Estimating heritability of complex traits from genome-wide association studies using IBS-based Haseman-Elston regression

Chen, Guo-Bo (2014) Estimating heritability of complex traits from genome-wide association studies using IBS-based Haseman-Elston regression. Frontiers in Genetics, 5 APR: 107.1-107.14. doi:10.3389/fgene.2014.00107

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Author Chen, Guo-Bo
Title Estimating heritability of complex traits from genome-wide association studies using IBS-based Haseman-Elston regression
Journal name Frontiers in Genetics   Check publisher's open access policy
ISSN 1664-8021
Publication date 2014-04-01
Sub-type Article (original research)
DOI 10.3389/fgene.2014.00107
Open Access Status DOI
Volume 5
Issue APR
Start page 107.1
End page 107.14
Total pages 14
Place of publication Lausanne, Switzerland
Publisher Frontiers Research Foundation
Collection year 2015
Abstract Exploring heritability of complex traits is a central focus of statistical genetics. Among various previously proposed methods to estimate heritability, variance component methods are advantageous when estimating heritability using markers. Due to the high-dimensional nature of data obtained from genome-wide association studies (GWAS) in which genetic architecture is often unknown, the most appropriate heritability estimator model is often unclear. The Haseman-Elston (HE) regression is a variance component method that was initially only proposed for linkage studies. However, this study presents a theoretical basis for a modified HE that models linkage disequilibrium for a quantitative trait, and consequently can be used for GWAS. After replacing identical by descent (IBD) scores with identity by state (IBS) scores, we applied the IBS-based HE regression to single-marker association studies (scenario I) and estimated the variance component using multiple markers (scenario II). In scenario II, we discuss the circumstances in which the HE regression and the mixed linear model are equivalent; the disparity between these two methods is observed when a covariance component exists for the additive variance. When we extended the IBS-based HE regression to case-control studies in a subsequent simulation study, we found that it provided a nearly unbiased estimate of heritability, more precise than that estimated via the mixed linear model. Thus, for the case-control scenario, the HE regression is preferable. GEnetic Analysis Repository (GEAR; http://sourceforge.net/p/gbchen/wiki/GEAR/) software implemented the HE regression method and is freely available.
Keyword Case-control
GWAS
Haseman-Elston regression
Identity by state
Missing heritability
Mixed linear model
REML
Variance component
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2015 Collection
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