Estimating Missing Heritability for Disease from Genome-wide Association Studies

Lee, Sang Hong; Wray, Naomi R; Goddard, Michael E; Visscher, Peter M

doi:10.1016/j.ajhg.2011.02.002

Author(s)

Lee, Sang Hong

Wray, Naomi R

Goddard, Michael E

Visscher, Peter M

Publication Date

2011

Abstract

Genome-wide association studies are designed to discover SNPs that are associated with a complex trait. Employing strict significance thresholds when testing individual SNPs avoids false positives at the expense of increasing false negatives. Recently, we developed a method for quantitative traits that estimates the variation accounted for when fitting all SNPs simultaneously. Here we develop this method further for case-control studies. We use a linear mixed model for analysis of binary traits and transform the estimates to a liability scale by adjusting both for scale and for ascertainment of the case samples. We show by theory and simulation that the method is unbiased. We apply the method to data from the Wellcome Trust Case Control Consortium and show that a substantial proportion of variation in liability for Crohn disease, bipolar disorder, and type I diabetes is tagged by common SNPs.

Citation

American Journal of Human Genetics, 88(3), p. 294-305

ISSN

1537-6605

0002-9297

Link

https://hdl.handle.net/1959.11/22044

Language

en

Publisher

Cell Press

Title

Estimating Missing Heritability for Disease from Genome-wide Association Studies

Type of document

Journal Article

Entity Type

Publication

Author(s)	Lee, Sang Hong Wray, Naomi R Goddard, Michael E Visscher, Peter M
Publication Date	2011
Abstract	Genome-wide association studies are designed to discover SNPs that are associated with a complex trait. Employing strict significance thresholds when testing individual SNPs avoids false positives at the expense of increasing false negatives. Recently, we developed a method for quantitative traits that estimates the variation accounted for when fitting all SNPs simultaneously. Here we develop this method further for case-control studies. We use a linear mixed model for analysis of binary traits and transform the estimates to a liability scale by adjusting both for scale and for ascertainment of the case samples. We show by theory and simulation that the method is unbiased. We apply the method to data from the Wellcome Trust Case Control Consortium and show that a substantial proportion of variation in liability for Crohn disease, bipolar disorder, and type I diabetes is tagged by common SNPs.
Citation	American Journal of Human Genetics, 88(3), p. 294-305
ISSN	1537-6605 0002-9297
Link	https://hdl.handle.net/1959.11/22044
Language	en
Publisher	Cell Press
Title	Estimating Missing Heritability for Disease from Genome-wide Association Studies
Type of document	Journal Article
Entity Type	Publication

Estimating Missing Heritability for Disease from Genome-wide Association Studies

Files: