GCTA: a tool for genome-wide complex trait analysis.
TLDR
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.Abstract:
For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the “missing heritability” problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.read more
Citations
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Second-generation PLINK: rising to the challenge of larger and richer datasets
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LD score regression distinguishes confounding from polygenicity in genome-wide association studies :
Brendan Bulik-Sullivan,Po-Ru Loh,Hilary K. Finucane,Stephan Ripke,Jian Yang,Nick Patterson,Mark J. Daly,Alkes L. Price,Benjamin M. Neale +8 more
TL;DR: It is found that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size, and the LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control.
Journal ArticleDOI
Second-generation PLINK: rising to the challenge of larger and richer datasets
Christopher C. Chang,Carson C. Chow,Laurent C. A. M. Tellier,Shashaank Vattikuti,Shaun Purcell,James J. Lee +5 more
TL;DR: PLINK as discussed by the authors is a C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics, which has been widely used in the literature.
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Genetic studies of body mass index yield new insights for obesity biology
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An atlas of genetic correlations across human diseases and traits.
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TL;DR: This work introduces a technique—cross-trait LD Score regression—for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap, and uses this method to estimate 276 genetic correlations among 24 traits.
References
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PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses
Shaun Purcell,Shaun Purcell,Benjamin M. Neale,Benjamin M. Neale,Kathe Todd-Brown,Lori Thomas,Manuel A. R. Ferreira,David Bender,David Bender,Julian Maller,Julian Maller,Pamela Sklar,Pamela Sklar,Paul I.W. de Bakker,Paul I.W. de Bakker,Mark J. Daly,Mark J. Daly,Pak C. Sham +17 more
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TL;DR: This study has demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in theBritish population is generally modest.
Journal ArticleDOI
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