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Guillenea A, Lund MS, Evans R, Boerner V, Karaman E. A breed-of-origin of alleles model that includes crossbred data improves predictive ability for crossbred animals in a multi-breed population. Genet Sel Evol 2023; 55:34. [PMID: 37189059 PMCID: PMC10184430 DOI: 10.1186/s12711-023-00806-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 04/24/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Recently, crossbred animals have begun to be used as parents in the next generations of dairy and beef cattle systems, which has increased the interest in predicting the genetic merit of those animals. The primary objective of this study was to investigate three available methods for genomic prediction of crossbred animals. In the first two methods, SNP effects from within-breed evaluations are used by weighting them by the average breed proportions across the genome (BPM method) or by their breed-of-origin (BOM method). The third method differs from the BOM in that it estimates breed-specific SNP effects using purebred and crossbred data, considering the breed-of-origin of alleles (BOA method). For within-breed evaluations, and thus for BPM and BOM, 5948 Charolais, 6771 Limousin and 7552 Others (a combined population of other breeds) were used to estimate SNP effects separately within each breed. For the BOA, the purebreds' data were enhanced with data from ~ 4K, ~ 8K or ~ 18K crossbred animals. For each animal, its predictor of genetic merit (PGM) was estimated by considering the breed-specific SNP effects. Predictive ability and absence of bias were estimated for crossbreds and the Limousin and Charolais animals. Predictive ability was measured as the correlation between PGM and the adjusted phenotype, while the regression of the adjusted phenotype on PGM was estimated as a measure of bias. RESULTS With BPM and BOM, the predictive abilities for crossbreds were 0.468 and 0.472, respectively, and with the BOA method, they ranged from 0.490 to 0.510. The performance of the BOA method improved as the number of crossbred animals in the reference increased and with the use of the correlated approach, in which the correlation of SNP effects across the genome of the different breeds was considered. The slopes of regression for PGM on adjusted phenotypes for crossbreds showed overdispersion of the genetic merits for all methods but this bias tended to be reduced by the use of the BOA method and by increasing the number of crossbred animals. CONCLUSIONS For the estimation of the genetic merit of crossbred animals, the results from this study suggest that the BOA method that accommodates crossbred data can yield more accurate predictions than the methods that use SNP effects from separate within-breed evaluations.
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Affiliation(s)
- Ana Guillenea
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus C, Denmark.
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus C, Denmark
| | - Ross Evans
- ICBF, Link Road, Carrigrohane, Ballincollig, Co. Cork, P31 D452, Ireland
| | - Vinzent Boerner
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus C, Denmark
| | - Emre Karaman
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus C, Denmark
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Ryan CA, Berry DP, O’Brien A, Pabiou T, Purfield DC. Evaluating the use of statistical and machine learning methods for estimating breed composition of purebred and crossbred animals in thirteen cattle breeds using genomic information. Front Genet 2023; 14:1120312. [PMID: 37274789 PMCID: PMC10237237 DOI: 10.3389/fgene.2023.1120312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 05/03/2023] [Indexed: 06/07/2023] Open
Abstract
