Multilocus statistics to uncover epistasis and heterogeneity in complex diseases: revisiting a set of multiple sclerosis data

Abstract

New statistics are developed to gather the contribution of many alleles at different loci to common diseases. Both inferential and descriptive statistics are included in order to uncover epistatic effects as well as heterogeneity. The problem of multiple testing is circumvented by considering a global null hypothesis. Global testing is supplemented by descriptive methods that make use of measures like odds ratio or the P-value of individually tested allele combinations. Visualization helps to reflect complex data sets. The methods described here have been scrutinized by statistical simulations, and we show that power gains can be substantial as compared to single locus statistics. Typing data of multiple sclerosis patients and controls are investigated, representing an example of larger scale information in screening candidate genes for their impact on complex diseases. New insights emerge from this data set demonstrating genetic heterogeneity and evidence for epistasis.