Genetic and non-genetic factors associated with the phenotype of exceptional longevity & normal cognition.
Journal Article (Journal Article)
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 [Formula: see text] 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 [Formula: see text] 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.
Full Text
Duke Authors
Cited Authors
- Han, B; Chen, H; Yao, Y; Liu, X; Nie, C; Min, J; Zeng, Y; Lutz, MW
Published Date
- November 5, 2020
Published In
Volume / Issue
- 10 / 1
Start / End Page
- 19140 -
PubMed ID
- 33154391
Pubmed Central ID
- PMC7645680
Electronic International Standard Serial Number (EISSN)
- 2045-2322
Digital Object Identifier (DOI)
- 10.1038/s41598-020-75446-2
Language
- eng
Conference Location
- England