Identifying diagnosis-specific genotype-phenotype associations via joint multitask sparse canonical correlation analysis and classification.

ABSTRACT:

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 regressi

SUBMITTER: Du L 

PROVIDER: S-EPMC7355274 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature