MODE for detecting and estimating genetic causal variants

Abstract

AbstractDetermining the genetic causal variants and estimating their effect sizes are considered to be correlated but independent problems. Fine-mapping studies often rely on the ability to integrate useful functional annotation information into genome wide association univariate/multivariate analysis. In the present study, by modeling the probability of a SNP being causal and its effect size as a set of correlated Gaussian/non-Gaussian random variables, we design an optimization routine for simultaneous fine-mapping and effect size estimation. The algorithm is released as an open source C package MODE.Availability and Implementation:http://sites.google.com/site/sundarvelkur/modeContact:amdale@ucsd.edu, svelkur@ucsd.edu