Pub Date : 2025-10-02DOI: 10.1186/s12711-025-01000-1
Tomasi Tusingwiire, Carolina Garcia-Baccino, Bruno Ligonesche, Catherine Larzul, Zulma G. Vitezica
Pigs in intensive production systems encounter various stressors that negatively impact their productivity and welfare. The primary aim of this study was to estimate the genetic correlations of the slope (indicator of sensitivity of the animals to environmental challenges) of the daily feed intake (DFI) across different environmental gradients (probability of the occurrence of a challenge on a given day) with growth, feed efficiency, carcass, and meat quality traits using a single-step reaction norm animal model (RNAM) in Piétrain pigs. In addition, genetic correlations of DFI (its total breeding value) with the same traits were also estimated. The probabilities of the occurrence of an unrecorded environmental challenge, inferred via a Gaussian mixture model, were taken as a reference and used in the genetic analysis as an environmental descriptor. Variance components were estimated via restricted maximum likelihood using the single-step genomic best linear unbiased prediction method, using a series of multivariate RNAM with two phenotypes (DFI and each of the traits of economic importance), with the probability of an unrecorded challenge on a given day included as an environmental descriptor for DFI only, because DFI is recorded daily but the other traits are not. Genetic correlations of the slope of DFI were 0.15 with age at 100 kg, 0.04 with backfat thickness, − 0.29 with loin muscle thickness, 0.05 with feed conversion ratio, − 0.07 with lean meat percentage, − 0.13 with pH of the ham at 24 h postmortem, 0.06 with drip loss percentage, and 0.15 with boneless ham weight. Complementary results showed that genetic correlations of DFI with other economic traits varied across the environmental gradients. Estimates of genetic correlations of DFI with other traits of economic importance varied across the environmental gradients, especially for growth rate, which suggests the presence of genotype-by-environment interactions. The slope of DFI is an indicator of sensitivity of the animals to environmental challenges. Most traits of economic importance exhibited weak genetic correlations with the slope of DFI, indicating that selection for resilience based on the environmental sensitivity (slope of DFI) can be performed without adversely affecting these other traits. Our results demonstrate the feasibility of improving resilience through genetic selection.
{"title":"Genetic correlations of environmental sensitivity based on daily feed intake perturbations with economically important traits in a male pig line","authors":"Tomasi Tusingwiire, Carolina Garcia-Baccino, Bruno Ligonesche, Catherine Larzul, Zulma G. Vitezica","doi":"10.1186/s12711-025-01000-1","DOIUrl":"https://doi.org/10.1186/s12711-025-01000-1","url":null,"abstract":"Pigs in intensive production systems encounter various stressors that negatively impact their productivity and welfare. The primary aim of this study was to estimate the genetic correlations of the slope (indicator of sensitivity of the animals to environmental challenges) of the daily feed intake (DFI) across different environmental gradients (probability of the occurrence of a challenge on a given day) with growth, feed efficiency, carcass, and meat quality traits using a single-step reaction norm animal model (RNAM) in Piétrain pigs. In addition, genetic correlations of DFI (its total breeding value) with the same traits were also estimated. The probabilities of the occurrence of an unrecorded environmental challenge, inferred via a Gaussian mixture model, were taken as a reference and used in the genetic analysis as an environmental descriptor. Variance components were estimated via restricted maximum likelihood using the single-step genomic best linear unbiased prediction method, using a series of multivariate RNAM with two phenotypes (DFI and each of the traits of economic importance), with the probability of an unrecorded challenge on a given day included as an environmental descriptor for DFI only, because DFI is recorded daily but the other traits are not. Genetic correlations of the slope of DFI were 0.15 with age at 100 kg, 0.04 with backfat thickness, − 0.29 with loin muscle thickness, 0.05 with feed conversion ratio, − 0.07 with lean meat percentage, − 0.13 with pH of the ham at 24 h postmortem, 0.06 with drip loss percentage, and 0.15 with boneless ham weight. Complementary results showed that genetic correlations of DFI with other economic traits varied across the environmental gradients. Estimates of genetic correlations of DFI with other traits of economic importance varied across the environmental gradients, especially for growth rate, which suggests the presence of genotype-by-environment interactions. The slope of DFI is an indicator of sensitivity of the animals to environmental challenges. Most traits of economic importance exhibited weak genetic correlations with the slope of DFI, indicating that selection for resilience based on the environmental sensitivity (slope of DFI) can be performed without adversely affecting these other traits. Our results demonstrate the feasibility of improving resilience through genetic selection.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"60 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145203220","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 : 2025-09-25DOI: 10.1186/s12711-025-00995-x
Hélène Gilbert, Yann Labrune, Katia Fève, David Renaudeau, Roseline Rosé, Mario Giorgi, Yvon Billon, Jean-Luc Gourdine, Juliette Riquet
