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Common SNPs explain a large proportion of the heritability for human height

TLDR
Evidence is provided that the remaining heritability is due to incomplete linkage disequilibrium between causal variants and genotyped SNPs, exacerbated by causal variants having lower minor allele frequency than the SNPs explored to date.
Abstract
SNPs discovered by genome-wide association studies (GWASs) account for only a small fraction of the genetic variation of complex traits in human populations. Where is the remaining heritability? We estimated the proportion of variance for human height explained by 294,831 SNPs genotyped on 3,925 unrelated individuals using a linear model analysis, and validated the estimation method with simulations based on the observed genotype data. We show that 45% of variance can be explained by considering all SNPs simultaneously. Thus, most of the heritability is not missing but has not previously been detected because the individual effects are too small to pass stringent significance tests. We provide evidence that the remaining heritability is due to incomplete linkage disequilibrium between causal variants and genotyped SNPs, exacerbated by causal variants having lower minor allele frequency than the SNPs explored to date.

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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

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.
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Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci

Andre Franke, +97 more
- 01 Dec 2010 - 
TL;DR: A meta-analysis of six Crohn's disease genome-wide association studies and a series of in silico analyses highlighted particular genes within these loci implicated functionally interesting candidate genes including SMAD3, ERAP2, IL10, IL2RA, TYK2, FUT2, DNMT3A, DENND1B, BACH2 and TAGAP.
Journal ArticleDOI

Five years of GWAS discovery

TL;DR: The past five years have seen many scientific and biological discoveries made through the experimental design of genome-wide association studies (GWASs), which were aimed at detecting variants at genomic loci that are associated with complex traits in the population and, in particular, at detecting associations between common single-nucleotide polymorphisms (SNPs) and common diseases such as heart disease, diabetes, auto-immune diseases, and psychiatric disorders.
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.
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Common polygenic variation contributes to risk of schizophrenia and bipolar disorder

Shaun Purcell, +81 more
- 06 Aug 2009 - 
TL;DR: The extent to which common genetic variation underlies the risk of schizophrenia is shown, using two analytic approaches, and the major histocompatibility complex is implicate, which is shown to involve thousands of common alleles of very small effect.
Journal ArticleDOI

Population structure and eigenanalysis

TL;DR: An approach to studying population structure (principal components analysis) is discussed that was first applied to genetic data by Cavalli-Sforza and colleagues, and results from modern statistics are used to develop formal significance tests for population differentiation.
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Common polygenic variation contributes to risk of schizophrenia and bipolar disorder

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