Abstract

In human populations, assortative mating is almost univer­sally positive, with similarities between partners for quantit­ative phenotypes1,​2,​3,​4,​5,​6, common disease risk1,3,7,​8,​9,​10, beha­vi­our6,11, social factors12,​13,​14 and personality4,5,11. The causes and genetic consequences of assortative mating remain un­re­solved because partner similarity can arise from different mechanisms: phenotypic assortment based on mate choice15,16, partner interaction and convergence in phenotype over time14,17, or social homogamy where individuals pair according to social or environmental background. Here, we present theory and an analytical approach to test for genetic evidence of assortative mating and find a correlation in genetic value among partners for a range of phenotypes. Across three independent samples of 24,662 spousal pairs in total, we infer a correlation at trait-associated loci between partners for height (0.200, 0.004 standard error, SE) that matched the phenotypic correlation (0.201, 0.004 SE), and a correlation at trait-associated loci for BMI (0.143, 0.007 SE) that was significantly lower than the phenotypic value (0.228, 0.004 SE). We extend our analysis to the UK Biobank study (7,780 pairs), finding evidence of a correlation at trait-associated loci for waist-to-hip ratio (0.101, 0.041 SE), systolic blood pressure (0.138, 0.064 SE) and educational attainment (0.654, 0.014 SE). Our results imply that mate choice, combined with widespread pleiotropy among traits, affects the genomic architecture of traits in humans.

Under direct phenotypic assortment for a heritable trait, pairing of phenotypically similar individuals will increase the proportion of homozygous progeny, create a directional build-up of gametic phase disequilibria after many generations16,18,​19,​20, affect trait correlations between relatives16,21,22 and influence traits that are genetically correlated. In contrast, there are no genetic consequences in the population if partner similarity arises by an environmental correlation from either social homogamy or an interaction between couples after pairing. Despite the fact that phenotypic similarity between partners for traits such as height and intelligence was first quantified over a century ago16,20,22,23, the genetic consequences of assortative mating remain unresolved, because many confounding factors affect partner similarity, making it difficult to distinguish among the different mechanisms. As elegantly summarized in the first ever textbook on quantitative genetics: “Assortative mating in man, however, probably seldom arises purely in this way [phenotypic resemblance as a cause of assortative mating] and caution is needed in applying the results to human data”24.

Studies have attempted to address this question empirically using classical twin designs13, finding mixed evidence for partner similarity due to initial choice for many phenotypes1. A number of recent studies have used genomic data to examine the genetic similarity between couples, by estimating the genome-wide sharing of single-nucleotide polymorphisms (SNPs) and testing whether the observed correlation is greater than expected in the population25,​26,​27. We show here that an extremely large sample size would be required in order to detect a deviation from expectation in genome-wide sharing (Supplementary Note, Supplementary Figure 1), which implies that results based on SNP sharing are most likely to be explained by other factors28. For example, if a phenotype is correlated with social, cultural or ethnic status, and there is social homogamy, then partners will generally be genetically similar29,​30,​31, but this will not affect the genetic architecture of traits in the population. A recent study of 13,068 pairs of adult male–female partners living in the same household found that the genotype of a person is correlated with the height of their partner32, with both genetic and environmental effects contributing to the observed phenotypic correlation of height between partners32. However, examining mate choice in a variance component framework when the data contains close relatives32 is unlikely to separate confounded environmental and genetic factors that affect partner similarity, meaning that the causes and genetic consequences of assortative mating remain obscured (Supplementary Note). In this study, we devise an analytical framework that is unbiased by environmental confounding or population stratification, to estimate the genetic association between partners for a phenotype, allowing for a determination of the degree to which phenotypic similarity of mates reflects a correlation among partners at trait-associated loci.

We first analysed height and body mass index (BMI) in three independent samples: a composite sample of 5,044 couples taken from a range of publicly available cohort studies; a sample of 7,780 couples from the UK Biobank study; and a sample of 11,908 couples from the 23andMe research participant cohort (Supplementary Table 1). In all samples, we selected heterosexual couples of European ethnicity, and we ensured that there were no close relatives within the data. We began by estimating the phenotypic correlation among couples for height and BMI after accounting for age and sex differences in both traits. We then predicted an individual’s phenotype from a genome-wide genetic predictor created from their partner’s genotype. To create the genetic predictor, we devised a random-effects approach. We first re-analysed results from recent genetic studies of height33 and BMI34 to ensure that the samples used in our study were independent of the discovery samples. We then re-estimated the SNP effects (SNPs on HapMap3) in a random-effect model that converts the least-squares SNP estimates into approximate best linear unbiased predictors (summary statistic BLUP, or SBLUP; see Methods). The SBLUP approach maximizes prediction power as it creates a genetic predictor with BLUP properties35,36 (Supplementary Figure 2). From summary statistics of the meta-analysed genome-wide association study (GWAS), our SBLUP predictors for height and BMI had BLUP properties (slope of the regression of phenotype on genetic predictor of ~1, Tables 1, 2), and explained 18% of the phenotypic variation of height and 8% of the phenotypic variation of BMI, as compared with estimates of 17% and 7%, respectively, obtained by using genetic predictors made directly from GWAS summary statistics33,34.

Table 1: Phenotypic and genetic correlations among partners for height.
Table 2: Phenotypic and genetic correlations among partners for BMI.

We subsequently estimated the regression coefficient from a linear regression of the phenotype of a female on the SBLUP genetic predictor of their male partner, and vice versa, within a mixed-effects model. To further account for population stratification, we adjusted the genetic predictor by the first 20 principal components generated from genotype data prior to the analysis33,34,37. We demonstrate by theory (Supplementary Methods) and through simulation (Supplementary Figures 2, 3 and 4) that if there is direct assortative mating for a phenotype, and the predictor has BLUP properties, then the regression coefficient from a linear regression of the phenotype of one partner on the genetic predictor of the other is expected to equal the phenotypic correlation among couples. Furthermore, we show by theory (Supplementary Methods) and simulation (Supplementary Figure 5) that indirect assortment for an unmeasured genetically correlated trait would also create a correlation among couples at trait-associated loci for the recorded phenotype, with the value dependent on the phenotypic and genetic correlations of the different phenotypes, the ratio of their heritability, and the degree of partner assortment (Supplementary Methods). Therefore, our approach provides a direct estimate of the correlation among couples at trait-associated loci but cannot differentiate between direct assortment on a phenotype and assortment on a genetically correlated trait. However, our approach does differentiate between assortative mating based on selection of phenotypic characteristics and assortative mating based on shared social or environmental factors, because under only social/environmental homogamy we would not expect an association between genetic predictors of phenotype within the mixed-effect model of equation (1). This is because the equation accounts for population stratification, both by regressing principal components from the genetic predictor, and by fitting a relationship matrix estimated from the SNP markers.

We find evidence for a genetic basis of assortative mating for both height and BMI in all samples (Tables 1,2, Fig. 1). Across all samples, the meta-analysed phenotypic correlation among partners was 0.201 for height (0.004 SE) and 0.228 for BMI (0.004 SE; Tables 1,2, Fig. 1). For height, the meta-analysed value of the regression coefficient from a linear regression of the SBLUP genetic predictor of males and the phenotype of their female partner, and vice versa (meta-analysed value 0.200 with SE of 0.007, Table 2, Fig. 1a), did not significantly differ from the phenotypic correlation. For BMI, the meta-analysed estimate of the regression coefficient was 0.143 (0.007 SE), which was lower than the phenotypic correlation (Table 2, Fig. 1b). The regression coefficients did not differ when using either the male or female partner as the focal individual (Tables 1 and 2; Fig. 1a and b). For both phenotypes, the regression coefficient was significantly different from the expectation of zero under only social homogamy or partner interaction (Supplementary Figure 3), and we demo­nstrate that correlation in ancestry among partners in our data would not drive the results we present (Supplementary Figure 6). For height, obtaining a genetic estimate equal to the phenotypic estimate under indirect assortment would require a combination of a partner correlation that is greater than 0.2, for a trait that has a genetic correlation of >0.5 with height, and a heritability of >0.8, which is unlikely given that there is no evidence for a trait fitting these criteria. Therefore, our results suggest that there is direct assortative mating on height across all studies. For BMI, there may be indirect assortment on a genetically correlated trait, or there may be a combination of direct assortment and environmental factors that lead to phenotypic similarity among partners. For example, couples may additionally converge in phenotype over time, creating a mismatch in phenotypic and genetic estimates. Regardless of the mechanism, we find evidence of assortment at height- and BMI-associated loci implying gametic phase disequilibrium at those loci in the human population.

Figure 1: Assortative mating for height and BMI creates a correlation at trait-associated loci among partners.
Figure 1

In blue (N = 5,044 couples) are the results of analysis conducted in a dataset that was a composite of the Atherosclerosis in Communities, Health and Retirement, LifeLines, and Minnesota Center for Twin and Family Research cohort studies. The analysis was repeated in the UK Biobank (cyan, N = 7,710) and 23andMe research participant cohort (green, N = 11,908), and then the results were meta-analysed (grey). a,b, The phenotypic correlation among spousal pairs is shown, after correcting for age and sex differences. ‘Male focal’ and ‘female focal’ refer to the focal individual used in the analysis to estimate the genetic association among partners for height (a) and BMI (b), with the combined meta-analysis value across studies in grey. c,d, Trait refers to the SNP heritability for height (c) and BMI (d), in males, females, and meta-analysed across sexes and studies. From the meta-analysis value, a theoretical expectation was derived for the heritability estimate gained when treating the phenotype of an individual’s partner as the phenotype of that individual, and then partner phenotype refers to those estimates gained from the data. Error bars give the SE of the estimates.

We estimated the heritability (h2SNP) associated with common SNPs for realized phenotypic mate choice in unrelated individuals, by treating an individual’s partner’s phenotype as their own, and we tested this estimate against a derived theoretical expectation (Supplementary Methods, Supplementary Figure 7). The meta-analysed estimate of h2SNP for height was 0.559 (0.012) and that for BMI was 0.243 (0.012) across samples, with no evidence for significant differences among samples or sexes (Fig. 1d). Using these meta-analysis estimates and the phenotypic partner correlations, we calculated expectations of the h2SNP for realized phenotypic mate choice of 0.023 for height and 0.016 for BMI (Supplementary Methods and Supplementary Figure 7). The estimates of h2SNP for partner phenotype were not significantly different from their expectation, giving meta-analysis values of 0.030 (0.012) for height and 0.026 (0.012) for BMI (Tables 1,2, Fig. 1c and d). Finally, we conducted a mixed linear model association analysis of assortative mating for height, in which we tested for associations between the phenotype of an individual and the genotype of their partner. We created a genetic predictor from the SNP estimates gained from this analysis and used this to predict height in an independent sample of individuals from the combined cohorts that were not part of, or related to, the couples used in the analysis (Supplementary Table 1). The genetic predictor generated from the SNP results of the composite sample was significantly associated with height in the independent prediction sample (Table 1, prediction R2 = 0.011, p < 2 × 10−16 for the female focal analysis; prediction R2 = 0.005, p < 4 × 10−6 for the male focal analysis), and this result was replicated in both the UK Biobank and 23andMe samples (Table 1). These results also conformed to our expectation from theory and simulation (Supplementary Methods and Supplementary Figure 8). Taken together, these analyses suggest that the same loci underlie the trait and assortment on the trait, and provide further support for a correlation among partners at height- and BMI-associated loci.

