Accounting for selection and correlation in the analysis of two-stage genome-wide association studies

Robertson, David s. and Prevost, A. Toby and Bowden, Jack,(2016), Accounting for selection and correlation in the analysis of two-stage genome-wide association studies. , Biostatistics, UNSPECIFIED

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The problem of selection bias has long been recognized in the analysis of two-stage trials, where promising candidates are selected in stage 1 for confirmatory analysis in stage 2. To eff iciently correct for bias, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been proposed for a wide variety of trial settings, but where the population parameter estimates are assumed to be independent. We relax this assumption and derive the UMVCUE in the multivariate normal setting with an arbitrary known covariance structure. One area of application is the estimation of odds ratios (ORs) when combining a genome-wide scan with a replication study. Our framework explicitly accounts for correlated single nucleotide polymorphisms, as might occur due to linkage disequilibrium. We illustrate our approach on the measurement of the association between 11 genetic variants and the risk of Crohn’s disease, as reported in Parkes and others (2007. Sequence variants in the autophagy gene IRGM and multiple other replicating loci contribute to Crohn’s disease susceptibility. Nat. Gen. 39(7), 830–832.), and show that the estimated ORs can vary substantially if both selection and correlation are taken into account.
Keywords : Correlated outcomes; Genome-wide scan; Selection bias; Two-stage sample; Uniformly minimum variance conditionally unbiased estimator., UNSPECIFIED
Journal or Publication Title: Biostatistics
Volume: 17
Number: 4
Item Type: Article
Subjects: Akuntansi
Depositing User: Gunawan Gunawan
Date Deposited: 30 Dec 2019 07:35
Last Modified: 30 Dec 2019 07:35

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