EQTL analysis of many thousands of expressed genes while simultaneously controlling for hidden factors

ABSTRACT: Motivation: Identification of eQTL, the genetic loci that contribute to heritable variation in gene expression, can be obstructed by factors that produce variation in expression profiles if these factors are unmeasured or hidden from direct analysis. Methods: We have developed a method for Hidden Expression Factor analysis (HEFT) that identifies individual and pleiotropic effects of eQTL in the presence of hidden factors. The HEFT model simultaneously accounts for the effects of genotypes while learning hidden factors, where we make use of the complete likelihood of a unified multivariate regression and factor analysis model to derive a ridge estimator for combined factor learning and detection of eQTL. HEFT requires no pre-estimation of hidden factor effects, no iterative model selection,

ORGANISM(S): Homo sapiens

SUBMITTER: Yael Strulovici-Barel 

PROVIDER: E-GEOD-40364 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress