A Simulation-Based Approach to Understanding Genomic Variants in Admixed Populations Using Graph-Based Reference Genomes
Track: Tiny Paper Track
Keywords: Human Admixture Simulation, Admixed Populations, Genomic Variants, Structural Variants (SVs)
TL;DR: This study employs a simulation-based approach to investigate genomic variant detection in admixed populations.
Abstract: Admixed populations represent a significant portion of global genetic diversity, yet their unique genomic architectures are often overlooked in genomic studies. This research aims to identify genomic variants associated with disease risk in admixed populations by employing a simulation-based approach. We will simulate genetic data from two distinct ancestral populations (pop1 and pop2) to create an admixed population (pop3), focusing on a single chromosome to maintain manageable data sets. Specific genetic variants, including copy number variations and complex structural variations, will be artificially introduced to assess their impact on disease risk. Following this, we will construct graph-based references using the simulated genomic data, allowing for a comparative analysis of variant detection capabilities between traditional linear reference genomes and graph-based approaches. The study will also simulate sequencing data to mimic real-world applications, facilitating the evaluation of variant detection across different reference genome types. This research is expected to enhance our understanding of genetic architecture in Admixed populations and improve methodologies for identifying disease risk variants.
Submission Number: 47
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