A kernel regression approach to gene-gene interaction detection for case-control studies

Genetic Epidemiology

Volumn 37, Issue 7, 2013, Pages 695-703

  Larson, Nicholas B ^a   Schaid, Daniel J ^a  

^a MAYO CLINIC   (United States)

Author keywords

Epistasis; Gene gene interaction; Kernel methods; Score tests; Variance component

Indexed keywords

ARTICLE; CASE CONTROL STUDY; COMPUTER SIMULATION; CONTROLLED STUDY; DISEASE MODEL; EPISTASIS; GENE GENE INTERACTION; GENE INTERACTION; GENE LINKAGE DISEQUILIBRIUM; GENETIC ANALYSIS; GENETIC ASSOCIATION; GENETIC MODEL; GENETIC RISK; GENOTYPE; HAPLOTYPE; HEREDITY; HUMAN; KERNEL METHOD; SINGLE NUCLEOTIDE POLYMORPHISM; VARIANCE;

EPISTASIS; GENE-GENE INTERACTION; KERNEL METHODS; SCORE TESTS; VARIANCE COMPONENT;

CASE-CONTROL STUDIES; EPISTASIS, GENETIC; GENES; GENETIC MARKERS; GENETIC PREDISPOSITION TO DISEASE; LOGISTIC MODELS; MODELS, GENETIC; POLYMORPHISM, SINGLE NUCLEOTIDE; RESEARCH DESIGN; SAMPLE SIZE; SOFTWARE;

EID: 84885840888     PISSN: 07410395     EISSN: 10982272     Source Type: Journal    
DOI: 10.1002/gepi.21749     Document Type: Article

References (40)

1. Aronszajn N. 1950. Theory of reproducing kernels. Trans Am Math Soc 68(4):337-404.
2. Aschard H, Chen J, Cornelis MC, Chibnik LB, Karlson EW, Kraft P. 2012. Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases. Am J Hum Genet 90(6):962-972.
3. Bach FR, Jordan MI. 2005. Predictive low-rank decomposition for kernel methods. Proceedings of the 22nd International Conference on Machine Learning. Bonn, Germany: ACM. p 33-40.
4. Bhattacharjee S, Wang Z, Ciampa J, Kraft P, Chanock S, Yu K, Chatterjee N. 2010. Using principal components of genetic variation for robust and powerful detection of gene-gene interactions in case-control and case-only studies. Am J Hum Genet 86(3):331-342.
5. Bonetta L. 2010. Protein-protein interactions: interactome under construction. Nature 468(7325):851-854.
6. Breslow NE, Clayton DG. 1993. Approximate Inference in Generalized Linear Mixed Models. J Am Stat Assoc 88(421):9-25.
7. Cordell HJ. 2009. Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet 10(6):392-404.
8. Greenland S. 2009. Interactions in epidemiology: relevance, identification, and estimation. Epidemiology 20(1):14-17.
9. He J, Wang K, Edmondson AC, Rader DJ, Li C, Li MY. 2011. Gene-based interaction analysis by incorporating external linkage disequilibrium information. Eur J Hum Genet 19(2):164-172.
10. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA. 2009. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA 106(23):9362-9367.
11. Howard TD, Koppelman GH, Xu JF, Zheng SQL, Postma DS, Meyers DA, Bleecker ER. 2002. Gene-gene interaction in asthma: IL4RA and IL13 in a Dutch population with asthma. Am J Hum Genet 70(1):230-236.
12. Imhof JP. 1961. Computing the distribution of quadratic forms in normal variables. Biometrika 48(3/4):419-426.
13. Jorgenson E, Witte JS. 2006. A gene-centric approach to genome-wide association studies. Nat Rev Genet 7(11):885-891.
14. Kempthorne O. 1954. The correlation between relatives in a random mating population. Proc R Soc Lond B 143(910):103-113.
15. Kershaw DS. 1978. Incomplete Cholesky-conjugate gradient method for iterative solution of systems of linear equations. J Comput Phys 26(1):43-65.
16. Kwee LC, Liu D, Lin X, Ghosh D, Epstein MP. 2008. A powerful and flexible multilocus association test for quantitative traits. Am J Hum Genet 82(2):386-397.
17. Larson NB, Jenkins GD, Larson MC, Vierkant RA, Sellers TA, Phelan CM, Schildkraut JM, Sutphen R, Pharoah PP, Gayther SA and others. 2013. Kernel canonical correlation analysis for assessing gene-gene interactions and application to ovarian cancer. Eur J Hum Genet, doi: 10.1038/ejhg.2013.69
18. Li S, Cui Y. 2012. Gene-centric gene-gene interaction: a model-based kernel machine method. Ann Appl Stat 6(3):1134-1161.
19. Li Z, Zhang Y, Wang Z, Chen J, Fan J, Guan Y, Zhang C, Yuan C, Hong W, Wang Y and others. 2012. The role of BDNF, NTRK2 gene and their interaction in development of treatment-resistant depression: data from multicenter, prospective, longitudinal clinic practice. J Psychiatr Res 47(1):8-14.
20. Lin XH. 1997. Variance component testing in generalised linear models with random effects. Biometrika 84(2):309-326.
21. Liu DW, Ghosh D, Lin XH. 2008. Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models. BMC Bioinform 9:292.
22. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A and others. 2009. Finding the missing heritability of complex diseases. Nature 461(7265):747-753.
23. Marchini J, Donnelly P, Cardon LR. 2005. Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet 37(4):413-417.
24. Moore JH. 2003. The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered 56(1-3):73-82.
25. Moore JH, Ritchie MD. 2004. STUDENTJAMA. The challenges of whole-genome approaches to common diseases. J Am Med Assoc 291(13):1642-1643.
26. Moore JH, Williams SM. 2002. New strategies for identifying gene-gene interactions in hypertension. Ann Med 34(2):88-95.
27. Moore JH, Williams SM. 2009. Epistasis and its implications for personal genetics. Am J Hum Genet 85(3):309-320.
28. Neale BM, Sham PC. 2004. The future of association studies: gene-based analysis and replication. Am J Hum Genet 75(3):353-362.
29. Schaid DJ. 2010a. Genomic similarity and kernel methods I: advancements by building on mathematical and statistical foundations. Hum Hered 70(2):109-131.
30. Schaid DJ. 2010b. Genomic similarity and kernel methods II: methods for genomic information. Hum Hered 70(2):132-140.
31. Schaid DJ, McDonnell SK, Hebbring SJ, Cunningham JM, Thibodeau SN. 2005. Nonparametric tests of association of multiple genes with human disease. Am J Hum Genet 76(5):780-793.
32. Schifano ED, Epstein MP, Bielak LF, Jhun MA, Kardia SL, Peyser PA, Lin X. 2012. SNP set association analysis for familial data. Genet Epidemiol 36(8):797-810.
33. Schölkopf B, Smola A. 2002. Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge: The MIT Press.
34. Sima X, Xu J, Li Q, Luo L, Liu J, You C. 2012. Gene-gene interactions between interleukin-12A and interleukin-12B with the risk of brain tumor. DNA Cell Biol 31(2):219-223.
35. Venables WN, Ripley BD. 2002. Modern Applied Statistics With S. New York: Springer.
36. Wang K, Abbott D. 2008. A principal components regression approach to multilocus genetic association studies. Genet Epidemiol 32(2):108-118.
37. Wu J, Vallenius T, Ovaska K, Westermarck J, Makela TP, Hautaniemi S. 2009. Integrated network analysis platform for protein-protein interactions. Nat Methods 6(1):75-77.
38. Wu MC, Kraft P, Epstein MP, Taylor DM, Chanock SJ, Hunter DJ, Lin X. 2010. Powerful SNP-set analysis for case-control genome-wide association studies. Am J Hum Genet 86(6):929-942.
39. Wu MC, Lee S, Cai TX, Li Y, Boehnke M, Lin XH. 2011. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 89(1):82-93.
40. Yuan ZS, Gao QS, He YG, Zhang XS, Li FY, Zhao JH, Xue FZ. 2012. Detection for gene-gene co-association via kernel canonical correlation analysis. BMC Genet 13:83.