Genotyping TOMM40'523 poly-T polymorphisms using whole-genome sequencing.

IF 3.6 Q2 GENETICS & HEREDITY

HGG Advances Pub Date : 2025-10-09 Epub Date: 2025-08-07 DOI:10.1016/j.xhgg.2025.100488

Ricardo A Vialle, Lei Yu, Yan Li, Roberto T Raittz, Jose M Farfel, Philip L De Jager, Julie A Schneider, Lisa L Barnes, Shinya Tasaki, David A Bennett

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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 (R² = 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.

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利用全基因组测序对TOMM40'523多态性进行基因分型

TOMM40'523多t重复多态性（rs10524523）与认知能力下降和阿尔茨海默病（AD）进展有关。传统上，处理全基因组测序（WGS）数据的挑战需要额外的PCR和靶向测序分析来对这些多态性进行基因分型。我们介绍了一个计算管道，该管道使用XGBoost在集成机器学习模型中集成了多个短串联重复（STR）检测工具。使用来自四个队列研究的1202名参与者的样本，我们将我们的方法与基于pcr的测量方法进行了基准比较。我们的集成模型优于单个STR工具，提高了重复长度估计的准确性（R2 = 0.92），与pcr衍生的基因型相比，准确率达到93.2%。此外，我们通过复制先前报道的TOMM40'523变异与认知能力下降之间的关联，验证了wgs衍生的基因型。我们的计算型基因分型工具是一种可扩展且可靠的替代pcr分析方法，可以在WGS数据的研究中更广泛地研究TOMM40变异。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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