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Functional annotation of noncoding causal variants in autoimmune diseases

机译:自身免疫性疾病中非编码因果变体的功能诠释

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Interpretation of noncoding disease variants, which comprise the vast majority of Genome-wide association studies (GWAS) hits, remains a momentous challenge due to haplotype structure and our limited understanding of the mechanisms and physiological contexts of noncoding elements. GWAS have identified loci underlying human diseases, but assigning the causal nucleotide changes still remain a controversial issue. Here we addressed these issues through the combination of high-density genotyping and epigenomic data using a random forest model to discover the noncoding causal variants. Focusing on autoimmune diseases, we triaged putative causal variants for atopic dermatitis and inflammatory bowel diseases. Making a filtering pipeline, we found three interesting single nucleotide polymorphisms (rs1800630, rs1799964 and rs4796793) in the upstream site of TNF and STAT3 genes, two frequent genes shared in some autoimmune diseases, and show how those variants affect on TNF and STAT3 expression levels. All data and source codes related to this manuscript are available at https://github.com/jieunjung511/Autoimmune-research.
机译:由于单倍型结构和对非编码元素的机制的有限理解,沉淀疾病变体的解释仍然是由于单倍型结构而仍然存在重大挑战。 Gwas已经确定了潜在的人类疾病,但分配因果核苷酸变化仍然是一个有争议的问题。在这里,我们通过使用随机林模型的高密度基因分型和表观胶质数据的组合来解决这些问题,以发现非编码因果变量。专注于自身免疫性疾病,我们提供了针对特应性皮炎和炎症性肠病的推定因果变形。制作过滤管道,我们发现三种有趣的单一核苷酸多态性(RS1800630,RS1799964和RS47967964和RS4796793和RS4796793),在某些自身免疫疾病中共有的两种频繁基因,并展示了这些变体如何影响TNF和STAT3表达水平。与此稿件相关的所有数据和源代码都可以在https://github.com/jieunjung511/autoimmune-research中获得。

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