德国癌症研究中心Oliver Stegle等研究人员合作发现，使用深度集合网络整合变异注释可提升稀有罕见关联测试。相关论文于2024年9月25日在线发表于国际学术期刊《自然—遗传学》。

研究人员提出深度罕见变异关联测试（DeepRVAT），这是一个基于集合神经网络的模型，从罕见变异注释和表型中学习无关特征的基因损伤评分，实现基因发现和表型预测。

在使用来自英国生物银行的全外显子测序数据，对34个定量特征和63个二元特征进行分析时，研究人员发现，DeepRVAT在基因发现和高遗传风险个体检测方面取得了显著提升。最后，研究人员展示了DeepRVAT如何在生物样本库规模上，实现校准和计算高效的罕见变异测试，进而促进人类疾病特征遗传风险因素的发现。

据悉，罕见遗传变异可以对表型产生强大影响，但由于等位基因携带者数量有限和多重检验负担，在遗传分析中考虑稀有变异在统计上具有挑战性。虽然丰富的变异注释有望实现高效的罕见变异关联测试，但缺乏以数据驱动方式整合变异注释的方法。

附：英文原文

Title: Integration of variant annotations using deep set networks boosts rare variant association testing

Author: Clarke, Brian, Holtkamp, Eva, ztrk, Hakime, Mck, Marcel, Wahlberg, Magnus, Meyer, Kayla, Munzlinger, Felix, Brechtmann, Felix, Hlzlwimmer, Florian R., Lindner, Jonas, Chen, Zhifen, Gagneur, Julien, Stegle, Oliver

Issue&Volume: 2024-09-25

Abstract: Rare genetic variants can have strong effects on phenotypes, yet accounting for rare variants in genetic analyses is statistically challenging due to the limited number of allele carriers and the burden of multiple testing. While rich variant annotations promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here we propose deep rare variant association testing (DeepRVAT), a model based on set neural networks that learns a trait-agnostic gene impairment score from rare variant annotations and phenotypes, enabling both gene discovery and trait prediction. On 34 quantitative and 63 binary traits, using whole-exome-sequencing data from UK Biobank, we find that DeepRVAT yields substantial gains in gene discoveries and improved detection of individuals at high genetic risk. Finally, we demonstrate how DeepRVAT enables calibrated and computationally efficient rare variant tests at biobank scale, aiding the discovery of genetic risk factors for human disease traits.

DOI: 10.1038/s41588-024-01919-z

Source: https://www.nature.com/articles/s41588-024-01919-z