期刊
JOURNAL OF COMPUTATIONAL BIOLOGY
卷 25, 期 7, 页码 809-824出版社
MARY ANN LIEBERT, INC
DOI: 10.1089/cmb.2018.0008
关键词
Alzheimer's disease; genotype-phenotype association prediction; longitudinal study; temporal structure auto-learning; low-rank model
类别
资金
- National Science Foundation at PITT [IIS 1302675, IIS 1344152, DBI 1356628, IIS 1619308, IIS 1633753]
- National Institutes of Health at PITT [R01 LM011360, U01 AG024904, RC2 AG036535, R01 AG19771, P30 AG10133, UL1 TR001108, R01 AG049371]
- National Science Foundation at IU [IIS 1622526]
With the rapid development of high-throughput genotyping and neuroimaging techniques, imaging genetics has drawn significant attention in the study of complex brain diseases such as Alzheimer's disease (AD). Research on the associations between genotype and phenotype improves the understanding of the genetic basis and biological mechanisms of brain structure and function. AD is a progressive neurodegenerative disease; therefore, the study on the relationship between single nucleotide polymorphism (SNP) and longitudinal variations of neuroimaging phenotype is crucial. Although some machine learning models have recently been proposed to capture longitudinal patterns in genotype-phenotype association studies, most machine-learning models base the learning on fixed structure among longitudinal prediction tasks rather than automatically learning the interrelationships. In response to this challenge, we propose a new automated time structure learning model to automatically reveal the longitudinal genotype-phenotype interactions and exploits such learned structure to enhance the phenotypic predictions. We proposed an efficient optimization algorithm for our model and provided rigorous theoretical convergence proof. We performed experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for longitudinal phenotype prediction, including 3123 SNPs and 2 biomarkers (Voxel-Based Morphometry and FreeSurfer). The empirical results validate that our proposed model is superior to the counterparts. In addition, the best SNPs identified by our model have been replicated in the literature, which justifies our prediction.
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