Functional variants identify sex-specific genes and pathways in Alzheimer’s Disease

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 ()
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Thomas Bourquard: Baylor College of Medicine
Kwanghyuk Lee: Baylor College of Medicine
Ismael Al-Ramahi: Baylor College of Medicine
Minh Pham: Baylor College of Medicine
Dillon Shapiro: Baylor College of Medicine
Yashwanth Lagisetty: Baylor College of Medicine
Shirin Soleimani: Baylor College of Medicine
Samantha Mota: Baylor College of Medicine
Kevin Wilhelm: Baylor College of Medicine
Maryam Samieinasab: Baylor College of Medicine
Young Won Kim: Baylor College of Medicine
Eunna Huh: Baylor College of Medicine
Jennifer Asmussen: Baylor College of Medicine
Panagiotis Katsonis: Baylor College of Medicine
Juan Botas: Baylor College of Medicine
Olivier Lichtarge: Baylor College of Medicine

Nature Communications, 2023, vol. 14, issue 1, 1-15

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.

Date: 2023
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DOI: 10.1038/s41467-023-38374-z

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