Title :
Network-based investigation of genetic modules associated with functional brain networks in schizophrenia
Author :
Dongdong Lin ; Hao He ; Jingyao Li ; Hong-Wen Deng ; Calhoun, Vince D. ; Yu-Ping Wang
Author_Institution :
Biomed. Eng. Dept., Tulane Univ., New Orleans, LA, USA
Abstract :
We developed a new sparse multivariate regression method, collaborative sparse reduced rank regression(C-sRRR) for detecting genetic networks associated with brain functional networks in schizophrenia (SZ). Our study: 1) introduced both genetic and brain network structure to group single nucleotide polymorphism (SNP) and voxels simultaneously for utilizing the interacting effects implied in both features; 2) used collaborative sparse group lasso to perform genetic variants selection and nuclear norm penalty to address the interrelationship among voxels; 3) developed an efficient algorithm for solving the non-smooth optimization. In real data analysis, we constructed 8605 genetic sub-networks (modules) from 722177 SNPs with a median module size of 9. A functional brain network was extracted which also showed significant discriminative characteristics between SZ and healthy controls. A sub sampling strategy was applied to identify 57 highly ranked genes from 14 high-ranking modules. 14 of them are SZ susceptibility genes and 6 genes were consistent with the findings in previous study.
Keywords :
brain; data analysis; diseases; genetics; medical disorders; optimisation; polymorphism; regression analysis; SNP; brain network structure; collaborative sparse reduced rank regression; functional brain networks; genetic modules; genetic subnetworks; genetic variant selection; network-based investigation; nonsmooth optimization; nuclear norm penalty; real data analysis; schizophrenia; single nucleotide polymorphism; sparse multivariate regression method; subsampling strategy; Collaboration; Databases; Genetics; Imaging; Proteins; Sparse matrices; Vectors; Collaborative sparse group lasso; imaging genetics; network analysis; reduced-rank regression;
Conference_Titel :
Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on
Conference_Location :
Shanghai
DOI :
10.1109/BIBM.2013.6732582