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Powerful multi‐marker association tests: unifying genomic distance‐based regression and logistic regression
Author(s) -
Han Fang,
Pan Wei
Publication year - 2010
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20529
Subject(s) - bonferroni correction , linkage disequilibrium , logistic regression , statistics , genetic association , multinomial logistic regression , regression , regression analysis , statistical power , statistical hypothesis testing , disequilibrium , multiple comparisons problem , ancestry informative marker , biology , genetics , allele frequency , mathematics , allele , genotype , single nucleotide polymorphism , haplotype , gene , medicine , ophthalmology
To detect genetic association with common and complex diseases, many statistical tests have been proposed for candidate gene or genome‐wide association studies with the case‐control design. Due to linkage disequilibrium (LD), multi‐marker association tests can gain power over single‐marker tests with a Bonferroni multiple testing adjustment. Among many existing multi‐marker association tests, most target to detect only one of many possible aspects in distributional differences between the genotypes of cases and controls, such as allele frequency differences, while a few new ones aim to target two or three aspects, all of which can be implemented in logistic regression. In contrast to logistic regression, a genomic distance‐based regression (GDBR) approach aims to detect some high‐order genotypic differences between cases and controls. A recent study has confirmed the high power of GDBR tests. At this moment, the popular logistic regression and the emerging GDBR approaches are completely unrelated; for example, one has to choose between the two. In this article, we reformulate GDBR as logistic regression, opening a venue to constructing other powerful tests while overcoming some limitations of GDBR. For example, asymptotic distributions can replace time‐consuming permutations for deriving P ‐values and covariates, including gene‐gene interactions, can be easily incorporated. Importantly, this reformulation facilitates combining GDBR with other existing methods in a unified framework of logistic regression. In particular, we show that Fisher's P ‐value combining method can boost statistical power by incorporating information from allele frequencies, Hardy–Weinberg disequilibrium, LD patterns, and other higher‐order interactions among multi‐markers as captured by GDBR. Genet. Epidemiol . 34:680–688, 2010. © 2010 Wiley‐Liss, Inc.