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DOI 10.1007/s11033-026-12221-y

Медицинская библиотека · Molecular biology reports · 2026

A case study of comprehensive association analysis and risk prediction of amyotrophic lateral sclerosis in a Chinese population.

Xie Xueying, Jiao Xiaoqing, Yang Ke, Zhang Qianqian

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Аннотация

BACKGROUND

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease with significant genetic heterogeneity. While large-scale studies have characterized its genetic architecture in European populations, the genetic basis of ALS in the Chinese population remains under-explored.

METHODS AND

RESULTS

To address this gap, we conducted a comprehensive genetic analysis on a cohort of 40 Chinese individuals (32 ALS patients and 8 controls) using whole genome sequencing. We employed the Phenotype-Covariate Genetic Correlation method to estimate SNP-based heritability on the liability scale and utilized LDAK-KVIK for gene-based association analysis. Our analysis revealed a SNP-based heritability (h2SNP) of approximately 25.1% in this Chinese cohort, with a positive correlation between minor allele frequency and heritability, highlighting the substantial contribution of common variants. Gene-based analysis prioritized candidate risk genes, including MIB1, TMED2, and DOC2B, which implicate ubiquitin-mediated protein degradation and intracellular vesicle trafficking in ALS pathogenesis. In risk prediction models, the BOLT-LMM approach achieved a robust mean Area Under the Curve (AUC) of 0.883.

CONCLUSIONS

This study provides the first comprehensive estimate of SNP-based heritability in a sequenced Chinese ALS cohort and supports the "polygenic background" hypothesis. The identification of candidate risk genes and the preliminary validation of polygenic risk scoring highlight the potential for future genetic stratification in Chinese patients.

Для цитирования: Xie Xueying, Jiao Xiaoqing, Yang Ke, Zhang Qianqian. A case study of comprehensive association analysis and risk prediction of amyotrophic lateral sclerosis in a Chinese population.. Molecular biology reports. 2026. DOI: 10.1007/s11033-026-12221-y.

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