Defining the role of common variation in the genomic and biological architecture of adult human height

By A. Wood, T. Esko, Jian Yang, S. Vedantam, T. Pers, S. Gustafsson, A. Chu, K. Estrada, J. Luan, Zoltán Kutalik and 435 more. 2014

Timothy Frayling, Joel Hirschhorn, Peter Visscher and colleagues report a meta-analysis of genome-wide association studies for adult height in 253,288 individuals. They identify 697 variants in 423 loci significantly associated with adult height and find that these variants cluster in pathways involved in growth and together explain one-fifth of the heritability for this trait. Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ∼2,000, ∼3,700 and ∼9,500 SNPs explained ∼21%, ∼24% and ∼29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/β-catenin and chondroitin sulfate–related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.

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Citations in the corpus

Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry (2018, methodology): “Regression of SNPs effect estimated from the meta-analysis of GWAS of height in UKB and GWAS of height from Wood et al. (2014) (panel a); and GWAS of body mass index (BMI) in UKB and GWAS of BMI from Locke et al. (2015) (panel b) onto SNP effects on height and BMI estimated in HRS. Predictive power…”
PRSice-2: Polygenic Risk Score software for biobank-scale data (2019, unknown): “For example, of the 725,459 post–quality control SNPs (see Supplementary Note 1) in the UK Biobank genotype data [10], only 31% (222,956) were found in the GIANT Height and Body Mass Index (BMI) GWAS [20, 21].”
A Molecular Census of Arcuate Hypothalamus and Median Eminence Cell Types (2017, result): “These results agree with and refine previous DEPICT height analyses showing enrichment in cardiovascular, endocrine, skeletal, respiratory, and urogenital tissues 50 .”
Integrative functional genomic analysis of human brain development and neuropsychiatric risks (2018, methodology): “Toward this end, we collected GWAS data concerning neuropsychiatric disorders or personality traits including schizophrenia (SCZ) from CLOZUK (37), Alzheimer’s disease (AD) from IGAP (38), Parkinson’s disease (PD) (39), autism spectrum disorder (ASD) (40), attention deficit hyperactivity disorder (A”
Impacts of Neanderthal-introgressed sequences on the landscape of human gene expression (2017, unknown): “These include rs950169, a SNP in extracellular matrix protein ADAMTSL3 that is significantly associated with both height (Table 1; Wood et al., 2014) and schizophrenia risk (Table 1; Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014).”
FINEMAP: efficient variable selection using summary data from genome-wide association studies (2015, background): “This step is crucial for fully exploiting the potential of GWAS: to unveil molecular biology of complex traits and, eventually, provide targets for therapeutic interventions.”
Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases (2018, background): “…above are associated to height in the lower-powered GWAS (Randall et al., 2013), 13 module genes are confirmed in the higher-powered GWAS (Wood et al., 2014) and 6 module genes further comprise coding variants associated to height in an independent ExomeChip study (Marouli et al., 2017)…”