{"doi":"10.1002/aro2.87","title":"Genome‐wide association study and genomic prediction for growth traits in spotted sea bass (<i>Lateolabrax maculatus</i>) using insertion and deletion markers","abstract":"<jats:title>Abstract</jats:title><jats:p>Spotted sea bass (<jats:italic>Lateolabrax maculatus</jats:italic>) is a species of significant economic importance in aquaculture. However, genetic degeneration, such as declining growth performance, has severely impeded industry development, necessitating urgent genetic improvement. Here, we conducted a genome‐wide association study (GWAS) and genomic prediction for growth traits using insertion and deletion (InDel) markers, and systematically compared the results with our previous studies using single nucleotide polymorphism (SNP) markers. A total of 97 significant InDels including a 6 bp insertion in an exon region were identified. It is worth noting that only 5 and 1 candidate genes for DY and TS populations were also detected in previous GWAS using SNPs, and numerous novel genes including <jats:italic>c4b</jats:italic>, <jats:italic>fgf4</jats:italic>, and <jats:italic>dnajb9</jats:italic> were identified as vital candidate genes. Moreover, several novel growth‐related procedures, such as the growth and development of the bone and muscle, were also detected. These findings indicated that InDel‐based GWAS can provide valuable complement to SNP‐based studies. The comparison of genomic predictive performance for total length trait under different marker selection strategies and genomic selection models indicated that GWAS selection strategy exhibits more stable predictive performance compared to the evenly selection strategy. Additionally, support vector machine model demonstrated better predictive accuracy and efficiency than traditional best linear unbiased prediction and Bayes models. Furthermore, the superior predictive performance using InDel markers compared to SNP markers highlighted the potential of InDels to enhance genomic predictive accuracy and efficiency. Our results carry significant implications for dissecting genetic mechanisms and contributing genetic improvement of growth traits in spotted sea bass through genomic resources.</jats:p>","journal":"Animal Research and One Health","year":2024,"id":650587,"datarank":0.41588830833596724,"base_score":2.772588722239781,"endowment":2.772588722239781,"self_citation_contribution":0.41588830833596724,"citation_network_contribution":0.0,"self_endowment_contribution":0.41588830833596724,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":15,"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":1696386,"name":"Yonghang Zhang","orcid":null,"position":1,"is_corresponding":false},{"id":1018568,"name":"Cong Liu","orcid":"0000-0001-7296-101X","position":2,"is_corresponding":false},{"id":288378,"name":"Lingyu Wang","orcid":"0000-0002-2736-7588","position":3,"is_corresponding":false},{"id":1696387,"name":"Yani Dong","orcid":null,"position":4,"is_corresponding":false},{"id":288228,"name":"Donglei Sun","orcid":"0000-0002-7930-7544","position":5,"is_corresponding":false},{"id":1696388,"name":"Haishen Wen","orcid":null,"position":6,"is_corresponding":false},{"id":1696389,"name":"Kaiqiang Zhang","orcid":null,"position":7,"is_corresponding":false},{"id":295389,"name":"Xin Qi","orcid":"0000-0003-2631-0456","position":8,"is_corresponding":false},{"id":603325,"name":"Yun Li","orcid":"0000-0003-0949-6519","position":9,"is_corresponding":false},{"id":513199,"name":"Chong Zhang","orcid":"0000-0003-0576-6016","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Genome‐wide association study and genomic prediction for growth traits in spotted sea bass (<i>Lateolabrax maculatus</i>) using insertion and deletion markers","abstract":"<jats:title>Abstract</jats:title><jats:p>Spotted sea bass (<jats:italic>Lateolabrax maculatus</jats:italic>) is a species of significant economic importance in aquaculture. However, genetic degeneration, such as declining growth performance, has severely impeded industry development, necessitating urgent genetic improvement. Here, we conducted a genome‐wide association study (GWAS) and genomic prediction for growth traits using insertion and deletion (InDel) markers, and systematically compared the results with our previous studies using single nucleotide polymorphism (SNP) markers. A total of 97 significant InDels including a 6 bp insertion in an exon region were identified. It is worth noting that only 5 and 1 candidate genes for DY and TS populations were also detected in previous GWAS using SNPs, and numerous novel genes including <jats:italic>c4b</jats:italic>, <jats:italic>fgf4</jats:italic>, and <jats:italic>dnajb9</jats:italic> were identified as vital candidate genes. Moreover, several novel growth‐related procedures, such as the growth and development of the bone and muscle, were also detected. These findings indicated that InDel‐based GWAS can provide valuable complement to SNP‐based studies. The comparison of genomic predictive performance for total length trait under different marker selection strategies and genomic selection models indicated that GWAS selection strategy exhibits more stable predictive performance compared to the evenly selection strategy. Additionally, support vector machine model demonstrated better predictive accuracy and efficiency than traditional best linear unbiased prediction and Bayes models. Furthermore, the superior predictive performance using InDel markers compared to SNP markers highlighted the potential of InDels to enhance genomic predictive accuracy and efficiency. Our results carry significant implications for dissecting genetic mechanisms and contributing genetic improvement of growth traits in spotted sea bass through genomic resources.</jats:p>","is_dataset_classified":null,"base_score":2.772588722239781,"endowment":2.772588722239781,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19965766","pmcid":null,"openalex_id":"https://openalex.org/W4403040560","authors":[],"funders":[{"funder_name":"National Key Research and Development Program of China","grant_id":"2022YFD2400503","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"NSFC, 32072947","title":null}],"total_grants":2,"fwci":5.8945,"citation_percentile":0.96708841,"influential_citations":0,"citation_trend":[{"year":2025,"count":9},{"year":2026,"count":6}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/aro2.87","host_type":"journal"},{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/aro2.87","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1002/aro2.87","host_type":"publisher"},{"url":"https://doi.org/10.1002/aro2.87","host_type":"journal"},{"url":"https://doaj.org/article/bd468cfe5df24ac6b96013b622de8041","host_type":"repository"}],"fields_of_study":["Genetic and phenotypic traits in livestock","Genetic Mapping and Diversity in Plants and Animals","Aquaculture Nutrition and Growth"],"mesh_terms":[],"keywords":["Biology","Lateolabrax","Sea bass","Genome-wide association study","Genetics","Bass (fish)","Genome","Evolutionary biology","Fishery","Gene","Fish <Actinopterygii>","Genotype","Single-nucleotide polymorphism"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-10T05:45:27.678867Z","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":[]}