The relative abundance of some bacteria in the gut of pigs is heritable, suggesting that host genetics may recursively influence boar semen quality by affecting the composition and function of gut microbiota. Therefore, it is essential to elucidate the specific contributions of heritable versus non-heritable gut microbiota to semen quality traits. Our study aimed to identify heritable and non-heritable bacterial taxa at the genus level in the boar gut and to predict their functions and respective contributions to semen quality traits. At the genus level, 39 heritable and 91 non-heritable bacterial taxa were identified. Functional analysis revealed that predicted microbial functions in both groups were primarily enriched in carbohydrate, nucleotide, and amino acid metabolism. We further analyzed the average microbiability of heritable and non-heritable bacteria on short-chain fatty acids (SCFAs) and semen quality traits. The relative abundance of heritable bacteria was found to contribute more to SCFAs levels and semen quality than non-heritable bacteria. Mediation analysis revealed that SCFAs could mediate the influence of the relative abundance of heritable bacteria on host phenotypes, identifying 99 significant genus-SCFAs-semen quality trait mediation links. Our findings underscore the substantial role of the relative abundance of heritable gut bacteria in shaping porcine semen quality through SCFAs mediation. These results highlight the potential of targeted microbiome interventions to enhance reproductive traits in pigs.
{"title":"Unraveling the role of bacteria with heritable versus non-heritable relative abundance in the gut on boar semen quality","authors":"Liangliang Guo, Xiaoqi Pei, Jiajian Tan, Haiqing Sun, Siwen Jiang, Hongkui Wei, Jian Peng","doi":"10.1186/s12711-025-00990-2","DOIUrl":"https://doi.org/10.1186/s12711-025-00990-2","url":null,"abstract":"The relative abundance of some bacteria in the gut of pigs is heritable, suggesting that host genetics may recursively influence boar semen quality by affecting the composition and function of gut microbiota. Therefore, it is essential to elucidate the specific contributions of heritable versus non-heritable gut microbiota to semen quality traits. Our study aimed to identify heritable and non-heritable bacterial taxa at the genus level in the boar gut and to predict their functions and respective contributions to semen quality traits. At the genus level, 39 heritable and 91 non-heritable bacterial taxa were identified. Functional analysis revealed that predicted microbial functions in both groups were primarily enriched in carbohydrate, nucleotide, and amino acid metabolism. We further analyzed the average microbiability of heritable and non-heritable bacteria on short-chain fatty acids (SCFAs) and semen quality traits. The relative abundance of heritable bacteria was found to contribute more to SCFAs levels and semen quality than non-heritable bacteria. Mediation analysis revealed that SCFAs could mediate the influence of the relative abundance of heritable bacteria on host phenotypes, identifying 99 significant genus-SCFAs-semen quality trait mediation links. Our findings underscore the substantial role of the relative abundance of heritable gut bacteria in shaping porcine semen quality through SCFAs mediation. These results highlight the potential of targeted microbiome interventions to enhance reproductive traits in pigs.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"36 2 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145447198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-31DOI: 10.1186/s12711-025-01009-6
Maulik Upadhyay, Alexander Graf, Neža Pogorevc, Doris Seichter, Ingolf Russ, Stefan Krebs, Saskia Meier, Ivica Medugorac
Breeding of genetically polled animals is a desirable approach in modern cattle husbandry for welfare and economic reasons. At least four different genetic variants associated with polledness in cattle have been identified, suggesting genetic heterogeneity. These dominant variants are located on chromosome 1 between approximately 2.42–2.73 Mb (reference: ARS-UCD1.3), also called the POLLED locus. Among these variants, Friesian (PF, ~ 80 kbp duplication) and Celtic (PC, 212 bp complex InDel) are the most common across breeds in the production systems globally, such as in Holstein–Friesian (HF) and Fleckvieh (FV). While studies have provided strong evidence supporting the association of the PF allele with the polledness, it has not yet been functionally validated, unlike the PC allele. Here, we conduct whole-genome sequencing analyses of two trios exhibiting unexpected inheritance patterns related to the PC and PF variants. In both instances, horned offspring were produced from mating pairs where one parent was homozygous for the polled variant and the other was homozygous for the ancestral horned variant. By analyzing the WGS data generated using Nanopore technology, we show that the de novo generation of the ancestral horned phenotype in both offspring was the result of distinct recombination events. Specifically, in the HF trio, it was the result of non-allelic homologous recombination in the gametes of the sire (PF/PF), while in the FV trio, it was the result of allelic homologous recombination in the gametes of the dam (PC/PF). The findings from the HF trio support the hypothesis that ~ 80-kbp duplication is the genetic variant responsible for the polled phenotype of Friesian origin. We show that different genomic arrangements in the POLLED locus can lead to the emergence of de novo ancestral horn phenotypes. Such arrangements can complicate phenotype prediction in offspring, even when sires or dams have been tested as genetically homozygous polled. Therefore, it is important, for a better understanding of the relationship between the POLLED locus and the POLLED phenotype, that any deviation from the expected result is critically analysed. Possibly, some of these cases can further narrow down the sequence motif that is essential for polledness in cattle.
{"title":"Recombination events restored the functional horned haplotypes in the offspring of polled parents","authors":"Maulik Upadhyay, Alexander Graf, Neža Pogorevc, Doris Seichter, Ingolf Russ, Stefan Krebs, Saskia Meier, Ivica Medugorac","doi":"10.1186/s12711-025-01009-6","DOIUrl":"https://doi.org/10.1186/s12711-025-01009-6","url":null,"abstract":"Breeding of genetically polled animals is a desirable approach in modern cattle husbandry for welfare and economic reasons. At least four different genetic variants associated with polledness in cattle have been identified, suggesting genetic heterogeneity. These dominant variants are located on chromosome 1 between approximately 2.42–2.73 Mb (reference: ARS-UCD1.3), also called the POLLED locus. Among these variants, Friesian (PF, ~ 80 kbp duplication) and Celtic (PC, 212 bp complex InDel) are the most common across breeds in the production systems globally, such as in Holstein–Friesian (HF) and Fleckvieh (FV). While studies have provided strong evidence supporting the association of the PF allele with the polledness, it has not yet been functionally validated, unlike the PC allele. Here, we conduct whole-genome sequencing analyses of two trios exhibiting unexpected inheritance patterns related to the PC and PF variants. In both instances, horned offspring were produced from mating pairs where one parent was homozygous for the polled variant and the other was homozygous for the ancestral horned variant. By analyzing the WGS data generated using Nanopore technology, we show that the de novo generation of the ancestral horned phenotype in both offspring was the result of distinct recombination events. Specifically, in the HF trio, it was the result of non-allelic homologous recombination in the gametes of the sire (PF/PF), while in the FV trio, it was the result of allelic homologous recombination in the gametes of the dam (PC/PF). The findings from the HF trio support the hypothesis that ~ 80-kbp duplication is the genetic variant responsible for the polled phenotype of Friesian origin. We show that different genomic arrangements in the POLLED locus can lead to the emergence of de novo ancestral horn phenotypes. Such arrangements can complicate phenotype prediction in offspring, even when sires or dams have been tested as genetically homozygous polled. Therefore, it is important, for a better understanding of the relationship between the POLLED locus and the POLLED phenotype, that any deviation from the expected result is critically analysed. Possibly, some of these cases can further narrow down the sequence motif that is essential for polledness in cattle.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"55 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-30DOI: 10.1186/s12711-025-01011-y
Leticia F. de Oliveira, Jenelle Dunkelberger, Claudia A. Sevillano, Saranya Arirangan, Robbee Wedow, Matthew Tegtmeyer, Mitchell Tuinstra, Luiz F. Brito
Porcine Reproductive and Respiratory Syndrome (PRRS) is a major challenge for the worldwide pig industry. Therefore, genetic selection for enhanced disease resilience is a priority for pig breeding programs. The objectives of this study were to evaluate genetic variation in reproductive performance during a PRRS outbreak and to assess the impact of selecting for enhanced reproductive performance using data collected under non-challenged conditions on reproductive performance in a PRRS challenged environment. These objectives were addressed by identifying natural PRRS outbreak periods from longitudinal performance data and estimating genetic parameters for reproductive performance traits, before, during, and after a PRRS outbreak, using data collected from purebred and crossbred sows on multiplier farms. During PRRS outbreaks, the number of piglets born alive decreased, while the number of stillborn and mummified piglets increased for both purebred and crossbred sows. Additive genetic variance and heritability estimates for reproductive performance traits varied by phase. For most traits, additive genetic variance was highest during the outbreak. Estimates of genetic correlations between a given trait measured across phases ranged from 0.09 to 0.99, but were > 0.3 for most traits. In general, estimates of genetic correlations were greatest between a given trait before and after an outbreak. Results also indicated reranking of animals based on estimated breeding values across outbreak phases, with Spearman correlations below 0.50 for most traits and low proportion of top-ranking animals retained across phases. PRRS outbreak periods can be detected by evaluating phenotypic variation in reproductive performance from longitudinal data. Reproductive performance is heritable, whether evaluated before, during, or after a PRRS outbreak, but estimates varied by phase. Favorable moderate-to-high genetic correlations were estimated for reproductive performance traits measured before vs. during a PRRS outbreak, suggesting that selection for improved reproductive performance under non-challenged conditions is also expected to improve reproductive performance under PRRS challenge conditions. However, the estimates of genetic correlation for most of the reproductive traits indicated genotype-by-environment interactions between the PRRS-free and challenge conditions. Therefore, incorporating data collected under PRRS challenge will capture additional genetic variation in PRRS resilience and, ultimately, aid in selecting sows with increased PRRS resilience.
