Pub Date : 2025-07-11DOI: 10.1186/s12711-025-00985-z
Berihu Welderufael, Isidore Houaga, R. Chris Gaynor, Gregor Gorjanc, John M. Hickey
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.
{"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}
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.
{"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}
Pub Date : 2025-07-01DOI: 10.1186/s12711-025-00981-3
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
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.
{"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}
Pub Date : 2025-06-23DOI: 10.1186/s12711-025-00984-0
Joe-Menwer Tabet, Ignacio Aguilar, Matias Bermann, Daniela Lourenco, Ignacy Misztal, Paul M. VanRaden, Zulma G. Vitezica, Andres Legarra
Differential treatment of daughters of the same sire within a herd is modelled as the herd-sire effect. Recent changes in management practices may have led to the extensive use of certain bulls in a limited number of herds. In that case, although the effect can be well accounted for in genetic evaluation models, some approximation methods for reliabilities do not consider it correctly, leading to an overestimation of some sires’ approximated reliabilities. This study assessed the potential bias of these approximated reliabilities due to the herd-sire effect in both simulated and real dairy cattle records. Two existing methods were tested: Misztal–Wiggans, which includes a specific modification for herd-sire, and Tier–Meyer, which does not. We also modified and tested a Tier–Meyer method considering the herd-sire effect. We observed that in the presence of the herd-sire effect, reliabilities obtained by approximations were overestimated by the Tier–Meyer method for sires with many daughters in a limited number of herds. This was true even for sires with a large number of daughters. The Misztal–Wiggans method performed correctly. We introduced a modified Tier–Meyer method that weighs the information transmitted by the daughter to the sire as a function of the herd-sire information. As a result, the modified Tier–Meyer method performed well in both simulated and real data. For cows, the inclusion of the herd-sire effect had minimal impact. This study identified possible overestimation of approximated reliabilities of sires with daughters concentrated in a few herds when there is a herd-sire effect. This bias occurs when the herd-sire effect is not correctly modeled in reliability approximation methods. Methods that specifically accounted for the herd-sire effect produced unbiased reliability estimates.
{"title":"Correcting overestimation of approximate traditional reliabilities with herd-sire interactions when young genomic bulls are used in few herds","authors":"Joe-Menwer Tabet, Ignacio Aguilar, Matias Bermann, Daniela Lourenco, Ignacy Misztal, Paul M. VanRaden, Zulma G. Vitezica, Andres Legarra","doi":"10.1186/s12711-025-00984-0","DOIUrl":"https://doi.org/10.1186/s12711-025-00984-0","url":null,"abstract":"Differential treatment of daughters of the same sire within a herd is modelled as the herd-sire effect. Recent changes in management practices may have led to the extensive use of certain bulls in a limited number of herds. In that case, although the effect can be well accounted for in genetic evaluation models, some approximation methods for reliabilities do not consider it correctly, leading to an overestimation of some sires’ approximated reliabilities. This study assessed the potential bias of these approximated reliabilities due to the herd-sire effect in both simulated and real dairy cattle records. Two existing methods were tested: Misztal–Wiggans, which includes a specific modification for herd-sire, and Tier–Meyer, which does not. We also modified and tested a Tier–Meyer method considering the herd-sire effect. We observed that in the presence of the herd-sire effect, reliabilities obtained by approximations were overestimated by the Tier–Meyer method for sires with many daughters in a limited number of herds. This was true even for sires with a large number of daughters. The Misztal–Wiggans method performed correctly. We introduced a modified Tier–Meyer method that weighs the information transmitted by the daughter to the sire as a function of the herd-sire information. As a result, the modified Tier–Meyer method performed well in both simulated and real data. For cows, the inclusion of the herd-sire effect had minimal impact. This study identified possible overestimation of approximated reliabilities of sires with daughters concentrated in a few herds when there is a herd-sire effect. This bias occurs when the herd-sire effect is not correctly modeled in reliability approximation methods. Methods that specifically accounted for the herd-sire effect produced unbiased reliability estimates.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"25 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144341292","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}
Pub Date : 2025-06-17DOI: 10.1186/s12711-025-00977-z
Solène Fresco, Marie-Pierre Sanchez, Didier Boichard, Sébastien Fritz, Pauline Martin
Due to their contribution to global warming, methane emissions from ruminants have been the subject of considerable scientific interest. It has been proposed that such emissions might be reduced using genetic selection; proposed phenotypes differ in the measurement methods used (direct or predicted methane emissions) and in the unit under consideration (g/d, g/kg of milk, g/kg of intake, residual methane emissions). Identifying the quantitative trait loci (QTLs) and candidate genes responsible for genetic variation in methane emissions allows a better understanding of the underlying genetic architecture of these phenotypes. Therefore, the aim of this study was to identify the genomic regions associated with six methane traits predicted from milk mid-infrared (MIR) spectra (0.33 ≤ R2 ≤ 0.88) in French Holstein dairy cows using genome-wide association studies at the whole-genome-sequence level. Six methane emission traits—in g/d, in g/kg of fat- and protein-corrected milk, and in g/kg of dry matter intake—were predicted from milk MIR spectra routinely collected by French milk recording companies. A genome-wide association study of the predicted methane emissions of 40,609 primiparous Holstein cows was conducted using imputed whole-genome-sequence data. This analysis revealed 57 genomic regions of interest; between 1 and 8 QTLs were identified on each of the autosomes except 4, 12, 21, 24 and 26. We identified multiple genomic regions that were shared by two or more predicted methane traits, illustrating their common genetic basis. Functional annotation revealed potential candidate genes, in particular FASN, DGAT1, ACSS2, and KCNIP4, which could be involved in biological pathways possibly related to methane production. The methane traits studied here, which were predicted from milk MIR spectra, appear to be highly polygenic. Several genomic regions associated with these traits contain candidate genes previously associated with milk traits. Functional annotation and comparisons with studies using direct methane measurements support some potential candidate genes involved in biological pathways related to methane production. However, the overlap with genes influencing milk traits highlights the challenge of distinguishing whether these regions genuinely influence methane emissions or reflect the use of milk MIR spectra to predict the phenotypes.
{"title":"Sequence-based GWAS reveals genes and variants associated with predicted methane emissions in French dairy cows","authors":"Solène Fresco, Marie-Pierre Sanchez, Didier Boichard, Sébastien Fritz, Pauline Martin","doi":"10.1186/s12711-025-00977-z","DOIUrl":"https://doi.org/10.1186/s12711-025-00977-z","url":null,"abstract":"Due to their contribution to global warming, methane emissions from ruminants have been the subject of considerable scientific interest. It has been proposed that such emissions might be reduced using genetic selection; proposed phenotypes differ in the measurement methods used (direct or predicted methane emissions) and in the unit under consideration (g/d, g/kg of milk, g/kg of intake, residual methane emissions). Identifying the quantitative trait loci (QTLs) and candidate genes responsible for genetic variation in methane emissions allows a better understanding of the underlying genetic architecture of these phenotypes. Therefore, the aim of this study was to identify the genomic regions associated with six methane traits predicted from milk mid-infrared (MIR) spectra (0.33 ≤ R2 ≤ 0.88) in French Holstein dairy cows using genome-wide association studies at the whole-genome-sequence level. Six methane emission traits—in g/d, in g/kg of fat- and protein-corrected milk, and in g/kg of dry matter intake—were predicted from milk MIR spectra routinely collected by French milk recording companies. A genome-wide association study of the predicted methane emissions of 40,609 primiparous Holstein cows was conducted using imputed whole-genome-sequence data. This analysis revealed 57 genomic regions of interest; between 1 and 8 QTLs were identified on each of the autosomes except 4, 12, 21, 24 and 26. We identified multiple genomic regions that were shared by two or more predicted methane traits, illustrating their common genetic basis. Functional annotation revealed potential candidate genes, in particular FASN, DGAT1, ACSS2, and KCNIP4, which could be involved in biological pathways possibly related to methane production. The methane traits studied here, which were predicted from milk MIR spectra, appear to be highly polygenic. Several genomic regions associated with these traits contain candidate genes previously associated with milk traits. Functional annotation and comparisons with studies using direct methane measurements support some potential candidate genes involved in biological pathways related to methane production. However, the overlap with genes influencing milk traits highlights the challenge of distinguishing whether these regions genuinely influence methane emissions or reflect the use of milk MIR spectra to predict the phenotypes.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"22 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144304448","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}
Pub Date : 2025-06-13DOI: 10.1186/s12711-025-00978-y
Dong-Feng Wang, Pablo Orozco-terWengel, Hosein Salehian-Dehkordi, Ali Esmailizadeh, Feng-Hua Lv
Asiatic mouflon (Ovis gmelini) consists of several subspecies mainly distributed in Armenia, southern Azerbaijan, Cyprus, northern, southern, and western regions of Iran, and eastern and central regions of Turkey nowadays. Genome analyses of Asiatic mouflon in Iran revealed that they could have diverged from the direct ancestor of domestic sheep, and showed genetic introgression into domestic sheep after domestication. However, the impact of the Asiatic mouflon subspecies in Iran on sheep domestication remains unclear. Here, we conducted a comprehensive population genomics analysis of Asiatic mouflon in Iran with 788 whole-genome sequences (including 40 from Asiatic mouflon), 1104 whole mitogenomes (105 from Asiatic mouflon), and 239 Y chromosomes (21 from Asiatic mouflon). Whole-genome sequence analyses revealed two subpopulations of Asiatic mouflon in Iran: O. gmelini_2 limited on Kaboodan Island in Urmia Lake National Park and O. gmelini_1 over a wide geographic area. Phylogenetic analyses of Asiatic mouflon in Iran based on uniparental variants revealed a monophyletic lineage with the mitochondrial haplogroups C/E, and clustered into a monophyletic with Y-chromosomal lineage HY2 of sheep. Additionally, introgression tests detected significant signals of genetic introgression from O. gmelini_2 to four sheep populations (e.g., Garut, Bangladeshi, Nellore, and Sumatra) in South and Southeast Asia. In the four sheep populations, selective tests and introgression signals revealed that the wild introgression could have contributed to their body size, fat metabolism and local adaptation to the hot and humid environments in the Indian Peninsula. Our results clarified subpopulation structure of Asiatic mouflon in Iran, identifying two distinct groups: O. gmelini_1 and O. gmelini_2. Additionally, we suggest a potential genetic contribution to domestic sheep by introgression, with maternal haplogroup C and paternal lineage HY2 likely originating from the Asiatic mouflon populations in Iran. Our findings offer new insights into domestication of sheep and subsequent introgressions events from wild relatives to domestic populations.
{"title":"Genomic analyses of Asiatic Mouflon in Iran provide insights into the domestication and evolution of sheep","authors":"Dong-Feng Wang, Pablo Orozco-terWengel, Hosein Salehian-Dehkordi, Ali Esmailizadeh, Feng-Hua Lv","doi":"10.1186/s12711-025-00978-y","DOIUrl":"https://doi.org/10.1186/s12711-025-00978-y","url":null,"abstract":"Asiatic mouflon (Ovis gmelini) consists of several subspecies mainly distributed in Armenia, southern Azerbaijan, Cyprus, northern, southern, and western regions of Iran, and eastern and central regions of Turkey nowadays. Genome analyses of Asiatic mouflon in Iran revealed that they could have diverged from the direct ancestor of domestic sheep, and showed genetic introgression into domestic sheep after domestication. However, the impact of the Asiatic mouflon subspecies in Iran on sheep domestication remains unclear. Here, we conducted a comprehensive population genomics analysis of Asiatic mouflon in Iran with 788 whole-genome sequences (including 40 from Asiatic mouflon), 1104 whole mitogenomes (105 from Asiatic mouflon), and 239 Y chromosomes (21 from Asiatic mouflon). Whole-genome sequence analyses revealed two subpopulations of Asiatic mouflon in Iran: O. gmelini_2 limited on Kaboodan Island in Urmia Lake National Park and O. gmelini_1 over a wide geographic area. Phylogenetic analyses of Asiatic mouflon in Iran based on uniparental variants revealed a monophyletic lineage with the mitochondrial haplogroups C/E, and clustered into a monophyletic with Y-chromosomal lineage HY2 of sheep. Additionally, introgression tests detected significant signals of genetic introgression from O. gmelini_2 to four sheep populations (e.g., Garut, Bangladeshi, Nellore, and Sumatra) in South and Southeast Asia. In the four sheep populations, selective tests and introgression signals revealed that the wild introgression could have contributed to their body size, fat metabolism and local adaptation to the hot and humid environments in the Indian Peninsula. Our results clarified subpopulation structure of Asiatic mouflon in Iran, identifying two distinct groups: O. gmelini_1 and O. gmelini_2. Additionally, we suggest a potential genetic contribution to domestic sheep by introgression, with maternal haplogroup C and paternal lineage HY2 likely originating from the Asiatic mouflon populations in Iran. Our findings offer new insights into domestication of sheep and subsequent introgressions events from wild relatives to domestic populations.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"22 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144278508","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}
The genetic basis of the phenotypic diversity of pigs is regulated by variants across the genome, especially the trait of early puberty, which is a crucial trait for enhancing the reproductive ability of pigs and the economy of the pig industry. However, the genetic basis of the early puberty trait in pigs remains largely unknown. Here, we report a comprehensive genomic variation map for pigs based on the resequencing of 493 accessions representing 59 different pig breeds or populations, which included 5,211,469 single-nucleotide polymorphisms (SNPs) and 487,725 small insertion/deletion structure variants (InDels). This sets included 45,640 high-quality structural variants (SVs). Our results suggested that Hanjiang black (HJB) pigs cluster with Jianghai-type pigs at the genetic level and that the genome characteristics of some HJB individuals exhibit a certain degree of European pig features. Using introgression and signature selection analysis, we identified several candidate genes associated with bone development and early puberty traits, such as TBX5, PAPPA2, IGFBP3, and MKRN3. Additionally, the GWAS and differential expression analysis results suggested that the PAPPA2 gene is associated with early puberty in pigs. This study revealed that past introgression events could impact the agronomical traits of pigs and contribute raw material of genetics and breeding in pig. Moreover, our results suggest that the PAPPA2 gene is a candidate gene associated with early sexual maturity in pigs and the genomic analysis provided important reference value for studying economic traits for pigs.
{"title":"Whole-genome resequencing reveals positive selection and introgression signatures and genetic loci associated with early puberty traits in Chinese indigenous pigs","authors":"Minghao Cao, Tiantian Yuan, Dong Li, Yulong Wang, Lin Zhang, Jingchun Sun, Guangquan Lv, Rongrong Ding, Taiyong Yu","doi":"10.1186/s12711-025-00975-1","DOIUrl":"https://doi.org/10.1186/s12711-025-00975-1","url":null,"abstract":"The genetic basis of the phenotypic diversity of pigs is regulated by variants across the genome, especially the trait of early puberty, which is a crucial trait for enhancing the reproductive ability of pigs and the economy of the pig industry. However, the genetic basis of the early puberty trait in pigs remains largely unknown. Here, we report a comprehensive genomic variation map for pigs based on the resequencing of 493 accessions representing 59 different pig breeds or populations, which included 5,211,469 single-nucleotide polymorphisms (SNPs) and 487,725 small insertion/deletion structure variants (InDels). This sets included 45,640 high-quality structural variants (SVs). Our results suggested that Hanjiang black (HJB) pigs cluster with Jianghai-type pigs at the genetic level and that the genome characteristics of some HJB individuals exhibit a certain degree of European pig features. Using introgression and signature selection analysis, we identified several candidate genes associated with bone development and early puberty traits, such as TBX5, PAPPA2, IGFBP3, and MKRN3. Additionally, the GWAS and differential expression analysis results suggested that the PAPPA2 gene is associated with early puberty in pigs. This study revealed that past introgression events could impact the agronomical traits of pigs and contribute raw material of genetics and breeding in pig. Moreover, our results suggest that the PAPPA2 gene is a candidate gene associated with early sexual maturity in pigs and the genomic analysis provided important reference value for studying economic traits for pigs.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"12 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144252313","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}
