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Letter
Nature Genetics  37, 413 - 417 (2005)
Published online: 27 March 2005; | doi:10.1038/ng1537

Genome-wide strategies for detecting multiple loci that influence complex diseases

Jonathan Marchini1, Peter Donnelly1 & Lon R Cardon2

1  Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.

2  Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.

Correspondence should be addressed to Lon R Cardon lon.cardon@well.ox.ac.uk
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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Nature Genetics
ISSN: 1061-4036
EISSN: 1546-1718
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