Penalized regression and risk prediction in genome‐wide association studies

摘要

Abstract An important task in personalized medicine is to predict disease risk based on a person's genome, e.g. on a large number of single-nucleotide polymorphisms (SNPs). Genome-wide association studies (GWAS) make SNP and phenotype data available to researchers. A critical question for researchers is how to best predict disease risk. Penalized regression equipped with variable selection, such as least absolute shrinkage and selection operator (LASSO) and smoothly clipped absolute deviation (.