Penalized regression and risk prediction in genome‐wide association studies

Statistical Analysis and Data Mining

Wei Pan¹  Erin Austin¹  Xiaotong Shen² 

[1] Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA;School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA

关键词: AUC;    GWAS;    LASSO;    logistic regression;    MLE;    ridge;    SCAD;    TLP;    elastic net;    SNP;   

DOI  :  10.1002/sam.11183

学科分类：社会科学、人文和艺术（综合）

来源: John Wiley & Sons, Inc.

【 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 (.

【 授权许可】

Unknown