1. Functional variants identify sex-specific genes and pathways in Alzheimer’s Disease
- Author
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Thomas Bourquard, Kwanghyuk Lee, Ismael Al-Ramahi, Minh Pham, Dillon Shapiro, Yashwanth Lagisetty, Shirin Soleimani, Samantha Mota, Kevin Wilhelm, Maryam Samieinasab, Young Won Kim, Eunna Huh, Jennifer Asmussen, Panagiotis Katsonis, Juan Botas, and Olivier Lichtarge
- Subjects
Science - Abstract
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.
- Published
- 2023
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