Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/18891
Title: Statistical Power to Detect Genetic (Co)Variance of Complex Traits Using SNP Data in Unrelated Samples
Contributor(s): Visscher, Peter M (author); Hemani, Gibran (author); Vinkhuyzen, Anna A E (author); Chen, Guo-Bo (author); Lee, Sang Hong  (author); Wray, Naomi R (author); Goddard, Michael E (author); Yang, Jian (author)
Publication Date: 2014
Open Access: Yes
DOI: 10.1371/journal.pgen.1004269Open Access Link
Handle Link: https://hdl.handle.net/1959.11/18891
Abstract: We have recently developed analysis methods (GREML) to estimate the genetic variance of a complex trait/disease and the genetic correlation between two complex traits/diseases using genome-wide single nucleotide polymorphism (SNP) data in unrelated individuals. Here we use analytical derivations and simulations to quantify the sampling variance of the estimate of the proportion of phenotypic variance captured by all SNPs for quantitative traits and case-control studies. We also derive the approximate sampling variance of the estimate of a genetic correlation in a bivariate analysis, when two complex traits are either measured on the same or different individuals. We show that the sampling variance is inversely proportional to the number of pairwise contrasts in the analysis and to the variance in SNP-derived genetic relationships. For bivariate analysis, the sampling variance of the genetic correlation additionally depends on the harmonic mean of the proportion of variance explained by the SNPs for the two traits and the genetic correlation between the traits, and depends on the phenotypic correlation when the traits are measured on the same individuals. We provide an online tool for calculating the power of detecting genetic (co)variation using genome-wide SNP data. The new theory and online tool will be helpful to plan experimental designs to estimate the missing heritability that has not yet been fully revealed through genome-wide association studies, and to estimate the genetic overlap between complex traits (diseases) in particular when the traits (diseases) are not measured on the same samples.
Publication Type: Journal Article
Source of Publication: PLoS Genetics, 10(4), p. 1-10
Publisher: Public Library of Science (PLoS)
Place of Publication: United States of America
ISSN: 1553-7404
Field of Research (FOR): 060412 Quantitative Genetics (incl. Disease and Trait Mapping Genetics)
060408 Genomics
070201 Animal Breeding
Socio-Economic Objective (SEO): 970106 Expanding Knowledge in the Biological Sciences
970107 Expanding Knowledge in the Agricultural and Veterinary Sciences
970111 Expanding Knowledge in the Medical and Health Sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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