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A data-adaptive sum test for disease association with multiple common or rare variants - PubMed

A data-adaptive sum test for disease association with multiple common or rare variants

Fang Han et al. Hum Hered. 2010.

Abstract

Since associations between complex diseases and common variants are typically weak, and approaches to genotyping rare variants (e.g. by next-generation resequencing) multiply, there is an urgent demand to develop powerful association tests that are able to detect disease associations with both common and rare variants. In this article we present such a test. It is based on data-adaptive modifications to a so-called Sum test originally proposed for common variants, which aims to strike a balance between utilizing information on multiple markers in linkage disequilibrium and reducing the cost of large degrees of freedom or of multiple testing adjustment. When applied to multiple common or rare variants in a candidate region, the proposed test is easy to use with 1 degree of freedom and without the need for multiple testing adjustment. We show that the proposed test has high power across a wide range of scenarios with either common or rare variants, or both. In particular, in some situations the proposed test performs better than several commonly used methods.

Copyright 2010 S. Karger AG, Basel.

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Figures

Fig. 1
Fig. 1

Distributions of the naive score test statistics U/√V (a), the adjusted score statistics (U − U0)/√V0 (c) and (aSum b)/a (e), and their observed values against the quantiles from N(0.3, 1) (b), N(0, 1) (d) and χ21 (f), respectively. All are based on n = 500 and 1,000 replicates of the simulated data with a compound symmetry covariance structure.

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References

    1. Azzopardi D, Dallosso AR, Eliason K, Hendrickson BC, Jones N, et al. Multiple rare nonsynonymous variants in the adenomatous polyposis coli gene predispose to colorectal adenomas. Cancer Res. 2008;68:358–363. - PubMed
    1. Barrett JC, Clayton DG, Concannon P, Akolkar B, Cooper JD, Erlich HA, Julier C, Morahan G, Nerup J, Nierras C, Plagnol V, Pociot F, Schuilenburg H, Smyth DJ, Stevens H, Todd JA, Walker NM, Rich SS, The Type 1 Diabetes Genetics Consortium: Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat Genet, Epub 2009. - PubMed
    1. Bodmer W, Bonilla C. Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet. 2008;40:695–701. - PMC - PubMed
    1. Chapman JM, Whittaker J. Analysis of multiple SNPs in a candidate gene or region. Genetic Epidemiology. 2008;32:560–566. - PMC - PubMed
    1. Clayton D, Chapman J, Cooper J. Use of unphased multilocus genotype data in indirect association studies. Genet Epidemiol. 2004;27:415–428. - PubMed

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