Format

Send to

Choose Destination
Genet Epidemiol. 2019 Sep;43(6):657-674. doi: 10.1002/gepi.22211. Epub 2019 May 18.

Integrated analysis of genomics, longitudinal metabolomics, and Alzheimer's risk factors among 1,111 cohort participants.

Darst BF1,2, Lu Q1,3, Johnson SC1,4,5,6, Engelman CD1,2,4,6.

Author information

1
University of Wisconsin, Madison, Wisconsin.
2
Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
3
Department of Biostatistics & Medical Informatics, Madison, Wisconsin.
4
Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
5
Geriatric Research Education and Clinical Center, William S. Middleton Memorial VA Hospital, Madison, Wisconsin.
6
Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.

Abstract

Although Alzheimer's disease (AD) is highly heritable, genetic variants are known to be associated with AD only explain a small proportion of its heritability. Genetic factors may only convey disease risk in individuals with certain environmental exposures, suggesting that a multiomics approach could reveal underlying mechanisms contributing to complex traits, such as AD. We developed an integrated network to investigate relationships between metabolomics, genomics, and AD risk factors using Wisconsin Registry for Alzheimer's Prevention participants. Analyses included 1,111 non-Hispanic Caucasian participants with whole blood expression for 11,376 genes (imputed from dense genome-wide genotyping), 1,097 fasting plasma metabolites, and 17 AD risk factors. A subset of 155 individuals also had 364 fastings cerebral spinal fluid (CSF) metabolites. After adjusting each of these 12,854 variables for potential confounders, we developed an undirected graphical network, representing all significant pairwise correlations upon adjusting for multiple testing. There were many instances of genes being indirectly linked to AD risk factors through metabolites, suggesting that genes may influence AD risk through particular metabolites. Follow-up analyses suggested that glycine mediates the relationship between carbamoyl-phosphate synthase 1 and measures of cardiovascular and diabetes risk, including body mass index, waist-hip ratio, inflammation, and insulin resistance. Further, 38 CSF metabolites explained more than 60% of the variance of CSF levels of tau, a detrimental protein that accumulates in the brain of AD patients and is necessary for its diagnosis. These results further our understanding of underlying mechanisms contributing to AD risk while demonstrating the utility of generating and integrating multiple omics data types.

KEYWORDS:

Alzheimer's disease; genomics; integrated analysis; metabolomics; multiomics

PMID:
31104335
PMCID:
PMC6687539
[Available on 2020-09-01]
DOI:
10.1002/gepi.22211

Supplemental Content

Loading ...
Support Center