{"doi":"10.7554/elife.73983","title":"Barcoded bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast","abstract":"<jats:p>Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.</jats:p>","journal":"eLife","year":2022,"id":32834,"datarank":1.7465093176553956,"base_score":4.406719247264253,"endowment":4.406719247264253,"self_citation_contribution":0.6610078870896381,"citation_network_contribution":1.0855014305657575,"self_endowment_contribution":0.6610078870896381,"citer_contribution":1.0855014305657575,"corpus_percentile":null,"corpus_rank":null,"citation_count":81,"citer_count":66,"citers_with_citation_signal":47,"citers_with_endowment":47,"datacite_reuse_total":1,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":173072,"name":"Katherine R Lawrence","orcid":null,"position":1,"is_corresponding":false},{"id":173073,"name":"Artur Rego-Costa","orcid":"0000-0001-9604-4208","position":2,"is_corresponding":false},{"id":173074,"name":"Shreyas Gopalakrishnan","orcid":null,"position":3,"is_corresponding":false},{"id":173075,"name":"Daniel Temko","orcid":null,"position":4,"is_corresponding":false},{"id":88477,"name":"Franziska Michor","orcid":"0009-0003-8552-7767","position":5,"is_corresponding":false},{"id":173076,"name":"Michael M Desai","orcid":"0000-0002-9581-1150","position":6,"is_corresponding":false},{"id":173071,"name":"Alex N Nguyen Ba","orcid":"0000-0003-1357-6386","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":4.406719247264253,"endowment":4.406719247264253,"datacite_reuse_total":1,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"35147078","pmcid":"PMC8979589","openalex_id":"https://openalex.org/W4225472713","authors":[],"funders":[{"funder_name":"Natural Sciences and Engineering Research Council of Canada","grant_id":"RGPIN-2021-02716","title":null},{"funder_name":"National Science Foundation","grant_id":"#1764269","title":null},{"funder_name":"National Institutes of Health","grant_id":"U54CA193461","title":null},{"funder_name":"National Science Foundation","grant_id":"PHY-1914916","title":null},{"funder_name":"National Institutes of Health","grant_id":"GM104239","title":null},{"funder_name":"Natural Sciences and Engineering Research Council of Canada","grant_id":"DGECR-2021-00117","title":null},{"funder_name":"Fannie & John Hertz Foundation","grant_id":"Graduate Fellowship Award","title":null},{"funder_name":"NIGMS NIH HHS","grant_id":"R01 GM104239","title":null},{"funder_name":"National Science Foundation","grant_id":"1764269","title":"NSF-Simons Center for Mathematical and Statistical Analysis of Biology"},{"funder_name":"Natural Sciences and Engineering Research Council of Canada","grant_id":"unidentified","title":"unidentified"},{"funder_name":"National Institutes of Health","grant_id":"5R01GM104239-13","title":"Microbial Adaptation and the Statistics of Epistasis and Pleiotropy"},{"funder_name":"National Institutes of Health","grant_id":"5U54CA193461-04","title":"Evolution and Treatment Response of Brain, Breast, and Hematologic Malignancies"},{"funder_name":"National Science Foundation","grant_id":"1914916","title":"The Evolution of Evolvability in Microbial Populations"}],"total_grants":13,"fwci":13.4218,"citation_percentile":0.99154809,"influential_citations":0,"citation_trend":[{"year":2021,"count":2},{"year":2022,"count":13},{"year":2023,"count":17},{"year":2024,"count":19},{"year":2025,"count":23},{"year":2026,"count":7}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.7554/elife.73983","host_type":"journal"},{"url":"https://doi.org/10.7554/elife.73983","host_type":"publisher"},{"url":"https://cdn.elifesciences.org/articles/73983/elife-73983-v2.pdf","host_type":"publisher"},{"url":"https://cdn.elifesciences.org/articles/73983/elife-73983-v2.xml","host_type":"publisher"},{"url":"https://elifesciences.org/articles/73983","host_type":"publisher"},{"url":"https://pubmed.ncbi.nlm.nih.gov/35147078","host_type":"repository"},{"url":"https://doaj.org/article/fc76b9c91a6b4f2d98062105470fe809","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8979589","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC8979589","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC8979589?pdf=render","host_type":"Europe_PMC"},{"url":"https://doi.org/10.1101/2021.09.08.459513","host_type":""},{"url":"https://www.biorxiv.org/content/biorxiv/early/2021/09/09/2021.09.08.459513.full.pdf","host_type":""},{"url":"https://elifesciences.org/download/aHR0cHM6Ly9jZG4uZWxpZmVzY2llbmNlcy5vcmcvYXJ0aWNsZXMvNzM5ODMvZWxpZmUtNzM5ODMtdjEucGRmP2Nhbm9uaWNhbFVyaT1odHRwczovL2VsaWZlc2NpZW5jZXMub3JnL2FydGljbGVzLzczOTgz/elife-73983-v1.pdf?_hash=QPi%2FndkgQdXoLIJVFljvY%2Bt5BWwvHm%2FaXPMgAKxtC%2B8%3D","host_type":""},{"url":"http://dx.doi.org/10.7554/eLife.73983","host_type":""},{"url":"https://dx.doi.org/10.1101/2021.09.08.459513","host_type":""}],"fields_of_study":["Genetic Mapping and Diversity in Plants and Animals","Bioinformatics and Genomic Networks","Fungal and yeast genetics research","0301 basic medicine","0303 health sciences","03 medical and health sciences","Chromosome Mapping","Epistasis, Genetic","Genome-Wide Association Study","Genotype","Multifactorial Inheritance","Phenotype","Quantitative Trait Loci","Saccharomyces cerevisiae"],"mesh_terms":["Chromosome Mapping","Epistasis, Genetic","Genotype","Phenotype","Saccharomyces cerevisiae","Multifactorial Inheritance","Quantitative Trait Loci","Genome-Wide Association Study"],"keywords":["Epistasis","Quantitative trait locus","Genetic architecture","Biology","Genome-wide association study","Family-based QTL mapping","Genetics","Inclusive composite interval mapping","Association mapping","Locus (genetics)","Heritability","Computational biology","Genetic association","Evolutionary biology","Gene mapping","Genotype","Gene","Single-nucleotide polymorphism","Chromosome","S. cerevisiae","Quantitative trait loci","Genomics","Pleiotropy","Polygenic Traits","Multifactorial Inheritance","QH301-705.5","Science","Q","R","Chromosome Mapping","Epistasis, Genetic","Saccharomyces cerevisiae","Phenotype","Medicine","Biology (General)"],"sdg_mappings":[],"linked_datasets":[{"doi":"10.5061/dryad.1rn8pk0vd","title":"Barcoded Bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast","publisher":"Dryad","resource_type":"Dataset"}],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"go"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-09T14:47:24.292885Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}