Bioinformatics Advance Access originally published online on August 4, 2009
Bioinformatics 2009 25(21):2802-2808; doi:10.1093/bioinformatics/btp476
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Multiple testing in genome-wide association studies via hidden Markov models
1 Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, 2 Department of Statistics, North Carolina State University, Raleigh, NC 27695, 3 Center for Applied Genomics, The Children's Hospital of Philadelphia and 4 Division of Genetics, Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
* To whom correspondence should be addressed.
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Abstract |
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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{at}njit.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: Martin Bishop
Received on June 6, 2009; revised on July 17, 2009; accepted on July 31, 2009
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