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

Overview

Journal J Am Stat Assoc

Specialty Public Health

Date 2017 Jul 25

PMID 28736464

Citations 36

Authors

Ian Barnett

Rajarshi Mukherjee

Xihong Lin

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

A Minimax Optimal Ridge-Type Set Test for Global Hypothesis with Applications in Whole Genome Sequencing Association Studies.

Liu Y, Li Z, Lin X J Am Stat Assoc. 2025; 117(538):897-908.

PMID: 40017563 PMC: 11865954. DOI: 10.1080/01621459.2020.1831926.

Ensemble methods for testing a global null.

Liu Y, Liu Z, Lin X J R Stat Soc Series B Stat Methodol. 2025; 86(2):461-486.

PMID: 40012608 PMC: 11864748. DOI: 10.1093/jrsssb/qkad131.

ADELLE: A global testing method for trans-eQTL mapping.

Akinbiyi T, McPeek M, Abney M PLoS Genet. 2025; 21(1):e1011563.

PMID: 39792937 PMC: 11756770. DOI: 10.1371/journal.pgen.1011563.

A multi-trait epigenome-wide association study identified DNA methylation signature of inflammation among men with HIV.

Chen J, Hui Q, Titanji B, So-Armah K, Freiberg M, Justice A Clin Epigenetics. 2024; 16(1):152.

PMID: 39488703 PMC: 11531128. DOI: 10.1186/s13148-024-01763-2.

A high-dimensional omnibus test for set-based association analysis.

Yang H, Wang X, Zhang Z, Chen F, Cao H, Yan L Brief Bioinform. 2024; 25(5).

PMID: 39288231 PMC: 11407446. DOI: 10.1093/bib/bbae456.

References

Lee S, Wu M, Lin X . Optimal tests for rare variant effects in sequencing association studies. Biostatistics. 2012; 13(4):762-75. PMC: 3440237. DOI: 10.1093/biostatistics/kxs014. View

Conneely K, Boehnke M . So many correlated tests, so little time! Rapid adjustment of P values for multiple correlated tests. Am J Hum Genet. 2007; 81(6):1158-68. PMC: 2276357. DOI: 10.1086/522036. View

Price A, Patterson N, Plenge R, Weinblatt M, Shadick N, Reich D . Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006; 38(8):904-9. DOI: 10.1038/ng1847. View

Barnett I, Lin X . Analytic P-value calculation for the higher criticism test in finite problems. Biometrika. 2015; 101(4):964-970. PMC: 4350786. DOI: 10.1093/biomet/asu033. View

Stevens V, Rodriguez C, Pavluck A, Thun M, Calle E . Association of polymorphisms in the paraoxonase 1 gene with breast cancer incidence in the CPS-II Nutrition Cohort. Cancer Epidemiol Biomarkers Prev. 2006; 15(6):1226-8. DOI: 10.1158/1055-9965.EPI-05-0930. View

Zhang Y, Liu J . Fast and Accurate Approximation to Significance Tests in Genome-Wide Association Studies. J Am Stat Assoc. 2011; 106(495):846-857. PMC: 3226809. DOI: 10.1198/jasa.2011.ap10657. View

Mukherjee R, Pillai N, Lin X . HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL SPARSE BINARY REGRESSION. Ann Stat. 2015; 43(1):352-381. PMC: 4522432. DOI: 10.1214/14-AOS1279. View

Wu M, Kraft P, Epstein M, Taylor D, Chanock S, Hunter D . Powerful SNP-set analysis for case-control genome-wide association studies. Am J Hum Genet. 2010; 86(6):929-42. PMC: 3032061. DOI: 10.1016/j.ajhg.2010.05.002. View

Schwartzman A, Lin X . The effect of correlation in false discovery rate estimation. Biometrika. 2012; 98(1):199-214. PMC: 3412603. DOI: 10.1093/biomet/asq075. View

10.

Moskvina V, Schmidt K . On multiple-testing correction in genome-wide association studies. Genet Epidemiol. 2008; 32(6):567-73. DOI: 10.1002/gepi.20331. View

11.

Jia P, Zheng S, Long J, Zheng W, Zhao Z . dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks. Bioinformatics. 2010; 27(1):95-102. PMC: 3008643. DOI: 10.1093/bioinformatics/btq615. View

12.

Eliassen A, Tworoger S, Mantzoros C, Pollak M, Hankinson S . Circulating insulin and c-peptide levels and risk of breast cancer among predominately premenopausal women. Cancer Epidemiol Biomarkers Prev. 2007; 16(1):161-4. DOI: 10.1158/1055-9965.EPI-06-0693. View

13.

Chen H, Meigs J, Dupuis J . Sequence kernel association test for quantitative traits in family samples. Genet Epidemiol. 2013; 37(2):196-204. PMC: 3642218. DOI: 10.1002/gepi.21703. View

14.

Hayes R, Reding D, Kopp W, Subar A, Bhat N, Rothman N . Etiologic and early marker studies in the prostate, lung, colorectal and ovarian (PLCO) cancer screening trial. Control Clin Trials. 2001; 21(6 Suppl):349S-355S. DOI: 10.1016/s0197-2456(00)00101-x. View

15.

Hunter D, Kraft P, Jacobs K, Cox D, Yeager M, Hankinson S . A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet. 2007; 39(7):870-4. PMC: 3493132. DOI: 10.1038/ng2075. View

16.

Boehm J, Zhao J, Yao J, Kim S, Firestein R, Dunn I . Integrative genomic approaches identify IKBKE as a breast cancer oncogene. Cell. 2007; 129(6):1065-79. DOI: 10.1016/j.cell.2007.03.052. View

17.

Manolio T, Collins F, Cox N, Goldstein D, Hindorff L, Hunter D . Finding the missing heritability of complex diseases. Nature. 2009; 461(7265):747-53. PMC: 2831613. DOI: 10.1038/nature08494. View

18.

Wu M, Lee S, Cai T, Li Y, Boehnke M, Lin X . Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet. 2011; 89(1):82-93. PMC: 3135811. DOI: 10.1016/j.ajhg.2011.05.029. View

19.

Su Z, Marchini J, Donnelly P . HAPGEN2: simulation of multiple disease SNPs. Bioinformatics. 2011; 27(16):2304-5. PMC: 3150040. DOI: 10.1093/bioinformatics/btr341. View

20.

Lee S, Abecasis G, Boehnke M, Lin X . Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet. 2014; 95(1):5-23. PMC: 4085641. DOI: 10.1016/j.ajhg.2014.06.009. View