Quantifying Effects of Partial Genetic Backgrounds to Decode Genetic Drivers of Clinical Phenotypes

Understanding partial genetic backgrounds illuminates the genetic architecture of complex traits and diseases, revealing how diverse genetic backgrounds contribute to phenotypic diversity. With this approach we could advance personalized medicine by identifying population-specific variants affecting drug metabolism, tailoring medical treatments to individual genetic profiles. Additionally, it offers evolutionary insights into human history, shedding light on past migrations and the spread of genetic traits. This research leverages cutting-edge statistical genetics techniques and novel machine learning approaches to efficiently analyze extensive population genomic datasets, distilling complex admixture signals into meaningful genetic markers. The study introduces Admix-AI, an innovative convolutional neural network-based tool for accurate classification of admixed genetic backgrounds. This computational advancement enables more precise identification of ancestry-specific genetic variants associated with disease risk and treatment response. The application of Admix-AI to self-reported datasets reveals critical insights into the inaccuracies of labels across genetic backgrounds. Furthermore, this work is one of the first to investigate the influence of Neanderthal-derived genetic variations on autism spectrum disorders, uncovering significant enrichment of rare variants in autistic individuals across multiple genetic backgrounds. These finding sheds light on the evolutionary origins of neurodevelopmental disorders and the ongoing impact of ancient admixture events on human health.