{"title":"Genetics of reproductive performance across Porcine Reproductive and Respiratory Syndrome (PRRS) outbreak phases in purebred and crossbred sows","authors":"Leticia F. de Oliveira, Jenelle Dunkelberger, Claudia A. Sevillano, Saranya Arirangan, Robbee Wedow, Matthew Tegtmeyer, Mitchell Tuinstra, Luiz F. Brito","doi":"10.1186/s12711-025-01011-y","DOIUrl":"https://doi.org/10.1186/s12711-025-01011-y","url":null,"abstract":"Porcine Reproductive and Respiratory Syndrome (PRRS) is a major challenge for the worldwide pig industry. Therefore, genetic selection for enhanced disease resilience is a priority for pig breeding programs. The objectives of this study were to evaluate genetic variation in reproductive performance during a PRRS outbreak and to assess the impact of selecting for enhanced reproductive performance using data collected under non-challenged conditions on reproductive performance in a PRRS challenged environment. These objectives were addressed by identifying natural PRRS outbreak periods from longitudinal performance data and estimating genetic parameters for reproductive performance traits, before, during, and after a PRRS outbreak, using data collected from purebred and crossbred sows on multiplier farms. During PRRS outbreaks, the number of piglets born alive decreased, while the number of stillborn and mummified piglets increased for both purebred and crossbred sows. Additive genetic variance and heritability estimates for reproductive performance traits varied by phase. For most traits, additive genetic variance was highest during the outbreak. Estimates of genetic correlations between a given trait measured across phases ranged from 0.09 to 0.99, but were > 0.3 for most traits. In general, estimates of genetic correlations were greatest between a given trait before and after an outbreak. Results also indicated reranking of animals based on estimated breeding values across outbreak phases, with Spearman correlations below 0.50 for most traits and low proportion of top-ranking animals retained across phases. PRRS outbreak periods can be detected by evaluating phenotypic variation in reproductive performance from longitudinal data. Reproductive performance is heritable, whether evaluated before, during, or after a PRRS outbreak, but estimates varied by phase. Favorable moderate-to-high genetic correlations were estimated for reproductive performance traits measured before vs. during a PRRS outbreak, suggesting that selection for improved reproductive performance under non-challenged conditions is also expected to improve reproductive performance under PRRS challenge conditions. However, the estimates of genetic correlation for most of the reproductive traits indicated genotype-by-environment interactions between the PRRS-free and challenge conditions. Therefore, incorporating data collected under PRRS challenge will capture additional genetic variation in PRRS resilience and, ultimately, aid in selecting sows with increased PRRS resilience.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"80 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145396779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-30DOI: 10.1186/s12711-025-01007-8
Andrew Lakamp, Seidu Adams, Larry Kuehn, Warren Snelling, James Wells, Kristin Hales, Bryan Neville, Samodha Fernando, Matthew L. Spangler
Host genomic and rumen metagenome data can predict feed efficiency traits, supporting management decisions and increasing profitability. This study estimated the proportion of variation of average daily dry matter intake and average daily gain explained by the rumen metagenome in beef cattle, evaluated prediction accuracy using genomic data, metagenomic data, or their combination, and explored methods for modelling the rumen metagenome to improve phenotypic prediction accuracy. Data from 717 animals on four diets (two concentrate-based and two forage-based) were analyzed. Animal genotypes consisted of 749,922 imputed sequence variants, while metagenomic data comprised 16,583 open reading frames from ruminal microbiota. The metagenome was modelled using six (co)variance matrices, based on combinations of two creation methods and three modifications. Nineteen mixed linear models were used per trait: one with genomic effects only, six with metagenomic effects, six combining genomic and metagenomic effects, and six adding interaction effects. Two cross-validation schemes were applied to evaluate prediction accuracy: fourfold cross-validation balanced for diet type with 5 replicates and leave-one-diet-out cross-validation, where three diets served as training and the fourth as testing. Prediction accuracy was measured as the correlation between an animal’s summed random effects and its adjusted phenotype. Although minimal, differences existed in parameter estimates and validation accuracy depending on how the metagenome effect was modelled. Median phenotype prediction accuracy ranged from −0.01 to 0.28. No specific set of model characteristics consistently lead to the highest accuracies. Models which combined genome and metagenome data outperformed those using either data source alone. Models where the rumen metagenome (co)variances matrix was scaled within each diet composition generally led to lower prediction accuracies in this study. The rumen metagenome can explain a significant proportion of variation in beef cattle feed efficiency traits. Those traits can also be predicted using either host genome or rumen metagenome, though using both sources of information proved more accurate. Multiple methods of forming the metagenome (co)variance matrix can lead to similar prediction accuracies.
{"title":"Prediction accuracy for feed intake and body weight gain using host genomic and rumen metagenomic data in beef cattle","authors":"Andrew Lakamp, Seidu Adams, Larry Kuehn, Warren Snelling, James Wells, Kristin Hales, Bryan Neville, Samodha Fernando, Matthew L. Spangler","doi":"10.1186/s12711-025-01007-8","DOIUrl":"https://doi.org/10.1186/s12711-025-01007-8","url":null,"abstract":"Host genomic and rumen metagenome data can predict feed efficiency traits, supporting management decisions and increasing profitability. This study estimated the proportion of variation of average daily dry matter intake and average daily gain explained by the rumen metagenome in beef cattle, evaluated prediction accuracy using genomic data, metagenomic data, or their combination, and explored methods for modelling the rumen metagenome to improve phenotypic prediction accuracy. Data from 717 animals on four diets (two concentrate-based and two forage-based) were analyzed. Animal genotypes consisted of 749,922 imputed sequence variants, while metagenomic data comprised 16,583 open reading frames from ruminal microbiota. The metagenome was modelled using six (co)variance matrices, based on combinations of two creation methods and three modifications. Nineteen mixed linear models were used per trait: one with genomic effects only, six with metagenomic effects, six combining genomic and metagenomic effects, and six adding interaction effects. Two cross-validation schemes were applied to evaluate prediction accuracy: fourfold cross-validation balanced for diet type with 5 replicates and leave-one-diet-out cross-validation, where three diets served as training and the fourth as testing. Prediction accuracy was measured as the correlation between an animal’s summed random effects and its adjusted phenotype. Although minimal, differences existed in parameter estimates and validation accuracy depending on how the metagenome effect was modelled. Median phenotype prediction accuracy ranged from −0.01 to 0.28. No specific set of model characteristics consistently lead to the highest accuracies. Models which combined genome and metagenome data outperformed those using either data source alone. Models where the rumen metagenome (co)variances matrix was scaled within each diet composition generally led to lower prediction accuracies in this study. The rumen metagenome can explain a significant proportion of variation in beef cattle feed efficiency traits. Those traits can also be predicted using either host genome or rumen metagenome, though using both sources of information proved more accurate. Multiple methods of forming the metagenome (co)variance matrix can lead to similar prediction accuracies.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"17 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145396846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Variance components of linear mixed models should be estimated with all the data and information available for a specific statistical model to avoid bias. Due to computational limitations, the estimation for large datasets or complex models is often carried out by subsetting the data, removing genomic information, or simplifying the statistical model. Monte Carlo REML (MC-REML) is a method developed to lift computational limitations, but so far there was no extension for genomic information under the single-step genomic methods. In this study, we extended MC-REML to include large genomic information. We developed a method to estimate variance components named Monte Carlo single-step genomic REML (MC-ss-GREML). The core of the method includes repeatedly simulating breeding values under a ssGBLUP model and solving the mixed model equations to approximate traces involving prediction error variances. The REML optimization strategies include Expectation Maximization and Average Information. We tested the accuracy of MC-ss-GREML with a three-trait growth model in beef cattle with maternal effects, with 14 parameters to estimate. The data set had 100,000 animals in the pedigree, of which about 33,000 had records, and 10,000 were genotyped. There were no differences in estimates between MC-ss-GREML and ss-GREML with the exact calculation of the traces (exact ss-GREML). MC-ss-GREML took 14% of the computing time and used 1% of the memory compared to the exact ss-GREML. We tested the computing performance of MC-ss-GREML by estimating variance components in a birth weight model, with much larger data that included 7 million animals in the pedigree, from which 330,000 were genotyped. The estimation converged in 11 rounds and took 121 h, with a peak memory usage of 53 Gb. The new method, MC-ss-GREML, can estimate variance components with large datasets including many genotyped individuals, at affordable time and memory costs.
线性混合模型的方差成分应该用特定统计模型的所有数据和信息来估计，以避免偏差。由于计算的限制，对于大型数据集或复杂模型的估计通常是通过细分数据、去除基因组信息或简化统计模型来进行的。Monte Carlo REML （MC-REML）是一种消除计算限制的方法，但目前在单步基因组方法下还没有对基因组信息的扩展。在这项研究中，我们扩展了MC-REML以包含大的基因组信息。我们开发了一种估算方差分量的方法，称为蒙特卡罗单步基因组REML （MC-ss-GREML）。该方法的核心是在ssGBLUP模型下反复模拟育种值，并求解混合模型方程来逼近涉及预测误差方差的迹线。REML优化策略包括期望最大化和平均信息。我们以具有母系效应的肉牛为研究对象，采用三性状生长模型对MC-ss-GREML的准确性进行了检验，模型中有14个参数需要估计。该数据集有10万只动物的家谱，其中约3.3万只有记录，1万只进行了基因分型。MC-ss-GREML和ss-GREML之间的估计与轨迹的精确计算（精确ss-GREML）没有差异。与精确的ss-GREML相比，MC-ss-GREML花费了14%的计算时间和1%的内存。我们通过估算出生体重模型中的方差成分来测试MC-ss-GREML的计算性能，该模型使用了更大的数据，包括家谱中的700万只动物，其中33万只进行了基因分型。估计在11轮中收敛，耗时121小时，峰值内存使用量为53 Gb。新方法MC-ss-GREML可以在可承受的时间和内存成本下估算包含许多基因型个体的大型数据集的方差成分。
{"title":"Estimation of (co)variance components for very large datasets and complex single-step genomic models","authors":"Matias Bermann, Andres Legarra, Ignacio Aguilar, Alejandra Alvarez-Munera, Ignacy Misztal, Daniela Lourenco","doi":"10.1186/s12711-025-01006-9","DOIUrl":"https://doi.org/10.1186/s12711-025-01006-9","url":null,"abstract":"Variance components of linear mixed models should be estimated with all the data and information available for a specific statistical model to avoid bias. Due to computational limitations, the estimation for large datasets or complex models is often carried out by subsetting the data, removing genomic information, or simplifying the statistical model. Monte Carlo REML (MC-REML) is a method developed to lift computational limitations, but so far there was no extension for genomic information under the single-step genomic methods. In this study, we extended MC-REML to include large genomic information. We developed a method to estimate variance components named Monte Carlo single-step genomic REML (MC-ss-GREML). The core of the method includes repeatedly simulating breeding values under a ssGBLUP model