Browsing School, Graduate by Title "Biases and caveats to implementing genomic medicine in diverse populations"
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Biases and caveats to implementing genomic medicine in diverse populationsGenetic science has traditionally focused on the study of European populations, which has resulted in the under-representation of other ancestral backgrounds across the vast majority of genetic resources. Since data derived from a limited number of ancestral backgrounds are unlikely to represent the biological variation inherent to diverse populations, genetic and genomic methods and models predicated on this Eurocentric data will have questionable accuracy and limited value when applied to diverse patient populations. This can drive scientific and/or clinical disparities, which can then seem opaque and be difficult to resolve. The work outlined in this thesis aims to increase the prevalence of ancestrally informed genetic science by characterizing bias deriving from a lack of ancestry awareness, evaluating ancestral representation in genomic resources, and identifying novel ancestry-informed biological signal. I first characterize bias by evaluating a typical clinical variant prioritization pipeline, and I demonstrate a significant positive correlation between African ancestry proportion and the identified number of clinically evaluable variants. I then more directly explore ancestral representation across translational resources by estimating the genetic ancestry for 1,018 common cancer cell lines. This analysis highlights the marked ancestral underrepresentation that exists among preclinical cancer cell line models, and identifies novel signals of ancestry-specific gene expression and somatic mutation. Finally, I evaluate how de novo mutation rates vary across diverse human populations from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. I find associations with heterozygosity, a reduced mutation rate in the Amish founder population, and near zero estimates of narrow-sense heritability. On the whole, these findings help to quantify the effects of ancestral diversity and under-representation on the application of genomic medicine.