Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood

Author(s)
Lee, Sang Hong
Yang, J
Goddard, M E
Visscher, P M
Wray, N R
Publication Date
2012
Abstract
Genetic correlations are the genome-wide aggregate effects of causal variants affecting multiple traits. Traditionally, genetic correlations between complex traits are estimated from pedigree studies, but such estimates can be confounded by shared environmental factors. Moreover, for diseases, low prevalence rates imply that even if the true genetic correlation between disorders was high, co-aggregation of disorders in families might not occur or could not be distinguished from chance. We have developed and implemented statistical methods based on linear mixed models to obtain unbiased estimates of the genetic correlation between pairs of quantitative traits or pairs of binary traits of complex diseases using population-based case-control studies with genome-wide single-nucleotide polymorphism data. The method is validated in a simulation study and applied to estimate genetic correlation between various diseases from Wellcome Trust Case Control Consortium data in a series of bivariate analyses. We estimate a significant positive genetic correlation between risk of Type 2 diabetes and hypertension of ~0.31 (SE 0.14, P = 0.024).
Citation
Bioinformatics, 28(19), p. 2540-2542
ISSN
1367-4811
1367-4803
Link
Language
en
Publisher
Oxford University Press
Title
Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood
Type of document
Journal Article
Entity Type
Publication

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