Publication Date
5-13-2023
Journal
Nature Communications
DOI
10.1038/s41467-023-38374-z
PMID
37179358
PMCID
PMC10183026
PubMedCentral® Posted Date
5-13-2023
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
Female, Humans, Male, Alzheimer Disease, Sex Factors, Genetics, Neuroscience
Abstract
The incidence of Alzheimer's Disease in females is almost double that of males. To search for sex-specific gene associations, we build a machine learning approach focused on functionally impactful coding variants. This method can detect differences between sequenced cases and controls in small cohorts. In the Alzheimer's Disease Sequencing Project with mixed sexes, this approach identified genes enriched for immune response pathways. After sex-separation, genes become specifically enriched for stress-response pathways in male and cell-cycle pathways in female. These genes improve disease risk prediction in silico and modulate Drosophila neurodegeneration in vivo. Thus, a general approach for machine learning on functionally impactful variants can uncover sex-specific candidates towards diagnostic biomarkers and therapeutic targets.
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Biological Phenomena, Cell Phenomena, and Immunity Commons, Biomedical Informatics Commons, Genetics and Genomics Commons, Medical Genetics Commons, Medical Molecular Biology Commons, Medical Specialties Commons, Neurosciences Commons