Genetics Selection Evolution最新文献

{"title":"Genome-environment association analysis reveals climate-driven adaptation of chickens","authors":"Xiurong Zhao, Jinxin Zhang, Junhui Wen, Xinye Zhang, Haiying Li, Huie Wang, Tao Zhu, Changsheng Nie, Xinghua Li, Weifang Yang, Guomin Cao, Wenjie Xiong, Xue Wang, Zhonghua Ning, Lujiang Qu","doi":"10.1186/s12711-025-00989-9","DOIUrl":"https://doi.org/10.1186/s12711-025-00989-9","url":null,"abstract":"Domestic chickens are one of the most widely raised and distributed bird species, exhibiting remarkable environmental adaptability, which makes them valuable model organisms for investigating the genetic mechanisms underlying climate adaptation. This study aimed to enhance our understanding of adaptive mechanisms in chickens by jointly analyzing genomic variations and climatic variables related to temperature and precipitation. To this end, whole-genome sequencing data were collected from 199 indigenous domestic chickens raised under diverse environmental conditions worldwide, and three genome-environment association analyses were performed. We identified 184 genes potentially associated with climate adaptation in chickens. Among these, the TSHR gene may play multiple roles in adaptation driven by different climatic factors. Immune-related genes also appear to contribute to climate adaptation in chickens. By calculating the allele frequencies of single nucleotide polymorphisms (SNPs) within candidate genes associated with temperature and precipitation adaptation, we identified five SNPs within four genes (ZNF536, ENSGALG00000049158, PAPPA, and EHMT1) that exhibited distinct geographic distribution patterns. Extended haplotype homozygosity (EHH) analysis of these SNPs revealed that haplotypes carrying the mutant allele exhibited slower decay in EHH compared to those carrying the wild-type allele. These results further indicate that the loci have experienced strong selective pressures, suggesting that the associated genes may play crucial roles in climate adaptation in chickens. Overall, this study provides new insights into the genetic mechanisms underlying climate adaptation in domestic chickens.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"266 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678158","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":"Towards assessing indirect genetic effects in dairy cattle","authors":"Ida Hansson, Piter Bijma, Freddy Fikse, Lars Rönnegård","doi":"10.1186/s12711-025-00988-w","DOIUrl":"https://doi.org/10.1186/s12711-025-00988-w","url":null,"abstract":"Social interactions in a dairy herd may impact an individual’s production, e.g., milk yield. These interactions can have a genetic component, so-called indirect genetic effects (IGE). IGEs contribute to heritable variation in other species, but studies on IGEs in cows are limited. Knowledge is needed on appropriate methods to monitor social interactions in cows. We evaluated with simulations whether we can estimate IGEs in cows. We used milk yield as an example trait, and we assessed how herd size, direct and indirect genetic correlations, and magnitude of IGE affected the variance component estimations and breeding value accuracies. We investigated the importance of knowing the contact intensity and direction by either including or ignoring them in the estimation model. Additionally, we investigated how random noise added to the intensities would affect the estimates and breeding values. The estimated variance components were unbiased and precise for scenarios with different herd sizes of 50, 100, or 200 cows and direct and indirect genetic correlations of either − 0.6, 0, or 0.6. The IGE breeding value accuracies were 0.55–0.65 for cows when the IGE explained 30% of the phenotypic variance. When the magnitude of the IGE became smaller, the precision of the estimated variances became lower. The IGE breeding value accuracies were 0.16–0.52 for cows when the IGE explained 1.5–15% of the phenotypic variance. Using imprecise intensities or ignoring the contact direction underestimated the variance of the indirect effects, and the breeding value accuracies became lower. Ignoring the variation in intensities in the model led to unbiased variance component estimates but a larger residual variance and lower breeding value accuracies than if we used imprecise intensities. We could estimate IGE in dairy cattle with high accuracy and precision in a simulated population of 10,000 phenotyped cows distributed over 50–200 herds. A smaller IGE variance led to less precise estimates and lower breeding value accuracies. Ignoring information about the intensity of contact in the model would be worse than using imprecise intensities, and using technology that also monitors the direction of contact may be beneficial to estimate variance components of IGE.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"14 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669658","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":"Improving genomic prediction accuracy for methane emission and feed efficiency in sheep: integrating rumen microbial PCA with host genomic variation using neural network GBLUP (NN-GBLUP)","authors":"Setegn Worku Alemu, Timothy P. Bilton, Patricia L. Johnson, Benjamin J. Perry, Hannah Henry, Ken G. Dodds, John C. McEwan, Suzanne J. Rowe","doi":"10.1186/s12711-025-00987-x","DOIUrl":"https://doi.org/10.1186/s12711-025-00987-x","url":null,"abstract":"Methane emissions from ruminant livestock pose a significant challenge to mitigating climate change. Genomic selection offers a promising approach to reduce methane emissions, but prediction accuracy remains low due to the high cost of measuring methane emissions. Integrating rumen microbiome composition (RMC) data may improve genomic prediction accuracy, yet the high dimensionality of RMC data presents computational challenges. This study aimed to (1) evaluate the effectiveness of principal component analysis (PCA) for reducing RMC data dimensionality while retaining essential information, and (2) assess whether incorporating PCA-reduced RMC data as intermediate traits in a Neural Network Genomic Best Linear Unbiased Prediction (NN-GBLUP) model improves genomic prediction accuracy for methane emissions and feed efficiency traits in sheep. For the first objective, Principal Components (PCs) explaining 100% of variation effectively captured RMC information, with microbiability estimates closely matching those from the full dataset. For the second objective, the NN-GBLUP model incorporating PCA-reduced RMC data improved prediction accuracy compared to standard GBLUP methods. Prediction accuracy for methane emissions increased from 0.09 to 0.30 in train-test validation and from 0.15 to 0.27 in five-fold cross-validation using PCA components explaining 25% of total RMC variation. For residual feed intake, accuracy improved from 0.25 to 0.37 in train-test validation and from 0.25 to 0.34 in cross-validation. Optimal PCA components varied by trait, with 25% and 50% components showing the best results. Prediction accuracy did not improve for carbon dioxide emissions, live weight, and mid-intake, indicating trait-dependent microbiome influence. Principal Component Analysis reduced the dimensionality of rumen microbiome data while preserving essential biological information. The integration of these PCA-reduced data with host genomic information through an NN-GBLUP model substantially improved genomic prediction accuracy for methane emissions and feed efficiency in sheep. Principal components explaining 25% and 50% of the variation yielded the highest accuracy, whereas higher components (75% and 95%) reduced accuracy for methane traits. This approach shows promise for implementing genomic selection strategies to reduce methane emissions and improve feed efficiency in ruminant livestock in a computationally efficient manner, thereby contributing to climate change mitigation efforts in agriculture.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"17 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144652103","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":"Positive selection on rare variants of IGF1R and BRD4 underlying the cold adaptation of wild boar","authors":"Jianhai Chen, Ivan Jakovlić, Mikhail Sablin, Shengqian Xia, Zhixiang Xu, Yapin Guo, Renzuo Kuang, Jie Zhong, Yangying Jia, Nhien Thuy Thi Tran, Hao Yang, Hong Ma, Nikica Šprem, Jianlin Han, Di Liu, Yunxia Zhao, Shuhong Zhao","doi":"10.1186/s12711-025-00986-y","DOIUrl":"https://doi.org/10.1186/s12711-025-00986-y","url":null,"abstract":"Domestic piglets often die of hypothermia, whereas Eurasian wild boar (Sus scrofa) thrives from tropical lowlands to subarctic forests. The thermoregulation of wild boar offers a natural experiment to uncover the genetic basis of cold adaptation. We conducted whole-genome resequencing on wild populations from cold regions (northern and northeastern Asia, with six samples) and warm regions (southeastern Asia and southern China, with five samples). By integrating publicly available data, we compiled a core dataset of 48 wild boar samples and an extended dataset of 445 wild boar and domestic pig samples to identify candidate genes related to cold adaptation. To investigate the functional effects of two candidate variants under positive selection, we performed CUT&Tag and RNA-seq using the northeastern Asian Min pig breed as a proxy for a cold-adapted population. Our study