The aggregation of Electronic Health Records (EHR) and personalized genetics leads to powerful discoveries relevant to population health. Here we perform genome-wide association studies (GWAS) and accompanying phenome-wide association studies (PheWAS) to validate phenotype-genotype associations of BMI, and to a greater extent, severe Class 2 obesity, using comprehensive diagnostic and clinical data from the EHR database of our cohort. Three GWASs of 500,000 variants on the Illumina platform of 6,645 Healthy Nevada participants identified several published and novel variants that affect BMI and obesity. Each GWAS was followed with two independent PheWASs to examine associations between extensive phenotypes (incidence of diagnoses, condition, or disease), significant SNPs, BMI, and incidence of extreme obesity. The first GWAS examines associations with BMI in a cohort with no type 2 diabetics, focusing exclusively on BMI. The second GWAS examines associations with BMI in a cohort that includes type 2 diabetics. In the second GWAS, type 2 diabetes is a comorbidity, and thus becomes a covariate in the statistical model. The intersection of significant variants of these two studies is surprising. The third GWAS is a case vs. control study, with cases defined as extremely obese (Class 2 or 3 obesity), and controls defined as participants with BMI between 18.5 and 25. This last GWAS identifies strong associations with extreme obesity, including established variants in the FTO and NEGR1 genes, as well as loci not yet linked to obesity. The PheWASs validate published associations between BMI and extreme obesity and incidence of specific diagnoses and conditions, yet also highlight novel links. This study emphasizes the importance of our extensive longitudinal EHR database to validate known associations and identify putative novel links with BMI and obesity.

译文

电子病历(EHR)和个性化遗传学的集合导致与人口健康相关的有力发现。在这里,我们使用来自EHR的综合诊断和临床数据,进行基因组范围的关联研究(GWAS)和伴随的表型范围的关联研究(PheWAS),以验证BMI的表型-基因型关联,并在更大程度上验证严重的2型肥胖。我们队列的数据库。在内华达州6,645名健康参与者的Illumina平台上,有500,000个变体的三个GWAS,确定了一些影响BMI和肥胖症的已发表和新颖的变体。每个GWAS后面都有两个独立的PheWAS,以检查广泛的表型(诊断,病状或疾病的发生率),重要的SNP,BMI和极端肥胖的发生率之间的关联。第一个GWAS在一个没有2型糖尿病的队列中研究了与BMI的关联,仅侧重于BMI。第二个GWAS在一个包括2型糖尿病的队列研究中检查了与BMI的关联。在第二个GWAS中,2型糖尿病是合并症,因此在统计模型中成为协变量。这两项研究的重要变体的交集令人惊讶。第三个GWAS是病例与对照研究,病例定义为极度肥胖(2或3级肥胖),对照定义为BMI在18.5至25之间的参与者。这最后一个GWAS确定了与极端肥胖的强烈关联,包括确定的变体FTO和NEGR1基因中的基因,以及尚未与肥胖相关的基因座。 PheWAS验证了BMI与极端肥胖以及特定诊断和病状的发生之间已发表的关联,但也突出了新颖的联系。这项研究强调了我们广泛的纵向EHR数据库对于验证已知关联并识别与BMI和肥胖症之间可能存在的新颖联系的重要性。

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