Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry

Yengo, Loïc; Sidorenko, Julia; Kemper, Kathryn E.; Zheng, Zhili; Wood, Andrew R.; Weedon, Michael N.; Frayling, Timothy M.; Hirschhorn, Joel N.; Yang, Jian; Visscher, Peter M.

doi:10.1101/274654

Cited by 137 publications

(176 citation statements)

References 32 publications

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“…The model estimate of SNP heritability for height is 17%, lower than the oft-reported value ∼50% (see Discussion). However, despite the huge differences in sample size, we find the same value, 17%, for the 2010 GWAS (N =133,735 [65]), and 19% for the 2018 GWAS (N = 707,868 [49,46]) -see Table 1. Figure 3 shows the sample size required so that a given proportion of chip heritability is captured by genome-wide significant SNPs for the phenotypes (assuming equal numbers of cases and controls for the qualitative phenotypes: N ef f = 4/(1/N cases + 1/N controls ), so that when N cases = N controls , N ef f = N cases + N controls = N , the total sample size, allowing for a straightforward comparison with quantitative traits).…”

Section: Simulationsmentioning

confidence: 51%

“…The number of causal SNPs (out of a total of approximately 11 million) is approximately 6k; although this is about one twentieth the estimate reported in [90], it remains large and allows for height to be interpreted as "omnigenic". For the 2010 GWAS (N=133,735 [65]) and 2018 GWAS (N=707,868 [49]), we estimate SNP heritability of 17% and 19% respectively (see Table 1 and Supplementary Material Fig. S8).…”

Section: Discussionmentioning

confidence: 99%

“…For height, we focused on the 2014 GWAS [46], not the more recent 2018 GWAS [49], although we also report below model results for the latter. There are issues pertaining to population structure in the various height GWAS [50,51], and the 2018 GWAS is a combination of GIANT and UKB GWAS, so some caution is warranted in interpreting results for these data.…”

Section: Data Preparationmentioning

confidence: 99%

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Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model

Holland¹,

Frei

Desikan³

et al. 2017

Preprint

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Of signal interest in the genetics of traits are estimating the proportion, π 1 , of causally associated single nucleotide polymorphisms (SNPs), and their effect size variance, σ 2 β , which are components of the mean heritabilities captured by the causal SNP. Here we present the first model, using detailed linkage disequilibrium structure, to estimate these quantities from genome-wide association studies (GWAS) summary statistics, assuming a Gaussian distribution of SNP effect sizes, β. We apply the model to three diverse phenotypes -schizophrenia, putamen volume, and educational attainment -and validate it with extensive simulations. We find that schizophrenia is highly polygenic, with ≃ 5 × 10 4 causal SNPs distributed with small effect size variance, σ 2 β = 3.5 × 10 −5 (in units where the phenotype variance is normalized to 1), requiring a GWAS study with more than 1/2-million samples in each arm for full discovery. In contrast, putamen volume involves only ≃ 3 × 10 2 causal SNPs, but with σ 2 β = 1.2 × 10 −3 , indicating a much larger proportion of the causal SNPs that are strongly associated. Educational attainment has similar polygenicity to schizophrenia, but with effects that are substantially weaker, σ 2 β = 5 × 10 −6 , leading to much lower heritability. Thus the model is able to describe the broad genetic architecture of phenotypes where both polygenicity and effect size variance range over several orders of magnitude, shows why only small proportions of heritability have been explained for discovered SNPs, and provides a roadmap for future GWAS discoveries.

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Section: Simulationsmentioning

confidence: 51%

Section: Discussionmentioning

confidence: 99%

Section: Data Preparationmentioning

confidence: 99%

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Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model

Holland¹,

Frei

Desikan³

et al. 2017

Preprint

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show abstract

“…Summary statistics for extraversion (34), openness to experience, agreeableness, and conscientiousness (35) were downloaded from the Genetics of Personality Consortium (GPC) repository. Finally, the summary statistics for body mass index (36) were obtained from the GIANT consortium repository. When summary statistics for an existing GWAS could not be found online, the authors of the relevant publication were contacted via email and asked to provide summary statistics, as was the case for alcohol dependence (37) and cannabis dependence (38).…”

Section: Search and Selection Strategymentioning

confidence: 99%

Testing structural models of psychopathology at the genomic level

Waldman

Poore

Luningham

et al. 2018

Preprint

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Genome-wide association studies (GWAS) have revealed hundreds of genetic loci that underlie major psychiatric disorders, and post-GWAS analyses have discovered substantial genetic correlations among these disorders. This suggests these disorders share a common genetic architecture, implying the presence of a higher-order structure of psychopathology at both the genetic and phenotypic levels. Nonetheless, despite recent efforts at elucidating this structure by collaborative consortia such as HiTOP, the exact structure of psychopathology remains unknown. Herein, in one of the largest genetic studies of psychopathology conducted to date, we tested multiple alternative structural models of psychopathology at the genomic level using the genetic correlations among fourteen psychiatric disorders and related psychological traits estimated from GWAS summary statistics. The best-fitting model includes four correlated higher-order factors -Externalizing, Internalizing, Thought Problems, and Neurodevelopmental Disorders. These higher-order factors were validated by examining their correlations with external criterion variables, which showed distinct patterns of genetic correlations with the 4 higher-order factors. Several model modifications were tested to explore the placement of a few disorders -such as bipolar disorder, OCD, and eating disorders -within the broader psychopathology structure. These findings show support for several of the HiTOP higher-order dimensions and suggest a similar structure of psychopathology at the genomic and phenotypic levels. Our results imply that such higher-order psychopathology dimensions might be better targets for genetic and genomic investigations than individual disorders, and might be more predictive of relevant outcomes of considerable public health and societal concern.

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“…Although obesity is a heritable and heterogeneous cardiometabolic risk factor, little is known about how genetic variation influences human adipocyte size across adipose depots or how such variability may confer risk to obesity and other cardiometabolic outcomes. [1][2][3][4] A defining feature of obesity is an excess of white adipose tissue (WAT). WAT mass expansion can occur in a range of adipose depots.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning based histology phenotyping to investigate epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits

Glastonbury

Pulit

Honecker

et al. 2019

Preprint

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Genetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the relationship between adipocyte area and obesity-related traits, and identify genetic factors associated with adipocyte cell size. To perform the first large-scale study of automatic adipocyte phenotyping using both histological and genetic data, we developed a deep learning-based method, the Adipocyte U-Net, to rapidly derive area estimates from histology images. We validate our method using three state-of-the-art approaches; CellProfiler, Adiposoft and floating adipocytes fractions, all run blindly on two external cohorts. We observe high concordance between our method and the state-of-the-art approaches (Adipocyte U-net vs. CellProfiler: R 2 visceral = 0.94, P < 2.2 × 10 −16 , R 2 subcutaneous = 0.91, P < 2.2 × 10 −16 ), and faster run times (10,000 images: 6mins vs 3.5hrs). We applied the Adipocyte U-Net to 4 cohorts with histology, genetic, and phenotypic data (total N = 820). After meta-analysis, we found that adipocyte area positively correlated with body mass index (BMI) ( P subq = 8.13 × 10 −69 , β subq = 0.45; P visc = 2.5 × 10 −55 , β visc = 0.49; average R 2 across cohorts = 0.49) and that adipocytes in subcutaneous depots are larger than their visceral counterparts ( P meta = 9.8 × 10 −7 ). Lastly, we performed the largest GWAS and subsequent meta-analysis of adipocyte area and intra-individual adipocyte variation (N = 820). Despite having twice the number of samples than any similar study, we found no genome-wide significant associations, suggesting that larger sample sizes and a homogenous collection of adipose tissue are likely needed to identify robust genetic associations.

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Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry

Abstract: Genome-wide association studies (GWASwas not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

Cited by 137 publications

References 32 publications

Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model

Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model

Testing structural models of psychopathology at the genomic level

Machine Learning based histology phenotyping to investigate epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits

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