利用具有基因特征的进化模型对基因约束进行贝叶斯估算，这一成果由美国斯坦福大学Jonathan K. Pritchard、Tony Zeng、Jeffrey P. Spence研究团队取得。该研究于2024年7月8日发表于国际学术期刊《自然-遗传学》杂志。

研究人员研发了一个将群体遗传学模型与基因特征相结合的机器学习方法，从而能够准确推断出可解释的约束指标Shet。该估算结果优于现有的对细胞本质、人类疾病和影响其他表型基因优先排序的方法，尤其是对短基因而言。该选择性约束的估计值在表征与人类疾病相关基因方面具有广泛实用性。最后，该算法（GeneBayes）提供了一个灵活的平台，可以改进许多基因水平的属性估算，如罕见变异负担或基因表达差异。

据了解，基因选择性约束的测量方法已得到广泛应用，包括罕见编码变异的临床解读、疾病基因的发现和基因组进化研究。然而，广泛使用的衡量标准在检测最短~25%基因限制方面存在不足，可能导致重要致病突变被忽视。

附：英文原文

Title: Bayesian estimation of gene constraint from an evolutionary model with gene features

Author: Zeng, Tony, Spence, Jeffrey P., Mostafavi, Hakhamanesh, Pritchard, Jonathan K.

Issue&Volume: 2024-07-08

Abstract: Measures of selective constraint on genes have been used for many applications, including clinical interpretation of rare coding variants, disease gene discovery and studies of genome evolution. However, widely used metrics are severely underpowered at detecting constraints for the shortest ~25% of genes, potentially causing important pathogenic mutations to be overlooked. Here we developed a framework combining a population genetics model with machine learning on gene features to enable accurate inference of an interpretable constraint metric, shet. Our estimates outperform existing metrics for prioritizing genes important for cell essentiality, human disease and other phenotypes, especially for short genes. Our estimates of selective constraint should have wide utility for characterizing genes relevant to human disease. Finally, our inference framework, GeneBayes, provides a flexible platform that can improve the estimation of many gene-level properties, such as rare variant burden or gene expression differences.

DOI: 10.1038/s41588-024-01820-9

Source: https://www.nature.com/articles/s41588-024-01820-9