@inproceedings{04a17de7fa81459980342cec03389e66,
title = "Detection of genetic factors associated with multiple correlated imaging phenotypes by a sparse regression model",
abstract = "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.",
keywords = "Sparse low rank regression, group ridge, imaging genetics, schizophrenia, significant test",
author = "Dongdong Lin and Jingyao Li and Calhoun, {Vince D.} and Wang, {Yu Ping}",
year = "2015",
month = jul,
day = "21",
doi = "10.1109/ISBI.2015.7164130",
language = "English (US)",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1368--1371",
booktitle = "2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015",
note = "12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 ; Conference date: 16-04-2015 Through 19-04-2015",
}