Introduction: The ability to accurately predict breed composition using genomic information has many potential uses including increasing the accuracy of genetic evaluations, optimising mating plans and as a parameter for genotype quality control. The objective of the present study was to use a database of genotyped purebred and crossbred cattle to compare breed composition predictions using a freely available software, Admixture, with those from a single nucleotide polymorphism Best Linear Unbiased Prediction (SNP-BLUP) approach; a supplementary objective was to determine the accuracy and general robustness of low-density genotype panels for predicting breed composition. Methods: All animals had genotype information on 49,213 autosomal single nucleotide polymorphism (SNPs). Thirteen breeds were included in the analysis and 500 purebred animals per breed were used to establish the breed training populations. Accuracy of breed composition prediction was determined using a separate validation population of 3,146 verified purebred and 4,330 two and three-way crossbred cattle. Results: When all 49,213 autosomal SNPs were used for breed prediction, a minimal absolute mean difference of 0.04 between Admixture vs. SNP-BLUP breed predictions was evident. For crossbreds, the average absolute difference in breed prediction estimates generated using SNP-BLUP and Admixture was 0.068 with a root mean square error of 0.08. Breed predictions from low-density SNP panels were generated using both SNP-BLUP and Admixture and compared to breed prediction estimates using all 49,213 SNPs (representing the gold standard). Breed composition estimates of crossbreds required more SNPs than predicting the breed composition of purebreds. SNP-BLUP required ≥3,000 SNPs to predict crossbred breed composition, but only 2,000 SNPs were required to predict purebred breed status. The absolute mean (standard deviation) difference across all panels <2,000 SNPs was 0.091 (0.054) and 0.315 (0.316) when predicting the breed composition of all animals using Admixture and SNP-BLUP, respectively compared to the gold standard prediction. Discussion: Nevertheless, a negligible absolute mean (standard deviation) difference of 0.009 (0.123) in breed prediction existed between SNP-BLUP and Admixture once ≥3,000 SNPs were considered, indicating that the prediction of breed composition could be readily integrated into SNP-BLUP pipelines used for genomic evaluations thereby avoiding the necessity for a stand-alone software.
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Affiliation(s)
- C. A. Ryan
- Teagasc, Co. Cork, Ireland
- Munster Technological University, Cork, Ireland
| | | | | | - T. Pabiou
- Irish Cattle Breeding Federation, Cork, Ireland
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Hu G, Do DN, Manafiazar G, Kelvin AA, Sargolzaei M, Plastow G, Wang Z, Miar Y. Population genomics of American mink using genotype data. Front Genet 2023; 14:1175408. [PMID: 37274788 PMCID: PMC10234291 DOI: 10.3389/fgene.2023.1175408] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 04/14/2023] [Indexed: 06/07/2023] Open
Abstract
Understanding the genetic structure of the target population is critically important to develop an efficient genomic selection program in domestic animals. In this study, 2,973 American mink of six color types from two farms (Canadian Centre for Fur Animal Research (CCFAR), Truro, NS and Millbank Fur Farm (MFF), Rockwood, ON) were genotyped with the Affymetrix Mink 70K panel to compute their linkage disequilibrium (LD) patterns, effective population size (Ne), genetic diversity, genetic distances, and population differentiation and structure. The LD pattern represented by average r 2, decreased to <0.2 when the inter-marker interval reached larger than 350 kb and 650 kb for CCFAR and MFF, respectively, and suggested at least 7,700 and 4,200 single nucleotide polymorphisms (SNPs) be used to obtain adequate accuracy for genomic selection programs in CCFAR and MFF respectively. The Ne for five generations ago was estimated to be 76 and 91 respectively. Our results from genetic distance and diversity analyses showed that American mink of the various color types had a close genetic relationship and low genetic diversity, with most of the genetic variation occurring within rather than between color types. Three ancestral genetic groups was considered the most appropriate number to delineate the genetic structure of these populations. Black (in both CCFAR and MFF) and pastel color types had their own ancestral clusters, while demi, mahogany, and stardust color types were admixed with the three ancestral genetic groups. This study provided essential information to utilize the first medium-density SNP panel for American mink in their genomic studies.
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Affiliation(s)
- Guoyu Hu
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
| | - Duy Ngoc Do
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
| | - Ghader Manafiazar
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
| | - Alyson A. Kelvin
- Vaccine and Infectious Disease Organization (VIDO), University of Saskatchewan, Saskatoon, SK, Canada
| | - Mehdi Sargolzaei
- Department of Pathobiology, University of Guelph, Guelph, ON, Canada
- Select Sires Inc, Plain City, OH, United States
| | - Graham Plastow
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Zhiquan Wang
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Younes Miar
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
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Ogawa S, Taniguchi Y, Watanabe T, Iwaisaki H. Fitting Genomic Prediction Models with Different Marker Effects among Prefectures to Carcass Traits in Japanese Black Cattle. Genes (Basel) 2022; 14:24. [PMID: 36672767 PMCID: PMC9859149 DOI: 10.3390/genes14010024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/16/2022] [Accepted: 12/20/2022] [Indexed: 12/25/2022] Open
Abstract
We fitted statistical models, which assumed single-nucleotide polymorphism (SNP) marker effects differing across the fattened steers marketed into different prefectures, to the records for cold carcass weight (CW) and marbling score (MS) of 1036, 733, and 279 Japanese Black fattened steers marketed into Tottori, Hiroshima, and Hyogo prefectures in Japan, respectively. Genotype data on 33,059 SNPs was used. Five models that assume only common SNP effects to all the steers (model 1), common effects plus SNP effects differing between the steers marketed into Hyogo prefecture and others (model 2), only the SNP effects differing between Hyogo steers and others (model 3), common effects plus SNP effects specific to each prefecture (model 4), and only the effects specific to each prefecture (model 5) were exploited. For both traits, slightly lower values of residual variance than that of model 1 were estimated when fitting all other models. Estimated genetic correlation among the prefectures in models 2 and 4 ranged to 0.53 to 0.71, all <0.8. These results might support that the SNP effects differ among the prefectures to some degree, although we discussed the necessity of careful consideration to interpret the current results.
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Affiliation(s)
- Shinichiro Ogawa
- Graduate School of Agriculture, Kyoto University, Kyoto 606-8502, Japan
- Division of Meat Animal and Poultry Research, Institute of Livestock and Grassland Science, Tsukuba 305-0901, Japan
| | - Yukio Taniguchi
- Graduate School of Agriculture, Kyoto University, Kyoto 606-8502, Japan
| | - Toshio Watanabe
- National Livestock Breeding Center, Fukushima 961-8511, Japan
- Maebashi Institute of Animal Science, Livestock Improvement Association of Japan, Inc., Maebashi 371-0121, Japan
| | - Hiroaki Iwaisaki
- Graduate School of Agriculture, Kyoto University, Kyoto 606-8502, Japan
- Sado Island Center for Ecological Sustainability, Niigata University, Niigata 952-0103, Japan
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Schmid M, Stock J, Bennewitz J, Wellmann R. Improving the Accuracy of Multi-Breed Prediction in Admixed Populations by Accounting for the Breed Origin of Haplotype Segments. Front Genet 2022; 13:840815. [PMID: 35401683 PMCID: PMC8987492 DOI: 10.3389/fgene.2022.840815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 03/04/2022] [Indexed: 11/13/2022] Open
Abstract
Numerically small breeds have often been upgraded with mainstream breeds. This historic introgression predisposes the breeds for joint genomic evaluations with mainstream breeds. The linkage disequilibrium structure differs between breeds. The marker effects of a haplotype segment may, therefore, depend on the breed from which the haplotype segment originates. An appropriate method for genomic evaluation would account for this dependency. This study proposes a method for the computation of genomic breeding values for small admixed breeds that incorporate phenotypic and genomic information from large introgressed breeds by considering the breed origin of alleles (BOA) in the evaluation. The proposed BOA model classifies haplotype segments according to their origins and assumes different but correlated SNP effects for the different origins. The BOA model was compared in a simulation study to conventional within-breed genomic best linear unbiased prediction (GBLUP) and conventional multi-breed GBLUP models. The BOA model outperformed within-breed GBLUP as well as multi-breed GBLUP in most cases.
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Affiliation(s)
- Markus Schmid
- Institute of Animal Science, Department of Animal Genetics and Breeding, University of Hohenheim, Stuttgart, Germany
| | - Joana Stock
- Institute of Animal Science, Department of Animal Genetics and Breeding, University of Hohenheim, Stuttgart, Germany
| | - Jörn Bennewitz
- Institute of Animal Science, Department of Animal Genetics and Breeding, University of Hohenheim, Stuttgart, Germany
| | - Robin Wellmann
- Institute of Animal Science, Department of Animal Genetics and Breeding, University of Hohenheim, Stuttgart, Germany
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6
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Evaluation of Bagging approach versus GBLUP and Bayesian LASSO in genomic prediction. J Genet 2022. [DOI: 10.1007/s12041-022-01358-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Guillenea A, Su G, Lund MS, Karaman E. Genomic prediction in Nordic Red dairy cattle considering breed origin of alleles. J Dairy Sci 2022; 105:2426-2438. [PMID: 35033341 DOI: 10.3168/jds.2021-21173] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 11/23/2021] [Indexed: 01/02/2023]
Abstract
This study investigated the reliability of genomic prediction (GP) using breed origin of alleles (BOA) approach in the Nordic Red (RDC) population, which has an admixed population structure. The RDC population consists of animals with varying degrees of genetic materials from the Danish Red (RDM), Swedish Red (SRB), Finnish Ayrshire (FAY), and Holstein (HOL) because bulls have been used across the breeds. The BOA approach was tested using 39,550 RDC animals in the reference population and 11,786 in the validation population. Deregressed proofs (DRP) of milk, fat and protein were used as response variable for GP. Direct genomic breeding values (DGV) for animals in the validation population were calculated with (BOA model) or without (joint model) considering breed origin of alleles. The joint model assumed homogeneous marker effects and a single set of marker effects were estimated, whereas BOA model assumed heterogeneous marker effects, and different sets of marker effects were estimated across the breeds. For the BOA approach, we tested scenarios assuming both correlated (BOA_cor) and uncorrelated (BOA_uncor) marker effects between the breeds. Additionally, we investigated GP using a standard Illumina 50K chip and including SNP selected from imputed whole-genome sequencing (50K+WGS). We also studied the effect of estimating (co)variances for genome regions of different sizes to exploit the information of the genome regions contributing to the (co)variance between the breeds. Region sizes were set as 1 SNP, a group of 30 or 100 adjacent SNP, or the whole genome. Reliability of DGV was measured as squared correlations between DGV and DRP divided by the reliability of DRP. Across the 3 traits, in general, RS30 and RS100 SNP yielded the highest reliabilities. Including WGS SNP improved reliabilities in almost all scenarios (0.297 on average for 50K and 0.307 on average for 50K+WGS). The BOA_uncor (0.233 on average) was inferior to the joint model (0.339 on average), but the reliabilities obtained using BOA_cor (0.334 on average) in most cases were not significantly different from those obtained using the joint model. The results indicate that both including additional whole-genome sequencing SNP and dividing the genome into fixed regions improve GP in the RDC. The BOA models have the potential to increase the reliability of GP, but the benefit is limited in populations with a high exchange of genetic material for a long time, as is the case for RDC.
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Affiliation(s)
- Ana Guillenea
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark.
