Detecting genes in complex disease: does phenotype accuracy limit the horizon?

Lee, Koon; Sawcer, Stephen

doi:10.1016/j.tig.2010.03.003

Cited by 4 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Phenotyping error decreases power, which can be problematic as most GWAS are not sufficiently powered to explain a significant fraction of the underlying heritability. Although increasing sample sizes can counteract the problems caused by misclassification, it is the very issue of needing ever larger samples that necessitates more efficient methods for collecting data [3] . A number of associations reported in very large meta-analyses have not been replicated, and may never be, simply because of the difficulty of assembling such a sizeable cohort of patients.…”

Section: Introductionmentioning

confidence: 99%

Efficient Replication of over 180 Genetic Associations with Self-Reported Medical Data

et al. 2011

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Efficient Replication of over 180 Genetic Associations with Self-Reported Medical Data

et al. 2011

View full text Add to dashboard Cite

show abstract

“…Phenotyping error decreases power, which can be problematic as most GWAS are not sufficiently powered to explain a significant fraction of the underlying heritability. While increasing sample sizes can counteract the problems caused by misclassification, it is the very issue of needing ever larger samples that necessitates more efficient methods for collecting data [3]. A number of associations reported in very large meta-analyses have not been replicated, and may never be, simply because of the difficulty of assembling such a sizeable cohort of patients.…”

Section: Introductionmentioning

confidence: 99%

Efficient Replication of Over 180 Genetic Associations with Self‐Reported Medical Data

Francke

Naughton

et al. 2011

Nat Prec

View full text Add to dashboard Cite

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 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 selfreported 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.

show abstract