We then extended our analysis to a range of phenotypes in the UK Biobank study. Of the 7,780 couples identified using household information (see Methods) with both phenotypic and genotypic data, all had measures of educational attainment (years), 4,323 had measures of bone mineral density, 7,773 had measures of waist-to-height ration (WHR) and 7,173 had measures of blood pressure. We corrected the phenotypes for age and sex differences and standardized to a z-score before estimating the phenotypic correlation. To estimate the genetic association, we reanalysed summary statistics from recent genetic studies38,​39,​40,​41 to create SBLUP statistics, and we then predicted an individual’s phenotype from a genome-wide SBLUP genetic predictor created from their partner’s genotype.

We find evidence for a correlation among partners at trait-associated loci for WHR, blood pressure and educational attainment (Fig. 2). In contrast, there was no evidence for either a phenotypic correlation for bone mineral density, or a correlation at bone mineral density associated loci, among partners (Fig. 2). Our findings for blood pressure, WHR and BMI probably reflect assortment on some combination of these phenotypes, or an alternative component of metabolism, given previous evidence for a genetic correlation between metabolic syndrome traits such as BMI, WHR and blood pressure42. For educational attainment, the correlation at trait-associated loci (0.654, 0.014 SE) was significantly higher than the phenotypic correlation (0.412, 0.011 SE). Previous studies indicate that a genetic predictor for educational attainment explains more variation in cognitive performance than educational attainment43, and provide evidence41,43 for a genetic correlation between educational attainment and cognitive performance that is higher than the phenotypic correlation of ~0.5. A partner correlation of ~0.65 for an unmeasured trait of cognitive performance with heritability ~0.7 that has phenotypic correlation ~0.6 and genetic correlation ~0.8 with educational attainment, and a heritability for educational attainment of ~0.35, would result in the estimates that we obtain here (Supplementary Methods). We support these results by directly estimating the correlation among partners for genetic predictors of both height and educational attainment, calculated from the ordinary least-squares association study estimates (Supplementary Figure S9). For educational attainment, we find that this direct estimate of the correlation at genetic value among partners is higher than the expected value given a phenotypic correlation of 0.4. In contrast, for height, the correlation at genetic value among partners conforms to the expectation given a phenotypic correlation of 0.2. While these findings on phenotypes other than height and BMI require replication that was not feasible in this study, they suggest that in addition to height there is phenotypic assortment in the UK population on traits that are associated with educational attainment and metabolism that creates a correlation among partners at trait-associated loci.

Figure 2: Genetic evidence for assortative mating across a range of phenotypes in the UK Biobank study.
Figure 2

Of the 7,780 couples identified in the UK Biobank with both phenotypic and genotypic data, all had measures of educational attainment (years), 4,323 had measures of bone mineral density, 7,773 had measures of waist-to-hip ratio and 7,173 had measures of blood pressure. We corrected the phenotypes for age and sex differences and standardized to a z-score before estimating the phenotypic correlation. To estimate the genetic association, we reanalysed summary statistics from recent genetic studies to create SBLUP statistics (see Methods). ‘Male focal’ (square) and ‘female focal’ (circle) refer to the focal individual used in the analysis to estimate the genetic association among partners, and ‘sexes combined’ refers to the meta-analysed value.

In summary, we show that the observed similarity in height, metabolic traits and educational attainment between partners reflects a correlation at trait-associated loci to differing degrees across traits. For height, there is likely to be direct phenotypic assortment, which is why our findings support a recent study32, despite the potential for bias by environmental confounding in that study. Secondary assortment on a genetically correlated trait probably leads to a correlation at trait-associated loci for educational attainment. Finally, for BMI, WHR and blood pressure there may be indirect assortment on a genetically correlated metabolic trait, or there may be a combination of direct assortment and environmental sharing that leads to phenotypic similarity among partners. For many phenotypes, shared environment probably plays a role in both phenotypic variation and mate choice. Our approach, which is free of environmental confounding, enables a direct estimation of the degree to which assortative mating creates a genetic correlation among partners at trait-associated loci for any phenotype in populations of any species.

Our results represent a snapshot of contemporary assortative mating in the human population, and we do not know whether mate choice was historically consistent, or whether equilibrium has been reached. If we assume equilibrium and an equilibrium heritability of 0.7 for height and 0.4 for BMI44, then our estimates of the degree to which the phenotypic correlation reflects a correlation at genetic values predict that the additive genetic variance and heritability are inflated by 17% and 5% for height, and 7% and 4% for BMI, respectively, relative to a population with random mating (see eq. 7.19 of previous work45). For educational attainment, assuming an equilibrium heritability of 0.4 implies an inflation of 27% and 24% for the additive genetic variance and heritability, respectively. These results have implications for the interpretation of resemblance between relatives and for estimates of genetic para­meters in populations.

Methods

We define assortative mating to be a phenotypic assortment that creates a directional build-up of gametic phase disequilibria at the underlying trait loci15,16,18,19,45. Phenotypic assortment can be based either directly on a phenotype, or indirectly on the phenotype of a genetically correlated trait. We distinguish this from assortative mating under heterogamy/homogamy where assortment occurs based on the environment (culture, social status, ethnicity), which can create a correlation in trait value if the phenotype is correlated with these environmental factors. Cultural homogamy can also create a correlation in genetic similarity among individuals if there is correlated population stratification among couples46. Our aim is to control for population stratification in order to quantify assortative mating genome-wide for height and BMI within populations.

Data

Composite cohort sample

We used a composite sample of data across a number of cohort studies (Supplementary Table 1). We selected heterosexual couples by identifying individuals of European ethnicity who had (i) a child together (inferred from genotype data and/or known pedigree structure), (ii) SNP genotype data, and (iii) phenotype data for height and BMI. Within each cohort, we adjusted the phenotype for age and standardized to z-scores in males and females separately, which removed differences in both mean and variance between males and females, and across cohorts. We then removed any couples that contained an outlying individual with a phenotypic value >7 SD from the mean.

All of the composite sample cohorts were independently imputed to a 1000 Genomes reference panel, using identical quality control (QC) procedures on the initial datasets of per-SNP missing data rate of <0.01, minor allele frequency >0.01, per-person missing data rate <0.01, and Hardy–Weinberg disequilibrium p-value <1 × 10−6. Imputation was performed in two stages. First, the target data were haplotyped using HAPI-UR. Second, Impute2 was used to impute the haplotypes to the 1,000-genome reference panel (release 1, version 3). We then extracted best-guess genotypes at common SNPs typed in the HapMap 3 European sample with imputation info score >0.5. We conducted principal component analysis within each cohort and removed individuals with principal eigenvector values that were >7 SD from the mean. We calculated allele frequencies within each of the cohorts and removed any SNPs with allele frequency differences across cohorts larger than 0.2. We then combined the cohorts together and conducted an additional round of QC of per-SNP missing data rate of <0.01, minor allele frequency >0.01, per-person missing data rate <0.01 and Hardy–Weinberg disequilibrium p-value <1 × 10−6. Finally, we removed one of any pair of individuals with estimated relatedness in a genetic relatedness matrix (see below) greater than a threshold of 0.05. All QC was conducted using PLINK v1.9.

23andMe research participant cohort

We repeated our analysis using data from the 23andMe research participant cohort, which is drawn from the customer base of 23andMe, a consumer genetics company. This cohort has been described in detail previously47,48. Participants provided informed consent and answered survey questions online, under a protocol approved by the external institutional review board Ethical & Independent Review Services (E&I Review), which is accredited by the Association for the Accreditation of Human Research Protection Programs. Couples were selected who had at least one child in the database, and for whom self-reported height and weight were available. Relatives were then excluded, by removing one from any pair of individuals that shared more than 700 cM of total identity by descent. Participant genotype data were phased out of sample using a modified version of BEAGLE, and were then imputed in batches of 8,000 to 9,000 individuals against the September 2013 release of the 1000 Genotypes Project haplotypes using Minimac2, with five rounds and 200 states for parameter estimation. Analyses were limited to 15.5 million SNPs with imputed R2 > 0.5 averaged across all batches and R2 > 0.3 in every batch.

UK Biobank Sample

We repeated our analyses using data from the UK Biobank following a recent study32. The UK Biobank Axiom (UKBA) array from Affymetrix was custom-designed for the purpose of genotyping the UK Biobank participants. The UKBA array is being used to genotype ~450,000 of the ~500,000 UK Biobank participants. The other ~50,000 samples were genotyped on the closely related UK BiLEVE (UKBL) array. The UKBA array is an updated version of the UKBL array that includes additional markers, which replaced a small fraction of the markers used for genome-wide coverage. The UKBL cohort and the rest of UK Biobank differ only in small details of the DNA processing stage and the two SNP arrays are very similar with over 95% common marker content. The ~50,000 samples genotyped on the UKBL array are included in the interim release. After QC procedures have been applied (see Supplementary Methods), the interim UK Biobank data release contains genotypes for 152,736 samples that passed sample QC (~99.9% of total samples), and 806,466 SNPs that passed SNP QC in at least one batch (>99% of the array content).

Imputed genotype data are provided as part of the data release. Prior to imputation, genotypes SNPs on the UKBA chip and UKBL chip were removed if (i) they were missing across multiple batches, (ii) they were multiallelic or (iii) they were of minor allele frequency, <1%. 1,037 sample outliers were also removed. These filters resulted in a dataset with 641,018 autosomal SNPs in 152,256 samples. The result of the imputation process using a merged reference panel from the UK10K and 1000 Genomes data (Supplementary Methods) is a dataset with 73,355,667 SNPs, short indels and large structural variants in 152,249 individuals. Selecting out only SNPs with imputation ‘info score’ >0.3 and minor allele count > = 5 gives ~40M SNPs in 152,249 individuals. Principal component analysis and the self-declared ethnicity were used to derive a ‘White British’ subset of samples. In addition, samples were excluded if they had (i) at least one identified closely related sample (r > 0.1); (ii) a genetically inferred sex that did not match the self-reported gender; (iii) ~500 extreme heterozygosity or missing genotype outliers. These filters resulted in a dataset with 112,338 samples, and further exclusion of one individual from a pair with an estimate SNP marker relatedness greater than 0.05 using GCTA (Supplementary Methods) resulted in a final sample of 108,042 samples. We then selected out 1,162,900 HapMap3 SNPs. BMI and height were recorded for every individual, and we selected only the first recorded measures. We then adjusted both phenotypes for age (factor with levels for each age between 40 and 73) and sex differences. BMI and height phenotypes 5 SD away from the mean were not included in the analyses. Both phenotypes were then converted to z-scores with zero mean and variance of 1.

From this set of 108,042 individuals, we used household sharing information to identify pairs of individuals who were less than 10 years apart in age, who both reported living with their spouse, in the same location, for the same length of time, with the same number of people in their household, and who had parents of different ages. This provided a set of 7,780 couples with complete height, BMI and genotype data. From these couples, 4,323 couples had complete bone mineral density data (UK Biobank unique data identifier 3148.0.0), 7,773 had measures of WHR (UK Biobank unique data identifier 48-0.0 and 49-0.0), 7,173 had measures of blood pressure (UK Biobank unique data identifiers 4079-0.0 and 4080-0.0) and all 7,780 had reported their educational attainment (UK Biobank unique data identifier 6138-0.0). We converted educational attainment to a continuous yearly measure as in a previous study41. We then adjusted the phenotypes for age (factor with levels for each age between 40 and 73) and sex differences, removed individuals 5 SD away from the mean, and standardized the phenotype to a z-score with zero mean and variance of 1.

Statistical analysis

Phenotypic correlation

We began by estimating the phenotypic correlation among couples for all phenotypes after accounting for age and sex differences in both traits.

Approximate best linear unbiased genetic predictor

We predicted an individual’s phenotype from a genome-wide genetic predictor created from their partner’s genotype. To create the genetic predictor, we devised a random-effect approach (Supplementary Methods). We first re-analysed results from recent genetic studies of height33 and BMI34 to ensure that the samples used in our study were independent of the discovery samples. For the extended UK Biobank analysis, we used results from genetic studies of bone mineral density38, systolic and diastolic blood pressure39, WHR40 and educational attainment41, ensuring that the UK Biobank sample was not included within the discovery meta-analysis. We then re-estimated the SNP effects (SNPs on HapMap3) in a random-effect model that converts the least-squares SNP estimates into approximate best linear unbiased predictors (summary statistic BLUP: SBLUP; Supplementary Methods). The SBLUP approach maximizes prediction power, as it creates a genetic predictor with BLUP properties35,36 (Supplementary Figure 2).

Prediction accuracy of a predictor with BLUP properties

We then estimated the amount of variation in height and BMI that can be explained by a predictor with BLUP properties. To do this, we estimated principal components of the HapMap 3 best-guess imputed SNPs for the combined cohort and we selected the top 20 principal components to create a N×P matrix Z of eigenvectors across the P selected principal components. We then regressed the estimated genetic predictor onto the eigenvectors as gˆm=μ+Zβm+em and gˆf=μ+Zβf+ef for males (m) and females (f), respectively, where μ is the mean and β is a P×1 vector of the regression coefficients, and e is the residual error. We adjusted the predictors as gˆpm=gˆmZβˆm and gˆpf=gˆfZβˆf. We then regressed the phenotypic values onto the adjusted genetic predictors as ym=μ+gˆpm+e and yf=μ+gˆpf+e, where ym and yf are N×1 vectors and represent the phenotype for males and females, respectively. In the UK Biobank sample and the 23andMe cohort, the same approach was followed, with the top 20 principal components computed from a subset of genotyped SNPs47,48. This approach removes population stratification (associated with the leading axes of genetic variation) in the predictor, before estimating the amount of variation in height and BMI explained by the genetic predictor, and the slope of the relationship between phenotype and genetic predictor49,​50,​51,​52. These two parameters are key to the later analysis.

Predicting an individual’s phenotype from the genotype of their partner

To estimate the degree to which assortative mating creates a genetic correlation at trait-associated loci, we first determined the relationship between the genetic predictor of males and the phenotype of their female partner, and vice versa, as: (1)ym=μm+gˆpf+um+em;yf=μf+gˆpm+uf+ef

where u is an N×1 vector of the total genetic effects of the individuals, with u=N(0,AσG2). Here, A is the genetic relationship matrix between either males (when estimating um) or females (when estimating uf), with its jlth element being Ajl=1Ni=1N(xij2pi)(xil2pi)2pi(1pi) where pi is the frequency of the minor allele of the imputed HapMap3 common SNP i, and x is the SNP genotype (best guess for the combined cohort and rounded imputed diploid dosage for the 23andMe cohort). The genetic relationship matrix accounts for population stratification in the phenotype, as it is equivalent to fitting all the principal components within the model. Equation (1) was estimated using the GREML function in GCTA v1.25. Under different types of assortative mating, we derive the expectation of the regression coefficient from a linear regression of the phenotype of males on the genetic predictor of their female partners, and vice versa, in the Supplementary Methods.

Common SNP heritability of realized mate choice

We then estimated the heritability associated with common SNPs (hSNP2) for realized mate choice of height and BMI as: (2)ym=μm+Zβm+uf+em,yf=μf+Zβf+um+ef

with notation the same as above. Equation (2) controls for population stratification by fitting the effects of the first 20 principal components estimated within the 23andMe data before then estimating the effects u=N(0,AσG2). We selected Hapmap3 common SNPs from the best-guess imputed SNP data to estimate A, and thus σG2 is the variance explained by those SNPs. Equation (2) was estimated using the GREML function in GCTA v 1.25. Again, we derive the expectation of the regression coefficient from a linear regression of the phenotype of males on the genetic predictor of their female partners, and vice versa, in the Supplementary Methods.

Mixed linear model association analysis of realized mate choice

To identify the genomic regions associated with realized mate choice and test for a single genetic basis of the trait and mate choice, which implies direct assortment on phenotype, we conducted a mixed linear model association analysis53 as: (3)ym=μm+Xfiβi+um+em;yf=μf+Xmiβi+uf+ef with notation the same as above, where βi is the regression coefficient, Xmi and Xfi are N×1 vectors of genotypes for each SNP i = 1, …, k (coded as 0, 1 or 2 defining the number of reference alleles), for males and females respectively, umanduf are the polygenic effects (random effect) for males and females respectively, and e is the residual. We selected HapMap3 common SNPs (MAF ≥ 0.01) from the best-guess imputed SNP data in equation (3) as we did for equations (1) and (2). Equation (3) was estimated using the MLMA function in GCTA v1.25. Again, we derive the expectation of the regression coefficient from a linear regression of the phenotype of males on the genetic predictor of their female partners and vice versa in the Supplementary Methods54,55.

Simulation study

To support our results we conducted a simulation study using real genotype data that is described in full in the Supplementary Methods.

Data availability

We utilize publicly available dbGaP data from the Atherosclerosis Risk in Communities (ARIC) Study (dbGaP phs000090.v1.p1), Health and Retirement Study (HRS: dbGaP phs000428.v1.p1), and Resource for Genetic Epidemiology Research on Adult Health and Aging (GERA: dbGaP phs000674.v1.p1). We also use data from the UK Biobank which is a publicly available resource on request. Access to individual-level phenotypic, genetic and partner identity data from the 23andMe cohort, ARIC, TWINGENE, Minnesota Center for Twin and Family Research (MCTFR) and the LifeLines Study is available with the obtainment of a research agreement. The summary data that support the findings of the study are available from M.R.R. upon request.

Additional information

How to cite this article: Robinson, M. R. et al. Genetic evidence of assortative mating in humans. Nat. Hum. Behav. 1, 0016 (2017).

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Acknowledgements

The University of Queensland group is supported by the Australian National Health and Medical Research Council (NHMRC grants 1078037, 1048853 and 1050218), the Australian Research Council (Discovery Project 160103860) and the National Institute of Health (NIH grants R21ESO25052-01, R01AG042568 and PO1GMO99568). J.Y. is supported by a Charles and Sylvia Viertel Senior Medical Research Fellowship. We thank the participants of the cohort studies, as well as C. Haley and our colleagues at the Program in Complex Trait Genomics, for comments and suggestions. 23andMe cohort thank the research participants and employees of 23andMe. This work was supported by the National Human Genome Research Institute of the NIH (grant number R44HG006981). The UK Biobank research was conducted using the UK Biobank Resource under project 12514. TWINGENE was supported by the Swedish Research Council (M-2005-1112), GenomEUtwin (EU/QLRT-2001-01254; QLG2-CT-2002-01254), NIH DK U01-066134, the Swedish Foundation for Strategic Research, and the Heart and Lung Foundation grant no. 20070481. The Atherosclerosis Risk in Communities Study (ARIC) is a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). K.E.N and M.G. are supported by NIDDK R01 DK089256. We thank the staff and participants of the ARIC study for their contributions. Generation and management of GWAS genotype data for the LifeLines Cohort Study is supported by the Netherlands Organization of Scientific Research (grant 175.010.2007.006), the Economic Structure Enhancing Fund of the Dutch government, the Ministry of Economic Affairs, the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the Northern Netherlands Collaboration of Provinces, the Province of Groningen, University Medical Center Groningen, the University of Groningen, Dutch Kidney Foundation and Dutch Diabetes Research Foundation. The authors acknowledge the services of the LifeLines Cohort Study, the contributing research centres delivering data to LifeLines, and all the study participants. Minnesota Center for Twin and Family Research (MCTFR) is funded by US Public Health Service grants from the National Institute on Alcohol Abuse and Alcoholism (AA09367, AA11886), National Institute on Drug Abuse (DA05147, DA13240, DA024417, DA036216) and National Institute of Mental Health (MH066140). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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  1. Institute of Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia

    • Matthew R. Robinson
    • , Anna A. E. Vinkhuyzen
    • , Jian Yang
    •  & Peter M. Visscher
  2. Department of Research, 23andMe Inc., Mountain View, California 94041, USA

    • Aaron Kleinman
  3. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina 27514, USA

    • Mariaelisa Graff
    •  & Kari E. North
  4. Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina 27514, USA

    • David Couper
  5. University of Minnesota, Department of Psychology, Minneapolis, Minnesota, USA

    • Michael B. Miller
    • , William G. Iacono
    •  & Matt McGue
  6. Department of Psychiatry, VU University Medical Centre & GGZ inGeest, Amsterdam, The Netherlands

    • Wouter J. Peyrot
  7. Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands

    • Abdel Abdellaoui
  8. School of Psychology, The University of Queensland, Brisbane, Queensland 4072, Australia

    • Brendan P. Zietsch
  9. Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands

    • Ilja M. Nolte
    • , Jana V. van Vliet-Ostaptchouk
    •  & Harold Snieder
  10. Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands

    • Jana V. van Vliet-Ostaptchouk
  11. QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, Queensland 4006, Australia

    • Sarah E. Medland
    •  & Nicholas G. Martin
  12. Karolinska Institutet, SE-171 77 Stockholm, Sweden

    • Patrik K. E. Magnusson
  13. Carolina Centre for Genome Sciences, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina 27514, USA

    • Kari E. North
  14. The Queensland Brain Institute, The University of Queensland, Brisbane, Queensland 4072, Australia

    • Jian Yang
    •  & Peter M. Visscher
  15. Department of Epidemiology, University of Groningen, University Medical Center Groningen, The Netherlands

    • Harold Snieder
    • , Behrooz Z. Alizadeh
    •  & H. Marike Boezen
  16. Department of Genetics, University of Groningen, University Medical Center Groningen, The Netherlands

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    • , Morris Swertz
    •  & Cisca Wijmenga
  17. Department of Cardiology, University of Groningen, University Medical Center Groningen, The Netherlands

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  18. Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, The Netherlands

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  19. Department of Medical Biology, University of Groningen, University Medical Center Groningen, The Netherlands

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  20. Department of Endocrinology, University of Groningen, University Medical Center Groningen, The Netherlands

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  21. Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA

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    •  & Cristen J. Willer
  22. Hudson Alpha Institute for Biotechnology, Huntsville, Alabama 35806, USA

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    •  & Lindsay L. Waite
  23. Estonian Genome Center, University of Tartu, Tartu 50410, Estonia

    • Helene Alavere
    • , Tõnu Esko
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    • , Andres Metspalu
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  24. Institute of Genetic Epidemiology, Helmholtz Zentrum Mìnchen - German Research Center for Environmental Health, 85764 Neuherberg, Germany

    • Eva Albrecht
    • , Christian Gieger
    •  & Martina Mìller-Nurasyid
  25. Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, EX1 2LU, UK

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    • , Timothy Frayling
    • , Andrew T. Hattersley
    • , John R. B. Perry
    • , Michael N. Weedon
    •  & Andrew R. Wood
  26. Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, 20502 Malmö, Sweden

    • Peter Almgren
    •  & Leif C. Groop
  27. Department of Epidemiology, Erasmus MC, Rotterdam, 3015GE, The Netherlands

    • Najaf Amin
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    • , Fernando Rivadeneira
    • , André G. Uitterlinden
    • , Sophie van Wingerden
    •  & Jacqueline C. M. Witteman
  28. Institut Pasteur de Lille, INSERM U744, Université Lille Nord de France, F-59000 Lille, France

    • Philippe Amouyel
  29. Telethon Institute for Child Health Research, West Perth Western Australia 6872, Australia

    • Denise Anderson
    •  & Aarno Palotie
  30. Centre for Child Health Research, The University of Western Australia, Australia

    • Denise Anderson
  31. Departments of Biostatistics, University of Washington, Seattle, Washington 98195, USA

    • Alice M. Arnold
    •  & Barbara McKnight
  32. Collaborative Health Studies Coordinating Center, Seattle, Washington 98115, USA

    • Alice M. Arnold
  33. Department of Epidemiology and Public Health, Faculty of Medicine, Strasbourg, France

    • Dominique Arveiler
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  34. Icelandic Heart Association, Kopavogur, Iceland

    • Thor Aspelund
    • , Gudny Eiriksdottir
    • , Vilmundur Gudnason
    •  & Albert Vernon Smith
  35. University of Iceland, Reykjavik, Iceland

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  36. Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, The Netherlands

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    • , Sarah E. Hunt
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  42. Department of Neurology, Boston University School of Medicine, Boston, Massachusetts 02118, USA

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  43. Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

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  44. INSERM CESP Centre for Research in Epidemiology and Public Health U1018, Epidemiology of diabetes, obesity and chronic kidney disease over the lifecourse, 94807 Villejuif, France

    • Beverley Balkau
  45. University Paris Sud 11, UMRS 1018, 94807 Villejuif, France

    • Beverley Balkau
  46. Multidisciplinary Cardiovascular Research Centre (MCRC), Leeds Institute of Genetics, Health and Therapeutics (LIGHT), University of Leeds, Leeds LS2 9JT, UK

    • Anthony J. Balmforth
  47. University of Milan, Department of Medicine, Surgery and Dentistry, 20139 Milano, Italy

    • Cristina Barlassina
  48. University of Cambridge Metabolic Research Labs, Institute of Metabolic Science Addenbrooke’s Hospital, CB2 OQQ, Cambridge, UK

    • Inês Barroso
  49. Department of Vascular Medicine, Academic Medical Center, Amsterdam, The Netherlands

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    •  & Mieke D. Trip
  50. Regensburg University Medical Center, Innere Medizin I, 93053 Regensburg, Germany

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    • , Christa Buechler
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  51. Department of Medical Genetics, University of Lausanne, 1005 Lausanne, Switzerland

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  52. Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois (CHUV) University Hospital, 1011 Lausanne, Switzerland

    • Jacques S. Beckmann
  53. PathWest Laboratory of Western Australia, Department of Molecular Genetics, J Block, QEII Medical Centre, Nedlands, Western Australia 6009, Australia

    • John P. Beilby
    •  & Jennie Hui
  54. Busselton Population Medical Research Foundation Inc., Sir Charles Gairdner Hospital, Nedlands, Western Australia 6009, Australia

    • John P. Beilby
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  55. School of Pathology and Laboratory Medicine, University of Western Australia, Nedlands, Western Australia 6009, Australia

    • John P. Beilby
    •  & Jennie Hui
  56. Department of Surgery and Pathology, University of Western Australia, Nedlands, 6009, Australia

    • John P. Beilby
  57. Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, OX3 7LJ, UK

    • Amanda J. Bennett
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  58. Department of Social Medicine, University of Bristol, Bristol, BS8 2PS, UK

    • Yoav Ben-Shlomo
  59. Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA

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    • , Thomas A. Buchanan
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  60. Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland

    • Sven Bergmann
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  61. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland 20892, USA

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  62. Zentrum fìr Zahn-, Mund- und Kieferheilkunde, 17489 Greifswald, Germany

    • Reiner Biffar
  63. Molecular Biology Department, Istituto Auxologico Italiano, Milano, Italy

    • Anna Maria Di Blasio
    •  & Davide Gentilini
  64. Division of Endocrinology and Diabetes, Department of Medicine, University Hospital, Ulm, Germany

    • Bernhard O. Boehm
  65. Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, 14558 Nuthetal, Germany

    • Heiner Boeing
    •  & Eva Fisher
  66. Human Genetics Center and Institute of Molecular Medicine, University of Texas Health Science Center, Houston, Texas 77030, USA

    • Eric Boerwinkle
  67. Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland

    • Jennifer L. Bolton
    • , Harry Campbell
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    • , Jackie F. Price
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  68. CNRS UMR8199-IBL-Institut Pasteur de Lille, F-59000 Lille, France

    • Amélie Bonnefond
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  69. National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA

    • Lori L. Bonnycastle
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  70. Genome Technology Branch, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA

    • Lori L. Bonnycastle
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    •  & Amy J. Swift
  71. Department of Biological Psychology, VU University Amsterdam, 1081 BT Amsterdam, The Netherlands

    • Dorret I. Boomsma
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  72. Department of Genetics, Washington University School of Medicine, St Louis, Missouri 63110, USA

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  73. Division of Biostatistics, Washington University School of Medicine, St Louis, Missouri 63110, USA

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  74. Department of Medicine III, University of Dresden, 01307 Dresden, Germany

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  75. Department of Medicine III, University of Dresden, Medical Faculty Carl Gustav Carus, Fetscherstrasse 74, 01307 Dresden, Germany

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  76. CNRS UMR8199-IBL-Institut Pasteur de Lille, F-59019 Lille, France

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    • , Christine Cavalcanti-Proença
    • , Philippe Froguel
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  77. University Lille Nord de France, 59000 Lille, France

    • Nabila Bouatia-Naji
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    • , Philippe Froguel
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    • , Boris Skrobek
    •  & Vincent Vatin
  78. Montreal Heart Institute, Montreal, Quebec, H1T 1C8, Canada

    • Gabrielle Boucher
    •  & Guillaume Lettre
  79. Dipartimento di Medicina Sperimentale. Università degli Studi Milano-Bicocca, Monza, Italy

    • Paolo Brambilla
  80. LifeLines Cohort Study, University Medical Center Groningen, University of Groningen, The Netherlands

    • Harold Snieder
    • , Marcel Bruinenberg
    • , Lude Franke
    • , Melanie M. Van der Klauw
    • , Ronald P. Stolk
    • , Jana V. Van Vliet-Ostaptchouk
    •  & Bruce H. R. Wolffenbuttel
  81. Division of Endocrinology, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA

    • Thomas A. Buchanan
  82. Genetic Epidemiology and Biostatistics Platform, Ontario Institute for Cancer Research, Toronto, M5G 1L7, Canada

    • Gemma Cadby
  83. Prosserman Centre for Health Research, Samuel Lunenfeld Research Institute, Toronto, M5G 1X5, Canada

    • Gemma Cadby
    •  & Lyle J. Palmer
  84. Clinical Pharmacology and Barts and The London Genome Centre, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK

    • Mark J. Caulfield
    • , Toby Johnson
    •  & Patricia B. Munroe
  85. Department of Clinical Medicine, University of Milano-Bicocca, Monza, Italy

    • Giancarlo Cesana
  86. Harvard Medical School, Boston, Massachusetts 02115, USA

    • Daniel I. Chasman
    • , Lee M. Kaplan
    •  & Paul M. Ridker
  87. Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, Massachusetts 02215, USA

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  88. Department of OB/GYN and Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

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  89. Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, California, USA

    • Yii-Der Ida Chen
  90. University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas Texas 75390-8854, USA

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    •  & Thomas Person
  91. Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, W2 1PG, UK

    • Lachlan Coin
    • , Paul Elliott
    • , Marjo-Riitta Jarvelin
    •  & Ulla Sovio
  92. British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, G12 8TA, UK

    • John M. Connell
  93. University of Dundee, Ninewells Hospital & Medical School, Dundee, DD1 9SY, UK

    • John M. Connell
  94. National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK

    • William Cookson
    •  & Miriam F. Moffatt
  95. Centre for Genetic Epidemiology and Biostatistics, University of Western Australia, Crawley, Western Australia 6009, Australia

    • Matthew N. Cooper
    • , Jennie Hui
    • , Robert W. Lawrence
    •  & Lyle J. Palmer
  96. Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA

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    • , Jennifer R. Kulzer
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  97. Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA

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    • , Julius S Ngwa
    •  & Charles C. White
  98. University of Milan, Department of Health Sciences, Ospedale San Paolo, 20139 Milano, Italy

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    •  & Francesca Frau
  99. Fondazione Filarete, Milano, Italy

    • Daniele Cusi
  100. MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, CB2 0QQ, UK

    • Felix R. Day
    • , Tuomas O. Kilpeläinen
    • , Claudia Langenberg
    • , Shengxu Li
    • , Ruth J. F. Loos
    • , Jian’an Luan
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    • , Nicholas J. Wareham
    •  & Jing Hua Zhao
  101. MRC Centre for Causal Analyses in Translational Epidemiology, Department of Social Medicine, Oakfield House, Bristol, BS8 2BN, UK

    • Ian N. M. Day
    • , Debbie A. Lawlor
    • , George Davey Smith
    •  & Nicholas John Timpson
  102. Department of Dietetics-Nutrition, Harokopio University, 70 El. Venizelou Str, Athens, Greece

    • George V. Dedoussis
    • , Maria Dimitriou
    •  & Eirini V. Theodoraki
  103. Istituto di Neurogenetica e Neurofarmacologia del CNR, Monserrato, 09042, Cagliari, Italy