This study aimed to identify genomic regions involved in animal responses to chronic and acute Heat challenges in 1149 pigs tested in three climatic environments (temperate, tropical, and temperate Heated to 30 °C for 3 weeks). Production (growth rate, feed intake and efficiency, backfat thicknesses) and thermoregulation (rectal and cutaneous temperatures) traits were recorded in a backcross between Large White and Créole pigs. Genome-wide association studies were applied to the full population assuming SNP effects to be the same in both environments or to depend on the environment (GxE), and to the population in each environment separately. The genetic models used linkage disequilibrium in all chromosomes (LD) or only in Large White chromosomes (LW), or breed-of-origin of F1 alleles through linkage analyses (LA). Fifty-two regions distributed on 16 autosomes were detected. Most were identified with the LW or LD analyses, indicating both a large variability of effects in Large White in response to Heat stress, and high variability among the 10 Créole genomes segregating in the design. However, for thermoregulation traits, the majority of QTLs were detected with the LW model, suggesting interesting segregation of susceptibility and resistance alleles within the Large White breed. Ten regions were detected with the GxE model, mainly corresponding to significant effects in the temperate environment and no effect in the tropical situation, except for two regions on chromosome 2, which affected backfat thickness and growth rate, respectively. Twenty-four regions were detected for thermoregulation traits, but none were significant for both rectal and cutaneous temperatures. Of the 13 QTL regions detected for traits recorded during acute stress, four were also detected for similar traits during chronic stress, suggesting some consistency of responses during both stresses, although nine QTL regions were only detected during acute heat stress. Measuring direct indicators of responses to heat stress, such as thermoregulatory responses, is essential to detect QTL and propose candidate genes involved in these responses. Multiple QTL for thermoregulatory responses segregate in the Large White breed were detected, paving the way for opportunities to select for heat stress resilience in European pig breeds.
{"title":"Detection of genomic regions affecting thermotolerance traits in growing pigs during acute and chronic heat stress","authors":"Hélène Gilbert, Yann Labrune, Katia Fève, David Renaudeau, Roseline Rosé, Mario Giorgi, Yvon Billon, Jean-Luc Gourdine, Juliette Riquet","doi":"10.1186/s12711-025-00995-x","DOIUrl":"https://doi.org/10.1186/s12711-025-00995-x","url":null,"abstract":"This study aimed to identify genomic regions involved in animal responses to chronic and acute Heat challenges in 1149 pigs tested in three climatic environments (temperate, tropical, and temperate Heated to 30 °C for 3 weeks). Production (growth rate, feed intake and efficiency, backfat thicknesses) and thermoregulation (rectal and cutaneous temperatures) traits were recorded in a backcross between Large White and Créole pigs. Genome-wide association studies were applied to the full population assuming SNP effects to be the same in both environments or to depend on the environment (GxE), and to the population in each environment separately. The genetic models used linkage disequilibrium in all chromosomes (LD) or only in Large White chromosomes (LW), or breed-of-origin of F1 alleles through linkage analyses (LA). Fifty-two regions distributed on 16 autosomes were detected. Most were identified with the LW or LD analyses, indicating both a large variability of effects in Large White in response to Heat stress, and high variability among the 10 Créole genomes segregating in the design. However, for thermoregulation traits, the majority of QTLs were detected with the LW model, suggesting interesting segregation of susceptibility and resistance alleles within the Large White breed. Ten regions were detected with the GxE model, mainly corresponding to significant effects in the temperate environment and no effect in the tropical situation, except for two regions on chromosome 2, which affected backfat thickness and growth rate, respectively. Twenty-four regions were detected for thermoregulation traits, but none were significant for both rectal and cutaneous temperatures. Of the 13 QTL regions detected for traits recorded during acute stress, four were also detected for similar traits during chronic stress, suggesting some consistency of responses during both stresses, although nine QTL regions were only detected during acute heat stress. Measuring direct indicators of responses to heat stress, such as thermoregulatory responses, is essential to detect QTL and propose candidate genes involved in these responses. Multiple QTL for thermoregulatory responses segregate in the Large White breed were detected, paving the way for opportunities to select for heat stress resilience in European pig breeds.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133581","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 : 2025-09-25DOI: 10.1186/s12711-025-00992-0
Ajoy Mandal, Indrajit Gayari, Sylvia Lalhmingmawii, David R. Notter, Hasan Baneh
The objective of this study was to investigate the use of principal components (PC) as potential selection criteria to improve growth in sheep. The PC were derived from body weights of 2223 Muzaffarnagari lambs at birth, 90, 180, 270 and 360 days of age. Univariate animal models including various combinations of direct and maternal effects were fitted to the PC. Genetic correlations among PC and with body weights and estimated growth curve parameters for the Brody and Richards functions were estimated using bivariate animal models. The first three PC explained 94% of multivariate variation in body weights. PC1 contrasted lambs with larger versus smaller body weights at all postnatal ages. PC2 contrasted lambs with heavier versus lighter birth weights, with little emphasis on postnatal weights. PC3 placed positive emphasis on weights at birth and after 6 months of age but negative emphasis on weight at 3 through 9 months of age. Direct heritabilities for PC1, PC2, and PC3 were 0.19, 0.12 and 0.08, respectively. Maternal genetic and permanent environmental effects affected PC1 (0.04 and 0.08, respectively). PC2 was influenced by maternal genetic effects (0.10). Direct genetic correlations of PC1 with PC2 and PC3 were 0.48 and 0.72. The maternal genetic correlation between PC1 and PC2 was 0.97. Genetic relationships of PC1 with yearling weight and with estimates of final body weight from both growth functions exceeded 0.65. PC2 was genetically correlated with birth weight (≥ 0.64) and degree of maturity for body weight at birth (u0; ≥ 0.83). PC3 had negative genetic correlations with measures of maturing rate (~ -0.86) and with u0 ( -0.52 and -0.49), but positive correlations with final body weight (0.85 and 0.90) and time required to reach 50% of mature weight (0.83). Maternal genetic correlations of PC1 and PC2 with birth weight and u0 exceeded 0.83. We conclude that PC could be used as selection criteria in genetic improvement programs in sheep. Also, selection on PC1 and PC2 would likely be adequate to describe and improve direct and maternal genetic potentials for postnatal growth and birth weight, respectively, in Muzaffarnagari lambs.
{"title":"Principal components-based selection criteria for genetic improvement of growth in sheep breeding programs","authors":"Ajoy Mandal, Indrajit Gayari, Sylvia Lalhmingmawii, David R. Notter, Hasan Baneh","doi":"10.1186/s12711-025-00992-0","DOIUrl":"https://doi.org/10.1186/s12711-025-00992-0","url":null,"abstract":"The objective of this study was to investigate the use of principal components (PC) as potential selection criteria to improve growth in sheep. The PC were derived from body weights of 2223 Muzaffarnagari lambs at birth, 90, 180, 270 and 360 days of age. Univariate animal models including various combinations of direct and maternal effects were fitted to the PC. Genetic correlations among PC and with body weights and estimated growth curve parameters for the Brody and Richards functions were estimated using bivariate animal models. The first three PC explained 94% of multivariate variation in body weights. PC1 contrasted lambs with larger versus smaller body weights at all postnatal ages. PC2 contrasted lambs with heavier versus lighter birth weights, with little emphasis on postnatal weights. PC3 placed positive emphasis on weights at birth and after 6 months of age but negative emphasis on weight at 3 through 9 months of age. Direct heritabilities for PC1, PC2, and PC3 were 0.19, 0.12 and 0.08, respectively. Maternal genetic and permanent environmental effects affected PC1 (0.04 and 0.08, respectively). PC2 was influenced by maternal genetic effects (0.10). Direct genetic correlations of PC1 with PC2 and PC3 were 0.48 and 0.72. The maternal genetic correlation between PC1 and PC2 was 0.97. Genetic relationships of PC1 with yearling weight and with estimates of final body weight from both growth functions exceeded 0.65. PC2 was genetically correlated with birth weight (≥ 0.64) and degree of maturity for body weight at birth (u0; ≥ 0.83). PC3 had negative genetic correlations with measures of maturing rate (~ -0.86) and with u0 ( -0.52 and -0.49), but positive correlations with final body weight (0.85 and 0.90) and time required to reach 50% of mature weight (0.83). Maternal genetic correlations of PC1 and PC2 with birth weight and u0 exceeded 0.83. We conclude that PC could be used as selection criteria in genetic improvement programs in sheep. Also, selection on PC1 and PC2 would likely be adequate to describe and improve direct and maternal genetic potentials for postnatal growth and birth weight, respectively, in Muzaffarnagari lambs.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"17 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133585","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 : 2025-09-25DOI: 10.1186/s12711-025-00999-7
Nantapong Kamprasert, Hassan Aliloo, Julius H. J. van der Werf, Christian J. Duff, Samuel A. Clark
The advent of next-generation sequencing enables the opportunity to use denser marker tools, up to whole-genome sequences (WGS), for genomic prediction in livestock. Improvement in genomic prediction (GP) accuracy from using WGS has been observed in simulation studies. In contrast, such advantage has found to be inconsistent once implemented in practice. The benefit of WGS appears to be from markers that are significant for the trait of interest. Thus, the main objective of this study was to investigate the predictive ability of adding preselected markers to the standard-industry 50k genotype for GP of economically important traits in Angus cattle, namely, birth weight (BW), scrotal circumference (SC), carcass weight (CWT) and carcass intramuscular fat (CIMF). Animals were genotyped with either commercial or customised SNP-genotyping arrays; then, the genotypes were imputed to WGS. The 50k genotype was used as the control group. Informative markers associated with the desired traits were extracted from WGS, then were added to the 50k genotype. Several methods were chosen to select different sets of informative markers, including LD-based pruning, top SNP from a genome-wide association study (GWAS), functional annotation based on Gene Ontology, cattle QTL database, and sequence annotation. In total, eight different sets of genotypes were investigated. We applied different statistical models to predict genomic breeding values, including GBLUP, BayesR, and BayesRC, and two-GRM GBLUP constructed separately from the 50k and the preselected genotype set. Heritability (h2) estimates were similarly calculated using different sets of genotypes and statistical methods across all traits. The log-likelihood ratio values revealed that two-GRM GBLUP was more suitable than the single-GRM GBLUP. There was no significant difference in accuracy and bias among the different sets of genotypes compared to the control group or the statistical methods, except for BW. For BW, the Bayesian models slightly outperformed GBLUP. The findings suggest that potential improvements may be achieved by using preselected SNPs from the GWAS, a method that has proven within the population. The performance of preselected markers on GP influenced by several factors, including population structure, method used to select significant markers, and genetic architecture of traits.
{"title":"Effect of using preselected markers from imputed whole-genome sequence for genomic prediction in Angus cattle","authors":"Nantapong Kamprasert, Hassan Aliloo, Julius H. J. van der Werf, Christian J. Duff, Samuel A. Clark","doi":"10.1186/s12711-025-00999-7","DOIUrl":"https://doi.org/10.1186/s12711-025-00999-7","url":null,"abstract":"The advent of next-generation sequencing enables the opportunity to use denser marker tools, up to whole-genome sequences (WGS), for genomic prediction in livestock. Improvement in genomic prediction (GP) accuracy from using WGS has been observed in simulation studies. In contrast, such advantage has found to be inconsistent once implemented in practice. The benefit of WGS appears to be from markers that are significant for the trait of interest. Thus, the main objective of this study was to investigate the predictive ability of adding preselected markers to the standard-industry 50k genotype for GP of economically important traits in Angus cattle, namely, birth weight (BW), scrotal circumference (SC), carcass weight (CWT) and carcass intramuscular fat (CIMF). Animals were genotyped with either commercial or customised SNP-genotyping arrays; then, the genotypes were imputed to WGS. The 50k genotype was used as the control group. Informative markers associated with the desired traits were extracted from WGS, then were added to the 50k genotype. Several methods were chosen to select different sets of informative markers, including LD-based pruning, top SNP from a genome-wide association study (GWAS), functional annotation based on Gene Ontology, cattle QTL database, and sequence annotation. In total, eight different sets of genotypes were investigated. We applied different statistical models to predict genomic breeding values, including GBLUP, BayesR, and BayesRC, and two-GRM GBLUP constructed separately from the 50k and the preselected genotype set. Heritability (h2) estimates were similarly calculated using different sets of genotypes and statistical methods across all traits. The log-likelihood ratio values revealed that two-GRM GBLUP was more suitable than the single-GRM GBLUP. There was no significant difference in accuracy and bias among the different sets of genotypes compared to the control group or the statistical methods, except for BW. For BW, the Bayesian models slightly outperformed GBLUP. The findings suggest that potential improvements may be achieved by using preselected SNPs from the GWAS, a method that has proven within the population. The performance of preselected markers on GP influenced by several factors, including population structure, method used to select significant markers, and genetic architecture of traits.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"40 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133582","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 : 2025-09-25DOI: 10.1186/s12711-025-00996-w
Jonathan D’Ambrosio, Yoannah François, Thierry Morin, Sébastien Courant, Alexandre Desgranges, Pierrick Haffray, Bertrand Collet, Pierre Boudinot, Florence Phocas
This study focuses on genetic resistance to infectious pancreatic necrosis (IPN), a highly contagious disease caused by an aquatic birnavirus (IPNV) which especially affects salmonids worldwide. The objectives were to estimate the heritability of IPN resistance and to fine map quantitative trait loci (QTL) using a Bayesian Sparse Linear Mixed Model to identify candidate genes possibly linked to IPN resistance in two successive generations from a French commercial strain of rainbow trout. For each generation, 2000 fish were experimentally exposed by bath to IPNV and mortalities were monitored daily during 5 weeks. All fish were genotyped using a medium-density 57 K single nucleotide polymorphism (SNP) chip and imputed to high-density genotypes (665 K SNPs). The mean survival rate was 70% after 37 days, with a higher survival rate in the second generation compared to the first one (78% versus 61%). Heritability was moderate (~ 0.20). Approximately 74% of the genetic variance of IPN resistance was explained by several tens of SNPs. In total, 25 QTL were mapped on 10 chromosomes, of which 7 were detected with very strong evidence, on chromosomes 1, 14, 16 and 28. The most interesting QTL were associated to top SNPs with mean survival rate differences over 20% between the beneficial and detrimental homozygous genotypes. Those SNPs were all located within promising functional candidate genes on chromosome 1 (uts2d, rc3h1, ga45b) and chromosome 16 (irf2bp, eif2ak2), which were all associated with regulation of inflammatory pathways. A key factor for the genetic differences in susceptibility to IPNV among fish is the dsRNA-dependent serine/threonine-protein kinase (PKR) encoded by the eif2ak2 gene. All genes associated with the most significant QTL on chromosomes 1 and 16 are involved in the regulation of inflammatory pathways, strongly suggesting a central role of inflammation in IPN resistance in rainbow trout. These findings offer the possibility of marker-assisted selection for rapid dissemination of genetic improvement for IPN resistance.
本研究的重点是对传染性胰腺坏死（IPN）的遗传抗性，这是一种由水生病毒（IPNV）引起的高度传染性疾病，尤其影响全世界的鲑鱼。目的是估计IPN抗性的遗传力，并使用贝叶斯稀疏线性混合模型精细定位数量性状位点（QTL），以确定法国虹鳟鱼商业品系连续两代中可能与IPN抗性相关的候选基因。在5周的时间里，对每一代2000条鱼进行了浸泡暴露于IPNV的实验，每天监测死亡率。所有鱼使用中密度57 K单核苷酸多态性（SNP）芯片进行基因分型，并导入高密度基因型（665 K SNP）。37天后的平均存活率为70%，第二代的存活率高于第一代（78%对61%）。遗传力中等（~ 0.20）。大约74%的IPN抗性遗传变异可以用几十个snp来解释。共在10条染色体上定位了25个QTL，其中在1、14、16和28号染色体上检测到7个QTL。最有趣的QTL与顶级snp相关，在有益和有害纯合基因型之间的平均存活率差异超过20%。这些snp都位于1号染色体（uts2d, rc3h1, ga45b）和16号染色体（irf2bp, eif2ak2）上有希望的功能候选基因上，这些基因都与炎症途径的调节有关。鱼类对IPNV易感性遗传差异的一个关键因素是由eif2ak2基因编码的dsrna依赖性丝氨酸/苏氨酸蛋白激酶（PKR）。所有与1号和16号染色体上最显著QTL相关的基因都参与了炎症途径的调控，这有力地表明炎症在虹鳟鱼IPN抗性中起着核心作用。这些发现为快速传播IPN抗性遗传改良提供了标记辅助选择的可能性。
{"title":"High-density genome-wide association study points out major candidate genes for resistance to infectious pancreatic necrosis in rainbow trout","authors":"Jonathan D’Ambrosio, Yoannah François, Thierry Morin, Sébastien Courant, Alexandre Desgranges, Pierrick Haffray, Bertrand Collet, Pierre Boudinot, Florence Phocas","doi":"10.1186/s12711-025-00996-w","DOIUrl":"https://doi.org/10.1186/s12711-025-00996-w","url":null,"abstract":"This study focuses on genetic resistance to infectious pancreatic necrosis (IPN), a highly contagious disease caused by an aquatic birnavirus (IPNV) which especially affects salmonids worldwide. The objectives were to estimate the heritability of IPN resistance and to fine map quantitative trait loci (QTL) using a Bayesian Sparse Linear Mixed Model to identify candidate genes possibly linked to IPN resistance in two successive generations from a French commercial strain of rainbow trout. For each generation, 2000 fish were experimentally exposed by bath to IPNV and mortalities were monitored daily during 5 weeks. All fish were genotyped using a medium-density 57 K single nucleotide polymorphism (SNP) chip and imputed to high-density genotypes (665 K SNPs). The mean survival rate was 70% after 37 days, with a higher survival rate in the second generation compared to the first one (78% versus 61%). Heritability was moderate (~ 0.20). Approximately 74% of the genetic variance of IPN resistance was explained by several tens of SNPs. In total, 25 QTL were mapped on 10 chromosomes, of which 7 were detected with very strong evidence, on chromosomes 1, 14, 16 and 28. The most interesting QTL were associated to top SNPs with mean survival rate differences over 20% between the beneficial and detrimental homozygous genotypes. Those SNPs were all located within promising functional candidate genes on chromosome 1 (uts2d, rc3h1, ga45b) and chromosome 16 (irf2bp, eif2ak2), which were all associated with regulation of inflammatory pathways. A key factor for the genetic differences in susceptibility to IPNV among fish is the dsRNA-dependent serine/threonine-protein kinase (PKR) encoded by the eif2ak2 gene. All genes associated with the most significant QTL on chromosomes 1 and 16 are involved in the regulation of inflammatory pathways, strongly suggesting a central role of inflammation in IPN resistance in rainbow trout. These findings offer the possibility of marker-assisted selection for rapid dissemination of genetic improvement for IPN resistance.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"35 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133584","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 : 2025-09-25DOI: 10.1186/s12711-025-00998-8
Mette D. Madsen, Julius H. J. van der Werf, Aaron Ingham, Brad Hine, Antonio Reverter, Sam A. Clark
Improving immune competence (IC) in livestock could reduce the incidence of disease and reliance on the use of antibiotics. In Australian Angus cattle, IC is a measure of an animal’s combined ability to mount antibody and cell-mediated immune responses (AMIR and CMIR). Immune competence may affect traits such as growth and related phenotypes as well as the variability of such phenotypes. However, the genetic relationship between IC and genetic sensitivity to individual environments, measured as micro-genetic environmental sensitivity (GES), is yet to be reported. In this study the genetic parameters of, and correlations between, AMIR or CMIR and micro-GES of live weaning weight (WW) and ultrasound scan records of rib (RIB) and rump (RUMP) fat depth and eye muscle area (EMA) measured between 501 and 900 days of age were estimated. This was accomplished by fitting eight multivariate models with AMIR or CMIR and a double hierarchical generalised linear model on a production trait. The heritabilities were 0.35 and 0.36 for AMIR and CMIR, respectively, and 0.25–0.70 for the production traits. The heritabilities and the genetic coefficient of variation of micro-GES of the production traits ranged from 0.01–0.04 and 18–82%, respectively, and were higher in RIB and RUMP than WW and EMA. The genetic correlations between AMIR and WW, RIB, RUMP, or EMA were -0.35 (SE 0.11), 0.11 (0.12), 0.06 (0.12) and -0.13 (0.12), respectively, while the genetic correlations between CMIR and WW, RIB, RUMP, or EMA were -0.26 (0.12), 0.15 (0.13), 0.16 (0.12) and 0.04 (0.13), respectively. The genetic correlations between IC and micro-GES of WW, RIB, RUMP or EMA were moderately negative to lowly positive and had large SEs rendering them non-significant. The unfavourable genetic correlation between the IC traits and WW supports the hypothesis that mounting an effective immune response can direct resources away from growth when resources are limited. Based on the heritabilities and genetic coefficient of variation of micro-GES, selection to increase uniformity is possible for WW, RIB, RUMP and EMA. The standard errors of the genetic correlations between IC and micro-GES of the production traits were too large to draw any definite conclusions about their relationships. Standard errors are expected to reduce as more IC records are collected.
{"title":"The genetic relationship between immune competence traits and micro-genetic environmental sensitivity of weight, fat, and muscle traits in Australian Angus cattle","authors":"Mette D. Madsen, Julius H. J. van der Werf, Aaron Ingham, Brad Hine, Antonio Reverter, Sam A. Clark","doi":"10.1186/s12711-025-00998-8","DOIUrl":"https://doi.org/10.1186/s12711-025-00998-8","url":null,"abstract":"Improving immune competence (IC) in livestock could reduce the incidence of disease and reliance on the use of antibiotics. In Australian Angus cattle, IC is a measure of an animal’s combined ability to mount antibody and cell-mediated immune responses (AMIR and CMIR). Immune competence may affect traits such as growth and related phenotypes as well as the variability of such phenotypes. However, the genetic relationship between IC and genetic sensitivity to individual environments, measured as micro-genetic environmental sensitivity (GES), is yet to be reported. In this study the genetic parameters of, and correlations between, AMIR or CMIR and micro-GES of live weaning weight (WW) and ultrasound scan records of rib (RIB) and rump (RUMP) fat depth and eye muscle area (EMA) measured between 501 and 900 days of age were estimated. This was accomplished by fitting eight multivariate models with AMIR or CMIR and a double hierarchical generalised linear model on a production trait. The heritabilities were 0.35 and 0.36 for AMIR and CMIR, respectively, and 0.25–0.70 for the production traits. The heritabilities and the genetic coefficient of variation of micro-GES of the production traits ranged from 0.01–0.04 and 18–82%, respectively, and were higher in RIB and RUMP than WW and EMA. The genetic correlations between AMIR and WW, RIB, RUMP, or EMA were -0.35 (SE 0.11), 0.11 (0.12), 0.06 (0.12) and -0.13 (0.12), respectively, while the genetic correlations between CMIR and WW, RIB, RUMP, or EMA were -0.26 (0.12), 0.15 (0.13), 0.16 (0.12) and 0.04 (0.13), respectively. The genetic correlations between IC and micro-GES of WW, RIB, RUMP or EMA were moderately negative to lowly positive and had large SEs rendering them non-significant. The unfavourable genetic correlation between the IC traits and WW supports the hypothesis that mounting an effective immune response can direct resources away from growth when resources are limited. Based on the heritabilities and genetic coefficient of variation of micro-GES, selection to increase uniformity is possible for WW, RIB, RUMP and EMA. The standard errors of the genetic correlations between IC and micro-GES of the production traits were too large to draw any definite conclusions about their relationships. Standard errors are expected to reduce as more IC records are collected.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"1 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133608","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}
Gene expression profiles hold potentially valuable information for the prediction of breeding values and phenotypes. However, in practical breeding programs, most reference population individuals typically have only genomic data, lacking transcriptomic data. Predicting gene expression based on genetic markers and integrating the genetically predicted gene expression data into genomic prediction may offer a potential solution. This study extends kernel ridge regression (KRR) to weighted multiple kernel ridge regression (WMKRR), which integrates genomic data and transcriptomic data predicted from genetic markers through a multiple kernel learning (MKL) approach. We evaluated the predictive ability of WMKRR compared to traditional genomic best linear unbiased prediction (GBLUP) and a combined genomic and transcriptomic best linear unbiased prediction (GTBLUP) in both genotype feature selection and non-feature selection scenarios in two datasets: (i) 3305 simulated data based on the Cattle Genotype-Tissue Expression (CattleGTEx) dataset, (ii) 5515 real dairy cattle data. Our results show that WMKRR yielded higher predictive abilities than GBLUP And GTBLUP in both simulated And real dairy cattle data. For the simulated data based on CattleGTEx, WMKRR achieved an average improvement in predictive ability of 1.12% And 1.13% over GBLUP And GTBLUP, respectively, under the non-feature selection scenario, And 3.17% And 3.23%, respectively, under the feature selection scenario. For the real dairy cattle data, in cross-validation, WMKRR improved over GBLUP And GTBLUP by An average of 5.56% And 7.23%, respectively, without feature selection, And by 5.66% And 6.40%, respectively, with feature selection. In forward validation, WMKRR improved over GBLUP And GTBLUP by An average of 5.68% And 8.41%, respectively, without feature selection, And by 4.66% And 7.06%, respectively, with feature selection. Our result demonstrates that the WMKRR model, which integrates genomic and genetically predicted transcriptomic data, achieves better prediction performance compared to traditional genomic prediction models. This study showed the potential of enhanced genomic breeding application using omics data with no further omics sequencing cost.
{"title":"Integrating gene expression data via weighted multiple kernel ridge regression improved accuracy of genomic prediction","authors":"Xue Wang, Jingfang Si, Yachun Wang, Lingzhao Fang, Zhe Zhang, Yi Zhang","doi":"10.1186/s12711-025-00997-9","DOIUrl":"https://doi.org/10.1186/s12711-025-00997-9","url":null,"abstract":"Gene expression profiles hold potentially valuable information for the prediction of breeding values and phenotypes. However, in practical breeding programs, most reference population individuals typically have only genomic data, lacking transcriptomic data. Predicting gene expression based on genetic markers and integrating the genetically predicted gene expression data into genomic prediction may offer a potential solution. This study extends kernel ridge regression (KRR) to weighted multiple kernel ridge regression (WMKRR), which integrates genomic data and transcriptomic data predicted from genetic markers through a multiple kernel learning (MKL) approach. We evaluated the predictive ability of WMKRR compared to traditional genomic best linear unbiased prediction (GBLUP) and a combined genomic and transcriptomic best linear unbiased prediction (GTBLUP) in both genotype feature selection and non-feature selection scenarios in two datasets: (i) 3305 simulated data based on the Cattle Genotype-Tissue Expression (CattleGTEx) dataset, (ii) 5515 real dairy cattle data. Our results show that WMKRR yielded higher predictive abilities than GBLUP And GTBLUP in both simulated And real dairy cattle data. For the simulated data based on CattleGTEx, WMKRR achieved an average improvement in predictive ability of 1.12% And 1.13% over GBLUP And GTBLUP, respectively, under the non-feature selection scenario, And 3.17% And 3.23%, respectively, under the feature selection scenario. For the real dairy cattle data, in cross-validation, WMKRR improved over GBLUP And GTBLUP by An average of 5.56% And 7.23%, respectively, without feature selection, And by 5.66% And 6.40%, respectively, with feature selection. In forward validation, WMKRR improved over GBLUP And GTBLUP by An average of 5.68% And 8.41%, respectively, without feature selection, And by 4.66% And 7.06%, respectively, with feature selection. Our result demonstrates that the WMKRR model, which integrates genomic and genetically predicted transcriptomic data, achieves better prediction performance compared to traditional genomic prediction models. This study showed the potential of enhanced genomic breeding application using omics data with no further omics sequencing cost.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"13 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133607","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 : 2025-09-25DOI: 10.1186/s12711-025-01002-z
Joana Jacinto, Anna Letko, Arcangelo Gentile, Arthur Otter, Tobias Floyd, Rachael Collins, Moyna Richey, Helen Carty, Sandra Scholes, Alwyn Jones, Harriet Fuller, Irene M. Häfliger, Ben Strugnell, Eveline Studer, Cinzia Benazzi, Marilena Bolcato, Jože Starič, Alessia Diana, Jim Weber, Markus Freick, Gesine Lühken, Imke Tammen, David C. E. Kraft, Celina M. Lindgren, Marlene Sickinger, Sara Soto, Brendon A. O’Rourke, Jørgen S. Agerholm, Cord Drögemüller
Genetic skeletal disorders are a heterogeneous group of syndromic or non-syndromic diseases characterized by abnormal bone, joint or cartilage development. These disorders generally occur sporadically in ruminants. Although a genetic etiology is often suspected, only a limited number of causal variants have been identified and no comprehensive genetic analyses of a cohort of bovine and ovine skeletal developmental defects have been published. The aims of our study were (1) to propose a nosology of genetic skeletal disorders in cattle and sheep and (2) to contribute to the nosology with a number of novel genomically characterized cases. Based on a literature review, the proposed nosology of skeletal disorders in cattle and sheep with a confirmed molecular cause was found to comprise 43 different disorders associated with 45 different genes. In addition, horn traits were also included. The disorders were grouped into 21 categories based on the human medical nosology. Thirty novel bovine and nine ovine cases of congenital skeletal disorders were investigated. These represented 19 different disorders, which were grouped into 9 categories. Whole-genome sequencing (WGS) data were generated based on sample availability for either complete trios, affected paternal halfsiblings or isolated single cases. We identified 21 SNVs or small indels for 12 skeletal disorders. Of these, 17 were considered candidate variants affecting 16 different genes, including 11 that were classified as pathogenic and six as likely pathogenic. Additionally, the remaining 4 SNVs were of uncertain significance. Two aneuploidies (trisomy and partial monosomy) were the cause of two different disorders. For eight cases affected by six disorders no variant could be identified. Different modes of inheritance were detected, including spontaneous dominant de novo mutations, autosomal recessive alleles, an X-linked dominant allele, as well as aneuploidies. The overall molecular genetic diagnostic rate was 64%. Genomic analysis revealed considerable heterogeneity of the described phenotypes in terms of mode of inheritance, affected genes, and variant type. We propose, for the first time in veterinary medicine, a nosology of genetic skeletal disorders in ruminants that may be useful for more precise differential clinicopathological diagnosis. We emphasize the potential of WGS to enhance genetic disease diagnosis and the importance of adopting a nosology for disease categorization.
{"title":"Exploring skeletal disorders in cattle and sheep: a WGS-based framework for diagnosis and classification","authors":"Joana Jacinto, Anna Letko, Arcangelo Gentile, Arthur Otter, Tobias Floyd, Rachael Collins, Moyna Richey, Helen Carty, Sandra Scholes, Alwyn Jones, Harriet Fuller, Irene M. Häfliger, Ben Strugnell, Eveline Studer, Cinzia Benazzi, Marilena Bolcato, Jože Starič, Alessia Diana, Jim Weber, Markus Freick, Gesine Lühken, Imke Tammen, David C. E. Kraft, Celina M. Lindgren, Marlene Sickinger, Sara Soto, Brendon A. O’Rourke, Jørgen S. Agerholm, Cord Drögemüller","doi":"10.1186/s12711-025-01002-z","DOIUrl":"https://doi.org/10.1186/s12711-025-01002-z","url":null,"abstract":"Genetic skeletal disorders are a heterogeneous group of syndromic or non-syndromic diseases characterized by abnormal bone, joint or cartilage development. These disorders generally occur sporadically in ruminants. Although a genetic etiology is often suspected, only a limited number of causal variants have been identified and no comprehensive genetic analyses of a cohort of bovine and ovine skeletal developmental defects have been published. The aims of our study were (1) to propose a nosology of genetic skeletal disorders in cattle and sheep and (2) to contribute to the nosology with a number of novel genomically characterized cases. Based on a literature review, the proposed nosology of skeletal disorders in cattle and sheep with a confirmed molecular cause was found to comprise 43 different disorders associated with 45 different genes. In addition, horn traits were also included. The disorders were grouped into 21 categories based on the human medical nosology. Thirty novel bovine and nine ovine cases of congenital skeletal disorders were investigated. These represented 19 different disorders, which were grouped into 9 categories. Whole-genome sequencing (WGS) data were generated based on sample availability for either complete trios, affected paternal halfsiblings or isolated single cases. We identified 21 SNVs or small indels for 12 skeletal disorders. Of these, 17 were considered candidate variants affecting 16 different genes, including 11 that were classified as pathogenic and six as likely pathogenic. Additionally, the remaining 4 SNVs were of uncertain significance. Two aneuploidies (trisomy and partial monosomy) were the cause of two different disorders. For eight cases affected by six disorders no variant could be identified. Different modes of inheritance were detected, including spontaneous dominant de novo mutations, autosomal recessive alleles, an X-linked dominant allele, as well as aneuploidies. The overall molecular genetic diagnostic rate was 64%. Genomic analysis revealed considerable heterogeneity of the described phenotypes in terms of mode of inheritance, affected genes, and variant type. We propose, for the first time in veterinary medicine, a nosology of genetic skeletal disorders in ruminants that may be useful for more precise differential clinicopathological diagnosis. We emphasize the potential of WGS to enhance genetic disease diagnosis and the importance of adopting a nosology for disease categorization.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"57 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133583","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 : 2025-08-28DOI: 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}
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