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|Title:||GCTA: A Tool for Genome-wide Complex Trait Analysis||Contributor(s):||Yang, Jian (author); Lee, Sang Hong (author); Goddard, Michael E. (author); Visscher, Peter M. (author)||Publication Date:||2011||Open Access:||Yes||DOI:||10.1016/j.ajhg.2010.11.011||Handle Link:||https://hdl.handle.net/1959.11/22043||Open Access Link:||https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3014363/||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.||Publication Type:||Journal Article||Source of Publication:||The American Journal of Human Genetics, 88(1), p. 76-82||Publisher:||Cell Press||Place of Publication:||United States of America||ISSN:||1537-6605
|Field of Research (FOR):||060405 Gene Expression (incl. Microarray and other genome-wide approaches)||Peer Reviewed:||Yes||HERDC Category Description:||C1 Refereed Article in a Scholarly Journal||Statistics to Oct 2018:||Visitors: 11
|Appears in Collections:||Journal Article|
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