A fast multilocus test with adaptive SNP selection for large-scale genetic-association studies

Type
Article

Authors
Zhang, Han
Shi, Jianxin
Liang, Faming
Wheeler, William
Stolzenberg-Solomon, Rachael
Yu, Kai

KAUST Grant Number
KUS-C1-016-04

Online Publication Date
2013-09-11

Print Publication Date
2014-05

Date
2013-09-11

Abstract
As increasing evidence suggests that multiple correlated genetic variants could jointly influence the outcome, a multilocus test that aggregates association evidence across multiple genetic markers in a considered gene or a genomic region may be more powerful than a single-marker test for detecting susceptibility loci. We propose a multilocus test, AdaJoint, which adopts a variable selection procedure to identify a subset of genetic markers that jointly show the strongest association signal, and defines the test statistic based on the selected genetic markers. The P-value from the AdaJoint test is evaluated by a computationally efficient algorithm that effectively adjusts for multiple-comparison, and is hundreds of times faster than the standard permutation method. Simulation studies demonstrate that AdaJoint has the most robust performance among several commonly used multilocus tests. We perform multilocus analysis of over 26,000 genes/regions on two genome-wide association studies of pancreatic cancer. Compared with its competitors, AdaJoint identifies a much stronger association between the gene CLPTM1L and pancreatic cancer risk (6.0 × 10(-8)), with the signal optimally captured by two correlated single-nucleotide polymorphisms (SNPs). Finally, we show AdaJoint as a powerful tool for mapping cis-regulating methylation quantitative trait loci on normal breast tissues, and find many CpG sites whose methylation levels are jointly regulated by multiple SNPs nearby.

Citation
Zhang H, Shi J, Liang F, Wheeler W, Stolzenberg-Solomon R, et al. (2013) A fast multilocus test with adaptive SNP selection for large-scale genetic-association studies. European Journal of Human Genetics 22: 696–702. Available: http://dx.doi.org/10.1038/ejhg.2013.201.

Acknowledgements
We thank three anonymous referees for their helpful comments. This study utilized the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health, Bethesda, MD. (http://biowulf.nih.gov). The work of H Zhang, J Shi, R Stolzenberg-Solomon and K Yu were supported by the Intramural Program of the National Institutes of Health and the National Cancer Institute. The work of F Liang was supported in part by the National Science Foundation (DMS-0607755, CMMI-0926803); and the award (KUS-C1-016-04) made by the King Abdullah University of Science and Technology.

Publisher
Springer Nature

Journal
European Journal of Human Genetics

DOI
10.1038/ejhg.2013.201

PubMed ID
24022295

PubMed Central ID
PMC3992564

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