NDLI: Detection of genetic factors associated with multiple correlated imaging phenotypes by a sparse regression model

Content Provider	IEEE Xplore Digital Library
Author	Dongdong Lin Jingyao Li Calhoun, V.D. Yu-Ping Wang
Copyright Year	2015
Description	Author affiliation: Mind Res. Network & LBERI, Albuquerque, NM, USA (Calhoun, V.D.) \|\| Biomed. Eng. Dept., Tulane Univ., New Orleans, LA, USA (Dongdong Lin; Jingyao Li; Yu-Ping Wang)
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.
Starting Page	1368
Ending Page	1371
File Size	798646
Page Count	4
File Format	PDF
ISBN	9781479923748
DOI	10.1109/ISBI.2015.7164130
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Imaging Bioinformatics Correlation Optimization Diseases Genomics schizophrenia Sparse low rank regression group ridge significant test imaging genetics
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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