Article

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: 5.47). 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.

0 Bookmarks
 · 
98 Views
  • Source
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Twin studies suggest that expressive vocabulary at textasciitilde24 months is modestly heritable. However, the genes influencing this early linguistic phenotype are unknown. Here we conduct a genome-wide screen and follow-up study of expressive vocabulary in toddlers of European descent from up to four studies of the EArly Genetics and Lifecourse Epidemiology consortium, analysing an early (15–18 months, ‘one-word stage’, NTotal=8,889) and a later (24–30 months, ‘two-word stage’, NTotal=10,819) phase of language acquisition. For the early phase, one single-nucleotide polymorphism (rs7642482) at 3p12.3 near ROBO2, encoding a conserved axon-binding receptor, reaches the genome-wide significance level (P=1.3 × 10−8) in the combined sample. This association links language-related common genetic variation in the general population to a potential autism susceptibility locus and a linkage region for dyslexia, speech-sound disorder and reading. The contribution of common genetic influences is, although modest, supported by genome-wide complex trait analysis (meta-GCTA h215–18-months=0.13, meta-GCTA h224–30-months=0.14) and in concordance with additional twin analysis (5,733 pairs of European descent, h224-months=0.20).
    Nature Communications 09/2014; 5. · 10.74 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: One of John Loehlin's many contributions to the field of behavioral genetics involves gene-environment (GE) correlation. The empirical base for GE correlation was research showing that environmental measures are nearly as heritable as behavioral measures and that genetic factors mediate correlations between environment and behavior. Attempts to identify genes responsible for these phenomena will come up against the 'missing heritability' problem that plagues DNA research on complex traits throughout the life sciences. However, DNA can also be used for quantitative genetic analyses of unrelated individuals (Genome-wide Complex Trait Analysis, GCTA) to investigate genetic influence on environmental measures and their behavioral correlates. A novel feature of GCTA is that it enables genetic analysis of family-level environments (e.g., parental socioeconomic status) and school-level environments (e.g., teaching quality) that cannot be investigated using within-family designs such as the twin method. An important implication of GE correlation is its shift from a passive model of the environment imposed on individuals to an active model in which individuals actively create their own experiences in part on the basis of their genetic propensities.
    Behavior Genetics 09/2014; · 2.61 Impact Factor