title: A LASSO penalized regression approach for genome-wide association analyses using related individuals: application to the Genetic Analysis Workshop 19 simulated data.
abstract: We propose a novel LASSO (least absolute shrinkage and selection operator) penalized regression method used to analyze samples consisting of (potentially) related individuals. Developed in the context of linear mixed models, our method models the relatedness of individuals in the sample through a random effect whose covariance structure is a linear function of known matrices with elements combinations of the condensed coefficients of identity between the individuals in the sample. We implement our method to analyze the simulated family data provided by the 19th Genetic Analysis Workshop in an effort to identify loci regulating the simulated trait of systolic blood pressure. The analyses were performed with full knowledge of the simulation model. Our findings demonstrate that we can significantly reduce the rate of false positive signals by incorporating the relatedness of the study participants.
authors.affiliation: Department of Mathematics, Physics, and Statistics, University of the Sciences, 600 S. 43rd Street, Philadelphia, PA 19104 USA ; Department of Mathematics, Rowan University, 201 Mullica Hill Road, Glassboro, NJ 08028 USA.
authors.forename: Charalampos
authors.initials: C
authors.lastname: Papachristou
authors.affiliation: Department of Human Genetics, University of Chicago, 920 E. 58th Street, CLSC 4th floor, Chicago, IL 60637 USA.
authors.forename: Carole
authors.initials: C
authors.lastname: Ober
authors.affiliation: Department of Human Genetics, University of Chicago, 920 E. 58th Street, CLSC 4th floor, Chicago, IL 60637 USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Abney