Qi Ting, Song Liyang, Guo Yazhou, Chen Chang, Yang Jian
Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
Trends Genet. 2024 Aug;40(8):642-667. doi: 10.1016/j.tig.2024.04.008. Epub 2024 May 11.
Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.
全基因组关联研究（GWAS）已经确定了许多与人类性状和疾病相关的基因座。然而，确定因果基因仍然是一项挑战，这阻碍了将GWAS研究结果转化为生物学见解和医学应用。在这篇综述中，我们深入概述了用于从GWAS基因座中对基因进行优先级排序的方法和技术，包括基于基因的关联测试、GWAS与分子定量性状基因座（xQTL）数据的综合分析、通过增强子-基因连接图谱将GWAS变异与靶基因联系起来，以及基于网络的优先级排序。我们还概述了生成上下文相关xQTL数据的策略及其在基因优先级排序中的应用。我们进一步强调了基因优先级排序在药物再利用中的潜力。最后，我们讨论了该领域未来的挑战和机遇。
