Pub Date : 2025-05-27DOI: 10.1186/s12711-025-00973-3
Timothy P. Bilton, Setegn W. Alemu, Ken G. Dodds, Hannah Henry, Melanie K. Hess, Ronan Jordan, Fern Booker, Sharon M. Hickey, Neville Amyes, Kevin Knowler, Edgar Sandoval, Jacqueline Peers-Adams, Tracey C. van Stijn, Hayley Baird, Trevor Watson, Wendy Bain, Barry Veenvliet, Gerard Pile, Brooke Bryson, Shannon M. Clarke, Patricia L. Johnson, John C. McEwan, Suzanne J. Rowe
Global targets to reduce greenhouse gas emissions to meet international climate change commitments have driven the livestock industry to develop solutions to reduce methane emission in ruminants while maintaining production. Research has shown that selective breeding for low methane emitting ruminants using genomic selection is one viable solution to meet methane targets at a national level. However, this requires obtaining sufficient measures of methane on individual animals across the national herd. In sheep, one affordable method for measuring methane on-farm to rank animals on their methane emissions is portable accumulation chambers (PAC), although this method is not without its challenges. An alternative is to use a proxy trait that is genetically correlated with PAC methane measures. One such trait that has shown promise is rumen metagenome community (RMC) profiles. In this study, we investigate the potential of using RMC profiles as a proxy trait for methane emissions from PAC using a large sheep dataset consisting of 4585 mixed-sex lambs from several flocks and years across New Zealand. RMC profiles were generated from rumen samples collected on the animals immediately after being measured through PAC using restriction enzyme-reduced representation sequencing. We predicted methane (CH4) and carbon dioxide (CO2) emissions (grams per day), as well as the ratio CH4/(CO2 + CH4) (CH4Ratio), from the RMC profiles and SNP-array genotype data. Heritability and microbiability estimates were similar to values found in the literature for all traits. The correlation of PAC methane with predicted methane was 1.9- to 2.3-fold (CH4) and 1.2- to 1.5-fold (CH4Ratio) greater for RMC profiles compared to host genomics only. The genetic correlation between methane predicted from RMC profiles and PAC methane was 0.75 ± 0.12 for CH4 and 0.64 ± 0.11 for CH4Ratio when using a validation set consisting of the animals with the most recent year of birth in the dataset. RMC profiles are predictive of, and genetically correlated, with PAC methane measures. Therefore, RMC profiles are a suitable proxy trait for determining the genetic merit of an animal’s methane emissions and could be incorporated into existing breeding programs to facilitate selective breeding for low methane emitting sheep.
{"title":"Rumen metagenome profiles are heritable and rank the New Zealand national sheep flock for enteric methane emissions","authors":"Timothy P. Bilton, Setegn W. Alemu, Ken G. Dodds, Hannah Henry, Melanie K. Hess, Ronan Jordan, Fern Booker, Sharon M. Hickey, Neville Amyes, Kevin Knowler, Edgar Sandoval, Jacqueline Peers-Adams, Tracey C. van Stijn, Hayley Baird, Trevor Watson, Wendy Bain, Barry Veenvliet, Gerard Pile, Brooke Bryson, Shannon M. Clarke, Patricia L. Johnson, John C. McEwan, Suzanne J. Rowe","doi":"10.1186/s12711-025-00973-3","DOIUrl":"https://doi.org/10.1186/s12711-025-00973-3","url":null,"abstract":"Global targets to reduce greenhouse gas emissions to meet international climate change commitments have driven the livestock industry to develop solutions to reduce methane emission in ruminants while maintaining production. Research has shown that selective breeding for low methane emitting ruminants using genomic selection is one viable solution to meet methane targets at a national level. However, this requires obtaining sufficient measures of methane on individual animals across the national herd. In sheep, one affordable method for measuring methane on-farm to rank animals on their methane emissions is portable accumulation chambers (PAC), although this method is not without its challenges. An alternative is to use a proxy trait that is genetically correlated with PAC methane measures. One such trait that has shown promise is rumen metagenome community (RMC) profiles. In this study, we investigate the potential of using RMC profiles as a proxy trait for methane emissions from PAC using a large sheep dataset consisting of 4585 mixed-sex lambs from several flocks and years across New Zealand. RMC profiles were generated from rumen samples collected on the animals immediately after being measured through PAC using restriction enzyme-reduced representation sequencing. We predicted methane (CH4) and carbon dioxide (CO2) emissions (grams per day), as well as the ratio CH4/(CO2 + CH4) (CH4Ratio), from the RMC profiles and SNP-array genotype data. Heritability and microbiability estimates were similar to values found in the literature for all traits. The correlation of PAC methane with predicted methane was 1.9- to 2.3-fold (CH4) and 1.2- to 1.5-fold (CH4Ratio) greater for RMC profiles compared to host genomics only. The genetic correlation between methane predicted from RMC profiles and PAC methane was 0.75 ± 0.12 for CH4 and 0.64 ± 0.11 for CH4Ratio when using a validation set consisting of the animals with the most recent year of birth in the dataset. RMC profiles are predictive of, and genetically correlated, with PAC methane measures. Therefore, RMC profiles are a suitable proxy trait for determining the genetic merit of an animal’s methane emissions and could be incorporated into existing breeding programs to facilitate selective breeding for low methane emitting sheep.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"16 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144145495","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 : 2025-05-21DOI: 10.1186/s12711-025-00972-4
Xiangyu Guo, Pernille Sarup, Anders Bay Nord, Mark Henryon, Tage Ostersen, Ole F. Christensen
Metabolomic profiling of blood samples can be done on selection candidates and could be a valuable information source for genetic evaluation of pigs. We hypothesized that integrating metabolomic data from pigs without individual phenotypes into the metabolomic-genomic best linear unbiased prediction (MGBLUP) model would generate estimated breeding values (EBVs) with a higher accuracy compared to what would be obtained without metabolomic data. We tested this hypothesis by predicting breeding values for average daily gain (ADG) using phenotypic, genomic, and metabolomic data. MGBLUP models were fitted to average daily gain of 8174 Duroc pigs that were genotyped and profiled for metabolomic features. Approximately half the pigs were males from a test station and the other half were females from breeding herds. Variance components were estimated, and we employed two validation schemes: test station to breeding herd validation and fivefold cross-validation. Accuracies of EBVs in the validation population were computed by combining results on predictive abilities with results on increases in accuracies from the linear regression method. Parameter estimates from MGBLUP showed a direct heritability of ADG of 0.15, a proportion of variance explained by metabolomic features of 0.18, and a heritability of metabolomic intensities of 0.14, together resulting in a total heritability of 0.17. Thus, the majority of the heritability was not mediated by the metabolome. For the test station to breeding herd validation, the accuracies of EBVs were 0.60 for genomic best linear unbiased prediction (GBLUP) with genotypes in validation population, 0.61 for MGBLUP with genotypes in validation population, 0.62 for MGBLUP with genotypes and metabolomic features in validation population, 0.72 for GBLUP with genotypes and phenotypes in validation population, and 0.74 for MGBLUP with genotypes, phenotypes and metabolomic features in validation population, whereas the corresponding numbers were 0.87, 0.87, 0.87, 0.91 and 0.92 for the fivefold cross-validation. Therefore, small increases in accuracies were observed when including metabolomic features. The inclusion of metabolomics data provided small improvements in the accuracy of genetic evaluations for average daily gain in pigs. Further work will be needed to investigate, e.g., alternative time points for blood sampling, metabolomics on samples of other tissues, and other traits.
