I was born in Nigeria, but I have called Georgia home since the age of three. I earned a B.S. in Neuroscience with a pre-med concentration, and upon graduation, I already had research experiences within computational neuroscience and bioinformatics under my belt. I decided to join the OGR program, because I wanted to develop my skills further in translational bioinformatics and learn more about the field of genomics. Beyond research, I believed the professional development offered from a top institution like WashU would be invaluable for preparing me for graduate school. I can say that these hopes were fully met, and I appreciate MGI’s goal to encourage collaboration. It aligns well with my interest in developing extensive professional networks in my future career as a physician-scientist.
Neurodevelopment disorders (NDDs), such as autism spectrum disorder and intellectual disabilities, have strong genetics components. It is alsowidely known that de novo variants (DNVs) are in excess within these conditions. Most of the emphasis thus far has been on likely gene-disruptive DNVs because they are deleterious and often lead to more severe phenotypes. Single nucleotide variants, such as missense DNVs that lead to an amino acid substitution, have not been as widely studied due to them being harder to interpret. Nonetheless, there have been efforts made to look at clusters of rare missense DNVs in high-impact risk genes that occur in domains, because these variants are more likely to have influence on protein activity than those occurring outside of domains. However, the assumption that rare missense DNVs of large effect will only occur in protein domains is potentially problematic, because we might miss out on functionally important regions that occur outside of domains. Therefore, my research topic is focused on using the Clustering by Mutation Position (CLUMP) algorithm to identify clusters of missense DNVs beyond a domain approach. The algorithm makes no a prioriassumptions about the importance of mutation positions in the context of protein domains or the number of clusters. After generating CLUMP results, we can visualize the clusters and compare them to previously mapped protein domains and regions to determine the functional relevance of significantly clustered variants in the context of NDDs.