The Generalized Higher Criticism for Testing SNP-Set Effects in Genetic Association Studies.

TitleThe Generalized Higher Criticism for Testing SNP-Set Effects in Genetic Association Studies.
Publication TypeJournal Article
Year of Publication2017
AuthorsBarnett, I, Mukherjee, R, Lin, X
JournalJ Am Stat Assoc
Date Published2017

It is of substantial interest to study the effects of genes, genetic pathways, and networks on the risk of complex diseases. These genetic constructs each contain multiple SNPs, which are often correlated and function jointly, and might be large in number. However, only a sparse subset of SNPs in a genetic construct is generally associated with the disease of interest. In this article, we propose the generalized higher criticism (GHC) to test for the association between an SNP set and a disease outcome. The higher criticism is a test traditionally used in high-dimensional signal detection settings when marginal test statistics are independent and the number of parameters is very large. However, these assumptions do not always hold in genetic association studies, due to linkage disequilibrium among SNPs and the finite number of SNPs in an SNP set in each genetic construct. The proposed GHC overcomes the limitations of the higher criticism by allowing for arbitrary correlation structures among the SNPs in an SNP-set, while performing accurate analytic -value calculations for any finite number of SNPs in the SNP-set. We obtain the detection boundary of the GHC test. We compared empirically using simulations the power of the GHC method with existing SNP-set tests over a range of genetic regions with varied correlation structures and signal sparsity. We apply the proposed methods to analyze the CGEM breast cancer genome-wide association study. Supplementary materials for this article are available online.

Alternate JournalJ Am Stat Assoc
PubMed ID28736464
PubMed Central IDPMC5517103
Grant ListR35 CA197449 / CA / NCI NIH HHS / United States
R37 CA076404 / CA / NCI NIH HHS / United States
T32 ES007142 / ES / NIEHS NIH HHS / United States
T32 GM074897 / GM / NIGMS NIH HHS / United States
P01 CA134294 / CA / NCI NIH HHS / United States
U01 HG009088 / HG / NHGRI NIH HHS / United States