{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/exploring-bayesian-approaches-to-eqtl-mapping","title":"Exploring Bayesian approaches to eQTL mapping through probabilistic programming","arxiv_id":"1906.05150","date":"2019-06-12","proceeding":null,"authors":[],"abstract":"The discovery of genomic polymorphisms influencing gene expression (also\nknown as expression quantitative trait loci or eQTLs) can be formulated as a\nsparse Bayesian multivariate/multiple regression problem. An important aspect\nin the development of such models is the implementation of bespoke inference\nmethodologies, a process which can become quite laborious, when multiple\ncandidate models are being considered. We describe automatic, black-box\ninference in such models using Stan, a popular probabilistic programming\nlanguage. The utilisation of systems like Stan can facilitate model prototyping\nand testing, thus accelerating the data modelling process. The code described\nin this chapter can be found at https://github.com/dvav/eQTLBookChapter.","url_abs":"http://arxiv.org/abs/1906.05150v1","url_pdf":"http://arxiv.org/pdf/1906.05150v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"exploring-bayesian-approaches-to-eqtl-mapping","repo_url":"https://github.com/dvav/eQTLBookChapter","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"probabilistic-programming","task_name":"Probabilistic Programming"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}