Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis.

ABSTRACT:

Motivation

Neuroimaging genetics identifies the relationships between genetic variants (i.e., the single nucleotide polymorphisms) and brain imaging data to reveal the associations from genotypes to phenotypes. So far, most existing machine-learning approaches are widely used to detect the effective associations between genetic variants and brain imaging data at one time-point. However, those associations are based on static phenotypes and ignore the temporal dynamics of the phenotypical changes. The phenotypes across multiple time-points may exhibit temporal patterns that can be used to facilitate the understanding of the degenerative process. In this article, we propose a novel temporally constrained group sparse canonical correlation analysis (TGSCCA) framework to identify genet

SUBMITTER: Hao X 

PROVIDER: S-EPMC5870577 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature