Pub Date : 2025-12-09DOI: 10.1186/s12711-025-01019-4
Bjarke G Poulsen,Daniela Lourenco,Tage Ostersen,Bjarne Nielsen,Natália G Leite,Mark Henryon,Ole F Christensen
BACKGROUNDThe aim of this study was to compare different statistical models for predicting breeding values for sow survival with right-censored phenotypes from rotationally crossbred and commercial sows. We tested two hypotheses. First, we hypothesized that single-trait relative risk models predict more accurate breeding values than single-trait linear repeatability models. Second, we hypothesized that a reproductive stage stratified linear repeatability model predicts more accurate breeding values than the standard single-trait linear repeatability models. The single-trait models predict breeding values for survival between farrowings, while the reproductive stage stratified models predict breeding values for both survival between a farrowing and the next service, and survival between a service and the next farrowing. The validation criterion was the Pearson correlation between adjusted phenotypes for the lifetime number of litters produced and predicted breeding values for survival converted to lifetime number of litters produced. All validation criteria were compared to one another and against zero using appropriate statistical tests and correction for multiple tests. Each model was constructed with two different multi-breed relationship matrices to ensure that the results were not affected by the choice between them.RESULTSThe values of the validation criteria for the single-trait models were significantly larger than zero and similar (0.02). The values of the validation criteria for the reproductive stage stratified linear repeatability models were both significantly larger than zero and significantly larger than those from the single-trait models (0.04 vs. 0.02).CONCLUSIONSThe relative risk and linear repeatability single-trait models for survival between subsequent farrowings predicted equally accurate breeding values (0.02), while the linear repeatability two-trait models for survival from services to their subsequent farrowings and farrowings to the subsequent services predicted more accurate breeding values than the single-trait models (0.04 vs. 0.02). However, the accuracy of breeding values was small for all models because the survival phenotypes used for prediction were censored and the heritability of complete survival times was moderate (8-9%). Therefore, the comparison would benefit from reevaluation in other populations, and the models should be improved upon before implementation in practical breeding programs.
背景：本研究的目的是比较旋转杂交母猪和商业母猪右删型母猪存活率的不同统计模型。我们检验了两个假设。首先，我们假设单性状相对风险模型比单性状线性可重复性模型预测更准确的育种值。其次，我们假设繁殖阶段分层线性可重复性模型比标准单性状线性可重复性模型预测更准确的育种值。单性状模型预测了两次分娩之间的存活率，而繁殖阶段分层模型预测了一次分娩和下一次分娩之间以及一次分娩和下一次分娩之间的存活率。验证标准是校正后的表型与转换为一生产仔数的预测繁殖值之间的Pearson相关性。使用适当的统计检验和多次检验的校正，将所有验证标准相互比较，并与零进行比较。每个模型都由两个不同的多品种关系矩阵构建，以确保结果不受它们之间选择的影响。结果单性状模型的验证标准值均显著大于零且相似（0.02）。生殖期分层线性重复性模型的验证标准值均显著大于零，且显著大于单性状模型的验证标准值（0.04 vs. 0.02）。结论相对危险度单性状模型和线性可重复性单性状模型预测的后续产仔之间的繁殖值准确度相同（0.02），而从产仔到后续产仔和产仔到后续产仔生存的线性可重复性双性状模型预测的繁殖值准确度高于单性状模型（0.04 vs. 0.02）。然而，所有模型的育种值的准确性都很小，因为用于预测的存活表型被剔除，并且完全存活时间的遗传力中等（8-9%）。因此，在其他种群中进行重新评估将有利于比较，在实际育种计划实施之前，应该对模型进行改进。
{"title":"Comparison between repeatability, reproductive stage stratified repeatability, and relative risk models for prediction of breeding values for functional survival in rotationally crossbred sows.","authors":"Bjarke G Poulsen,Daniela Lourenco,Tage Ostersen,Bjarne Nielsen,Natália G Leite,Mark Henryon,Ole F Christensen","doi":"10.1186/s12711-025-01019-4","DOIUrl":"https://doi.org/10.1186/s12711-025-01019-4","url":null,"abstract":"BACKGROUNDThe aim of this study was to compare different statistical models for predicting breeding values for sow survival with right-censored phenotypes from rotationally crossbred and commercial sows. We tested two hypotheses. First, we hypothesized that single-trait relative risk models predict more accurate breeding values than single-trait linear repeatability models. Second, we hypothesized that a reproductive stage stratified linear repeatability model predicts more accurate breeding values than the standard single-trait linear repeatability models. The single-trait models predict breeding values for survival between farrowings, while the reproductive stage stratified models predict breeding values for both survival between a farrowing and the next service, and survival between a service and the next farrowing. The validation criterion was the Pearson correlation between adjusted phenotypes for the lifetime number of litters produced and predicted breeding values for survival converted to lifetime number of litters produced. All validation criteria were compared to one another and against zero using appropriate statistical tests and correction for multiple tests. Each model was constructed with two different multi-breed relationship matrices to ensure that the results were not affected by the choice between them.RESULTSThe values of the validation criteria for the single-trait models were significantly larger than zero and similar (0.02). The values of the validation criteria for the reproductive stage stratified linear repeatability models were both significantly larger than zero and significantly larger than those from the single-trait models (0.04 vs. 0.02).CONCLUSIONSThe relative risk and linear repeatability single-trait models for survival between subsequent farrowings predicted equally accurate breeding values (0.02), while the linear repeatability two-trait models for survival from services to their subsequent farrowings and farrowings to the subsequent services predicted more accurate breeding values than the single-trait models (0.04 vs. 0.02). However, the accuracy of breeding values was small for all models because the survival phenotypes used for prediction were censored and the heritability of complete survival times was moderate (8-9%). Therefore, the comparison would benefit from reevaluation in other populations, and the models should be improved upon before implementation in practical breeding programs.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"1 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145710787","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 : 2025-11-27DOI: 10.1186/s12711-025-01016-7
Moh Sallam,Lina Göransson,Anne Larsen,Helena Wall,Wael Alhamid,Stefan Gunnarsson,Martin Johnsson,Dirk-Jan de Koning
BACKGROUNDPoultry is a global industry with laying hens that are genetically optimized for high egg yield. Keel bone fractures can affect up to 80% of laying hens, posing welfare and production problems. Therefore, genetic selection to reduce keel fractures is important. However, the lack of a reliable, automated, and heritable phenotypes for keel bones makes this a challenging task. The aim of this study was to (1) develop automated analyses of radiographic images to phenotype keel bones, and (2) investigate whether the proposed phenotypes are heritable and genetically correlated with the post-dissection scores of keel bone fractures and deviations. A total of 1051 laying hens (Bovans Brown and Lohmann Brown) from a commercial farm were x-rayed, followed by keel bone dissection and scoring for deviations and fractures. Furthermore, blood was sampled for genotyping using 50 K Illumina SNP chips. Keel bones were segmented (with ~ 0.90 accuracy) from the radiographic images using deep learning models, after which the images were automatically measured for general geometry and radiopacity. Multi-trait genomic restricted maximum likelihood was used to estimate genetic parameters.RESULTSHeritability estimates ranged from 0.28 to 0.30 for both keel deviations and fractures observed post-dissection. The automated phenotypes had heritability estimates ranging from 0.07 to 0.10 for keel radiopacity and from 0.11 to 0.39 for keel geometry. Estimates of genetic correlations of keel geometry with keel deviation and fractures ranged from -0.57 to 0.72.CONCLUSIONSAutomated methods were developed for measuring keel bone radiopacity and geometry. Keel concave area was found to be a reliable and heritable phenotype that breeding companies can use to reduce keel deviations and fractures. These methods can also be adapted to measure other bones (e.g., tibiotarsal) or objects (e.g., eggs), allowing breeders to quickly compute phenotypes for keel, tibia, and egg size from the same radiographic image. The developed methods are well-suited for large-scale studies to assess different housing environments and nutrition strategies aimed at improving keel bone conditions.
家禽业是一个全球性的产业，其蛋鸡是经过基因优化的高产蛋鸡。高达80%的蛋鸡会受到龙骨骨折的影响，造成福利和生产问题。因此，通过遗传选择来减少龙骨骨折是很重要的。然而，缺乏可靠的、自动化的和可遗传的龙骨表型使得这项任务具有挑战性。本研究的目的是：(1)开发自动分析的影像学图像，以龙骨表型，(2)调查所提出的表型是否可遗传，并与龙骨骨折和偏差的解剖后评分遗传相关。对来自某商业农场的1051只蛋鸡（Bovans Brown和Lohmann Brown）进行x光检查，随后进行龙骨解剖并对偏差和骨折进行评分。此外，使用50 K Illumina SNP芯片采集血液进行基因分型。使用深度学习模型从放射图像中分割龙骨（精度约为0.90），然后自动测量图像的一般几何形状和放射不透明度。采用多性状基因组限制性最大似然法估计遗传参数。结果解剖后观察到的龙骨偏差和骨折的评分范围为0.28至0.30。自动表型的遗传率估计范围从0.07到0.10的龙骨放射度和从0.11到0.39的龙骨几何。龙骨几何形状与龙骨偏差和骨折的遗传相关性估计在-0.57到0.72之间。结论建立了测量龙骨放射透明度和几何形状的自动化方法。龙骨凹区被发现是一种可靠的遗传表型，育种公司可以利用它来减少龙骨偏差和骨折。这些方法也可以用于测量其他骨骼（例如，胫跖骨）或物体（例如，鸡蛋），使育种者能够快速计算龙骨、胫骨和鸡蛋大小的表型。开发的方法非常适合大规模研究，以评估不同的住房环境和营养策略，旨在改善龙骨状况。
{"title":"Genetics of digital phenotypes of keel bone in layer chickens and correlations with keel bone fractures and deviations.","authors":"Moh Sallam,Lina Göransson,Anne Larsen,Helena Wall,Wael Alhamid,Stefan Gunnarsson,Martin Johnsson,Dirk-Jan de Koning","doi":"10.1186/s12711-025-01016-7","DOIUrl":"https://doi.org/10.1186/s12711-025-01016-7","url":null,"abstract":"BACKGROUNDPoultry is a global industry with laying hens that are genetically optimized for high egg yield. Keel bone fractures can affect up to 80% of laying hens, posing welfare and production problems. Therefore, genetic selection to reduce keel fractures is important. However, the lack of a reliable, automated, and heritable phenotypes for keel bones makes this a challenging task. The aim of this study was to (1) develop automated analyses of radiographic images to phenotype keel bones, and (2) investigate whether the proposed phenotypes are heritable and genetically correlated with the post-dissection scores of keel bone fractures and deviations. A total of 1051 laying hens (Bovans Brown and Lohmann Brown) from a commercial farm were x-rayed, followed by keel bone dissection and scoring for deviations and fractures. Furthermore, blood was sampled for genotyping using 50 K Illumina SNP chips. Keel bones were segmented (with ~ 0.90 accuracy) from the radiographic images using deep learning models, after which the images were automatically measured for general geometry and radiopacity. Multi-trait genomic restricted maximum likelihood was used to estimate genetic parameters.RESULTSHeritability estimates ranged from 0.28 to 0.30 for both keel deviations and fractures observed post-dissection. The automated phenotypes had heritability estimates ranging from 0.07 to 0.10 for keel radiopacity and from 0.11 to 0.39 for keel geometry. Estimates of genetic correlations of keel geometry with keel deviation and fractures ranged from -0.57 to 0.72.CONCLUSIONSAutomated methods were developed for measuring keel bone radiopacity and geometry. Keel concave area was found to be a reliable and heritable phenotype that breeding companies can use to reduce keel deviations and fractures. These methods can also be adapted to measure other bones (e.g., tibiotarsal) or objects (e.g., eggs), allowing breeders to quickly compute phenotypes for keel, tibia, and egg size from the same radiographic image. The developed methods are well-suited for large-scale studies to assess different housing environments and nutrition strategies aimed at improving keel bone conditions.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"126 1","pages":"69"},"PeriodicalIF":4.1,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145613293","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 : 2025-11-13DOI: 10.1186/s12711-025-01013-w
A. Bouquet, M. Slagboom, J. R. Thomasen, M. Kargo, N. C. Friggens, L. Puillet
Predicting selection response for lactation efficiency in dairy cows is challenging, as the expression of this complex trait depends on dynamic interactions between the ability of cows to acquire nutrients and allocate them to different life functions. Moreover, the relative emphasis of these components may change due to energetic trade-offs between life functions when kept in limiting environments. The objective of this study is to present a new approach combining mechanistic and breeding scheme simulations to predict selection response on components of lactation efficiency of dairy cows under a non-limiting nutritional environment and when transferred to a limiting environment with a moderate feed restriction. These predictions were compared to the ones obtained with the conventional method used in quantitative genetics considering a typical dairy cattle breeding scheme and several breeding goals (BG) aiming at improving milk production, lactation efficiency and fertility. In the non-limiting environment, selection responses predicted by the two methods differed for both milk production and fertility. The sign and magnitude of differences depended on BGs. Selection response predictions were consistent only for BGs that did not change much the body reserve mobilization patterns of cows, and hence their conception probability. Indeed, pregnancy status impacted energy allocation of cows and consequently milk production, more energy being allocated to lactation in case of reproductive failure. Differences in selection response between modelling approaches were slightly increased when cows were reared in the limiting environment. Overall, the choice of prediction method led to substantial BG reranking with respect to selection response on milk production and fertility. Mechanistic-based predictions of selection response for lifetime efficiency were also sensitive to the nutritional environment and BG. Combining mechanistic and genetic modelling is a promising approach to benchmark breeding strategies of dairy cow lactation efficiency and better anticipate the impact of changes in energetic trade-offs induced both by selection and changes in the nutritional environment. Moreover, the simulations of phenotypic trajectories over cow lifetime before and after selection enabled a better understanding of the mechanisms underlying genetic improvement.
{"title":"Interfacing mechanistic and breeding scheme simulation to predict selection response on lactation efficiency in dairy cows under different nutritional environments","authors":"A. Bouquet, M. Slagboom, J. R. Thomasen, M. Kargo, N. C. Friggens, L. Puillet","doi":"10.1186/s12711-025-01013-w","DOIUrl":"https://doi.org/10.1186/s12711-025-01013-w","url":null,"abstract":"Predicting selection response for lactation efficiency in dairy cows is challenging, as the expression of this complex trait depends on dynamic interactions between the ability of cows to acquire nutrients and allocate them to different life functions. Moreover, the relative emphasis of these components may change due to energetic trade-offs between life functions when kept in limiting environments. The objective of this study is to present a new approach combining mechanistic and breeding scheme simulations to predict selection response on components of lactation efficiency of dairy cows under a non-limiting nutritional environment and when transferred to a limiting environment with a moderate feed restriction. These predictions were compared to the ones obtained with the conventional method used in quantitative genetics considering a typical dairy cattle breeding scheme and several breeding goals (BG) aiming at improving milk production, lactation efficiency and fertility. In the non-limiting environment, selection responses predicted by the two methods differed for both milk production and fertility. The sign and magnitude of differences depended on BGs. Selection response predictions were consistent only for BGs that did not change much the body reserve mobilization patterns of cows, and hence their conception probability. Indeed, pregnancy status impacted energy allocation of cows and consequently milk production, more energy being allocated to lactation in case of reproductive failure. Differences in selection response between modelling approaches were slightly increased when cows were reared in the limiting environment. Overall, the choice of prediction method led to substantial BG reranking with respect to selection response on milk production and fertility. Mechanistic-based predictions of selection response for lifetime efficiency were also sensitive to the nutritional environment and BG. Combining mechanistic and genetic modelling is a promising approach to benchmark breeding strategies of dairy cow lactation efficiency and better anticipate the impact of changes in energetic trade-offs induced both by selection and changes in the nutritional environment. Moreover, the simulations of phenotypic trajectories over cow lifetime before and after selection enabled a better understanding of the mechanisms underlying genetic improvement.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"54 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145498983","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}
Pork is a primary source of animal protein worldwide, and intramuscular fat (IMF) content is a key determinant of meat quality and consumer preference. To identify genetic regulators of IMF content, we leveraged RNA sequencing and whole-genome resequencing data from 79 Laiwu pigs renowned for high IMF content to conduct expression quantitative trait locus (eQTL) mapping. We integrated eQTL results with genome-wide association study (GWAS) data from 453 Chinese Lulai Black pigs (a crossbreed of Laiwu and Yorkshire pigs), and systematically identified candidate regulatory genes for IMF content by incorporating weighted gene co-expression network analysis (WGCNA) and correlation analysis in this population. We identified 9,763 cis-eQTLs at the genome-wide level (p < 5E−08) and 1,337 cis-eQTLs at the suggestive level (p < 5E−06). A 2.02 Mb cis-QTL region on Sus scrofa chromosome 9, containing 587 cis-eQTLs regulating MED17 expression, overlapped with an IMF-associated QTL detected by GWAS in Lulai Black pigs, a Laiwu-Yorkshire crossbreed. WGCNA identified three critical co-expression modules related to IMF content, with MED17 acting as a critical gene in a module linked to adipogenesis and lipid metabolism. Correlation analysis showed MED17 expression was negatively correlated with IMF content (FDR = 1.58E−02). In 3T3-L1 preadipocytes, adenovirus-mediated Med17 overexpression significantly reduced adipogenic differentiation and altered expression of adipogenesis-related genes (Pparg, Adipoq, Srebf1, Cpt1a, and Atgl), indicating that Med17 modulates adipocyte differentiation and lipid metabolism. This study identifies MED17 as a novel regulator of IMF content in pigs, bridging genomic variation, gene expression networks, and phenotypic traits. These findings provide mechanistic insights into IMF deposition and highlight the potential of integrative multi-omics approaches for genetic improvement of pork quality traits in breeding programs.
{"title":"Integrative analysis of genome and transcriptome reveals a novel regulator for pork intramuscular fat content","authors":"Xueyan Zhao, Jingxuan Li, Wanli Jia, Yifan Ren, Yanping Wang, Tizhong Shan, Jiying Wang","doi":"10.1186/s12711-025-01014-9","DOIUrl":"https://doi.org/10.1186/s12711-025-01014-9","url":null,"abstract":"Pork is a primary source of animal protein worldwide, and intramuscular fat (IMF) content is a key determinant of meat quality and consumer preference. To identify genetic regulators of IMF content, we leveraged RNA sequencing and whole-genome resequencing data from 79 Laiwu pigs renowned for high IMF content to conduct expression quantitative trait locus (eQTL) mapping. We integrated eQTL results with genome-wide association study (GWAS) data from 453 Chinese Lulai Black pigs (a crossbreed of Laiwu and Yorkshire pigs), and systematically identified candidate regulatory genes for IMF content by incorporating weighted gene co-expression network analysis (WGCNA) and correlation analysis in this population. We identified 9,763 cis-eQTLs at the genome-wide level (p < 5E−08) and 1,337 cis-eQTLs at the suggestive level (p < 5E−06). A 2.02 Mb cis-QTL region on Sus scrofa chromosome 9, containing 587 cis-eQTLs regulating MED17 expression, overlapped with an IMF-associated QTL detected by GWAS in Lulai Black pigs, a Laiwu-Yorkshire crossbreed. WGCNA identified three critical co-expression modules related to IMF content, with MED17 acting as a critical gene in a module linked to adipogenesis and lipid metabolism. Correlation analysis showed MED17 expression was negatively correlated with IMF content (FDR = 1.58E−02). In 3T3-L1 preadipocytes, adenovirus-mediated Med17 overexpression significantly reduced adipogenic differentiation and altered expression of adipogenesis-related genes (Pparg, Adipoq, Srebf1, Cpt1a, and Atgl), indicating that Med17 modulates adipocyte differentiation and lipid metabolism. This study identifies MED17 as a novel regulator of IMF content in pigs, bridging genomic variation, gene expression networks, and phenotypic traits. These findings provide mechanistic insights into IMF deposition and highlight the potential of integrative multi-omics approaches for genetic improvement of pork quality traits in breeding programs.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"24 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145447197","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 relative abundance of some bacteria in the gut of pigs is heritable, suggesting that host genetics may recursively influence boar semen quality by affecting the composition and function of gut microbiota. Therefore, it is essential to elucidate the specific contributions of heritable versus non-heritable gut microbiota to semen quality traits. Our study aimed to identify heritable and non-heritable bacterial taxa at the genus level in the boar gut and to predict their functions and respective contributions to semen quality traits. At the genus level, 39 heritable and 91 non-heritable bacterial taxa were identified. Functional analysis revealed that predicted microbial functions in both groups were primarily enriched in carbohydrate, nucleotide, and amino acid metabolism. We further analyzed the average microbiability of heritable and non-heritable bacteria on short-chain fatty acids (SCFAs) and semen quality traits. The relative abundance of heritable bacteria was found to contribute more to SCFAs levels and semen quality than non-heritable bacteria. Mediation analysis revealed that SCFAs could mediate the influence of the relative abundance of heritable bacteria on host phenotypes, identifying 99 significant genus-SCFAs-semen quality trait mediation links. Our findings underscore the substantial role of the relative abundance of heritable gut bacteria in shaping porcine semen quality through SCFAs mediation. These results highlight the potential of targeted microbiome interventions to enhance reproductive traits in pigs.
