美国加州大学洛杉矶分校Sriram Sankararaman等研究人员合作发现，基于深度学习的人群规模生物库数据表型估算可提高基因发现率。相关论文于2023年11月20日在线发表在《自然—遗传学》杂志上。

研究人员提出的AutoComplete是一种基于深度学习的估算方法，用于估算或“填补”群体规模生物库数据集中的缺失表型。与现有方法相比，AutoComplete在应用于英国生物库约30万名个体的表型测量集合时，大大提高了估算的准确性。在三个有显著缺失的性状上，研究人员发现AutoComplete所得到的估算表型在遗传学上与最初观察到的表型相似，同时有效样本量平均增加了约两倍。此外，对得到的推算表型进行全基因组关联分析后，相关基因位点的数量大幅增加。这些研究结果表明，基于深度学习的表型估算可以提高现有生物库数据集的遗传发现能力。

研究人员表示，生物库收集了许多个体的深度表型和基因组数据，已成为人类遗传学的重要资源。然而，生物库中的表型往往在许多个体中缺失，这限制了它们的效用。

附：英文原文

Title: Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries

Author: An, Ulzee, Pazokitoroudi, Ali, Alvarez, Marcus, Huang, Lianyun, Bacanu, Silviu, Schork, Andrew J., Kendler, Kenneth, Pajukanta, Pivi, Flint, Jonathan, Zaitlen, Noah, Cai, Na, Dahl, Andy, Sankararaman, Sriram

Issue&Volume: 2023-11-20

Abstract: Biobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplete, a deep learning-based imputation method to impute or ‘fill-in’ missing phenotypes in population-scale biobank datasets. When applied to collections of phenotypes measured across ~300,000 individuals from the UK Biobank, AutoComplete substantially improved imputation accuracy over existing methods. On three traits with notable amounts of missingness, we show that AutoComplete yields imputed phenotypes that are genetically similar to the originally observed phenotypes while increasing the effective sample size by about twofold on average. Further, genome-wide association analyses on the resulting imputed phenotypes led to a substantial increase in the number of associated loci. Our results demonstrate the utility of deep learning-based phenotype imputation to increase power for genetic discoveries in existing biobank datasets.

DOI: 10.1038/s41588-023-01558-w

Source: https://www.nature.com/articles/s41588-023-01558-w