Citations 1. Vidal, M., Cusick, M., & Barabasi, A.L., 2011. Interactome Networks and Human Disease. Cell. 986-998. 2. Sahni et al 2015. Macromolecular Interaction Perturbation Human Disorders. Cell. 161:3, 647-660. 3. Fragoza, R., et al 2019. Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations. Nat Comm. 4.Albers & McVean 2020. Dating genomic variants and shared ancestry in population-scale sequencing data. PLoS Biology. 18(1):e30000586. Conclusion Takeaways ●Taken together, the younger age and lower chance of lying in long ROH suggest that PPI disruptive variants are indeed deleterious. ●And simulations show that recent demography can push recessive deleterious mutations to higher frequencies as observed in PPI disruptive variants, although the vast majority of common polymorphisms are neutral. Population Genetic Attributes of Variants That Disrupt Protein-Protein Interactions Mitchell G. Lokey1,2, Robert Fragoza1, Philipp W. Messer2, Haiyuan Yu1,2, Andrew G. Clark1,2 1Department of Molecular Biology & Genetics, Cornell University, Ithaca, New York 14853, USA 2Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA Email: mgl77@cornell.edu @mitch_lokey Background • We seek improved methods to discover genotype to phenotype relationships. One approach to this problem focuses on the network of macromolecular interactions known as the interactome. Of great importance for cellular function, and our focus here, are networks of Protein-Protein Interactions (PPIs).1 • Yeast-2-Hybrid (Y2H) and similar assays have allowed mapping of the human protein interaction network by identifying large numbers of binary PPIs.1 • Studies of the effects of disease-associated variants on the known network of PPIs have shown that ~1/2 of tested Mendelian disease- associated variants disrupt PPIs.2 • Until recently, no work had looked at the extent to which naturally segregating variants in the human population affect PPIs. Fragoza et al (2019) sampled variants from the ExAC database and tested PPIs of >2,000 non-synonymous SNPs (nsSNPs) with the Y2H assay.3 • Fragoza et al (2019) showed that 10.5% of tested variants disrupted PPIs, a larger estimate of functional variation than previously reported. Surprisingly they also found that disruptive variants often segregate at relatively high allele frequencies.3 • As the links between cellular phenotypes, such as PPIs, and impacts on fitness are not straightforward we seek to illuminate population genetic attributes of PPI disruptive variants by leveraging large human databases and simulations. Future Directions ●Investigating the evolutionary signatures of PPI disruptive variants may help us to better identify which variants within a protein interaction network are important for disease. ●The results presented here should be tested against a control set of variants with similar allele frequencies. Acknowledgements ●This work is supported by NIH grant R01 GM125639 to H. Yu and A.G. Clark ●Clark Lab ●Messer Lab ●Yu Lab ●Sarah Kunz Naturally Segregating Variants & PPIs • Fragoza et al investigated fitness impacts of PPI variants using PolyPhen-2 a machine learning tool that probabilistically classifies the deleteriousness of missense mutations. PolyPhen-2 scores show that PPI disruptive variants are more deleterious than non-disruptive and even more so for variants that disrupt all of their PPIs. • Theory tells us that deleterious variants such as disease-associated variants, and likely PPI disruptive variants, will be kept at low allele frequencies due to purging selection. However, Fragoza et al found that although ~20% of very rare variants disrupt PPIs, ~9.6% of common variants (allele frequency > 10%) also disrupt PPIs.3 • To better understand the evolutionary history and impact of PPI disruptive variants we first looked at the ages of our disruptive and non-disruptive variants using Genealogical Estimati