Large-scale studies in eMERGE to discover the genetic determinants of uterine fibroids

子宫肌瘤是人类子宫的良性肿瘤，在美国影响77%的绝经妇女， 占每年医疗保健费用的59 - 344亿美元。直到最近，肿瘤组织和细胞培养研究 研究纤维瘤生长是了解纤维瘤病理生理学的主要来源。遗传 在双胞胎研究中已经记录了子宫肌瘤的易感性，其中高达69%的风险是遗传的。我们 建议通过GWAS、全基因组和 外显子组重测序我们召集了一个研究联盟，利用GWAS数据和成像证实， 电子病历和基因组学（eMERGE）网络中的纤维瘤病例和对照。我们有 在范德比尔特的BioVU DNA数据库中开发并验证了表型分析算法， 入选/排除标准要求盆腔成像。我们的表型分析算法是在七岁时实施的， eMERGE研究中心生成按照病例和对照的通用标准分类的协调表型 eMERGE中的GWAS数据。在具有GWAS数据的受试者中，外显子组测序可用于3，045 来自Geisinger卫生系统MyCode社区健康倡议（MyCode）的病例和8，598例对照， 来自BioVU的500例病例和500例对照。我们将进行多阶段的跨种族GWAS和协会 下一代子宫肌瘤风险测序数据的研究，利用现有的遗传数据， 其他资金（R 01 HD 074711、R 03 HD 078567）和本申请的新数据。我们的具体目标是：1。 对子宫肌瘤风险进行多阶段经胸EHR联盟荟萃分析。我们将进行固定效果 来自eMERGE for Discovery的7，242例病例和14，895例对照的SNP汇总统计的荟萃分析。 我们将在超过7,000例病例和14,000例对照中对最强相关的SNP进行计算机分析， 复制的2.使用BioVU进行大规模的全基因组和外显子组重测序研究， 我的代码我们将使用来自MyCode的3，645例病例和9，098例对照的现有外显子组测序数据，放大75倍。 和BioVU在50倍下评估罕见的编码变体，并在15倍下进行全基因组测序， 来自BioVU Discovery的另外1，000例病例和1，000例对照。我们使用了4,500个独立的案例， 12，000个对照将在MyCode中通过外显子组测序增加，另外还有1，920个现有病例， 来自BioVU的1，920份对照品，对50，000个SNP进行重新基因分型，以复制结果。3.评价 使用生物信息学方法研究相关变异在纤维瘤风险中的生物学意义。我们将使用 PrediXcan和PheWAS进一步检查目的1和2中鉴定的SNP/基因，以获得 通过调节基因表达进行调节。关于遗传因素的风险知之甚少， 纤维瘤病我们提出了一种有效的和具有成本效益的方法来确定遗传风险因素的子宫肌瘤 通过利用可用的成像、现有的GWAS、测序和DNA。本研究将提供 无与伦比的基因组覆盖率为大量的黄金标准收集图像确认的参与者。

Uterine fibroids, benign tumors of the human uterus, affect 77% of women by menopause in the U.S. and account for $5.9-34.4 billion in annual healthcare costs. Until recently, tumor tissue and cell culture studies investigating fibroid growth have been the primary sources for understanding fibroid pathophysiology. Genetic predisposition to fibroids has been documented in twin studies, where up to 69% of risk is heritable. We propose to identify common and rare variants associated with fibroid risk through GWAS, whole genome, and exome resequencing. We have convened a consortium of studies with GWAS data and imaging-confirmed fibroid cases and controls in the Electronic Medical Record and Genomics (eMERGE) Network. We have developed and validated a phenotyping algorithm in the BioVU DNA databank at Vanderbilt with stringent inclusion/exclusion criteria that requires pelvic imaging. Our phenotyping algorithm was implemented at seven eMERGE sites to generate harmonized phenotypes classified by common criteria across cases and controls with GWAS data in eMERGE. Among subjects with GWAS data, exome sequencing is available for 3,045 cases and 8,598 controls from Geisinger Health System MyCode Community Health Initiative (MyCode) and 500 cases and 500 controls from BioVU. We will conduct multi-stage transethnic GWAS and association studies in next-generation sequencing data for fibroid risk, leveraging existing genetic data, data generated by other funding (R01HD074711, R03HD078567), and new data from this application. Our Specific Aims are to: 1. Conduct a multi-stage transethnic EHR consortium meta-analysis of fibroid risk. We will conduct fixed effects meta-analysis of SNP summary statistics with 7,242 cases and 14,895 controls from eMERGE for Discovery. We will conduct in silico analysesof the strongest associated SNPs in over 7,000 cases and 14,000 controls for Replication. 2. Conduct a large-scale whole genome and exome resequencing study using BioVU and MyCode. We will use existing exome sequencing data for 3,645 cases and 9,098 controls from MyCode at 75X and BioVU at 50X to assess rare coding variants, and conduct whole genome sequencing at 15X in an additional 1,000 cases and 1,000 controls from BioVU for Discovery. We use 4,500 independent cases and 12,000 controls that will accrue in MyCode with exome sequencing, and additional existing 1,920 cases and 1,920 controls from BioVU with de novo genotyping of 50,000 SNPs for Replication of findings. 3. Evaluate the biological significance of associated variants in fibroid risk using bioinformatics approaches. We will use PrediXcan and PheWAS to further examine the SNPs/genes identified in Aims 1 and 2 for evidence of pleiotropy and mediation by regulation of gene expression. Little is known about the genetic causes of risk in fibroid disease. We propose an efficient and cost-effective approach to identify genetic risk factors for fibroids by taking advantage of available imaging, existing GWAS, sequencing, and DNA. This study will provide unparalleled genomic coverage for a large gold-standard collection of image-confirmed participants.