Division of Epidemiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Mayo Clin Proc. 2011 Jul;86(7):606-14. doi: 10.4065/mcp.2011.0178. Epub 2011 Jun 6.
To create a cohort for cost-effective genetic research, the Mayo Genome Consortia (MayoGC) has been assembled with participants from research studies across Mayo Clinic with high-throughput genetic data and electronic medical record (EMR) data for phenotype extraction.
Eligible participants include those who gave general research consent in the contributing studies to share high-throughput genotyping data with other investigators. Herein, we describe the design of the MayoGC, including the current participating cohorts, expansion efforts, data processing, and study management and organization. A genome-wide association study to identify genetic variants associated with total bilirubin levels was conducted to test the genetic research capability of the MayoGC.
Genome-wide significant results were observed on 2q37 (top single nucleotide polymorphism, rs4148325; P=5.0 × 10(-62)) and 12p12 (top single nucleotide polymorphism, rs4363657; P=5.1 × 10(-8)) corresponding to a gene cluster of uridine 5'-diphospho-glucuronosyltransferases (the UGT1A cluster) and solute carrier organic anion transporter family, member 1B1 (SLCO1B1), respectively.
Genome-wide association studies have identified genetic variants associated with numerous phenotypes but have been historically limited by inadequate sample size due to costly genotyping and phenotyping. Large consortia with harmonized genotype data have been assembled to attain sufficient statistical power, but phenotyping remains a rate-limiting factor in gene discovery research efforts. The EMR consists of an abundance of phenotype data that can be extracted in a relatively quick and systematic manner. The MayoGC provides a model of a unique collaborative effort in the environment of a common EMR for the investigation of genetic determinants of diseases.
为了进行具有成本效益的遗传研究,Mayo 基因组联盟 (MayoGC) 由 Mayo 诊所多个研究项目的参与者组成,这些参与者提供了高通量遗传数据和电子病历 (EMR) 数据以提取表型。
合格的参与者包括那些在参与研究中给予了一般研究同意的人,同意将高通量基因分型数据与其他研究人员共享。在这里,我们描述了 MayoGC 的设计,包括当前参与的队列、扩展努力、数据处理以及研究管理和组织。进行了一项全基因组关联研究,以确定与总胆红素水平相关的遗传变异,以测试 MayoGC 的遗传研究能力。
在 2q37(最高单核苷酸多态性,rs4148325;P=5.0×10(-62))和 12p12(最高单核苷酸多态性,rs4363657;P=5.1×10(-8))上观察到全基因组显著结果,分别对应于尿苷 5'-二磷酸葡萄糖醛酸基转移酶 (UGT1A 簇)和溶质载体有机阴离子转运蛋白家族成员 1B1 (SLCO1B1)的基因簇。
全基因组关联研究已经确定了与许多表型相关的遗传变异,但由于昂贵的基因分型和表型分析,历史上由于样本量不足而受到限制。已经组装了大型联盟以获得足够的统计能力,但表型仍然是基因发现研究工作中的一个限制因素。EMR 包含大量可以以相对快速和系统的方式提取的表型数据。MayoGC 提供了一种在共同 EMR 环境下进行疾病遗传决定因素研究的独特协作努力的模式。