Abstract
The TOMM40'523 poly-T repeat polymorphism (rs10524523) has been associated with cognitive decline and Alzheimer's disease (AD) progression. Challenges in processing whole-genome sequencing (WGS) data traditionally require additional PCR and targeted sequencing assays to genotype these polymorphisms. We introduce a computational pipeline that integrates multiple short tandem repeat (STR) detection tools in an ensemble machine learning model using XGBoost. Using a sample of 1,202 participants from 4 cohort studies, we benchmarked our method against PCR-based measures. Our ensemble model outperformed individual STR tools, improving repeat length estimation accuracy (R(2) = 0.92) and achieving an accuracy rate of 93.2% compared with PCR-derived genotypes. Additionally, we validated our WGS-derived genotypes by replicating previously reported associations between TOMM40'523 variants and cognitive decline. Our computational genotyping tool is a scalable and reliable alternative to PCR-based assays, enabling broader investigations of TOMM40 variation in studies with WGS data.