Pub Date : 2025-06-10DOI: 10.1186/s12711-025-00980-4
Filippo Cendron, Christian Persichilli, Gabriele Senczuk, Francesco Perini, Emiliano Lasagna, Martino Cassandro, Mauro Penasa
Chickens, domesticated around 3500 years ago, are crucial in global agriculture, resulting in hundreds of breeds worldwide. In Europe, intensive breeding has led to the creation of numerous distinct commercial lines at the expense of local breeds. As a result, local breeds, which are not subject to rigorous selective practices, face higher risks of genetic problems due to a narrower genetic base. Modern genotyping and bioinformatic approaches allow detailed genetic analysis. This study offers a comprehensive genetic overview of Italian chicken biodiversity compared to global breeds, emphasizing the importance of preserving local genetic diversity. Hundred and ninety-two chicken breeds from various countries were analyzed, with Italian breeds being highly represented. Genetic relationships showed that Italian breeds clustered with some European, African, and Asian breeds. The ADMIXTURE analysis identified 25 distinct populations and highlighted genetic similarities of certain Italian breeds with German, French, and Swiss ones. Genetic diversity was high in African and some Asian and European breeds, with Italian breeds exhibiting moderate diversity and variability. The TreeMix analysis revealed significant migration events and evolutionary clustering. The Italian breeds had close genetic ties and some highlighted evidence of genetic introgression from common ancestors. Italian chicken breeds have significant genetic relationships with European, Asian, and African breeds, reflecting historical trade and breeding exchanges. Southern Italian breeds form a distinct genetic group, highlighting regional uniqueness. Overall, the research points out the need for conservation strategies to preserve genetic diversity and account for historical and contemporary gene flows, ensuring the sustainability of Italian chicken biodiversity in the face of environmental and agricultural challenges.
{"title":"Italian local chicken breeds: a comparative analysis of biodiversity on a global scale","authors":"Filippo Cendron, Christian Persichilli, Gabriele Senczuk, Francesco Perini, Emiliano Lasagna, Martino Cassandro, Mauro Penasa","doi":"10.1186/s12711-025-00980-4","DOIUrl":"https://doi.org/10.1186/s12711-025-00980-4","url":null,"abstract":"Chickens, domesticated around 3500 years ago, are crucial in global agriculture, resulting in hundreds of breeds worldwide. In Europe, intensive breeding has led to the creation of numerous distinct commercial lines at the expense of local breeds. As a result, local breeds, which are not subject to rigorous selective practices, face higher risks of genetic problems due to a narrower genetic base. Modern genotyping and bioinformatic approaches allow detailed genetic analysis. This study offers a comprehensive genetic overview of Italian chicken biodiversity compared to global breeds, emphasizing the importance of preserving local genetic diversity. Hundred and ninety-two chicken breeds from various countries were analyzed, with Italian breeds being highly represented. Genetic relationships showed that Italian breeds clustered with some European, African, and Asian breeds. The ADMIXTURE analysis identified 25 distinct populations and highlighted genetic similarities of certain Italian breeds with German, French, and Swiss ones. Genetic diversity was high in African and some Asian and European breeds, with Italian breeds exhibiting moderate diversity and variability. The TreeMix analysis revealed significant migration events and evolutionary clustering. The Italian breeds had close genetic ties and some highlighted evidence of genetic introgression from common ancestors. Italian chicken breeds have significant genetic relationships with European, Asian, and African breeds, reflecting historical trade and breeding exchanges. Southern Italian breeds form a distinct genetic group, highlighting regional uniqueness. Overall, the research points out the need for conservation strategies to preserve genetic diversity and account for historical and contemporary gene flows, ensuring the sustainability of Italian chicken biodiversity in the face of environmental and agricultural challenges.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"7 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144252310","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}
Pub Date : 2025-06-05DOI: 10.1186/s12711-025-00974-2
Fernando Bussiman, Daniela Lourenco, Jorge Hidalgo, Ching-Yi Chen, Justin Holl, Ignacy Misztal, Zulma G. Vitezica
In traditional genetic prediction models, environments are typically treated as uncorrelated effects, either fixed or random. Environments can be correlated when they share the same location, management practices, or climate conditions. The temperature-humidity index (THI) is often used to address environmental effects related to climate or heat stress. However, it does not fully describe the complete climate profile of a specific location. Therefore, it is more appropriate to use multiple environmental covariates (ECs), when available, to describe the weather in a specific environment. This raises the question of whether publicly available weather information (such as NASA POWER) is useful for genomic predictions. Genotype-by-environment interaction (GxE) can be modeled using multiple-trait models or reaction norms. However, the former requires a substantial number of records per environment, while the latter can result in over-parametrized models when the number of ECs is large. This study investigated whether using ECs is a suitable strategy to correlate environments (herds) and to model GxE in the genomic prediction of purebred pigs for production traits. We evaluated different models to account for environmental effects and GxE. When environments were correlated based on ECs, we observed an increase in environmental variance, which was accompanied by an increase in phenotypic variance and a decrease in heritability. Furthermore, including environments as an uncorrelated random effect yielded the same accuracy of estimated breeding values as treating them as correlated based on weather information. All the tested models exhibited the same bias, but the predictions from the multiple-trait models were under-dispersed. Evidence of GxE was observed for both traits; however, there were more genetically unconnected environments for backfat thickness than for average daily gain. Using outdoor weather information to correlate environments and model GxE offers limited advantages for genomic predictions in pigs. Although it adds complexity to the model and increases computing time without improving accuracy, it does enhance model fit. Including environment information (e.g. herd effect) as an uncorrelated random effect in the model could help address GxE and environmental effects.
{"title":"Genotype-by-environment interaction with high-dimensional environmental data: an example in pigs","authors":"Fernando Bussiman, Daniela Lourenco, Jorge Hidalgo, Ching-Yi Chen, Justin Holl, Ignacy Misztal, Zulma G. Vitezica","doi":"10.1186/s12711-025-00974-2","DOIUrl":"https://doi.org/10.1186/s12711-025-00974-2","url":null,"abstract":"In traditional genetic prediction models, environments are typically treated as uncorrelated effects, either fixed or random. Environments can be correlated when they share the same location, management practices, or climate conditions. The temperature-humidity index (THI) is often used to address environmental effects related to climate or heat stress. However, it does not fully describe the complete climate profile of a specific location. Therefore, it is more appropriate to use multiple environmental covariates (ECs), when available, to describe the weather in a specific environment. This raises the question of whether publicly available weather information (such as NASA POWER) is useful for genomic predictions. Genotype-by-environment interaction (GxE) can be modeled using multiple-trait models or reaction norms. However, the former requires a substantial number of records per environment, while the latter can result in over-parametrized models when the number of ECs is large. This study investigated whether using ECs is a suitable strategy to correlate environments (herds) and to model GxE in the genomic prediction of purebred pigs for production traits. We evaluated different models to account for environmental effects and GxE. When environments were correlated based on ECs, we observed an increase in environmental variance, which was accompanied by an increase in phenotypic variance and a decrease in heritability. Furthermore, including environments as an uncorrelated random effect yielded the same accuracy of estimated breeding values as treating them as correlated based on weather information. All the tested models exhibited the same bias, but the predictions from the multiple-trait models were under-dispersed. Evidence of GxE was observed for both traits; however, there were more genetically unconnected environments for backfat thickness than for average daily gain. Using outdoor weather information to correlate environments and model GxE offers limited advantages for genomic predictions in pigs. Although it adds complexity to the model and increases computing time without improving accuracy, it does enhance model fit. Including environment information (e.g. herd effect) as an uncorrelated random effect in the model could help address GxE and environmental effects.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"9 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144218898","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}
Pub Date : 2025-06-04DOI: 10.1186/s12711-025-00979-x
Ioanna-Theoni Vourlaki, Raquel Rio-Lopez, Adrià Clavell-Sansalvador, Lino C. Ramírez-Ayala, Maria Ballester, Juan P. Sanchez, Miriam Piles, Raquel Quintanilla, Angela C. da Fonseca de Oliveira, Leandro Batista Costa, Antoni Dalmau, Yuliaxis Ramayo-Caldas
The pig gut microbiota is a complex ecosystem composed of microbial guilds that remain largely unexplored. Here we decomposed the pig fecal microbiota of two cohorts of 648 healthy Duroc pigs during the transition (n = 400) and growing finish (n = 248) periods in co-occurring bacterial guilds defined as pig enterosignatures (ES). Our results indicate that fecal microbial ecosystems can accurately be described by combinations of at least six ES, driven by the Prevotella (ES-Prev), Treponema (ES-Trep), Lactobacillus (ES-Lact), Clostridium (ES-Clost), Streptococcus (ES-Strep), and UBA2810 (ES-UBA2) genera. We observed a dynamic shift with age in the composition of ES, where ES-Prev, ES-Strep, and ES-Lact seem to be core components. Our results suggest partial genetic control by the host, with heritabilities of ES composition ranging from 0.24 to 0.36. Furthermore, our findings indicate that stress on the host is associated with assembly of the ES, decreasing ES-Lact abundance, and increasing prevalence of ES-Strep. We noted a positive association of ES-Prev with growth rate at 60-days, which later evolved to become negative, impacting feed efficiency during the growing period. Remarkably, a negative association of the abundance of ES-Lact with levels of hair cortisol was also found during this period. Our findings provide novel insights into the pig gut microbiota and reveal novels associations with relevant porcine physiological and performance traits. Moreover, while the ES concept has proven valuable in dissecting microbial communities into assemblies of underlying microbial guilds, our results emphasize the relevance of customizing microbial interventions strategies based on the nutritional and health requirements at each stage of the porcine production cycle.
{"title":"Co-occurring microbial guilds in pig fecal microbiota: key drivers and effects on host performance","authors":"Ioanna-Theoni Vourlaki, Raquel Rio-Lopez, Adrià Clavell-Sansalvador, Lino C. Ramírez-Ayala, Maria Ballester, Juan P. Sanchez, Miriam Piles, Raquel Quintanilla, Angela C. da Fonseca de Oliveira, Leandro Batista Costa, Antoni Dalmau, Yuliaxis Ramayo-Caldas","doi":"10.1186/s12711-025-00979-x","DOIUrl":"https://doi.org/10.1186/s12711-025-00979-x","url":null,"abstract":"The pig gut microbiota is a complex ecosystem composed of microbial guilds that remain largely unexplored. Here we decomposed the pig fecal microbiota of two cohorts of 648 healthy Duroc pigs during the transition (n = 400) and growing finish (n = 248) periods in co-occurring bacterial guilds defined as pig enterosignatures (ES). Our results indicate that fecal microbial ecosystems can accurately be described by combinations of at least six ES, driven by the Prevotella (ES-Prev), Treponema (ES-Trep), Lactobacillus (ES-Lact), Clostridium (ES-Clost), Streptococcus (ES-Strep), and UBA2810 (ES-UBA2) genera. We observed a dynamic shift with age in the composition of ES, where ES-Prev, ES-Strep, and ES-Lact seem to be core components. Our results suggest partial genetic control by the host, with heritabilities of ES composition ranging from 0.24 to 0.36. Furthermore, our findings indicate that stress on the host is associated with assembly of the ES, decreasing ES-Lact abundance, and increasing prevalence of ES-Strep. We noted a positive association of ES-Prev with growth rate at 60-days, which later evolved to become negative, impacting feed efficiency during the growing period. Remarkably, a negative association of the abundance of ES-Lact with levels of hair cortisol was also found during this period. Our findings provide novel insights into the pig gut microbiota and reveal novels associations with relevant porcine physiological and performance traits. Moreover, while the ES concept has proven valuable in dissecting microbial communities into assemblies of underlying microbial guilds, our results emphasize the relevance of customizing microbial interventions strategies based on the nutritional and health requirements at each stage of the porcine production cycle.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"14 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211035","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}
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