TY - GEN
T1 - Detection of genetic factors associated with multiple correlated imaging phenotypes by a sparse regression model
AU - Lin, Dongdong
AU - Li, Jingyao
AU - Calhoun, Vince D.
AU - Wang, Yu Ping
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/21
Y1 - 2015/7/21
N2 - Recently, more evidence of polygenicity and pleiotropy has been found in genome-wide association (GWA) studies of complex psychiatric diseases (e.g., schizophrenia), where multiple interacting genetic variants may affect multiple phenotypic traits simultaneously. In this work, we propose a new sparse collaborative group-ridge low-rank regression model (sCGRLR) to study the pleiotropic effects of a group of genetic variants on multiple imaging-derived quantitative traits (i.e., endophenotype). In the method, we enforce sparse and low-rank regularizations to reduce the number of features and then construct an effective gene or gene-set based statistic test to evaluate the significance of selected features. We show the advantage of our method with other gene-set pleiotropy analysis methods and other sparse multivariate regression methods in terms of type I error and power on simulated data. Finally, we demonstrate its application to real data analysis on the study of schizophrenia.
AB - Recently, more evidence of polygenicity and pleiotropy has been found in genome-wide association (GWA) studies of complex psychiatric diseases (e.g., schizophrenia), where multiple interacting genetic variants may affect multiple phenotypic traits simultaneously. In this work, we propose a new sparse collaborative group-ridge low-rank regression model (sCGRLR) to study the pleiotropic effects of a group of genetic variants on multiple imaging-derived quantitative traits (i.e., endophenotype). In the method, we enforce sparse and low-rank regularizations to reduce the number of features and then construct an effective gene or gene-set based statistic test to evaluate the significance of selected features. We show the advantage of our method with other gene-set pleiotropy analysis methods and other sparse multivariate regression methods in terms of type I error and power on simulated data. Finally, we demonstrate its application to real data analysis on the study of schizophrenia.
KW - Sparse low rank regression
KW - group ridge
KW - imaging genetics
KW - schizophrenia
KW - significant test
UR - http://www.scopus.com/inward/record.url?scp=84944320899&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944320899&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2015.7164130
DO - 10.1109/ISBI.2015.7164130
M3 - Conference contribution
AN - SCOPUS:84944320899
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1368
EP - 1371
BT - 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PB - IEEE Computer Society
T2 - 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Y2 - 16 April 2015 through 19 April 2015
ER -