NATURE：自身免疫病致病变种的遗传及表观遗传精确图谱

全基因组扫描相关的研究已经识别出了可能导致潜在人类疾病的位点，但是它在所导致的核苷酸变异和机制还并不为人知。我们在此开发了精确制图的算法，用基因型分型数据来识别21种可能由自身免疫疾病导致的致病变种。

我们将这些预判和转录以及顺式调控的元件注释相结合，元件注释来自于包括静息态和兴奋态的CD4+T-细胞、调节性T细胞、CD8+T细胞、B细胞和单核细胞等在内的原发性免疫细胞的RNA和染色质制图。我们发现大约90%的致病变种是非编码的，并且大约60%的定位位于免疫细胞增强剂上，大部分定位点含有组蛋白乙酰化并且当免疫系统受刺激时转录增强剂相关的RNA。免疫分化和刺激相关的基因激活的主要调控者的结合位点附近，通常是致病变种产生的位置，但只有10%-20%的变种直接改变可识别的转录因子的结合基序。与此恰恰相反，大多数的非编码的风险变种影响现在基因调控无法很好解释的非标准的序列调控因素，包括哪些改变基因表达的变种。（转化医学网360zhyx.com）

Genome-wide association studies have identified loci underlying human diseases, but the causal nucleotide changes and mechanisms remain largely unknown. Here we developed a fine-mapping algorithm to identify candidate causal variants for 21 autoimmune diseases from genotyping data. We integrated these predictions with transcription and cis-regulatory element annotations, derived by mapping RNA and chromatin in primary immune cells, including resting and stimulated CD4⁺ T-cell subsets, regulatory T cells, CD8⁺ T cells, B cells, and monocytes. We find that ~90% of causal variants are non-coding, with ~60% mapping to immune-cell enhancers, many of which gain histone acetylation and transcribe enhancer-associated RNA upon immune stimulation. Causal variants tend to occur near binding sites for master regulators of immune differentiation and stimulus-dependent gene activation, but only 10–20% directly alter recognizable transcription factor binding motifs. Rather, most non-coding risk variants, including those that alter gene expression, affect non-canonical sequence determinants not well-explained by current gene regulatory models.

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