identified candidate genes associated with cold adaptation, which are significantly enriched in thermogenesis, fat cell development, and adipose tissue pathways. We discovered two enhancer variants under positive selection: an intronic variant of IGF1R (rs341219502) and an exonic variant of BRD4 (rs327139795). These variants exhibited the highest differentiation between populations of wild boar and domestic pigs in cold and warm region populations. Furthermore, these rare variants were absent in outgroup species and warm-region wild boars but were nearly fixed in cold-region populations. The H3K27ac CUT&Tag profiling revealed that the rs341219502 variant of IGF1R is linked to the gain of novel binding sites for three transcription factors involving regulatory changes in enhancer function. In contrast, the rs327139795 variant of BRD4 may result in the loss of a phosphorylation site due to an alteration in the amino acid sequence. Our study identified candidate genes for cold adaptation in wild boar. The variant rs341219502 in the IGF1R enhancer and the variant rs327139795 in the BRD4 exon, both of which were under positive selection and nearly fixed in populations from cold regions, suggest they may have originated de novo in these populations. Further analysis indicated that rs341219502 could influence enhancer function, while rs327139795 may affect amino acid alterations. Overall, our study highlights the adaptive evolution of genomic molecules that contribute to the remarkable environmental flexibility of wild boar.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"4 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144640363","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":"Estimating the genetic parameters of resilience toward known and unknown disturbances in sheep using wool fibre diameter and body weight variability","authors":"Erin G. Smith, Samuel F. Walkom, Dominic L. Waters, Sam A. Clark","doi":"10.1186/s12711-025-00983-1","DOIUrl":"https://doi.org/10.1186/s12711-025-00983-1","url":null,"abstract":"General resilience in animals can be quantified by analysing the variability in longitudinal data. However, it is unclear whether resilience indicators derived from different longitudinal data series can predict resilience to known or unknown disturbances in sheep. This study aimed to use two sources of longitudinal data, wool fibre diameter and body weight, to develop potential indicators for resilience to the known stress of weaning and overall resilience to unknown disturbances. The genetic parameters of these traits were assessed, along with the genetic correlations between traits from different data series and different definitions of resilience. Additionally, correlations between resilience indicators, health and production traits were estimated to evaluate the suitability of including resilience indicators in breeding programs. Fibre diameter and body weight records from approximately 6500 yearling Merino sheep were used to estimate four resilience indicators of resilience towards unknown disturbances: log-transformed variance (Lnvar), lag-1 Auto (Auto), skewness (Skewness) and absolute difference in the deviations (ABS) from these curves. Three other traits, rate of change in the response and recovery (ROC_response and ROC_recovery) and area between curves (ABC) during a known disturbance of weaning, were also estimated. Resilience indicators were found to be lowly heritable (0.03 ± 0.01 to 0.18 ± 0.04). Genetic correlations between the general resilience indicator and the indicator of resilience to weaning stress were generally moderate, particularly in the wool fibre diameter data, suggesting these may represent similar traits. Genetic correlations between resilience indicators derived from wool fibre diameter and body weight data were typically weak to moderate, which indicates that they possibly capture different aspects of resilience. The genetic correlations between resilience indicators and health traits were mostly low, except for body condition score. Correlations between resilience and production traits were low to moderate and favourable. Resilience indicators based on deviations in wool fibre diameter and body weight can be used to potentially select animals that are less affected by environmental disturbances. The genetic correlations between resilience indicators and health and production traits suggest that these traits could be included in breeding programs to improve resilience without adversely affecting production traits.