期刊
BIOSTATISTICS
卷 12, 期 1, 页码 18-32出版社
OXFORD UNIV PRESS
DOI: 10.1093/biostatistics/kxq042
关键词
Feature selection; GENICA; Importance measure; logicFS; Logic regression
资金
- Deutsche Forschungsgemeinschaft [SFB 475, SCHW 1508/1-1]
- National Institutes of Health [HL090577]
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [R01HL090577] Funding Source: NIH RePORTER
A major goal of genetic association studies concerned with single nucleotide polymorphisms (SNPs) is the detection of SNPs exhibiting an impact on the risk of developing a disease. Typically, this problem is approached by testing each of the SNPs individually. This, however, can lead to an inaccurate measurement of the influence of the SNPs on the disease risk, in particular, if SNPs only show an effect when interacting with other SNPs, as the multivariate structure of the data is ignored. In this article, we propose a testing procedure based on logic regression that takes this structure into account and therefore enables a more appropriate quantification of importance and ranking of the SNPs than marginal testing. Since even SNP interactions often exhibit only a moderate effect on the disease risk, it can be helpful to also consider sets of SNPs (e.g. SNPs belonging to the same gene or pathway) to borrow strength across these SNP sets and to identify those genes or pathways comprising SNPs that are most consistently associated with the response. We show how the proposed procedure can be adapted for testing SNP sets, and how it can be applied to blocks of SNPs in linkage disequilibrium (LD) to overcome problems caused by LD.
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