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.
Papachristou, Charalampos; Ober, Carole; and Abney, Mark, "A LASSO penalized regression approach for genome-wide association analyses using related individuals: application to the Genetic Analysis Workshop 19 simulated data" (2016). Faculty Scholarship for the College of Science & Mathematics. 48.
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Papachristou, C., Ober, C., & Abney, M. (2016). A LASSO penalized regression approach for genome-wide association analyses using related individuals: Application to the genetic analysis workshop 19 simulated data. BMC Proceedings, 10(S7), 221-226.