期刊
SCIENTIFIC REPORTS
卷 10, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41598-020-75446-2
关键词
-
资金
- National Institutes of Health/National Institute of Aging (NIH/NIA) P01 Grant [AG031719]
- NIA [P30AG10161, R01AG15819, R01AG17917, U01AG32984, U01AG46152]
- Illinois Department of Public Health (ROSMAP)
- Translational Genomics Research Institute
In this study, we split 2156 individuals from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) data into two groups, establishing a phenotype of exceptional longevity & normal cognition versus cognitive impairment. We conducted a genome-wide association study (GWAS) to identify significant genetic variants and biological pathways that are associated with cognitive impairment and used these results to construct polygenic risk scores. We elucidated the important and robust factors, both genetic and non-genetic, in predicting the phenotype, using several machine learning models. The GWAS identified 28 significant SNPs at p-value < 3 x 10(-5) significance level and we pinpointed four genes, ESR1, PHB, RYR3, GRIK2, that are associated with the phenotype though immunological systems, brain function, metabolic pathways, inflammation and diet in the CLHLS cohort. Using both genetic and non-genetic factors, four machine learning models have close prediction results for the phenotype measured in Area Under the Curve: random forest (0.782), XGBoost (0.781), support vector machine with linear kernel (0.780), and l(2) penalized logistic regression (0.780). The top four important and congruent features in predicting the phenotype identified by these four models are: polygenic risk score, sex, age, and education.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据