{"title":"Unraveling the role of bacteria with heritable versus non-heritable relative abundance in the gut on boar semen quality","authors":"Liangliang Guo, Xiaoqi Pei, Jiajian Tan, Haiqing Sun, Siwen Jiang, Hongkui Wei, Jian Peng","doi":"10.1186/s12711-025-00990-2","DOIUrl":"https://doi.org/10.1186/s12711-025-00990-2","url":null,"abstract":"The relative abundance of some bacteria in the gut of pigs is heritable, suggesting that host genetics may recursively influence boar semen quality by affecting the composition and function of gut microbiota. Therefore, it is essential to elucidate the specific contributions of heritable versus non-heritable gut microbiota to semen quality traits. Our study aimed to identify heritable and non-heritable bacterial taxa at the genus level in the boar gut and to predict their functions and respective contributions to semen quality traits. At the genus level, 39 heritable and 91 non-heritable bacterial taxa were identified. Functional analysis revealed that predicted microbial functions in both groups were primarily enriched in carbohydrate, nucleotide, and amino acid metabolism. We further analyzed the average microbiability of heritable and non-heritable bacteria on short-chain fatty acids (SCFAs) and semen quality traits. The relative abundance of heritable bacteria was found to contribute more to SCFAs levels and semen quality than non-heritable bacteria. Mediation analysis revealed that SCFAs could mediate the influence of the relative abundance of heritable bacteria on host phenotypes, identifying 99 significant genus-SCFAs-semen quality trait mediation links. Our findings underscore the substantial role of the relative abundance of heritable gut bacteria in shaping porcine semen quality through SCFAs mediation. These results highlight the potential of targeted microbiome interventions to enhance reproductive traits in pigs.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"36 2 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145447198","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 : 2025-10-31DOI: 10.1186/s12711-025-01009-6
Maulik Upadhyay, Alexander Graf, Neža Pogorevc, Doris Seichter, Ingolf Russ, Stefan Krebs, Saskia Meier, Ivica Medugorac
Breeding of genetically polled animals is a desirable approach in modern cattle husbandry for welfare and economic reasons. At least four different genetic variants associated with polledness in cattle have been identified, suggesting genetic heterogeneity. These dominant variants are located on chromosome 1 between approximately 2.42–2.73 Mb (reference: ARS-UCD1.3), also called the POLLED locus. Among these variants, Friesian (PF, ~ 80 kbp duplication) and Celtic (PC, 212 bp complex InDel) are the most common across breeds in the production systems globally, such as in Holstein–Friesian (HF) and Fleckvieh (FV). While studies have provided strong evidence supporting the association of the PF allele with the polledness, it has not yet been functionally validated, unlike the PC allele. Here, we conduct whole-genome sequencing analyses of two trios exhibiting unexpected inheritance patterns related to the PC and PF variants. In both instances, horned offspring were produced from mating pairs where one parent was homozygous for the polled variant and the other was homozygous for the ancestral horned variant. By analyzing the WGS data generated using Nanopore technology, we show that the de novo generation of the ancestral horned phenotype in both offspring was the result of distinct recombination events. Specifically, in the HF trio, it was the result of non-allelic homologous recombination in the gametes of the sire (PF/PF), while in the FV trio, it was the result of allelic homologous recombination in the gametes of the dam (PC/PF). The findings from the HF trio support the hypothesis that ~ 80-kbp duplication is the genetic variant responsible for the polled phenotype of Friesian origin. We show that different genomic arrangements in the POLLED locus can lead to the emergence of de novo ancestral horn phenotypes. Such arrangements can complicate phenotype prediction in offspring, even when sires or dams have been tested as genetically homozygous polled. Therefore, it is important, for a better understanding of the relationship between the POLLED locus and the POLLED phenotype, that any deviation from the expected result is critically analysed. Possibly, some of these cases can further narrow down the sequence motif that is essential for polledness in cattle.
{"title":"Recombination events restored the functional horned haplotypes in the offspring of polled parents","authors":"Maulik Upadhyay, Alexander Graf, Neža Pogorevc, Doris Seichter, Ingolf Russ, Stefan Krebs, Saskia Meier, Ivica Medugorac","doi":"10.1186/s12711-025-01009-6","DOIUrl":"https://doi.org/10.1186/s12711-025-01009-6","url":null,"abstract":"Breeding of genetically polled animals is a desirable approach in modern cattle husbandry for welfare and economic reasons. At least four different genetic variants associated with polledness in cattle have been identified, suggesting genetic heterogeneity. These dominant variants are located on chromosome 1 between approximately 2.42–2.73 Mb (reference: ARS-UCD1.3), also called the POLLED locus. Among these variants, Friesian (PF, ~ 80 kbp duplication) and Celtic (PC, 212 bp complex InDel) are the most common across breeds in the production systems globally, such as in Holstein–Friesian (HF) and Fleckvieh (FV). While studies have provided strong evidence supporting the association of the PF allele with the polledness, it has not yet been functionally validated, unlike the PC allele. Here, we conduct whole-genome sequencing analyses of two trios exhibiting unexpected inheritance patterns related to the PC and PF variants. In both instances, horned offspring were produced from mating pairs where one parent was homozygous for the polled variant and the other was homozygous for the ancestral horned variant. By analyzing the WGS data generated using Nanopore technology, we show that the de novo generation of the ancestral horned phenotype in both offspring was the result of distinct recombination events. Specifically, in the HF trio, it was the result of non-allelic homologous recombination in the gametes of the sire (PF/PF), while in the FV trio, it was the result of allelic homologous recombination in the gametes of the dam (PC/PF). The findings from the HF trio support the hypothesis that ~ 80-kbp duplication is the genetic variant responsible for the polled phenotype of Friesian origin. We show that different genomic arrangements in the POLLED locus can lead to the emergence of de novo ancestral horn phenotypes. Such arrangements can complicate phenotype prediction in offspring, even when sires or dams have been tested as genetically homozygous polled. Therefore, it is important, for a better understanding of the relationship between the POLLED locus and the POLLED phenotype, that any deviation from the expected result is critically analysed. Possibly, some of these cases can further narrow down the sequence motif that is essential for polledness in cattle.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"55 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405123","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 : 2025-10-30DOI: 10.1186/s12711-025-01011-y
Leticia F. de Oliveira, Jenelle Dunkelberger, Claudia A. Sevillano, Saranya Arirangan, Robbee Wedow, Matthew Tegtmeyer, Mitchell Tuinstra, Luiz F. Brito
Porcine Reproductive and Respiratory Syndrome (PRRS) is a major challenge for the worldwide pig industry. Therefore, genetic selection for enhanced disease resilience is a priority for pig breeding programs. The objectives of this study were to evaluate genetic variation in reproductive performance during a PRRS outbreak and to assess the impact of selecting for enhanced reproductive performance using data collected under non-challenged conditions on reproductive performance in a PRRS challenged environment. These objectives were addressed by identifying natural PRRS outbreak periods from longitudinal performance data and estimating genetic parameters for reproductive performance traits, before, during, and after a PRRS outbreak, using data collected from purebred and crossbred sows on multiplier farms. During PRRS outbreaks, the number of piglets born alive decreased, while the number of stillborn and mummified piglets increased for both purebred and crossbred sows. Additive genetic variance and heritability estimates for reproductive performance traits varied by phase. For most traits, additive genetic variance was highest during the outbreak. Estimates of genetic correlations between a given trait measured across phases ranged from 0.09 to 0.99, but were > 0.3 for most traits. In general, estimates of genetic correlations were greatest between a given trait before and after an outbreak. Results also indicated reranking of animals based on estimated breeding values across outbreak phases, with Spearman correlations below 0.50 for most traits and low proportion of top-ranking animals retained across phases. PRRS outbreak periods can be detected by evaluating phenotypic variation in reproductive performance from longitudinal data. Reproductive performance is heritable, whether evaluated before, during, or after a PRRS outbreak, but estimates varied by phase. Favorable moderate-to-high genetic correlations were estimated for reproductive performance traits measured before vs. during a PRRS outbreak, suggesting that selection for improved reproductive performance under non-challenged conditions is also expected to improve reproductive performance under PRRS challenge conditions. However, the estimates of genetic correlation for most of the reproductive traits indicated genotype-by-environment interactions between the PRRS-free and challenge conditions. Therefore, incorporating data collected under PRRS challenge will capture additional genetic variation in PRRS resilience and, ultimately, aid in selecting sows with increased PRRS resilience.
{"title":"Genetics of reproductive performance across Porcine Reproductive and Respiratory Syndrome (PRRS) outbreak phases in purebred and crossbred sows","authors":"Leticia F. de Oliveira, Jenelle Dunkelberger, Claudia A. Sevillano, Saranya Arirangan, Robbee Wedow, Matthew Tegtmeyer, Mitchell Tuinstra, Luiz F. Brito","doi":"10.1186/s12711-025-01011-y","DOIUrl":"https://doi.org/10.1186/s12711-025-01011-y","url":null,"abstract":"Porcine Reproductive and Respiratory Syndrome (PRRS) is a major challenge for the worldwide pig industry. Therefore, genetic selection for enhanced disease resilience is a priority for pig breeding programs. The objectives of this study were to evaluate genetic variation in reproductive performance during a PRRS outbreak and to assess the impact of selecting for enhanced reproductive performance using data collected under non-challenged conditions on reproductive performance in a PRRS challenged environment. These objectives were addressed by identifying natural PRRS outbreak periods from longitudinal performance data and estimating genetic parameters for reproductive performance traits, before, during, and after a PRRS outbreak, using data collected from purebred and crossbred sows on multiplier farms. During PRRS outbreaks, the number of piglets born alive decreased, while the number of stillborn and mummified piglets increased for both purebred and crossbred sows. Additive genetic variance and heritability estimates for reproductive performance traits varied by phase. For most traits, additive genetic variance was highest during the outbreak. Estimates of genetic correlations between a given trait measured across phases ranged from 0.09 to 0.99, but were > 0.3 for most traits. In general, estimates of genetic correlations were greatest between a given trait before and after an outbreak. Results also indicated reranking of animals based on estimated breeding values across outbreak phases, with Spearman correlations below 0.50 for most traits and low proportion of top-ranking animals retained across phases. PRRS outbreak periods can be detected by evaluating phenotypic variation in reproductive performance from longitudinal data. Reproductive performance is heritable, whether evaluated before, during, or after a PRRS outbreak, but estimates varied by phase. Favorable moderate-to-high genetic correlations were estimated for reproductive performance traits measured before vs. during a PRRS outbreak, suggesting that selection for improved reproductive performance under non-challenged conditions is also expected to improve reproductive performance under PRRS challenge conditions. However, the estimates of genetic correlation for most of the reproductive traits indicated genotype-by-environment interactions between the PRRS-free and challenge conditions. Therefore, incorporating data collected under PRRS challenge will capture additional genetic variation in PRRS resilience and, ultimately, aid in selecting sows with increased PRRS resilience.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"80 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145396779","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 : 2025-10-30DOI: 10.1186/s12711-025-01007-8
Andrew Lakamp, Seidu Adams, Larry Kuehn, Warren Snelling, James Wells, Kristin Hales, Bryan Neville, Samodha Fernando, Matthew L. Spangler
Host genomic and rumen metagenome data can predict feed efficiency traits, supporting management decisions and increasing profitability. This study estimated the proportion of variation of average daily dry matter intake and average daily gain explained by the rumen metagenome in beef cattle, evaluated prediction accuracy using genomic data, metagenomic data, or their combination, and explored methods for modelling the rumen metagenome to improve phenotypic prediction accuracy. Data from 717 animals on four diets (two concentrate-based and two forage-based) were analyzed. Animal genotypes consisted of 749,922 imputed sequence variants, while metagenomic data comprised 16,583 open reading frames from ruminal microbiota. The metagenome was modelled using six (co)variance matrices, based on combinations of two creation methods and three modifications. Nineteen mixed linear models were used per trait: one with genomic effects only, six with metagenomic effects, six combining genomic and metagenomic effects, and six adding interaction effects. Two cross-validation schemes were applied to evaluate prediction accuracy: fourfold cross-validation balanced for diet type with 5 replicates and leave-one-diet-out cross-validation, where three diets served as training and the fourth as testing. Prediction accuracy was measured as the correlation between an animal’s summed random effects and its adjusted phenotype. Although minimal, differences existed in parameter estimates and validation accuracy depending on how the metagenome effect was modelled. Median phenotype prediction accuracy ranged from −0.01 to 0.28. No specific set of model characteristics consistently lead to the highest accuracies. Models which combined genome and metagenome data outperformed those using either data source alone. Models where the rumen metagenome (co)variances matrix was scaled within each diet composition generally led to lower prediction accuracies in this study. The rumen metagenome can explain a significant proportion of variation in beef cattle feed efficiency traits. Those traits can also be predicted using either host genome or rumen metagenome, though using both sources of information proved more accurate. Multiple methods of forming the metagenome (co)variance matrix can lead to similar prediction accuracies.
{"title":"Prediction accuracy for feed intake and body weight gain using host genomic and rumen metagenomic data in beef cattle","authors":"Andrew Lakamp, Seidu Adams, Larry Kuehn, Warren Snelling, James Wells, Kristin Hales, Bryan Neville, Samodha Fernando, Matthew L. Spangler","doi":"10.1186/s12711-025-01007-8","DOIUrl":"https://doi.org/10.1186/s12711-025-01007-8","url":null,"abstract":"Host genomic and rumen metagenome data can predict feed efficiency traits, supporting management decisions and increasing profitability. This study estimated the proportion of variation of average daily dry matter intake and average daily gain explained by the rumen metagenome in beef cattle, evaluated prediction accuracy using genomic data, metagenomic data, or their combination, and explored methods for modelling the rumen metagenome to improve phenotypic prediction accuracy. Data from 717 animals on four diets (two concentrate-based and two forage-based) were analyzed. Animal genotypes consisted of 749,922 imputed sequence variants, while metagenomic data comprised 16,583 open reading frames from ruminal microbiota. The metagenome was modelled using six (co)variance matrices, based on combinations of two creation methods and three modifications. Nineteen mixed linear models were used per trait: one with genomic effects only, six with metagenomic effects, six combining genomic and metagenomic effects, and six adding interaction effects. Two cross-validation schemes were applied to evaluate prediction accuracy: fourfold cross-validation balanced for diet type with 5 replicates and leave-one-diet-out cross-validation, where three diets served as training and the fourth as testing. Prediction accuracy was measured as the correlation between an animal’s summed random effects and its adjusted phenotype. Although minimal, differences existed in parameter estimates and validation accuracy depending on how the metagenome effect was modelled. Median phenotype prediction accuracy ranged from −0.01 to 0.28. No specific set of model characteristics consistently lead to the highest accuracies. Models which combined genome and metagenome data outperformed those using either data source alone. Models where the rumen metagenome (co)variances matrix was scaled within each diet composition generally led to lower prediction accuracies in this study. The rumen metagenome can explain a significant proportion of variation in beef cattle feed efficiency traits. Those traits can also be predicted using either host genome or rumen metagenome, though using both sources of information proved more accurate. Multiple methods of forming the metagenome (co)variance matrix can lead to similar prediction accuracies.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"17 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145396846","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}
Variance components of linear mixed models should be estimated with all the data and information available for a specific statistical model to avoid bias. Due to computational limitations, the estimation for large datasets or complex models is often carried out by subsetting the data, removing genomic information, or simplifying the statistical model. Monte Carlo REML (MC-REML) is a method developed to lift computational limitations, but so far there was no extension for genomic information under the single-step genomic methods. In this study, we extended MC-REML to include large genomic information. We developed a method to estimate variance components named Monte Carlo single-step genomic REML (MC-ss-GREML). The core of the method includes repeatedly simulating breeding values under a ssGBLUP model and solving the mixed model equations to approximate traces involving prediction error variances. The REML optimization strategies include Expectation Maximization and Average Information. We tested the accuracy of MC-ss-GREML with a three-trait growth model in beef cattle with maternal effects, with 14 parameters to estimate. The data set had 100,000 animals in the pedigree, of which about 33,000 had records, and 10,000 were genotyped. There were no differences in estimates between MC-ss-GREML and ss-GREML with the exact calculation of the traces (exact ss-GREML). MC-ss-GREML took 14% of the computing time and used 1% of the memory compared to the exact ss-GREML. We tested the computing performance of MC-ss-GREML by estimating variance components in a birth weight model, with much larger data that included 7 million animals in the pedigree, from which 330,000 were genotyped. The estimation converged in 11 rounds and took 121 h, with a peak memory usage of 53 Gb. The new method, MC-ss-GREML, can estimate variance components with large datasets including many genotyped individuals, at affordable time and memory costs.
线性混合模型的方差成分应该用特定统计模型的所有数据和信息来估计，以避免偏差。由于计算的限制，对于大型数据集或复杂模型的估计通常是通过细分数据、去除基因组信息或简化统计模型来进行的。Monte Carlo REML （MC-REML）是一种消除计算限制的方法，但目前在单步基因组方法下还没有对基因组信息的扩展。在这项研究中，我们扩展了MC-REML以包含大的基因组信息。我们开发了一种估算方差分量的方法，称为蒙特卡罗单步基因组REML （MC-ss-GREML）。该方法的核心是在ssGBLUP模型下反复模拟育种值，并求解混合模型方程来逼近涉及预测误差方差的迹线。REML优化策略包括期望最大化和平均信息。我们以具有母系效应的肉牛为研究对象，采用三性状生长模型对MC-ss-GREML的准确性进行了检验，模型中有14个参数需要估计。该数据集有10万只动物的家谱，其中约3.3万只有记录，1万只进行了基因分型。MC-ss-GREML和ss-GREML之间的估计与轨迹的精确计算（精确ss-GREML）没有差异。与精确的ss-GREML相比，MC-ss-GREML花费了14%的计算时间和1%的内存。我们通过估算出生体重模型中的方差成分来测试MC-ss-GREML的计算性能，该模型使用了更大的数据，包括家谱中的700万只动物，其中33万只进行了基因分型。估计在11轮中收敛，耗时121小时，峰值内存使用量为53 Gb。新方法MC-ss-GREML可以在可承受的时间和内存成本下估算包含许多基因型个体的大型数据集的方差成分。
{"title":"Estimation of (co)variance components for very large datasets and complex single-step genomic models","authors":"Matias Bermann, Andres Legarra, Ignacio Aguilar, Alejandra Alvarez-Munera, Ignacy Misztal, Daniela Lourenco","doi":"10.1186/s12711-025-01006-9","DOIUrl":"https://doi.org/10.1186/s12711-025-01006-9","url":null,"abstract":"Variance components of linear mixed models should be estimated with all the data and information available for a specific statistical model to avoid bias. Due to computational limitations, the estimation for large datasets or complex models is often carried out by subsetting the data, removing genomic information, or simplifying the statistical model. Monte Carlo REML (MC-REML) is a method developed to lift computational limitations, but so far there was no extension for genomic information under the single-step genomic methods. In this study, we extended MC-REML to include large genomic information. We developed a method to estimate variance components named Monte Carlo single-step genomic REML (MC-ss-GREML). The core of the method includes repeatedly simulating breeding values under a ssGBLUP model and solving the mixed model equations to approximate traces involving prediction error variances. The REML optimization strategies include Expectation Maximization and Average Information. We tested the accuracy of MC-ss-GREML with a three-trait growth model in beef cattle with maternal effects, with 14 parameters to estimate. The data set had 100,000 animals in the pedigree, of which about 33,000 had records, and 10,000 were genotyped. There were no differences in estimates between MC-ss-GREML and ss-GREML with the exact calculation of the traces (exact ss-GREML). MC-ss-GREML took 14% of the computing time and used 1% of the memory compared to the exact ss-GREML. We tested the computing performance of MC-ss-GREML by estimating variance components in a birth weight model, with much larger data that included 7 million animals in the pedigree, from which 330,000 were genotyped. The estimation converged in 11 rounds and took 121 h, with a peak memory usage of 53 Gb. The new method, MC-ss-GREML, can estimate variance components with large datasets including many genotyped individuals, at affordable time and memory costs.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"8 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145396777","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 : 2025-10-23DOI: 10.1186/s12711-025-01008-7
Valentin P Haas,Robin Wellmann,Pascal Duenk,Michael Oster,Siriluck Ponsuksili,Jörn Bennewitz,Mario P L Calus
BACKGROUNDSince genomic selection has been established in animal breeding, attention has turned towards other omics layers that are seen as promising to improve prediction accuracy. Transcriptomic data provide insights into gene expression patterns, which are shaped by both genetic and environmental factors, offering a more comprehensive understanding of the expression of phenotypes. This study utilized various statistical methods to assess the applicability of transcriptomic data derived from intestinal tissue to the prediction of efficiency-related phenotypes. The focus was on formal derivation of the previously described GTCBLUP model, which was adapted to create GTCBLUPi and compared with other BLUP models. The GTCBLUPi model addresses redundant information between genomic and transcriptomic information. We compared estimated variance components and accuracies of prediction of phenotypes for efficiency-related traits in an F2 cross of 480 Japanese quail using different models. Additionally, we estimated transcriptomic correlations between the traits using animal effects based on transcriptomic similarity, and the effects of individual transcript abundances on the phenotypes.RESULTSThis study showed that transcript abundances from the ileum explain a larger portion of the phenotypic variance of the traits than host genetics. Models incorporating both genetic and transcriptomic information outperformed those using only one type of information, with regard to the phenotypic variances explained. The combination of both data types resulted in higher trait prediction accuracies, confirming that transcriptomic information complements genetic data effectively. The derived GTCBLUPi model proved to be a suitable framework for integrating both information types. Additionally, polygenic backgrounds were identified for the traits studied based on transcriptomic profiles, along with high transcriptomic correlations between the traits.CONCLUSIONSTranscriptomic data account for a high portion of phenotypic expression for all phenotypes and incorporating them enables more accurate predictions of phenotypes for efficiency and performance traits. Models that integrate both genetic and transcriptomic information are the most effective, offering valuable insights for improving phenotype prediction accuracy and insights in biological mechanisms underlying phenotypic variation of traits.
{"title":"Incorporating transcriptomic data into genomic prediction models to improve the prediction accuracy of phenotypes of efficiency traits.","authors":"Valentin P Haas,Robin Wellmann,Pascal Duenk,Michael Oster,Siriluck Ponsuksili,Jörn Bennewitz,Mario P L Calus","doi":"10.1186/s12711-025-01008-7","DOIUrl":"https://doi.org/10.1186/s12711-025-01008-7","url":null,"abstract":"BACKGROUNDSince genomic selection has been established in animal breeding, attention has turned towards other omics layers that are seen as promising to improve prediction accuracy. Transcriptomic data provide insights into gene expression patterns, which are shaped by both genetic and environmental factors, offering a more comprehensive understanding of the expression of phenotypes. This study utilized various statistical methods to assess the applicability of transcriptomic data derived from intestinal tissue to the prediction of efficiency-related phenotypes. The focus was on formal derivation of the previously described GTCBLUP model, which was adapted to create GTCBLUPi and compared with other BLUP models. The GTCBLUPi model addresses redundant information between genomic and transcriptomic information. We compared estimated variance components and accuracies of prediction of phenotypes for efficiency-related traits in an F2 cross of 480 Japanese quail using different models. Additionally, we estimated transcriptomic correlations between the traits using animal effects based on transcriptomic similarity, and the effects of individual transcript abundances on the phenotypes.RESULTSThis study showed that transcript abundances from the ileum explain a larger portion of the phenotypic variance of the traits than host genetics. Models incorporating both genetic and transcriptomic information outperformed those using only one type of information, with regard to the phenotypic variances explained. The combination of both data types resulted in higher trait prediction accuracies, confirming that transcriptomic information complements genetic data effectively. The derived GTCBLUPi model proved to be a suitable framework for integrating both information types. Additionally, polygenic backgrounds were identified for the traits studied based on transcriptomic profiles, along with high transcriptomic correlations between the traits.CONCLUSIONSTranscriptomic data account for a high portion of phenotypic expression for all phenotypes and incorporating them enables more accurate predictions of phenotypes for efficiency and performance traits. Models that integrate both genetic and transcriptomic information are the most effective, offering valuable insights for improving phenotype prediction accuracy and insights in biological mechanisms underlying phenotypic variation of traits.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"140 1","pages":"59"},"PeriodicalIF":4.1,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145351800","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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