Journal Article

Multiple testing in genome-wide association studies via hidden Markov models

Zhi Wei, Wenguang Sun, Kai Wang and Hakon Hakonarson

in Bioinformatics

Volume 25, issue 21, pages 2802-2808
Published in print November 2009 | ISSN: 1367-4803
Published online May 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp476
Multiple testing in genome-wide association studies via hidden Markov models

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Motivation: Genome-wide association studies (GWAS) interrogate common genetic variation across the entire human genome in an unbiased manner and hold promise in identifying genetic variants with moderate or weak effect sizes. However, conventional testing procedures, which are mostly P-value based, ignore the dependency and therefore suffer from loss of efficiency. The goal of this article is to exploit the dependency information among adjacent single nucleotide polymorphisms (SNPs) to improve the screening efficiency in GWAS.

Results: We propose to model the linear block dependency in the SNP data using hidden Markov models (HMMs). A compound decision–theoretic framework for testing HMM-dependent hypotheses is developed. We propose a powerful data-driven procedure [pooled local index of significance (PLIS)] that controls the false discovery rate (FDR) at the nominal level. PLIS is shown to be optimal in the sense that it has the smallest false negative rate (FNR) among all valid FDR procedures. By re-ranking significance for all SNPs with dependency considered, PLIS gains higher power than conventional P-value based methods. Simulation results demonstrate that PLIS dominates conventional FDR procedures in detecting disease-associated SNPs. Our method is applied to analysis of the SNP data from a GWAS of type 1 diabetes. Compared with the Benjamini–Hochberg (BH) procedure, PLIS yields more accurate results and has better reproducibility of findings.

Conclusion: The genomic rankings based on our procedure are substantially different from the rankings based on the P-values. By integrating information from adjacent locations, the PLIS rankings benefit from the increased signal-to-noise ratio, hence our procedure often has higher statistical power and better reproducibility. It provides a promising direction in large-scale GWAS.

Availability: An R package PLIS has been developed to implement the PLIS procedure. Source codes are available upon request and will be available on CRAN (http://cran.r-project.org/).

Contact: zhiwei@njit.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5667 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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