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Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-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: 4.62). 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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    • "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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    • "Some research (Conley et al., 2014) suggests that GCTA results may be robust to violations of this assumption, but more work in this area is needed. This univariate GCTA model has been recently extended to a bivariate specification in which the same genetic relationship can be used to examine covariance between two traits as a function of genetic similarity (Lee et al., 2012). The model is derived from twin-sibling approaches in which cross-twin-cross-trait covariance is compared among monozygotic and dizygotic twin pairs to see if there is evidence that the covariance is due to common genetic influences. "
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    • "However, our hypothesis for an association between loci associated with schizophrenia and adolescent psychotic experiences is supported by phenotypic associations reported between earlier psychotic experiences and adult psychosis [4],[5]. Fifth, the genetic correlation between adolescent psychotic experiences and adult schizophrenia is not known because no twin data or genome-wide complex trait analysis (GCTA) [40],[41] findings on this bivariate relationship have been reported. It is not known the degree to which the relationship between adolescent psychotic experiences and psychotic disorders is influenced by genetic or environmental influences. "
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