替代合成提高了对人生物库中部分缺失表型进行全基因组关联研究（GWAS）的能力，这一成果由加拿大多伦多大学Gronsbell  Jessica研究组经过不懈努力而取得。2024年6月13日出版的《自然-遗传学》发表了这项成果。

研究人员开发了一种新方法-替代合成（SynSurr）分析，它能使对归因表型的全基因组关联研究不受归因错误的影响。SynSurr不是替换缺失值，而是联合分析原始性状和归因性状。研究研究表明，SynSurr估算出的遗传效应与标准GWAS估算出的遗传效应相同，并能提高与估算质量成正比的作用力。

SynSurr需要一个常见的随机缺失假设，但不需要正确的模型规范，从而放宽了对现有归因方法的要求。研究人员通过大量的模拟和缺失分析验证了SynSurr，并将其应用于英国生物库中双能X射线吸收测量特征性GWAS。

研究人员表示，在人生物库中，对某些性状的不完全监测限制了基因发现的能力。机器学习越来越多地应用于对现有数据中缺失值的估算。然而，对估算性状进行全基因组关联研究可能会引入虚假关联，识别出与原始性状无关的遗传变异。

附：英文原文

Title: Synthetic surrogates improve power for genome-wide association studies of partially missing phenotypes in population biobanks

Author: McCaw, Zachary R., Gao, Jianhui, Lin, Xihong, Gronsbell, Jessica

Issue&Volume: 2024-06-13

Abstract: Within population biobanks, incomplete measurement of certain traits limits the power for genetic discovery. Machine learning is increasingly used to impute the missing values from the available data. However, performing genome-wide association studies (GWAS) on imputed traits can introduce spurious associations, identifying genetic variants that are not associated with the original trait. Here we introduce a new method, synthetic surrogate (SynSurr) analysis, which makes GWAS on imputed phenotypes robust to imputation errors. Rather than replacing missing values, SynSurr jointly analyzes the original and imputed traits. We show that SynSurr estimates the same genetic effect as standard GWAS and improves power in proportion to the quality of the imputations. SynSurr requires a commonly made missing-at-random assumption but relaxes the requirements of existing imputation methods by not requiring correct model specification. We present extensive simulations and ablation analyses to validate SynSurr and apply it to empower the GWAS of dual-energy X-ray absorptiometry traits within the UK Biobank.

DOI: 10.1038/s41588-024-01793-9

Source: https://www.nature.com/articles/s41588-024-01793-9