TY - JOUR
T1 - Identifying diagnosis-specific genotype-phenotype associations via joint multitask sparse canonical correlation analysis and classification
AU - Alzheimer's Disease Neuroimaging Initiative
AU - Du, Lei
AU - Liu, Fang
AU - Liu, Kefei
AU - Yao, Xiaohui
AU - Risacher, Shannon L.
AU - Han, Junwei
AU - Guo, Lei
AU - Saykin, Andrew J.
AU - Shen, Li
N1 - Publisher Copyright:
© The Author(s) 2020. Published by Oxford University Press.
PY - 2020
Y1 - 2020
N2 - Motivation: Brain imaging genetics studies the complex associations between genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs). The neurodegenerative disorders usually exhibit the diversity and heterogeneity, originating from which different diagnostic groups might carry distinct imaging QTs, SNPs and their interactions. Sparse canonical correlation analysis (SCCA) is widely used to identify bi-multivariate genotype-phenotype associations. However, most existing SCCA methods are unsupervised, leading to an inability to identify diagnosis-specific genotype-phenotype associations. Results: In this article, we propose a new joint multitask learning method, named MT-SCCALR, which absorbs the merits of both SCCA and logistic regression. MT-SCCALR learns genotype-phenotype associations of multiple tasks jointly, with each task focusing on identifying one diagnosis-specific genotype-phenotype pattern. Meanwhile, MT-SCCALR cannot only select relevant SNPs and imaging QTs for each diagnostic group alone, but also allows the selection of those shared by multiple diagnostic groups. We derive an efficient optimization algorithm whose convergence to a local optimum is guaranteed. Compared with two state-of-the-art methods, MT-SCCALR yields better or similar canonical correlation coefficients and classification performances. In addition, it owns much better discriminative canonical weight patterns of great interest than competitors. This demonstrates the power and capability of MTSCCAR in identifying diagnostically heterogeneous genotype-phenotype patterns, which would be helpful to understand the pathophysiology of brain disorders. Availability and implementation: The software is publicly available at https://github.com/dulei323/MTSCCALR.
AB - Motivation: Brain imaging genetics studies the complex associations between genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs). The neurodegenerative disorders usually exhibit the diversity and heterogeneity, originating from which different diagnostic groups might carry distinct imaging QTs, SNPs and their interactions. Sparse canonical correlation analysis (SCCA) is widely used to identify bi-multivariate genotype-phenotype associations. However, most existing SCCA methods are unsupervised, leading to an inability to identify diagnosis-specific genotype-phenotype associations. Results: In this article, we propose a new joint multitask learning method, named MT-SCCALR, which absorbs the merits of both SCCA and logistic regression. MT-SCCALR learns genotype-phenotype associations of multiple tasks jointly, with each task focusing on identifying one diagnosis-specific genotype-phenotype pattern. Meanwhile, MT-SCCALR cannot only select relevant SNPs and imaging QTs for each diagnostic group alone, but also allows the selection of those shared by multiple diagnostic groups. We derive an efficient optimization algorithm whose convergence to a local optimum is guaranteed. Compared with two state-of-the-art methods, MT-SCCALR yields better or similar canonical correlation coefficients and classification performances. In addition, it owns much better discriminative canonical weight patterns of great interest than competitors. This demonstrates the power and capability of MTSCCAR in identifying diagnostically heterogeneous genotype-phenotype patterns, which would be helpful to understand the pathophysiology of brain disorders. Availability and implementation: The software is publicly available at https://github.com/dulei323/MTSCCALR.
UR - http://www.scopus.com/inward/record.url?scp=85087868546&partnerID=8YFLogxK
U2 - 10.1093/BIOINFORMATICS/BTAA434
DO - 10.1093/BIOINFORMATICS/BTAA434
M3 - 文章
C2 - 32657360
AN - SCOPUS:85087868546
SN - 1367-4803
VL - 36
SP - I371-I379
JO - Bioinformatics
JF - Bioinformatics
ER -