Efficient replication of over 180 genetic associations with self-reported medical data

Abstract

While 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 obtaining large amounts of medical information from 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 suggest that online collection of self-reported data from a recontactable cohort may be a viable method for both broad and deep phenotyping in large populations.

Figure 1. Replicated SNPs for binary traits.

Our log ORs and 95% confidence intervals are shown as black circles and lines. Published ORs are shown as blue Xs.

Figure 2. Success rate (versus total power) by disease class.

Replications = number of associations we successfully replicated. Expected = number of associations we expected to replicate. Attempts = number of associations we attempted to replicate. The blue dot represents our success ratio (number of successful replications divided by number of expected replications). The black line represents the 95% prediction interval for the success ratio. The nine associations that we had high power to detect but had known conflicting data were not included in this figure (see text and Table 1). Conditions assigned to each class (also see Methods S1): Asthma: childhood asthma; Autoimmune: Crohn's disease, inflammatory bowel disease, lupus, multiple sclerosis, psoriasis, type 1 diabetes, ulcerative colitis; Cancer: basal cell carcinoma, bladder cancer, breast cancer, colorectal cancer, prostate cancer, lung cancer, melanoma, pancreatic cancer, scleroderma, testicular cancer, thyroid cancer; Celiac: celiac disease; Diabetes: type 2 diabetes; Heart: blood clots, coronary artery disease, heart attack; Pigment/Hair: eye color, freckling, hair color, red hair color, male pattern baldness; Neuro: Alzheimer's disease, autism, Parkinson's disease; Other: chronic obstructive pulmonary disease, kidney stones, stroke, osteoarthritis; Psychiatric: alcohol abuse, bipolar disorder, schizophrenia.