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 m...
| Published in: | BioMed Research International Vol. 2015; pp. 1 - 11 |
|---|---|
| Main Authors: | , , , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
8/4/2015
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109031045&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109031045 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/4/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109031045 109031045 109031045 10.1155/2015/605891 109031045 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Evaluation of Penalized and Nonpenalized Methods for Disease Prediction with Large-Scale Genetic Data. aug: au: Won, Sungho Choi, Hosik Park, Suyeon Lee, Juyoung Park, Changyi Kwon, Sunghoon affil: Department of Public Health Science, Seoul National University, Seoul, Republic of Korea sug: subj: Disease Susceptibility Familial and Genetic Data Analysis Human Regression South Korea Descriptive Statistics Genotype Diabetes Mellitus, Type 2 Familial and Genetic Obesity Familial and Genetic Hypertension Familial and Genetic Smoking Familial and Genetic Models, Statistical Male Female Adult Adult: 19-44 years Male Female ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|