Over the past 15 years, genome-wide association studies (GWASs) have enabled the systematic identification of genetic loci associated with traits and diseases. However, due to resolution issues and methodological limitations, the true causal variants and genes associated with traits remain difficult to identify. In this post-GWAS era, many biological and computational fine-mapping approaches now aim to solve these issues. Here, we review fine-mapping and gene prioritization approaches that, when combined, will improve the understanding of the underlying mechanisms of complex traits and diseases. Fine-mapping of genetic variants has become increasingly sophisticated: initially, variants were simply overlapped with functional elements, but now the impact of variants on regulatory activity and direct variant-gene 3D interactions can be identified. Moreover, gene manipulation by CRISPR/Cas9, the identification of expression quantitative trait loci and the use of co-expression networks have all increased our understanding of the genes and pathways affected by GWAS loci. However, despite this progress, limitations including the lack of cell-type- and disease-specific data and the ever-increasing complexity of polygenic models of traits pose serious challenges. Indeed, the combination of fine-mapping and gene prioritization by statistical, functional and population-based strategies will be necessary to truly understand how GWAS loci contribute to complex traits and diseases.

中文翻译：

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后全基因组关联时代精细作图和基因优先级的实用观点。

在过去的 15 年里，全基因组关联研究 (GWAS) 已经能够系统地识别与性状和疾病相关的遗传位点。然而，由于分辨率问题和方法学限制，真正的因果变异和与性状相关的基因仍然难以识别。在后 GWAS 时代，许多生物和计算精细绘图方法现在旨在解决这些问题。在这里，我们回顾了精细绘图和基因优先排序方法，将它们结合起来将提高对复杂性状和疾病的潜在机制的理解。遗传变异的精细绘图变得越来越复杂：最初，变异只是与功能元件重叠，但现在可以识别变异对调控活动和直接变异-基因 3D 相互作用的影响。此外，CRISPR/Cas9的基因操作、表达数量性状位点的鉴定以及共表达网络的使用都增加了我们对受GWAS位点影响的基因和通路的理解。然而，尽管取得了这些进展，但包括缺乏细胞类型和疾病特异性数据以及性状多基因模型不断增加的复杂性等限制带来了严峻的挑战。事实上，要真正了解 GWAS 基因座如何影响复杂的性状和疾病，必须将统计、功能和基于群体的策略的精细绘图和基因优先排序相结合。