Dataset Information

A Bayesian Approach to the Overlap Analysis of Epidemiologically Linked Traits.

ABSTRACT: 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.

SUBMITTER: Asimit JL

PROVIDER: S-EPMC4832282 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Bayesian Approach to the Overlap Analysis of Epidemiologically Linked Traits.

Asimit Jennifer L JL Panoutsopoulou Kalliope K Wheeler Eleanor E Berndt Sonja I SI Cordell Heather J HJ Morris Andrew P AP Zeggini Eleftheria E Barroso Inês I

Genetic epidemiology 20150928 8

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. ...[more]

PMID: 26411566

Similar Datasets

Project description:BackgroundGiardia duodenalis is a leading cause of gastroenteritis worldwide. Humans are mainly infected by two different subtypes, i.e., assemblage A and B. Genotyping is hampered by allelic sequence heterozygosity (ASH) mainly in assemblage B, and by occurrence of mixed infections. Here we assessed the suitability of current genotyping protocols of G. duodenalis for epidemiological applications such as molecular tracing of transmission chains.Methodology/principal findingsTwo G. duodenalis isolate collections, from an outpatient tropical medicine clinic and from several primary care laboratories, were characterized by assemblage-specific qPCR (TIF, CATH gene loci) and a common multi locus sequence typing (MLST; TPI, BG, GDH gene loci). Assemblage A isolates were further typed at additional loci (HCMP22547, CID1, RHP26, HCMP6372, DIS3, NEK15411). Of 175/202 (86.6%) patients the G. duodenalis assemblage could be identified: Assemblages A 25/175 (14.3%), B 115/175 (65.7%) and A+B mixed 35/175 (20.0%). By incorporating allelic sequence heterozygosity in the analysis, the three marker MLST correctly identified 6/9 (66,7%) and 4/5 (80.0%) consecutive samples from chronic assemblage B infections in the two collections, respectively, and identified a cluster of five independent patients carrying assemblage B parasites of identical MLST type. Extended MLST for assemblage A altogether identified 5/6 (83,3%) consecutive samples from chronic assemblage A infections and 15 novel genotypes. Based on the observed A+B mixed infections it is estimated that only 75% and 50% of assemblage A or B only cases represent single strain infections, respectively. We demonstrate that typing results are consistent with this prediction.Conclusions/significanceTyping of assemblage A and B isolates with resolution for epidemiological applications is possible but requires separate genotyping protocols. The high frequency of multiple infections and their impact on typing results are findings with immediate consequences for result interpretation in this field.

Dataset Information

A Bayesian Approach to the Overlap Analysis of Epidemiologically Linked Traits.

Publications

A Bayesian Approach to the Overlap Analysis of Epidemiologically Linked Traits.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets