美国斯坦福大学Jonathan K. Pritchard和Hakhamanesh Mostafavi共同合作，近期取得重要工作进展。他们研究发现了基因表达和复杂性状遗传效应的系统差异。相关研究成果2023年10月19日在线发表于《自然—遗传学》杂志上。

据介绍，复杂性状的全基因组关联研究（GWAS）中的大多数信号都涉及具有假定基因调控作用的非编码遗传变异。然而，目前确定的调控变异，特别是表达数量性状基因座（eQTL），只能解释GWAS信号的一小部分。

研究人员发现，GWAS和顺式eQTL存在系统性的差异：eQTL在转录起始位点附近强烈聚集，而GWAS则没有。GWAS附近的基因富含关键功能注释，受到强烈的选择性约束，在不同组织/细胞类型中具有复杂的调控概况，而eQTL附近的基因缺乏大多数功能注释，表现出宽松的约束，并且具有更简单的调控景观。研究人员描述了一个模型来理解这些观察结果，包括复杂性状的自然选择如何阻碍功能相关eQTL的发现。

总之，这一研究结果表明，GWAS和eQTL的研究系统偏向于不同类型的变体，并支持在下一代eQTL研究的同时使用互补的功能方法。

附：英文原文

Title: Systematic differences in discovery of genetic effects on gene expression and complex traits

Author: Mostafavi, Hakhamanesh, Spence, Jeffrey P., Naqvi, Sahin, Pritchard, Jonathan K.

Issue&Volume: 2023-10-19

Abstract: Most signals in genome-wide association studies (GWAS) of complex traits implicate noncoding genetic variants with putative gene regulatory effects. However, currently identified regulatory variants, notably expression quantitative trait loci (eQTLs), explain only a small fraction of GWAS signals. Here, we show that GWAS and cis-eQTL hits are systematically different: eQTLs cluster strongly near transcription start sites, whereas GWAS hits do not. Genes near GWAS hits are enriched in key functional annotations, are under strong selective constraint and have complex regulatory landscapes across different tissue/cell types, whereas genes near eQTLs are depleted of most functional annotations, show relaxed constraint, and have simpler regulatory landscapes. We describe a model to understand these observations, including how natural selection on complex traits hinders discovery of functionally relevant eQTLs. Our results imply that GWAS and eQTL studies are systematically biased toward different types of variant, and support the use of complementary functional approaches alongside the next generation of eQTL studies.

DOI: 10.1038/s41588-023-01529-1

Source: https://www.nature.com/articles/s41588-023-01529-1