Identifying causal variants at loci with multiple signals of association
| العنوان: | Identifying causal variants at loci with multiple signals of association |
|---|---|
| المؤلفون: | Eleazar Eskin, Bogdan Pasaniuc, Eun Yong Kang, Farhad Hormozdiari, Emrah Kostem |
| المصدر: | BCB Genetics, vol 198, iss 2 |
| بيانات النشر: | ACM, 2014. |
| سنة النشر: | 2014 |
| مصطلحات موضوعية: | Linkage disequilibrium, Empirical data, Quantitative Trait Loci, Posterior probability, causal variants, Genome-wide association study, Locus (genetics), Coronary Artery Disease, Computational biology, Investigations, Biology, Quantitative trait locus, Polymorphism, Single Nucleotide, Linkage Disequilibrium, Genetic, Models, Genetics, Humans, 2.1 Biological and endogenous factors, Genetic Predisposition to Disease, Polymorphism, Aetiology, Genetic Association Studies, Mathematics, Genetic association, Models, Genetic, Chitinases, Human Genome, Case-control study, Single Nucleotide, association studies, Case-Control Studies, fine mapping, Expression quantitative trait loci, Developmental Biology, Common disease-common variant |
| الوصف: | Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility of an arbitrary number of causal variants when estimating the posterior probability of a variant being causal. A direct benefit of our approach is that we predict a set of variants for each locus that under reasonable assumptions will contain all of the true causal variants with a high confidence level (e.g., 95%) even when the locus contains multiple causal variants. We use simulations to show that our approach provides 20–50% improvement in our ability to identify the causal variants compared to the existing methods at loci harboring multiple causal variants. We validate our approach using empirical data from an expression QTL study of CHI3L2 to identify new causal variants that affect gene expression at this locus. CAVIAR is publicly available online at http://genetics.cs.ucla.edu/caviarTest/. |
| وصف الملف: | application/pdf |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::245feda77ad3658d32ee5882fcdafa31Test https://doi.org/10.1145/2649387.2660800Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....245feda77ad3658d32ee5882fcdafa31 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |