title: Exploring the potential benefits of stratified false discovery rates for region-based testing of association with rare genetic variation.
abstract: When analyzing the data that arises from exome or whole-genome sequencing studies, window-based tests, (i.e., tests that jointly analyze all genetic data in a small genomic region), are very popular. However, power is known to be quite low for finding associations with phenotypes using these tests, and therefore a variety of analytic strategies may be employed to potentially improve power. Using sequencing data of all of chromosome 3 from an interim release of data on 2432 individuals from the UK10K project, we simulated phenotypes associated with rare genetic variation, and used the results to explore the window-based test power. We asked two specific questions: firstly, whether there could be substantial benefits associated with incorporating information from external annotation on the genetic variants, and secondly whether the false discovery rate (FDRs) would be a useful metric for assessing significance. Although, as expected, there are benefits to using additional information (such as annotation) when it is associated with causality, we confirmed the general pattern of low sensitivity and power for window-based tests. For our chosen example, even when power is high to detect some of the associations, many of the regions containing causal variants are not detectable, despite using lax significance thresholds and optimal analytic methods. Furthermore, our estimated FDR values tended to be much smaller than the true FDRs. Long-range correlations between variants-due to linkage disequilibrium-likely explain some of this bias. A more sophisticated approach to using the annotation information may improve power, however, many causal variants of realistic effect sizes may simply be undetectable, at least with this sample size. Perhaps annotation information could assist in distinguishing windows containing causal variants from windows that are merely correlated with causal variants.
authors: Lady Davis Institute for Medical Research, Jewish General Hospital Montreal, QC, Canada ; Department of Epidemiology, Biostatistics and Occupational Health, McGill University Montreal, QC, Canada.
authors: Changjiang
authors: C
authors: Xu
authors: Department of Epidemiology, Biostatistics and Occupational Health, McGill University Montreal, QC, Canada.
authors: Antonio
authors: A
authors: Ciampi
authors: Lady Davis Institute for Medical Research, Jewish General Hospital Montreal, QC, Canada ; Department of Epidemiology, Biostatistics and Occupational Health, McGill University Montreal, QC, Canada ; Departments of Oncology and Human Genetics, McGill University Montreal, QC, Canada.
authors: Celia M T
authors: CM
authors: Greenwood
keywords: SNV; false discovery rate; genomic annotation; multiple testing; rare genetic variants; stratified false discovery rate; whole genome sequencing; window-based tests