Yeshiva College (YC)

Permanent URI for this communityhttps://hdl.handle.net/20.500.12202/8

Browse

Browsing Yeshiva College (YC) by Subject "1000 Genomes Project"

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Thumbnail Image
    ItemEmbargo
    Investigating population differences and the utility of pathogenicity predictors in primary immunodeficiency diseases
    (Yeshiva University, 2024-08) Carroll, Eitan S.; Viswanathan, Raji; Asgari, Samira
    Abstract Inborn errors of immunity (IEIs) are a heterogeneous group of genetic disorders that compromise immune function. Despite advances in genetic sequencing technologies, the full extent of the genetic architecture of IEIs remains underexplored, particularly across diverse populations. This thesis aims to improve our understanding of the genetic landscape of IEI-associated genes by analyzing genetic variants within these genes using whole-genome sequencing data from the Human Genome Diversity Project and 1000 Genomes Project, encompassing individuals from nine distinct genetic ancestral backgrounds. We identified and annotated 1,688,213 variants across 450 IEI genes and conducted a comprehensive analysis of these variants, focusing on population-specific differences in variant frequency and the application of pathogenicity predictors. Population-specific analyses demonstrated that the majority of genetic variation in these genes are population-specific, which may contribute to unique susceptibilities to IEIs in diverse populations. Then, we employed seven variant annotation tools, including REVEL, CADD, SIFT, PolyPhen-2, PrimateAI, BayesDel, and AlphaMissense, to predict the potential impact of these variants and evaluate the concordance among these tools. Our findings reveal significant discordance among variant effect prediction tools, highlighting the challenges in predicting genetic variants effects. This problem was particularly significant for variants classified as variants of uncertain significance (VUS) based on clinical genetic testing criteria (i.e. ClinVar). Our results underscore the complexity of IEI genetics, the need for improved variant effect prediction and pathogenicity strategies, and highlight the importance of diverse population sampling in genomic research to enhance our understanding of IEIs, improving diagnosis and treatment across all populations.