Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis
Abstract
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 genetic associations with longitudinal phenotypic markers.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 12, 2017
- Source ID
- 10.1093/bioinformatics/btx245
Entities
People
- Andrew J. Saykin
- Chanxiu Li
- Daoqiang Zhang
- For The Alzheimer’s Disease Neuroimaging Initiative*
- Jingwen Yan
- Shannon L. Risacher
- Shen Li
- Xiaohui Yao
- Xiaoke Hao
Organizations
- Indiana University
- Nanjing University of Aeronautics and Astronautics
- National Natural Science Foundation of China
- United States Department of Defense