Department of Informatics and Predictive Sciences, Bristol Myers Squibb, Princeton, NJ 08540, USA.
Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Am J Hum Genet. 2021 Apr 1;108(4):549-563. doi: 10.1016/j.ajhg.2021.03.009.
Genome-wide association studies (GWASs) have enabled unbiased identification of genetic loci contributing to common complex diseases. Because GWAS loci often harbor many variants and genes, it remains a major challenge to move from GWASs' statistical associations to the identification of causal variants and genes that underlie these association signals. Researchers have applied many statistical and functional fine-mapping strategies to prioritize genetic variants and genes as potential candidates. There is no gold standard in fine-mapping approaches, but consistent results across different approaches can improve confidence in the fine-mapping findings. Here, we combined text mining with a systematic review and formed a catalog of 85 studies with evidence of fine mapping for at least one autoimmune GWAS locus. Across all fine-mapping studies, we compiled 230 GWAS loci with allelic heterogeneity estimates and predictions of causal variants and trait-relevant genes. These 230 loci included 455 combinations of locus-by-disease association signals with 15 autoimmune diseases. Using these estimates, we assessed the probability of mediating disease risk associations across genes in GWAS loci and identified robust signals of causal disease biology. We predict that this comprehensive catalog of GWAS fine-mapping efforts in autoimmune disease will greatly help distill the plethora of information in the field and inform therapeutic strategies.
全基因组关联研究 (GWAS) 使我们能够在不受影响的情况下鉴定出与常见复杂疾病相关的遗传基因座。由于 GWAS 基因座通常包含许多变体和基因,因此从 GWAS 的统计关联转移到鉴定这些关联信号背后的因果变体和基因仍然是一个主要挑战。研究人员已经应用了许多统计和功能精细映射策略来优先考虑遗传变体和基因作为潜在的候选者。精细映射方法没有黄金标准,但不同方法的一致结果可以提高对精细映射发现的信心。在这里,我们结合文本挖掘和系统评价,形成了一个至少有一个自身免疫性 GWAS 基因座精细映射证据的 85 项研究目录。在所有精细映射研究中,我们汇编了 230 个具有等位基因异质性估计和因果变体及与性状相关基因预测的 GWAS 基因座。这 230 个基因座包括 455 个与 15 种自身免疫性疾病相关的基因座 - 疾病关联信号组合。使用这些估计值,我们评估了 GWAS 基因座中基因介导疾病风险关联的概率,并确定了因果疾病生物学的稳健信号。我们预测,这个自身免疫性疾病 GWAS 精细映射工作的综合目录将极大地帮助我们提取该领域的大量信息,并为治疗策略提供信息。