与男性不育相关的全基因组关联研究(GWAS)数据的途径分析

IF 1.1 Q4 OBSTETRICS & GYNECOLOGY
Rupashree Salvi, Ulka Gawde, Susan Idicula-Thomas, B. Biswas
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引用次数: 0

摘要

背景:不孕症是影响大约10-20%育龄人口的常见疾病。特发性不孕症病例被认为有遗传基础,但潜在的原因在很大程度上是未知的。然而,人类男性不育的遗传基础只被部分理解。本研究的目的是了解男性不育的遗传学研究现状及其与重要生物学机制的关联。结果:我们使用全基因组关联研究(GWAS)数据集和NCBI-PubMed检索进行了识别候选因果snp和通路(ICSN通路)分析,分别包括GWAS中的632个snp和PubMed服务器中的451个snp。ICSN通路分析产生了与男性不育相关的三种假设的生物学机制:(1)rs8084和rs7192→HLA-DRA→炎症途径和细胞粘附;rs7550231和rs2234167→TNFRSF14→TNF受体超家族成员14→T淋巴细胞增殖活化;rs1105879和rs2070959→UGT1A6→UDP葡萄糖醛酸糖基转移酶家族1成员A6→外源代谢、雄激素、雌激素、视黄醇和碳水化合物。结论:本研究结果可能有助于研究男性不育的遗传机制。基于途径的方法已应用于男性不育的GWAS数据集来研究生物学机制,并报道了一些新的男性不育的风险途径。使用GWAS数据集进行的途径分析表明,与炎症和代谢相关的生物学过程可能与男性不育易感性有关。我们的分析表明,基因对男性不育的贡献是通过影响常见炎症疾病的多个基因在功能途径中相互作用来实现的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pathway Analysis of Genome Wide Association Studies (GWAS) Data Associated with Male Infertility
Background: Infertility is a common condition affecting approximately 10–20% of the reproductive age population. Idiopathic infertility cases are thought to have a genetic basis, but the underlying causes are largely unknown. However, the genetic basis underlying male infertility in humans is only partially understood. The Purpose of the study is to understand the current state of research on the genetics of male infertility and its association with significant biological mechanisms. Results: We performed an Identify Candidate Causal SNPs and Pathway (ICSN Pathway) analysis using a genome-wide association study (GWAS) dataset, and NCBI-PubMed search which included 632 SNPs in GWAS and 451 SNPs from the PubMed server, respectively. The ICSN Pathway analysis produced three hypothetical biological mechanisms associated with male infertility: (1) rs8084 and rs7192→HLA-DRA→inflammatory pathways and cell adhesion; rs7550231 and rs2234167→TNFRSF14→TNF Receptor Superfamily Member 14→T lymphocyte proliferation and activation; rs1105879 and rs2070959→UGT1A6→UDP glucuronosyltransferase family 1 member A6→Metabolism of Xenobiotics, androgen, estrogen, retinol, and carbohydrates. Conclusions: We believe that our results may be helpful to study the genetic mechanisms of male infertility. Pathway-based methods have been applied to male infertility GWAS datasets to investigate the biological mechanisms and reported some novel male infertility risk pathways. This pathway analysis using GWAS dataset suggests that the biological process related to inflammation and metabolism might contribute to male infertility susceptibility. Our analysis suggests that genetic contribution to male infertility operates through multiple genes affecting common inflammatory diseases interacting in functional pathways.
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