{"doi":"10.1093/bib/bbae276","title":"Improving multi-population genomic prediction accuracy using multi-trait GBLUP models which incorporate global or local genetic correlation information","abstract":"<jats:title>Abstract</jats:title>\n               <jats:p>In the application of genomic prediction, a situation often faced is that there are multiple populations in which genomic prediction (GP) need to be conducted. A common way to handle the multi-population GP is simply to combine the multiple populations into a single population. However, since these populations may be subject to different environments, there may exist genotype-environment interactions which may affect the accuracy of genomic prediction. In this study, we demonstrated that multi-trait genomic best linear unbiased prediction (MTGBLUP) can be used for multi-population genomic prediction, whereby the performances of a trait in different populations are regarded as different traits, and thus multi-population prediction is regarded as multi-trait prediction by employing the between-population genetic correlation. Using real datasets, we proved that MTGBLUP outperformed the conventional multi-population model that simply combines different populations together. We further proposed that MTGBLUP can be improved by partitioning the global between-population genetic correlation into local genetic correlations (LGC). We suggested two LGC models, LGC-model-1 and LGC-model-2, which partition the genome into regions with and without significant LGC (LGC-model-1) or regions with and without strong LGC (LGC-model-2). In analysis of real datasets, we demonstrated that the LGC models could increase universally the prediction accuracy and the relative improvement over MTGBLUP reached up to 163.86% (25.64% on average).</jats:p>","journal":"Briefings in Bioinformatics","year":2024,"id":633700,"datarank":0.26876392038420827,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.0,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"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":414361,"name":"Tingting Zhai","orcid":"0000-0001-5812-4055","position":1,"is_corresponding":false},{"id":817866,"name":"Xinyi Zhang","orcid":"0000-0003-4996-4698","position":2,"is_corresponding":false},{"id":1643145,"name":"Changheng Zhao","orcid":null,"position":3,"is_corresponding":false},{"id":202935,"name":"Wenwen Wang","orcid":"0000-0002-1545-4369","position":4,"is_corresponding":false},{"id":326993,"name":"Hui Tang","orcid":"0000-0002-4232-4607","position":5,"is_corresponding":false},{"id":371072,"name":"Dan Wang","orcid":"0000-0002-9758-3614","position":6,"is_corresponding":false},{"id":334713,"name":"Yingli Shang","orcid":"0000-0001-5052-8346","position":7,"is_corresponding":false},{"id":3070,"name":"Chao Ning","orcid":"0000-0002-8848-3961","position":8,"is_corresponding":false},{"id":396791,"name":"Qin Zhang","orcid":"0000-0002-1023-480X","position":9,"is_corresponding":false},{"id":1625095,"name":"Jun Teng","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Improving multi-population genomic prediction accuracy using multi-trait GBLUP models which incorporate global or local genetic correlation information","abstract":"<jats:title>Abstract</jats:title>\n               <jats:p>In the application of genomic prediction, a situation often faced is that there are multiple populations in which genomic prediction (GP) need to be conducted. A common way to handle the multi-population GP is simply to combine the multiple populations into a single population. However, since these populations may be subject to different environments, there may exist genotype-environment interactions which may affect the accuracy of genomic prediction. In this study, we demonstrated that multi-trait genomic best linear unbiased prediction (MTGBLUP) can be used for multi-population genomic prediction, whereby the performances of a trait in different populations are regarded as different traits, and thus multi-population prediction is regarded as multi-trait prediction by employing the between-population genetic correlation. Using real datasets, we proved that MTGBLUP outperformed the conventional multi-population model that simply combines different populations together. We further proposed that MTGBLUP can be improved by partitioning the global between-population genetic correlation into local genetic correlations (LGC). We suggested two LGC models, LGC-model-1 and LGC-model-2, which partition the genome into regions with and without significant LGC (LGC-model-1) or regions with and without strong LGC (LGC-model-2). In analysis of real datasets, we demonstrated that the LGC models could increase universally the prediction accuracy and the relative improvement over MTGBLUP reached up to 163.86% (25.64% on average).</jats:p>","is_dataset_classified":null,"base_score":1.791759469228055,"endowment":1.791759469228055,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38856170","pmcid":"PMC11163384","openalex_id":"https://openalex.org/W4399498214","authors":[],"funders":[{"funder_name":"National Key Research and Development Program of China","grant_id":"2021YFD1200900","title":null},{"funder_name":"Yangzhou University Interdisciplinary Research Foundation for Animal Science Discipline of Targeted Support","grant_id":"yzuxk202016","title":null},{"funder_name":"Project of Genetic Improvement for Agricultural Species of Shandong Province","grant_id":"2019LZGC011","title":null},{"funder_name":"Project of Genetic Improvement for Agricultural Species of Shandong Province","grant_id":"2022LZGCQY007","title":null},{"funder_name":"Shandong Provincial Natural Science Foundation","grant_id":"ZR2020QC175","title":null},{"funder_name":"Shandong Provincial Natural Science Foundation","grant_id":"ZR2020QC176","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"32002172","title":null}],"total_grants":7,"fwci":1.9404,"citation_percentile":0.8605179,"influential_citations":0,"citation_trend":[{"year":2025,"count":5}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://academic.oup.com/bib/article-pdf/25/4/bbae276/58180424/bbae276.pdf","host_type":"journal"},{"url":"https://academic.oup.com/bib/article-pdf/25/4/bbae276/58180424/bbae276.pdf","host_type":"publisher"},{"url":"https://doi.org/10.1093/bib/bbae276","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38856170","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11163384","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11163384/pdf/bbae276.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC11163384","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11163384?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Genetic and phenotypic traits in livestock","Genetic Mapping and Diversity in Plants and Animals","Genetics and Plant Breeding","Genomics","Models, Genetic","Genetics, Population","Quantitative Trait Loci","Humans","Algorithms","Genotype"],"mesh_terms":["Algorithms","Genetics, Population","Genotype","Humans","Models, Genetic","Genomics","Quantitative Trait Loci"],"keywords":["Correlation","Trait","Population","Computer science","Computational biology","Machine learning","Data mining","Artificial intelligence","Biology","Mathematics","Demography","Genomic Prediction","Multi-population","Local Genetic Correlation","Global Genetic Correlation","Mtgblup"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T12:31:24.655589Z","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":[]}