TY - JOUR
T1 - Evaluation of penalized and nonpenalized methods for disease prediction with large-scale genetic data
AU - Won, Sungho
AU - Choi, Hosik
AU - Park, Suyeon
AU - Lee, Juyoung
AU - Park, Changyi
AU - Kwon, Sunghoon
N1 - Publisher Copyright:
© 2015 Sungho Won et al.
PY - 2015
Y1 - 2015
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=84939475495&partnerID=8YFLogxK
U2 - 10.1155/2015/605891
DO - 10.1155/2015/605891
M3 - Article
C2 - 26346893
AN - SCOPUS:84939475495
SN - 2314-6133
VL - 2015
JO - BioMed Research International
JF - BioMed Research International
M1 - 605891
ER -