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"23 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144622229","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":"The impact of deregressed foreign breeding values on national beef cattle single-step genomic evaluation","authors":"Damilola Adekale, Zengting Liu, Ross Evans, Thierry Pabiou, Reinhard Reents, Dierck Segelke, Jens Tetens","doi":"10.1186/s12711-025-00982-2","DOIUrl":"https://doi.org/10.1186/s12711-025-00982-2","url":null,"abstract":"In recent years, genetic evaluations in cattle breeding have shifted from purely national evaluations to multinational evaluations considering relatives from other countries. Integrating international estimated breeding values (EBVs) into national genomic evaluations presents challenges due to differences in evaluation methodologies and data sources. This study focused on the impact of blending internationally derived EBVs with national EBVs in the single-step genomic evaluation of German beef cattle using three approaches to deregressing EBVs. The national phenotypic data for four breeds (Angus, Charolais, Limousin, and Simmental) were obtained from the routine German beef cattle evaluation of December 2022, and the international EBVs were obtained from the routine Interbeef evaluation of January 2023. Scalar (Garrick (GA), Van Raden (VR)) and matrix deregression approaches were compared for reversibility of EBVs. A forward validation study was used to evaluate the accuracy, dispersion and level bias obtained in a purely national single-step evaluation, and single-step genomic evaluations blended with DRPs obtained from the three deregression approaches. A validation study based on forward prediction showed improved accuracy, and reduced dispersion bias in the EBVs blended with international EBVs compared to purely national EBVs, particularly for the direct and maternal effects of 200-day weight. As expected, Pearson correlation analysis revealed that the matrix deregression (> 0.99) approach outperformed the scalar deregression approaches (0.75–0.99), exhibiting a greater correlation between the EBVs obtained from DRPs and the EBVs obtained from phenotypes across the various breeds and traits in our study. A forward validation study with and without integrating foreign data across the three deregression methods showed improvement in reducing dispersion bias, as indicated by the regression coefficient. The GEBVs from an evaluation incorporating foreign information with national data showed a higher correlation to the GEBVs from a truncated evaluation than those from an evaluation without foreign information. These findings underscore the importance of accurately integrating foreign EBVs to enhance national genomic evaluations and genetic progress in livestock populations. Our results show that the matrix approach to deregressing EBVs performs optimally across traits and breeds. However, the VR deregression approach can serve as an alternative in situations where the matrix deregression approach might be too technical to implement.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"14 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144622210","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 parameters and potential of reducing tail and ear damage in pigs through breeding","authors":"Bernadett Hegedűs, Natália Galoro Leite, J. Elizabeth Bolhuis, Piter Bijma","doi":"10.1186/s12711-025-00976-0","DOIUrl":"https://doi.org/10.1186/s12711-025-00976-0","url":null,"abstract":"Ear and tail biting are behaviours in pigs that cause both welfare problems and financial losses. Data collection of behaviour is difficult at the large scale needed for breeding. The damage inflicted on victims can, however, serve as a proxy for animal breeding. Here, we analysed tail and ear damage scores on their original scale, binary scale, and summed versions of these damage traits to investigate which trait definition is best for genetic selection. Using data from six purebred lines (33,329 animals in total) we aimed to (1) estimate genetic parameters for ear and tail damage using direct genetic models, (2) estimate the genetic correlation between tail and ear damage, (3) compare different trait definitions and their impact on accuracy, dispersion, and bias of estimated breeding values (EBV), and (4) compare expected responses to selection for each trait definition. The heritability of the damage traits ranged from 0.04 to 0.06. Ear and tail damage were moderately correlated (0.41–0.45), meaning that the genetic propensity of being a victim is a different trait for tail versus ear biting. Estimates of the accuracy of the EBV for the traits with a five-fold cross-validation and the linear regression method based on pedigree relationships ranged from 0.27 to 0.57, the dispersion from 0.91 to 1.18, and the bias was negligible. With a selected proportion of 5%, genetic progress of ~ 0.20–0.78 genetic standard