Adaptive combination of Bayes factors as a powerful method for the joint analysis of rare and common variants.

ABSTRACT: Multi-marker association tests can be more powerful than single-locus analyses because they aggregate the variant information within a gene/region. However, combining the association signals of multiple markers within a gene/region may cause noise due to the inclusion of neutral variants, which usually compromises the power of a test. To reduce noise, the "adaptive combination of P-values" (ADA) method removes variants with larger P-values. However, when both rare and common variants are considered, it is not optimal to truncate variants according to their P-values. An alternative summary measure, the Bayes factor (BF), is defined as the ratio of the probability of the data under the alternative hypothesis to that under the null hypothesis. The BF quantifies the "relative" evidence support

SUBMITTER: Lin WY 

PROVIDER: S-EPMC5654754 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature