小柯机器人

英国威康基因组园区威康桑格研究所Maya Ghoussaini团队经过不懈努力而取得最新进展。他们开发了一种在所有已发表的人类全基因组关联研究 (GWAS) 性状相关位点上系统地优先考虑因果变异和基因的开放方法。相关论文于2021年10月28日发表在《自然—遗传学》杂志上。

他们提供了一个开放资源，可在 133,441 个已发表的人类 GWAS 基因座中提供系统的精细定位和基因优先排序。他们将遗传学（GWAS Catalog 和 UK Biobank）与转录组学、蛋白质组学和表观基因组数据相结合，包括跨越 92 种细胞类型和组织的系统疾病-疾病和疾病-分子性状共定位结果。他们确定了 729 个精确映射到单编码因果变异并与单个基因共定位的基因座。他们使用精细映射的遗传学和功能基因组学数据以及 445 个黄金标准精选的 GWAS 基因座训练了一个机器学习模型，以区分因果基因和邻近基因，优于基于距离的朴素模型。

他们的优先基因针对已知的批准药物靶点进行了富集（比值比 = 8.1，95% 置信区间 = 5.7、11.5）。这些结果可通过门户网站 (http://genetics.opentargets.org) 公开获得，使用户能够轻松地对疾病相关位点的基因进行优先排序，并评估它们作为药物靶点的潜力。

研究人员表示，GWAS已经确定了许多与复杂性状相关的变异，但确定因果基因是一项重大挑战。

附：英文原文

Title: An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci

Author: Mountjoy, Edward, Schmidt, Ellen M., Carmona, Miguel, Schwartzentruber, Jeremy, Peat, Gareth, Miranda, Alfredo, Fumis, Luca, Hayhurst, James, Buniello, Annalisa, Karim, Mohd Anisul, Wright, Daniel, Hercules, Andrew, Papa, Eliseo, Fauman, Eric B., Barrett, Jeffrey C., Todd, John A., Ochoa, David, Dunham, Ian, Ghoussaini, Maya

Issue&Volume: 2021-10-28

Abstract: Genome-wide association studies (GWASs) have identified many variants associated with complex traits, but identifying the causal gene(s) is a major challenge. In the present study, we present an open resource that provides systematic fine mapping and gene prioritization across 133,441 published human GWAS loci. We integrate genetics (GWAS Catalog and UK Biobank) with transcriptomic, proteomic and epigenomic data, including systematic disease–disease and disease–molecular trait colocalization results across 92 cell types and tissues. We identify 729 loci fine mapped to a single-coding causal variant and colocalized with a single gene. We trained a machine-learning model using the fine-mapped genetics and functional genomics data and 445 gold-standard curated GWAS loci to distinguish causal genes from neighboring genes, outperforming a naive distance-based model. Our prioritized genes were enriched for known approved drug targets (odds ratio=8.1, 95% confidence interval=5.7, 11.5). These results are publicly available through a web portal (http://genetics.opentargets.org), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets.

DOI: 10.1038/s41588-021-00945-5

Source: https://www.nature.com/articles/s41588-021-00945-5

Nature Genetics：《自然—遗传学》，创刊于1992年。隶属于施普林格·自然出版集团，最新IF：41.307
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