Genetics Selection Evolution最新文献

{"title":"Genetic variation of hypoxia tolerance in farmed fish: a systematic review for selective breeding purposes.","authors":"Sergio P Barahona, Nicolás Salinas-Parra, Rodrigo Pulgar, José Gallardo-Matus","doi":"10.1186/s12711-026-01032-1","DOIUrl":"https://doi.org/10.1186/s12711-026-01032-1","url":null,"abstract":"","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146133679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blood lipidome profiling reveals potential biomarkers linked to health and carcass quality traits in pigs.","authors":"Carles Hernández-Banqué,Teodor Jové-Juncà,Manel Portero-Otin,Elia Obis,Olga González-Rodríguez,Maria Ballester,Raquel Quintanilla","doi":"10.1186/s12711-026-01030-3","DOIUrl":"https://doi.org/10.1186/s12711-026-01030-3","url":null,"abstract":"BACKGROUNDThe modulation, activation, and differentiation of several immune cells is highly dependent on lipid metabolism. The objective of this study was to analyse the genetic determinism of the porcine plasma lipidome and its association to the animal immune capacity and production performance. The analysis of the blood lipidome of 300 60-day-old Duroc pigs allowed semi-quantification of 982 circulating lipid molecules. We evaluated the genetic determinism of the lipidome abundances, as well as their phenotypic and genetic correlations with health, stress, and carcass phenotypes.RESULTSTriacylglycerols were the most abundant lipid class among the plasma lipid features, followed by glycerophosphocholines, glycerophosphoethanolamines, diacylglycerols, and fatty acids/esters. Lipidome abundances showed low to moderate phenotypic correlations with the health and production traits, which clustered in two groups with opposite phenotypic correlation patterns with the lipidome. Mean heritability estimates for the circulating lipids abundance was generally low, but 184 lipid molecules showed significant heritability ranging between 0.25 and 0.85. At the genetic level, the percentage and phagocytic capacity of lymphocytes, the proportion of γδ T lymphocytes, and the cortisol concentration in hair were especially correlated with the lipidome, showing more than 200 significant genetic correlations with different lipidic compounds. Putative identification of associated metabolites by mass similarity revealed a large presence of phospholipids and glycerolipids among lipid molecules genetically correlated with immunity traits. Regarding production traits, fatness and lean meat measures showed an opposite pattern of genetic correlations with the porcine lipidome. Lipids positively correlated with fatness were mainly composed of diacyl- and triacyl-glycerides, while potential ceramides and phospholipids were more abundant among the lipids positively correlated with lean meat content at the genetic level.CONCLUSIONSOur results demonstrate a genetic determinism of the porcine blood lipidomic profile and suggest genetic correlations of the lipidome abundances with health and production performance phenotypes. We identify potential lipid biomarkers for assessing animal health and productivity.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"43 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146088995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the genetic basis of susceptibility to amoebic gill disease and idiopathic gill lesions in Atlantic salmon populations using field data.","authors":"Afees A Ajasa,Solomon A Boison,Muhammad L Aslam,Marie Lillehammer,Hans M Gjøen","doi":"10.1186/s12711-025-01025-6","DOIUrl":"https://doi.org/10.1186/s12711-025-01025-6","url":null,"abstract":"BACKGROUNDGill-related morbidity and mortality have become a major concern to the Atlantic salmon industry worldwide. Understanding the genetic mechanisms underlying susceptibility to gill diseases or lesions can help guide mitigation efforts. Genome-wide association analysis was conducted on gill scores from two large cohorts of Atlantic salmon populations, reared in Norway and Canada, that were phenotyped during amoebic gill disease (AGD) outbreaks and at harvest (referred to as idiopathic gill lesions (IGL)), respectively.RESULTSWhereas one novel quantitative trait locus (QTL) region on chromosome 12 was associated with susceptibility to AGD, two QTL regions on chromosomes 2 and 12 were associated with IGL. There was an overlap between the QTL region on chromosome 12 for AGD and IGL. The lead variant(s) identified explained approximately 7% of the additive genetic variance for AGD, and 3 and 10% for IGL, for the QTL on chromosomes 2 and 12, respectively. Putative candidate genes identified within or close to the lead variants include tfeb, zscan12l, and ifi44l, with the majority of these genes playing roles relating to immune functions. Fine-mapping the identified QTL region associated with AGD using re-sequence data revealed a lead intergenic variant explaining 9% of the additive genetic variance.CONCLUSIONSOur results provide valuable insight into the genetic architecture of susceptibility to AGD and IGL, suggesting that both traits may be partly under the same genetic control. Future studies are warranted, especially on the genetic correlation between AGD and IGL.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"47 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial modelling improves genomic evaluation in Tanzanian smallholder admixed dairy cattle.","authors":"Isidore Houaga, Raphael Mrode, Julie Ojango, Chinyere Charlote Ekine-Dzivenu, Mwai Okeyo, Zabron Nziku, Athumani Nguluma, Eva Lavrenčič, Finn Lindgren, Ivan Pocrnic, Appolinaire Djikeng, Gregor Gorjanc","doi":"10.1186/s12711-025-01021-w","DOIUrl":"10.1186/s12711-025-01021-w","url":null,"abstract":"<p><strong>Background: </strong>Smallholder dairy production systems in low-and middle-income countries are characterised by large phenotypic variance due to diverse environmental effects, farming practices, and crossbreeding. Furthermore, small herds, low genetic connectedness, and limited data recording challenge accurate separation of environmental and genetic effect in such settings, limiting genetic improvement. Here, we evaluated the impact of modelling spatial variation between herds to address these challenges and improve the accuracy of genomic evaluation for Tanzanian smallholder dairy cattle.</p><p><strong>Results: </strong>We analysed 19,375 test-day milk yield records of 1894 dairy cows from 1386 herds across four distinct geographical regions in Tanzania. The cows had 664,822 SNP marker genotypes after quality control and were highly admixed. We fitted a series of GBLUP models to evaluate the impact of modelling the herd effect and the spatial effect on. The herd effect was fitted as an independent random effect, while the spatial effect was fitted as a random effect with Euclidean distance-based Matérn covariance function. The models were compared based on: model fit; estimates of variance components and breeding values; correlations between the estimated contribution of breeding values, herd effect, and spatial effect to phenotype values; and the accuracy of phenotype prediction in cross-validation and forward validation. The results showed large differences in milk yield between and within regions, as well as significant variation due to the spatial effect, which were not fully captured by modelling the herd effect. The results also strongly indicate that a model with just the herd effect underestimated breeding values of animals in less favourable environments and overestimated breeding values of animals in more favourable environments.</p><p><strong>Conclusions: </strong>This study demonstrated the challenge of achieving accurate genomic evaluation in smallholder settings. By leveraging spatial modelling we maximised the use of available data and improved the separation of genetic and environmental effects. Further work is required to improve smallholder genetic evaluations by understanding environmental and genetic processes that drive the large phenotypic variance in African smallholder setting.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":" ","pages":"8"},"PeriodicalIF":3.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12829002/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146020856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of optimal contribution selection, trait distribution management, and group selection in layer chickens.","authors":"Anna Wolc,Brian P Kinghorn,Alexander J Kinghorn,Jack C M Dekkers","doi":"10.1186/s12711-025-01029-2","DOIUrl":"https://doi.org/10.1186/s12711-025-01029-2","url":null,"abstract":"BACKGROUNDOptimal Contribution Selection (OCS) aims at maximizing long term response to selection by balancing short term genetic gain and inbreeding rate. In this study, OCS, trait management, and group mating methods were applied to poultry data to evaluate their potential impact. Real data from a White Leghorn line with 7 generations of pedigree and estimated breeding values (EBV) for 18 traits were used to evaluate responses from a single round of selection. It was shown that the current inbreeding rate is low and cannot be substantially reduced without significant loss of genetic gain, unless implemented in parallel with genomics to enable flexibility in population structure.RESULTSAllowing flexible mating ratios under OCS resulted in 5.3 to 23.8% more genetic gain plus lower loss of genetic diversity compared to fixed-ratio ocs. However, in the case of multiple males per female, implementation is logistically challenging and requires genotyping of all hatched chicks. Using predicted progeny trait distribution management, a 3.2 g difference in mean EBV for egg weight was obtained for two market-targeted groups, without impact on the predicted progeny mean for the multi-trait index used for selection, or on average parental coancestry, but with a small increase in progeny inbreeding. While maintaining these two egg weight groups, tactical desired gains using trait EBV was used to favourably reduce predicted progeny genetic merit for feed intake from + 0.850 to -0.005 g/day, with little impact on genetic gain for other traits, mean index values, mean parental coancestry, or mean progeny inbreeding. Using pooled semen or multi-sire mating, while accounting for variation in male reproductive success, resulted in only a 0.5% reduction in response in predicted mean progeny index and in small increases in mean parental coancestry (from 0.014 to 0.015) and mean progeny inbreeding (from 0.005 to 0.007).CONCLUSIONSEvaluating the longer-term impacts of OCS and other methods employed requires multi-generation simulations, ideally starting from the current real data as a base. However, the current approach of using a real implementation scenario is important in decision making for real-life applications. Similar benefits from the selection and mating strategies used here are expected in breeding programs for other species.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"16 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic diversity and population structure of U.S. Suffolk sheep participating in the national sheep improvement program.","authors":"Carrie S Wilson,Brenda M Murdoch,Luiz F Brito,J Bret Taylor,Artur O Rocha,Brad A Freking,Thomas W Murphy,Ronald M Lewis","doi":"10.1186/s12711-025-01027-4","DOIUrl":"https://doi.org/10.1186/s12711-025-01027-4","url":null,"abstract":"BACKGROUNDThe Suffolk is the primary terminal sire breed in the U.S. As a breed that participates in the National Sheep Improvement Program (NSIP), Suffolk breeders are attempting to accumulate enough genomic information to provide genomic-enhanced estimated breeding values as part of the national genetic evaluations. The effective implementation of genomic selection and management of genetic diversity in the breed require a comprehensive assessment of current genetic diversity and population structure. Therefore, the primary objective of this study is to assess the genetic diversity and population structure present in U.S. Suffolk sheep included in the NSIP using both pedigree- and genomic-based methods. A secondary objective is to compare the levels of genomic diversity of U.S. Suffolk to those from other selected countries.RESULTSBased on pedigree (n = 75,161) analyses, the generation interval was 2.8 years, and the effective number of founders and ancestors were 504 and 300, respectively. Effective population size ranged from 28 to 194 based on pedigree-based measures and 75 to 85 based on genomic-based metrics. When the mean inbreeding was compared for the 1,878 genotyped animals (GGP Ovine 50 K BeadChip) that passed quality control, pedigree-based inbreeding; and, inbreeding based on heterozygosity, runs of homozygosity, diagonal of the genomic relationship matrix, and homozygous-by-descent segments were 4.8%, 3.3%, 4.6%, 3.3%, and 3.4%, respectively. Of the 16 flocks with genotyped animals, four had fixation index values that exceeded 0.10, but the model-based population structure showed admixture across all flocks. For the principal component analysis and the model-based population structure with international genomic datasets, the U.S. Suffolks were distinct, the United Kingdom Suffolks were placed in-between but distinct from the other countries, and the Australian, Irish, and New Zealand Suffolks were grouped together.CONCLUSIONSThe current level of genetic diversity and population structure was quantified for the U.S. Suffolk breed. While the rate of inbreeding was at an acceptable level, the effective population size was modest, indicating that monitoring of genetic diversity and strategic mating of less related animals in the breed should continue. As the sheep industry moves forward, regular assessments of genetic diversity and population structure are needed.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"24 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ReaGP: integrating residual units and attention mechanisms in convolution neural network for genomic prediction.","authors":"Jing Li,Peng Guo,Yuanxu Zhang,Haoran Ma,Zhida Zhao,Yuanqing Wang,Zezhao Wang,Yan Chen,Lingyang Xu,Lupei Zhang,Huijiang Gao,Xue Gao,Junya Li,Bo Zhu","doi":"10.1186/s12711-025-01015-8","DOIUrl":"https://doi.org/10.1186/s12711-025-01015-8","url":null,"abstract":"BACKGROUNDVarious methods have been widely utilized to estimate the genomic breeding values (GEBVs) for genomic prediction. Traditional approaches often relied on the assumption of linear regression models, which struggle to effectively capture the nonlinear relationships between limited phenotypic data and high-dimensional genotypic data. Deep learning (DL) provided a powerful solution for addressing nonlinear problems. Herein, we proposed a novel deep learning method, named residual attention genomic prediction (ReaGP), which was characterized by two main features. It employed residual units to mitigate gradient instability and network degradation issues, while leveraging attention mechanisms to enhance the mining of critical feature information. Moreover, genomic data processed with frequency encoding was integrated into ReaGP to achieve a richer feature representation.RESULTSWhen assessing the predictive accuracy across three animal datasets and two plant datasets covering 15 traits with varying heritabilities, ReaGP improved predictive performance by 14.41% and 7.78% over linear models specifically genomic best linear unbiased prediction (GBLUP) and BayesB, and by 34.41% and 10.09% over kernel methods namely support vector regression (SVR) and reproducing kernel Hilbert space (RKHS), respectively. ReaGP achieved a 4.35% enhancement on average compared to deep neural network genomic prediction (DNNGP). Furthermore, while ReaGP has more trainable parameters than DNNGP, it requires only half the number of floating-point operations.CONCLUSIONSWe introduced a novel deep learning method for genomic prediction, which integrates residual units, attention mechanisms and frequency-encoded genomic data. Comprehensive evaluation on pig, dairy cow, Huaxi cattle, wheat and rice datasets demonstrated that ReaGP was a promising tool for most traits. Thus, ReaGP could be considered as an efficient deep learning method for genomic prediction in farm animals and crops. The source code in this study is available at https://github.com/LiJing5467/ReaGP .","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"13 1","pages":"6"},"PeriodicalIF":4.1,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrative analysis of GWAS, Bayesian fine-mapping, Mendelian randomization and colocalization reveals genetic determinants underlying milk-related traits in dairy cattle.","authors":"Jun Teng,Xiuxin Zhao,Qingxia Yan,Jian Yang,Fen Pei,Xinyi Zhang,Chongwei Duan,Zhujun Chen,Qianwen Xu,Yan Liu,Guanghui Xue,Shuwen Xia,Huili Wang,Yao Gu,Lingzhao Fang,Huiming Liu,Hongding Gao,Jing An,Li Jiang,Chao Ning,Rongling Li,Yundong Gao,Xiao Wang,Jianbin Li,Qin Zhang","doi":"10.1186/s12711-025-01028-3","DOIUrl":"https://doi.org/10.1186/s12711-025-01028-3","url":null,"abstract":"","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"29 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GWAS on early sexual maturation across freshwater and seawater environments in domesticated Lochy strain of Atlantic salmon.","authors":"Patricia Rivera,M Angélica Rueda-Calderón,Nicol Delgado,María Eugenia López,Anti Vasemägi,Carlos Soto,Alfonso Romero,José Gallardo-Matus","doi":"10.1186/s12711-025-01026-5","DOIUrl":"https://doi.org/10.1186/s12711-025-01026-5","url":null,"abstract":"BACKGROUNDEarly sexual maturation is a challenging obstacle to overcome in Atlantic salmon farming. This trait primarily affects males and occurs in both freshwater fish farms and sea culture cages during the fattening phase. Current strategies for preventing early maturation include a combination of genetic selection and management practices. However, the genetic architecture of early maturation appears to vary across populations, strains and environments. Our study aimed to elucidate the genetic architecture of early maturation in the Lochy strain of Atlantic salmon using genome-wide SNP panels. This European-origin strain grows rapidly but is prone to high rates of precocious male maturation if not properly managed.RESULTSWe report two genome-wide association (GWAS) results focusing on males of the Lochy strain of Atlantic salmon. The first included seawater-cultured fish (Group-SA: 714 males, 80 precocious and 634 immature) with an artificial continuous light photoperiod, while the second included freshwater-cultured fish (Group-FN: 707 males, 333 precocious and 374 immature) with a natural photoperiod. Group-SA was genotyped using a custom 46,115-SNP Illumina microarray, whereas Group-FN employed a custom 62,044-SNP Thermo microarray. Genomic heritability of early maturation in males was consistently high across models-ranging from 0.62-0.79 in seawater and from 0.54-0.62 in freshwater. In Group-SA, one significant SNP associated with early sexual maturation were identified on chromosome Ssa25. In Group-FN, sixty significant SNPs associated with early sexual maturation were identified on chromosomes Ssa5, Ssa7, and Ssa25. The genetic variance explained by these SNPs ranged from 16.1-53.7%, while the proportion of phenotypic variance explained varied from 8.7% to 29.1%. The identified candidate genes included chmp2b and vgll3, both previously reported in other domesticated European-origin populations, suggesting some degree of convergence.CONCLUSIONSThe SNPs associated with early maturation are promising candidates for application in breeding programs in the Lochy strain aimed at implementing improved control strategies against early maturation in both freshwater and sea environments.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"86 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A 3' UTR polymorphism g.1618 G > A in the MAFA gene modulates miR-3678-3p binding and enhances meat production in sheep via the MAFA/GHR/JAK2 pathway.","authors":"Cuiyu Lai,Dandan Tan,Xuewen Han,Yu Fu,Jinlin Chen,Xiaofan Yang,Xuesong Shan,Yang Chen,Huaizhi Jiang","doi":"10.1186/s12711-025-01024-7","DOIUrl":"https://doi.org/10.1186/s12711-025-01024-7","url":null,"abstract":"BACKGROUNDMAFA (musculoaponeurotic fibrosarcoma oncogene Family A) is a specific transcriptional activator of the insulin gene (INS), playing a critical role in regulating insulin secretion and thereby indirectly influencing growth and development in animals. Previously, MAFA's role in meat production remained speculative, despite its known function in endocrine regulation. However, our recent publication provides direct evidence linking MAFA to muscular phenotypes in sheep. These findings support a direct role for MAFA in muscle growth, complementing its canonical role in insulin-mediated growth regulation. To further investigate this proposition, we conducted genotype-phenotype association analyses to assess the potential impact of MAFA polymorphisms on meat production performance in sheep. In parallel, we employed a combination of dual-luciferase reporter assays, transcriptome profiling, and ChIP-PCR to dissect the underlying molecular mechanisms. These findings were further validated in ovine myoblasts.RESULTSWe found that the g.1618 G/A polymorphism in the 3' UTR of the MAFA gene is associated with meat production in sheep. Individuals with the GG genotype exhibited a 36.9% higher proportion of longissimus dorsi muscle mass compared to those with the AA genotype. Further analysis, including binding site prediction and dual-luciferase reporter assays, revealed that this mutation may regulate MAFA translation efficiency by altering the binding affinity of miR-3678-3p. Subsequently, ChIP-PCR experiments confirmed that the growth hormone receptor (GHR) gene is a direct target of the transcription factor MAFA. By conducting miR-3678-3p transfection and MAFA overexpression experiments in sheep myoblasts, we further validated the miR-3678-3p/MAFA/GHR regulatory axis and the classical GHR/JNK signaling pathway. These findings elucidate the molecular mechanism by which the g.1618 G/A polymorphism in the MAFA gene affects meat production in sheep, providing a novel molecular marker with potential application in molecular breeding for improved meat performance in sheep.CONCLUSIONSIn summary, The G/A polymorphism at position g.1618 in the 3' UTR of the MAFA gene affects the binding of miR-3678-3p, thereby regulating the expression of the transcription factor MAFA at the post-transcriptional level. MAFA, in turn, directly influences the transcription of its target gene GHR, which affects JAK2 phosphorylation, ultimately regulating myoblast proliferation and muscle growth.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"119 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145801277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}