Associations between genetic loci and increased susceptibility to autoimmune disease have been well characterized, however, translating this knowledge into mechanistic insight and patient benefit remains a challenge. While improvements in the precision, completeness and accuracy of our genetic understanding of autoimmune diseases will undoubtedly be helpful, meeting this challenge will require two interlinked problems to be addressed: first which of the highly correlated variants at an individual locus is responsible for increased disease risk, and second what are the downstream effects of this variant. Given that the majority of loci are thought to affect non-coding regulatory elements, the second question is often reframed as what are the target gene(s) and pathways affected by causal variants. Currently, these questions are being addressed using a wide variety of novel techniques and datasets. In many cases, these approaches are complementary and it is likely that the most accurate picture will be generated by consolidating information relating to transcription, regulatory activity, chromatin accessibility, chromatin conformation and readouts from functional experiments, such as genome editing and reporter assays. It is clear that it will be necessary to gather this information from disease relevant cell types and conditions and that by doing so our understanding of disease etiology will be improved. This review is focused on the field of autoimmune disease functional genomics with a particular focus on the most exciting and significant research to be published within the last couple of years.

中文翻译：

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自身免疫性疾病的功能基因组学。

遗传位点与自身免疫性疾病易感性增加之间的关联已得到很好的表征，然而，将这些知识转化为机制见解和患者利益仍然是一个挑战。虽然提高我们对自身免疫性疾病的遗传理解的精确度、完整性和准确性无疑会有所帮助，但应对这一挑战需要解决两个相互关联的问题：首先，单个位点的高度相关变异中的哪一个会导致疾病风险增加，第二这个变体的下游影响是什么。鉴于大多数基因座被认为影响非编码调控元件，第二个问题通常被重新定义为受因果变异影响的目标基因和途径是什么。目前，这些问题正在通过各种新技术和数据集得到解决。在许多情况下，这些方法是互补的，通过整合与转录、调控活性、染色质可及性、染色质构象和功能实验（例如基因组编辑和报告基因检测）读数相关的信息，可能会生成最准确的图像。显然，有必要从疾病相关的细胞类型和条件中收集这些信息，通过这样做，我们对疾病病因学的理解将会得到提高。本综述重点关注自身免疫性疾病功能基因组学领域，特别关注过去几年内发表的最令人兴奋和最重要的研究。