Abstract
After nearly 10 years of intense academic and commercial research effort, large genome-wide association studies for common complex diseases are now imminent. Although these conditions involve a complex relationship between genotype and phenotype, including interactions between unlinked loci1, the prevailing strategies for analysis of such studies focus on the locus-by-locus paradigm. Here we consider analytical methods that explicitly look for statistical interactions between loci. We show first that they are computationally feasible, even for studies of hundreds of thousands of loci, and second that even with a conservative correction for multiple testing, they can be more powerful than traditional analyses under a range of models for interlocus interactions. We also show that plausible variations across populations in allele frequencies among interacting loci can markedly affect the power to detect their marginal effects, which may account in part for the well-known difficulties in replicating association results. These results suggest that searching for interactions among genetic loci can be fruitfully incorporated into analysis strategies for genome-wide association studies.
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References
Phillips, P.C. The language of gene interaction. Genetics 149, 1167–1171 (1998).
Risch, N.J. Searching for genetic determinants in the new millennium. Nature 405, 847–856 (2000).
Ozaki, K. et al. Functional SNPs in the lymphotoxin-alpha gene that are associated with susceptibility to myocardial infarction. Nat. Genet. 32, 650–654 (2002).
Roses, A. The genome era begins. Nat. Genet. 33 (Supp 2), 217 (2003).
Thomas, D.C. Statistical Methods in Genetic Epidemiology (Oxford University Press, Oxford, 2004).
Mackay, T.F. Quantitative trait loci in Drosophila. Nat. Rev. Genet. 2, 11–20 (2001).
Williams, S.M., Haines, J.L. & Moore, J.H. The use of animal models in the study of complex disease: all else is never equal or why do so many human studies fail to replicate animal findings? Bioessays 26, 170–179 (2004).
Routman, E.J. & Cheverud, J.M. Gene effects on a quantitative trait: Two-locus epistatic effects measured at microsatellite markers and at estimated QTL. Evolution 51, 1654–1662 (1997).
Segre, D., Deluna, A., Church, G.M. & Kishony, R. Modular epistasis in yeast metabolism. Nat. Genet. 37, 77–83 (2005).
Sing, C.F. & Davignon, J. Role of the apolipoprotein E polymorphism in determining normal plasma lipid and lipoprotein variation. Am. J. Hum. Genet. 37, 268–285 (1985).
Zerba, K.E., Ferrell, R.E. & Sing, C.F. Complex adaptive systems and human health: the influence of common genotypes of the apolipoprotein E (ApoE) gene polymorphism and age on the relational order within a field of lipid metabolism traits. Hum. Genet. 107, 466–475 (2000).
Hoh, J. & Ott, J. Mathematical multi-locus approaches to localizing complex human trait genes. Nat. Rev. Genet. 4, 701–709 (2003).
Culverhouse, R., Suarez, B.K., Lin, J. & Reich, T. A perspective on epistasis: limits of models displaying no main effect. Am. J. Hum. Genet. 70, 461–471 (2002).
Moore, J.H. & Ritchie, M.D. The challenges of whole-genome approaches to common diseases. JAMA 291, 1642–1643 (2004).
Kempthorne, O. An Introduction to Genetic Statistics (John Wiley & Sons, New York, 1957).
Carlson, C.S., Newman, T.L. & Nickerson, D.A. SNPing in the human genome. Curr. Opin. Chem. Biol. 5, 78–85 (2001).
Lohmueller, K.E., Pearce, C.L., Pike, M., Lander, E.S. & Hirschhorn, J.N. Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat. Genet. 33, 177–182 (2003).
Carlson, C.S., Eberle, M.A., Kruglyak, L. & Nickerson, D.A. Mapping complex disease loci in whole-genome association studies. Nature 429, 446–452 (2004).
Templeton, A.R. Epistasis and complex traits. in Epistasis and the Evolutionary Process (eds. Wolf, J.B., Brodie, E.D.I. & Wade, M.J.) 41–57 (Oxford University Press, New York, 2000).
Zondervan, K.T. & Cardon, L.R. The complex interplay among factors that influence allelic association. Nat. Rev. Genet. 5, 89–100 (2004).
Ioannidis, J.P., Ntzani, E.E., Trikalinos, T.A. & Contopoulos-Ioaunidis, D.G. Replication validity of genetic association studies. Nat. Genet. 29, 306–309 (2001).
Moore, J.H. The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum. Hered. 56, 73–82 (2003).
Sham, P., Bader, J.S., Craig, I., O'Donovan, M. & Owen, M. DNA pooling: A tool for large-scale association studies. Nat. Rev. Genet. 3, 862–871 (2002).
Tiwari, H.K. Deriving components of genetic variance for multilocus models. Genet. Epidemiol. 14, 1131–1136 (1997).
Schlesselman, J.J. Case-Control Studies: Design, Conduct, Analysis (Oxford University Press, Oxford, 1982).
Sham, P. Statistics in Human Genetics (Hodder Arnold, London, 1997).
Clayton, D. Population association. in Handbook of Statistical Genetics (eds. Balding, D.J., Bishop, M. & Cannings, C.) 519–540 (John Wiley & Sons, New York, 2001).
Hoh, J. et al. Selecting SNPs in two-stage analysis of disease association data: a model-free approach. Ann. Hum. Genet. 64, 413–417 (2000).
Marchini, J., Cardon, L.R., Phillips, M.S. & Donnelly, P. The effects of human population structure on large genetic association studies. Nat. Genet. 36, 512–517 (2004).
Acknowledgements
We thank the Wellcome Trust, the US National Institutes of Health and the SNP Consortium for support.
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Marchini, J., Donnelly, P. & Cardon, L. Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet 37, 413–417 (2005). https://doi.org/10.1038/ng1537
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DOI: https://doi.org/10.1038/ng1537
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