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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    • "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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    ABSTRACT: Control over the number of mtDNA molecules per cell appears to be tightly regulated, but the mechanisms involved are largely unknown. Reversible alterations in the amount of mtDNA occur in response to stress suggesting that control over the amount of mtDNA is involved in stress-related diseases including major depressive disorder (MDD). Using low-coverage sequence data from 10,442 Chinese women to compute the normalized numbers of reads mapping to the mitochondrial genome as a proxy for the amount of mtDNA, we identified two loci that contribute to mtDNA levels: one within the TFAM gene on chromosome 10 (rs11006126, p value = 8.73 × 10−28, variance explained = 1.90%) and one over the CDK6 gene on chromosome 7 (rs445, p value = 6.03 × 10−16, variance explained = 0.50%). Both loci replicated in an independent cohort. CDK6 is thus a new molecule involved in the control of mtDNA. We identify increased rates of heteroplasmy in women with MDD, and show from an experimental paradigm using mice that the increase is likely due to stress. Furthermore, at least one heteroplasmic variant is significantly associated with changes in the amount of mtDNA (position 513, p value = 3.27 × 10−9, variance explained = 0.48%) suggesting site-specific heteroplasmy as a possible link between stress and increase in amount of mtDNA. These findings indicate the involvement of mitochondrial genome copy number and sequence in an organism’s response to stress.
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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.
    Full-text · Article · Jul 2015 · Genetics
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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.
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