and solving the mixed model equations to approximate traces involving prediction error variances. The REML optimization strategies include Expectation Maximization and Average Information. We tested the accuracy of MC-ss-GREML with a three-trait growth model in beef cattle with maternal effects, with 14 parameters to estimate. The data set had 100,000 animals in the pedigree, of which about 33,000 had records, and 10,000 were genotyped. There were no differences in estimates between MC-ss-GREML and ss-GREML with the exact calculation of the traces (exact ss-GREML). MC-ss-GREML took 14% of the computing time and used 1% of the memory compared to the exact ss-GREML. We tested the computing performance of MC-ss-GREML by estimating variance components in a birth weight model, with much larger data that included 7 million animals in the pedigree, from which 330,000 were genotyped. The estimation converged in 11 rounds and took 121 h, with a peak memory usage of 53 Gb. The new method, MC-ss-GREML, can estimate variance components with large datasets including many genotyped individuals, at affordable time and memory costs.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"8 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145396777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-23DOI: 10.1186/s12711-025-01008-7
Valentin P Haas,Robin Wellmann,Pascal Duenk,Michael Oster,Siriluck Ponsuksili,Jörn Bennewitz,Mario P L Calus
BACKGROUNDSince genomic selection has been established in animal breeding, attention has turned towards other omics layers that are seen as promising to improve prediction accuracy. Transcriptomic data provide insights into gene expression patterns, which are shaped by both genetic and environmental factors, offering a more comprehensive understanding of the expression of phenotypes. This study utilized various statistical methods to assess the applicability of transcriptomic data derived from intestinal tissue to the prediction of efficiency-related phenotypes. The focus was on formal derivation of the previously described GTCBLUP model, which was adapted to create GTCBLUPi and compared with other BLUP models. The GTCBLUPi model addresses redundant information between genomic and transcriptomic information. We compared estimated variance components and accuracies of prediction of phenotypes for efficiency-related traits in an F2 cross of 480 Japanese quail using different models. Additionally, we estimated transcriptomic correlations between the traits using animal effects based on transcriptomic similarity, and the effects of individual transcript abundances on the phenotypes.RESULTSThis study showed that transcript abundances from the ileum explain a larger portion of the phenotypic variance of the traits than host genetics. Models incorporating both genetic and transcriptomic information outperformed those using only one type of information, with regard to the phenotypic variances explained. The combination of both data types resulted in higher trait prediction accuracies, confirming that transcriptomic information complements genetic data effectively. The derived GTCBLUPi model proved to be a suitable framework for integrating both information types. Additionally, polygenic backgrounds were identified for the traits studied based on transcriptomic profiles, along with high transcriptomic correlations between the traits.CONCLUSIONSTranscriptomic data account for a high portion of phenotypic expression for all phenotypes and incorporating them enables more accurate predictions of phenotypes for efficiency and performance traits. Models that integrate both genetic and transcriptomic information are the most effective, offering valuable insights for improving phenotype prediction accuracy and insights in biological mechanisms underlying phenotypic variation of traits.
{"title":"Incorporating transcriptomic data into genomic prediction models to improve the prediction accuracy of phenotypes of efficiency traits.","authors":"Valentin P Haas,Robin Wellmann,Pascal Duenk,Michael Oster,Siriluck Ponsuksili,Jörn Bennewitz,Mario P L Calus","doi":"10.1186/s12711-025-01008-7","DOIUrl":"https://doi.org/10.1186/s12711-025-01008-7","url":null,"abstract":"BACKGROUNDSince genomic selection has been established in animal breeding, attention has turned towards other omics layers that are seen as promising to improve prediction accuracy. Transcriptomic data provide insights into gene expression patterns, which are shaped by both genetic and environmental factors, offering a more comprehensive understanding of the expression of phenotypes. This study utilized various statistical methods to assess the applicability of transcriptomic data derived from intestinal tissue to the prediction of efficiency-related phenotypes. The focus was on formal derivation of the previously described GTCBLUP model, which was adapted to create GTCBLUPi and compared with other BLUP models. The GTCBLUPi model addresses redundant information between genomic and transcriptomic information. We compared estimated variance components and accuracies of prediction of phenotypes for efficiency-related traits in an F2 cross of 480 Japanese quail using different models. Additionally, we estimated transcriptomic correlations between the traits using animal effects based on transcriptomic similarity, and the effects of individual transcript abundances on the phenotypes.RESULTSThis study showed that transcript abundances from the ileum explain a larger portion of the phenotypic variance of the traits than host genetics. Models incorporating both genetic and transcriptomic information outperformed those using only one type of information, with regard to the phenotypic variances explained. The combination of both data types resulted in higher trait prediction accuracies, confirming that transcriptomic information complements genetic data effectively. The derived GTCBLUPi model proved to be a suitable framework for integrating both information types. Additionally, polygenic backgrounds were identified for the traits studied based on transcriptomic profiles, along with high transcriptomic correlations between the traits.CONCLUSIONSTranscriptomic data account for a high portion of phenotypic expression for all phenotypes and incorporating them enables more accurate predictions of phenotypes for efficiency and performance traits. Models that integrate both genetic and transcriptomic information are the most effective, offering valuable insights for improving phenotype prediction accuracy and insights in biological mechanisms underlying phenotypic variation of traits.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"140 1","pages":"59"},"PeriodicalIF":4.1,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145351800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-23DOI: 10.1186/s12711-025-01005-w
Katie L M Eager,Robert D Jolly,Leah Manning,Cali E Willet,Russell G Snell,Klaus Lehnert,Natasha E Mckean,Nick W Sneddon,Brendon A O'Rourke,Keren E Dittmer,Imke Tammen,Matt Littlejohn
BACKGROUNDSegmental axonopathy is a recessively inherited neurodegenerative disorder that has affected Merino sheep since the early 1930s. Despite its long-standing recognition, the genetic basis of the condition remained unknown. This study aimed to identify the genetic cause of segmental axonopathy and confirm its pathological features to improve diagnostic accuracy and inform breeding strategies.RESULTSWhole genome sequencing and genotyping of affected and unaffected Merino sheep identified a novel homozygous frameshift variant in the ALS2 gene that segregated with disease. RNA sequencing of cerebellar peduncle tissue confirmed the nonsense consequence on the ALS2 transcript. Histological analysis highlighted the hallmarks of the disease as large, foamy eosinophilic axonal swellings predominantly in the trigeminal ganglia, with additional degenerative changes in both the brain and spinal cord. These findings support the value of targeted sampling of sensory roots of the trigeminal nerve, spinal cord tracts, and dorsal nerve rootlets to enhance diagnostic accuracy. The same ALS2 variant was found across multiple unrelated flocks in both Australia and New Zealand, indicating a broader presence within the fine-wool Merino sheep population.CONCLUSIONSThis study identifies a novel ALS2 frameshift variant associated with segmental axonopathy in Merino sheep and provides both genetic and histological evidence supporting its role in disease pathology. The development of a DNA diagnostic test will enable more informed breeding decisions, reduce the prevalence of this condition, and improve animal welfare and productivity in the Merino industry. Moreover, the findings offer a potential large-animal model for exploring early-onset forms of human motor neuron diseases, including amyotrophic lateral sclerosis, in which ALS2 variants are implicated.
{"title":"A novel frameshift variant in ALS2 associated with segmental axonopathy in Merino sheep.","authors":"Katie L M Eager,Robert D Jolly,Leah Manning,Cali E Willet,Russell G Snell,Klaus Lehnert,Natasha E Mckean,Nick W Sneddon,Brendon A O'Rourke,Keren E Dittmer,Imke Tammen,Matt Littlejohn","doi":"10.1186/s12711-025-01005-w","DOIUrl":"https://doi.org/10.1186/s12711-025-01005-w","url":null,"abstract":"BACKGROUNDSegmental axonopathy is a recessively inherited neurodegenerative disorder that has affected Merino sheep since the early 1930s. Despite its long-standing recognition, the genetic basis of the condition remained unknown. This study aimed to identify the genetic cause of segmental axonopathy and confirm its pathological features to improve diagnostic accuracy and inform breeding strategies.RESULTSWhole genome sequencing and genotyping of affected and unaffected Merino sheep identified a novel homozygous frameshift variant in the ALS2 gene that segregated with disease. RNA sequencing of cerebellar peduncle tissue confirmed the nonsense consequence on the ALS2 transcript. Histological analysis highlighted the hallmarks of the disease as large, foamy eosinophilic axonal swellings predominantly in the trigeminal ganglia, with additional degenerative changes in both the brain and spinal cord. These findings support the value of targeted sampling of sensory roots of the trigeminal nerve, spinal cord tracts, and dorsal nerve rootlets to enhance diagnostic accuracy. The same ALS2 variant was found across multiple unrelated flocks in both Australia and New Zealand, indicating a broader presence within the fine-wool Merino sheep population.CONCLUSIONSThis study identifies a novel ALS2 frameshift variant associated with segmental axonopathy in Merino sheep and provides both genetic and histological evidence supporting its role in disease pathology. The development of a DNA diagnostic test will enable more informed breeding decisions, reduce the prevalence of this condition, and improve animal welfare and productivity in the Merino industry. Moreover, the findings offer a potential large-animal model for exploring early-onset forms of human motor neuron diseases, including amyotrophic lateral sclerosis, in which ALS2 variants are implicated.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"11 1","pages":"60"},"PeriodicalIF":4.1,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145351789","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}
Background: The current cattle reference genome assembly, a pseudo-linear sequence produced using sequences from a single Hereford cow, represents a limitation when performing genetic studies, especially when investigating the whole spectrum of genetic variations within the species. Detecting structural variations (SVs) poses significant challenges when relying solely on conventional methods of sequencing read mapping to the current bovine genome assembly.
Results: In this study, we used long-reads (LR) and bioinformatic tools to construct a comprehensive bovine pangenome, using as a backbone the Hereford ARS-UCD1.2 reference genome assembly, and incorporating genetic diversity of 64 good quality de novo genome assemblies representing 14 French dairy and beef cattle breeds. Using a combination of complementary approaches, we explored the pangenome graph and identified 2.563 Gb of sequences common to all samples, and cumulated 0.295 Gb of variable sequences. Notably, we discovered 0.159 Gb of novel sequences not present in the current reference genome assembly. Our analysis also revealed 109,275 SVs, of which 84,612 were bi-allelic. These included 27,171 insertions and 24,592 deletions, while the remaining 32,849 SVs corresponded to alternate allele sequences defined as sequence substitutions between the reference genome and the sample sequence. Genome-wide association studies using SNPs and a panel of 221 SVs, shared between the pangenome and the EuroGMD chip, revealed well-known QTLs across the genome for the Holstein, Montbéliarde and Normande breeds. Among those, a QTL on chromosome 11 presents an SV with a highly significant effect on stature in the Holstein breed. This SV is a 6.2 kb deletion affecting the 5'UTR, first exon and part of the first intron of the MATN3 gene, suggesting a potential regulatory and coding effect.
