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


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 ( Supplementary Information: Supplementary data are available at Bioinformatics online.

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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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    ABSTRACT: The availability of dense panels of common single nucleotide polymorphisms and of sequence variants has facilitated the study of statistical features of the genetic architecture of complex traits and diseases via whole-genome regressions (WGR). At the onset, traits were analyzed trait by trait but recently WGR have been extended for analysis of several traits jointly. The expectation is that such an approach would offer insight into mechanisms that cause trait associations, such as pleiotropy. We demonstrate that correlation parameters inferred using markers can give a distorted picture of the genetic correlation between traits. In the absence of knowledge of linkage disequilibrium relationships between quantitative or disease trait loci and markers, speculating about genetic correlation and its causes (such as pleiotropy) using genomic data is conjectural. Copyright © 2015, The Genetics Society of America.
    Genetics 07/2015; 201(1). DOI:10.1534/genetics.115.179978 · 5.96 Impact Factor
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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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    ABSTRACT: Peer behaviour plays an important role in the development of social adjustment, though little is known about its genetic architecture. We conducted a twin study combined with a genome-wide complex trait analysis (GCTA) and a genome-wide screen to characterise genetic influences on problematic peer behaviour during childhood and adolescence. This included a series of longitudinal measures (parent-reported Strengths-and-Difficulties Questionnaire) from a UK population-based birth-cohort (ALSPAC, 4-17 years), and a UK twin sample (TEDS, 4-11 years). Longitudinal twin analysis (TEDS; N ≤ 7,366 twin pairs) showed that peer problems in childhood are heritable (4-11 years, 0.60 < twin-h (2) ≤ 0.71) but genetically heterogeneous from age to age (4-11 years, twin-r g = 0.30). N ≤ 5,608, TEDS: N ≤ 2,691) provided furthermore little support for the contribution of measured common genetic variants during childhood (4-12 years, 0.02<[Formula: see text] ≤ 0.11) though these influences become stronger in adolescence (13-17 years, 0.14<[Formula: see text] ≤ 0.27). A subsequent cross-sectional genome-wide screen in ALSPAC (N ≤ 6,000) focussed on peer problems with the highest GCTA-heritability (10, 13 and 17 years, 0.0002 < GCTA-P ≤ 0.03). Single variant signals (P ≤ 10(-5)) were followed up in TEDS (N ≤ 2835, 9 and 11 years) and, in search for autism quantitative trait loci, explored within two autism samples (AGRE: N Pedigrees = 793; ACC: N Cases = 1,453/N Controls = 7,070). There was, however, no evidence for association in TEDS and little evidence for an overlap with the autistic continuum. In summary, our findings suggest that problematic peer relationships are heritable but genetically complex and heterogeneous from age to age, with an increase in common measurable genetic variation during adolescence.
    Human Genetics 12/2014; 134(6). DOI:10.1007/s00439-014-1514-5 · 4.82 Impact Factor
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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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    ABSTRACT: We use genome wide data from respondents of the Health and Retirement Study (HRS) to evaluate the possibility that common genetic influences are associated with education and three health outcomes: depression, self-rated health, and body mass index. We use a total of 1.7 million single nucleotide polymorphisms obtained from the Illumina HumanOmni2.5-4v1 chip from 4233 non-Hispanic white respondents to characterize genetic similarities among unrelated persons in the HRS. We then used the Genome Wide Complex Trait Analysis (GCTA) toolkit, to estimate univariate and bivariate heritability. We provide evidence that education (h(2) = 0.33), BMI (h(2) = 0.43), depression (h(2) = 0.19), and self-rated health (h(2) = 0.18) are all moderately heritable phenotypes. We also provide evidence that some of the correlation between depression and education as well as self-rated health and education is due to common genetic factors associated with one or both traits. We find no evidence that the correlation between education and BMI is influenced by common genetic factors.
    Social Science & Medicine 08/2014; 127. DOI:10.1016/j.socscimed.2014.08.001 · 2.89 Impact Factor
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