Evaluation of Penalized and Nonpenalized Methods for Disease Prediction with Large-Scale Genetic Data.

ABSTRACT: Owing to recent improvement of genotyping technology, large-scale genetic data can be utilized to identify disease susceptibility loci and this successful finding has substantially improved our understanding of complex diseases. However, in spite of these successes, most of the genetic effects for many complex diseases were found to be very small, which have been a big hurdle to build disease prediction model. Recently, many statistical methods based on penalized regressions have been proposed to tackle the so-called "large P and small N" problem. Penalized regressions including least absolute selection and shrinkage operator (LASSO) and ridge regression limit the space of parameters, and this constraint enables the estimation of effects for very large number of SNPs. Various extensions ha

SUBMITTER: Won S 

PROVIDER: S-EPMC4539442 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature