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

Lee, S. H., Yang, J., Goddard, M. E., Visscher, P. M. and Wray, N. R. (2012) Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics, 28 19: 2540-2542. doi:10.1093/bioinformatics/bts474


Author Lee, S. H.
Yang, J.
Goddard, M. E.
Visscher, P. M.
Wray, N. R.
Title Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood
Journal name Bioinformatics   Check publisher's open access policy
ISSN 1367-4803
1367-4811
Publication date 2012-10
Sub-type Article (original research)
DOI 10.1093/bioinformatics/bts474
Open Access Status DOI
Volume 28
Issue 19
Start page 2540
End page 2542
Total pages 3
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Collection year 2013
Language eng
Formatted abstract
Summary: 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, P¼0.024).
Availability: Our methods, appropriate for both quantitative and binary traits, are implemented in the freely available software GCTA (http://www.complextraitgenomics.com/software/gcta/reml_bivar.html).
Keyword Average Information Reml
Variance-Components
Common Snps
Proportion
Models
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 2013 Collection
UQ Diamantina Institute Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 124 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 128 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Thu, 15 Nov 2012, 11:45:02 EST by System User on behalf of Queensland Brain Institute