publication . Article . 2018

A LASSO penalized regression approach for genome-wide association analyses using related individuals: application to the Genetic Analysis Workshop 19 simulated data

Papachristou, Charalampos; Ober, Carole; Abney, Mark;
Open Access
  • Published: 13 Nov 2018 Journal: BMC Proceedings, volume 10 (eissn: 1753-6561, Copyright policy)
  • Publisher: Springer Nature
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 blo...
Subjects
free text keywords: Lasso (statistics), Genome-wide association study, Genetics, Regression, Medicine, business.industry, business, Bioinformatics, Generalized linear mixed model, Linear function, Trait, Covariance, Random effects model, Proceedings
Funded by
NIH| Genetic Analysis of Common Diseases: An Evaluation
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01GM031575-22
  • Funding stream: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
,
NIH| Genomic Studies of Sex-Specific Architecture of Asthma-Associated Traits
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01HL085197-07
  • Funding stream: NATIONAL HEART, LUNG, AND BLOOD INSTITUTE

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