Evaluation of Penalized and Nonpenalized Methods for Disease Prediction with Large-Scale Genetic Data.
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 have been suggested, and, in this report, we compare their accuracy by applying them to several complex diseases. Our results show that penalized regressions are usually robust and provide better accuracy than the existing methods for at least diseases under consideration.
Department of Public Health Science, Seoul National University, Seoul, Republic of Korea.
Sungho
S
Won
Department of Applied Information Statistics, Kyonggi University, Suwon, Republic of Korea.
Hosik
H
Choi
Department of Biostatistics, Soonchunhyang University, College of Medicine, Seoul, Republic of Korea ; Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Seoul, Republic of Korea.
Suyeon
S
Park
Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Seoul, Republic of Korea.
Juyoung
J
Lee
Department of Statistics, University of Seoul, Seoul, Republic of Korea.
Changyi
C
Park
Department of Applied Statistics, Konkuk University, Seoul, Republic of Korea.
Sunghoon
S
Kwon
D000818:Animals; D020022:Genetic Predisposition to Disease; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D011237:Predictive Value of Tests