deviations per generation can be reached, depending on the trait. It was trait dependent whether direct or indirect selection yielded the most response. Ear and tail damage are heritable traits and are moderately positively correlated. The EBV for the evaluated traits related to ear and tail damage showed moderate accuracies, minor dispersion, and no bias. We hypothesize that from a welfare perspective, ear and tail damage on the original scale are the relevant breeding goal traits. For ear damage on the original scale, the highest response to selection can be expected when selecting on the trait itself, whereas for tail damage on the original scale, selection on summed damage showed the highest gain. Results from this study show that genetic improvement of the direct genetic effect of ear and tail damage is possible.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"38 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144622209","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":"Accurate determination of breed origin of alleles in a simulated smallholder crossbred dairy cattle population","authors":"Berihu Welderufael, Isidore Houaga, R. Chris Gaynor, Gregor Gorjanc, John M. Hickey","doi":"10.1186/s12711-025-00985-z","DOIUrl":"https://doi.org/10.1186/s12711-025-00985-z","url":null,"abstract":"Accurate assignment of breed origin of alleles (BOA) at a heterozygote locus may help to introduce a resilient or adaptive haplotype in crossbreeding. In this study, we developed and tested a method to assign breed of origin for individual alleles in crossbred dairy cattle. After generations of mating within and between local breeds as well as the importation of exotic bulls, five rounds of selected crossbred cows were simulated to mimic a dairy breeding program in the low- and middle-income countries (LMICs). In each round of selection, the alleles of those crossbred animals were phased and assigned to their breed of origin (being either local or exotic). Across all core lengths and modes of phasing (with offset—move 50% of the core length forward or no-offset), the average percentage of alleles correctly assigned a breed origin was 95.76%, with only 1.39% incorrectly assigned and 2.85% missing or unassigned. On consensus, the average percentage of alleles correctly assigned a breed origin was 93.21%, with only 0.46% incorrectly assigned and 6.33% missing or unassigned. This high proportion of alleles correctly assigned a breed origin resulted in a high core-based mean accuracy of 0.99 and a very high consensus-based (most frequently observed assignment across all the scenarios) mean accuracy of 1.00. The algorithm’s assignment yield and accuracy were affected by the choice of threshold levels for the best match of assignments. The threshold level had the opposite effect on assignment yield and assignment accuracy. A less stringent threshold generated higher assignment yields and lower assignment accuracy. We developed an algorithm that accurately assigns a breed origin to alleles of crossbred animals designed to represent breeding programs in the LMICs. The developed algorithm is straightforward in its application and does not require prior knowledge of pedigree, which makes it more relevant and applicable in LMICs breeding programs.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"275 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144603163","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":"Bivariate GWAS performed on rabbits divergently selected for intramuscular fat content reveals pleiotropic genomic regions and genes related to meat and carcass quality traits","authors":"Bolívar Samuel Sosa-Madrid, Agostina Zubiri-Gaitán, Noelia Ibañez-Escriche, Agustín Blasco, Pilar Hernández","doi":"10.1186/s12711-025-00971-5","DOIUrl":"https://doi.org/10.1186/s12711-025-00971-5","url":null,"abstract":"Meat quality plays an important economic role in the meat industry and livestock breeding programmes. Intramuscular fat content (IMF) is one of the main meat quality parameters and its genetic improvement has led breeders to investigate its genomic architecture and correlation with other relevant traits. Genetic markers associated with causal variants for these traits can be identified by bivariate analyses. In this study, we used two rabbit lines divergently selected for IMF to perform bivariate GWAS with the aim of detecting pleiotropic genomic regions between IMF and several weight, fat, and meat quality traits. Additionally, whole-genome sequencing data from these lines were used to identify potential causal variants associated with the genetic markers. The main pleiotropic region was found on Oryctolagus cuniculus chromosome (OCC) 1 between 35.4 Mb and 38.2 