Article

Estimation of pleiotropy between complex diseases using SNP-derived genomic relationships and restricted maximum likelihood

The University of Queensland, Queensland Brain Institute, Brisbane, QLD 4072, The University of Queensland Diamantina Institute, Princess Alexandra Hospital, Brisbane, QLD 4102 and Department of Agriculture and Food Systems, University of Melbourne, VIC 3010, Melbourne, Australia.
Bioinformatics (Impact Factor: 4.98). 07/2012; 28(19):2540-2. DOI: 10.1093/bioinformatics/bts474
Source: PubMed

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).
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).
hong.lee@uq.edu.au Supplementary Information: Supplementary data are available at Bioinformatics online.

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    • "Therefore, our data indicate effects at both loci on the amount of mtDNA are independent of MDD disease status. Even though MDD is a strong predictor of the amount of mtDNA [6], and even though the genetic correlation between amount of mtDNA and MDD computed from genome-wide SNPs [8, 17] was 46.7% (SE = 14.3%, p value = 3.53 3 10 À4 ), SNPs at TFAM and CDK6 were not associated with MDD (p value = 0.70 for rs11006126, p value = 0.53 for rs445, from a linear mixed model). Furthermore, no homoplasmic variant was associated with MDD in our cohort, nor were any of the six heteroplasmic sites with verified degrees of heteroplasmy associated with MDD (Table S5). "
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    • "For instance, Yang et al. (2010) suggested using whole-genome regressions (WGRs) (Meuwissen et al. 2001) to assess the proportion of variance of a trait or disease risk that can be explained by a regression of phenotypes on common SNPs or genomic heritability and a related parameter, the " missing heritability. " More recently, WGR models have been extended for the analysis of systems of multiple traits, so the concept of genomic correlation also has entered into the picture (Jia and Jannink 2012; Lee et al. 2012). For instance, Maier et al. (2015) used multivariate WGR models and reported estimates of genetic correlations between psychiatric disorders, and Furlotte and Eskin (2015) presented a methodology that incorporates genetic marker information for the analysis of multiple traits that, according to the authors, " provide fundamental insights into the nature of co-expressed genes. "
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    • "We also used bivariate GCTA (Lee et al. 2012) to estimate the extent to which the same genes contribute to the observed phenotypic correlation between two variables. These estimations are based on the genetic covariance between untransformed peer problem measures at different ages, which is due to common measured genetic variation. "
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