Pathway Analysis Based on a Genome-Wide Association Study of Polycystic Ovary Syndrome.
Pathway Analysis Based on a Genome-Wide Association Study of Polycystic Ovary Syndrome.
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DOI：
10.1371/journal.pone.0136609
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Kim HL
中科院分区：
文献类型：
--
作者：
Shim U;Kim HN;Lee H;Oh JY;Sung YA;Kim HL
多囊卵巢综合征(PCOS)是育龄妇女最常见的内分泌疾病之一，受环境因素和遗传因素的共同影响。尽管多囊卵巢综合征的遗传成分很明显，但旨在识别易感基因的研究显示出了有争议的结果。这项研究利用全基因组关联研究(GWAS)获得的数据集，进行了基于途径的分析，以阐明导致PCOS易感性的生物学途径和相关基因。我们使用来自1221名个体(432名PCOS患者和789名对照)的636,797个常染色体单核苷酸多态(SNPs)数据进行分析。采用变关联基因集富集法(品红)进行通径分析。确定了与多囊卵巢综合征相关的顶级通路或基因集，并分析了通路中的重要基因。GWAs数据集的通径分析确定了与卵母细胞减数分裂以及乙酰胆碱和游离脂肪酸调节胰岛素分泌有关的重要途径(所有标称的基因集浓缩分析(GSEA)P值均为0.05)。此外，INS、GNAQ、STXBP1、PLCB3、PLCB2、SMC3和PLCZ1是在生物学途径中观察到的显著基因(所有基因P值均为0.05)。通过将洋红色途径分析应用于PCOS Gwas数据，我们确定了与PCOS相关的重要途径和候选基因。我们的发现可能为理解多囊卵巢综合征的发生机制提供新的线索。
Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders in women of reproductive age, and it is affected by both environmental and genetic factors. Although the genetic component of PCOS is evident, studies aiming to identify susceptibility genes have shown controversial results. This study conducted a pathway-based analysis using a dataset obtained through a genome-wide association study (GWAS) to elucidate the biological pathways that contribute to PCOS susceptibility and the associated genes. We used GWAS data on 636,797 autosomal single nucleotide polymorphisms (SNPs) from 1,221 individuals (432 PCOS patients and 789 controls) for analysis. A pathway analysis was conducted using meta-analysis gene-set enrichment of variant associations (MAGENTA). Top-ranking pathways or gene sets associated with PCOS were identified, and significant genes within the pathways were analyzed. The pathway analysis of the GWAS dataset identified significant pathways related to oocyte meiosis and the regulation of insulin secretion by acetylcholine and free fatty acids (all nominal gene-set enrichment analysis (GSEA) P-values < 0.05). In addition, INS, GNAQ, STXBP1, PLCB3, PLCB2, SMC3 and PLCZ1 were significant genes observed within the biological pathways (all gene P-values < 0.05). By applying MAGENTA pathway analysis to PCOS GWAS data, we identified significant pathways and candidate genes involved in PCOS. Our findings may provide new leads for understanding the mechanisms underlying the development of PCOS.