Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure.

ABSTRACT:

Background

In genome-wide association studies the extent and impact of confounding due to population structure have been well recognized. Inadequate handling of such confounding is likely to lead to spurious associations, hampering replication, and the identification of causal variants. Several strategies have been developed for protecting associations against confounding, the most popular one is based on Principal Component Analysis. In contrast, the extent and impact of confounding due to population structure in gene-gene interaction association epistasis studies are much less investigated and understood. In particular, the role of nonlinear genetic population substructure in epistasis detection is largely under-investigated, especially outside a regression framework.

Methods<

SUBMITTER: Abegaz F 

PROVIDER: S-EPMC7893746 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature