SVSI: fast and powerful set-valued system identification approach to identifying rare variants in sequencing studies for ordered categorical traits.

ABSTRACT: In genetic association studies of an ordered categorical phenotype, it is usual to either regroup multiple categories of the phenotype into two categories and then apply the logistic regression (LG), or apply ordered logistic (oLG), or ordered probit (oPRB) regression, which accounts for the ordinal nature of the phenotype. However, they may lose statistical power or may not control type I error due to their model assumption and/or instable parameter estimation algorithm when the genetic variant is rare or sample size is limited. To solve this problem, we propose a set-valued (SV) system model to identify genetic variants associated with an ordinal categorical phenotype. We couple this model with a SV system identification algorithm to identify all the key system parameters. Simulations an

SUBMITTER: Bi W 

PROVIDER: S-EPMC4474746 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature