Abstract
AbstractWhile the cost and speed of generating genomic data have come down dramatically in recent years, the slow pace of collecting medical data for large cohorts continues to hamper genetic research. Here we evaluate a novel online framework for amassing large amounts of medical information in a recontactable cohort by assessing our ability to replicate genetic associations using these data. Using web-based questionnaires, we gathered self-reported data on 50 medical phenotypes from a generally unselected cohort of over 20,000 genotyped individuals. Of a list of genetic associations curated by NHGRI, we successfully replicated about 75% of the associations that we expected to (based on the number of cases in our cohort and reported odds ratios, and excluding a set of associations with contradictory published evidence). Altogether we replicated over 180 previously reported associations, including many for type 2 diabetes, prostate cancer, cholesterol levels, and multiple sclerosis. We found significant variation across categories of conditions in the percentage of expected associations that we were able to replicate, which may reflect systematic inflation of the effects in some initial reports, or differences across diseases in the likelihood of misdiagnosis or misreport. We also demonstrated that we could improve replication success by taking advantage of our recontactable cohort, offering more in-depth questions to refine self-reported diagnoses. Our data suggests that online collection of self-reported data in a recontactable cohort may be a viable method for both broad and deep phenotyping in large populations.
Highlights
In the last few years, the cost of collecting genomic data has declined rapidly
New techniques are needed to complement the wealth of genomic data and build the large cohorts needed for highly-powered genome-wide association studies (GWAS)
Phenotyping error decreases power, which can be problematic as most GWAS are not sufficiently powered to explain a significant fraction of the underlying heritability
Summary
In the last few years, the cost of collecting genomic data has declined rapidly. advances in the collection of phenome data (the set of all phenotypic information from a single organism) have not kept pace [1,2]. There is a need for more straightforward methods to quickly and reliably gather retrospective phenotype information from large cohorts of people, to validate existing associations, but to discover new ones. We evaluate a research model in which a large, recontactable cohort is surveyed online across a broad range of phenotypes. Subsets of this cohort with particular characteristics can be contacted for further research with more in-depth phenotyping on specific topics as appropriate. By assessing our ability to replicate previously reported genetic associations across a wide range of conditions, we demonstrate that broad self-reported data collection online is useful for medically-related conditions as well. We show that the ability to recontact the cohort facilitates rapid refinement of phenotype characterization
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