Pub Date : 2025-08-28DOI: 10.1186/s12711-025-00994-y
Hanbin Lee, Rosalind Françoise Craddock, Gregor Gorjanc, Hannes Becher
Pedigrees continue to be extremely important in agriculture and conservation genetics, with the pedigrees of modern breeding programmes easily comprising millions of records. This size can make visualising the structure of such pedigrees challenging. Being graphs, pedigrees can be represented as matrices, including, most commonly, the additive (numerator) relationship matrix, $$varvec{A}$$ , and its inverse. With these matrices, the structure of pedigrees can then, in principle, be visualised via principal component analysis (PCA). However, the naive PCA of matrices for large pedigrees is challenging due to computational and memory constraints. Furthermore, computing a few leading principal components is usually sufficient for visualising the structure of a pedigree. We present the open-access R package randPedPCA for rapid pedigree PCA using sparse matrices. Our rapid pedigree PCA builds on the fact that matrix-vector multiplications with the additive relationship matrix can be carried out implicitly using the extremely sparse inverse relationship factor, $$varvec{L}^{-1}$$ , which can be directly obtained from a given pedigree. We implemented two methods. Randomised singular value decomposition tends to be faster when very few principal components are requested, and Eigen decomposition via the RSpectra library tends to be faster when more principal components are of interest. On simulated data, our package delivers a speed-up greater than 10,000 times compared to naive PCA. It further enables analyses that are impossible with naive PCA. When only two principal components are desired, the randomised PCA method can half the running time required compared to RSpectra, which we demonstrate by analysing the pedigree of the UK Kennel Club registered Labrador Retriever population of almost 1.5 million individuals. The leading principal components of pedigree matrices can be efficiently obtained using randomised singular value decomposition and other methods. Scatter plots of these scores allow for intuitive visualisation of large pedigrees. For large pedigrees, this is considerably faster than rendering plots of a pedigree graph.
{"title":"randPedPCA: rapid approximation of principal components from large pedigrees","authors":"Hanbin Lee, Rosalind Françoise Craddock, Gregor Gorjanc, Hannes Becher","doi":"10.1186/s12711-025-00994-y","DOIUrl":"https://doi.org/10.1186/s12711-025-00994-y","url":null,"abstract":"Pedigrees continue to be extremely important in agriculture and conservation genetics, with the pedigrees of modern breeding programmes easily comprising millions of records. This size can make visualising the structure of such pedigrees challenging. Being graphs, pedigrees can be represented as matrices, including, most commonly, the additive (numerator) relationship matrix, $$varvec{A}$$ , and its inverse. With these matrices, the structure of pedigrees can then, in principle, be visualised via principal component analysis (PCA). However, the naive PCA of matrices for large pedigrees is challenging due to computational and memory constraints. Furthermore, computing a few leading principal components is usually sufficient for visualising the structure of a pedigree. We present the open-access R package randPedPCA for rapid pedigree PCA using sparse matrices. Our rapid pedigree PCA builds on the fact that matrix-vector multiplications with the additive relationship matrix can be carried out implicitly using the extremely sparse inverse relationship factor, $$varvec{L}^{-1}$$ , which can be directly obtained from a given pedigree. We implemented two methods. Randomised singular value decomposition tends to be faster when very few principal components are requested, and Eigen decomposition via the RSpectra library tends to be faster when more principal components are of interest. On simulated data, our package delivers a speed-up greater than 10,000 times compared to naive PCA. It further enables analyses that are impossible with naive PCA. When only two principal components are desired, the randomised PCA method can half the running time required compared to RSpectra, which we demonstrate by analysing the pedigree of the UK Kennel Club registered Labrador Retriever population of almost 1.5 million individuals. The leading principal components of pedigree matrices can be efficiently obtained using randomised singular value decomposition and other methods. Scatter plots of these scores allow for intuitive visualisation of large pedigrees. For large pedigrees, this is considerably faster than rendering plots of a pedigree graph.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"178 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911009","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}
Improvement of protein efficiency (PE) is a key factor for a sustainable pig production, as nitrogen excretion contributes substantially to environmental pollution. Protein efficiency has been shown to be heritable and genetically correlated with performance traits such as feed conversion ratio (FCR) and average daily feed intake (ADFI). This study aimed to identify genomic regions associated with these traits through single-variant genome-wide association studies (GWAS) and regional heritability mapping (RHM) using whole-genome sequence variants from low-pass sequencing of more than 1000 Swiss Large White pigs. Genomic heritability estimates using ~ 15 million variants were moderate to high, ranging from 0.33 to 0.47. GWAS did not identify significant variants for PE and FCR, but identified 45 variants at suggestive significance levels for ADFI on chromosome 1 and one for ADG on chromosome 14. Similarly, RHM detected no significant regions for PE and FCR, but five suggestive regions for ADFI (chromosome 1) and one for ADG (chromosome 14). However, by combining leading signals from GWAS and RHM, i.e. overlapping leading variants and significant regions, we highlighted putative candidate genes for PE, including PHYKPL, COL23A1, PPFIBP2, GVIN1, SYT9, RBMXL2, ZNF215, and olfactory receptor genes. Combining GWAS and RHM allowed us to identify genomic regions that may influence PE and production traits. Our apparent difficulty in detecting significant regions for these traits probably reflects the relatively small sample size, differences in genetic architecture across study designs and experimental conditions, and that polymorphisms explaining large proportions of the trait variation may not segregate in this population. Nevertheless, we identified plausible functional candidate genes in the highlighted regions, including those involved in nutrient sensing, the urea cycle, and metabolic pathways, in particular IGF1-insulin, and that have previously been reported to be associated with nitrogen metabolism in cattle and with muscle and adipose tissue metabolism and feed intake in pigs. We also highlighted a range of noncoding RNAs. Their targets and roles in gene regulation should be further investigated in this context.
{"title":"Single-variant genome-wide association study and regional heritability mapping of protein efficiency and performance traits in Large White pigs","authors":"Esther Oluwada Ewaoluwagbemiga, Audald Lloret-Villas, Adéla Nosková, Hubert Pausch, Claudia Kasper","doi":"10.1186/s12711-025-00993-z","DOIUrl":"https://doi.org/10.1186/s12711-025-00993-z","url":null,"abstract":"Improvement of protein efficiency (PE) is a key factor for a sustainable pig production, as nitrogen excretion contributes substantially to environmental pollution. Protein efficiency has been shown to be heritable and genetically correlated with performance traits such as feed conversion ratio (FCR) and average daily feed intake (ADFI). This study aimed to identify genomic regions associated with these traits through single-variant genome-wide association studies (GWAS) and regional heritability mapping (RHM) using whole-genome sequence variants from low-pass sequencing of more than 1000 Swiss Large White pigs. Genomic heritability estimates using ~ 15 million variants were moderate to high, ranging from 0.33 to 0.47. GWAS did not identify significant variants for PE and FCR, but identified 45 variants at suggestive significance levels for ADFI on chromosome 1 and one for ADG on chromosome 14. Similarly, RHM detected no significant regions for PE and FCR, but five suggestive regions for ADFI (chromosome 1) and one for ADG (chromosome 14). However, by combining leading signals from GWAS and RHM, i.e. overlapping leading variants and significant regions, we highlighted putative candidate genes for PE, including PHYKPL, COL23A1, PPFIBP2, GVIN1, SYT9, RBMXL2, ZNF215, and olfactory receptor genes. Combining GWAS and RHM allowed us to identify genomic regions that may influence PE and production traits. Our apparent difficulty in detecting significant regions for these traits probably reflects the relatively small sample size, differences in genetic architecture across study designs and experimental conditions, and that polymorphisms explaining large proportions of the trait variation may not segregate in this population. Nevertheless, we identified plausible functional candidate genes in the highlighted regions, including those involved in nutrient sensing, the urea cycle, and metabolic pathways, in particular IGF1-insulin, and that have previously been reported to be associated with nitrogen metabolism in cattle and with muscle and adipose tissue metabolism and feed intake in pigs. We also highlighted a range of noncoding RNAs. Their targets and roles in gene regulation should be further investigated in this context.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"12 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144840133","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}
Likelihood-based inferences such as variance components estimation and hypothesis testing need logarithms of the determinant (log-determinant) of high dimensional matrices. Calculating the log-determinant is memory and time-consuming, making it impossible to perform likelihood-based inferences for large datasets. We presented a method for approximating the log-determinant of positive semi-definite matrices based on repeated matrix–vector products and complex calculus. We tested the approximation of the log-determinant in beef and dairy cattle, chicken, and pig datasets including single and multiple-trait models. Average absolute relative differences between the approximated and exact log-determinant were around 10–3. The approximation was between 2 and 500 times faster than the exact calculation for medium and large matrices. We compared the restricted likelihood with (approximated) and without (exact) the approximation of the log-determinant for different values of heritability for a single-trait model. We also compared estimated variance components using exact expectation–maximization (EM) and average information (AI) REML algorithms, against two derivative-free approaches using the restricted likelihood calculated with the log-determinant approximation. The approximated and exact restricted likelihood showed maxima at the same heritability value. Derivative-free estimation of variance components with the approximated log-determinant converged to the same values as EM and AI-REML. The proposed approach is feasible to apply to any data size. The method presented in this study allows to approximate the log-determinant of positive semi-definite matrices and, therefore, the likelihood for datasets of any size. This opens the possibility of performing likelihood-based inferences for large datasets in animal and plant breeding.
{"title":"Monte Carlo approximation of the logarithm of the determinant of large matrices with applications for linear mixed models in quantitative genetics","authors":"Matias Bermann, Alejandra Alvarez-Munera, Andres Legarra, Ignacio Aguilar, Ignacy Misztal, Daniela Lourenco","doi":"10.1186/s12711-025-00991-1","DOIUrl":"https://doi.org/10.1186/s12711-025-00991-1","url":null,"abstract":"Likelihood-based inferences such as variance components estimation and hypothesis testing need logarithms of the determinant (log-determinant) of high dimensional matrices. Calculating the log-determinant is memory and time-consuming, making it impossible to perform likelihood-based inferences for large datasets. We presented a method for approximating the log-determinant of positive semi-definite matrices based on repeated matrix–vector products and complex calculus. We tested the approximation of the log-determinant in beef and dairy cattle, chicken, and pig datasets including single and multiple-trait models. Average absolute relative differences between the approximated and exact log-determinant were around 10–3. The approximation was between 2 and 500 times faster than the exact calculation for medium and large matrices. We compared the restricted likelihood with (approximated) and without (exact) the approximation of the log-determinant for different values of heritability for a single-trait model. We also compared estimated variance components using exact expectation–maximization (EM) and average information (AI) REML algorithms, against two derivative-free approaches using the restricted likelihood calculated with the log-determinant approximation. The approximated and exact restricted likelihood showed maxima at the same heritability value. Derivative-free estimation of variance components with the approximated log-determinant converged to the same values as EM and AI-REML. The proposed approach is feasible to apply to any data size. The method presented in this study allows to approximate the log-determinant of positive semi-definite matrices and, therefore, the likelihood for datasets of any size. This opens the possibility of performing likelihood-based inferences for large datasets in animal and plant breeding.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"64 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144786611","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}
Domestic chickens are one of the most widely raised and distributed bird species, exhibiting remarkable environmental adaptability, which makes them valuable model organisms for investigating the genetic mechanisms underlying climate adaptation. This study aimed to enhance our understanding of adaptive mechanisms in chickens by jointly analyzing genomic variations and climatic variables related to temperature and precipitation. To this end, whole-genome sequencing data were collected from 199 indigenous domestic chickens raised under diverse environmental conditions worldwide, and three genome-environment association analyses were performed. We identified 184 genes potentially associated with climate adaptation in chickens. Among these, the TSHR gene may play multiple roles in adaptation driven by different climatic factors. Immune-related genes also appear to contribute to climate adaptation in chickens. By calculating the allele frequencies of single nucleotide polymorphisms (SNPs) within candidate genes associated with temperature and precipitation adaptation, we identified five SNPs within four genes (ZNF536, ENSGALG00000049158, PAPPA, and EHMT1) that exhibited distinct geographic distribution patterns. Extended haplotype homozygosity (EHH) analysis of these SNPs revealed that haplotypes carrying the mutant allele exhibited slower decay in EHH compared to those carrying the wild-type allele. These results further indicate that the loci have experienced strong selective pressures, suggesting that the associated genes may play crucial roles in climate adaptation in chickens. Overall, this study provides new insights into the genetic mechanisms underlying climate adaptation in domestic chickens.
{"title":"Genome-environment association analysis reveals climate-driven adaptation of chickens","authors":"Xiurong Zhao, Jinxin Zhang, Junhui Wen, Xinye Zhang, Haiying Li, Huie Wang, Tao Zhu, Changsheng Nie, Xinghua Li, Weifang Yang, Guomin Cao, Wenjie Xiong, Xue Wang, Zhonghua Ning, Lujiang Qu","doi":"10.1186/s12711-025-00989-9","DOIUrl":"https://doi.org/10.1186/s12711-025-00989-9","url":null,"abstract":"Domestic chickens are one of the most widely raised and distributed bird species, exhibiting remarkable environmental adaptability, which makes them valuable model organisms for investigating the genetic mechanisms underlying climate adaptation. This study aimed to enhance our understanding of adaptive mechanisms in chickens by jointly analyzing genomic variations and climatic variables related to temperature and precipitation. To this end, whole-genome sequencing data were collected from 199 indigenous domestic chickens raised under diverse environmental conditions worldwide, and three genome-environment association analyses were performed. We identified 184 genes potentially associated with climate adaptation in chickens. Among these, the TSHR gene may play multiple roles in adaptation driven by different climatic factors. Immune-related genes also appear to contribute to climate adaptation in chickens. By calculating the allele frequencies of single nucleotide polymorphisms (SNPs) within candidate genes associated with temperature and precipitation adaptation, we identified five SNPs within four genes (ZNF536, ENSGALG00000049158, PAPPA, and EHMT1) that exhibited distinct geographic distribution patterns. Extended haplotype homozygosity (EHH) analysis of these SNPs revealed that haplotypes carrying the mutant allele exhibited slower decay in EHH compared to those carrying the wild-type allele. These results further indicate that the loci have experienced strong selective pressures, suggesting that the associated genes may play crucial roles in climate adaptation in chickens. Overall, this study provides new insights into the genetic mechanisms underlying climate adaptation in domestic chickens.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"266 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-21DOI: 10.1186/s12711-025-00988-w
Ida Hansson, Piter Bijma, Freddy Fikse, Lars Rönnegård
Social interactions in a dairy herd may impact an individual’s production, e.g., milk yield. These interactions can have a genetic component, so-called indirect genetic effects (IGE). IGEs contribute to heritable variation in other species, but studies on IGEs in cows are limited. Knowledge is needed on appropriate methods to monitor social interactions in cows. We evaluated with simulations whether we can estimate IGEs in cows. We used milk yield as an example trait, and we assessed how herd size, direct and indirect genetic correlations, and magnitude of IGE affected the variance component estimations and breeding value accuracies. We investigated the importance of knowing the contact intensity and direction by either including or ignoring them in the estimation model. Additionally, we investigated how random noise added to the intensities would affect the estimates and breeding values. The estimated variance components were unbiased and precise for scenarios with different herd sizes of 50, 100, or 200 cows and direct and indirect genetic correlations of either − 0.6, 0, or 0.6. The IGE breeding value accuracies were 0.55–0.65 for cows when the IGE explained 30% of the phenotypic variance. When the magnitude of the IGE became smaller, the precision of the estimated variances became lower. The IGE breeding value accuracies were 0.16–0.52 for cows when the IGE explained 1.5–15% of the phenotypic variance. Using imprecise intensities or ignoring the contact direction underestimated the variance of the indirect effects, and the breeding value accuracies became lower. Ignoring the variation in intensities in the model led to unbiased variance component estimates but a larger residual variance and lower breeding value accuracies than if we used imprecise intensities. We could estimate IGE in dairy cattle with high accuracy and precision in a simulated population of 10,000 phenotyped cows distributed over 50–200 herds. A smaller IGE variance led to less precise estimates and lower breeding value accuracies. Ignoring information about the intensity of contact in the model would be worse than using imprecise intensities, and using technology that also monitors the direction of contact may be beneficial to estimate variance components of IGE.
{"title":"Towards assessing indirect genetic effects in dairy cattle","authors":"Ida Hansson, Piter Bijma, Freddy Fikse, Lars Rönnegård","doi":"10.1186/s12711-025-00988-w","DOIUrl":"https://doi.org/10.1186/s12711-025-00988-w","url":null,"abstract":"Social interactions in a dairy herd may impact an individual’s production, e.g., milk yield. These interactions can have a genetic component, so-called indirect genetic effects (IGE). IGEs contribute to heritable variation in other species, but studies on IGEs in cows are limited. Knowledge is needed on appropriate methods to monitor social interactions in cows. We evaluated with simulations whether we can estimate IGEs in cows. We used milk yield as an example trait, and we assessed how herd size, direct and indirect genetic correlations, and magnitude of IGE affected the variance component estimations and breeding value accuracies. We investigated the importance of knowing the contact intensity and direction by either including or ignoring them in the estimation model. Additionally, we investigated how random noise added to the intensities would affect the estimates and breeding values. The estimated variance components were unbiased and precise for scenarios with different herd sizes of 50, 100, or 200 cows and direct and indirect genetic correlations of either − 0.6, 0, or 0.6. The IGE breeding value accuracies were 0.55–0.65 for cows when the IGE explained 30% of the phenotypic variance. When the magnitude of the IGE became smaller, the precision of the estimated variances became lower. The IGE breeding value accuracies were 0.16–0.52 for cows when the IGE explained 1.5–15% of the phenotypic variance. Using imprecise intensities or ignoring the contact direction underestimated the variance of the indirect effects, and the breeding value accuracies became lower. Ignoring the variation in intensities in the model led to unbiased variance component estimates but a larger residual variance and lower breeding value accuracies than if we used imprecise intensities. We could estimate IGE in dairy cattle with high accuracy and precision in a simulated population of 10,000 phenotyped cows distributed over 50–200 herds. A smaller IGE variance led to less precise estimates and lower breeding value accuracies. Ignoring information about the intensity of contact in the model would be worse than using imprecise intensities, and using technology that also monitors the direction of contact may be beneficial to estimate variance components of IGE.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"14 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1186/s12711-025-00987-x
Setegn Worku Alemu, Timothy P. Bilton, Patricia L. Johnson, Benjamin J. Perry, Hannah Henry, Ken G. Dodds, John C. McEwan, Suzanne J. Rowe
Methane emissions from ruminant livestock pose a significant challenge to mitigating climate change. Genomic selection offers a promising approach to reduce methane emissions, but prediction accuracy remains low due to the high cost of measuring methane emissions. Integrating rumen microbiome composition (RMC) data may improve genomic prediction accuracy, yet the high dimensionality of RMC data presents computational challenges. This study aimed to (1) evaluate the effectiveness of principal component analysis (PCA) for reducing RMC data dimensionality while retaining essential information, and (2) assess whether incorporating PCA-reduced RMC data as intermediate traits in a Neural Network Genomic Best Linear Unbiased Prediction (NN-GBLUP) model improves genomic prediction accuracy for methane emissions and feed efficiency traits in sheep. For the first objective, Principal Components (PCs) explaining 100% of variation effectively captured RMC information, with microbiability estimates closely matching those from the full dataset. For the second objective, the NN-GBLUP model incorporating PCA-reduced RMC data improved prediction accuracy compared to standard GBLUP methods. Prediction accuracy for methane emissions increased from 0.09 to 0.30 in train-test validation and from 0.15 to 0.27 in five-fold cross-validation using PCA components explaining 25% of total RMC variation. For residual feed intake, accuracy improved from 0.25 to 0.37 in train-test validation and from 0.25 to 0.34 in cross-validation. Optimal PCA components varied by trait, with 25% and 50% components showing the best results. Prediction accuracy did not improve for carbon dioxide emissions, live weight, and mid-intake, indicating trait-dependent microbiome influence. Principal Component Analysis reduced the dimensionality of rumen microbiome data while preserving essential biological information. The integration of these PCA-reduced data with host genomic information through an NN-GBLUP model substantially improved genomic prediction accuracy for methane emissions and feed efficiency in sheep. Principal components explaining 25% and 50% of the variation yielded the highest accuracy, whereas higher components (75% and 95%) reduced accuracy for methane traits. This approach shows promise for implementing genomic selection strategies to reduce methane emissions and improve feed efficiency in ruminant livestock in a computationally efficient manner, thereby contributing to climate change mitigation efforts in agriculture.
{"title":"Improving genomic prediction accuracy for methane emission and feed efficiency in sheep: integrating rumen microbial PCA with host genomic variation using neural network GBLUP (NN-GBLUP)","authors":"Setegn Worku Alemu, Timothy P. Bilton, Patricia L. Johnson, Benjamin J. Perry, Hannah Henry, Ken G. Dodds, John C. McEwan, Suzanne J. Rowe","doi":"10.1186/s12711-025-00987-x","DOIUrl":"https://doi.org/10.1186/s12711-025-00987-x","url":null,"abstract":"Methane emissions from ruminant livestock pose a significant challenge to mitigating climate change. Genomic selection offers a promising approach to reduce methane emissions, but prediction accuracy remains low due to the high cost of measuring methane emissions. Integrating rumen microbiome composition (RMC) data may improve genomic prediction accuracy, yet the high dimensionality of RMC data presents computational challenges. This study aimed to (1) evaluate the effectiveness of principal component analysis (PCA) for reducing RMC data dimensionality while retaining essential information, and (2) assess whether incorporating PCA-reduced RMC data as intermediate traits in a Neural Network Genomic Best Linear Unbiased Prediction (NN-GBLUP) model improves genomic prediction accuracy for methane emissions and feed efficiency traits in sheep. For the first objective, Principal Components (PCs) explaining 100% of variation effectively captured RMC information, with microbiability estimates closely matching those from the full dataset. For the second objective, the NN-GBLUP model incorporating PCA-reduced RMC data improved prediction accuracy compared to standard GBLUP methods. Prediction accuracy for methane emissions increased from 0.09 to 0.30 in train-test validation and from 0.15 to 0.27 in five-fold cross-validation using PCA components explaining 25% of total RMC variation. For residual feed intake, accuracy improved from 0.25 to 0.37 in train-test validation and from 0.25 to 0.34 in cross-validation. Optimal PCA components varied by trait, with 25% and 50% components showing the best results. Prediction accuracy did not improve for carbon dioxide emissions, live weight, and mid-intake, indicating trait-dependent microbiome influence. Principal Component Analysis reduced the dimensionality of rumen microbiome data while preserving essential biological information. The integration of these PCA-reduced data with host genomic information through an NN-GBLUP model substantially improved genomic prediction accuracy for methane emissions and feed efficiency in sheep. Principal components explaining 25% and 50% of the variation yielded the highest accuracy, whereas higher components (75% and 95%) reduced accuracy for methane traits. This approach shows promise for implementing genomic selection strategies to reduce methane emissions and improve feed efficiency in ruminant livestock in a computationally efficient manner, thereby contributing to climate change mitigation efforts in agriculture.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"17 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144652103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-16DOI: 10.1186/s12711-025-00986-y
Jianhai Chen, Ivan Jakovlić, Mikhail Sablin, Shengqian Xia, Zhixiang Xu, Yapin Guo, Renzuo Kuang, Jie Zhong, Yangying Jia, Nhien Thuy Thi Tran, Hao Yang, Hong Ma, Nikica Šprem, Jianlin Han, Di Liu, Yunxia Zhao, Shuhong Zhao
Domestic piglets often die of hypothermia, whereas Eurasian wild boar (Sus scrofa) thrives from tropical lowlands to subarctic forests. The thermoregulation of wild boar offers a natural experiment to uncover the genetic basis of cold adaptation. We conducted whole-genome resequencing on wild populations from cold regions (northern and northeastern Asia, with six samples) and warm regions (southeastern Asia and southern China, with five samples). By integrating publicly available data, we compiled a core dataset of 48 wild boar samples and an extended dataset of 445 wild boar and domestic pig samples to identify candidate genes related to cold adaptation. To investigate the functional effects of two candidate variants under positive selection, we performed CUT&Tag and RNA-seq using the northeastern Asian Min pig breed as a proxy for a cold-adapted population. Our study identified candidate genes associated with cold adaptation, which are significantly enriched in thermogenesis, fat cell development, and adipose tissue pathways. We discovered two enhancer variants under positive selection: an intronic variant of IGF1R (rs341219502) and an exonic variant of BRD4 (rs327139795). These variants exhibited the highest differentiation between populations of wild boar and domestic pigs in cold and warm region populations. Furthermore, these rare variants were absent in outgroup species and warm-region wild boars but were nearly fixed in cold-region populations. The H3K27ac CUT&Tag profiling revealed that the rs341219502 variant of IGF1R is linked to the gain of novel binding sites for three transcription factors involving regulatory changes in enhancer function. In contrast, the rs327139795 variant of BRD4 may result in the loss of a phosphorylation site due to an alteration in the amino acid sequence. Our study identified candidate genes for cold adaptation in wild boar. The variant rs341219502 in the IGF1R enhancer and the variant rs327139795 in the BRD4 exon, both of which were under positive selection and nearly fixed in populations from cold regions, suggest they may have originated de novo in these populations. Further analysis indicated that rs341219502 could influence enhancer function, while rs327139795 may affect amino acid alterations. Overall, our study highlights the adaptive evolution of genomic molecules that contribute to the remarkable environmental flexibility of wild boar.
{"title":"Positive selection on rare variants of IGF1R and BRD4 underlying the cold adaptation of wild boar","authors":"Jianhai Chen, Ivan Jakovlić, Mikhail Sablin, Shengqian Xia, Zhixiang Xu, Yapin Guo, Renzuo Kuang, Jie Zhong, Yangying Jia, Nhien Thuy Thi Tran, Hao Yang, Hong Ma, Nikica Šprem, Jianlin Han, Di Liu, Yunxia Zhao, Shuhong Zhao","doi":"10.1186/s12711-025-00986-y","DOIUrl":"https://doi.org/10.1186/s12711-025-00986-y","url":null,"abstract":"Domestic piglets often die of hypothermia, whereas Eurasian wild boar (Sus scrofa) thrives from tropical lowlands to subarctic forests. The thermoregulation of wild boar offers a natural experiment to uncover the genetic basis of cold adaptation. We conducted whole-genome resequencing on wild populations from cold regions (northern and northeastern Asia, with six samples) and warm regions (southeastern Asia and southern China, with five samples). By integrating publicly available data, we compiled a core dataset of 48 wild boar samples and an extended dataset of 445 wild boar and domestic pig samples to identify candidate genes related to cold adaptation. To investigate the functional effects of two candidate variants under positive selection, we performed CUT&Tag and RNA-seq using the northeastern Asian Min pig breed as a proxy for a cold-adapted population. Our study identified candidate genes associated with cold adaptation, which are significantly enriched in thermogenesis, fat cell development, and adipose tissue pathways. We discovered two enhancer variants under positive selection: an intronic variant of IGF1R (rs341219502) and an exonic variant of BRD4 (rs327139795). These variants exhibited the highest differentiation between populations of wild boar and domestic pigs in cold and warm region populations. Furthermore, these rare variants were absent in outgroup species and warm-region wild boars but were nearly fixed in cold-region populations. The H3K27ac CUT&Tag profiling revealed that the rs341219502 variant of IGF1R is linked to the gain of novel binding sites for three transcription factors involving regulatory changes in enhancer function. In contrast, the rs327139795 variant of BRD4 may result in the loss of a phosphorylation site due to an alteration in the amino acid sequence. Our study identified candidate genes for cold adaptation in wild boar. The variant rs341219502 in the IGF1R enhancer and the variant rs327139795 in the BRD4 exon, both of which were under positive selection and nearly fixed in populations from cold regions, suggest they may have originated de novo in these populations. Further analysis indicated that rs341219502 could influence enhancer function, while rs327139795 may affect amino acid alterations. Overall, our study highlights the adaptive evolution of genomic molecules that contribute to the remarkable environmental flexibility of wild boar.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"4 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144640363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-14DOI: 10.1186/s12711-025-00983-1
Erin G. Smith, Samuel F. Walkom, Dominic L. Waters, Sam A. Clark
General resilience in animals can be quantified by analysing the variability in longitudinal data. However, it is unclear whether resilience indicators derived from different longitudinal data series can predict resilience to known or unknown disturbances in sheep. This study aimed to use two sources of longitudinal data, wool fibre diameter and body weight, to develop potential indicators for resilience to the known stress of weaning and overall resilience to unknown disturbances. The genetic parameters of these traits were assessed, along with the genetic correlations between traits from different data series and different definitions of resilience. Additionally, correlations between resilience indicators, health and production traits were estimated to evaluate the suitability of including resilience indicators in breeding programs. Fibre diameter and body weight records from approximately 6500 yearling Merino sheep were used to estimate four resilience indicators of resilience towards unknown disturbances: log-transformed variance (Lnvar), lag-1 Auto (Auto), skewness (Skewness) and absolute difference in the deviations (ABS) from these curves. Three other traits, rate of change in the response and recovery (ROC_response and ROC_recovery) and area between curves (ABC) during a known disturbance of weaning, were also estimated. Resilience indicators were found to be lowly heritable (0.03 ± 0.01 to 0.18 ± 0.04). Genetic correlations between the general resilience indicator and the indicator of resilience to weaning stress were generally moderate, particularly in the wool fibre diameter data, suggesting these may represent similar traits. Genetic correlations between resilience indicators derived from wool fibre diameter and body weight data were typically weak to moderate, which indicates that they possibly capture different aspects of resilience. The genetic correlations between resilience indicators and health traits were mostly low, except for body condition score. Correlations between resilience and production traits were low to moderate and favourable. Resilience indicators based on deviations in wool fibre diameter and body weight can be used to potentially select animals that are less affected by environmental disturbances. The genetic correlations between resilience indicators and health and production traits suggest that these traits could be included in breeding programs to improve resilience without adversely affecting production traits.
动物的一般恢复力可以通过分析纵向数据的可变性来量化。然而，尚不清楚来自不同纵向数据系列的弹性指标是否可以预测绵羊对已知或未知干扰的弹性。本研究旨在利用两种纵向数据来源，羊毛纤维直径和体重，以开发对已知断奶应激和对未知干扰的整体恢复能力的潜在指标。分析了这些性状的遗传参数，以及不同数据序列和不同弹性定义性状之间的遗传相关性。此外，还估计了恢复力指标、健康和生产性状之间的相关性，以评估将恢复力指标纳入育种计划的适用性。利用约6500只美利奴羊的纤维直径和体重记录，估计了对未知干扰的弹性的4个指标：对数变换方差（Lnvar）、lag-1 Auto （Auto）、偏度（skewness）和这些曲线的绝对偏差差（ABS）。另外三个特征，在已知的断奶干扰期间，反应和恢复的变化率（ROC_response和ROC_recovery）和曲线间面积（ABC）也被估计。恢复力指标的可遗传性较低（0.03±0.01 ~ 0.18±0.04）。一般恢复力指标和断奶应激恢复力指标之间的遗传相关性一般是中等的，特别是在羊毛纤维直径数据中，这表明它们可能代表相似的性状。从羊毛纤维直径和体重数据得出的弹性指标之间的遗传相关性通常为弱至中等，这表明它们可能捕获了弹性的不同方面。韧性指标与健康性状的遗传相关性除体质评分外，其余均较低。恢复力与生产性状的相关性为低至中等和有利。基于羊毛纤维直径和体重偏差的弹性指标可用于潜在地选择受环境干扰影响较小的动物。抗逆性指标与健康和生产性状之间的遗传相关性表明，这些性状可以纳入育种计划，以提高抗逆性，而不会对生产性状产生不利影响。
{"title":"Estimating the genetic parameters of resilience toward known and unknown disturbances in sheep using wool fibre diameter and body weight variability","authors":"Erin G. Smith, Samuel F. Walkom, Dominic L. Waters, Sam A. Clark","doi":"10.1186/s12711-025-00983-1","DOIUrl":"https://doi.org/10.1186/s12711-025-00983-1","url":null,"abstract":"General resilience in animals can be quantified by analysing the variability in longitudinal data. However, it is unclear whether resilience indicators derived from different longitudinal data series can predict resilience to known or unknown disturbances in sheep. This study aimed to use two sources of longitudinal data, wool fibre diameter and body weight, to develop potential indicators for resilience to the known stress of weaning and overall resilience to unknown disturbances. The genetic parameters of these traits were assessed, along with the genetic correlations between traits from different data series and different definitions of resilience. Additionally, correlations between resilience indicators, health and production traits were estimated to evaluate the suitability of including resilience indicators in breeding programs. Fibre diameter and body weight records from approximately 6500 yearling Merino sheep were used to estimate four resilience indicators of resilience towards unknown disturbances: log-transformed variance (Lnvar), lag-1 Auto (Auto), skewness (Skewness) and absolute difference in the deviations (ABS) from these curves. Three other traits, rate of change in the response and recovery (ROC_response and ROC_recovery) and area between curves (ABC) during a known disturbance of weaning, were also estimated. Resilience indicators were found to be lowly heritable (0.03 ± 0.01 to 0.18 ± 0.04). Genetic correlations between the general resilience indicator and the indicator of resilience to weaning stress were generally moderate, particularly in the wool fibre diameter data, suggesting these may represent similar traits. Genetic correlations between resilience indicators derived from wool fibre diameter and body weight data were typically weak to moderate, which indicates that they possibly capture different aspects of resilience. The genetic correlations between resilience indicators and health traits were mostly low, except for body condition score. Correlations between resilience and production traits were low to moderate and favourable. Resilience indicators based on deviations in wool fibre diameter and body weight can be used to potentially select animals that are less affected by environmental disturbances. The genetic correlations between resilience indicators and health and production traits suggest that these traits could be included in breeding programs to improve resilience without adversely affecting production traits.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"23 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144622229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, genetic evaluations in cattle breeding have shifted from purely national evaluations to multinational evaluations considering relatives from other countries. Integrating international estimated breeding values (EBVs) into national genomic evaluations presents challenges due to differences in evaluation methodologies and data sources. This study focused on the impact of blending internationally derived EBVs with national EBVs in the single-step genomic evaluation of German beef cattle using three approaches to deregressing EBVs. The national phenotypic data for four breeds (Angus, Charolais, Limousin, and Simmental) were obtained from the routine German beef cattle evaluation of December 2022, and the international EBVs were obtained from the routine Interbeef evaluation of January 2023. Scalar (Garrick (GA), Van Raden (VR)) and matrix deregression approaches were compared for reversibility of EBVs. A forward validation study was used to evaluate the accuracy, dispersion and level bias obtained in a purely national single-step evaluation, and single-step genomic evaluations blended with DRPs obtained from the three deregression approaches. A validation study based on forward prediction showed improved accuracy, and reduced dispersion bias in the EBVs blended with international EBVs compared to purely national EBVs, particularly for the direct and maternal effects of 200-day weight. As expected, Pearson correlation analysis revealed that the matrix deregression (> 0.99) approach outperformed the scalar deregression approaches (0.75–0.99), exhibiting a greater correlation between the EBVs obtained from DRPs and the EBVs obtained from phenotypes across the various breeds and traits in our study. A forward validation study with and without integrating foreign data across the three deregression methods showed improvement in reducing dispersion bias, as indicated by the regression coefficient. The GEBVs from an evaluation incorporating foreign information with national data showed a higher correlation to the GEBVs from a truncated evaluation than those from an evaluation without foreign information. These findings underscore the importance of accurately integrating foreign EBVs to enhance national genomic evaluations and genetic progress in livestock populations. Our results show that the matrix approach to deregressing EBVs performs optimally across traits and breeds. However, the VR deregression approach can serve as an alternative in situations where the matrix deregression approach might be too technical to implement.
近年来，牛的遗传评价已从单纯的国家评价转向考虑其他国家亲缘关系的多国评价。由于评估方法和数据来源的差异，将国际估计育种值（ebv）整合到国家基因组评估中存在挑战。本研究的重点是在德国肉牛的单步基因组评估中，使用三种方法去除ebv，将国际衍生ebv与本国ebv混合的影响。4个品种（安格斯、夏洛莱、利穆赞和西蒙塔尔）的全国表型数据来自2022年12月的德国肉牛常规评估，国际ebv数据来自2023年1月的Interbeef常规评估。比较标量(Garrick （GA), Van Raden (VR)）和矩阵去回归方法对ebv可逆性的影响。采用前向验证研究来评估纯国家单步评估获得的准确性、离散度和水平偏差，以及单步基因组评估与三种去回归方法获得的DRPs混合。一项基于前向预测的验证研究表明，与纯粹的国内ebv相比，ebv与国际ebv混合的准确性更高，分散偏差减少，特别是对200天体重的直接和母体影响。正如预期的那样，Pearson相关分析显示，矩阵去回归方法（> 0.99）优于标量去回归方法（0.75-0.99），表明在我们的研究中，从DRPs中获得的ebv与从各种品种和性状的表型中获得的ebv之间存在更大的相关性。一项有和没有整合国外数据的前向验证研究表明，三种去回归方法在减少分散偏差方面有所改善，如回归系数所示。与不包含外国信息的评估相比，包含外国信息和国家数据的评估的gebv与截断评估的gebv具有更高的相关性。这些发现强调了准确整合外来ebv对加强国家基因组评估和牲畜种群遗传进展的重要性。我们的研究结果表明，矩阵方法解除ebv在性状和品种中表现最佳。然而，在矩阵解算方法可能过于技术性而无法实现的情况下，VR解算方法可以作为一种替代方案。
{"title":"The impact of deregressed foreign breeding values on national beef cattle single-step genomic evaluation","authors":"Damilola Adekale, Zengting Liu, Ross Evans, Thierry Pabiou, Reinhard Reents, Dierck Segelke, Jens Tetens","doi":"10.1186/s12711-025-00982-2","DOIUrl":"https://doi.org/10.1186/s12711-025-00982-2","url":null,"abstract":"In recent years, genetic evaluations in cattle breeding have shifted from purely national evaluations to multinational evaluations considering relatives from other countries. Integrating international estimated breeding values (EBVs) into national genomic evaluations presents challenges due to differences in evaluation methodologies and data sources. This study focused on the impact of blending internationally derived EBVs with national EBVs in the single-step genomic evaluation of German beef cattle using three approaches to deregressing EBVs. The national phenotypic data for four breeds (Angus, Charolais, Limousin, and Simmental) were obtained from the routine German beef cattle evaluation of December 2022, and the international EBVs were obtained from the routine Interbeef evaluation of January 2023. Scalar (Garrick (GA), Van Raden (VR)) and matrix deregression approaches were compared for reversibility of EBVs. A forward validation study was used to evaluate the accuracy, dispersion and level bias obtained in a purely national single-step evaluation, and single-step genomic evaluations blended with DRPs obtained from the three deregression approaches. A validation study based on forward prediction showed