Correction for hidden confounders in the genetic analysis of gene expression.

ABSTRACT: Understanding the genetic underpinnings of disease is important for screening, treatment, drug development, and basic biological insight. One way of getting at such an understanding is to find out which parts of our DNA, such as single-nucleotide polymorphisms, affect particular intermediary processes such as gene expression. Naively, such associations can be identified using a simple statistical test on all paired combinations of genetic variants and gene transcripts. However, a wide variety of confounders lie hidden in the data, leading to both spurious associations and missed associations if not properly addressed. We present a statistical model that jointly corrects for two particular kinds of hidden structure--population structure (e.g., race, family-relatedness), and microarray expre

SUBMITTER: Listgarten J 

PROVIDER: S-EPMC2944732 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature