Polygenic prediction of body mass index and obesity through the life course and across ancestries | Nature Medicine

Polygenic prediction of body mass index and obesity through the life course and across ancestries | Nature Medicine
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Polygenic prediction of body mass index and obesity through the life course and across ancestries
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Subjects
Obesity
Predictive markers
Abstract
Polygenic scores (PGSs) for body mass index (BMI) may guide early prevention and targeted treatment of obesity. Using genetic data from up to 5.1 million people (4.6% African ancestry, 14.4% American ancestry, 8.4% East Asian ancestry, 71.1% European ancestry and 1.5% South Asian ancestry) from the GIANT consortium and 23andMe, Inc., we developed ancestry-specific and multi-ancestry PGSs. The multi-ancestry score explained 17.6% of BMI variation among UK Biobank participants of European ancestry. For other populations, this ranged from 16% in East Asian-Americans to 2.2% in rural Ugandans. In the ALSPAC study, children with higher PGSs showed accelerated BMI gain from age 2.5 years to adolescence, with earlier adiposity rebound. Adding the PGS to predictors available at birth nearly doubled explained variance for BMI from age 5 onward (for example, from 11% to 21% at age 8). Up to age 5, adding the PGS to early-life BMI improved prediction of BMI at age 18 (for example, from 22% to 35% at age 5). Higher PGSs were associated with greater adult weight gain. In intensive lifestyle intervention trials, individuals with higher PGSs lost modestly more weight in the first year (0.55 kg per s.d.) but were more likely to regain it. Overall, these data show that PGSs have the potential to improve obesity prediction, particularly when implemented early in life.
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Main
Obesity is a major public health concern that causes or exacerbates many chronic diseases and leads to reduced life expectancy
. By 2035, more than half of the global population is projected to be living with overweight or obesity
. Although intensive lifestyle interventions (ILIs), bariatric surgery and weight loss medications are effective treatment options
, they are not without risk and likely to remain inaccessible to most people. Thus, preventing obesity remains paramount.
In contrast to many other chronic conditions, obesity often manifests itself during childhood and tends to persist into adulthood
. Therefore, predictors available in early life, such as genetic variants, which are fixed at conception, could be of particular value. In recent years, PGSs that capture an individual’s inherited polygenic susceptibility to a trait or disease have shown great promise in enhancing disease risk prediction and population screening
10
11
. However, it remains unclear how, when and under what circumstances PGSs for obesity might demonstrate utility for risk prediction.
The widely used PGS for obesity by Khera et al.
12
, based on a genome-wide association study (GWAS) of BMI in over 339,000 people of predominantly European ancestry, explains approximately 8.5% of the variation in BMI in adults. However, as a PGS based on one ancestry population may have weak transferability to other ancestry populations
13
14
, there is a growing recognition that PGSs that represent a broad range of populations are needed to ensure quality healthcare for all
15
By leveraging the results of the largest GWAS meta-analyses for BMI from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and 23andMe, we derived ancestry-specific and multi-ancestry PGSs for BMI and obesity to examine their performance (1) across diverse adult populations, (2) across childhood and adolescence and (3) in the context of ILIs aimed at weight loss (Fig.
).
Fig. 1: Study overview.
The alternative text for this image may have been generated using AI.
Full size image
PGSs were constructed using ancestry-specific GWAS summary statistics, using ancestry-specific (PRS-CS) and ancestry-combined (PRS-CSx) approaches. Tuning of the global shrinkage parameter (ϕ) and optimal weights for the linear combination version of PRS-CSx was performed in the UKBB. The best-performing score across multiple ancestries was taken forward to independent validation studies (linear combination version of PRS-CSx with ϕ 1 × 10
−2
). Population descriptors shown in the figure reflect a combination of self-identified ethnicity and genetic similarity. Created in BioRender: Smit, R. (2025):
Results
Selecting the best-performing PGS
To develop a PGS for BMI, we used GWAS meta-analysis summary statistics for BMI from over 200 studies from the GIANT consortium and 23andMe (Supplementary Tables
and
), excluding eight studies used for tuning parameters and for testing performance (
Methods
). The GWAS summary statistics included contributions from over 5.1 million people of diverse populations based on a combination of self-identified ethnicity and genetic similarity: 71.1% of participants were of predominantly European ancestry; 14.4% were of Hispanic ethnicity with typically admixed ancestries; 8.4% were of predominantly East Asian ancestry; 4.6% were of predominantly African ancestry (primarily admixed African American populations); and 1.5% were of predominantly South Asian ancestry. We refer to these groups, and to the most closely aligning, genetically inferred population groups from our PGS tuning and testing studies, as being of European-like ancestry (EUR), East Asian-like ancestry (EAS), American-like ancestry (AMR), African-like ancestry (AFR) and South Asian-like ancestry (SAS), respectively, while acknowledging that these groupings oversimplify the actual genetic diversity among participants (see Supplementary Tables
and
for study-specific population descriptors). Using PRS-CS(x)
16
17
, we created ancestry-specific and multi-ancestry PGSs leveraging up to 1.3 million common variants. We first identified the optimal genome-wide shrinkage parameter and linear combination weights for PRS-CS(x) that achieved the highest explained variance for BMI in six ancestry subpopulations of the UK Biobank (UKBB)
18
, including individuals of Middle Eastern-like ancestry (MID). For the EUR-tuning population, we selected a random subset of 20,000 unrelated individuals (
Methods
and Supplementary Tables
and
).
Overall, a multi-ancestry PGS consisting of a linear combination of five ancestry-specific PGSs (PGS
LC
) was the best-performing score (
Methods
, Fig.
2a
and Supplementary Tables
and
). In absolute terms, the explained variance of this multi-ancestry PGS for BMI ranged between 7.2% (AFR) and 17.5% (EUR), with a median of 14.0% (Fig.
2a
and Supplementary Table
). The multi-ancestry PGS resulted in a higher explained variance than the PGSs trained only with GWAS summary statistics most closely corresponding to the target population (‘ancestry-matched’) (Supplementary Table
). This was particularly evident for the populations of African-like and Central/South Asian-like ancestry (2.6-fold and 2.8-fold increase, respectively), consistent with the smaller GWAS sample sizes available for these populations. The performance of a PGS consisting of near-independent, genome-wide significant variants from the overall multi-ancestry GWAS meta-analysis was generally intermediate to that of the ancestry-matched and multi-ancestry PGSs, with the exception of populations of East Asian-like and European-like ancestry, in whom it performed worse than either (Fig.
2a
).
Fig. 2: Explained variance for BMI in adult populations.
The alternative text for this image may have been generated using AI.
Full size image
, Explained variance for BMI in UKBB tuning populations, defined as adjusted R
of the rank-based inverse-normal transformed BMI (by sex) predicted by the PGS, incremental to age, genotyping array and ancestry principal components. Error bars represent 95% confidence intervals (CIs) from 1,000 bootstrap resamples. The ancestry-matched PGS is the best-performing PRS-CS score using ancestry-specific GWAS summary statistics. The genome-wide significant score reflects a weighted sum of near-independent SNPs obtained from approximate COJO multi-SNP analyses of a fixed-effect meta-analysis of all contributing GWASs. The multi-ancestral PGS
LC
reflects the best-performing PRS-CSx score consisting of a linear combination of five ancestry-specific scores, with weights being specific to the validation population (for example, AFR). Population labels follow PAN-UKBB assignment of genetically determined ancestry. Sample sizes (distinct individuals): African 6,154; Admixed American 971; Middle Eastern 1,553; East Asian 2,660; Central/South Asian 8,005; European 20,000.
, Explained variance for BMI within validation populations, comparing the multi-ancestry PGS
LC
to a previously published score (PGS
Khera
) based on a smaller BMI GWAS meta-analysis. Same R
definition and CI estimation as in
. Population descriptors reflect a combination of self-identified ethnicity and genetic similarity. For the MVP’s non-Hispanic Asian (AS) group, the result shown is for the PGS
LC
using the linear combination weights derived from UKBB-EAS. Sample sizes (distinct individuals), from left to right: AFR 12,263, 2,332, 18,701; AMR 10,281, 8,096; AS 4,201; EAS 1,359; SAS 1,177; EUR 13,673, 69,828, 340,224.
, Separation in BMI, body fat percentage (BF%) and waist-to-hip ratio (WHR) across deciles of the PGS
LC
within the validation subset of the UKBB participants of European-like ancestry (
~ 340,000). All traits were rank-based inverse-normal transformed by sex.
Predicting prevalent obesity in adulthood
Having tuned parameters in a subset of the UKBB, we took PGS
LC
forward to independent validation populations, estimating the prediction accuracy for BMI and obesity in 482,135 participants from the UKBB, the Million Veteran Program (MVP)
19
20
, the Bio
Me
Biobank
21
and the Uganda General Population Cohort (GPC-UGR)
22
. Individuals were grouped by population group (AMR 22,612, AFR 29,454, EAS 1,617, EUR 423,420, SAS 1,164 and non-Hispanic Asian (AS) 4,201) (MVP-specific population label) (
Methods
and Supplementary Tables
and
). The prevalence of obesity varied substantially across populations and cohorts, with those having obesity class I or higher (that is, BMI ≥ 30 kg m
in AFR, AMR and EUR populations and BMI ≥ 27.5 kg m
for Asian-like ancestry populations
23
) ranging from 4.3% for the GPC-UGR to more than 45% for all ancestry subgroups in the MVP and mean BMI ranging from 22.2 kg m
to 30.6 kg m
(Extended Data Fig.
and Supplementary Table
).
The performance of the PGS
LC
was highest in individuals of European-like ancestry from the UKBB, with an explained variance of 17.6%. A pronounced lower performance was seen for populations with greater proportions of African-like ancestry, with the explained variance being 6.3% and 5.1% in African American populations (from Bio
Me
and MVP, respectively) and 2.2% in the GPC-UGR population from rural southwestern Uganda (Fig.
2b
and Supplementary Table
). Overall, we observed a median explained variance of 10.3% across our testing populations.
Compared to the previously reported PGS by Khera et al. (PGS
Khera
12
, which was based on a smaller BMI GWAS of up to 339,224 individuals of primarily European ancestry
24
, we observed a 1.9–2.6-fold increase in explained variance for BMI (Fig.
2b
).
Within the UKBB participants of European-like ancestry, the explained variance was marginally higher in males than in females (males: 17.9%; females: 17.3%) and higher in younger compared to older participants (≤50 years: 18.8%; >50 years: 17.0%) (Supplementary Table
), both directionally consistent with sex-specific and age-specific genetic differences previously observed for BMI
25
26
. Within this same cohort, an s.d. increase of PGS
LC
was associated with a 1.98 kg m
higher BMI (equivalent to 5.7 kg of body weight for a 1.7-m tall person) (Supplementary Table
). The corresponding mean separation across deciles of PGS
LC
was less pronounced for body fat percentage and waist-to-hip ratio compared to BMI (Fig.
2c
), revealing that the score does not equally capture differences in body composition.
We further evaluated the overall performance and discrimination of the PGS for prevalent obesity. Among European-like ancestry population groups, the PGS
LC
showed an improved capacity to differentiate between participants with and without obesity (BMI ≥ 30 kg m
) compared to PGS
Khera
. Specifically, the prevalence in the top 1% of the PGS
LC
was 69.5% versus 54.9% for PGS
Khera
and, in the bottom 1%, 1.7% versus 5.1%, respectively (Fig.
3a
and Extended Data Fig.
). Across populations, an s.d. increase in PGS
LC
was associated with a median 1.9–2.6-fold increase in odds of obesity class I or higher (Fig.
3b
). We observed larger effects for more severe obesity (Extended Data Fig.
and Supplementary Tables
and
10
). The area under the receiver operating characteristic curve (AUC) similarly increased with the severity of obesity and neared 0.80 for severe obesity in multiple populations (Fig.
3c
). The AUC for PGS
LC
on its own was consistently larger than those for age and sex, PGS
Khera
and within the UKBB self-reported comparative body size at age 10 (Fig.
3c
and Supplementary Table
11
).
Fig. 3: Prediction of prevalent obesity outcomes in adults.
The alternative text for this image may have been generated using AI.
Full size image
, Separation in prevalence of obesity (BMI ≥ 30 kg m
) across 1% groups of PGS
Khera
and PGS
LC
within the validation subset of the UKBB participants of European-like ancestry (
~ 340,000), with reference lines for the bottom and top 1% groups. Error bars show 95% confidence intervals (CIs) based on the normal approximation to the binomial distribution. The horizontal lines correspond to the average prevalence (black, dotted) and the prevalence of obesity within the top and bottom 1% of PGS
Khera
and PGS
LC
(red and blue, respectively).
, Odds ratios with 95% CIs for prevalent obesity class I or higher, per s.d. of PGS
Khera
and PGS
LC
, adjusted for age, sex, principal components of ancestry and genotyping array. All PGSs were standardized using the mean and s.d. of the PGS within individuals who did not have obesity class I or higher, to account for differences in prevalence across validation populations. Sample sizes (distinct individuals), from left to right: AFR 12,263, 2,332, 18,701; AMR 10,281, 8,096; AS 4,201; EAS 1,359; SAS 1,177; EUR 13,673, 69,828, 340,224.
, AUC classification of prevalent obesity outcomes in the Bio
Me
Biobank, the MVP and the UKBB. Models including PGSs (included as a continuous predictor) additionally include principal components of ancestry. CBS10, self-reported comparative body size at age 10 years. Restricted to estimates where the number of individuals with the obesity outcome was at least 50.
After an ancestry adjustment (
Methods
and Extended Data Fig.
), the median odds ratio for obesity class I or higher across Bio
Me
’s population groups was 3.6 for those in the top 10% of PGS
LC
(compared to bottom 90%) and 4.1 for the top 5% (versus those in the bottom 95%) (Supplementary Table
12
), with greater separation between these two tail estimates for European-like and Asian-like ancestry groups.
Childhood and adolescence
We investigated whether PGS
LC
has predictive value at an early age within the Avon Longitudinal Study of Parents and Children (ALSPAC), a geographically homogeneous prospective birth cohort from the southwest of England with follow-up until early adulthood (
Methods
and Supplementary Table
27
28
29
. Repeated cross-sectional associations with BMI showed small effects for PGS
LC
soon after birth with much stronger effects emerging in early childhood, from 0.12 s.d. per s.d. of PGS
LC
at 12 months to nearly quadruple that size (0.45 s.d. per s.d. of PGS
LC
) by age 12 years, after which effects plateaued (Fig.
4a
). The PGS was also associated, albeit less strongly, with height in early childhood with effects increasing until age 12 years, after which they returned to zero by mid-adolescence, suggesting that genetic predisposition for higher BMI early in life promotes increased body size in general, including postnatal linear growth, but that this early growth does not translate into differences in height after puberty (Extended Data Fig.
). This is in line with childhood overweight and obesity’s known associations with earlier pubertal timing
30
. Over time, BMI in children with a higher genetic predisposition (PGS ≥10th percentile) increased at a faster rate than those with a lower genetic predisposition, most evident after age 2.5 years (
Methods
, Fig.
4b
and Supplementary Table
13
). Both boys and girls with a very early age of adiposity rebound (≤43 months), a well-established predictor of future obesity risk
31
, had a higher mean PGS than those with later ages of adiposity rebound (Supplementary Table
14
).
Fig. 4: PGS
LC
performance during childhood and adolescence.
The alternative text for this image may have been generated using AI.
Full size image
, Repeated cross-sectional linear regression associations of standardized PGS
LC
with BMI and height, with both standardized within sample by sex and timepoint. Data are presented as regression coefficient with 95% confidence interval (CI). Ponderal index was used instead of BMI at birth. Associations were adjusted for age and principal components of ancestry. Sample sizes, based on repeated measurements, from left to right, for BMI: 4,740, 638, 847, 814, 769, 41, 732, 729, 725, 720, 699, 5,816, 4,863, 5,570, 5,368, 5,187, 4,910, 4,556, 4,024, 3,603, 2,780; and height: 4,802, 638, 847, 814, 771, 741, 734, 729, 726, 723, 701, 5,820, 5,160, 5,572, 5,379, 5,188, 4,956, 4,561, 4,032, 3,606, 2,782.
, Sequentially plotted mean BMI trajectories from the age of 4 months to 24 years with knot points from linear spline multilevel models, accounting for sex and principal components of ancestry, of PGS
LC
(bottom 10%, middle 80%, top 10%).
, Contribution of PGS
LC
to explained variance (adjusted R
) for BMI, rank-based inverse-normal transformed by sex and timepoint. Ponderal index was used instead of BMI at birth. Data are presented as R
values computed from the original dataset, with error bars representing 95% confidence intervals (2.5th–97.5th percentiles) estimated from 1,000 bootstrap resamples. Predictors available at birth were birthweight, maternal education, pre-pregnancy maternal BMI, maternal age at date of birth and household social status. The left panel shows explained variance for BMI at the timepoint shown on the
axis. In contrast, the right panel shows explained variance for BMI measured at 18 years, with early-life BMI measurement shown on the
axis used as predictors. Sample sizes, based on repeated measurements, from left to right: 3,800, 839, 714, 1,192, 4,062, 3,044, 940, 594, 737, 725, 890, 3,310.
We then examined whether the PGS adds predictive value over and above clinically available predictors of obesity. When added to predictors that are measurable at birth (birthweight, maternal education, pre-pregnancy maternal BMI, maternal age at date of birth and household social status), the PGS showed no clear added value for predicting ponderal index at birth or BMI at ages 3 years and 5 years (
Methods
and Fig.
4c
, left panel). However, for predicting BMI at later ages (8, 11 and 15 years), the contribution of the predictors at birth plateaued, whereas inclusion of the PGS roughly doubled the total explained variance from 11% to 21% at age 8 and from 13% to 26% at age 15 (Fig.
4c
, left panel).
For the prediction of BMI in early adulthood, the contribution of the PGS especially shines through in the first few years after birth. BMI measured at age 8 explained 44% of the variation in BMI at age 18, and adding the PGS to the prediction model only raised this to 49%. In contrast, at younger ages, adding the PGS to measured BMI led to larger relative increases in the explained variance for BMI at 18 years, from 1.5-fold at age 5 (from 22% to 35%) to more than three-fold (from 8% to 26%) at 1 year of age (Fig.
4c
, right panel, and Supplementary Table
15
).
From early adulthood to middle life
Within the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial (
Methods
32
, both males and females with a higher PGS experienced a greater change in self-reported weight and BMI between age 20 and age 50. Across populations, the per-5-year change per s.d. in PGS
LC
was consistently larger for females than for males (median of 0.35 kg for females versus 0.21 kg for males and 0.14 kg m
versus 0.07 kg m
for BMI), also when corrected for initial weight or BMI (Supplementary Table
16
).
The addition of the PGS to a model with birth year, sex and BMI at age 20 was associated with modest increases in discriminative ability for predicting obesity at age 50 (incremental AUC ranged from 0.01 to 0.03; Fig.
). There was evidence of statistical interaction between the PGS and BMI at age 20 for obesity at age 50 (
Methods
and Supplementary Table
17
), which translated into a larger incremental AUC of the PGS for those who did not have overweight or obesity at age 20 (range, 0.02–0.05; Fig.
), without strong evidence of sex specificity (Extended Data Fig.
).
Fig. 5: AUC for obesity classification at age 50, stratified by having overweight or obesity at age 20.
The alternative text for this image may have been generated using AI.
Full size image
Analyses performed within the PLCO Cancer Screening Trial. Restricted to estimates where the number of individuals with the obesity outcome was at least 50. Population descriptors were provided by the PLCO investigators and reflect genetically determined ancestry using Genetic Relationship and Fingerprinting (GRAF). PCs, principal components; y, years; M1, model with birth year, sex, BMI at 20y, and PCs.
Response to ILIs
Lifestyle modification remains a cornerstone of weight management, with multicomponent ILIs having shown success in achieving clinically relevant weight loss
33
. However, ILIs require substantial, sustained investment from both healthcare providers and patients, with large variation in weight loss and unintentional weight regain being common
34
35
36
. To examine whether polygenic predisposition to obesity might modify the effectiveness of such interventions, we examined whether the PGS was associated with weight loss during the first year of an ILI, and with weight regain thereafter, in two randomized controlled trials with similar intervention arms aimed at achieving 7% weight loss: the Diabetes Prevention Program (DPP
33
) and the Look AHEAD (Action for Health in Diabetes
37
) studies (
Methods
).
Among 3,909 participants (Supplementary Table
), individuals with a higher PGS
LC
lost more weight during the first year in response to the ILI compared to the control group (−0.55 kg per s.d. in PGS, 95% confidence interval: −0.94 to −0.16) (Fig.
), when adjusting for starting weight. In addition, among those who lost at least 3% of their baseline weight during the first year, a higher PGS was associated with more weight regain in the following years (up to 3 years) (0.48 kg per PGS s.d., 95% confidence interval: 0.00–0.95) (Fig.
). These findings were directionally consistent across (self-)reported population groups.
Fig. 6: Impact of PGS
LC
on weight change due to ILIs.
The alternative text for this image may have been generated using AI.
Full size image
Interaction effect between PGS
LC
and trial arm (ILI versus comparison arm) for weight change within the first year of follow-up (left) and weight change after the first year in the subset of individuals who had lost ≥3% of baseline weight at year 1 (right). Data are presented as study-specific and study-combined interaction regression coefficients (with 95% confidence interval (CI)), pooled through inverse-variance weighted fixed-effect meta-analysis. Weight change was assessed by using weight at follow-up timepoint(s) as outcome in the linear mixed models, adjusting for initial weight (baseline or year 1, respectively). LA, Look AHEAD. Population descriptors reflect race and ethnicity, as reported by dbGAP variable phv00201855.v2.p1 (DPP) or self-report by participants (LA). Study-specific sample sizes (distinct individuals, with pooled sample sizes for ‘All’), from top to bottom, for forest plot on the left: 295, 374, 251, 428, 839, 1,722, 1,385, 2,524; and on the right: 118, 186, 128, 233, 428, 951, 674, 1,370.
Discussion
We used GWAS summary statistics from the GIANT consortium and 23andMe, encompassing over 5.1 million people, to create ancestry-specific and multi-ancestry PGSs capturing genetic predisposition to weight gain and obesity. Our multi-ancestry PGS more than doubled the explained variance for BMI compared to the widely used PGS by Khera et al.
12
. Additionally, we demonstrate the potential added value of the PGS in two distinct clinical applications: predicting adult BMI at an early age and weight change in response to ILIs.
Across diverse populations, the increases in explained variance for BMI were accompanied by substantial improvements in effect sizes and discrimination metrics for obesity, with the PGSs’ standalone AUC nearing 0.80 for severe obesity. Consistent with other polygenic traits, our results underscore the importance of training sample size in driving PGS performance improvements. Nonetheless, our results also show diminishing returns of sample size increases, as has been observed for other polygenic traits
38
, with a 15-fold increase in total sample size (compared to PGS
Khera
, ~10-fold in EUR alone) leading to a 2–3-fold increase in prediction accuracy. We leveraged PRS-CSx’s unique ability to integrate GWAS summary statistics from all five broad ancestry population groups from the meta-analyses from GIANT and 23andMe
17
. However, recent Bayesian methods have shown promising results with smaller sample sizes by including additional variants and functional annotation, meriting consideration for future comparisons
39
40
. Although our results highlight the value of multi-ancestry PGS to increase PGS performance, a considerable performance gap persists for populations with substantial African-like genetic ancestry compared to other populations. This discrepancy is likely due to the underrepresentation of individuals with African-like ancestry in the training GWAS, particularly from continental Africa
41
, and differences in minor allele frequency (MAF) and linkage disequilibrium (LD) patterns
42
43
. The lower performance of the PGS in the GPC-UGR is in line with those seen therein for PGSs for other traits and the limited transferability of PGS performance between African ancestry populations, reflecting genetic and environmental differences as well as their interactions
22
44
45
46
47
, emphasising that much remains to be done to improve the performance of genetic risk prediction across the genetically diverse populations within Africa. For example, it remains unclear whether a finer classification of broad ancestry groups in GWAS meta-analyses and PGS development could improve downstream PGS generalizability, especially given the dominant role of GWAS sample size. Given the observed performance gap, careful implementation of the score is essential to avoid potentially introducing public health disparities. For example, establishing a standardized ancestry threshold
14
48
may improve the accuracy of population-specific risk stratification and ensure that the score is returned only in populations where it meets an acceptable performance threshold.
Within ALSPAC, the PGS—which was developed for adult BMI—already shows an effect on BMI early in life. Effect sizes rapidly increased with age and were accompanied by clear divergence of BMI trajectories throughout childhood and adolescence. As a result, the PGS showed added value beyond clinically available predictors assessed at birth for predicting BMI measured after age 5. This is in line with previous research on the dynamic relationship between genetic variation and adiposity in early life
12
49
50
. Particularly elucidating are the observations from the Norwegian Mother, Father and Child Cohort Study (MoBa) with repeated measurements across narrow age windows
51
. They show that BMI has a rapidly changing genetic architecture during the first 8 years of life with a ‘late rise’ cluster of variants, emerging in late childhood, which show limited association before the adiposity rebound but have persistent effects on BMI into adult life
51
. This also explains why our PGS shows its value for predicting adult BMI during the earliest years of life, up to age 5, when measured BMI has limited value as a predictor. With a growing number of obesity prevention trials commencing in early life
52
53
, our results provide important context at what age(s) PGSs for obesity, potentially in tandem with PGSs of obesity-related complications, could be considered promising candidate predictors to help guide risk stratification and the implementation of such interventions.
In the PLCO study, a higher PGS was associated with weight gain over a 30-year period from early adulthood to midlife. Additionally, for individuals aged 20, knowing their PGS modestly improved the prediction of obesity at age 50, more so for those without overweight or obesity at age 20. This suggests that particularly individuals with an early adulthood BMI below their genetically predicted BMI are at risk of gaining weight to match their innate predisposition. Our findings are consistent with those from the CARDIA study
54
, showing that PGSs for obesity only marginally improve the prediction of midlife BMI when combined with early adulthood BMI measurements, contrasting with its predictive value in early life.
Analyzing clinical trial data of ILIs, we observed that individuals with a higher PGS lost modestly more weight during the first year. However, this group was also at higher risk of weight regain after this most intensive portion of the intervention had concluded. This may seem counterintuitive to the expectation that those with a higher genetic risk will benefit less from weight loss interventions. However, this observation is supported by a strong body of literature reporting that those most genetically predisposed to obesity are also those most responsive to changes in an obesogenic environment
55
56
57
58
59
60
61
62
63
. However, the literature on the role of genetics modifying response to weight loss interventions remains sparse and ambiguous, reflecting differences in variants considered, methods and study design, highlighting the need for well-powered PGSs
64
65
66
67
68
69
70
71
72
73
. It will be of interest to investigate whether PGSs for obesity have predictive utility for the fast-growing arsenal of pharmacotherapies aimed at weight loss. Separately, the current results offer crucial context when conveying genetic results of obesity risk to participants, which has generally shown either no or even short-lived adverse effects on risk-reducing behavior
74
75
76
77
. Rather, our findings emphasize that individuals with a high genetic predisposition to obesity may respond more to lifestyle changes and, thus, contrast with the determinist view that genetic predisposition is unmodifiable
78
. This is further reinforced by evidence that polygenic susceptibility can mitigate or exacerbate the impact of pathogenic variants in
MC4R
, underscoring the complexity of genetic influences on obesity
79
. We look forward to the findings of the Electronic MEdical Records and GEnomics (eMERGE) network’s PGS-based genome-informed risk assessment being returned to 25,000 diverse adults and children for 11 conditions, including obesity/BMI
14
Taken together, we show that BMI PGSs can be used for prediction of adult obesity throughout the life course, particularly in early life, and for severe obesity. This PGS represents a substantial improvement compared to previous scores and may help to identify individuals at high risk to allow for timely prevention or treatment of obesity, such as through integration in a broader predictive framework jointly modeling genetic and environmental risk.
Methods
Study populations
The list of studies contributing to the GWAS meta-analyses for BMI, which served as the training data for the presented PGSs, is provided in Supplementary Table
. The study populations described below are those that contributed as a PGS tuning or validation population.
The UKBB is a prospective cohort study that enrolled approximately 500,000 people from across the UK, aged 40–69 years at recruitment, between 2006 and 2010 (ref.
18
). At recruitment, participants completed detailed questionnaires, underwent a range of physical measures and provided blood, urine and saliva samples. BMI was calculated using height (measured in whole centimeters) and weight (to the nearest 0.1 kg). Females who were pregnant at the time of assessment were excluded from our analysis. As a measure of comparative childhood body size, participants were asked: ‘When you were 10 years old, compared to average, would you describe yourself as: (i) thinner, (ii) plumper, (iii) about average?’.
The MVP has recruited over 1 million people from Veteran Affairs (VA) Medical Centers across the United States since 2011 (ref.
19
). Veterans who volunteer provide a blood sample for biobanking, complete baseline and lifestyle questionnaires and consent to allow access to clinical data from VA electronic health records (EHRs). For the current study, PGS performance was assessed in the MVP’s third release of genomic data (R3) using additional participants without overlap, with earlier data releases contributing to the GIANT GWAS meta-analyses. Phenotypic and genomic data related to the association analysis of BMI were previously described
20
The Institute for Personalized Medicine Bio
Me
Biobank, founded in 2007, is an ancestrally and culturally highly diverse EHR-linked biorepository enrolling participants non-selectively from across the Mount Sinai Health System in New York City
21
. At enrollment, participants consent to link their DNA and plasma samples to deidentified EHRs. The clinical and EHR information is complemented by a baseline questionnaire that gathers demographic and lifestyle information. BMI was calculated using weight and height from baseline and outpatient EHR measurements, using median height and weight measured at or within 60 days before/after the enrollment visit, after several cleaning steps (Supplementary Table
).
The GPC-UGR is a population-based open cohort study established in 1989 by the Medical Research Council (UK) in collaboration with the Uganda Virus Research Institute (UVRI) to monitor the HIV epidemic and its determinants in neighboring villages in rural southwestern Uganda, in the Kyamulibwa subcounty of the Kalungu district, approximately 120 km from Entebbe town. Since 2010, its mandate has expanded to incorporate the epidemiology and genetics of both communicable and non-communicable diseases
22
. The GPC-UGR was initially recruited and assessed through annual house-to-house census and survey rounds until 2012, when biannual surveys commenced. For the current study, we included individuals aged 18 years and older who have been either whole-genome genotyped or whole-genome sequenced
80
81
The ALSPAC is a prospective birth cohort from the southwest of England established to investigate environmental and genetic characteristics that influence health, development and growth of children and their parents
27
28
29
. Full details of the cohort and study design are available at
. In brief, pregnant females residing in Avon, UK, with expected dates of delivery between 1 April 1991 and 31 December 1992 were invited to take part in the study. The initial number of pregnancies enrolled was 14,541, with 13,988 children who were alive at 1 year of age. The children resulting from these pregnancies have been followed-up to date with measures obtained through regular questionnaires and clinical visits, providing information on a range of behavioral, lifestyle and biological data. More specifically, a 10% sample of the ALSPAC cohort, known as the Children in Focus (CiF) group, attended clinics at the University of Bristol at various time intervals between 4 months and 61 months of age, whereas the entire ALSPAC cohort was invited to attend regular research clinics from age 7. When the oldest children were approximately age 7, an attempt was made to bolster the initial sample with eligible individuals who had failed to join the study originally. The total sample size available for analyses using any data collected after the age of 7 is, therefore, 15,447 pregnancies, with 14,901 children who were alive at 1 year of age. Study data were collected and managed using Research Electronic Data Capture (REDCap) tools hosted at the University of Bristol
82
. REDCap is a secure, web-based software platform designed to support data capture for research studies. Details on sample selection for the current analyses are presented in Supplementary Table
. Please note that the study website contains details of all data that are available through a fully searchable data dictionary and variable search tool (
).
The PLCO Cancer Screening Trial was a multicenter randomized controlled trial in the United States that enrolled males and females aged 55–74 years from 1993 to 2001 to evaluate the effectiveness of different screening programs on cancer mortality
32
. In the baseline questionnaire, participants self-reported their weight at age 20 years, age 50 years and baseline as well as their height. For the current study, we restricted the analyses to genotyped participants who had not contributed to the underlying GWAS meta-analyses and had information available on their weight and BMI at both age 20 and age 50.
The DPP was a 27-site parallel-arm randomized controlled trial designed to determine whether either the oral diabetes drug metformin or an ILI (primarily fat gram, calorie and physical activity goals) aimed at approximately 7% weight loss, compared to inactive tablets and standard lifestyle recommendations, could prevent or delay type 2 diabetes onset in ethnically diverse high-risk individuals with prediabetes and overweight or obesity
33
. For the current study, we excluded individuals from the metformin arm as well as those from a fourth intervention arm, troglitazone, which was discontinued at an early stage. Moreover, we focused on the individuals comprising the three largest race and ethnicity categories (White, Black and Hispanic, as reported by database of Genotypes and Phenotypes (dbGAP) variable phv00201855.v2.p1) who had consented to being genotyped.
Look AHEAD was a 16-site parallel-arm randomized controlled trial that assessed the long-term effects of an ILI in ethnically diverse patients with overweight or obesity and type 2 diabetes on cardiovascular morbidity and mortality
37
. The lifestyle intervention was modeled after the DPP lifestyle intervention and similarly aimed at 7% weight loss but with more ambitious individual goals for several intervention components
83
. Although those in both trial arms were provided one session of education on diabetes and cardiovascular risk at baseline, the comparison arm thereafter received the option of attending only three sessions per year on nutrition, physical activity and social support, with no explicit weight loss goals. As for the DPP, we restricted our analyses to genotyped participants who self-reported as being of African American/Black, Hispanic and White race and ethnicity on the baseline questionnaire.
Population descriptors and study-provided population labels and genotyping, phenotyping and participant characteristics are presented in Supplementary Tables
and
. No new data (that is, measurements) were collected for this study, and no participant compensation was provided for the analyses conducted.
Our research complies with all relevant ethical regulations. The UKBB study was approved by the North West Multi-Centre Research Ethics Committee (ref. 11/NW/0382), and all participants provided written informed consent to participate in the UKBB study. The VA central institutional review board (IRB) approved the MVP study protocol in accordance with the principles outlined in the Declaration of Helsinki. Informed consent was obtained from all participants. The Bio
Me
Biobank Program (IRB no. 07-0529) operates under a Mount Sinai IRB-approved research protocol. All study participants provided written informed consent. The Uganda Genome Resource was approved by the Science and Ethics Committee of the UVRI Research and Ethics Committee (UVRI-REC no. HS 1978), the Uganda National Council for Science and Technology (UNCST no. SS 4283) and the East of England-Cambridge South (formerly Cambridgeshire 4) NHS Research Ethics Committee UK. Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the local research ethics committees. Consent for biological samples has been collected in accordance with the Human Tissue Act (2004). Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. The PLCO study was approved by the human subjects review boards at the National Cancer Institute and at the 10 study centers (National Institutes of Health IRB no. OH97CN041). Written informed consent was given by all participants. The DPP and Look AHEAD trials were approved by the IRB at each center, and all participants gave written informed consent.
PGS construction and tuning
To derive our scores, we used summary statistics from the ongoing GWAS meta-analyses for BMI conducted by the GIANT consortium (
) in collaboration with 23andMe. Prior to each GWAS, BMI (kg m
) underwent rank-based inverse-normal transformation by sex, similarity to major ancestry group (AFR, AMR, EAS, EUR, SAS) and case–control status as appropriate, with age, age
, principal components of population structure and study-specific covariates regressed out. Ancestry group assignment was defined independently by each contributing study, as previously described
84
. Additive GWAS analyses were performed primarily using RVTESTS for 1000 Genomes Project (1000G) phase 3 or Haplotype Reference Consortium (HRC)-imputed data
85
. For 23andMe, which performed its GWAS on BMI using self-reported height and weight, see Supplementary Table
for details on phenotyping, ancestry group assignment and GWAS methodology. Quality control of the study-specific files was conducted using EasyQC
86
, followed by fixed-effect meta-analysis by ancestry group using RAREMETAL
87
. When performing the GWAS meta-analyses to train the PGSs with, we purposely excluded data from several populations (ALSPAC, Bio
Me
and UKBB). No additional genomic control was applied to the resulting summary statistics.
For PGS derivation, we employed PRS-CS and its multi-ancestral extension PRS-CSx (versions 1.0.0)
16
17
. In short, PRS-CS(x) uses a Bayesian regression framework and assumes a continuous shrinkage prior on single-nucleotide polymorphism (SNP) effects. Whereas PRS-CS focuses on creating ancestry-specific scores, informed only by the GWAS summary statistics and LD information of a given population, PRS-CSx uses a shared prior to couple SNP effects across populations and explicitly models population-specific allele frequencies and LD patterns to create multi-ancestry scores. Both methods were implemented using the software’s default settings (that is, gamma–gamma priors set to a = 1 and b = 0.5, Markov chain Monte Carlo (MCMC) total and burn-in iterations set at 1,000 times and 500 times the number of discovery populations and MC thinning factor set at 5), with input being ancestry-matched summary statistics and PRS-CS(x) developer-provided 1000G LD reference panels of common HapMap3 variants (AFR, AMR, EAS, EUR, SAS). PRS-CS(x) uses a global scaling parameter (Φ), which requires tuning. In addition to small-scale grid testing of Φ (1 × 10
−6
, 1 × 10
−4
, 1 × 10
−2
, 1), we ran PRS-CS(x) using its ‘auto’ option, which learns Φ automatically from the GWAS summary statistics. Finally, PRS-CSx was performed separately using its default ‘linear combination’ as well as its ‘meta’ version. In the first, a set of population-specific PGSs is outputted (that is, five in our case), for which the optimal linear combination needs to be derived for each target population (for example, AFR) in a population-matched tuning dataset. For ‘meta’, the population-specific posterior SNP effects are integrated using an inverse-variance weighted meta-analysis in the Gibbs sampler, producing a single score that can theoretically be applied regardless of the target population.
The variants considered for PGS derivation were those present in the 1000G reference panels mentioned above (HapMap3 variants (
= 1–1.2 million across panels), which were common in 1000G populations, excluding ambiguous A/T or G/C variants), which additionally had an INFO > 0.3 (that is, imputation quality) in the overall UKBB. Ancestry-specific summary statistics were further restricted to the variants with a variant-specific sample size of at least a third of the maximum sample size for that ancestry.
Tuning of Φ and derivation of PRS-CSx’s linear combination weights were performed in the same subsets of the UKBB. More specifically, we used genetically determined relatedness and ancestry assignments provided by the Pan-UKBB Team (
, UKBB return 2442) to subset the UKBB into groups of unrelated individuals of African-like, Admixed American-like, Central/South Asian-like, East Asian-like, European-like (random subset of 20,000 individuals) and Middle Eastern-like ancestry. The target-population-specific (for example, AFR) linear combination weights for PRS-CSx (for each Φ) were derived from a joint linear regression model of rank-based inverse-normal transformed BMI (by sex and population) on the five population-specific scores (each standardized to mean zero and unit variance in the corresponding tuning dataset—for example, UKBB
AFR
), age, 10 principal components and genotyping array.
Explained variance for BMI was defined as the incremental adjusted R
from linear regression for rank-based inverse-normal transformed BMI (by sex and population), when adding the PGS predictor to a model containing age, principal components and genotyping array. Confidence intervals (95%) were determined using bootstrapping with 1,000 repetitions. As comparator, we created a score of quasi-independent associations after performing approximate conditional and joint (COJO) multiple-SNP analysis
88
, as implemented in GCTA
89
, using the results from the multi-ancestral GWAS summary statistics when leaving out only the UKBB. We used genotypes from a previously described set of 50,000 unrelated UKBB
EUR
participants
84
as our LD reference panel for GCTA-COJO, with parameters set to: –diff-freq 0.1; –maf 0.01; –cojo-collinear 0.9; –cojo-p 5 × 10
−9
. The scores with the highest explained variance across multiple tuning population datasets (PRS-CSx linear combination and meta versions, with Φ 1 × 10
−2
) were then taken forward to the validation populations, which performed study-level variant filtering on imputation quality when constructing each PGS (Supplementary Table
). The results of PGS
meta
are presented throughout the supplementary tables.
Of note, in August 2023, a new version of PRS-CSx was released (version 1.1.0) to address potential reductions in performance due to truncating of GWAS summary statistics
values below 1 × 10
−323
. Given our inclusion of several variants with
values below this threshold (among the PGS variants:
= 154 for EUR,
= 12 for AMR), we reran PRS-CSx with Φ 1 × 10
−2
but did not observe markedly different results in PGS performance within the UKBB tuning populations (Supplementary Table
18
).
PGS performance in adulthood
In addition to explained variance (%) for BMI, several population-stratified metrics for overall performance and discrimination were calculated in the validation studies with adult participants:
A.
Nagelkerke R
, with a null model of age, sex, principal components and genotyping array, for prevalent obesity categories based on World Health Organization (WHO) BMI cutoff points (membership of category or above versus rest), using Asian-specific cutoffs where applicable (Supplementary Table
23
. Confidence intervals (95%) were calculated using bootstrapping with 1,000 repetitions.
B.
Mean difference in BMI, both untransformed and after rank-based inverse-normal transformation by sex, per s.d. increase in PGS. Covariates included age, sex (where applicable), principal components and genotyping array.
C.
Odds ratio per s.d. of PGS for prevalent WHO obesity outcome. Same covariates as under (B). Here, the PGSs were standardized to mean zero and unit variance using the mean and s.d. of the PGS observed within participants without the outcome (for example, individuals who do not fall within obesity class I (or higher), where this was the outcome), to account for prevalence differences across studies.
D.
AUC statistic for prevalent WHO obesity outcomes. We compare models including (i) age and sex, (ii) genotyping array, principal components and PGS
Khera
, (iii) genotyping array, principal components and PGS
LC
and (iv) models (i)/(iii) combined. In the UKBB, we additionally ran models including and excluding self-reported comparative body size at age 10 (UKBB field ID 1687). Confidence intervals (95%) were calculated using DeLong’s method.
In addition, the explained variance for BMI was calculated separately in age strata (≤50 years or >50 years, with adjustment for residual age differences within age strata) and sex strata of the European UKBB participants. The main comparator for all analyses was a previously published LDpred-derived PGS for BMI (PGS
Khera
12
, which was based on the results from a GIANT consortium GWAS meta-analysis including up to 339,224 individuals of primarily European ancestry
24
. As Bio
Me
had contributed to this meta-analysis, metrics for PGS
Khera
were determined in individuals who had not contributed nor were related to those who did (second or stronger degree of relatedness, based on KING-derived kinship coefficients). We chose PGS
Khera
as our comparator as it is the most widely recognized and extensively used PGS for BMI within the obesity research community, thereby serving as a well-established benchmark.
Tail comparisons after ancestry correction
Due to on-average differences in LD structure and allele frequencies, the distribution of a PGS (for example, mean and variance) can differ across ancestral populations. This was also observed for the five population-specific PGSs that were linearly combined to create PGS
LC
(Extended Data Fig.
). As performance metrics that rely on thresholding the PGS distribution (for example, top 10%) may be impacted by such on-average differences
90
, particularly in admixed populations, we modeled the mean and variance of the population-specific PGSs through principal component analysis of the 1000G reference panel. For this, we applied a correction described by the eMERGE network
14
91
, which represents a modified version of the approach by Khera et al.
92
. Starting with a publicly available, curated version of the 1000G reference panel (
; gs://broad-gotc-test-storage/imputation/1000 G_reference_panel), we first excluded variants with MAF < 1% across 1000G superpopulations, before determining the overlap with genotyped variants available for both the UKBB and Bio
Me
. After excluding long-range LD regions, we pruned the list of variants using plink (–indep-pairwise 1000 50 0.05) and ran principal component analysis with flashpca
93
, whereafter we projected UKBB and Bio
Me
participants to the same 1000G principal component space. For each of the five population-specific PGSs underlying PGS
LC
(that is, AFR, AMR, EAS, EUR and SAS), we regressed the PGS of 1000G participants against the first 10 principal components of ancestry:
$${PGS}={\alpha }_{0}\,+\,\mathop{\sum }\limits_{i=1}^{10}{\alpha }_{i}\times {{PC}}_{i}$$
In addition, we modeled its residual variance (
\({\delta }^{2}\)
) as a function of the same principal components:
$${\delta }^{2}={\beta }_{0}\,+\,\mathop{\sum }\limits_{i=1}^{10}{\beta }_{i}\times {{PC}}_{i}$$
Then, using the projected principal component, predicted mean and residual s.d. were calculated and used to create ancestry-adjusted
-scores of each PGS for UKBB and Bio
Me
participants:
$${adjusted\; zPGS}=\frac{\mathop{\sum}\nolimits_{j=1}^{M}{w}_{\!j}\times {{dosage}}_{j}\,-\,({\alpha }_{0}\,+\,\mathop{\sum}\nolimits_{i=1}^{10}{\alpha }_{i}\times {{PC}}_{i})}{\sqrt{{\beta }_{0}\,+\,\mathop{\sum }\nolimits_{i=1}^{10}{\beta}_{i}\times {{PC}}_{i}}}$$
with
\({\sum }_{j=1}^{M}{w}_{\!j}\times {{dosage}}_{j}\)
representing the raw PGS.
We observed that mean and variance differences of scores across ancestry populations were largely, but not fully, resolved through this correction in both study populations (Extended Data Fig.
). In keeping with PRS-CSx’s linear combination approach, weights to linearly combine scores were derived within each UKBB tuning population and applied to Bio
Me
validation populations. The ancestry-adjusted PGS
LC
was then thresholded at its top 5% and 10% separately (versus bottom 95% and 90%, respectively), and odds ratios for prevalent obesity were calculated, adjusting for age, sex and genotyping array. Given the relatively lower number of East Asian and South Asian individuals available for these tail comparisons, we present pooled estimates.
Multi-ancestry PGS trained only with GIANT consortium data
Although we primarily report on the development and performance of the PGS
LC
trained with the larger GIANT+23andMe BMI GWAS meta-analyses results, we initially developed a multi-ancestry PGS with the GIANT-only results. For this, we used PRS-CS, combining GIANT’s combined multi-ancestral GWAS summary statistics (including Bio
Me
but excluding the UKBB and the ALSPAC study) with a previously described multi-ancestry 1000G-based LD reference panel created following the same protocol as described by the PRS-CS authors
94
. We restricted variants to those present in the LD reference panel, which additionally had MAF ≥ 0.1% and INFO ≥ 0.8 in the overall UKBB, resulting in a total of 1,217,710 variants included. We did not specify a genome-wide shrinkage parameter for PRS-CS, which it therefore learned from the training data using a fully Bayesian approach (its ‘auto’ option). In collaboration with the eMERGE network, this GIANT-only PGS was included among a select group of trait-specific scores currently being returned as part of a genome-informed risk assessment (GIRA) to 25,000 diverse adults and children and their healthcare providers in an ongoing prospective cohort study across 10 clinical sites, as recently described
14
48
. In line with the aim of returning a high-risk versus a not-high-risk GIRA status to participants, we estimated the odds ratio for obesity class I (or higher) for being in the top 3% of the PGS distribution (versus bottom 97%) within the subset of Bio
Me
participants genotyped with the Global Screening Array (with 1000G imputation) who had not contributed to the underlying GIANT GWAS meta-analysis nor were related to those who did. Accounting for age, sex and four principal components, we observed that values within the top 3% of this PGS were associated with 4.08 (95% confidence interval: 3.02–5.52) times as high odds for prevalent obesity in individuals of self-reported European descent, with 2.54 (95% confidence interval: 1.55–3.98) for individuals of self-reported African descent, with 2.33 (95% confidence interval: 1.64–3.31) for individuals of self-reported Hispanic/Latino descent and with 5.73 (95% confidence interval: 2.28–14.57) for individuals of self-reported Asian descent. The weights for this GIANT-only PGS are made available through the PGS Catalog alongside the weights for PGS
LC
and PGS
meta
(see ‘Data availability’).
Childhood and adolescence
Within the ALSPAC study, repeated anthropometric measurements are available from birth to age 24. We employed both cross-sectional and multilevel analyses to provide complementary perspectives: cross-sectional analyses facilitate granular comparisons at each timepoint, whereas multilevel models capture longitudinal trajectories and population-level divergence over time. First, we assessed repeated cross-sectional associations between the standardized PGS (PGS
LC
using linear combination weights from UKBB
EUR
) and all available measures of BMI (or ponderal index at birth) and height. Anthropometric measurements were rank-based inverse-normal transformed separately by sex, at each timepoint, and analyses were adjusted for age at timepoint and principal components.
Longitudinal analyses using linear spline multilevel models were conducted to examine the association between the PGS (bottom 10%, middle 80%, top 10%) and change in untransformed BMI and height between 4 months and 24 years. Multilevel models estimate the mean trajectories of each anthropometric trait while accounting for non-independence of repeated measures within individuals, change in scale and variance of measures over time and differences in the number and timing of measurements between individuals (using all available data from all eligible participants under a missing-at-random assumption). Linear splines allow knot points to be fitted at different ages to derive periods of change that are approximately linear. All participants with at least one measure of the anthropometric traits were included under a missing-at-random assumption to minimize selection bias in trajectories estimated using linear spline multilevel models (with two levels of random effects: measurement occasion (that is, age to the nearest integer in years) and individual), allowing individuals to have different intercepts and slopes and, thus, their own trajectories. Knot points were placed as follows for each anthropometric trait based on the distribution and longitudinal pattern of measures between the earliest measure and 24 years: at ages 4 months and 8 months and at ages 2.5 years, 5 years, 8 years and 15 years for BMI and at ages 1 year, 5 years and 15 years for height. Interaction terms between PGS and each spline were included in the models to estimate the difference in the intercepts (earliest anthropometric trait measurement) and slopes (change in anthropometric trait from the earliest measure to 24 years) between the three PGS categories. Additionally, interaction terms between sex and the first 10 genetic principal components with each spline were included to estimate the difference in intercepts and slopes between males and females; therefore, models were adjusted for sex and principal components. All longitudinal models were created in MLwiN version 3.04 called from Stata version 15 using the ‘runmlwin’ command
95
In the CiF subset of the cohort, age at adiposity rebound was available, categorized as very early (at/before 43 months), early (from 49 months but before 61 months) and later (after 61 months)
96
. These children represent a 10% sample of the cohort, randomly selected for more detailed investigations, and are representative of the entire cohort (
). One-way ANOVA was used to test for differences in mean PGS across categories, in boys and girls separately and combined.
Added value of the PGS for prediction of BMI (rank-based inverse-normal transformed by sex) at given timepoints was quantified as incremental explained variance, when adding the PGS to different sets of predictors. First, we examined R
for BMI at birth and at 3, 5, 8, 11 and 15 years, when adding the PGS to a model containing multiple relatively easily obtainable clinical variables that would be available at birth (birthweight, maternal education, pre-pregnancy maternal BMI, maternal age at date of birth and household social status). Details regarding phenotyping of these variables can be found in Supplementary Table
. Separately, for R
for BMI at age 18, we added the PGS to separate models containing BMI from a given early-life timepoint (1, 2, 3, 4, 5 and 8 years). Confidence intervals (95%) were calculated using bootstrapping with 1,000 repetitions.
From early adulthood to middle age
Within PLCO, we assessed the population-statified and sex-stratified association between the standardized PGS
LC
(using linear combination weights from the UKBB most closely corresponding to each testing population) and per-5-year change between ages 20 and 50 in weight and BMI, with and without adjustment for the initial measurement at age 20. These analyses were additionally adjusted for birth year, sex (where appropriate) and principal components.
We calculated the population-stratified AUC of the PGS for obesity outcomes at age 50. For this, we ran models including (i) birth year and sex, (ii) birth year, sex, principal components and PGS
LC
, (iii) birth year, sex, principal components and BMI at age 20 and (iv) birth year, sex, principal components, BMI at age 20 and PGS
LC
. We separately ran model (iv) when including an interaction term between the PGS and BMI at age 20. Based on evidence for statistical interaction between the PGS and BMI at age 20 for predicting obesity at age 50 across multiple populations (Supplementary Table
16
), we stratified our analyses on the presence of overweight or obesity at age 20 and, additionally, on sex. Confidence intervals (95%) for AUC were calculated using DeLong’s method.
PGS and weight change due to ILIs
Although neither intervention nor comparison arms were identical across DPP and Look AHEAD, both intervention arms were aimed at 7% weight loss while focused on the same process features
83
, and both represent a large shift in lifestyle behavior relative to the comparison arms. As such, we decided to pool study-specific effect estimates via inverse-variance weighted meta-analysis. In each study, we ran analyses after pooling the three largest population groups present in both trials: Black and/or African American, Hispanic and White (Supplementary Table
). Population-stratified analyses were separately run as well. Within each study-specific population, we applied the linear combination weights for PGS
LC
most closely aligned with the target population (AFR, AMR and EUR, respectively). These scores were then standardized within the matching population, ignoring trial arm, whereafter we standardized again across all individuals for the population-pooled analyses. Due to restricting analyses to genotyped individuals from the three largest population groups, trial randomization was, by definition, broken. To investigate potential shifts in the PGS, we plotted its distribution by trial arm, which showed minimal shifts in distribution (Extended Data Fig.
).
To investigate weight change, we focused on annual weight measurements. The weight loss nadir in both trials is known to occur at the 1-year mark, after which many individuals start regaining weight. As such, we separately examined the association of the PGS with (i) weight at year 1 (adjusting for baseline weight) and (ii) repeated weight measurements beyond year 1 up to year 4 (adjusting for weight at year 1) in the subset of individuals who had lost ≥3% of their initial body weight in the first year. Analyses were run by trial arm using linear mixed models with random intercepts, with additional covariates being age, sex, principal components, the initial weight measurement (baseline or year 1) and random slopes for time (years). In addition, we ran trial–arm combined models in which we examined interaction effects between trial arm and PGS. These interaction terms are our main estimates of interest, as they reflect how genetic predisposition to obesity modifies the intervention’s effect on weight change, relative to the comparison arm. Study-specific main and interaction effect estimates were separately pooled by means of inverse-variance weighted fixed-effect meta-analysis. Participant characteristics are presented in Supplementary Table
Reporting summary
Further information on research design is available in the
Nature Portfolio Reporting Summary
linked to this article.
Data availability
The PGS variant-level weights for the GIANT-only and GIANT+23andMe multi-ancestry PGSs are available for download through the PGS Catalog (
, publication ID PGP000724, score IDs PGS005198–PGS005204). The source data of the figures are provided in the supplementary tables. This research has been conducted using UKBB research under application number 1251. The UKBB genotype and phenotype data are available through the UKBB at
. Pan-UKBB relatedness and ancestry determinations can be obtained via UKBB return 2442. Due to US Department of Veterans Affairs (VA) regulations and our ethics agreements, the analytic datasets used for this study are not permitted to leave the Million Veteran Program (MVP) research environment and the VA firewall. This limitation is consistent with other MVP studies based on VA data. However, the MVP data are made available to researchers with an approved VA and MVP study protocol. Information regarding access to the Bio
Me
Biobank is available at
. For access to the Uganda GPC-UGR data, contact should be made to the director of the MRC/UVRI & LSHTM Uganda Research Unit, Entebbe, by email to mrc@mrcuganda.org. The data underlying this article are available upon reasonable request to the PLCO Cancer Data Access System:
. Requests from researchers to access ALSPAC data and samples are welcomed; relevant procedures can be found at
. Data from the Diabetes Prevention Program (V9) (
) reported here are available for request at the NIDDK Central Repository (NIDDK-CR) website, Resources for Research (R4R) (
). The Diabetes Prevention Program genotype data were downloaded from the dbGaP website under
phs000681.v2.p1
. Researchers can access data from the primary Look AHEAD data trial on the NIDDK repository. Due to limitations specified in informed consent, the Look AHEAD genetic data are not publicly available.
Code availability
We used publicly available software tools for all analyses. These software tools are listed in the main text and in the
Methods
. Of particular note, we used PRS-CS (
) and PRS-CSx (
) to develop the PGSs.
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Acknowledgements
We acknowledge the participants in each cohort contributing to this study. Additional acknowledgements are provided in the Supplementary Note. Support for title page creation and format was provided by AuthorArranger (
), a tool developed at the National Cancer Institute. This research was supported by the following funding bodies, which had no role in study design, data collection and analysis, preparation of the manuscript or decision to publish. US National Institutes of Health (NIH): 01HG006934-01 (G.R.A.), 1 U19 CA148537-01, 1K08 HG010155 (A.V.K.), 1RC2 MH089951, 1RC2 MH089951-01, 1RC2 MH089995, 1U01 HG011717 (S.F.), 1U01 HG011719 (A.V.K.), 1U01 HL172180 (S.F.), 1U19 CA148065, 1U19 CA148112, 1U19 CA148537, 5-P60-AR30701, 5-P60-AR49465, 5UM1 CA182883, 75N92020D00001, 75N92020D00002, 75N92020D00003, 75N92020D00004, 75N92020D00005, 75N92020D00006, 75N92020D00007, 75N92021D00001, 75N92021D00002, 75N92021D00003, 75N92021D00004, 75N92021D00005, 75N92021D00006, AA-00145, AA-09203, AA-12502, AA009367, AA014041, AA07535, AA10248, AA13320, AA13321, AA13326, AA15416, AG000513, CA047988 (J.E.Bu.), CA054281, CA063464, CA098758, CA141298, CA164973, DA005147, DA036216, DA037904, DA044283, DA12854, DK056350, DK063491, DK078150, DK125187 (Y.V.S.), DK56990, DK56992, DK57002, DK57008, DK57078, DK57131, DK57135, DK57136, DK57149, DK57151, DK57154, DK57171, DK57177, DK57178, DK57182, DK57219, ES010126, EY014684, EY014684-03S1, EY014684-04S1, Fogarty grant D43 TW009077, HG011052 (X. Zhu), HHSC268200782096C, HHSN268200625226C, HHSN268200782096C, HHSN268200800007C, HHSN268200960009C, HHSN268201200008I, HHSN268201200036C, HHSN268201300001I/N01-HC-65233, HHSN268201300002I/N01-HC-65235, HHSN268201300003I/N01-HC-65236, HHSN268201300004I/N01-HC-65234, HHSN268201300005I/N01-HC-65237, HHSN268201500001I, HHSN268201500003I, HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I, HHSN268201700005I, HHSN268201800001C, HHSN268201800003I, HHSN268201800004I, HHSN268201800005I, HHSN268201800006I, HHSN268201800007I, HHSN268201800010I, HHSN268201800011I, HHSN268201800012I, HHSN268201800013I, HHSN268201800014I, HHSN268201800015I, HHSN271201100005C, HHSN271201200022C, HL043851 (J.E.Bu.), HL046389 (Su.R.), HL080467 (J.E.Bu.), HL085144, HL113338 (Su.R.), HL129982 (H.M.H.), HL154996, HL54471, HL54472, HL54473, HL54495, HL54496, HL54509, HL54515, intramural project number 1Z01-HG000024 (F.S.C.), K01 HL135405 (B.E.C.), K02 AA018755, K05 CA175147, K99/R00 HL130580 (A.E.J.), KL2 RR024990, KL2 TR002490 (L.M.R.), N.1-AG-1-1, N.1-AG-1-2111, N01-AG-1-2100, N01-AG-1-2109, N01-AG-6-2101, N01-AG-6-2103, N01-AG-6-2106, N01-HC-25195, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, N01-HD-1-3228, N01-HD-1-3329, N01-HD-1-3330, N01-HD-1-3331, N01-HD-1-3332, N01-HD-1-3333, N01-HG-65403, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, N02-HL-64278, National Institute on Aging Intramural Research Program (E.M.S.), NR013520 (Y.V.S.), P01 CA055075, P01 CA87969, P30 AG10161, P30 AG72975, P30 DK072488, P30-CA015704, P30AR072580, P50 GM115305, R01 24799, R01 AG023629, R01 AG028050, R01 AG034454, R01 AG056477 (M.Ki.), R01 AG065357, R01 AG17917, R01 AG30146, R01 CA056678, R01 CA080122, R01 CA082664, R01 CA092579, R01 CA128813 (J.Y.P), R01 CA128978, R01 CA144034, R01 CA148677, R01 CA15847, R01 CA49449, R01 CA55069, R01 CA64277, R01 CA67262, R01 CA80205, R01 D0042157-01A, R01 D0042157-01A1, R01 DA042755, R01 DK053591, R01 DK062370 (M.B.), R01 DK066358, R01 DK072193 (CHNS, E.P.W., K.L.M.), R01 DK075787 (J.N.H., R.J.F.L., DGI), R01 DK087914, R01 DK089256 (C.-T.L., X.D., FamHS), R01 DK092127-04, R01 DK101855, R01 DK104371, R01 DK107786 (R.J.F.L.), R01 DK110113 (R.J.F.L.), R01 DK118011 (C.N.S.), R01 DK118427, R01 DK122503 (A.E.J., C.-T.L., G.C., K.E.N. and Q.M.), R01 DK124097 (R.J.F.L.), R01 DK135938 (A.E.J. and Y.L.), R01 DK136671 (C.N.S.), R01 DK139598 (K.E.N.), R01 EY022310, R01 HD056465 (S.F.A.G., CHOP), R01 HD074711, R01 HD100406, R01 HD30880, R01 HD58886, R01 HG003054, R01 HG010297 (R.J.F.L. and V.L.B.), R01 HG011035, R01 HL046380, R01 HL053353, R01 HL074166, R01 HL085251, R01 HL086694, R01 HL086718, R01 HL087641, R01 HL087652, R01 HL087660, R01 HL091357, R01 HL09135701, R01 HL092577 (P.T.E.), R01 HL095056, R01 HL103612, R01 HL104135, R01 HL105756, R01 HL108427, R01 HL111249, R01 HL111249-04S1, R01 HL113338, R01 HL117078, R01 HL119443, R01 HL120393, R01 HL139731 (S.A.Lu.), R01 HL142302 (K.E.N. and R.J.F.L.), R01 HL151152 (K.E.N., R.J.F.L. and V.L.B.), R01 HL156991 (K.E.N.), R01 HL157635 (P.T.E. and S.A.Lu.), R01 HL158884 (Y.L.), R01 HL160689 (R.W.M.), R01 HL163262 (K.E.N.), R01 HL163262-01A1 (M.Gr.), R01 HL172803, R01 HL177209 (P.T.E.), R01 HL77398, R01 MH081802, R01 MH58799-03, R01 NR012459, R01 NS032830, R01 NS100178 (J.W.C.), R01 NS105150 (J.W.C.), R01 NS114045 (J.W.C.), R01 NS45012, R03 HL154284 (B.E.C.), R21 DK105913, R35 CA53890, R35 HL135818 (Su.R., CFS), R35 HL135824 (C.J.W.), R37 HL045508, R56 HL150186 (Y.L.), RC2 AG036495, RC2 GM092618, RC4 AG039029, RF1 AG15819, RR020649, S10OD017985, S10OD026880, S10OD030463, S10RR025141, T32 HL007055 (H.M.H.), T32 HL129982 (L.M.R.), TW005596, U01 167552, U01 AG009740, U01 AG023746, U01 CA176726, U01 CA188392, U01 DK062418, U01 DK105535, U01 DK105556, U01 HG004402, U01 HG004423, U01 HG004436, U01 HG004438, U01 HG004446, U01 HG004798, U01 HG006378, U01 HG007416 (K.E.N.), U01 HG011723 (R.J.F.L.), U01 HL054457, U01 HL054464, U01 HL054481, U01 HL054512, U01 HL080295, U01 HL130114, U01 HL54498, U01 HL54527, U01 NS069208-01, U01AG46152, U01AG61256, U01HG006379, U01HG008657, U01HG008664, U01HG008666, U01HG008672, U01HG008673, U01HG008676, U01HG008679, U01HG008680, U01HG008684, U01HG008685, U01HG008701, U10 CA37429, U19 AG063893, U19 CA148065, U19 CA148537, U19 HL065962, U24 MH068457-06, U54MD007593, UL1 RR024975, UL1 TR000077, UL1 TR000445, UL1 TR001878, UL1 TR001881, UL1 TR002243, UL1 TR004419, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, UL1RR025005, UL1TR001881, UM1 CA182876, UM1 CA182910, UM1 CA182913 (J.E.Bu.), UM1 CA186107, UM1 DK126194 (S.F.A.G.), X01 HG007492, Z01 CP010119, ZIA CP010152-20. The European Union: EMBO 3573 (Kr.L.), ERDF European Regional Development Fund 539/2010 A31592, European Regional Development Fund (Kr.L.), 018996, EUROSPAN (LSHG-CT-2006-018947), 223004, HEALTH-F4-2007-201413 by the European Commission (QLG2-CT-2002-01254), EUROHEADPAIN (602633), HEALTH-F4-2007-9602768, HEALTH-F2-2009-223175, EpiMigrant, 279143, RiskyCAD (305739), HEALTH-F2-2013-601456 (CVGenes@Target), research and innovation programme (G.E.D.), EU H2020 Marie Sklodowska-Curie grant agreement no. 786833 (J.R.), EU H2020 research and innovation programme (634935 and 633784), EU H2020 TO_AITION 848146 (S.W.v.d.L.), EU H2020 (iHealth-T2D, 643774), EU H2020 (755320 for TAXINOMISIS and 848146 for To Aition), EU H2020 contract no. 733100 (P.C.), 2014-2020.4.01.16-0125 (A.M), EU HORIZON NextGen 101136962 (S.W.v.d.L.), EU HORIZON MIRACLE 101115381 (S.W.v.d.L.), LSHM-CT-2007-037273, the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115317 (DIRECT), the Innovative Medicines Initiative 2 Joint Undertaking 115974, the ERA-Net Cofund action no. 727565, MAESTRIA 965286 (P.T.E and, P.Ki.), the European Social Fund (M.S.-L.), SOC 95201408 05F02, the European Community (SOC 98200769 05F02), QLG1-CT-2001-01252, the European Science Foundation (EuroSTRESS project FP-006), EU pBETTER4U_EU 101080117, LSHM-CT-2006-037593, EXGENESIS, European Commission 018947 (LSHG-CT-2006-01947), EHRA-PATHS (945260). The UK National Institute for Health Research (NIHR) Centers: RP-PG-0407-10371, the NIHR Biomedical Research Centre at the Institute of Cancer Research, the NIHR Cambridge Biomedical Research Centre, NIHR Cardiovascular Biomedical Research, NIHR Comprehensive Biomedical Research Centre Imperial College Healthcare NHS Trust, the NIHR Exeter Clinical Research Facility, the NIHR Health Protection Research Unit on Health Impact of Environmental Hazards, the NIHR Leicester Cardiovascular Biomedical Research Centre (BRC-1215-20010) (C.P.N., P.S.B. and N.J.S.), the NIHR Manchester Biomedical Research Centre (K.M. and A.L.), the NIHR Official Development Assistance (ODA, 16/136/68), the NIHR Oxford Biomedical Research Centre, the NIHR to the Biomedical Research Centre at the Institute of Cancer Research and Royal Marsden Foundation NHS Trust, the NIHR-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St. Thomas’ NHS Foundation Trust in partnership with King’s College London (TwinsUK and M.M.), the UK National Institute for Health Research Academic Clinical Fellowship (S.J.H.), University of Bristol NIHR Biomedical Research Centre (BRC-1215-2001) (N.J.T.). Netherlands Organization for Scientific Research (NWO): NWO 175.010.2003.005, NWO 480-05-003, NWO 481-08-013, NWO 481-11-001, NWO Gravitation 024.001.003, NWO Vi.C.191.021, NWO Vici 016.130.002, 453-16-007/2735, NWO/SPI 480-15-001/674, NWO/SPI 56-464-14192, 10-000-1002, 911-03-012, 175.010.2005.011, NWO Division for the Social Sciences (MaGW) VIDI 016-065-318 (D.P.), the Netherlands Genomics Initiative (NGI)/NWO Netherlands Consortium for Healthy Aging (NCHA) 050-060-810, NGI/NWO - the Consortium for Systems Biology (NCSB), the Center for Medical Systems Biology (CSMB, NWO Genomics), The Netherlands Organization for Health Research and Development (ZonMw) 6130.0031, NWO/ZonMw Rubicon grant 452182321 (R.A.J.S.), ZonMW 100-001-004, ZonMW 261-98-710, ZonMW 60-60600-97-118, ZonMW 636340002, ZonMW 916.19.151 (H.H.H.A.), ZonMw 95103007, the Dutch Research Council (Vidi Innovational Research Award 016.096.309), Medical Research Council program GB-MW 940-38-011. UK Medical Research Council (MRC): G0000934, G0601966, G0700931, G1001799, G9521010D, K013351, MC_PC_13046, MC_PC_13049, MC_U137686851, MC_UU_00007/10 (V.V. and A.F.W.), MC_UU_00017/1, MC_UU_12015/1, MC_UU_12026/2, MC-PC-14135, MR/M016560/1, MR/N013166/1 (K.A.K.), MRC Integrative Epidemiology Unit MC_UU_00011/1 (N.J.T. and R.E.Mi.), National Health and Medical Research Council Leadership Investigator GNT 2025349 (E.Hy.), R024227 (M.Ki.), the Medical Research Council Doctoral Training Programme in Precision Medicine (MR/N013166/1) (P.R.H.J.T.), the MRC-PHE Centre for Environment and Health, the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement (GPC-UGR), the UKRI Medical Research Council MC_UU_00019/1, the UKRI Medical Research Council MR/Y014022/1 (A.W., N.C.), U.MC_UU_00007/10, Y014154 (M.Ki.). Wellcome Trust: 068545/Z/02, 072960/Z/03/Z, 076113, 076113/B/04/Z, 084723/Z/08/Z, 084726/Z/08/Z, 084727/Z/08/Z, 085475/B/08/Z, 085475/Z/08/Z, 088158/Z/09/Z, 090532, 090532/Z/09/Z, 098051, 098381, 104036/Z/14/Z, 104085/Z/14/Z, 106130, 201543/B/16/Z, 202922/Z/16/Z, 203141, 203141/Z/16/Z, 212259, 212904/Z/18/Z, 212946/Z/18/Z, 216767/Z/19/Z, 217065/Z/19/Z (N.J.T.), 220740/Z/20/Z (S.F.), 221854/Z/20/Z (M.Ki.), Institutional Strategic Support Fund (204813) (K.H.W.), the NIHR/Wellcome Trust Imperial Clinical Research Facility, Wellcome Trust Investigator 202802/Z/16/Z (N.J.T.), Wellcome Trust Strategic Award (079895), WT095219MA, WT098051. The Novo Nordisk Foundation (NNF): 12955 (B.F.), NNF NNF20OC0062294 (Tu.K.), NNF15OC0015896, NNF18CC0034900 (M.R.C., N.G., R.J.F.L. and T.O.K.), NNF22OC0074128 (T.O.K.), NNF20OC0059313 (R.J.F.L.), NNF23SA0084103 (M.R.C., N.G., R.J.F.L. and T.O.K.), grant NNF20SA0067242 from the Danish Cardiovascular Academy, which is funded by the Novo Nordisk Foundation (M.G.-M.), the Danish Diabetes Academy, Novo Nordisk Foundation, NNF17SA0031406 (M.R.C.), the TriPartite Immunometabolism Consortium (TrIC)-NNF NNF15CC0018486. Australian National Health and Medical Research Council (NHMRC): NHMRC Practitioner Fellowship GNT1103329 (A.W.H.), NHMRC Practitioner Fellowship GNT1154518 (D.A.M.), NHMRC Investigator Grant APP1172917 (S.E.M.), NHMRC Career Development Fellowship (J.B), 209057, 211069, 241944, 302010, 389875, 389891, 389892, 389938, 390130, 396414, 403981, 442915, 442981, 457349, 496688, 496739, 512423, 552485, 571013, 572613, 613601, 613672, 614296, 974159, 1009458, 1011506, 1021858, 1027449, 1044840, 1059711, 1074383, 1113400 (P.M.Vis.), 1186500 (D.F). Academy of Finland: 100499 (J.K.), 117797, 118555 (J.K.), 124243, 126925, 121584, 124282, 129378, 141054 (J.K.), 141071, 205585 (J.K.), 206374, 251360, 264146 (J.K.), 276861, 285547, 308248 (J.K.), 312073 (J.K.), 322098, 336823 (J.K.), 350426 (M.Ki.), 352792 (J.K.), 356405, 286284, 134309, 77299, Center of Excellence in Complex Disease Genetics (312062) (Sa.R.). Cancer Research UK (CRUK): C12292/A11174, C1281/A12014, C1287/A10118, C1287/A10710, C1287/A16563, C16077/A29186, C16913/A6135, C490/A10124, C500/A16896, C5047/A10692, C5047/A1232, C5047/A15007, C5047/A3354, C5047/A7357, C5047/A8384, C8197/A16565, C18281/A29019 (N.J.T.), RCCPDF\100007 (K.H.W.). The Swedish Research Council: 2016-06830 (G.H.), 2017-00641, 2021-02273 (J.G.S.), grant for the Swedish Infrastructure for Medical Population-based Life-course Environmental Research – SIMPLER, Linnaeus grant Dnr 349-2006-237, Strategic Research Area Exodiab Dnr 2009-1039, the Swedish Heart-Lung Foundation (2022-0344 and 2023-03332) (J.G.S.). The Netherlands Heart Foundation: NHS2010B233, NHS2010B280, 2001 D 032 (J.W.J.), Dutch Heart Foundation (DHF) (P.Ki. and M.R.), RACE V (CVON 2014–9), RED-CVD (CVON2017-11), from Top Sector Life Sciences & Health to the Dutch Heart Foundation (PPP Allowance; CVON-AI (2018B017)), Dutch Heart Foundation and ZonMW 01-002-2022-0118 EmbRACE. The European Research Council (ERC): ERC-2011-StG 280559-SEPI, ERC-STG-2015-679242 (J.G.S.), ERC-230374, ERC-2017-STG-757364, ERC-CoG-2015-681466, ERC Grant 310644 MACULA, CoG-2015_681742_NASCENT, ERC 742927. The British Heart Foundation (BHF): 32334, AA/18/2/34218 (P.Ki.), CH/1996001/9454, FS/14/66/3129 (O.G.), RG/14/5/30893 (Pa.D.), RG/17/1/32663, SP/04/002, SP/16/4/32697 (C.P.N), the British Heart Foundation Centre for Research Excellence (H.W.). German Research Foundation (Deutsche Forschungsgemeinschaft (DFG)): 209933838 – SFB 1052; B03, C01; SPP 1629 TO 718/2- 1, 387509280 – SFB 1350 (I.M.H.), 431984000 - SFB 1453 (M.Wu.), BR 6028/2-1 (Ca.B), DFG BO 3815/4-1 (I.M.H., C.A.B.), HE 3690/7-1 (I.M.H), Ki 509167694 (P.Ki.). German Research Council (DFG): EI 969/2-3, ER 155/6-1;6-2, HO 3314/2-1;2-2;2-3;4-3, INST 58219/32-1, JO 170/8-1, KN 885/3-1, PE 2309/2-1, SI 236/8-1;9-1;10-1. The American Heart Association (AHA): 15GRNT25880008, 15POST24470131 (C.N.S.), 17IBDG33700328 (J.W.C.), 15GPSPG23770000 (J.W.C.), 17POST33650016 (C.N.S.), 18SFRN34250007 (S.A.Lu.), 961045 (P.T.E.). The American Diabetes Association (ADA): Jr Faculty Development 11-22-JDFPM-06 (C.N.S.), 11-23-PDF-35 (A.H.-C.). German Federal Ministry of Education and Research (BMBF): 01ZZ0103, 01ZZ0403, 01ZZ9603, 03IS2061A, 03ZIK012, BMBF 01EA1801A, BMBF 01ER0804 (K.-U.E.), BMBF 01ER1206, BMBF 01ER1507 (I.M.H.). German Ministry of Education and Science: 01EG0401, 01ER1001D, 01GI0205, 01GI0856, 01GI0860, 01GS0820_WB2-C, the Federal Ministry of Science, Germany (01 EA 9401). Australian Research Council (ARC): DP0770096, DP1093502, FL180100072 (P.M.Vis.), FT220100069 (Lo.Y.). The US Department of Veterans Affairs: Baltimore Geriatrics Research, Education, and Clinical Center, Merit Grant BX004821, BX003340, BX003362 and BX005831, I01-BX004821 (MVP 001, P.W.F.W. and Kelly Cho). Additional funding came from the following sources. ‘Indremedisinsk Forskningsfond’ at Bærum Hospital; 23andMe; a Royal Society URF (J.F.W.); Abbott; Accare Centre for Child and Adolescent Psychiatry; ADAS software (S.N.); AFNET; Agence Nationale de sécurité sanitaire de l’alimentation, de l’environnement et du travail (ANSES); AICR Netherlands A10-0227; Amgen (P.M.R. and D.I.C.); Amsterdam Public Health (APH); Arthritis Research UK; Astellas Pharma, Inc.; AstraZeneca; Avera Institute for Human Genetics; Bayer Pharma AG; Beijing Municipal Center for Disease Prevention and Control; Beijing Municipal of Health Reform and Development Project no. 2019-4 (Beijing Eye Study); Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL), CP 32; Biosense Webster (S.N.); Boston University Chobanian & Avedisian School of Medicine; Bristol Myers Squibb; Budgetary funds from the National Institute for Health and Welfare (THL) of Finland; Business Finland through the Personalized Diagnostics and Care program coordinated by SalWe Ltd. (3986/31/2013); California Cancer Research Fund 99-00527V-10182; Cancer Australia; Cancer Australia PdCCRS (J.B); Cancer Centre at Barts and QMUL (H.R.W.); Cancer Council Queensland (J.B); Cancer Council Tasmania; Cancer Councils of Victoria and South Australia; Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6); Chinese National Human Genome Center at Shanghai; Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (X51001); Concerted Research Action of the KU Leuven (GOA/15/017) (F.C. and S.J.); Council of Scientific and Industrial Research (CSIR), Government of India, grant no. BSC0122; Curtin University; D70-RESRICGIROTTO (G.G.); Daiichi Sankyo Co., Ltd.; Danish Ministry of Internal Affairs and Health; Dell Loy Hansen Heart Foundation (M.J.Cu.); Department of Biotechnology, Government of India (C.S.Y.); Department of Science and Technology, Government of India through PURSE II CDST/SR/PURSE PHASE II/11 provided to Jawaharlal Nehru University; Departments, Division and the Board of Directors of the Leiden University Medical Centre; Dutch Ministry of Economic Affairs, Agriculture and Innovation (project KB-15-004-003); Edith Cowan University; EFPIA companies; EHRA; Erasmus MC, Rotterdam; Estonian Research Council PUT 1371 (Kr.L.), PUT PRG1291 (T.E); Finska Läkaresällskapet (E.W.); Fondation ARC; Fondation de France; Fondazione di Sardegna (U1301.2015/AI.1157. BE Prat. 2015-1651); Fondo Sectorial de Investigación en Salud y Seguridad Social (SSA/IMSS/ISSSTE CONACYT, project 150352) in Mexico; Foundation for the National Institutes of Health through the Accelerating Medicines Partnership (no. HART17AMP) and the Parelsnoer Initiative (PSI); Foundation Leducq: ‘PlaqOmics’ and ‘AtheroGen’, 24CVD01 (P.T.E.); Funding Program for Next-Generation World-Leading Researchers (NEXT Program, no. LS056).; FWO Vlaanderen (G.0684.12N and G.0830.13N) (F.C. and S.J.); Generalitat de Catalunya; Genome Quebec and Genome Canada (G.L.); German Cancer Aid (70-2488-Ha I, ULM); German Centre for Cardiovascular Research (DZHK): FKZ 81×2800182 (P.Ki.), FKZ 81Z0710116 (P.Ki.), FKZ 81Z0710110 (P.Ki.); GlaxoSmithKline; governmental funding of clinical research within the Swedish National Health Service (J.G.S.); Greek Secretariat for Research and Technology (Cooperation 890/2009); Health Care Centers in Vasa, Närpes and Korsholm; Health~Holland PPP Allowance ‘Getting the Perfect Image’ (S.W.v.d.L.); Heinz Nixdorf Foundation; Helmholtz Zentrum München–German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria; Hjartavernd (the Icelandic Heart Association); Hong Kong Kadoorie Charitable Foundation; ImriCor (S.N.); Institut National du Cancer (INCa); Instituto de Salud Carlos III: CPII22/00007 (M.S.-L.), FEDER (PI08/1770, PI09/00773-Cantabria, PI11/01889-FEDER, PI12/00265, PI12/01270, PI12/00715, PI15/00069, RD09/0076/00036); Italian Ministry of Health and the US National Institute on Aging (263 MD 9164 and 263 MD 821336); Italian Ministry of Health through RC 01/21 (M.P.C.).; Juvenile Diabetes Research Foundation International (JDRF) (BEAT-DKD); Komen Foundation for the Cure; Korea Healthcare Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (A102065); Korea National Institute of Health intramural grant 2019-NG-053-02; Kuwait Foundation for the Advancements of Sciences (KFAS) (T.A.T. and F.A.-M.); Leiden University Medical Center; Leo Laboratories; Ligue départementale du Val de Marne; Ligue Nationale Contre le Cancer; Medical University of Graz (ASPS); Merck Sharp & Dohme Corp.; MEXT (16H05250, 15K19242, 16H06277, 19K19434, 20K10514, 21H03206, 22H04923, 22H03350, 23K24608, 24K02690); Mitsubishi Tanabe Pharma Corporation; Murdoch University; National Institute of Health, Republic of Korea (2022-NI-067-02); National Institute of Health, Republic of Korea (4845–301, 4851–302, 4851–307); National Key Research and Development Program of China (2016YFC 0900500, 0900501, 0900504, 1303904); National Research Foundation of Korea (NRF): RS-2023-00245960 (Y.S.C.), NRF-2023R1A2C2006416 (H.-N.K.); NCHA (Netherlands Consortium Healthy Ageing) Leiden/Rotterdam; Netherlands Twin Registry Repository and the Biobanking and Biomolecular Resources Research Infrastructure (BBMRI–NL, 184.021.007 and 184.033.111); Neuroscience Campus Amsterdam (NCA); NextGenerationEU Project code CN_00000033, CUP B83C22002930006; Nordic Center of Excellence in Health-Related e-Sciences (Xueping.L.); Novartis Institute for Biomedical Research; Novo Nordisk, Inc.; NZO (Dutch Dairy Association); Oak Foundation fellowship (B.F.); Otsuka Pharmaceutical Co., Ltd.; Pfizer; philanthropic donation to Northshore University Health System; Pla Director d’Oncologia de Catalunya (XBTC); Prostate Cancer Canada Movember Discovery Grant (D2013-17) (RJH); Prostate Research Campaign UK (now PCUK/Prostate Action); Queen’s University Belfast; Region of Southern Denmark; Research Foundation of Copenhagen County; Research Project for Excellence IKY/SIEMENS; Rosetrees Trust; Royal Marsden Foundation NHS Trust; Safe Work Australia, WA Health, Government of Western Australia (WADOH) (Future Health WA G06302); Sanofi; Sardinian Autonomous Region (L.R. no. 7/2009) cRP3-154; Servier Research Group; Shanghai Municipal Science and Technology Major Project (2017SHZDZX01); Siemens Healthineers; Social Sciences Council medium-sized investment grants GB-MaGW 480-01-006 and GB-MaGW 480-07-001; Social Sciences Council project grants GB-MaGW 452-04-314 and GB-MaGW 452-06-004; Special Governmental Grants for Health Sciences Research, Turku University Hospital; Stanley Medical Research Institute; Taisho Pharmaceutical Co., Ltd.; Takeda Medical Research Foundation; Takeda Pharmaceutical Co., Ltd.; Temas Prioritarios de Salud Instituto Mexicano del Seguro Social (2014-FIS/IMSS/PROT/PRIO/14/34); the Aarhus and Copenhagen universities and university hospitals; the Accelerating Clinical Trials funding stream in Canada (P.Ki.); the Agency for Science, Technology and Research, Singapore; the ALF/LUA research grant in Gothenburg; the Althingi (the Icelandic Parliament); the Amsterdam University Medical Center, location VUmc; the Anthony DeNovi Fund; the Assessorato Ricerca Regione Campania (POR CAMPANIA 2000/2006 MISURA 3.16); the Atlantic Philanthropies; the Augustinus Foundation; the Augustinus Foundation; the Austrian Ministry of Science; the Austrian National Bank Anniversary Fund, P15435; the Austrian Science Fond (FWF) P20545-P05, P13180, P20545-B05; the Baden Württemberg Ministry of Science, Research and Arts; the Becket Foundation; the Belgian federal government (National Cancer Plan KPC_29_023) (F.C. and S.J.); the Biomedical Research Council (BMRC) of Singapore (08/1/35/19/550, 09/1/35/19/616); the Breast Cancer Research Foundation; the Broad Institute; the C.G. Sundell Foundation; the Canada Research Chair Program (G.L.); the Canadian Cancer Society Research Institute (2013-702108) (RJH); Cancer Foundation Finland; the Catalan Government DURSI 2009SGR1489; the Center for Translational Molecular Medicine; the Center of Innovation Program, the Global University Project from the Ministry of Education, Culture, Sports, Science and Technology of Japan; the Centre for Ageing Research and Development in Ireland; the Chief Scientist Office of the Scottish Government (CZB/4/276, CZB/4/710); the Children’s Hospital of Philadelphia; the Childrenʼs Hospital of Philadelphia Endowed Chair in Genomic Research; the China-Japan Friendship Hospital, Ministry of Health for support for CHNS 2009; the CNR project FOE-2021 DBA.AD005.225 NUTRAGE; the Competitive Research Funding of the Tampere University Hospital (9M048, 9N035); the Consejo Nacional de Ciencia y Tecnología (CONACYT) in Mexico (2092, M9303, F677, 251); the Core Research for Evolutional Science and Technology (CREST) from the Japan Science Technology Agency; the Cotswold Foundation; the Council of Scientific and Industrial Research, Indian Ministry of Science and Technology (G.R.C.); the Daniel B. Burke Endowed Chair for Diabetes Research (S.F.A.G., CHOP); the Danish Centre for Health Technology Assessment; the Danish Diabetes Association; the Danish Heart Foundation (M.G.-M., Inter99); the Danish National Research Foundation; the Danish Pharmaceutical Association; the Danish Pharmacists’ Fund; The Danish Research Council for Independent Research; the Department of Innovation, Research, and University of the Autonomous Province of Bolzano-South Tyrol; the Diabetes Research Foundation of Finnish Diabetes Association; the Donald C. McGraw Foundation; the Dutch Brain Foundation; the Dutch Kidney Foundation (grant E0.13); the Dutch Ministry of Justice (WODC); the Economic and Social Research Council (ESRC) UK: ES/H029745/1, ES/L008459/1, ES/M001660/1, ES/S007253/1 (M.Ku.), ES/T002611/1 (M.Ku.), ES/T014083/1 (M.Ku.); the Egmont Foundation; the Elizabeth Blackwell Institute for Health Research, University of Bristol (K.H.W.); the Else Kröner-Fresenius-Stiftung: P.Ki., 2012_A147; the Emil Aaltonen Foundation; The Everyman Campaign; the Eye Birth Defects Foundation, Inc.; the Faculty of Biology and Medicine of Lausanne; the Federal State of Mecklenburg-West Pomerania; the Finnish Cardiac Research Foundation; the Finnish Cultural Foundation; the Finnish Diabetes Research Foundation; the Finnish Foundation for Cardiovascular Research (Ve.S. and Sa.R.); the Finnish innovation fund Sitra (E.W.); the Finnish Medical Society; the Finnish Ministry of Education and Culture; the Finnish Society of Clinical Chemistry; the Folkhälsan Research Foundation; the Foundation for Pediatric Research, Finland; the Foundation for Strategic Research and the Stockholm County Council (560283); the Free State of Saxony; the French Ministry of Research; the Fundación IMSS Mexico; the Fundación Marqués de Valdecilla (API 10/09); the FWO (TVDB); the Galician Foundation of Genomic Medicine; the General Clinical Research Center Program (GCRC); the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health; the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC; the German Heart Foundation (DSF); the German Ministry of Cultural Affairs; Gothenburg University; the Government of Canada through Genome Canada, Genome Québec, the Canadian Institutes of Health Research (CIHR): FRN-CCT-83028, GPH-129344, MOP-82893, operating grants (E.J.P.) and a New Investigator Award (E.J.P.), PJT no. 156248 (G.L.)., the Department of Defence (W81XWH-10-1-0341), the Ministère de l’Économie et de l’Innovation du Québec, the Ministère de l’Économie, Science et Innovation du Québec, the PSRSIIRI-701 grant.; grant of the National Center for Global Health and Medicine (NCGM); the Gratama Foundation; the Greek General Secretary of Research and Technology (PENED 2003); the Health and Social Care Research and Development Division of the Irish Public Health Agency; the Health Fund of the Danish Health Insurance Societies; the Helsinki University Central Hospital Research Foundation; the Ib Henriksen Foundation; the Italian Ministry of Education, University and Research (MIUR) 5571/DSPAR/2002; the Italian Ministry of Universities; the Jan Dekker Foundation; the Japan Agency for Medical Research and Development (AMED): the Program for an Integrated Database of Clinical and Genomic Information (kk0205008), the Practical Research Project for Rare/Intractable Diseases (ek0109070, ek0109283, ek0109196, ek0109348); the Juho Vainio Foundation (Ve.S., YFS); the KfH Foundation for Preventive Medicine (the KfH Stiftung Präventivmedizin); the KfH Stiftung Präventivmedizin e.V. (C.A.B.); the Kids Research Institute Australia; the Knut and Alice Wallenberg Foundation; the Leiden University, Research Profile Area ‘Vascular and Regenerative Medicine’; the Leipzig Research Center for Civilization Diseases (LIFE); the Lundbeck Foundation (R155-2013-14070); the Lundberg Foundation; the March of Dimes Birth Defects Foundation; the Medical Research Council of Canada; the Ministry of Innovation, Science, Research and Technology, North Rhine-Westphalia (MIWFT-NRW); the Montreal Heart Institute Biobank; the Montreal Heart Institute Foundation (G.L.); the Morris and Horowitz Families Endowed Professorship; the Movember Foundation grant no. D2013-36; the Municipal Health Care Center and Hospital in Jakobstad; the Närpes Health Care Foundation; the National Cancer Research Institute (NCRI) UK; the National Cancer Research Network UK; the National Health Research Institutes, Taiwan (MG-112-SP-09, MG-112-PP-18, MG-113-GP-03); the National Natural Science Foundation of China (81970684, 91846303); the National Recovery and Resilience Plan (NRRP), European Union; the National Research Foundation, Singapore (HUJ-CREATE Programme 370062002); the National Science Council, Taiwan (NSC 98-2314-B-075A-002-MY3); the National University Health System, Singapore; the National University of Singapore; the Netherlands Cardiovascular Research Initiative (DECISION project 848090001); the Nordic Center of Excellence in Disease Genetics; the Norwegian Atrial Fibrillation Research Network; the Office of Research on Minority Health (ORMH); the Office of the First Minister and Deputy First Minister of Ireland; the Ollqvist Foundation; the Oogfonds, MaculaFonds, Landelijke Stichting voor Blinden en Slechtzienden, Stichting Blindenhulp, Stichting A.F. Deutman Oogheelkunde Researchfonds; the Orchid Cancer Appeal; the Oulu University Hospital, 24301140; the Ovarian Cancer Research Fund; the Paavo Nurmi Foundation; the Perelman School of Medicine at the University of Pennsylvania; the Perklén Foundation; the Program for Promotion of Fundamental Studies in Health Sciences, National Institute of Biomedical Innovation Organization (NIBIO); the Prostate Cancer Foundation of Australia; the Prostate Cancer Research Foundation; the Quebec Breast Cancer Foundation; the Raine Study Medical Research Foundation; the Republic of Croatia Ministry of Science, Education and Sports grant 108-1080315-0302 (I.R.); the Research Council of Norway (287086); the Research Institute for Diseases in the Elderly (014-93-015; RIDE2); the Robert Dawson Evans Endowment of the Department of Medicine at Boston University Chobanian & Avedisian School of Medicine and Boston Medical Center; the Ronald and Rita McAulay Foundation; the Royal Marsden NHS Foundation Trust; the Science Foundation Ireland-Department for the Economy (SFI-DfE) Investigator Program Partnership Award (15/IA/3152); the Scottish Funding Council (HR03006); the Signe and Ane Gyllenberg Foundation; the Sigrid Juselius Foundation; the Singapore Ministry of Health (Population Health Metrics and Analytics PHMA); the Singapore National Medical Research Council (NMRC): 1270/2010, 0796/2003, 1176/2008, 1149/2008, Singapore Translational Research Investigator (STaR) Award (NMRC/STaR/0028/2017) (J.C.C.), STaR/0003/2008, 1249/2010, CG/SERI/2010, CIRG/1371/2013, CIRG/1417/2015; the Skåne University Hospital; the Smilow family; the Social Insurance Institution of Finland; the Social Ministry of the Federal State of Mecklenburg-West Pomerania; the South-Eastern Norway Regional Health Authority (2019122); the Spanish Association Against Cancer (AECC) Scientific Foundation; the Spanish Ministry of Science and Innovation (CEX2018-000806-S); the St. Louis Men’s Group Against Cancer; the State of Brandenburg (DZD grant 82DZD00302); the Strategic Cardiovascular Program of Karolinska Institutet and Stockholm County Council; the Strategic Priority CAS Project (XDB38000000); the Swedish Cancer Foundation; the Swedish Foundation for Strategic Research (Dnr IRC15-0067); the Swedish Ministry of Education; the Swiss National Science Foundation (33CSCO-122661, 33CS30-139468, 33CS30-148401, 33CS30_177535, 3247730_204523 and 320030_220190); the Swiss Personalized Health Network (2018DRI01); the Taichung Veterans General Hospital, Taichung, Taiwan (TCVGH-1003001C); the Tampere Tuberculosis Foundation; the Tampere University Hospital Supporting Foundation; the Torsten and Ragnar Soderberg Foundation; the Turku University Foundation; the UKCRC Centre of Excellence for Public Health Northern Ireland; the University Hospital Regensburg; the University Medical Center Groningen; the University of Notre Dame Australia; the University of Oulu, 24000692; the University of Western Australia; the US Centers for Disease Control and Prevention (CDC): U01 DP003206; the US Centers for Disease Control and Prevention (CDC)/Association of Schools of Public Health: S043, S1734, S3486; the Victor Chang Cardiac Research Institute, NSW Health (D.F.); the Wellcome Trust/Wolfson Foundation; the Yrjö Jahnsson Foundation; TrygFonden (7-11-0213); University of Helsinki HiLIFE Fellow and Grand Challenge grant (Sa.R.); the University of Michigan; the University of Newcastle; Vestre Viken Hospital Trust; VIAgenomics (SP/19/2/344612); VicHealth and Cancer Council Victoria; Vincent Fairfax Family Foundation; VU University Medical Center, Amsterdam; VU University’s Institutes for Health and Care Research (EMGO+); Wageningen University, Wageningen; Wake Forest School of Medicine grant M01 RR07122 and Venture Fund; Wellcome Trust Sanger Institute; and the Women and Infants Research Foundation.
Author information
Author notes
Xiaoyin Li
Present address: Department of Mathematics and Statistics, St. Cloud State University, St. Cloud, MN, USA
Deceased: Richard S. Cooper.
Deceased: Jeanette Erdmann.
Deceased: David M. Hougaard.
Full lists of members and their affiliations appear in the Supplementary Information.
Authors and Affiliations
The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Roelof A. J. Smit, Michael H. Preuss, Ghislain Rocheleau, Marta Guindo-Martínez, Daiane Hemerich, Zhe Wang & Ruth J. F. Loos
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
Roelof A. J. Smit, Raymond Noordam, Dennis O. Mook-Kanamori & Frits R. Rosendaal
Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
Roelof A. J. Smit, Malene R. Christiansen, Marta Guindo-Martínez, Mette K. Andersen, Jette Bork-Jensen, Kristoffer S. Burgdorf, Anette P. Gjesing, Anna Jonsson, Niels Grarup, Torben Hansen, Oluf Pedersen, Tuomas O. Kilpeläinen & Ruth J. F. Loos
Department of Environmental Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Roelof A. J. Smit, Michael H. Preuss & Ruth J. F. Loos
MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
Kaitlin H. Wade, Simon J. Haworth, Ruth E. Mitchell & Nicholas J. Timpson
Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
Kaitlin H. Wade, Ruth E. Mitchell & Nicholas J. Timpson
Atlanta VA Health Care System, Decatur, GA, USA
Qin Hui, Peter W. F. Wilson & Yan V. Sun
Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
Qin Hui & Yan V. Sun
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
Joshua D. Arias, Moara Machado, Shengchao A. Li, Stephen J. Chanock, Wen-Yi Huang & Sonja I. Berndt
Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
Xianyong Yin
Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
Xianyong Yin, Lars G. Fritsche, Anne U. Jackson, Anita Pandit, Laura J. Scott, Goncalo R. Abecasis & Michael Boehnke
Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
Loic Yengo, Julia Sidorenko & Peter M. Visscher
Department of Public Health, Section of Epidemiology, Algorithmic Fairness Research Group, University of Copenhagen, Copenhagen, Denmark
Mariam Nakabuye
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Ghislain Rocheleau
Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Ghislain Rocheleau
Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
Sarah E. Graham, Whitney E. Hornsby, Tori L. Melendez & Cristen J. Willer
Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Victoria L. Buchanan
Department of Population Health Sciences, Geisinger, Danville, PA, USA
Geetha Chittoor & Anne E. Justice
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Marielisa Graff, Heather M. Highland, Daeeun Kim, Rebecca Rohde, Anne E. Justice, Kari E. North & Kristin L. Young
Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
Yingchang Lu
William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
Eirini Marouli, Olga Giannakopoulou, Stavroula Kanoni, Ioanna Ntalla, Julia Ramirez, Helen R. Warren, Mark J. Caulfield, Patricia B. Munroe & Panos Deloukas
Digital Environment Research Institute, Queen Mary University of London, London, UK
Eirini Marouli
Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
Saori Sakaue, Masato Akiyama, Masahiro Kanai & Yoichiro Kamatani
Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
Saori Sakaue, Masahiro Kanai & Yukinori Okada
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
Saori Sakaue, Sailaja Vedantam, Eric Bartell, Anders U. Eliasen, George Hindy, Masahiro Kanai, Richa Saxena, Wei Zhou, Samuli Ripatti & Cecilia M. Lindgren
Divisions of Genetics and Rheumatology, Brigham and Women’s Hospital and Department of Medicine, Harvard Medical School, Boston, MA, USA
Saori Sakaue
Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Cassandra N. Spracklen, Emma P. Wilson, Daeeun Kim, Laura M. Raffield & Karen L. Mohlke
Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA
Cassandra N. Spracklen
Division of Endocrinology, Boston Children’s Hospital, Boston, MA, USA
Sailaja Vedantam, Eric Bartell & Joel N. Hirschhorn
Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
Shyh-Huei Chen
Nuffield Department of Population Health, University of Oxford, Oxford, UK
Teresa Ferreira, Deborah E. Malden, Zammy Fairhurst-Hunter, Kuang Lin, Jun Liu, Iona Y. Millwood, Zhengming Chen, Cornelia M. van Duijn, Cecilia M. Lindgren, Peter M. Visscher & Robin G. Walters
Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK
Yingjie Ji, Andrew R. Wood & Timothy M. Frayling
Center for Health Data Science, Section for Health Data Science and Artificial Intelligence, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
Tugce Karaderi
Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK
Tugce Karaderi, Anuj Goel, Anubha Mahajan, Mark I. McCarthy, Hugh Watkins & Cecilia M. Lindgren
Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
Kreete Lüll, Andres Metspalu & Tõnu Esko
Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
Carolina Medina-Gomez, Fernando Rivadeneira, Nathalie Van der Velde & M. Carola Zillikens
Division of Biostatistics and Epidemiology, RTI International, Durham, NC, USA
Amy Moore
Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
Sina Rüeger, Aaron F. McDaid & Zoltan Kutalik
Swiss Institute of Bioinformatics, Lausanne, Switzerland
Sina Rüeger, Aaron F. McDaid & Zoltan Kutalik
Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
Masato Akiyama
Department of Family Medicine, University of California, San Diego, La Jolla, CA, USA
Matthew A. Allison
Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
Marcus Alvarez & Päivi Pajukanta
Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Copenhagen, Denmark
Vivek Appadurai & Thomas M. Werge
Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Liubov Arbeeva, Yvonne M. Golightly & Amanda E. Nelson
Harvard Medical School, Boston, MA, USA
Eric Bartell, Brian E. Cade, Saiju Pyarajan, Julie E. Buring, Paul L. Huang, Amit V. Khera, JoAnn E. Manson, Susan Redline, Paul M. Ridker, Daniel I. Chasman & Christopher J. O’Donnell
Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology, Hyderabad, India
Seema Bhaskar, Suraj S. Nongmaithem & Giriraj R. Chandak
Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
Lawrence F. Bielak, Jennifer A. Smith, Wei Zhao, Sharon L. R. Kardia & Patricia A. Peyser
Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
Joshua C. Bis, Jennifer A. Brody, Rozenn N. Lemaitre & Bruce M. Psaty
Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
Sailalitha Bollepalli, Aki S. Havulinna, Sanni E. Ruotsalainen, Jaakko Kaprio, Samuli Ripatti, Tiinamaija Tuomi & Elisabeth Widen
Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
Jonathan P. Bradfield, Struan F. A. Grant & Hakon Hakonarson
Quantinuum Research, LLC, Wayne, PA, USA
Jonathan P. Bradfield
Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
Yuki Bradford, Xinyuan Zhang, Scott M. Damrauer, Struan F. A. Grant, Daniel J. Rader, Marylyn D. Ritchie & Babette S. Zemel
Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
Caroline Brandl, Alexander Dietl, Mathias Gorski, Martina E. Zimmermann, Iris M. Heid, Klaus J. Stark & Thomas W. Winkler
Department of Ophthalmology, University Hospital Regensburg, Regensburg, Germany
Caroline Brandl
Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
Peter S. Braund, Christopher P. Nelson & Nilesh J. Samani
NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
Peter S. Braund, Christopher P. Nelson & Nilesh J. Samani
Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA
Ulrich Broeckel
Department of Congenital Disorders and Danish National Biobank, Statens Serum Institut, Copenhagen, Denmark
Kristoffer S. Burgdorf & David M. Hougaard
Department of Public Health, University of Copenhagen, Copenhagen, Denmark
Kristoffer S. Burgdorf
Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
Brian E. Cade, Saiju Pyarajan, JoAnn E. Manson, Susan Redline & Christopher J. O’Donnell
Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
Qiuyin Cai, Jirong Long, Wanqing Wen, Xiao-Ou Shu & Wei Zheng
Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
Silvia Camarda, Giuseppe Giovanni Nardone, Francesco Piluso, Aurora Santin, Paola Tesolin, Giorgia Girotto & Beatrice Spedicati
Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
Archie Campbell, Caroline Hayward & David J. Porteous
Usher Institute, University of Edinburgh, Edinburgh Bioquarter, Edinburgh, UK
Archie Campbell
Centre for Public Health, Queen’s University of Belfast, Northern Ireland, UK
Marisa Cañadas-Garre, Laura J. Smyth, Frank Kee & Amy Jayne McKnight
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
Jin-Fang Chai, Xueling Sim, E. Shyong Tai & Rob M. van Dam
Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
Alessandra Chesi & Shefali S. Verma
Center for Spatial and Functional Genomics, Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
Alessandra Chesi & Babette S. Zemel
Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
Seung Hoan Choi, Patrick T. Ellinor & Steven A. Lubitz
Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
Paraskevi Christofidou, Massimo Mangino & Tim D. Spector
Department of Kinesiology, Faculty of Medicine, Université Laval, Québec, QC, Canada
Christian Couture & Louis Perusse
Diamantina Institute, The University of Queensland, Brisbane, Queensland, Australia
Gabriel Cuellar-Partida
23andMe, Inc., Sunnyvale, CA, USA
Gabriel Cuellar-Partida, Yunxuan Jiang, Jingchunzi Shi & Adam Auton
Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA
Rebecca Danning, Franco Giulianini, Julie E. Buring, JoAnn E. Manson, Paul M. Ridker & Daniel I. Chasman
Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
Frauke Degenhardt & Andre Franke
Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
Graciela E. Delgado, Marcus E. Kleber & Winfried März
Dipartimento di Scienze Mediche Chirurgiche e Sperimentali, Università degli Studi di Sassari, Sassari, Italy
Alessandro Delitala
Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
Ayşe Demirkan, Maria J. Knol, Jun Liu, Natalie Terzikhan, M. Arfan Ikram, Maryam Kavousi & Cornelia M. van Duijn
Section of Statistical Multi-omics, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK
Ayşe Demirkan
Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
Xuan Deng, Quanshun Mei & Ching-Ti Liu
Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
Alexander Dietl
Department of Nutrition Science and Dietetics, School of Health Sciences, University of the Peloponnese, Kalamata, Greece
Maria Dimitriou
Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
Maria Dimitriou, Aliki-Eleni Farmaki, Ioanna P. Kalafati & George V. Dedoussis
Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
Latchezar Dimitrov & Donald W. Bowden
Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
Rajkumar Dorajoo, Chiea Chuen Khor & Jianjun Liu
Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
Rajkumar Dorajoo & Chew-Kiat Heng
Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
Fabian Eichelmann & Matthias B. Schulze
German Center for Diabetes Research (DZD), Neuherberg, Germany
Fabian Eichelmann, Christian Gieger, Harald Grallert, Annette Peters & Matthias B. Schulze
COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
Anders U. Eliasen
Department of Pediatrics, Division of Endocrinology, Boston Children’s Hospital, Boston, MA, USA
Anders U. Eliasen
Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
Jorgen E. Engmann
Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
Michael R. Erdos & Francis S. Collins
Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
Jessica D. Faul, Jennifer A. Smith, Wei Zhao & David R. Weir
Department of Computational Genomics, National Institute of Genomic Medicine, Mexico City, Mexico
Juan-Carlos Fernandez-Lopez
Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
Lukas Forer
Institute for Medical Informatics, Biometry and Epidemiology, University Hospital Essen, Essen, Germany
Mirjam Frank, Karl-Heinz Jöckel & Börge Schmidt
Institute of Medical Informatics and Statistics, Kiel University, Kiel, Germany
Sandra Freitag-Wolf
Rogel Cancer Center, University of Michigan Medicine, Ann Arbor, MI, USA
Lars G. Fritsche
Eurac Research Institute for Biomedicine, Bolzano, Italy
Christian Fuchsberger, Cristian Pattaro & Peter P. Pramstaller
Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands
Tessel E. Galesloot & Lambertus A. L. M. Kiemeney
Jackson Heart Study, Department of Medicine, University of Mississippi, Jackson, MS, USA
Yan Gao
Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
Frank Geller, Xueping Liu & Bjarke Feenstra
Cardiovascular Medicine/Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
Anuj Goel & Hugh Watkins
Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Scott D. Gordon & Nicholas G. Martin
Department of Biomedicine (Human Genetics) and iSEQ Center, Aarhus University, Aarhus, Denmark
Jakob Grove & Manuel Mattheisen
The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
Jakob Grove, Liselotte V. Petersen & David M. Hougaard
BiRC - Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
Jakob Grove
The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
Xiuqing Guo, Marie Lauzon, Xiaohui Li, Kent D. Taylor, Jie Yao, Yii-Der Ida Chen & Jerome I. Rotter
Department of Medical Sciences, Uppsala University, Uppsala, Sweden
Stefan Gustafsson & Lars Lind
Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
Jeffrey Haessler, Charles Kooperberg & Alexander P. Reiner
Translational Research Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
Thomas F. Hansen
Danish Headache Center, Department of Neurology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
Thomas F. Hansen
Danish Multiple Sclerosis Center, Department of Neurology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
Thomas F. Hansen
Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
Aki S. Havulinna, Pekka Jousilahti, Heikki A. Koistinen, Veikko Salomaa & Jaakko Tuomilehto
Bristol Dental School, University of Bristol, Bristol, UK
Simon J. Haworth
Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
Nancy Heard-Costa
Framingham Heart Study, Framingham, MA, USA
Nancy Heard-Costa
Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
George Hindy & Sölve Elmståhl
Veterans Affairs Boston Healthcare System, Boston, MA, USA
Yuk-Lam Ho & Jennifer E. Huffman
Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
Edith Hofer & Reinhold Schmidt
Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
Edith Hofer
School of Medicine and Public Health, University of Newcastle, Callaghan, New South Wales, Australia
Elizabeth Holliday, Carol A. Wang, John Attia & Craig E. Pennell
Leipzig University, Medical Faculty, Institute for Medical Informatics, Statistics and Epidemiology, Leipzig, Germany
Katrin Horn, Markus Scholz & Markus Loeffler
Leipzig University, Medical Faculty, LIFE Research Center for Civilization Diseases, Leipzig, Germany
Katrin Horn, Markus Scholz & Markus Loeffler
Department of Biological Psychology, Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
Jouke-Jan Hottenga, Eco J. C. de Geus & Gonneke Willemsen
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
Hongyan Huang, Constance Turman, A. Heather Eliassen, Peter Kraft, Lorelei A. Mucci, Meir J. Stampfer & Walter C. Willett
School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, Guangdong, China
Jie Huang
Institute for Global Health and Development, Peking University, Beijing, China
Jie Huang
Program in Metabolism and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
Alicia Huerta-Chagoya, Jose C. Florez & Joel N. Hirschhorn
Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
Alicia Huerta-Chagoya
Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
Shaofeng Huo, Liang Sun, Huaixing Li & Xu Lin
Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongwon-gun, Republic of Korea
Mi Yeong Hwang, Young Jin Kim & Bong-Jo Kim
Division of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
Chii-Min Hwu
Biomedical Technology Research Center, Tokushima Research Institute, Otsuka Pharmaceutical Co., Ltd., Tokushima, Japan
Hiroyuki Iha, Daisuke D. Ikeda & Haretsugu Hishigaki
Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
Masato Isono, Fumihiko Takeuchi & Norihiro Kato
Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
Iris E. Jansen & Danielle Posthuma
Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam, The Netherlands
Iris E. Jansen
Department of Biobank Research, Umeå University, Umeå, Sweden
Ingegerd Johansson
Department of Odontology, Umeå University, Umeå, Sweden
Ingegerd Johansson
Center for Clinical Research and Prevention, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Frederiksberg, Denmark
Torben Jørgensen, Line L. Kårhus, Line T. Møllehave, Thomas M. Dantoft & Allan Linneberg
Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
Torben Jørgensen
Department of Nutrition and Dietetics, School of Physical Education, Sport Science and Dietetics, University of Thessaly, Trikala, Greece
Ioanna P. Kalafati
Faculty of Medicine, University of Kelaniya, Colombo, Sri Lanka
Anuradhani Kasturiratne, Ananda R. Wickremasinghe & H. Janaka de Silva
Osaka University Graduate School of Medicine, Osaka, Japan
Tomohiro Katsuya & Hiromi Rakugi
Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
Takahisa Kawaguchi, Fumihiko Matsuda & Yasuharu Tabara
Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
Rachel L. Kember
Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
Rachel L. Kember & Scott M. Damrauer
Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
Katherine A. Kentistou, Paul R. H. J. Timmers, Peter K. Joshi, Igor Rudan & James F. Wilson
Centre for Cardiovascular Sciences, Queens Medical Research Institute, University of Edinburgh, Edinburgh, Scotland
Katherine A. Kentistou
Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
Han-Na Kim & Seungho Ryu
Center for Clinical Epidemiology, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
Han-Na Kim
SYNLAB MVZ Humangenetik Mannheim, Mannheim, Germany
Marcus E. Kleber
Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
Azra Kurbasic & Alaitz Poveda
Department of Computer Science, University of Toronto, Toronto, ON, Canada
Phuong Le
Department of Anthropology, University of Toronto at Mississauga, Mississauga, ON, Canada
Phuong Le & Esteban J. Parra
Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Qld University of Technology, Kelvin Grove, Queensland, Australia
Rodney Lea & Lyn Griffiths
Oneomics, Soonchunhyang Mirai Medical Center, Gyeonggi-do, Republic of Korea
Jong-Young Lee
Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
Wen-Jane Lee
Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
Hampton L. Leonard, Mike A. Nalls & Andrew B. Singleton
Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD, USA
Hampton L. Leonard & Mike A. Nalls
Data Tecnica International, Glen Echo, MD, USA
Hampton L. Leonard
Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
Hengtong Li & Ching-Yu Cheng
Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
Hengtong Li
Cancer Genomics Research Laboratory, Leidos Biomedical Research, Inc., Rockville, MD, USA
Shengchao A. Li
Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
Xiaoyin Li, Jingjing Liang & Xiaofeng Zhu
Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
Honghuang Lin
Montreal Heart Institute, Montreal, QC, Canada
Ken Sin Lo, Jean-Claude Tardif & Guillaume Lettre
Departments of Ophthalmology and Human Genetics, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
Laura Lores-Motta, Marc Pauper, Anneke I. den Hollander & Carel B. Hoyng
MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
Jian’an Luan, Claudia Langenberg & Nicholas J. Wareham
Department of Clinical Science, Center for Diabetes Research, University of Bergen, Bergen, Norway
Valeriya Lyssenko
Department of Clinical Sciences, Lund University Diabetes Centre, Malmö, Sweden
Valeriya Lyssenko & Tiinamaija Tuomi
Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
Leo-Pekka Lyytikäinen, Pashupati P. Mishra & Terho Lehtimäki
Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
Leo-Pekka Lyytikäinen, Pashupati P. Mishra, Nina Mononen & Terho Lehtimäki
Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, Finland
Leo-Pekka Lyytikäinen & Kjell Nikus
Department of Genetics & Bioinformatics, Dasman Diabetes Institute, Kuwait City, Kuwait
Md Zubbair Malik, Fahd Al-Mulla & Thangavel A. Thanaraj
National and Kapodistrian University of Athens, Dromokaiteio Psychiatric Hospital, Athens, Greece
Vasiliki Mamakou
NIHR Biomedical Research Centre at Guy’s and St. Thomas’ Foundation Trust, London, UK
Massimo Mangino
Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
Ani Manichaikul
MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, Scotland
Jonathan Marten, Paul R. H. J. Timmers, Caroline Hayward, Veronique Vitart, James F. Wilson & Alan F. Wright
Department of Community Health & Epidemiology and Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
Manuel Mattheisen
Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
Manuel Mattheisen
Department of Nephrology and Hypertension, University Hospital Erlangen, Friedrich Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
Heike Meiselbach & Kai-Uwe Eckardt
Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience Amsterdam UMC/Vrije Universiteit, Amsterdam, The Netherlands
Yuri Milaneschi & Brenda W. J. H. Penninx
Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA
Jason E. Miller
Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Jason E. Miller
Department of Clinical Chemistry, Tampere University Hospital, Wellbeing Services County of Pirkanmaa, Tampere, Finland
Nina Mononen
Institute for Cardiogenetics, University of Lübeck, DZHK (German Research Centre for Cardiovascular Research), partner site Hamburg/Lübeck/Kiel and University Heart Center Lübeck, Lübeck, Germany
Sören Mucha, Matthias Munz & Jeanette Erdmann
Institute of Epidemiology and Biobank PopGen, University Hospital Schleswig Holstein, Campus Kiel and Kiel University, Kiel, Germany
Sören Mucha
Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
Juha Mykkänen, Harri Niinikoski, Katja Pahkala & Olli T. Raitakari
Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
Juha Mykkänen, Harri Niinikoski, Katja Pahkala & Olli T. Raitakari
Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
Masahiro Nakatochi
Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
Maria Nethander, Dan Mellström & Claes Ohlsson
Bioinformatics Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
Maria Nethander
Korea Institute of Science and Technology, Gangneung Institute of Natural Products, Gangneung, Republic of Korea
Chu Won Nho
Department of Clinical Biochemistry, Lillebaelt Hospital, Kolding, Denmark
Aneta A. Nielsen
Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
Ilja M. Nolte, Peter J. van der Most & Harold Snieder
Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
Suraj S. Nongmaithem
Health Campus The Hague/Public Health and Primary Care, Leiden University Medical Center, The Hague, The Netherlands
Raymond Noordam
Institute of Genetics and Biophysics A. Buzzati-Traverso, CNR, Naples, Italy
Teresa Nutile, Daniela Ruggiero & Marina Ciullo
Department of Clinical Biochemistry, Lillebaelt Hospital, Vejle, Denmark
Eva R. B. Petersen & Ivan Brandslund
The National Centre for Register-based Research, University of Aarhus, Aarhus, Denmark
Liselotte V. Petersen
Medical School, University of Split, Šoltanska 2, Split, Croatia
Ozren Polašek
Algebra University College, Zagreb, Croatia
Ozren Polašek
Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
Saiju Pyarajan
Center for Non-Communicable Diseases, Karachi, Pakistan
Asif Rasheed & Danish Saleheen
Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
Dennis Raven, Catharina A. Hartman & Albertine J. Oldehinkel
Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
Nigel W. Rayner, Lorraine Southam & Eleftheria Zeggini
Hunter Medical Research Institute, New Lambton Heights, New South Wales, Australia
Carlos Riveros, Carol A. Wang & Craig E. Pennell
Department of Medicine, Division of Endocrinology, Diabetes & Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
Kathleen A. Ryan, Braxton D. Mitchell & Huichun Xu
Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
Kathleen A. Ryan, Braxton D. Mitchell & Huichun Xu
Unit of Genomics of Complex Diseases, Institut de Recerca Sant Pau (IR Sant Pau), Barcelona, Spain
Maria Sabater-Lleal
Cardiovascular Medicine Unit, Department of Medicine, Karolinska Institutet, Center for Molecular Medicine, Stockholm, Sweden
Maria Sabater-Lleal
Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
Maria Sabater-Lleal
Institute for Maternal and Child Health, IRCCS Burlo Garofolo, Trieste, Italy
Aurora Santin, Paola Tesolin, Maria Pina Concas, Giorgia Girotto & Beatrice Spedicati
Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
Richa Saxena, Jose C. Florez & Christopher Newton-Cheh
Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
Botong Shen, Michele K. Evans, Lenore J. Launer & Alan B. Zonderman
Department of Biomedical Science, Hallym University, Chuncheon, Republic of Korea
Jae Hun Shin & Yoon Shin Cho
Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Cagliari, Italy
Carlo Sidore, Simona Vaccargiu, Magdalena Zoledziewska & Francesco Cucca
Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
Roderick C. Slieker & Leen M. ‘t Hart
Department of Epidemiology and Data Science, Amsterdam Public Health Institute, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
Roderick C. Slieker, Leen M. ‘t Hart & Femke Rutters
Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
Albert V. Smith
Icelandic Heart Association, Kopavogur, Iceland
Albert V. Smith & Vilmundur Gudnason
deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
Valgerdur Steinthorsdottir, Gudmar Thorleifsson, Kari Stefansson & Unnur Thorsteinsdottir
Department of Public Health Sciences, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, IL, USA
Bamidele O. Tayo & Richard S. Cooper
VA Palo Alto Health Care System, Palo Alto, CA, USA
Catherine Tcheandjieu & Themistocles L. Assimes
Gladstone Institute of Data Science and Biotechnology, Gladstone Institute, San Francisco, CA, USA
Catherine Tcheandjieu
Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
Alexander Teumer
DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
Alexander Teumer, Uwe Völker & Henry Völzke
Cardiology Division, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
Elizabeth Theusch & Ronald M. Krauss
Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
Deborah J. Thompson & Douglas F. Easton
Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
Stella Trompet
Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
Stella Trompet & J. Wouter Jukema
Central Diagnostic Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
Sander W. van der Laan
Department of Genome Sciences, University of Virginia, Charlottesville, VA, USA
Sander W. van der Laan
Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
Jan B. van Klinken
Laboratory of Genetic Metabolic Diseases, Amsterdam UMC, University of Amsterdam, Amsterdam Gastroenterology and Metabolism, Amsterdam Cardiovascular Sciences Institute, Amsterdam, The Netherlands
Jan B. van Klinken
Core Facility Metabolomics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
Jan B. van Klinken
Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
Jessica van Setten, Adriaan Kraaijeveld & Pim van der Harst
Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
Niek Verweij, Michiel Rienstra & Pim van der Harst
Department of Biobehavioral Health, Pennsylvania State University, University Park, PA, USA
Yogasudha Veturi
Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
Chaolong Wang
Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
Jun-Sing Wang
Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
Lihua Wang, Mary F. Feitosa & Michael A. Province
Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing, China
Ya Xing Wang & Zi-Bing Jin
Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
Zhe Wang
NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
Helen R. Warren, Mark J. Caulfield & Patricia B. Munroe
Department of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
Wen Bin Wei
Information Management Services, Inc., Calverton, UK
William A. Wheeler
Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
Matthias Wielscher & Marjo-Riitta Jarvelin
Department of Dermatology, Medical University of Vienna, Vienna, Austria
Matthias Wielscher
Department of Research and Innovation, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
Bendik S. Winsvold & John-Anker Zwart
Department of Neurology, Oslo University Hospital, Oslo, Norway
Bendik S. Winsvold
Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
Andrew Wong & Nish Chaturvedi
Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
Matthias Wuttke
Department of Medicine IV – Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
Matthias Wuttke
Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
Rui Xia & Myriam Fornage
Department of Medical Biochemistry, Kurume University School of Medicine, Kurume, Japan
Ken Yamamoto
Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
Jingyun Yang, Lei Yu & David A. Bennett
Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
Jingyun Yang, Lei Yu & David A. Bennett
Department of Psychology, University of Minnesota, Minneapolis, MN, USA
Hannah Young, William G. Iacono, Matt McGue & Scott Vrieze
Department of Genetic Medicine, Weill Cornell Medicine, Doha, Qatar
Noha A. Yousri
Department of Computer and Systems Engineering, Alexandria University, Alexandria, Egypt
Noha A. Yousri
Department of Cardiology, German Heart Centre Munich, Technical University Munich, Munich, Germany
Lingyao Zeng & Thorsten Kessler
Department of Epidemiology and Biostatistics, Imperial College London, London, UK
Weihua Zhang & John C. Chambers
Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
Jing-Hua Zhao
Victor Phillip Dahdaleh Heart & Lung Research Institute, Cambridge Biomedical Campus, Cambridge, UK
Jing-Hua Zhao
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
Wei Zhou & Cristen J. Willer
Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
Wei Zhou
Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
Wei Zhou
Amsterdam Cardiovascular Sciences Institute, Amsterdam, The Netherlands
Leen M. ‘t Hart
Molecular Epidemiology Section, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
Leen M. ‘t Hart
Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Linda S. Adair, Shufa Du & Penny Gordon-Larsen
Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Linda S. Adair, Shufa Du & Penny Gordon-Larsen
Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
Hieab H. H. Adams
Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
Hieab H. H. Adams
Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
Carlos A. Aguilar-Salinas & Maria-Teresa Tusié-Luna
Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Mexico
Carlos A. Aguilar-Salinas
Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
Donna K. Arnett
Department of Cardiology, Amsterdam Cardiovascular Sciences Institute, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
Folkert W. Asselbergs
Institute of Health Informatics, University College London, London, UK
Folkert W. Asselbergs
The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
Folkert W. Asselbergs
HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
Bjørn Olav Åsvold, Ben Brumpton & Kristian Hveem
HUNT Research Center, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway
Bjørn Olav Åsvold & Kristian Hveem
Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
Bjørn Olav Åsvold
Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
Bernhard Banas, Tobias Bergler & Carsten A. Böger
Geriatric Unit, Azienda Toscana Centro, Florence, Italy
Stefania Bandinelli
Medical School, Royal Perth Hospital Unit, University of Western Australia, Perth, Western Australia, Australia
Lawrence J. Beilin & Trevor A. Mori
Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University (JNU), New Delhi, India
Dwaipayan Bharadwaj
Institute of Molecular Genetics, National Research Council of Italy, Pavia, Italy
Ginevra Biino
Human Genetics Center and Department of Epidemiology, The University of Texas Health Science Center at Houston, Houston, TX, USA
Eric Boerwinkle
Department of Nephrology and Rheumatology, Kliniken Südostbayern, Regensburg, Germany
Carsten A. Böger
KfH Kidney Center Traunstein, Traunstein, Germany
Carsten A. Böger
USC-Office of Population Studies Foundation, Inc., University of San Carlos, Cebu City, Philippines
Judith B. Borja & Nanette R. Lee
Department of Nutrition and Dietetics, University of San Carlos, Cebu City, Philippines
Judith B. Borja
Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
Claude Bouchard
Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
Ivan Brandslund
Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
Ben Brumpton
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
John C. Chambers
Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UK
John C. Chambers & Jaspal S. Kooner
Imperial College Healthcare NHS Trust, Imperial College London, London, UK
John C. Chambers & Jaspal S. Kooner
Lightening Lives, Hyderabad, India
Giriraj R. Chandak
Department of Ophthalmology, National University of Singapore and National University Health System, Singapore, Singapore
Ching-Yu Cheng
Ocular Epidemiology, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
Ching-Yu Cheng & Charumathi Sabanayagam
Danish Aging Research Center, University of Southern Denmark, Odense, Denmark
Kaare Christensen
Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
Ingrid E. Christophersen
Department of Medical Research, Bærum Hospital, Vestre Viken Hospital Trust, Gjettum, Norway
Ingrid E. Christophersen
IRCCS Neuromed, Pozzilli (IS), Pozzilli, Italy
Marina Ciullo
Department of Neurology, Division of Vascular Neurology, University of Maryland School of Medicine, Baltimore, MD, USA
John W. Cole
Department of Neurology, Baltimore Veterans Affairs Medical Center, Baltimore, MD, USA
John W. Cole
Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
Miguel Cruz
Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy
Francesco Cucca
Intermountain Heart Institute, Intermountain Medical Center, Murray, UT, USA
Michael J. Cutler
Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA
Scott M. Damrauer
Department of Vascular Surgery, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
Gert J. de Borst & Dominique P. V. de Kleijn
Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC/Vrije Universiteit, Amsterdam, The Netherlands
Eco J. C. de Geus
Department of Human Nutrition, Wageningen University, Wageningen, The Netherlands
Lisette C. P. G. M. de Groot
Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
Philip L. De Jager
Department of Oncology, University of Cambridge, Cambridge, UK
Douglas F. Easton
Department of Nephrology and Medical Intensive Care, Charité – Universitätsmedizin Berlin, Berlin, Germany
Kai-Uwe Eckardt
Department of General Practice, Amsterdam Public Health Institute, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
Petra J. M. Elders
Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
A. Heather Eliassen & Meir J. Stampfer
Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
A. Heather Eliassen, Paul W. Franks, Meir J. Stampfer & Walter C. Willett
Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, MA, USA
Patrick T. Ellinor & Steven A. Lubitz
Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
Patrick T. Ellinor, Paul L. Huang, Steven A. Lubitz & Christopher Newton-Cheh
Molecular Cardiology Division, Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia
Diane Fatkin
Department of Cardiology, St. Vincent’s Hospital, Darlinghurst, New South Wales, Australia
Diane Fatkin
Faculty of Medicine, UNSW Sydney, Kensington, New South Wales, Australia
Diane Fatkin
Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
Luigi Ferrucci & Eleanor M. Simonsick
Department of Medicine, Harvard Medical School, Boston, MA, USA
Jose C. Florez
Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
Jose C. Florez
Robertson Center for Biostatistics, University of Glasgow, Glasgow, UK
Ian Ford
Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
Myriam Fornage
Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Helsingborg, Sweden
Paul W. Franks
Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, USA
Paul W. Franks & Braxton D. Mitchell
Section on Nephrology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
Barry I. Freedman
Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
Christian Gieger & Harald Grallert
College of Allied Health Professions, University of Nebraksa Medical Center, Omaha, NE, USA
Yvonne M. Golightly
Centro de Investigacion en Salud Poblacional Instituto Nacional de Salud Publica and Centro de Estudios en Diabetes, Cuernavaca Morelos, Mexico
Clicerio Gonzalez-Villalpando
Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
Struan F. A. Grant & Hakon Hakonarson
Division of Endocrinology and Diabetes, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
Struan F. A. Grant & Babette S. Zemel
Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Struan F. A. Grant, Hakon Hakonarson & Babette S. Zemel
Faculty of Medicine, University of Iceland, Reykjavik, Iceland
Vilmundur Gudnason, Kari Stefansson & Unnur Thorsteinsdottir
Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA
Christopher Haiman
Division of Pulmonary Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
Hakon Hakonarson
Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
Andrew T. Hattersley
Khoo Teck Puat - National University Children’s Medical Institute, National University Health System, Singapore, Singapore
Chew-Kiat Heng
Department of Internal Medicine II, Division of Cardiology, Medical University of Vienna, Vienna, Austria
Christian Hengstenberg
Research Unit of Biomedicine and Internal Medicine, Biocenter of Oulu, Medical Research Center, University of Oulu, Oulu, Finland
Karl-Heinz Herzig
Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, Poznań, Poland
Karl-Heinz Herzig
Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
Alex W. Hewitt & David A. Mackey
Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Victoria, Australia
Alex W. Hewitt
Lions Eye Institute, Centre for Ophthalmology and Visual Science, University of Western Australia, Perth, Western Australia, Australia
Alex W. Hewitt & David A. Mackey
Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
Paul L. Huang
Department of Genetics, Shanghai-MOST Key Laboratory of Heath and Disease Genomics, Chinese National Human Genome Center and Shanghai Industrial Technology Institute, Shanghai, China
Wei Huang
Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
Steven C. Hunt
Department of Pediatrics, Tampere University Hospital, Tampere, Finland
Nina Hutri
Department of Pediatrics, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
Nina Hutri
Australian Centre for Precision Health, Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
Elina Hyppönen
South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
Elina Hyppönen
Department of Environmental and Preventive Medicine, Jichi Medical University School of Medicine, Shimotsuke, Japan
Sahoko Ichihara
Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
Carmen R. Isasi & Qibin Qi
Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulun, Finland
Marjo-Riitta Jarvelin
Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
Marjo-Riitta Jarvelin
Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Uxbridge, UK
Marjo-Riitta Jarvelin
The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, China
Zi-Bing Jin, Fan Lu & Jia Qu
Institut Français de Myopie, Hôpital Fondation Rothschild, Paris, France
Jost B. Jonas
Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
Jost B. Jonas & Tien Y. Wong
Privatpraxis Prof. Jonas und Dr. Panda-Jonas, Heidelberg, Germany
Jost B. Jonas
L V Prasad Eye Institute, Hyderabad, India
Jost B. Jonas
Einthoven Laboratory for Experimental Vascular Medicine, LUMC, Leiden, The Netherlands
J. Wouter Jukema
Netherlands Heart Institute, Utrecht, The Netherlands
J. Wouter Jukema
Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
Mika Kähönen
Department of Clinical Physiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
Mika Kähönen
Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
Yoichiro Kamatani
Department of Ophthalmology, The Catholic University of Korea Incheon St. Mary’s Hospital, Incheon, Republic of Korea
Kui Dong Kang
Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Oxford, UK
Fredrik Karpe, Mark I. McCarthy & Matt J. Neville
NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, UK
Fredrik Karpe & Matt J. Neville
German Centre for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
Thorsten Kessler & Moritz F. Sinner
Verve Therapeutics, Boston, MA, USA
Amit V. Khera
Brigham and Women’s Hospital, Boston, MA, USA
Amit V. Khera
Department of Urology, Radboud University Medical Center, Nijmegen, The Netherlands
Lambertus A. L. M. Kiemeney
Corneal Dystrophy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
Eung Kweon Kim
Saevit Eye Hospital, Goyang-si, Republic of Korea
Eung Kweon Kim
Department of Biochemistry, College of Medicine, Ewha Womans University, Seoul, Republic of Korea
Hyung-Lae Kim
Department of Cardiology, University Heart and Vascular Center UKE Hamburg, Hamburg, Germany
Paulus Kirchhof
Institute of Cardiovascular Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
Paulus Kirchhof
German Center for Cardiovascular Research, Partner Site, Hamburg/Kiel/Lübeck, Germany
Paulus Kirchhof
Atrial Fibrillation NETwork, Münster, Germany
Paulus Kirchhof
UCL Brain Sciences, University College London, London, UK
Mika Kivimaki
Clinicum, University of Helsinki, Helsinki, Finland
Mika Kivimaki
Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
Woon-Puay Koh
Department of Medicine, University of Helsinki, Helsinki University Hospital, Helsinki, Finland
Heikki A. Koistinen
Minerva Foundation Institute for Medical Research, Helsinki, Finland
Heikki A. Koistinen
First Department of Propaedeutic Internal Medicine, Medical School, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
Alexander Kokkinos
MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
Jaspal S. Kooner
National Heart and Lung Institute, Imperial College London, London, UK
Jaspal S. Kooner & Peter Sever
Medical Department III – Endocrinology, Nephrology, Rheumatology, Leipzig University Medical Center, Leipzig, Germany
Peter Kovacs & Anke Tönjes
Institute for Social and Economic Research, University of Essex, Wivenhoe Park, UK
Meena Kumari
Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
Markku Laakso
Department of Medicine, University of Colorado at Denver, Aurora, CO, USA
Leslie A. Lange
Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
Claudia Langenberg
Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
Claudia Langenberg & Segun Fatumo
Department of Internal Medicine, Ewha Womans University School of Medicine, Seoul, Republic of Korea
Hyejin Lee
Department of Epidemiology and Biostatistics, Peking University Health Science Center, Beijing, China
Liming Li
Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
Liming Li
Institute of Epidemiology and Biobank Popgen, Kiel University, Kiel, Germany
Wolfgang Lieb
Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
Xu Lin
Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
Allan Linneberg & Thomas M. Werge
Division of Cardiovascular Medicine and Abboud Cardiovascular Research Center, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
Barry London
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
Patrik K. E. Magnusson
Division of Cardiology, University of California, San Francisco, San Francisco, CA, USA
Gregory M. Marcus
Department of Medicine, Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland
Pedro Marques Vidal & Peter Vollenweider
University of Lausanne, Lausanne, Switzerland
Pedro Marques Vidal & Peter Vollenweider
SYNLAB Academy, SYNLAB Holding Deutschland GmbH, Mannheim, Germany
Winfried März
Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
Winfried März
Department of Medicine, Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
Robert W. McGarrah
Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
Robert W. McGarrah
Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Sarah E. Medland
Geriatric Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
Dan Mellström
Centre for Vision Research and Department of Ophthalmology, Westmead Millennium Institute of Medical Research, University of Sydney, Sydney, New South Wales, Australia
Paul Mitchell
Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
Dennis O. Mook-Kanamori
Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
Andrew D. Morris
Data Tecnica, Washington, DC, USA
Mike A. Nalls
Electrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Saman Nazarian
Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Amanda E. Nelson
Department of Chronic Diseases, Norwegian Institute of Public Health, Oslo, Norway
Christopher S. Nielsen
Department of Pain Management and Research, Oslo University Hospital, Oslo, Norway
Christopher S. Nielsen
Department of Cardiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology Tampere University, Tampere, Finland
Kjell Nikus
Institute of Human Genetics, School of Medicine & University Hospital Bonn, Bonn, Germany
Markus M. Nöthen
Department of Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria
Adesola Ogunniyi
Department of Drug Treatment, Sahlgrenska University Hospital, Gothenburg, Sweden
Claes Ohlsson
Laboratorio de Inmunogenómica y Enfermedades Metabólicas, Instituto Nacional de Medicina Genómica, CDMX, Mexico City, Mexico
Lorena Orozco
Paavo Nurmi Centre and Unit for Health and Physical Activity, University of Turku, Turku, Finland
Katja Pahkala
Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
Päivi Pajukanta
Pat MacPherson Centre for Pharmacogenetics and Pharmacogenomics, Division of Population Health and Genomics, School of Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
Colin N. A. Palmer
Centre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods, Université Laval, Québec, QC, Canada
Louis Perusse & Marie-Claude Vohl
Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
Annette Peters
Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig Maximilians Universität München, Munich, Germany
Annette Peters
Population, Policy and Practice, UCL Great Ormond Street Hospital Institute of Child Health, London, UK
Chris Power
Department of Epidemiology, University of Washington, Seattle, WA, USA
Bruce M. Psaty & Alexander P. Reiner
Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
Bruce M. Psaty
Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Daniel J. Rader
Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
Olli T. Raitakari
InFLAMES Research Flagship, University of Turku, Turku, Finland
Olli T. Raitakari
Second Department of Cardiology, Medical School, National and Kapodistrian University of Athens, University General Hospital Attikon, Athens, Greece
Loukianos S. Rallidis
Center for Biostatistics and Data Science, Washington University School of Medicine, St. Louis, MO, USA
Dabeeru C. Rao
Genetics, Merck Sharp & Dohme Corp., Kenilworth, NJ, USA
Dermot F. Reilly
Department of Endocrinology and Metabolism, Kyung Hee University School of Medicine, Dongdaemun-gu, Seoul, Republic of Korea
Sang Youl Rhee & Jeong-Taek Woo
Department of Public Health, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
Samuli Ripatti
Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
Dan M. Roden
Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
Seungho Ryu
Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
Seungho Ryu
Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
Charumathi Sabanayagam
Department of Medicine, University of Ibadan, Ibadan, Nigeria
Babatunde Salako
Department of Cardiology and Department of Medicine, Columbia University, Columbia, NY, USA
Danish Saleheen
Department of Pediatrics, Section of Genetics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
Dharambir K. Sanghera
Department of Pharmaceutical Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
Dharambir K. Sanghera
Department of Physiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
Dharambir K. Sanghera
Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
Dharambir K. Sanghera
Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
Naveed Sattar
Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Medical University of Graz, Graz, Austria
Helena Schmidt
Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
Matthias B. Schulze
Deutsches Herzzentrum München, Cardiology, Deutsches Zentrum für Herz- und Kreislaufforschung (DZHK) – Munich Heart Alliance and Technische Universität München, München, Germany
Heribert Schunkert
School of Biomedical Science and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia
Rodney J. Scott
Institute of Molecular and Genomic Medicine, National Health Research Institutes, Zhunan, Taiwan
Wayne H. H. Sheu
Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
M. Benjamin Shoemaker
Department of Social Medicine, Population and Public Health, School of Medicine, University of California, Riverside, Riverside, CA, USA
Mario Sims
Department of Medicine I, University Hospital, LMU Munich, Munich, Germany
Moritz F. Sinner
Department of Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Lund, Sweden
J. Gustav Smith
The Wallenberg Laboratory/Department of Molecular and Clinical Medicine, Institute of Medicine, Gothenburg University and the Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden
J. Gustav Smith
Wallenberg Center for Molecular Medicine and Lund University Diabetes Center, Lund University, Lund, Sweden
J. Gustav Smith
Population Health Research Institute, St. George’s University of London, London, UK
David P. Strachan
Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
E. Shyong Tai
Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
Hua Tang
Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
Jean-Claude Tardif & Guillaume Lettre
Helsinki University Hospital, Helsinki, Finland
Tiinamaija Tuomi
Helsinki University Central Hospital, Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland
Tiinamaija Tuomi
Folkhälsan Research Center, Helsinki, Finland
Tiinamaija Tuomi
Department of Public Health, University of Helsinki, Helsinki, Finland
Jaakko Tuomilehto
Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
Jaakko Tuomilehto
Unidad de Biología Molecular y Medicina Genómica, Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
Maria-Teresa Tusié-Luna
Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
Rob M. van Dam
Department of Geriatric Medicine, Amsterdam Public Health, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands
Nathalie Van der Velde
Department of Epidemiology and Data Science, Amsterdam UMC, Amsterdam, The Netherlands
Natasja M. van Schoor
School of Nutrition, Faculty of Agricultural and Food Sciences, Université Laval, Québec, QC, Canada
Marie-Claude Vohl
Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
Uwe Völker
Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
Henry Völzke
Unidad de Investigación Médica en Epidemiología Clínica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
Niels H. Wacher-Rodarte
Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
Mark Walker
Hero DMC Heart Institute, Dayanand Medical College, Ludhiana, India
Gurpreet S. Wander
Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
Richard M. Watanabe
Department of Physiology & Neuroscience, Keck School of Medicine of USC, Los Angeles, CA, USA
Richard M. Watanabe
USC Diabetes and Obesity Research Institute, Keck School of Medicine of USC, Los Angeles, CA, USA
Richard M. Watanabe
Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
Peter W. F. Wilson
School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua Medicine, Tsinghua University, Beijing, China
Tien Y. Wong
Diabetes Unit, KEM Hospital and Research Centre, Pune, India
Chittaranjan S. Yajnik
School of Life Sciences, Westlake University, Hangzhou, China
Jian Yang
Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
Jian Yang
Kurume University School of Medicine, Kurume, Japan
Mitsuhiro Yokota
Cancer Epidemiology and Prevention Program, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
Jian-Min Yuan
Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
Jian-Min Yuan
Technical University of Munich and Klinikum Rechts der Isar, TUM School of Medicine and Health, Munich, Germany
Eleftheria Zeggini
Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
John-Anker Zwart
Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
Themistocles L. Assimes
Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
Maggie C. Y. Ng
Division of Genetic Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
Maggie C. Y. Ng
Department of Medicine, Veterans Affairs Boston Healthcare System, Boston, MA, USA
Christopher J. O’Donnell
Institute for Research and Medical Consultations, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
Panos Deloukas
Department of Genetic Medicine and Development, Centre Medical Universitaire, University of Geneva, Geneva, Switzerland
Timothy M. Frayling
McDonnell Genome Institute and Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
Adam E. Locke
Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
Yukinori Okada
Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
Yukinori Okada
Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka, Japan
Yukinori Okada
Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
Cristen J. Willer
MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda
Segun Fatumo
Department of Allied Health Sciences, University of Connecticut, Storrs, CT, USA
Jeanne M. McCaffery
Departments of Pediatrics and Genetics, Harvard Medical School, Boston, MA, USA
Joel N. Hirschhorn
The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Ruth J. F. Loos
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Consortia
23andMe Research Team
Adam Auton
, Yunxuan Jiang
& Jingchunzi Shi
DiscovEHR (DiscovEHR and MyCode Community Health Initiative)
Geetha Chittoor
, Anne E. Justice
, Jason E. Miller
& Shefali S. Verma
eMERGE (Electronic Medical Records and Genomics Network)
Yuki Bradford
, Marylyn D. Ritchie
, Yogasudha Veturi
& Xinyuan Zhang
GPC-UGR
Segun Fatumo
& Mariam Nakabuye
The PRACTICAL Consortium
Sonja I. Berndt
Understanding Society Scientific Group
Meena Kumari
, Lorraine Southam
& Eleftheria Zeggini
VA Million Veteran Program
Themistocles L. Assimes
, Yuk-Lam Ho
, Jie Huang
, Jennifer E. Huffman
, Qin Hui
, Christopher J. O’Donnell
, Saiju Pyarajan
, Yan V. Sun
, Catherine Tcheandjieu
& Peter W. F. Wilson
Contributions
Steering committee: G.R.A., T.L.A., S.I.B., M.B., D.I.C., T.E., T.M.F., J.N.H., G.L., C.M.L., A.E.L., R.J.F.L., K.L.M., K.E.N., C.J.O., Y.O., Y.V.S., U.T., P.M.Vis., R.G.W. and C.J.W. Conveners of GIANT working groups: S.I.B., Pa.D., J.N.H., A.E.J., G.L., C.M.L., A.E.L., R.J.F.L., E.M., K.L.M., K.E.N., Y.O., C.N.S., Sa.V., P.M.Vis., R.G.W., C.J.W., A.R.Wo. and Lo.Y. Writing group, coordination or supervision of data collection or analysis specific to the manuscript: J.D.A., S.I.B., M.R.C., S.F., S.E.G., J.N.H., Q.H., J.M.F., R.J.F.L., M.Nak., M.H.P., G.Ro., R.A.J.S., Y.V.S., N.J.T., K.H.W., Lo.Y. and X.Y. Data preparation group (checked and prepared data from contributing cohorts for GWAS meta-analyses): J.D.A., S.I.B., S.-H.C., T.F., M.Gr., Yi.J., Tu.K., Kr.L., M.Ma., D.E.M., C.M.-G., E.M., A.Moo., M.C.Y.N., A.E.L., A.E.J., Sa.V., C.N.S., Si.R., T.W.W., A.R.Wo, X.Y. and K.L.Y. Primary BMI working group: J.D.A., S.I.B., V.L.B., G.C., Pa.D., T.M.F., M.Gr., M.G.-M., J.N.H., A.E.J., A.E.L., R.J.F.L., Y.L., E.M., K.L.M., K.E.N., Y.O., M.H.P., Sa.S., R.A.J.S., Y.V.S., E.P.W., C.J.W., Lo.Y., X.Y. and K.L.Y. All other authors were involved in the design, management, coordination or analysis of contributing studies. Consortium representatives: publication-review@23andme.com (23andMe Research Team); aejustice1@geisinger.edu (DiscovEHR); marylyn@pennmedicine.upenn.edu (eMERGE); segun.fatumo@lshtm.ac.uk (GPC-UGR); practical@icr.ac.uk (PRACTICAL); mkumari@essex.ac.uk (Understanding Society Scientific Group); yan.v.sun@emory.edu (VA Million Veteran Program).
Corresponding author
Correspondence to
Ruth J. F. Loos
Ethics declarations
Competing interests
The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. M.I.McC. (since June 2019) and A.Mah (since January 2020) are employees of Genentech and holders of Roche stock. Yu.J., Ji.S. and Ad.A. are employed by and hold stock or stock options in 23andMe, Inc. S.W.v.d.L. has received Roche funding for unrelated work. M.J.Ca. is Chief Scientist for Genomics England, a UK Government company. P.S. has received research awards from Pfizer, Inc. D.H. is currently employed at Bristol Myers Squibb. G.C.-P. is an employee of 23andMe, Inc. Since completing the work contributed to this paper, D.J.T. has left the University of Cambridge and is now employed by Genomics plc. I.N. is an employee and stock owner of Gilead Sciences; this work was conducted before employment by Gilead Sciences. P.Ki. received research support for basic, translational and clinical research projects from the German Research Foundation (DFG), the European Union, the British Heart Foundation, the Leducq Foundation, the Else-Kröner-Fresenius Foundation, the Dutch Heart Foundation (DHF), the Accelerating Clinical Trials funding stream in Canada, the Medical Research Council (UK) and the German Center for Cardiovascular Research and from several drug and device companies active in atrial fibrillation and has received honoraria from several such companies in the past but not in the last 5 years. P.Ki. is listed as inventor on two issued patents held by the University of Hamburg (Atrial Fibrillation Therapy WO 2015140571 and Markers for Atrial Fibrillation WO 2016012783). M.R. received consultancy fees from Bayer (OCEANIC-AF national PI) and InCarda Therapeutics (RESTORE-SR national PI) to the institution, unrelated to this research/dataset. M.J.Cu. has served on advisory boards or consulted for Boston Scientific, Biosense Webster, Janssen Scientific Affairs and Johnson & Johnson. R.J.F.L. has acted as a member of advisory boards and as a speaker for Eli Lilly and the Novo Nordisk Foundation, for which she has received fees. S.N. is a scientific advisor to Circle software, ADAS software, CardioSolv and ImriCor and receives grant support from Biosense Webster, ADAS software and ImriCor. S.A.Lu. is an employee of Novartis. S.A.Lu. received sponsored research support from Bristol Myers Squibb, Pfizer, Boehringer Ingelheim, Fitbit, Medtronic, Premier and IBM and has consulted for Bristol Myers Squibb, Pfizer, Blackstone Life Sciences and Invitae. P.T.E. receives sponsored research support from Bayer AG, Bristol Myers Squibb, Pfizer and Novo Nordisk; he has also served on advisory boards or consulted for Bayer AG. Her.S. has received honoraria for consulting from AstraZeneca, MSD/Merck, Daiichi, Servier, Amgen and Takeda Pharma. He has additionally received honoraria for lectures and/or chairs from AstraZeneca, BayerVital, BRAHMS, Daiichi, Medtronic, Novartis, Sanofi and Servier. S.E.G. is employed by Regeneron. C.J.W. is employed by Regeneron. B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. K.S. is employed by deCODE Genetics/Amgen, Inc. Va.S. is employed by deCODE Genetics/Amgen, Inc. G.T. is employed by deCODE Genetics/Amgen, Inc. U.T. is employed by deCODE Genetics/Amgen, Inc. E.Ba. is an employee of Empirico, Inc. Al.K. declares having received research grants through his affiliation from Novo Nordisk and Pharmaserve Lilly and consulting honoraria from Pharmaserve Lilly, Sanofi-Aventis, Novo Nordisk, MSD, AstraZeneca, ELPEN Pharma, Boehringer Ingelheim, Galenica Pharma, Epsilon Health and Winmedica. J.N.H. holds equity in Camp4 Therapeutics. A.E.L. is currently employed by and holds stock in Regeneron Pharmaceuticals, Inc. G.R.A. is an employee of Regeneron Pharmaceuticals and owns stock and stock options in Regeneron Pharmaceuticals. A.Pa. is currently employed by and holds stock in Regeneron Pharmaceuticals. O.G. is an employee and stock owner of UCB Pharma; this work was conducted before employment by UCB. N.G. is currently employed at Novo Nordisk A/S. M.E.K. is employed by SYNLAB Holding Deutschland GmbH. W.M. reports grants and personal fees from AMGEN, BASF, Sanofi, Siemens Diagnostics, Aegerion Pharmaceuticals, AstraZeneca, Danone Research, Numares, Pfizer and Hoffmann-La Roche; personal fees from MSD and Alexion; and grants from Abbott Diagnostics, all outside the submitted work. W.M. is employed by Synlab Holding Deutschland GmbH. C.J.O. is a current employee of Novartis Biomedical Research. S.M.D. receives research support from RenalytixAI and in-kind research support from Novo Nordisk, outside the scope of the current research. Ca.S. is currently employed by and holds stock in Regeneron Pharmaceuticals. J.C.F. received speaking honoraria from AstraZeneca, Merck and Novo Nordisk for talks over which he had full control of content and a consulting honorarium from AstraZeneca. H.L.L. receives support from a consulting contract between Data Tecnica International and the National Institute on Aging (NIA), National Institutes of Health (NIH). M.A.N. receives support from a consulting contract between Data Tecnica and the NIA, NIH. T.D.S. is a co-founder and shareholder of ZOE, Ltd. P.R.H.J.T. is a salaried employee of BioAge Labs, Inc. A.I.d.H. is currently an employee of AbbVie. The remaining authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Distribution of obesity categories and average BMI in adult tuning and validation populations.
Distribution of obesity categories and mean BMI (+/- standard deviation) across adult tuning and validation populations. Asian-specific BMI cutoffs (see Methods) were applied to East Asian (EAS), South Asian (SAS), and non-Hispanic Asian (AS) populations, in contrast to African (AFR), American (AMR), European (EUR), and Middle Eastern (MID) populations. UKBB, UK Biobank; MVP, the Million Veteran Program; GPC-UGR, the Uganda General Population Cohort. Sample sizes (distinct individuals), from left to right, for
Tuning
: 6154, 971, 1553, 2660, 8005, 20000;
Validation
: 12263, 2332, 18701, 10281, 8096, 4201, 1359, 1177, 13673, 69828, 340224; and
BMI
: 12263, 10281, 1359, 1177, 13673, 2332, 18701, 8096, 4201, 69828, 6154, 971, 1553, 2660, 8005, 340224.
Extended Data Fig. 2 Prevalence of obesity categories across 1%-groups of PGSs.
Prevalence of obesity category, with 95% confidence intervals based on the normal approximation to the binomial distribution, across 1%-groups of PGS
Khera
and PGS
LC
within the validation subset of the UKBB participants of European ancestry (N ~ 340k). The black dotted line denotes the average prevalence in the entire subset. The red and blue lines correspond to the prevalence of the obesity category within the top and bottom 1% of PGS
Khera
and PGS
LC
, respectively.
Extended Data Fig. 3 Odds ratio per standard deviation (SD) increase of PGS
LC
for prevalent obesity categories.
All PGS were standardised using the mean and SD of the PGS of individuals who did not have the outcome of interest (that is, those with a BMI value below the threshold for a given obesity category), to account for differences in prevalence across validation populations. MVP, Million Veteran Program; GPC-UGR, Uganda General Population Cohort; UKB, UK Biobank. Sample sizes (distinct individuals) per grouped set of estimates, left to right: AFR 12263, 2332, 18701; AMR 10281, 8096; AS 4201; EAS 1359; SAS 1177; EUR 13673, 69828, 340224.
Extended Data Fig. 4 Distributional shifts of ancestry-specific scores before and after ancestry-calibration.
Distributional shifts of ancestry-specific scores which are linearly combined to create PGS
LC
, across UK Biobank-tuning and Bio
Me
-validation populations, before (top) and after (bottom) ancestry–calibration using 1000 Genomes reference data (Methods). Each plot shows a single ancestry-specific score, with separate lines showing the distribution across ancestries. For these plots, each score was standardised to mean zero and variance one once across all participants of a given study.
Extended Data Fig. 5 Association between PGS
LC
and height trajectory between birth and age 24.
Estimated using linear spline multilevel models with repeated measures from the ALSPAC cohort.
Extended Data Fig. 6 AUC for obesity outcomes at age 50, stratified by sex and overweight/obesity status at age 20.
Estimated in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. Restricted to estimates where the number of individuals with the obesity outcome was at least 50. Population labels as provided by PLCO.
Extended Data Fig. 7 Distribution of PGS across intensive lifestyle intervention trial arms.
Scores were standardised to mean zero and variance one by population group, ignoring assigned trial arm. Vertical lines indicate the mean PGS per arm.
Supplementary information
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Supplementary Table 1 (download XLSX
Combined spreadsheet of supplementary tables (
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Smit, R.A.J., Wade, K.H., Hui, Q.
et al.
Polygenic prediction of body mass index and obesity through the life course and across ancestries.
Nat Med
31
, 3151–3168 (2025). https://doi.org/10.1038/s41591-025-03827-z
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