Mb, explaining up to 2.66% of the IMF genetic variance and being associated with all traits analysed, except muscle lightness. In this region, the potentially causal variants found pointed to PLIN2, SH3GL2, CNTLN, and BNC2 as the main candidate genes affecting the different weight, fat depots and meat quality traits. Other relevant pleiotropic regions found were those on OCC3 (148.94–150.89 Mb) and on OCC7 (27.07–28.44 Mb). The first was associated with all fat depot traits and explained the highest percentage of genetic variance, up to 10.90% for scapular fat. Several allelic variants were found in this region, all located in the novel gene ENSOCUG00000000157 (orthologous to ST3GAL1 in other species), involved in lipid metabolism, suggesting it as the main candidate affecting fat deposition. The region on OCC7 was associated with most meat quality traits and explained 8.48% of the genetic variance for pH. No allele variants were found to segregate differently between the lines in this region; however, it remains a promising region for future functional studies. Our results showed that bivariate models assuming pleiotropic effects are valuable tools to identify genomic regions simultaneously associated with IMF and several weight, fat and meat quality traits. Overall, our results provided relevant insights into the correlations and relationships between traits at the genomic level, together with potential functional mutations, which would be relevant for exploration in rabbit and other livestock breeding programmes.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"107 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144611388","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":"Multiple cell types guided by neurocytes orchestrate horn bud initiation in dairy goats","authors":"Hegang Li, Mengmeng Du, Xiaokun Lin, Xinxin Cao, Lu Leng, F. M. Perez Campo, Dongliang Xu, Lele Hou, Xiaoxiao Gao, Jianyu Zhou, Ming Cheng, Jianguang Wang, Qinan Zhao, Yin Chen, Feng Yang, Jinshan Zhao","doi":"10.1186/s12711-025-00981-3","DOIUrl":"https://doi.org/10.1186/s12711-025-00981-3","url":null,"abstract":"Horn development is a key ruminant trait involving multi-cell type coordination via molecular pathways. This study used scRNA-seq to analyze cellular heterogeneity and fate trajectories during early horn bud niche formation, revealing key gene expression profiles. Combining with hematoxylin–eosin (HE) staining and immunohistochemical analysis, we further verified the asynchronous developmental pathways of key cells in the skin tissue of fetal goat horn bud at induction (embryonic day (E) 50; E50), organogenesis (E60), and cytodifferentiation (E70) stages, and demonstrated the signal transmission routes for the development of early horn buds. We revealed temporal and spatial differences of the main signal transmission of horn bud development combining with existing literatures. We speculated that multiple cell types under the guidance of nerve cells collaborated on horn bud initiation in dairy goats. In detail, neural cells receive initial horn bud signals, stimulating hair follicle cell degeneration and transmitting to dermal cells, which evolve through intermediates, amplify signals to epithelial cells, and differentiate into mesenchymal cells. Nerve cell branches also trigger neural crest cell production/migration, working with chondrocytes to promote keratinocyte differentiation for horn bud formation. In addition, we further identified the early horn bud developmental specific events, including the screening of biological functions, signaling pathways and key candidate genes. This study employed scRNA-seq to characterize cell fate trajectories and gene expression profiles in goat fetal horn buds. Histological comparisons between hornless and horned fetuses revealed cellular heterogeneity in epithelial, dermal, nerve, and hair follicle cells, with pseudo-time analysis identifying distinct differentiation paths. Dermal and epithelial cell transcriptional dynamics were critical for horn bud initiation (branch 1), supported by immunohistochemistry. Keratinocyte and nerve cell state transitions actively regulated horn development, with asynchronous cell development visualized via immunohistochemistry. Functional enrichment analyses (GO/KEGG) highlighted neural crest development and keratinocyte differentiation pathways, identifying candidate genes (EGR1, ZEB2, SFRP2, KRT10, FMOD, CENPW, LDB1, TWIST1) involved in horn morphogenesis. These findings advance understanding of goat horn development and genetic determinants.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"36 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520821","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}