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Diseases often cooccur in individuals more often than expected by chance, and may be explained by shared underlying genetic etiology. A common approach to genetic overlap analyses is to use summary genome-wide association study data to identify single-nucleotide polymorphisms (SNPs) that are associated with multiple traits at a selected P-value threshold. However, P-values do not account for differences in power, whereas Bayes' factors (BFs) do, and may be approximated using summary statistics. We use simulation studies to compare the power of frequentist and Bayesian approaches with overlap analyses, and to decide on appropriate thresholds for comparison between the two methods. It is empirically illustrated that BFs have the advantage over P-values of a decreasing type I error rate as study size increases for single-disease associations. Consequently, the overlap analysis of traits from different-sized studies encounters issues in fair P-value threshold selection, whereas BFs are adjusted automatically. Extensive simulations show that Bayesian overlap analyses tend to have higher power than those that assess association strength with P-values, particularly in low-power scenarios. Calibration tables between BFs and P-values are provided for a range of sample sizes, as well as an approximation approach for sample sizes that are not in the calibration table. Although P-values are sometimes thought more intuitive, these tables assist in removing the opaqueness of Bayesian thresholds and may also be used in the selection of a BF threshold to meet a certain type I error rate. An application of our methods is used to identify variants associated with both obesity and osteoarthritis.

Original publication

DOI

10.1002/gepi.21919

Type

Journal article

Journal

Genet Epidemiol

Publication Date

12/2015

Volume

39

Pages

624 - 634

Keywords

Bayes’ factor, P-value, obesity, osteoarthritis, overlap analysis, threshold calibration, Bayes Theorem, Body Mass Index, Computer Simulation, Data Interpretation, Statistical, Genome-Wide Association Study, Humans, Models, Genetic, Obesity, Osteoarthritis, Phenotype, Polymorphism, Single Nucleotide, Probability, Quantitative Trait, Heritable, Sample Size