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Fast eQTL Analysis for Twin Studies.

ABSTRACT: Twin data are commonly used for studying complex psychiatric disorders, and mixed effects models are one of the most popular tools for modeling dependence structures between twin pairs. However, for eQTL (expression quantitative trait loci) data where associations between thousands of transcripts and millions of single nucleotide polymorphisms need to be tested, mixed effects models are computationally inefficient and often impractical. In this paper, we propose a fast eQTL analysis approach for twin eQTL data where we randomly split twin pairs into two groups, so that within each group the samples are unrelated, and we then apply a multiple linear regression analysis separately to each group. A score statistic that automatically adjusts the (hidden) correlation between the two groups is constructed for combining the results from the two groups. The proposed method has well-controlled type I error. Compared to mixed effects models, the proposed method has similar power but drastically improved computational efficiency. We demonstrate the computational advantage of the proposed method via extensive simulations. The proposed method is also applied to a large twin eQTL data from the Netherlands Twin Register.

SUBMITTER: Yin Z

PROVIDER: S-EPMC4469571 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Fast eQTL Analysis for Twin Studies.

Yin Zhaoyu Z Xia Kai K Chung Wonil W Sullivan Patrick F PF Zou Fei F

Genetic epidemiology 20150410 5

Twin data are commonly used for studying complex psychiatric disorders, and mixed effects models are one of the most popular tools for modeling dependence structures between twin pairs. However, for eQTL (expression quantitative trait loci) data where associations between thousands of transcripts and millions of single nucleotide polymorphisms need to be tested, mixed effects models are computationally inefficient and often impractical. In this paper, we propose a fast eQTL analysis approach for ...[more]

PMID: 25865703

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Project description:BackgroundThe etiology of Autism Spectrum Disorder (ASD) has been recently debated due to emerging findings on the importance of shared environmental influences. However, two recent twin studies do not support this and instead re-affirm strong genetic effects on the liability to ASD, a finding consistent with previous reports. This study conducts a systematic review and meta-analysis of all twin studies of ASD published to date and explores the etiology along the continuum of a quantitative measure of ASD.MethodsA PubMed Central, Science Direct, Google Scholar, Web of Knowledge structured search conducted online, to identify all twin studies on ASD published to date. Thirteen primary twin studies were identified, seven were included in the meta-analysis by meeting Systematic Recruitment criterion; correction for selection and ascertainment strategies, and applied prevalences were assessed for these studies. In addition, a quantile DF extremes analysis was carried out on Childhood Autism Spectrum Test scores measured in a population sample of 6,413 twin pairs including affected twins.ResultsThe meta-analysis correlations for monozygotic twins (MZ) were almost perfect at .98 (95% Confidence Interval, .96-.99). The dizygotic (DZ) correlation, however, was .53 (95% CI .44-.60) when ASD prevalence rate was set at 5% (in line with the Broad Phenotype of ASD) and increased to .67 (95% CI .61-.72) when applying a prevalence rate of 1%. The meta-analytic heritability estimates were substantial: 64-91%. Shared environmental effects became significant as the prevalence rate decreased from 5-1%: 07-35%. The DF analyses show that for the most part, there is no departure from linearity in heritability.ConclusionsWe demonstrate that: (a) ASD is due to strong genetic effects; (b) shared environmental effects become significant as a function of lower prevalence rate; (c) previously reported significant shared environmental influences are likely a statistical artefact of overinclusion of concordant DZ twins.

Dataset Information

Fast eQTL Analysis for Twin Studies.

Publications

Fast eQTL Analysis for Twin Studies.

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