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

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 efficiently correct for bias, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been propo...

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Main Authors:	Robertson, David S., Prevost, A. Toby, Bowden, Jack
Format:	Online
Language:	English
Published:	Oxford University Press 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5031943/

id	pubmed-5031943
recordtype	oai_dc
spelling	pubmed-50319432016-09-23 Accounting for selection and correlation in the analysis of two-stage genome-wide association studies Robertson, David S. Prevost, A. Toby Bowden, Jack Articles 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 efficiently 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. Oxford University Press 2016-10 2016-03-18 /pmc/articles/PMC5031943/ /pubmed/26993061 http://dx.doi.org/10.1093/biostatistics/kxw012 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Robertson, David S. Prevost, A. Toby Bowden, Jack
spellingShingle	Robertson, David S. Prevost, A. Toby Bowden, Jack Accounting for selection and correlation in the analysis of two-stage genome-wide association studies
author_facet	Robertson, David S. Prevost, A. Toby Bowden, Jack
author_sort	Robertson, David S.
title	Accounting for selection and correlation in the analysis of two-stage genome-wide association studies
title_short	Accounting for selection and correlation in the analysis of two-stage genome-wide association studies
title_full	Accounting for selection and correlation in the analysis of two-stage genome-wide association studies
title_fullStr	Accounting for selection and correlation in the analysis of two-stage genome-wide association studies
title_full_unstemmed	Accounting for selection and correlation in the analysis of two-stage genome-wide association studies
title_sort	accounting for selection and correlation in the analysis of two-stage genome-wide association studies
description	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 efficiently 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.
publisher	Oxford University Press
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5031943/
_version_	1613657575838973952

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

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