{"title":"Metabolomic-genomic prediction realizes small increases in accuracy of estimated breeding values for daily gain in pigs","authors":"Xiangyu Guo, Pernille Sarup, Anders Bay Nord, Mark Henryon, Tage Ostersen, Ole F. Christensen","doi":"10.1186/s12711-025-00972-4","DOIUrl":"https://doi.org/10.1186/s12711-025-00972-4","url":null,"abstract":"Metabolomic profiling of blood samples can be done on selection candidates and could be a valuable information source for genetic evaluation of pigs. We hypothesized that integrating metabolomic data from pigs without individual phenotypes into the metabolomic-genomic best linear unbiased prediction (MGBLUP) model would generate estimated breeding values (EBVs) with a higher accuracy compared to what would be obtained without metabolomic data. We tested this hypothesis by predicting breeding values for average daily gain (ADG) using phenotypic, genomic, and metabolomic data. MGBLUP models were fitted to average daily gain of 8174 Duroc pigs that were genotyped and profiled for metabolomic features. Approximately half the pigs were males from a test station and the other half were females from breeding herds. Variance components were estimated, and we employed two validation schemes: test station to breeding herd validation and fivefold cross-validation. Accuracies of EBVs in the validation population were computed by combining results on predictive abilities with results on increases in accuracies from the linear regression method. Parameter estimates from MGBLUP showed a direct heritability of ADG of 0.15, a proportion of variance explained by metabolomic features of 0.18, and a heritability of metabolomic intensities of 0.14, together resulting in a total heritability of 0.17. Thus, the majority of the heritability was not mediated by the metabolome. For the test station to breeding herd validation, the accuracies of EBVs were 0.60 for genomic best linear unbiased prediction (GBLUP) with genotypes in validation population, 0.61 for MGBLUP with genotypes in validation population, 0.62 for MGBLUP with genotypes and metabolomic features in validation population, 0.72 for GBLUP with genotypes and phenotypes in validation population, and 0.74 for MGBLUP with genotypes, phenotypes and metabolomic features in validation population, whereas the corresponding numbers were 0.87, 0.87, 0.87, 0.91 and 0.92 for the fivefold cross-validation. Therefore, small increases in accuracies were observed when including metabolomic features. The inclusion of metabolomics data provided small improvements in the accuracy of genetic evaluations for average daily gain in pigs. Further work will be needed to investigate, e.g., alternative time points for blood sampling, metabolomics on samples of other tissues, and other traits.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"45 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144113993","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}
Genetic defects that occur naturally in livestock species provide valuable models for investigating the molecular mechanisms underlying rare human diseases. Livestock breeds are subject to the regular emergence of recessive genetic defects due to genetic drift and recent inbreeding. At the same time, their large population sizes provide easy access to case and control individuals and to massive amounts of pedigree, genomic and phenotypic information recorded for management and selection purposes. In this study, we investigated a lethal form of recessive chondrodysplasia observed in 21 stillborn calves of the Aubrac beef cattle breed. Detailed examinations of three affected calves revealed proximal limb shortening, epiphyseal calcific deposits, and other pathological signs consistent with human rhizomelic chondrodysplasia punctata, a rare peroxisomal disorder caused by recessive variants in one of five genes (AGPS, FAR1, GNPAT, PEX5, and PEX7). Using homozygosity mapping, whole genome sequencing of two affected individuals, and filtering for variants found in 1867 control genomes, we reduced the list of candidate variants to a single deep intronic substitution in GNPAT (NC_037355.1:g.4039268G > A on chromosome 28 of the ARS-UCD1.2 bovine genome assembly). For verification, we performed large-scale genotyping of this variant using a custom SNP array and found a perfect genotype–phenotype correlation in 21 cases and 26 of their parents, and a complete absence of homozygotes in 1195 unaffected Aubrac controls. The g.4039268A allele segregated at a frequency of 2.6% in this population and was absent in 375,535 additional individuals from 17 breeds. Then, using in vivo and in vitro analyses, we demonstrated that the derived allele activates cryptic splice sites within intron 11 resulting in abnormal transcripts. Finally, by mining the wealth of records available in the French bovine database, we also reported suggestive effects on juvenile mortality (and not just stillbirth) in homozygotes and on muscle development in heterozygotes, which merit further investigation. We report the first spontaneous large animal model of rhizomelic chondrodysplasia punctata and provide a diagnostic test to select against this defect in cattle. Our work also brings interesting insights into the molecular consequences of complete or partial GNPAT insufficiency in mammals.
{"title":"A bovine model of rhizomelic chondrodysplasia punctata caused by a deep intronic splicing variant in the GNPAT gene","authors":"Arnaud Boulling, Julien Corbeau, Cécile Grohs, Anne Barbat, Jérémy Mortier, Sébastien Taussat, Vincent Plassard, Hélène Leclerc, Sébastien Fritz, Cyril Leymarie, Lorraine Bourgeois-Brunel, Alain Ducos, Raphaël Guatteo, Didier Boichard, Mekki Boussaha, Aurélien Capitan","doi":"10.1186/s12711-025-00969-z","DOIUrl":"https://doi.org/10.1186/s12711-025-00969-z","url":null,"abstract":"Genetic defects that occur naturally in livestock species provide valuable models for investigating the molecular mechanisms underlying rare human diseases. Livestock breeds are subject to the regular emergence of recessive genetic defects due to genetic drift and recent inbreeding. At the same time, their large population sizes provide easy access to case and control individuals and to massive amounts of pedigree, genomic and phenotypic information recorded for management and selection purposes. In this study, we investigated a lethal form of recessive chondrodysplasia observed in 21 stillborn calves of the Aubrac beef cattle breed. Detailed examinations of three affected calves revealed proximal limb shortening, epiphyseal calcific deposits, and other pathological signs consistent with human rhizomelic chondrodysplasia punctata, a rare peroxisomal disorder caused by recessive variants in one of five genes (AGPS, FAR1, GNPAT, PEX5, and PEX7). Using homozygosity mapping, whole genome sequencing of two affected individuals, and filtering for variants found in 1867 control genomes, we reduced the list of candidate variants to a single deep intronic substitution in GNPAT (NC_037355.1:g.4039268G > A on chromosome 28 of the ARS-UCD1.2 bovine genome assembly). For verification, we performed large-scale genotyping of this variant using a custom SNP array and found a perfect genotype–phenotype correlation in 21 cases and 26 of their parents, and a complete absence of homozygotes in 1195 unaffected Aubrac controls. The g.4039268A allele segregated at a frequency of 2.6% in this population and was absent in 375,535 additional individuals from 17 breeds. Then, using in vivo and in vitro analyses, we demonstrated that the derived allele activates cryptic splice sites within intron 11 resulting in abnormal transcripts. Finally, by mining the wealth of records available in the French bovine database, we also reported suggestive effects on juvenile mortality (and not just stillbirth) in homozygotes and on muscle development in heterozygotes, which merit further investigation. We report the first spontaneous large animal model of rhizomelic chondrodysplasia punctata and provide a diagnostic test to select against this defect in cattle. Our work also brings interesting insights into the molecular consequences of complete or partial GNPAT insufficiency in mammals.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"148 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144097516","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 : 2025-05-02DOI: 10.1186/s12711-025-00968-0
Cristina Óvilo, Rita Benítez, Yolanda Núñez, Ramón Peiró-Pastor, Fabian García, Eduardo De Mercado, Emilio Gómez-Izquierdo, Juan García-Casco, Clemente López-Bote, María Muñoz
Taste receptor genes are expressed in sensory cells located in the tongue and influence food preferences, voluntary feed intake, and other relevant traits. Taste perception may differ between livestock breeds that show differences in eating behaviour and between animals that receive different diets or show phenotypic variation in feed intake or related-traits. The objectives of this work were to deepen the understanding of the regulation of the function of taste receptor genes in the circumvallate papillae of obese Iberian pigs in comparison to Duroc pigs, and to characterize their genetic variation and associations with relevant production traits. We performed a gene expression and structural analysis of ten taste receptor genes in Iberian and Duroc pigs. Gene expression was quantified in the circumvallate papillae of 48 growing Iberian and Duroc pigs maintained under identical management conditions but fed isocaloric diets differing in energy source: either high concentration of fat rich in oleic acid (HO) or carbohydrates (CH); and sacrificed after 47 days of treatment (50.5 kg live weight). Gene expression differed between the two breeds for most of the analyzed genes, with the TAS1R1, TAS1R2, TAS1R3, TAS2R4, TAS2R38, TAS2R39, GPR84, and CD36 genes being overexpressed in Duroc pigs. The diet effect was modulated by breed, with TAS1R1, TAS1R3, and TAS2R4 genes being overexpressed only in Duroc pigs fed the HO diet. Detection of genetic variants (single nucleotide polymorphisms, SNPs) for this panel of genes was performed on muscle RNA-seq data, and three SNPs in the TAS1R1, TAS1R3, and CD36 genes were selected for association studies. All three SNPs were associated with various growth, fattening, tissue fat content, and composition traits. Moreover, the CD36:c.910G/T SNP was associated with oral CD36 gene expression and with differences in the predicted mRNA secondary structure. Most taste receptor genes are expressed at lower level in circumvallate papillae from Iberian than Duroc pigs. This aligns with lower overall taste sensitivity, higher feed intake, and obese nature of Iberian pigs. Significant association results were observed for SNPs in the TAS1R1 and TAS1R3 genes with meat quality traits and liver composition, which showed segregation in world-wide distributed breeds, but particularly for a potential causal SNP in the CD36 gene, associated with growth and tissue composition, which segregates in Iberian populations.
{"title":"Expression and structural analysis of taste receptor genes in Iberian and Duroc pigs","authors":"Cristina Óvilo, Rita Benítez, Yolanda Núñez, Ramón Peiró-Pastor, Fabian García, Eduardo De Mercado, Emilio Gómez-Izquierdo, Juan García-Casco, Clemente López-Bote, María Muñoz","doi":"10.1186/s12711-025-00968-0","DOIUrl":"https://doi.org/10.1186/s12711-025-00968-0","url":null,"abstract":"Taste receptor genes are expressed in sensory cells located in the tongue and influence food preferences, voluntary feed intake, and other relevant traits. Taste perception may differ between livestock breeds that show differences in eating behaviour and between animals that receive different diets or show phenotypic variation in feed intake or related-traits. The objectives of this work were to deepen the understanding of the regulation of the function of taste receptor genes in the circumvallate papillae of obese Iberian pigs in comparison to Duroc pigs, and to characterize their genetic variation and associations with relevant production traits. We performed a gene expression and structural analysis of ten taste receptor genes in Iberian and Duroc pigs. Gene expression was quantified in the circumvallate papillae of 48 growing Iberian and Duroc pigs maintained under identical management conditions but fed isocaloric diets differing in energy source: either high concentration of fat rich in oleic acid (HO) or carbohydrates (CH); and sacrificed after 47 days of treatment (50.5 kg live weight). Gene expression differed between the two breeds for most of the analyzed genes, with the TAS1R1, TAS1R2, TAS1R3, TAS2R4, TAS2R38, TAS2R39, GPR84, and CD36 genes being overexpressed in Duroc pigs. The diet effect was modulated by breed, with TAS1R1, TAS1R3, and TAS2R4 genes being overexpressed only in Duroc pigs fed the HO diet. Detection of genetic variants (single nucleotide polymorphisms, SNPs) for this panel of genes was performed on muscle RNA-seq data, and three SNPs in the TAS1R1, TAS1R3, and CD36 genes were selected for association studies. All three SNPs were associated with various growth, fattening, tissue fat content, and composition traits. Moreover, the CD36:c.910G/T SNP was associated with oral CD36 gene expression and with differences in the predicted mRNA secondary structure. Most taste receptor genes are expressed at lower level in circumvallate papillae from Iberian than Duroc pigs. This aligns with lower overall taste sensitivity, higher feed intake, and obese nature of Iberian pigs. Significant association results were observed for SNPs in the TAS1R1 and TAS1R3 genes with meat quality traits and liver composition, which showed segregation in world-wide distributed breeds, but particularly for a potential causal SNP in the CD36 gene, associated with growth and tissue composition, which segregates in Iberian populations.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"4 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143897918","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 : 2025-04-14DOI: 10.1186/s12711-025-00967-1
Christina M. Rochus, Marije J. Steensma, Marco C. A. M. Bink, Abe E. Huisman, Barbara Harlizius, Martijn F. L. Derks, Richard P. M. A. Crooijmans, Bart J. Ducro, Piter Bijma, Martien A. M. Groenen, Han A. Mulder
Direct estimates of mutation rates in humans have changed our understanding of evolutionary timing and de novo mutations (DNM) have been associated with several developmental disorders in humans. Livestock species, including pigs, can contribute to the study of DNM because of their ideal population structure and routine phenotype collection. In principle, there is the potential for livestock populations to quickly accumulate new genetic variants because of short generation intervals and high selection intensity. However, the impact of DNM on the fitness of individuals is not known and with current genomic selection programs they cannot contribute to estimated breeding values. The aims of our project were to detect and validate single nucleotide DNM in two commercial pig breeding lines, estimate the single nucleotide mutation rate, and characterise DNM. We sequenced (150 bp paired end reads, 30X coverage) 46 pig trios from two commercial lines. Single nucleotide DNM were detected using a trio-aware method. We defined candidate DNM as single nucleotide variants (SNVs) found in heterozygous state in trio-offspring with both trio-parents homozygous for the reference allele. In this study, we estimate a lower threshold of the DNM rate in pigs of 6.3 × 10–9 per site per gamete. Our findings are consistent with those from other mammals and those published for a small number of livestock species. Most DNM we detected were in introns (47%) and intergenic regions (49%). The mutational spectrum in pigs differs from that in humans and we found several DNM predicted to have an effect on animal’s fitness based on the base pair change and their location in the genome. With this study, we have generated fundamental knowledge on mutation rate in a non-primate species and identified DNM that could have an impact on the fitness of individuals.
{"title":"Estimating mutation rate and characterising single nucleotide de novo mutations in pigs","authors":"Christina M. Rochus, Marije J. Steensma, Marco C. A. M. Bink, Abe E. Huisman, Barbara Harlizius, Martijn F. L. Derks, Richard P. M. A. Crooijmans, Bart J. Ducro, Piter Bijma, Martien A. M. Groenen, Han A. Mulder","doi":"10.1186/s12711-025-00967-1","DOIUrl":"https://doi.org/10.1186/s12711-025-00967-1","url":null,"abstract":"Direct estimates of mutation rates in humans have changed our understanding of evolutionary timing and de novo mutations (DNM) have been associated with several developmental disorders in humans. Livestock species, including pigs, can contribute to the study of DNM because of their ideal population structure and routine phenotype collection. In principle, there is the potential for livestock populations to quickly accumulate new genetic variants because of short generation intervals and high selection intensity. However, the impact of DNM on the fitness of individuals is not known and with current genomic selection programs they cannot contribute to estimated breeding values. The aims of our project were to detect and validate single nucleotide DNM in two commercial pig breeding lines, estimate the single nucleotide mutation rate, and characterise DNM. We sequenced (150 bp paired end reads, 30X coverage) 46 pig trios from two commercial lines. Single nucleotide DNM were detected using a trio-aware method. We defined candidate DNM as single nucleotide variants (SNVs) found in heterozygous state in trio-offspring with both trio-parents homozygous for the reference allele. In this study, we estimate a lower threshold of the DNM rate in pigs of 6.3 × 10–9 per site per gamete. Our findings are consistent with those from other mammals and those published for a small number of livestock species. Most DNM we detected were in introns (47%) and intergenic regions (49%). The mutational spectrum in pigs differs from that in humans and we found several DNM predicted to have an effect on animal’s fitness based on the base pair change and their location in the genome. With this study, we have generated fundamental knowledge on mutation rate in a non-primate species and identified DNM that could have an impact on the fitness of individuals.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"6 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143827681","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 : 2025-04-11DOI: 10.1186/s12711-025-00966-2
Setegn Worku Alemu, Thomas J. Lopdell, Alexander J. Trevarton, Russell G. Snell, Mathew D. Littlejohn, Dorian J. Garrick
Genomic selection, typically employing genetic markers from SNP chips, is routine in modern dairy cattle breeding. This study assessed the impact of functional sequence variants on genomic prediction accuracy relative to 50 k SNP chip markers for fat percent, protein percent, milk volume, fat yield, and protein yield in lactating dairy cattle. The functional variants were identified through GWAS, RNA-seq, Histone modification ChIP-seq, ATAC-seq, or were coding variants. The genomic prediction accuracy obtained using each class of functional variants was compared with matched numbers of SNPs randomly selected from the Illumina 50 k SNP chip. The investigation revealed that variants identified by GWAS or RNA-seq, significantly improved the prediction accuracy across all five traits. Contributions from ChIP-seq, ATAC-seq, and coding variants varied. Some variants identified using ChIP-seq showed marked improvements, while others reduced accuracy in protein yield predictions. Relative to a matched number of 32,595 SNPs from the SNP chip, pooling all the functional variants demonstrated prediction accuracy increases of 1.76% for fat percent, 2.97% for protein percent, 0.51% for milk volume, and 0.26% for fat yield, but with a slight decrease of 0.43% in protein yield. The study demonstrates that functional variants can improve prediction accuracy relative to equivalent numbers of variants from a generic SNP panel, with percent traits showing more significant gains than yield traits. The main advantage of using functional variants for genomic prediction was achievement of comparable accuracy using a smaller, more selective set of loci. This is particularly evident in trait-specific scenarios. Our findings indicate that specific combinations of functional variants comprising 16 k variants can achieve genomic prediction accuracy comparable to employing a standard panel of twice the size (32.6 k), especially for percent traits. This highlights the potential for the development of more efficient, trait-focused SNP panels utilizing functional variants.
基因组选择，通常使用来自SNP芯片的遗传标记，在现代奶牛育种中是常规的。本研究评估了功能序列变异对50 k SNP芯片标记对泌乳奶牛脂肪百分比、蛋白质百分比、产奶量、脂肪产量和蛋白质产量的基因组预测准确性的影响。功能变异通过GWAS、RNA-seq、组蛋白修饰ChIP-seq、ATAC-seq或编码变异进行鉴定。利用每一类功能变异获得的基因组预测精度与从Illumina 50 k SNP芯片中随机选择的匹配数量的SNP进行比较。调查显示，通过GWAS或RNA-seq鉴定的变异显著提高了所有五个性状的预测准确性。ChIP-seq、ATAC-seq和编码变体的贡献有所不同。使用ChIP-seq识别的一些变异显示出明显的改善，而其他变异则降低了蛋白质产量预测的准确性。相对于来自SNP芯片的32,595个SNP的匹配数量，汇集所有功能变异表明，脂肪百分比的预测准确率提高了1.76%，蛋白质百分比提高了2.97%，奶量提高了0.51%，脂肪产量提高了0.26%，但蛋白质产量略有下降0.43%。该研究表明，相对于来自通用SNP面板的相同数量的变异，功能变异可以提高预测准确性，百分比性状比产量性状表现出更显著的收益。使用功能变异进行基因组预测的主要优点是使用更小、更具选择性的基因座集实现相当的准确性。这在特定于性格的场景中尤为明显。我们的研究结果表明，包含16k变体的功能变体的特定组合可以达到与使用两倍大小的标准面板（32.6 k）相当的基因组预测精度，特别是对于百分比性状。这凸显了利用功能变异开发更高效、以性状为重点的SNP面板的潜力。
{"title":"Comparison of genomic prediction accuracies in dairy cattle lactation traits using five classes of functional variants versus generic SNP","authors":"Setegn Worku Alemu, Thomas J. Lopdell, Alexander J. Trevarton, Russell G. Snell, Mathew D. Littlejohn, Dorian J. Garrick","doi":"10.1186/s12711-025-00966-2","DOIUrl":"https://doi.org/10.1186/s12711-025-00966-2","url":null,"abstract":"Genomic selection, typically employing genetic markers from SNP chips, is routine in modern dairy cattle breeding. This study assessed the impact of functional sequence variants on genomic prediction accuracy relative to 50 k SNP chip markers for fat percent, protein percent, milk volume, fat yield, and protein yield in lactating dairy cattle. The functional variants were identified through GWAS, RNA-seq, Histone modification ChIP-seq, ATAC-seq, or were coding variants. The genomic prediction accuracy obtained using each class of functional variants was compared with matched numbers of SNPs randomly selected from the Illumina 50 k SNP chip. The investigation revealed that variants identified by GWAS or RNA-seq, significantly improved the prediction accuracy across all five traits. Contributions from ChIP-seq, ATAC-seq, and coding variants varied. Some variants identified using ChIP-seq showed marked improvements, while others reduced accuracy in protein yield predictions. Relative to a matched number of 32,595 SNPs from the SNP chip, pooling all the functional variants demonstrated prediction accuracy increases of 1.76% for fat percent, 2.97% for protein percent, 0.51% for milk volume, and 0.26% for fat yield, but with a slight decrease of 0.43% in protein yield. The study demonstrates that functional variants can improve prediction accuracy relative to equivalent numbers of variants from a generic SNP panel, with percent traits showing more significant gains than yield traits. The main advantage of using functional variants for genomic prediction was achievement of comparable accuracy using a smaller, more selective set of loci. This is particularly evident in trait-specific scenarios. Our findings indicate that specific combinations of functional variants comprising 16 k variants can achieve genomic prediction accuracy comparable to employing a standard panel of twice the size (32.6 k), especially for percent traits. This highlights the potential for the development of more efficient, trait-focused SNP panels utilizing functional variants.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"39 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819556","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 : 2025-04-09DOI: 10.1186/s12711-025-00965-3
Joseph L. Matt, Jessica Moss Small, Peter D. Kube, Standish K. Allen
Triploid oysters, bred by crossing tetraploid and diploid oysters, are common worldwide in commercial oyster aquaculture and make up much of the hatchery-produced Crassostrea virginica farmed in the mid-Atlantic and southeast of the United States. Breeding diploid and tetraploid animals for genetic improvement of triploid progeny is unique to oysters and can proceed via several possible breeding strategies. Triploid oysters, along with their diploid or tetraploid relatives, have yet been subject to quantitative genetic analyses that could inform a breeding strategy of triploid improvement. The importance of quantitative genetic analyses involving triploid C. virginica has been emphasized by the occurrence of mortality events of near-market sized triploids in late spring. Genetic parameters for survival and weight of triploid and tetraploid C. virginica were estimated from twenty paternal half-sib triploid families and thirty-nine full-sib tetraploid families reared at three sites in the Chesapeake Bay (USA). Traits were analyzed using linear mixed models in ASReml-R. Genetic relationship matrices appropriate for pedigrees with triploid and tetraploid animals were produced using the polyAinv package in R. A mortality event in triploids occurred at one site located on the bayside of the Eastern Shore of Virginia. Between early May and early July, three triploid families had survival of less than 0.70, while most had survival greater than 0.90. The heritability for survival during this period in triploids at this affected site was 0.57 ± 0.23. Triploid survival at the affected site was adversely related to triploid survival at the low salinity site (− 0.50 ± 0.23) and unrelated to tetraploid survival at the site with similar salinity (0.05 ± 0.39). Survival during a late spring mortality event in triploids had a substantial additive genetic basis, suggesting selective breeding of tetraploids can reduce triploid mortalities. Genetic correlations revealed evidence of genotype by environment interactions for triploid survival and weak genetic correlations between survival of tetraploids and triploids. A selective breeding strategy with phenotyping of tetraploid and triploid half-sibs is recommended for genetic improvement of triploid oysters.
{"title":"Quantitative genetic analysis of late spring mortality in triploid Crassostrea virginica","authors":"Joseph L. Matt, Jessica Moss Small, Peter D. Kube, Standish K. Allen","doi":"10.1186/s12711-025-00965-3","DOIUrl":"https://doi.org/10.1186/s12711-025-00965-3","url":null,"abstract":"Triploid oysters, bred by crossing tetraploid and diploid oysters, are common worldwide in commercial oyster aquaculture and make up much of the hatchery-produced Crassostrea virginica farmed in the mid-Atlantic and southeast of the United States. Breeding diploid and tetraploid animals for genetic improvement of triploid progeny is unique to oysters and can proceed via several possible breeding strategies. Triploid oysters, along with their diploid or tetraploid relatives, have yet been subject to quantitative genetic analyses that could inform a breeding strategy of triploid improvement. The importance of quantitative genetic analyses involving triploid C. virginica has been emphasized by the occurrence of mortality events of near-market sized triploids in late spring. Genetic parameters for survival and weight of triploid and tetraploid C. virginica were estimated from twenty paternal half-sib triploid families and thirty-nine full-sib tetraploid families reared at three sites in the Chesapeake Bay (USA). Traits were analyzed using linear mixed models in ASReml-R. Genetic relationship matrices appropriate for pedigrees with triploid and tetraploid animals were produced using the polyAinv package in R. A mortality event in triploids occurred at one site located on the bayside of the Eastern Shore of Virginia. Between early May and early July, three triploid families had survival of less than 0.70, while most had survival greater than 0.90. The heritability for survival during this period in triploids at this affected site was 0.57 ± 0.23. Triploid survival at the affected site was adversely related to triploid survival at the low salinity site (− 0.50 ± 0.23) and unrelated to tetraploid survival at the site with similar salinity (0.05 ± 0.39). Survival during a late spring mortality event in triploids had a substantial additive genetic basis, suggesting selective breeding of tetraploids can reduce triploid mortalities. Genetic correlations revealed evidence of genotype by environment interactions for triploid survival and weak genetic correlations between survival of tetraploids and triploids. A selective breeding strategy with phenotyping of tetraploid and triploid half-sibs is recommended for genetic improvement of triploid oysters.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"108 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813775","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 : 2025-04-07DOI: 10.1186/s12711-025-00948-4
Lisa Büttgen, Henner Simianer, Torsten Pook
Genomic selection has become an integral component of modern animal breeding programs, having the potential to improve the efficiency of layer breeding programs both by obtaining higher prediction accuracies and reducing the generation interval, particularly for males, who cannot be phenotyped for sex-limited traits such as laying performance. In the current study, we investigate different strategies to reduce the generation interval either for both sexes or only for the male side of the breeding scheme based on stochastic simulation using the software MoBPS. Additionally, prediction accuracies based on varying proportions of genotyping and phenotype- and pedigree-based selection as well as genomic breeding values are compared. Selection of hens based on estimated breeding values, either pedigree-based or genomic, increased genetic gain compared to selection based on phenotypes only. The use of two time-shifted subpopulations with exchange of males between subpopulations to reduce the generation interval on the male side led to significantly higher genetic gains. Reducing the generation interval for both males and females was only efficient when population sizes were maintained, which result in doubling of the number of females to genotype and phenotype within the same time frame compared to the scenarios with the longer generation intervals. Although substantially higher gains were obtained by in particular pedigree-based selection of females and a reduction of generation intervals this led to substantially greater rates of inbreeding per year. The use of a genomic relationship matrix in breeding value estimation instead of a pedigree-based relationship matrix not only increased genetic gains but also reduced inbreeding rates. The use of optimum contribution selection led to basically the same genetic gains as without it but reduced inbreeding rates. However, overall differences obtained with optimal contribution selection were small compared to differences caused by the other effects that were considered. The reduction of the generation interval on the male side by the use of genomic estimated breeding values was highly beneficial. Reduction of the generation interval on the female side was only beneficial when a high proportion of hens was genotyped and housing capacities were increased. On the female side of a layer breeding program, selection based on pedigree-based estimated breeding values was inferior to phenotypic selection, as it resulted in a substantial increase in inbreeding rates.
{"title":"Analysis of different genotyping and selection strategies in laying hen breeding programs","authors":"Lisa Büttgen, Henner Simianer, Torsten Pook","doi":"10.1186/s12711-025-00948-4","DOIUrl":"https://doi.org/10.1186/s12711-025-00948-4","url":null,"abstract":"Genomic selection has become an integral component of modern animal breeding programs, having the potential to improve the efficiency of layer breeding programs both by obtaining higher prediction accuracies and reducing the generation interval, particularly for males, who cannot be phenotyped for sex-limited traits such as laying performance. In the current study, we investigate different strategies to reduce the generation interval either for both sexes or only for the male side of the breeding scheme based on stochastic simulation using the software MoBPS. Additionally, prediction accuracies based on varying proportions of genotyping and phenotype- and pedigree-based selection as well as genomic breeding values are compared. Selection of hens based on estimated breeding values, either pedigree-based or genomic, increased genetic gain compared to selection based on phenotypes only. The use of two time-shifted subpopulations with exchange of males between subpopulations to reduce the generation interval on the male side led to significantly higher genetic gains. Reducing the generation interval for both males and females was only efficient when population sizes were maintained, which result in doubling of the number of females to genotype and phenotype within the same time frame compared to the scenarios with the longer generation intervals. Although substantially higher gains were obtained by in particular pedigree-based selection of females and a reduction of generation intervals this led to substantially greater rates of inbreeding per year. The use of a genomic relationship matrix in breeding value estimation instead of a pedigree-based relationship matrix not only increased genetic gains but also reduced inbreeding rates. The use of optimum contribution selection led to basically the same genetic gains as without it but reduced inbreeding rates. However, overall differences obtained with optimal contribution selection were small compared to differences caused by the other effects that were considered. The reduction of the generation interval on the male side by the use of genomic estimated breeding values was highly beneficial. Reduction of the generation interval on the female side was only beneficial when a high proportion of hens was genotyped and housing capacities were increased. On the female side of a layer breeding program, selection based on pedigree-based estimated breeding values was inferior to phenotypic selection, as it resulted in a substantial increase in inbreeding rates.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"6 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143790194","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 : 2025-04-01DOI: 10.1186/s12711-025-00956-4
Christopher M. Pooley, Glenn Marion, Jamie Prentice, Ricardo Pong-Wong, Stephen C. Bishop, Andrea Doeschl-Wilson
Genetic selection of individuals that are less susceptible to infection, less infectious once infected, and recover faster, offers an effective and long-lasting solution to reduce the incidence and impact of infectious diseases in farmed animals. However, computational methods for simultaneously estimating genetic parameters for host susceptibility, infectivity and recoverability from real-word data have been lacking. Our previously developed methodology and software tool SIRE 1.0 (Susceptibility, Infectivity and Recoverability Estimator) allows estimation of host genetic effects of a single nucleotide polymorphism (SNP), or other fixed effects (e.g. breed, vaccination status), for these three host traits using individual disease data typically available from field studies and challenge experiments. SIRE 1.0, however, lacks the capability to estimate genetic parameters for these traits in the likely case of underlying polygenic control. This paper introduces novel Bayesian methodology and a new software tool SIRE 2.0 for estimating polygenic contributions (i.e. variance components and additive genetic effects) for host susceptibility, infectivity and recoverability from temporal epidemic data, assuming that pedigree or genomic relationships are known. Analytical expressions for prediction accuracies (PAs) for these traits are derived for simplified scenarios, revealing their dependence on genetic and phenotypic variances, and the distribution of related individuals within and between contact groups. PAs for infectivity are found to be critically dependent on the size of contact groups. Validation of the methodology with data from simulated epidemics demonstrates good agreement between numerically generated PAs and analytical predictions. Genetic correlations between infectivity and other traits substantially increase trait PAs. Incomplete data (e.g. time censored or infrequent sampling) generally yield only small reductions in PAs, except for when infection times are completely unknown, which results in a substantial reduction. The method presented can estimate genetic parameters for host susceptibility, infectivity and recoverability from individual disease records. The freely available SIRE 2.0 software provides a valuable extension to SIRE 1.0 for estimating host polygenic effects underlying infectious disease transmission. This tool will open up new possibilities for analysis and quantification of genetic determinates of disease dynamics.
{"title":"SIRE 2.0: a novel method for estimating polygenic host effects underlying infectious disease transmission, and analytical expressions for prediction accuracies","authors":"Christopher M. Pooley, Glenn Marion, Jamie Prentice, Ricardo Pong-Wong, Stephen C. Bishop, Andrea Doeschl-Wilson","doi":"10.1186/s12711-025-00956-4","DOIUrl":"https://doi.org/10.1186/s12711-025-00956-4","url":null,"abstract":"Genetic selection of individuals that are less susceptible to infection, less infectious once infected, and recover faster, offers an effective and long-lasting solution to reduce the incidence and impact of infectious diseases in farmed animals. However, computational methods for simultaneously estimating genetic parameters for host susceptibility, infectivity and recoverability from real-word data have been lacking. Our previously developed methodology and software tool SIRE 1.0 (Susceptibility, Infectivity and Recoverability Estimator) allows estimation of host genetic effects of a single nucleotide polymorphism (SNP), or other fixed effects (e.g. breed, vaccination status), for these three host traits using individual disease data typically available from field studies and challenge experiments. SIRE 1.0, however, lacks the capability to estimate genetic parameters for these traits in the likely case of underlying polygenic control. This paper introduces novel Bayesian methodology and a new software tool SIRE 2.0 for estimating polygenic contributions (i.e. variance components and additive genetic effects) for host susceptibility, infectivity and recoverability from temporal epidemic data, assuming that pedigree or genomic relationships are known. Analytical expressions for prediction accuracies (PAs) for these traits are derived for simplified scenarios, revealing their dependence on genetic and phenotypic variances, and the distribution of related individuals within and between contact groups. PAs for infectivity are found to be critically dependent on the size of contact groups. Validation of the methodology with data from simulated epidemics demonstrates good agreement between numerically generated PAs and analytical predictions. Genetic correlations between infectivity and other traits substantially increase trait PAs. Incomplete data (e.g. time censored or infrequent sampling) generally yield only small reductions in PAs, except for when infection times are completely unknown, which results in a substantial reduction. The method presented can estimate genetic parameters for host susceptibility, infectivity and recoverability from individual disease records. The freely available SIRE 2.0 software provides a valuable extension to SIRE 1.0 for estimating host polygenic effects underlying infectious disease transmission. This tool will open up new possibilities for analysis and quantification of genetic determinates of disease dynamics.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"34 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143757995","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 : 2025-03-28DOI: 10.1186/s12711-025-00962-6
Manu Kumar Gundappa, Diego Robledo, Alastair Hamilton, Ross D. Houston, James G. D. Prendergast, Daniel J. Macqueen
Whole genome sequencing (WGS), despite its advantages, is yet to replace methods for genotyping single nucleotide variants (SNVs) such as SNP arrays and targeted genotyping assays. Structural variants (SVs) have larger effects on traits than SNVs, but are more challenging to accurately genotype. Using low-coverage WGS with genotype imputation offers a cost-effective strategy to achieve genome-wide variant coverage, but is yet to be tested for SVs. Here, we investigate combined SNV and SV imputation with low-coverage WGS data in Atlantic salmon (Salmo salar). As the reference panel, we used genotypes for high-confidence SVs and SNVs for n = 365 wild individuals sampled from diverse populations. We also generated 15 × WGS data (n = 20 samples) for a commercial population external to the reference panel, and called SVs and SNVs with gold-standard approaches. An imputation method selected for its established performance using low-coverage sequencing data (GLIMPSE) was tested at WGS depths of 1 × , 2 × , 3 × , and 4 × for samples within and external to the reference panel. SNVs were imputed with high accuracy and recall across all WGS depths, including for samples out-with the reference panel. For SVs, we compared imputation based purely on linkage disequilibrium (LD) with SNVs, to that supplemented with SV genotype likelihoods (GLs) from low-coverage WGS. Including SV GLs increased imputation accuracy, but as a trade-off with recall, requiring 3–4 × depth for best performance. Combining strategies allowed us to capture 84% of the reference panel deletions with 87% accuracy at 1 × depth. We also show that SV length affects imputation performance, with provision of SV GLs greatly enhancing accuracy for the longest SVs in the dataset. This study highlights the promise of reference panel imputation using low-coverage WGS, including novel opportunities to enhance the resolution of genome-wide association studies by capturing SVs.
{"title":"High performance imputation of structural and single nucleotide variants using low-coverage whole genome sequencing","authors":"Manu Kumar Gundappa, Diego Robledo, Alastair Hamilton, Ross D. Houston, James G. D. Prendergast, Daniel J. Macqueen","doi":"10.1186/s12711-025-00962-6","DOIUrl":"https://doi.org/10.1186/s12711-025-00962-6","url":null,"abstract":"Whole genome sequencing (WGS), despite its advantages, is yet to replace methods for genotyping single nucleotide variants (SNVs) such as SNP arrays and targeted genotyping assays. Structural variants (SVs) have larger effects on traits than SNVs, but are more challenging to accurately genotype. Using low-coverage WGS with genotype imputation offers a cost-effective strategy to achieve genome-wide variant coverage, but is yet to be tested for SVs. Here, we investigate combined SNV and SV imputation with low-coverage WGS data in Atlantic salmon (Salmo salar). As the reference panel, we used genotypes for high-confidence SVs and SNVs for n = 365 wild individuals sampled from diverse populations. We also generated 15 × WGS data (n = 20 samples) for a commercial population external to the reference panel, and called SVs and SNVs with gold-standard approaches. An imputation method selected for its established performance using low-coverage sequencing data (GLIMPSE) was tested at WGS depths of 1 × , 2 × , 3 × , and 4 × for samples within and external to the reference panel. SNVs were imputed with high accuracy and recall across all WGS depths, including for samples out-with the reference panel. For SVs, we compared imputation based purely on linkage disequilibrium (LD) with SNVs, to that supplemented with SV genotype likelihoods (GLs) from low-coverage WGS. Including SV GLs increased imputation accuracy, but as a trade-off with recall, requiring 3–4 × depth for best performance. Combining strategies allowed us to capture 84% of the reference panel deletions with 87% accuracy at 1 × depth. We also show that SV length affects imputation performance, with provision of SV GLs greatly enhancing accuracy for the longest SVs in the dataset. This study highlights the promise of reference panel imputation using low-coverage WGS, including novel opportunities to enhance the resolution of genome-wide association studies by capturing SVs.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"57 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723122","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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