{"doi":"10.1017/s0016672399004255","title":"Estimating the genetic architecture of quantitative traits","abstract":"<jats:p>Understanding and estimating the structure and parameters associated with the genetic architecture \nof quantitative traits is a major research focus in quantitative genetics. With the availability of a \nwell-saturated genetic map of molecular markers, it is possible to identify a major part of the \nstructure of the genetic architecture of quantitative traits and to estimate the associated \nparameters. Multiple interval mapping, which was recently proposed for simultaneously mapping \nmultiple quantitative trait loci (QTL), is well suited to the identification and estimation of the \ngenetic architecture parameters, including the number, genomic positions, effects and interactions \nof significant QTL and their contribution to the genetic variance. With multiple traits and multiple \nenvironments involved in a QTL mapping experiment, pleiotropic effects and QTL by environment \ninteractions can also be estimated. We review the method and discuss issues associated with \nmultiple interval mapping, such as likelihood analysis, model selection, stopping rules and \nparameter estimation. The potential power and advantages of the method for mapping multiple \nQTL and estimating the genetic architecture are discussed. We also point out potential problems \nand difficulties in resolving the details of the genetic architecture as well as other areas that require \nfurther investigation. One application of the analysis is to improve genome-wide marker-assisted \nselection, particularly when the information about epistasis is used for selection with mating.</jats:p>","journal":"Genetical Research","year":1999,"id":32784,"datarank":13.696583191024274,"base_score":5.8111409929767,"endowment":5.8111409929767,"self_citation_contribution":0.8716711489465051,"citation_network_contribution":12.824912042077768,"self_endowment_contribution":0.8716711489465051,"citer_contribution":12.824912042077768,"corpus_percentile":null,"corpus_rank":null,"citation_count":333,"citer_count":200,"citers_with_citation_signal":200,"citers_with_endowment":200,"datacite_reuse_total":6,"is_dataset":false,"is_dataset_confidence":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":172905,"name":"CHEN-HUNG KAO","orcid":null,"position":1,"is_corresponding":false},{"id":172906,"name":"CHRISTOPHER J. BASTEN","orcid":null,"position":2,"is_corresponding":false},{"id":172904,"name":"ZHAO-BANG ZENG","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":5.8111409929767,"endowment":5.8111409929767,"datacite_reuse_total":6,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"10689805","pmcid":null,"openalex_id":"https://openalex.org/W2073654881","authors":[],"funders":[{"funder_name":"NIGMS NIH HHS","grant_id":"GM 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Mapping and Diversity in Plants and Animals","Genetic and phenotypic traits in livestock","Genetics and Plant Breeding","Computer Science","Medicine","Biology","Algorithms","Animals","Chromosome Mapping","Genetic Markers","Genotype","Models, Genetic","Mutation","Quantitative Trait, Heritable","Selection, Genetic"],"mesh_terms":["Algorithms","Animals","Chromosome Mapping","Genetic Markers","Genotype","Models, Genetic","Mutation","Selection, Genetic","Quantitative Trait, Heritable"],"keywords":["Genetic architecture","Quantitative trait locus","Epistasis","Inclusive composite interval mapping","Selection (genetic algorithm)","Family-based QTL mapping","Association mapping","Quantitative genetics","Biology","Computer science","Computational biology","Genetic model","Gene mapping","Genetics","Machine learning","Genetic variation","Gene","Single-nucleotide polymorphism","Genotype"],"sdg_mappings":[],"linked_datasets":[{"doi":"10.6084/m9.figshare.19711394.v1","title":"Additional file 2 of 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