Document Type

Article

Version

Published Open Access

Publication Date

10-18-2016

Publication Title

BMC Proceedings

DOI

http://dx.doi.org/10.1186/s12919-016-0034-9

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.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Published Citation

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.

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