Abstract
The Nurses’ Health Study (NHS), Nurses’ Health Study II (NHSII), Health Professionals Follow Up Study (HPFS) and the Physicians Health Study (PHS) have collected detailed longitudinal data on multiple exposures and traits for approximately 310,000 study participants over the last 35 years. Over 160,000 study participants across the cohorts have donated a DNA sample and to date, 20,691 subjects have been genotyped as part of genome-wide association studies (GWAS) of twelve primary outcomes. However, these studies utilized six different GWAS arrays making it difficult to conduct analyses of secondary phenotypes or share controls across studies. To allow for secondary analyses of these data, we have created three new datasets merged by platform family and performed imputation using a common reference panel, the 1,000 Genomes Phase I release. Here, we describe the methodology behind the data merging and imputation and present imputation quality statistics and association results from two GWAS of secondary phenotypes (body mass index (BMI) and venous thromboembolism (VTE)). We observed the strongest BMI association for the FTO SNP rs55872725 (β = 0.45, p = 3.48x10-22), and using a significance level of p = 0.05, we replicated 19 out of 32 known BMI SNPs. For VTE, we observed the strongest association for the rs2040445 SNP (OR = 2.17, 95% CI: 1.79–2.63, p = 2.70x10-15), located downstream of F5 and also observed significant associations for the known ABO and F11 regions. This pooled resource can be used to maximize power in GWAS of phenotypes collected across the cohorts and for studying gene-environment interactions as well as rare phenotypes and genotypes.
Highlights
Large, well-phenotyped cohort studies have constituted the backbone of epidemiology for several decades
Collected longitudinal information on exposures and outcomes enables a broad spectrum of analyses and has led to novel insights into disease etiology, such as the link between smoking and lung cancer [1,2] as well as the link between both high cholesterol levels and trans fatty acids with coronary heart disease [3,4] Many existing cohorts collect biological specimens from their participants, allowing for studies of inherited genetic variation as well as prospectively measured biomarkers such as metabolomic profiles [5] and circulating hormone levels [6]
The average imputation quality score by minor frequency for each platform family is shown in Fig 2 and the distribution of imputation quality score for rare (MAF 0.01) variants is shown in S1 Table
Summary
Well-phenotyped cohort studies have constituted the backbone of epidemiology for several decades. Genome-wide association studies (GWAS) are currently a main engine of genetic epidemiology and have led to the identification of thousands of loci for hundreds of traits (for an overview and its clinical applications, see Manolio [7]). When designing a GWAS, cost is still the determining factor and GWAS within cohorts are often conducted within nested case-control studies or sub-cohorts. The Women’s Genome Health Study (WGHS) [8] genotyped the entire cohort of 27,000 women and the Genetic Epidemiology Research on Adult Health and Aging (GERA) Cohort has generated GWAS data on almost 100,000 individuals [9]. In many instances, GWAS are tied to specific funding sources acquired for studying a pre-defined outcome and only a small fraction of the cohort is genotyped at a specific time
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