    • Mariano Dei
    • , Andrea Maschio
    • , Serena Sanna
    • , Manuela Uda
    •  & Gianluca Usala
  104. Istituto di Ricerca Genetica e Biomedicadel CNR, Monserrato, 09042, Cagliari, Italy

    • Mariano Dei
    •  & Serena Sanna
  105. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva 1211, Switzerland

    • Emmanouil T. Dermitzakis
    •  & Antigone S. Dimas
  106. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK

    • Antigone S. Dimas
    • , Teresa Ferreira
    • , Jon P. Krohn
    • , Cecilia M. Lindgren
    • , Reedik Mägi
    • , Iain Mathieson
    • , Mark I. McCarthy
    • , Josine L. Min
    • , Andrew P. Morris
    • , John F. Peden
    • , Inga Prokopenko
    • , Joshua C. Randall
    • , Nigel W. Rayner
    •  & Neil R. Robertson
  107. Biomedical Sciences Research Center Al. Fleming, 16672 Vari, Greece

    • Antigone S. Dimas
  108. Department of Pharmacy and Pharmacology, University of Bath, Bath, BA1 1RL, UK

    • Anna L. Dixon
  109. Department of Internal Medicine B, Ernst-Moritz-Arndt University, 17475 Greifswald, Germany

    • Marcus Dörr
  110. Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), The Netherlands

    • Cornelia M. van Duijn
    • , Karol Estrada
    • , Albert Hofman
    • , Carolina Medina-Gomez
    • , Joyce B. J. van Meurs
    • , Ben A. Oostra
    • , Marjolein J. Peters
    • , Fernando Rivadeneira
    • , André G. Uitterlinden
    • , Jacqueline C. M. Witteman
    •  & M. Carola Zillikens
  111. Center of Medical Systems Biology, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands

    • Cornelia M. van Duijn
    •  & Gert-Jan van Ommen
  112. The London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK

    • Shah Ebrahim
  113. South Asia Network for Chronic Disease

    • Shah Ebrahim
  114. National Institute for Health and Welfare, Department of Chronic Disease Prevention, Unit of Public Health Genomics, 00014, Helsinki, Finland

    • Niina Eklund
    • , Johannes Kettunen
    • , Kati Kristiansson
    • , Niina Pellikka
    • , Markus Perola
    • , Samuli Ripatti
    • , Kaisa Silander
    •  & Ida Surakka
  115. Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia

    • Niina Eklund
    • , Tõnu Esko
    • , Andres Metspalu
    • , Evelin Mihailov
    • , Mari Nelis
    •  & Maris Teder-Laving
  116. MRC-HPA Centre for Environment and Health, London W2 1PG, UK

    • Paul Elliott
  117. Clinic of Cardiology, West German Heart Centre, University Hospital of Essen, University Duisburg-Essen, Germany

    • Raimund Erbel
  118. Nordic Center of Cardiovascular Research (NCCR), 23538 Lìbeck, Germany

    • Jeanette Erdmann
    •  & Heribert Schunkert
  119. Universität zu Lìbeck, Medizinische Klinik II, 23562 Lìbeck, Germany

    • Jeanette Erdmann
    • , Christina Loley
    •  & Heribert Schunkert
  120. Universität zu Lìbeck, Medizinische Klinik II, 23538 Lìbeck, Germany

    • Jeanette Erdmann
    • , Michael Preuss
    •  & Heribert Schunkert
  121. Deutsches Zentrum fìr Herz-Kreislaufforschung e. V. (DZHK), Universität zu Lìbeck, 23538 Lìbeck, Germany

    • Jeanette Erdmann
    •  & Heribert Schunkert
  122. Department of General Practice and Primary health Care, University of Helsinki, Helsinki, Finland

    • Johan G. Eriksson
  123. National Institute for Health and Welfare, 00271 Helsinki, Finland

    • Johan G. Eriksson
    • , Eero Kajantie
    •  & Leena Peltonen
  124. Helsinki University Central Hospital, Unit of General Practice, 00280 Helsinki, Finland

    • Johan G. Eriksson
  125. Folkhalsan Research Centre, 00250 Helsinki, Finland

    • Johan G. Eriksson
    • , Bo Isomaa
    •  & Tiinamaija Tuomi
  126. Vasa Central Hospital, 65130 Vasa, Finland

    • Johan G. Eriksson
  127. Estonian Biocenter, Tartu 51010, Estonia

    • Tõnu Esko
    • , Andres Metspalu
    • , Mari Nelis
    •  & Maris Teder-Laving
  128. Department of Internal Medicine, Erasmus MC, Rotterdam, 3015GE, The Netherlands

    • Karol Estrada
    • , Carolina Medina-Gomez
    • , Joyce B. J. van Meurs
    • , Marjolein J. Peters
    • , Fernando Rivadeneira
    • , André G. Uitterlinden
    •  & M. Carola Zillikens
  129. MRC Centre for Causal Analyses in Translational Epidemiology, Department of Social Medicine, University of Bristol, Bristol, BS8 2BN, UK

    • David M Evans
    •  & Lavinia Paternoster
  130. Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

    • Ulf de Faire
    •  & Bruna Gigante
  131. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden

    • Tove Fall
    • , Andrea Ganna
    • , Henrik Grönberg
    • , Stefan Gustafsson
    • , Per Hall
    • , Erik Ingelsson
    • , Yudi Pawitan
    • , Nancy L. Pedersen
    • , Emil Rehnberg
    •  & Patrik K. E. Magnusson
  132. Cardiovascular Medicine, University of Oxford, Wellcome Trust Centre for Human Genetics, Oxford, OX3 7BN, UK

    • Martin Farrall
    • , Anuj Goel
    •  & Hugh Watkins
  133. Epidemiology and Preventive Medicine Research Center, Department of Clinical and Experimental Medicine, University of Insubria, Varese, Italy

    • Marco M. Ferrario
  134. Department of Cardiology, Toulouse University School of Medicine, Rangueil Hospital, Toulouse, France

    • Jean Ferrières
  135. Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham Heart Study, Framingham, Massachusetts 01702, USA

    • Caroline S. Fox
  136. Department of Genetics, University Medical Center Groningen, University of Groningen, The Netherlands

    • Lude Franke
    •  & Pim van der Harst
  137. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Skåne University Hospital Malmö, Lund University, Malmö, Sweden

    • Paul W. Franks
    •  & Dmitry Shungin
  138. Department of Nutrition, Harvard School of Public Health, Boston, MA, USA

    • Paul W. Franks
  139. Department of Public Health & Clinical Medicine, Umeå University,Umeå, Sweden

    • Paul W. Franks
    • , Göran Hallmans
    •  & Dmitry Shungin
  140. Center for Neurobehavioral Genetics, University of California, Los Angeles, California 90095, USA

    • Nelson B. Freimer
  141. Department of Genomics of Common Disease, School of Public Health, Imperial College London, W12 0NN, London, UK

    • Philippe Froguel
  142. Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA

    • Mao Fu
    • , Jeffrey R. O’Connell
    •  & Alan R. Shuldiner
  143. University of Chicago, Chicago, IL

    • Pablo V. Gejman
    •  & Alan R. Sanders
  144. Northshore University Healthsystem, Evanston, Ilinois 60201, USA

    • Pablo V. Gejman
    •  & Alan R. Sanders
  145. Hagedorn Research Institute, 2820 Gentofte, Denmark

    • Anette P. Gjesing
    • , Torben Hansen
    • , Oluf Pedersen
    •  & Camilla Sandholt
  146. Department of Medicine, University of Washington, Seattle, Washington 98101, USA

    • Nicole L. Glazer
  147. Cardiovascular Health Research Unit, University of Washington, Seattle, Washington 98101, USA

    • Nicole L. Glazer
    • , Guo Li
    •  & Bruce M. Psaty
  148. University of Melbourne, Parkville 3010, Australia

    • Michael E. Goddard
  149. Department of Primary Industries, Melbourne, Victoria 3001, Australia

    • Michael E. Goddard
  150. Institute of Epidemiology, Helmholtz Zentrum Mìnchen - German Research Center for Environmental Health, 85764 Neuherberg, Germany

    • Harald Grallert
    • , Iris M. Heid
    • , Thomas Illig
    • , Claudia Lamina
    •  & H-Erich Wichmann
  151. Unit for Molecular Epidemiology, Helmholtz Zentrum Mìnchen - German Research Center for Environmental Health, Neuherberg, Germany

    • Harald Grallert
    •  & Thomas Illig
  152. Research Unit for Molecular Epidemiology, Helmholtz Zentrum Mìnchen - German Research Center for Environmental Health, Neuherberg, Germany

    • Harald Grallert
    • , Thomas Illig
    •  & Annette Peters
  153. Department of Medicine III, Pathobiochemistry, University of Dresden, 01307 Dresden, Germany

    • Jìrgen Gräßler
  154. Department of Medicine, University of Iceland, Reykjavik, Iceland

    • Vilmundur Gudnason
    •  & Albert Vernon Smith
  155. Metabolism Initiative and Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA

    • Candace Guiducci
    • , Joel N. Hirschhorn
    • , Elizabeth K. Speliotes
    • , Brian Thomson
    •  & Sailaja Vedantam
  156. Department of Genetics and Pathology, Rudbeck Laboratory, University of Uppsala, SE-75185 Uppsala, Sweden

    • Ulf Gyllensten
    • , Wilmar Igl
    •  & Åsa Johansson
  157. Department of Immunology, Genetics and Pathology, Uppsala University, Sweden

    • Ulf Gyllensten
    •  & Åsa Johansson
  158. Division of Cardiovascular and Neuronal Remodelling, Multidisciplinary Cardiovascular Research Centre, Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, UK

    • Alistair S. Hall
  159. Atherosclerosis Research Unit, Department of Medicine, Solna,Karolinska Institutet, Karolinska University Hospital, 171 76 Stockholm, Sweden

    • Anders Hamsten
    •  & Rona J Strawbridge
  160. Faculty of Health Science, University of Southern Denmark, 5000 Odense, Denmark

    • Torben Hansen
  161. Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, USA

    • Talin Haritunians
  162. Laboratory of Epidemiology, Demography, Biometry, National Institute on Aging, National Institutes of Health, Bethesda, Maryland 20892, USA

    • Tamara B. Harris
    •  & Lenore J. Launer
  163. Department of Cardiology, University Medical Center Groningen, University of Groningen, The Netherlands

    • Pim van der Harst
    •  & Irene M. Leach
  164. Department of Clinical Sciences/Obstetrics and Gynecology, University of Oulu, 90014 Oulu, Finland

    • Anna-Liisa Hartikainen
    •  & Anneli Pouta
  165. National Institute for Health and Welfare, Department of Chronic Disease Prevention, Chronic Disease Epidemiology and Prevention Unit, 00014, Helsinki, Finland

