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Integrated analysis of genomics, longitudinal metabolomics, and Alzheimer’s risk factors among 1,111 individuals


Dr. Burcu Darst






Leadership Auditorium, 2501 Student Center

Although Alzheimer’s disease (AD) is highly heritable, genetic variants known to be associated with AD only explain a small proportion of its heritability. It is possible that some genetic factors only convey disease risk in individuals with certain environmental exposures, suggesting that a multi-omics approach could reveal the underlying mechanisms contributing to complex traits, such as AD. We investigated such complex inter-omics relationships by developing an integrated network using genomics, longitudinal metabolomics, and longitudinal AD risk factors from participants in the Wisconsin Registry for Alzheimer’s Prevention. This network revealed many instances of genes being indirectly linked to AD risk factors through metabolites, suggesting that genes may influence AD risk through particular metabolites. Some such relationships were further investigated in follow-up analyses that corroborated their biological relevance. These results further our understanding of underlying mechanisms contributing to AD risk while demonstrating the utility of generating and integrating multiple omics data types.