Sriram V, MA - University of Pennsylvania Woerner J - University of Pennsylvania Ahn Y, PhD - Indiana University Bloomington Kim D, PhD - University of Pennsylvania
Poster # 15
Disease multimorbidities, occurring as a result of biologically related associations across phenotypes, can lead to increased risk of severe patient health burdens. Given that many individual diseases exhibit sex-specific differences in their genetic underpinnings, we aimed to determine if cross-phenotype associations are also influenced by such genotype-by-sex (GxS) interactions. Through the comparison of sex-stratified disease-disease networks (DDNs) - graphs where nodes represent diseases and edges represent relationships between diseases - we investigate differences across the sexes in patterns of genetic polygenicity and pleiotropy between diseases. We built a male- and a female-specific DDN for 103 disease phenotypes using sex-stratified phenome-wide association study summary data from the UK Biobank. Comparing the two graphs reveals that the diseasomes of males and females behave similarly to one another in terms of topology and key central diseases (e.g. hypertensive, chronic respiratory, and thyroid-based disorders). Some phenotypes, however, are found to exhibit sex-specific influence in cross-phenotype associations. For instance, autoimmune and inflammatory disorders are centrally involved only in the female-specific DDN, while cardiometabolic diseases and skin cancer are more prominent only in the male-specific DDN. A comparison of edges present in the two graphs indicates similar patterns of polygenicity across the sexes relative to a random model of genetic associations between diseases. Deviations in embedding distances and clustering patterns across the networks indicate a broader spread of genetic influence on multimorbidity risk for females compared to males. An evaluation of the pleiotropic contributions for two sexually dimorphic single-nucleotide polymorphisms related to disorders of the thyroid provide evidence of differential genetic architecture across the sexes that influence associations with the phenotype. In sum, our analysis affirms the presence of GxS interactions in cross-phenotype associations, emphasizing the continued need for investigation of the role of sex in disease onset and its importance in biomedical discovery and precision medicine research.
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