    • Aki S. Havulinna
    • , Pekka Jousilahti
    • , Seppo Koskinen
    •  & Veikko Salomaa
  166. MRC Human Genetics Unit, Institute for Genetics and Molecular Medicine, Western General Hospital, Edinburgh, EH4 2XU, Scotland, UK

    • Caroline Hayward
    • , Jennifer E. Huffman
    • , Veronique Vitart
    •  & Alan F. Wright
  167. Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA

    • Andrew C Heath
    •  & Pamela A. Madden
  168. Department of Child and Adolescent Psychiatry, University of Duisburg-Essen, 45147 Essen, Germany

    • Johannes Hebebrand
    •  & Anke Hinney
  169. Regensburg University Medical Center, Department of Epidemiology and Preventive Medicine, 93053 Regensburg, Germany

    • Iris M. Heid
    • , Michael F. Leitzmann
    •  & Thomas W. Winkler
  170. Public Health and Gender Studies, Institute of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Regensburg, Germany

    • Iris M. Heid
    •  & Thomas W. Winkler
  171. Institute of Epidemiology I, Helmholtz Zentrum Mìnchen - German Research Center for Environmental Health, Neuherberg, Germany

    • Iris M. Heid
    •  & H-Erich Wichmann
  172. Department of Internal Medicine, VU University Medical Centre, Amsterdam, The Netherlands

    • Martin den Heijer
  173. Klinik und Poliklinik fìr Innere Medizin II, Universität Regensburg, 93053 Regensburg, Germany

    • Christian Hengstenberg
  174. Regensburg University Medical Center, Innere Medizin II, 93053 Regensburg, Germany

    • Christian Hengstenberg
  175. Klinik und Poliklinik fìr Innere Medizin II, Universitätklinikum Regensburg, 93053 Regensburg, Germany

    • Christian Hengstenberg
    •  & Klaus Stark
  176. Biocenter Oulu, University of Oulu, 90014 Oulu, Finland

    • Karl-Heinz Herzig
    • , Marjo-Riitta Jarvelin
    •  & Marika Kaakinen
  177. Institute of Biomedicine, Department of Physiology, University of Oulu, 90014 Oulu, Finland

    • Karl-Heinz Herzig
  178. Department of Psychiatry, Kuopio University Hospital and University of Kuopio, 70210 Kuopio, Finland

    • Karl-Heinz Herzig
  179. Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Affiliated Institute of the University of Lìbeck, Lìbeck, Germany, Bolzano/Bozen, 39100, Italy

    • Andrew A. Hicks
    • , Irene Pichler
    • , Peter P. Pramstaller
    •  & Claudia B. Volpato
  180. Center for Biomedicine, European Academy Bozen/Bolzano (EURAC), Affiliated Institute of the University of Lìbeck, Lìbeck, Germany, Bolzano/Bozen, 39100, Italy

    • Andrew A. Hicks
    •  & Peter P. Pramstaller
  181. Center for Biomedicine, European Academy Bozen/Bolzano (EURAC), Affiliated Institute of the University of Lìbeck, Lìbeck, Germany, Bolzano/Bozen, 39100, Italy

    • Andrew A. Hicks
    •  & Peter P. Pramstaller
  182. Department of Epidemiology and Public Health, University College London, 1-19 Torrington Place, London WC1E 6BT, UK

    • Aroon Hingorani
    • , Mika Kivimaki
    • , Mika Kivmaki
    • , Meena Kumari
    •  & Claudia Langenberg
  183. Divisions of Genetics and Endocrinology and Program in Genomics, Children’s Hospital, Boston, Massachusetts 02115, USA

    • Joel N. Hirschhorn
    •  & Sailaja Vedantam
  184. Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA

    • Joel N. Hirschhorn
    •  & Sailaja Vedantam
  185. Divisions of Genetics and Endocrinology and Centerfor Basic and Translational Obesity Research, Children’s Hospital, Boston, Massachusetts 02115, USA

    • Joel N. Hirschhorn
    •  & Sailaja Vedantam
  186. MRC Harwell, Harwell Science and Innovation Campus, Oxfordshire, OX11 0RD, UK

    • Christopher C. Holmes
  187. Department of Statistics, University of Oxford, Oxford OX1 3TG, UK

    • Christopher C. Holmes
    •  & George Nicholson
  188. Interfaculty Institute for Genetics and Functional Genomics, Ernst-Moritz-Arndt-University Greifswald, 17487 Greifswald, Germany

    • Georg Homuth
    •  & Alexander Teumer
  189. Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts 02115, USA

    • Frank B. Hu
    • , David Hunter
    • , Lu Qi
    •  & Tsegaselassie Workalemahu
  190. Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA

    • Frank B. Hu
    • , David Hunter
    • , Lu Qi
    •  & Tsegaselassie Workalemahu
  191. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA

    • Frank B. Hu
    • , David Hunter
    • , Peter Kraft
    •  & Liming Liang
  192. Department of Biostatistics andBioinformatics, Emory University, Atlanta, Georgia 30322, USA

    • Yi-Juan Hu
  193. School of Population Health, The University of Western Australia, Nedlands WA 6009, Australia

    • Jennie Hui
    •  & Arthur W. Musk
  194. Institute of Clinical Medicine, Department of Internal Medicine, University of Oulu, 90014 Oulu, Finland

    • Heikki Huikuri
  195. Cardiovascular Genetics, British Heart Foundation Laboratories, Rayne Building, University College London, London, United Kingdom

    • Steve E. Humphries
  196. School of Medicine and Pharmacology, The University of Western Australia, Nedlands WA 6009, Australia

    • Joseph Hung
  197. HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, 7600 Levanger, Norway

    • Kristian Hveem
    • , Kirsti Kvaloy
    • , Kristian Midthjell
    •  & Carl G. P. Platou
  198. Centre For Paediatric Epidemiolgy and Biostatistics/MRC Centre of Epidemiology for Child Health, University College of London Institute of Child Health, London, UK

    • Elina Hyppönen
    •  & Chris Power
  199. Hannover Unified Biobank, Hannover Medical School, 30625 Hannover, Germany

    • Thomas Illig
  200. Division of Research, Kaiser Permanente Northern California, Oakland, California 94612, USA

    • Carlos Iribarren
  201. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California 94107, USA

    • Carlos Iribarren
  202. Department of Social Services and Health Care, 68601 Jakobstad, Finland

    • Bo Isomaa
  203. Core Genotyping Facility, SAIC-Frederick, Inc., NCI-Frederick, Frederick, Maryland 21702, USA

    • Kevin B. Jacobs
    •  & Zhaoming Wang
  204. School of Medicine and Pharmacology, University of Western Australia, Perth, Western Australia 6009, Australia

    • Alan L. James
  205. Department of Physiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden

    • John-Olov Jansson
  206. Institute of Medical Biometry and Epidemiology, University of Marburg, 35037 Marburg, Germany

    • Ivonne Jarick
    •  & Martina Mìller-Nurasyid
  207. Institute of Health Sciences, University of Oulu, 90014 Oulu, Finland

    • Marjo-Riitta Jarvelin
    •  & Marika Kaakinen
  208. National Institute for Health and Welfare, 90101 Oulu, Finland

    • Marjo-Riitta Jarvelin
    •  & Anneli Pouta
  209. Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany

    • Karl-Heinz Jöckel
    • , Susanne Moebus
    • , Sonali Pechlivanis
    • , Carolin Pìtter
    •  & Andre Scherag
  210. Department of Cancer Research and Molecular Medicine, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, N-7489, Norway

    • Åsa Johansson
  211. Uppsala Clinical Research Center, Uppsala university hospital, Sweden

    • Åsa Johansson
  212. Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary, University of London, London, UK

    • Toby Johnson
  213. Research Centre for Prevention and Health, Glostrup University Hospital, 2600 Glostrup, Denmark

    • Torben Jørgensen
  214. Faculty of Health Science, University of Copenhagen, 2100 Copenhagen, Denmark

    • Torben Jørgensen
  215. National Institute for Health and Welfare, Department of Chronic Disease Prevention, Population Studies Unit, 20720 Turku, Finland

    • Antti Jula
  216. Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, USA

    • Anne E. Justice
    • , Keri L. Monda
    •  & Kari E. North
  217. Department of Clinical Physiology, University of Tampere and Tampere University Hospital, 33520 Tampere, Finland

    • Mika Kähönen
  218. Hospital for Children and Adolescents, Helsinki University Central Hospital and University of Helsinki, 00029 HUS, Finland

    • Eero Kajantie
  219. Department of Epidemiology and Medicine, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA

    • W. H. Linda Kao
  220. Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts 02114, USA

    • Lee M. Kaplan
    •  & Elizabeth K. Speliotes
  221. MGH Weight Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA

    • Lee M. Kaplan
  222. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York 10461, USA

    • Robert C. Kaplan
  223. Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014, Helsinki, Finland

    • Jaakko Kaprio
    • , Johannes Kettunen
    • , Kati Kristiansson
    • , Aarno Palotie
    • , Niina Pellikka
    • , Leena Peltonen
    • , Markus Perola
    • , Samuli Ripatti
    • , Kaisa Silander
    • , Ida Surakka
    •  & Elisabeth Widen
  224. Finnish Twin Cohort Study, Department of Public Health, University of Helsinki, 00014, Helsinki, Finland

    • Jaakko Kaprio
    •  & Kirsi H. Pietiläinen
  225. National Institute for Health and Welfare, Department of Mental Health and Substance Abuse Services, Unit for Child and Adolescent Mental Health, 00271 Helsinki, Finland

    • Jaakko Kaprio
    •  & Robert N. Luben
  226. National Institute for Health and Welfare, Unit for Child and Adolescent Psychiatry, Helsinki, Finland

    • Jaakko Kaprio
  227. NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, OX3 7LJ, UK

    • Fredrik Karpe
    •  & Mark I. McCarthy
  228. Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Old Road Headington, Oxford, OX3 7LJ, UK

    • Fredrik Karpe
    •  & Mark I. McCarthy
  229. Cardiovascular Research Center and Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts 02114, USA

    • Sekar Kathiresan
  230. Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA

    • Sekar Kathiresan
    • , Steven A. McCaroll
    • , Shaun Purcell
    •  & Benjamin F. Voight
  231. Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA

    • Sekar Kathiresan
    • , Steven A. McCaroll
    • , James Nemesh
    •  & Benjamin F. Voight
  232. UKCRC Centre of Excellence for Public Health (NI) Queens University, Belfast

    • Frank Kee
  233. Faculty of Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland

    • Sirkka M. Keinanen-Kiukaanniemi
  234. Unit of General Practice, Oulu University Hospital, Oulu, Finland

    • Sirkka M. Keinanen-Kiukaanniemi
  235. Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge CB2 2SR, UK

    • Kay-Tee Khaw
  236. Department of Epidemiology, Biostatistics and HTA, Radboud University Nijmegen Medical Centre, 6500 HB Nijmegen, The Netherlands

    • Lambertus A. Kiemeney
    • , Femmie de Vegt
    •  & Sita H. Vermeulen
  237. Department of Urology, Radboud University Nijmegen Medical Centre, 6500 HB Nijmegen, The Netherlands

    • Lambertus A. Kiemeney
  238. Comprehensive Cancer Center East, 6501 BG Nijmegen, The Netherlands

    • Lambertus A. Kiemeney
  239. National Institute for Health and Welfare, Diabetes Prevention Unit, 00271 Helsinki, Finland

    • Leena Kinnunen
    • , Jaana Lindström
    • , Jaakko Tuomilehto
    •  & Timo T. Valle
  240. Department of Endocrinology, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands

    • Melanie M. Van der Klauw
    • , Jana V. Van Vliet-Ostaptchouk
    •  & Bruce H. R. Wolffenbuttel
  241. LURIC Study nonprofit LLC, Freiburg, Germany

    • Marcus E. Kleber
  242. Mannheim Institute of Public Health, Social and Preventive Medicine, Medical Faculty of Mannheim, University of Heidelberg, Mannheim, Germany

    • Marcus E. Kleber
    •  & Winfried März
  243. Department of Internal Medicine II – Cardiology, University of Ulm Medical Center, Ulm, Germany

    • Wolfgang Koenig
  244. Andrija Stampar School of Public Health, Medical School, University of Zagreb, 10000 Zagreb, Croatia

    • Ivana Kolcic
    • , Ozren Polasek
    •  & Lina Zgaga
  245. 1st Cardiology Department, Onassis Cardiac Surgery Center 356, Sygrou Ave., Athens, Greece

    • Genovefa Kolovou
  246. Institut fìr Medizinische Biometrie und Statistik, Universität zu Lìbeck, Universitätsklinikum Schleswig-Holstein, Campus Lìbeck, 23562 Lìbeck, Germany

    • Inke R. König
    • , Christina Loley
    •  & Michael Preuss
  247. Interdisciplinary Centre for Clinical Research, University of Leipzig, 04103 Leipzig, Germany

    • Peter Kovacs
  248. Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA

    • Peter Kraft
    •  & Liming Liang
  249. Institut fìr Pharmakologie, Universität Greifswald, 17487 Greifswald, Germany

    • Heyo K. Kroemer
  250. Croatian Centre for Global Health, School of Medicine, University of Split, Split 21000, Croatia

    • Vjekoslav Krzelj
    •  & Igor Rudan
  251. MRC Unit for Lifelong Health & Ageing, London, UK

    • Diana Kuh
    • , Ken K. Ong
    •  & Andrew Wong
  252. National Institute for Health and Welfare, Department of Chronic Disease Prevention, Chronic Disease Epidemiology and Prevention Unit, 00271, Helsinki, Finland

    • Kari Kuulasmaa
    • , Veikko Salomaa
    •  & Jarmo Virtamo
  253. Department of Medicine, University of Kuopio and Kuopio University Hospital, 70210 Kuopio, Finland

    • Johanna Kuusisto
    •  & Markku Laakso
  254. Department of Medicine, University of Eastern Finland, Kuopio Campus and Kuopio University Hospital, 70210 Kuopio, Finland

    • Johanna Kuusisto
    •  & Markku Laakso
  255. Finnish Institute of Occupational Health, 90220 Oulu, Finland

    • Jaana H. Laitinen
  256. Kuopio Research Institute of Exercise Medicine, Kuopio, Finland

    • Timo A. Lakka
    •  & Rainer Rauramaa
  257. Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, 6020 Innsbruck, Austria

    • Claudia Lamina
  258. Institut inter-regional pour la sante (IRSA), F-37521 La Riche, France

    • Olivier Lantieri
  259. Centre National de Genotypage, Evry, Paris 91057, France

    • G. Mark Lathrop
  260. The Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia

    • Sang Hong Lee
    •  & Peter M. Visscher
  261. Department of Clinical Chemistry, University of Tampere and Tampere University Hospital, 33520 Tampere, Finland

    • Terho Lehtimäki
  262. Department of Clinical Chemistry, Fimlab Laboratories, University of Tampere and Tampere University Hospital, 33520 Tampere, Finland

    • Terho Lehtimäki
  263. Department of Medicine, Université de Montréal, Montreal, Quebec, H3T 1J4, Canada

    • Guillaume Lettre
  264. Stanford University School of Medicine, Stanford, California 93405, USA

    • Douglas F. Levinson
  265. Department of Epidemiology, Tulane School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA

    • Shengxu Li
  266. Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA

    • Dan-Yu Lin
  267. Department of Medical Sciences, Uppsala University, Akademiska sjukhuset, 751 85 Uppsala, Sweden

    • Lars Lind
  268. Human Genetics, Genome Institute of Singapore, Singapore 138672, Singapore

    • Jianjun Liu
  269. Department of Internal Medicine, Istituto Auxologico Italiano, Verbania, Italy

    • Antonio Liuzzi
  270. Transplantation Laboratory, Haartman Institute, University of Helsinki, 00014, Helsinki, Finland

    • Marja-Liisa Lokki
  271. The Charles Bronfman Institute of Personalized Medicine, Mount Sinai School of Medicine, New York, NY 10029, USA

    • Ruth J. F. Loos
  272. Child Health and Development Institute, Mount Sinai School of Medicine, New York, NY 10029, USA

    • Ruth J. F. Loos
  273. Department of Preventive Medicine, Mount Sinai School of Medicine, New York, NY 10029, USA

    • Ruth J. F. Loos
  274. Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden

    • Mattias Lorentzon
    • , Claes Ohlsson
    •  & Liesbeth Vandenput
  275. Department of Twin Research and Genetic Epidemiology, King’s College London, London, SE1 7EH, UK

    • Massimo Mangino
    • , Nicole Soranzo
    •  & Timothy D. Spector
  276. Università Vita-Salute San Raffaele, Chair of Nephrology San Raffaele Scientific Institute, OU Nephrology and Dialysis, 20132 Milan, Italy

    • Paolo Manunta
  277. Department of Endocrinology, Diabetology and Nutrition, Bichat-Claude Bernard University Hospital, Assistance Publique des Hôpitaux de Paris, F-75018 Paris, France

    • Michel Marre
  278. Cardiovascular Genetics Research Unit, Université Henri Poincaré-Nancy 1, 54000, Nancy, France

    • Michel Marre
  279. Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Queensland 4006, Australia

    • Sarah E. Medland
    •  & Nicholas G. Martin
  280. Queensland Institute of Medical Research, Queensland 4029, Australia

    • Grant W. Montgomery
    • , Sarah E. Medland
    • , Nicholas G. Martin
    •  & Jian Yang
  281. Synlab Academy, Mannheim, Germany

    • Winfried März
  282. Avon Longitudinal Study of Parents and Children (ALSPAC) Laboratory, Department of Social Medicine, University of Bristol, Bristol, BS8 2BN, UK

    • Wendy L. McArdle
  283. School of Social and Community Medicine, University of Bristol, UK

    • Wendy L. McArdle
  284. Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts 02114, USA

    • Steven A. McCaroll
    •  & Benjamin F. Voight
  285. Division of Health, Research Board, An Bord Taighde Sláinte, Dublin, 2, Ireland

    • Anne McCarthy
  286. Institute of Human Genetics, Klinikum rechts der Isar der Technischen Universität Mìnchen, 81675 Munich, Germany

    • Thomas Meitinger
  287. Institute of Human Genetics, Helmholtz Zentrum Mìnchen - German Research Center for Environmental Health, 85764 Neuherberg, Germany

    • Thomas Meitinger
  288. Department of Clinical Epidemiology and Biostatistics, McMasterUniversity, Hamilton, Ontario L8S 4L8, Canada

    • David Meyre
  289. Human Genetics, Leiden University Medical Center, Leiden 2333, The Netherlands

    • Josine L. Min
  290. Merck Research Laboratories, Merck & Co., Inc., Boston, Massachusetts 02115, USA

    • Cliona Molony
  291. Center for Observational Research, Amgen, Thousands Oaks, CA, 91320

    • Keri L. Monda
  292. Molecular Epidemiology Laboratory, Queensland Institute of Medical Research, Queensland 4006, Australia

    • Grant W. Montgomery
  293. Genetics Division, GlaxoSmithKline, King of Prussia, Pennsylvania 19406, USA

    • Vincent Mooser
  294. Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School. Dundee, DD1 9SY

    • Andrew D. Morris
    •  & Colin N. A. Palmer
  295. Institute of Human Genetics, University of Bonn, Bonn, Germany

    • Thomas W. Mìhleisen
    •  & Markus M. Nöthen
  296. Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany

    • Thomas W. Mìhleisen
    •  & Markus M. Nöthen
  297. Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany

    • Martina Mìller-Nurasyid
  298. Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany

    • Martina Mìller-Nurasyid
  299. Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Nedlands, 6009, Australia

    • Arthur W. Musk
  300. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114

    • Benjamin M. Neale
  301. MRC Harwell, Harwell, UK

    • George Nicholson
  302. Division of Cardiology, Cardiovascular Laboratory, Helsinki University Central Hospital, 00029 Helsinki, Finland

    • Markku S. Nieminen
    •  & Juha Sinisalo
  303. Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway

    • Inger Njølstad
    •  & Tom Wilsgaard
  304. Department of Clinical Medicine, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway

    • Inger Njølstad
  305. Department of Public Health, Section of Epidemiology, Aarhus University, Denmark

    • Ellen A. Nohr
  306. Unit of Genetic Epidemiology and Bioinformatics, Dept of Epidemiology, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands

    • Ilja M. Nolte
    •  & Harold Snieder
  307. Department of Epidemiology, University of Groningen, University Medical Center Groningen, The Netherlands

    • Ilja M. Nolte
    •  & Ronald P. Stolk
  308. Carolina Center for Genome Sciences, School of Public Health, University of North Carolina Chapel Hill, Chapel Hill, North Carolina 27514, USA

    • Kari E. North
  309. Neurogenetics Laboratory, Queensland Institute of Medical Research, Queensland 4006, Australia

    • Dale R. Nyholt
  310. Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen, The Netherlands

    • Albertine J. Oldehinkel
  311. Department of Human Genetics, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands

    • Gert-Jan van Ommen
  312. Department of Clinical Genetics, Erasmus MC, Rotterdam, 3015GE, The Netherlands

    • Ben A. Oostra
  313. Centre for Medical Systems Biology & Netherlands Consortium on Healthy Aging, Leiden, The Netherlands

    • Ben A. Oostra
  314. NHS Blood and Transplant, Cambridge Centre, Cambridge, CB2 0PT, UK

    • Willem H. Ouwehand
    • , Aparna Radhakrishnan
    • , Augusto Rendon
    • , Jennifer G. Sambrook
    •  & Jonathan C. Stephens
  315. Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario L8N3Z5, Canada

    • Guillaume Paré
  316. Amgen, Cambridge, Massachusetts 02139, USA

    • Alex N. Parker
  317. Department of Cardiovascular Medicine, University of Oxford, Level 6 West Wing, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU

    • John F. Peden
  318. Illumina Inc. Cambridge, USA

    • John F. Peden
  319. Institute of Biomedical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark

    • Oluf Pedersen
  320. Faculty of Health Science, University of Aarhus, 8000 Aarhus, Denmark

    • Oluf Pedersen
  321. The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA

    • Leena Peltonen
    •  & Shaun Purcell
  322. Department of Medical Genetics, University of Helsinki, 00014 Helsinki, Finland

    • Leena Peltonen
  323. Department of Psychiatry/EMGO Institute, VU University Medical Center, 1081 BT Amsterdam, The Netherlands

    • Brenda Penninx
    •  & Jan H. Smit
  324. Department of Psychiatry, Leiden University Medical Centre, 2300 RC Leiden, The Netherlands

    • Brenda Penninx
  325. Department of Psychiatry, University Medical Centre Groningen, 9713 GZ Groningen, The Netherlands

    • Brenda Penninx
  326. Institute of Epidemiology II, Helmholtz Zentrum Mìnchen - German Research Center for Environmental Health, Neuherberg, Germany

    • Annette Peters
    •  & Barbara Thorand
  327. Munich Heart Alliance, Munich, Germany

    • Annette Peters
  328. Obesity Research unit, Department of Psychiatry, Helsinki University Central Hospital, Helsinki, Finland

    • Kirsi H. Pietiläinen
    •  & Aila Rissanen
  329. Department of Medicine, Levanger Hospital, The Nord-Trøndelag Health Trust, 7600 Levanger, Norway

    • Carl G. P. Platou
  330. Gen-Info Ltd, 10000 Zagreb, Croatia

    • Ozren Polasek
  331. Faculty of Medicine, University of Split, Croatia

    • Ozren Polasek
  332. Department of Neurology, General Central Hospital, Bolzano, Italy

    • Peter P. Pramstaller
  333. Department of Neurology, University of Lìbeck, Lìbeck, Germany

    • Peter P. Pramstaller
  334. Departments of Epidemiology, Medicine and Health Services, University of Washington, Seattle, Washington 98195, USA

    • Bruce M. Psaty
  335. Group Health Research Institute, Group Health, Seattle, Washington 98101, USA

    • Bruce M. Psaty
  336. Department of Psychiatry, Harvard Medical School, Boston, Massachusetts 02115, USA

    • Shaun Purcell
  337. Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20520 Turku, Finland

    • Olli Raitakari
  338. The Department of Clinical Physiology, Turku University Hospital, 20520 Turku, Finland

    • Olli Raitakari
  339. The Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, 20520 Turku, Finland

    • Olli Raitakari
  340. Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland

    • Rainer Rauramaa
  341. MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK

    • Augusto Rendon
  342. Department of Clinical Sciences, Lund University, 20502 Malmö, Sweden

    • Martin Ridderstråle
  343. Finnish Diabetes Association, Kirjoniementie 15, 33680, Tampere, Finland

    • Timo E. Saaristo
  344. Pirkanmaa Hospital District, Tampere, Finland

    • Timo E. Saaristo
  345. Medizinische Klinik II, Universität zu Lìbeck Ratzeburger Allee 160, D-23538 Lìbeck, Germany

    • Hendrik Sager
  346. Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, LE3 9QP, UK

    • Nilesh J. Samani
  347. Leicester NIHR Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital, Leicester, LE3 9QP, UK

    • Nilesh J. Samani
  348. South Karelia Central Hospital, 53130 Lappeenranta, Finland

    • Jouko Saramies
  349. Pacific Biosciences, Menlo Park, California 94025, USA

    • Eric E. Schadt
  350. Sage Bionetworks, Seattle, Washington 98109, USA

    • Eric E. Schadt
  351. Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, One Gustave L. Levy Place, Box 1498, New York, NY 10029-6574 USA

    • Eric E. Schadt
  352. Institute of Genomics and Multiscale Biology, Mount Sinai School of Medicine, One Gustave L. Levy Place, Box 1498, New York, NY 10029-6574 USA

    • Eric E. Schadt
  353. Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany

    • Sabine Schipf
  354. Laboratory of Genetics, National Institute on Aging, Baltimore, Maryland 21224, USA

    • David Schlessinger
  355. Institut fìr Klinische Molekularbiologie, Christian-Albrechts Universität, Kiel, Germany

    • Stefan Schreiber
  356. Department of Medicine III, Prevention and Care of Diabetes, University of Dresden, 01307 Dresden, Germany

    • Peter E. H. Schwarz
  357. Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, Maryland 21201, USA

    • Alan R. Shuldiner
  358. Department of Odontology, Umeå University, Sweden

    • Dmitry Shungin
  359. Azienda ospedaliera di Desio e Vimercate, Milano, Italy

    • Stefano Signorini
  360. Institute of Preventive Medicine, Bispebjerg University Hospital, Copenhagen, and Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Denmark

    • Thorkild I. A. Sørensen
  361. Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA

    • Elizabeth K. Speliotes
  362. Department of Internal Medicine, Division of Gastroenterology, University of Michigan, Ann Arbor, Michigan, USA

    • Elizabeth K. Speliotes
  363. University of Eastern Finland and Kuopio University Hospital, 70210 Kuopio, Finland

    • Alena Stančáková
  364. Regensburg University Medical Center, Clinic and Policlinic for Internal Medicine II, 93053 Regensburg, Germany

    • Klaus Stark
  365. deCODE Genetics, 101 Reykjavik, Iceland

    • Kari Stefansson
    • , Valgerdur Steinthorsdottir
    • , Gudmar Thorleifsson
    • , Unnur Thorsteinsdottir
    •  & G. Bragi Walters
  366. Faculty of Medicine, University of Iceland, 101 Reykjavík, Iceland

    • Kari Stefansson
    •  & Unnur Thorsteinsdottir
  367. Division of Community Health Sciences, St George’s, University of London, London, SW17 0RE, UK

    • David P Strachan
  368. Division of Population Health Sciences and Education, St George’s, University of London, London, SW17 0RE, UK

    • David P Strachan
  369. Department of Medicine, University of Leipzig, 04103 Leipzig, Germany

    • Michael Stumvoll
    •  & Anke Tönjes
  370. LIFE Study Centre, University of Leipzig, Leipzig, Germany

    • Michael Stumvoll
  371. University of Leipzig, IFB Adiposity Diseases, Leipzig, Germany

    • Michael Stumvoll
    •  & Anke Tönjes
  372. Uppsala University / Dept. of Medical Sciences, Molecular Medicine, 751 85 Uppsala, Sweden

    • Ann-Christine Syvanen
  373. Coordination Centre for Clinical Trials, University of Leipzig, Härtelstr. 16-18, 04103 Leipzig, Germany

    • Anke Tönjes
  374. INSERM UMR_S 937, ICAN Institute, Pierre et Marie Curie Medical School, Paris 75013, France

    • David-Alexandre Tregouet
  375. Department of Pharmacological Sciences, University of Milan, Monzino Cardiology Center, IRCCS, Milan, Italy

    • Elena Tremoli
  376. Heart Failure Research Centre, Department of Clinical and Experimental Cardiology, Academic Medical Center, Amsterdam, The Netherlands

    • Mieke D. Trip
  377. Department of Medicine, Helsinki University Central Hospital, 00290 Helsinki, Finland

    • Tiinamaija Tuomi
  378. Research Program of Molecular Medicine, University of Helsinki, 00014 Helsinki, Finland

    • Tiinamaija Tuomi
  379. Hjelt Institute, Department of Public Health, University of Helsinki, 00014 Helsinki, Finland

    • Jaakko Tuomilehto
  380. South Ostrobothnia Central Hospital, 60220 Seinajoki, Finland

    • Jaakko Tuomilehto
  381. Red RECAVA Grupo RD06/0014/0015, Hospital Universitario La Paz, 28046 Madrid, Spain

    • Jaakko Tuomilehto
  382. Centre for Vascular Prevention, Danube-University Krems, 3500 Krems, Austria

    • Jaakko Tuomilehto
  383. Department of Oncology, University of Cambridge, Cambridge, CB1 8RN, UK

    • Jonathan Tyrer
  384. Department of Public Health and Clinical Nutrition, University of Eastern Finland, Finland

    • Matti Uusitupa
  385. Research Unit, Kuopio University Hospital, Kuopio, Finland

    • Matti Uusitupa
  386. Department of Human Genetics, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands

    • Sita H. Vermeulen
  387. Department of Medicine, University of Turku and Turku University Hospital, 20520 Turku, Finland

    • Jorma Viikari
  388. Queensland Statistical Genetics Laboratory, Queensland Institute of Medical Research, Queensland 4006, Australia

    • Jian Yang
    •  & Peter M. Visscher
  389. Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV) University Hospital, 1011 Lausanne, Switzerland

    • Peter Vollenweider
    •  & Gérard Waeber
  390. Institut fìr Community Medicine, 17489 Greifswald, Germany

    • Henry Völzke
  391. Institute for Community Medicine, Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany

    • Henry Völzke
  392. Institut fìr Klinische Chemie und Laboratoriumsmedizin, Universität Greifswald, 17475 Greifswald, Germany

    • Henri Wallaschofski
  393. Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, 17475 Greifswald, Germany

    • Henri Wallaschofski
  394. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089, USA

    • Richard M. Watanabe
  395. Klinikum Grosshadern, 81377 Munich, Germany

    • H-Erich Wichmann
  396. Ludwig-Maximilians-Universität, Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, 81377 Munich, Germany

    • H-Erich Wichmann
  397. Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, and Klinikum Grosshadern, Munich, Germany

    • H-Erich Wichmann
  398. Klinikum Grosshadern, Munich, Germany

    • H-Erich Wichmann
  399. Cardiology Group, Frankfurt-Sachsenhausen, Germany

    • Bernhard R. Winkelmann
  400. Steno Diabetes Center, 2820 Gentofte, Denmark

    • Daniel R. Witte
  401. Centre for Public Health, Queen’s University, Belfast, UK

    • John W. G. Yarnell
  402. Department of Physiatrics, Lapland Central Hospital, 96101 Rovaniemi, Finland

    • Paavo Zitting
  403. Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, OX3 7BN, Oxford.

    • Krina T. Zondervan

Consortia

  1. The LifeLines Cohort Study

  2. Genetic Investigation of Anthropometric Traits (GIANT) consortium

Authors

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Contributions

M.R.R., J.Y. and P.M.V. conceived and designed the study. M.R.R., A.K. and M.G. analysed the data. M.R.R. devised and performed the simulations. A.A.E.V., W.J.P., A.A., B.Z., S.M. provided statistical support. 23andMe Inc., The LifeLines cohort, GIANT consortium, G.W.M., N.G.M., M.L., P.L., D.C., J.V.V.O., M.B.M., H.S., W.G.I., P.K.E.M, N.L.P, M.McG. and K.E.N. provided study oversight, sample collection and management. M.R.R. and P.M.V. derived the theory and wrote the manuscript. All collaborators reviewed and approved the final manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Matthew R. Robinson or Peter M. Visscher.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    Supplementary Figures 1–9, Supplementary Table 1, Supplementary Note, Supplementary Methods, Supplementary References, Author Lists