TY - JOUR
T1 - Brief review of regression-based and machine learning methods in genetic epidemiology: The Genetic Analysis Workshop 17 experience
AU - Dasgupta, Abhijit
AU - Sun, Yan V.
AU - König, Inke R.
AU - Bailey-Wilson, Joan E.
AU - Malley, James D.
PY - 2011
Y1 - 2011
N2 - Genetics Analysis Workshop 17 provided common and rare genetic variants from exome sequencing data and simulated binary and quantitative traits in 200 replicates. We provide a brief review of the machine learning and regression-based methods used in the analyses of these data. Several regression and machine learning methods were used to address different problems inherent in the analyses of these data, which are high-dimension, low-sample-size data typical of many genetic association studies. Unsupervised methods, such as cluster analysis, were used for data segmentation and, subset selection. Supervised learning methods, which include regression-based methods (e.g., generalized linear models, logic regression, and regularized regression) and tree-based methods (e.g., decision trees and random forests), were used for variable selection (selecting genetic and clinical features most associated or predictive of outcome) and prediction (developing models using common and rare genetic variants to accurately predict outcome), with the outcome being case-control status or quantitative trait value. We include a discussion of cross-validation for model selection and assessment, and a description of available software resources for these methods.
AB - Genetics Analysis Workshop 17 provided common and rare genetic variants from exome sequencing data and simulated binary and quantitative traits in 200 replicates. We provide a brief review of the machine learning and regression-based methods used in the analyses of these data. Several regression and machine learning methods were used to address different problems inherent in the analyses of these data, which are high-dimension, low-sample-size data typical of many genetic association studies. Unsupervised methods, such as cluster analysis, were used for data segmentation and, subset selection. Supervised learning methods, which include regression-based methods (e.g., generalized linear models, logic regression, and regularized regression) and tree-based methods (e.g., decision trees and random forests), were used for variable selection (selecting genetic and clinical features most associated or predictive of outcome) and prediction (developing models using common and rare genetic variants to accurately predict outcome), with the outcome being case-control status or quantitative trait value. We include a discussion of cross-validation for model selection and assessment, and a description of available software resources for these methods.
UR - http://www.scopus.com/inward/record.url?scp=82455175511&partnerID=8YFLogxK
U2 - 10.1002/gepi.20642
DO - 10.1002/gepi.20642
M3 - Journal articles
C2 - 22128059
AN - SCOPUS:82455175511
SN - 0741-0395
VL - 35
SP - S5-S11
JO - Genetic Epidemiology
JF - Genetic Epidemiology
IS - SUPPL. 1
ER -