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

Dongdong Lin, Jingyao Li, Vince D. Calhoun, Yu Ping Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PublisherIEEE Computer Society
Pages1368-1371
Number of pages4
ISBN (Electronic)9781479923748
DOIs
StatePublished - Jul 21 2015
Externally publishedYes
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: Apr 16 2015Apr 19 2015

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2015-July
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
CountryUnited States
CityBrooklyn
Period4/16/154/19/15

Keywords

  • Sparse low rank regression
  • group ridge
  • imaging genetics
  • schizophrenia
  • significant test

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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