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Open AccessJournal ArticleDOI

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

Andrew R. Wood, +444 more
- 01 Nov 2014 - 
- Vol. 46, Iss: 11, pp 1173-1186
TLDR
This article identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height, and all common variants together captured 60% of heritability.
Abstract
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
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Genetic studies of body mass index yield new insights for obesity biology

TL;DR: A genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals provide strong support for a role of the central nervous system in obesity susceptibility.

Genetic studies of body mass index yield new insights for obesity biology

Adam E. Locke, +481 more
TL;DR: This paper conducted a genome-wide association study and meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals.
Journal ArticleDOI

10 Years of GWAS Discovery: Biology, Function, and Translation

TL;DR: The remarkable range of discoveriesGWASs has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics are reviewed.
References
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Journal ArticleDOI

PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses

TL;DR: This work introduces PLINK, an open-source C/C++ WGAS tool set, and describes the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation, which focuses on the estimation and use of identity- by-state and identity/descent information in the context of population-based whole-genome studies.
Journal ArticleDOI

Principal components analysis corrects for stratification in genome-wide association studies

TL;DR: This work describes a method that enables explicit detection and correction of population stratification on a genome-wide scale and uses principal components analysis to explicitly model ancestry differences between cases and controls.
Book

Genetics and Analysis of Quantitative Traits

Michael Lynch, +1 more
TL;DR: This book discusses the genetic Basis of Quantitative Variation, Properties of Distributions, Covariance, Regression, and Correlation, and Properties of Single Loci, and Sources of Genetic Variation for Multilocus Traits.
Journal ArticleDOI

GCTA: a tool for genome-wide complex trait analysis.

TL;DR: The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets and focuses on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation.
Journal ArticleDOI

KEGG for integration and interpretation of large-scale molecular data sets

TL;DR: KEGG Mapper, a collection of tools for KEGG PATHWAY, BRITE and MODULE mapping, enabling integration and interpretation of large-scale data sets and recent enhancements to the K EGG content, especially the incorporation of disease and drug information used in practice and in society, to support translational bioinformatics.
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