Assessing a Spatial Boost Model for Quantitative Trait GWAS

Pages: 337 - 346
Published: Jan 1, 2015
Abstract
Bayesian variable selection provides a principled framework for incorporating prior information to regularize parameters in high-dimensional large-p-small-n regression models such as genomewide association studies (GWAS). Although these models produce more informative results, researchers often disregard them in favor of simpler models because of their high computational cost. We explore our recently proposed spatial boost model for GWAS on...
Paper Details
Title
Assessing a Spatial Boost Model for Quantitative Trait GWAS
Published Date
Jan 1, 2015
Pages
337 - 346
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