DOI: 10.1101/451377Oct 24, 2018Paper

Permutation methods for assessing significance in binary trait association mapping with structured samples

BioRxiv : the Preprint Server for Biology
Joelle MbatchouMary Sara McPeek


In genetic association analysis of complex traits, permutation testing can be a valuable tool for assessing significance when the distribution of the test statistic is unknown or not well-approximated. This commonly arises when the association test statistic is itself a function of multiple correlated statistics, e.g, in tests of gene-set, pathway or genome-wide significance, as well as omnibus tests that combine test statistics that perform well in different scenarios. For genetic association testing in samples with population structure and/or relatedness, use of naive permutation can lead to inflated type 1 error. To address this in quantitative traits, the MVNpermute method was developed. However, for association mapping of a binary trait, the relationship between the mean and variance makes both naive permutation and the MVNpermute method invalid. We propose BRASS, a permutation method for binary trait association mapping in samples that have related individuals and/or population structure. BRASS allows for covariates, ascertainment and simultaneous testing of multiple markers, and it accommodates a wide range of test statistics. We use an estimating equation approach that can be viewed as a hybrid of logistic regression an...Continue Reading

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