Conclusions: Our study provides new insights into the genetic diversity of 14 French dairy and beef breeds and highlights the utility of pangenome graphs in capturing structural variation. The identified SV associated with stature highlights the importance of integrating SVs into GWAS for a more comprehensive understanding of complex traits.
{"title":"Application of a French cattle pangenome, from structural variant discovery to association studies on key phenotypes.","authors":"Valentin Sorin, Maulana Mughitz Naji, Clément Birbes, Cécile Grohs, Clémentine Escouflaire, Sébastien Fritz, Camille Eché, Camille Marcuzzo, Amandine Suin, Cécile Donnadieu, Christine Gaspin, Carole Iampietro, Denis Milan, Laurence Drouilhet, Gwenola Tosser-Klopp, Didier Boichard, Christophe Klopp, Marie-Pierre Sanchez, Mekki Boussaha","doi":"10.1186/s12711-025-01012-x","DOIUrl":"10.1186/s12711-025-01012-x","url":null,"abstract":"<p><strong>Background: </strong>The current cattle reference genome assembly, a pseudo-linear sequence produced using sequences from a single Hereford cow, represents a limitation when performing genetic studies, especially when investigating the whole spectrum of genetic variations within the species. Detecting structural variations (SVs) poses significant challenges when relying solely on conventional methods of sequencing read mapping to the current bovine genome assembly.</p><p><strong>Results: </strong>In this study, we used long-reads (LR) and bioinformatic tools to construct a comprehensive bovine pangenome, using as a backbone the Hereford ARS-UCD1.2 reference genome assembly, and incorporating genetic diversity of 64 good quality de novo genome assemblies representing 14 French dairy and beef cattle breeds. Using a combination of complementary approaches, we explored the pangenome graph and identified 2.563 Gb of sequences common to all samples, and cumulated 0.295 Gb of variable sequences. Notably, we discovered 0.159 Gb of novel sequences not present in the current reference genome assembly. Our analysis also revealed 109,275 SVs, of which 84,612 were bi-allelic. These included 27,171 insertions and 24,592 deletions, while the remaining 32,849 SVs corresponded to alternate allele sequences defined as sequence substitutions between the reference genome and the sample sequence. Genome-wide association studies using SNPs and a panel of 221 SVs, shared between the pangenome and the EuroGMD chip, revealed well-known QTLs across the genome for the Holstein, Montbéliarde and Normande breeds. Among those, a QTL on chromosome 11 presents an SV with a highly significant effect on stature in the Holstein breed. This SV is a 6.2 kb deletion affecting the 5'UTR, first exon and part of the first intron of the MATN3 gene, suggesting a potential regulatory and coding effect.</p><p><strong>Conclusions: </strong>Our study provides new insights into the genetic diversity of 14 French dairy and beef breeds and highlights the utility of pangenome graphs in capturing structural variation. The identified SV associated with stature highlights the importance of integrating SVs into GWAS for a more comprehensive understanding of complex traits.</p>","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"57 1","pages":"61"},"PeriodicalIF":3.1,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12551211/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145356826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-23DOI: 10.1186/s12711-025-01010-z
Suyun Fang,Chao Guo,Hang Liu,Yuzhan Wang,Cheng Tan,Zhenfang Wu,Yiqiang Zhao,Xiaoxiang Hu,Ruifei Yang
BACKGROUNDThe historical importation of Chinese pigs into Western countries has facilitated the introduction of Chinese haplotypes into European pig breeds, thereby shaping their genetic diversity and phenotypic traits. However, the genetic and biological implications of this introgression remain poorly understood.RESULTSBased on SNP chip and resequencing data, we confirmed significant genetic introgression from Chinese pigs into commercial European lines. The genetic origins of the introgressed segments predominantly derive from Southern Chinese domestic pigs (CSDP), with additional contributions from other populations, such as Eastern Chinese domestic pigs (CEDP). Our study demonstrates that the selection pressure for Chinese pig introgression was stronger in Duroc pigs compared to the Large White and Landrace breeds. Based on ancestral haplotypes from CEDP and CSDP, we conducted a genome-wide association study (GWAS) and identified 10 quantitative trait loci (QTLs), five of which were not identified in previous studies or using SNPs. Expression genome-wide association studies (eGWAS) based on these introgressed haplotypes, using gene expression profiles from the duodenum, liver, and muscle tissues in the Duroc population, revealed eGWAS signals that were enriched near transcript start sites. By integrating GWAS signals for loin muscle depth with eGWAS signals in muscle tissue, we confirmed that a region 300 Kb from TAF11, which is enriched with open chromatin regions and encompasses a super-enhancer located within the same topologically associating domain as TAF11, was associated with both TAF11 expression and loin muscle depth, highlighting the profound influence of Chinese introgression.CONCLUSIONSThese findings offer valuable insights into the genetic influences of Chinese pig introgression on the Duroc breed, as well as the molecular basis for its effects on economically important traits in Duroc pigs.
{"title":"The impact of haplotypes derived from Chinese pigs on genetic variation and economic traits in the Duroc breed.","authors":"Suyun Fang,Chao Guo,Hang Liu,Yuzhan Wang,Cheng Tan,Zhenfang Wu,Yiqiang Zhao,Xiaoxiang Hu,Ruifei Yang","doi":"10.1186/s12711-025-01010-z","DOIUrl":"https://doi.org/10.1186/s12711-025-01010-z","url":null,"abstract":"BACKGROUNDThe historical importation of Chinese pigs into Western countries has facilitated the introduction of Chinese haplotypes into European pig breeds, thereby shaping their genetic diversity and phenotypic traits. However, the genetic and biological implications of this introgression remain poorly understood.RESULTSBased on SNP chip and resequencing data, we confirmed significant genetic introgression from Chinese pigs into commercial European lines. The genetic origins of the introgressed segments predominantly derive from Southern Chinese domestic pigs (CSDP), with additional contributions from other populations, such as Eastern Chinese domestic pigs (CEDP). Our study demonstrates that the selection pressure for Chinese pig introgression was stronger in Duroc pigs compared to the Large White and Landrace breeds. Based on ancestral haplotypes from CEDP and CSDP, we conducted a genome-wide association study (GWAS) and identified 10 quantitative trait loci (QTLs), five of which were not identified in previous studies or using SNPs. Expression genome-wide association studies (eGWAS) based on these introgressed haplotypes, using gene expression profiles from the duodenum, liver, and muscle tissues in the Duroc population, revealed eGWAS signals that were enriched near transcript start sites. By integrating GWAS signals for loin muscle depth with eGWAS signals in muscle tissue, we confirmed that a region 300 Kb from TAF11, which is enriched with open chromatin regions and encompasses a super-enhancer located within the same topologically associating domain as TAF11, was associated with both TAF11 expression and loin muscle depth, highlighting the profound influence of Chinese introgression.CONCLUSIONSThese findings offer valuable insights into the genetic influences of Chinese pig introgression on the Duroc breed, as well as the molecular basis for its effects on economically important traits in Duroc pigs.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"42 1","pages":"58"},"PeriodicalIF":4.1,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145351821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-16DOI: 10.1186/s12711-025-01001-0
Alice Etourneau, Rachel Rupp, Bertrand Servin
Recombination is a fundamental biological process, both in participating to the creation of viable gametes and as a driver of genetic diversity. Characterising recombination is therefore of strong interest in breeding populations. In this study, we used ~ 50 K genotyped data and pedigree from two French populations (Alpine and Saanen) of domestic goats (Capra hircus) to build sex-specific recombination maps, and to explore the genetic basis of two recombination phenotypes: genome-wide recombination rate (GRR) and intra-chromosomal shuffling. Sex-specific recombination maps showed higher recombination in males than females for both breeds (Alpine autosomal map size = 35.1 M in males and 30.5 M in females; Saanen map size = 34.0 M in males and 29.0 M in females). Heterochiasmy is particularly notable on small chromosomes, and at both ends of the chromosomes. Yet, no difference in shuffling has been found between populations. Genetic parameters on recombination phenotypes could only be estimated in males, due to lack of data in females. Both phenotypes are significantly heritable (h2 = 0.12 (0.03) for GRR and h2 = 0.034 (0.015) for shuffling, when pooling breeds). GWAS on male GRR identified several significant loci, including RNF212, RNF212B and SSH1, the last one being a novel locus for this phenotype. Correlation of SNP effects between breeds is low for both recombination phenotypes (0.25 for GRR and 0.04 for shuffling), indicating different genetic determinants in the two breeds. This study contributes to the understanding of recombination evolution in ruminants, both between breeds and species.
{"title":"Genome landscape and genetic architecture of recombination in domestic goats (Capra hircus)","authors":"Alice Etourneau, Rachel Rupp, Bertrand Servin","doi":"10.1186/s12711-025-01001-0","DOIUrl":"https://doi.org/10.1186/s12711-025-01001-0","url":null,"abstract":"Recombination is a fundamental biological process, both in participating to the creation of viable gametes and as a driver of genetic diversity. Characterising recombination is therefore of strong interest in breeding populations. In this study, we used ~ 50 K genotyped data and pedigree from two French populations (Alpine and Saanen) of domestic goats (Capra hircus) to build sex-specific recombination maps, and to explore the genetic basis of two recombination phenotypes: genome-wide recombination rate (GRR) and intra-chromosomal shuffling. Sex-specific recombination maps showed higher recombination in males than females for both breeds (Alpine autosomal map size = 35.1 M in males and 30.5 M in females; Saanen map size = 34.0 M in males and 29.0 M in females). Heterochiasmy is particularly notable on small chromosomes, and at both ends of the chromosomes. Yet, no difference in shuffling has been found between populations. Genetic parameters on recombination phenotypes could only be estimated in males, due to lack of data in females. Both phenotypes are significantly heritable (h2 = 0.12 (0.03) for GRR and h2 = 0.034 (0.015) for shuffling, when pooling breeds). GWAS on male GRR identified several significant loci, including RNF212, RNF212B and SSH1, the last one being a novel locus for this phenotype. Correlation of SNP effects between breeds is low for both recombination phenotypes (0.25 for GRR and 0.04 for shuffling), indicating different genetic determinants in the two breeds. This study contributes to the understanding of recombination evolution in ruminants, both between breeds and species.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"10 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145295624","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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