improved accuracy, and reduced dispersion bias in the EBVs blended with international EBVs compared to purely national EBVs, particularly for the direct and maternal effects of 200-day weight. As expected, Pearson correlation analysis revealed that the matrix deregression (> 0.99) approach outperformed the scalar deregression approaches (0.75–0.99), exhibiting a greater correlation between the EBVs obtained from DRPs and the EBVs obtained from phenotypes across the various breeds and traits in our study. A forward validation study with and without integrating foreign data across the three deregression methods showed improvement in reducing dispersion bias, as indicated by the regression coefficient. The GEBVs from an evaluation incorporating foreign information with national data showed a higher correlation to the GEBVs from a truncated evaluation than those from an evaluation without foreign information. These findings underscore the importance of accurately integrating foreign EBVs to enhance national genomic evaluations and genetic progress in livestock populations. Our results show that the matrix approach to deregressing EBVs performs optimally across traits and breeds. However, the VR deregression approach can serve as an alternative in situations where the matrix deregression approach might be too technical to implement.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"14 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144622210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-14DOI: 10.1186/s12711-025-00976-0
Bernadett Hegedűs, Natália Galoro Leite, J. Elizabeth Bolhuis, Piter Bijma
Ear and tail biting are behaviours in pigs that cause both welfare problems and financial losses. Data collection of behaviour is difficult at the large scale needed for breeding. The damage inflicted on victims can, however, serve as a proxy for animal breeding. Here, we analysed tail and ear damage scores on their original scale, binary scale, and summed versions of these damage traits to investigate which trait definition is best for genetic selection. Using data from six purebred lines (33,329 animals in total) we aimed to (1) estimate genetic parameters for ear and tail damage using direct genetic models, (2) estimate the genetic correlation between tail and ear damage, (3) compare different trait definitions and their impact on accuracy, dispersion, and bias of estimated breeding values (EBV), and (4) compare expected responses to selection for each trait definition. The heritability of the damage traits ranged from 0.04 to 0.06. Ear and tail damage were moderately correlated (0.41–0.45), meaning that the genetic propensity of being a victim is a different trait for tail versus ear biting. Estimates of the accuracy of the EBV for the traits with a five-fold cross-validation and the linear regression method based on pedigree relationships ranged from 0.27 to 0.57, the dispersion from 0.91 to 1.18, and the bias was negligible. With a selected proportion of 5%, genetic progress of ~ 0.20–0.78 genetic standard deviations per generation can be reached, depending on the trait. It was trait dependent whether direct or indirect selection yielded the most response. Ear and tail damage are heritable traits and are moderately positively correlated. The EBV for the evaluated traits related to ear and tail damage showed moderate accuracies, minor dispersion, and no bias. We hypothesize that from a welfare perspective, ear and tail damage on the original scale are the relevant breeding goal traits. For ear damage on the original scale, the highest response to selection can be expected when selecting on the trait itself, whereas for tail damage on the original scale, selection on summed damage showed the highest gain. Results from this study show that genetic improvement of the direct genetic effect of ear and tail damage is possible.
{"title":"Genetic parameters and potential of reducing tail and ear damage in pigs through breeding","authors":"Bernadett Hegedűs, Natália Galoro Leite, J. Elizabeth Bolhuis, Piter Bijma","doi":"10.1186/s12711-025-00976-0","DOIUrl":"https://doi.org/10.1186/s12711-025-00976-0","url":null,"abstract":"Ear and tail biting are behaviours in pigs that cause both welfare problems and financial losses. Data collection of behaviour is difficult at the large scale needed for breeding. The damage inflicted on victims can, however, serve as a proxy for animal breeding. Here, we analysed tail and ear damage scores on their original scale, binary scale, and summed versions of these damage traits to investigate which trait definition is best for genetic selection. Using data from six purebred lines (33,329 animals in total) we aimed to (1) estimate genetic parameters for ear and tail damage using direct genetic models, (2) estimate the genetic correlation between tail and ear damage, (3) compare different trait definitions and their impact on accuracy, dispersion, and bias of estimated breeding values (EBV), and (4) compare expected responses to selection for each trait definition. The heritability of the damage traits ranged from 0.04 to 0.06. Ear and tail damage were moderately correlated (0.41–0.45), meaning that the genetic propensity of being a victim is a different trait for tail versus ear biting. Estimates of the accuracy of the EBV for the traits with a five-fold cross-validation and the linear regression method based on pedigree relationships ranged from 0.27 to 0.57, the dispersion from 0.91 to 1.18, and the bias was negligible. With a selected proportion of 5%, genetic progress of ~ 0.20–0.78 genetic standard deviations per generation can be reached, depending on the trait. It was trait dependent whether direct or indirect selection yielded the most response. Ear and tail damage are heritable traits and are moderately positively correlated. The EBV for the evaluated traits related to ear and tail damage showed moderate accuracies, minor dispersion, and no bias. We hypothesize that from a welfare perspective, ear and tail damage on the original scale are the relevant breeding goal traits. For ear damage on the original scale, the highest response to selection can be expected when selecting on the trait itself, whereas for tail damage on the original scale, selection on summed damage showed the highest gain. Results from this study show that genetic improvement of the direct genetic effect of ear and tail damage is possible.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"38 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144622209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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