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Mogens Sand Lund
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
| | - Emre Karaman
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark
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8
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Karaman E, Su G, Croue I, Lund MS. Genomic prediction using a reference population of multiple pure breeds and admixed individuals. Genet Sel Evol 2021; 53:46. [PMID: 34058971 PMCID: PMC8168010 DOI: 10.1186/s12711-021-00637-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 05/11/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In dairy cattle populations in which crossbreeding has been used, animals show some level of diversity in their origins. In rotational crossbreeding, for instance, crossbred dams are mated with purebred sires from different pure breeds, and the genetic composition of crossbred animals is an admixture of the breeds included in the rotation. How to use the data of such individuals in genomic evaluations is still an open question. In this study, we aimed at providing methodologies for the use of data from crossbred individuals with an admixed genetic background together with data from multiple pure breeds, for the purpose of genomic evaluations for both purebred and crossbred animals. A three-breed rotational crossbreeding system was mimicked using simulations based on animals genotyped with the 50 K single nucleotide polymorphism (SNP) chip. RESULTS For purebred populations, within-breed genomic predictions generally led to higher accuracies than those from multi-breed predictions using combined data of pure breeds. Adding admixed population's (MIX) data to the combined pure breed data considering MIX as a different breed led to higher accuracies. When prediction models were able to account for breed origin of alleles, accuracies were generally higher than those from combining all available data, depending on the correlation of quantitative trait loci (QTL) effects between the breeds. Accuracies varied when using SNP effects from any of the pure breeds to predict the breeding values of MIX. Using those breed-specific SNP effects that were estimated separately in each pure breed, while accounting for breed origin of alleles for the selection candidates of MIX, generally improved the accuracies. Models that are able to accommodate MIX data with the breed origin of alleles approach generally led to higher accuracies than models without breed origin of alleles, depending on the correlation of QTL effects between the breeds. CONCLUSIONS Combining all available data, pure breeds' and admixed population's data, in a multi-breed reference population is beneficial for the estimation of breeding values for pure breeds with a small reference population. For MIX, such an approach can lead to higher accuracies than considering breed origin of alleles for the selection candidates, and using breed-specific SNP effects estimated separately in each pure breed. Including MIX data in the reference population of multiple breeds by considering the breed origin of alleles, accuracies can be further improved. Our findings are relevant for breeding programs in which crossbreeding is systematically applied, and also for populations that involve different subpopulations and between which exchange of genetic material is routine practice.
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Affiliation(s)
- Emre Karaman
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | | | - Mogens S Lund
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
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9
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Karimi K, Ngoc Do D, Sargolzaei M, Miar Y. Population Genomics of American Mink Using Whole Genome Sequencing Data. Genes (Basel) 2021; 12:genes12020258. [PMID: 33670138 PMCID: PMC7916864 DOI: 10.3390/genes12020258] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 02/04/2021] [Accepted: 02/05/2021] [Indexed: 12/11/2022] Open
Abstract
Characterizing the genetic structure and population history can facilitate the development of genomic breeding strategies for the American mink. In this study, we used the whole genome sequences of 100 mink from the Canadian Centre for Fur Animal Research (CCFAR) at the Dalhousie Faculty of Agriculture (Truro, NS, Canada) and Millbank Fur Farm (Rockwood, ON, Canada) to investigate their population structure, genetic diversity and linkage disequilibrium (LD) patterns. Analysis of molecular variance (AMOVA) indicated that the variation among color-types was significant (p < 0.001) and accounted for 18% of the total variation. The admixture analysis revealed that assuming three ancestral populations (K = 3) provided the lowest cross-validation error (0.49). The effective population size (Ne) at five generations ago was estimated to be 99 and 50 for CCFAR and Millbank Fur Farm, respectively. The LD patterns revealed that the average r2 reduced to <0.2 at genomic distances of >20 kb and >100 kb in CCFAR and Millbank Fur Farm suggesting that the density of 120,000 and 24,000 single nucleotide polymorphisms (SNP) would provide the adequate accuracy of genomic evaluation in these populations, respectively. These results indicated that accounting for admixture is critical for designing the SNP panels for genotype-phenotype association studies of American mink.
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Affiliation(s)
- Karim Karimi
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS B2N 5E3, Canada; (K.K.); (D.N.D.)
| | - Duy Ngoc Do
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS B2N 5E3, Canada; (K.K.); (D.N.D.)
| | - Mehdi Sargolzaei
- Department of Pathobiology, University of Guelph, Guelph, ON N1G 2W1, Canada;
- Select Sires Inc., Plain City, OH 43064, USA
| | - Younes Miar
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS B2N 5E3, Canada; (K.K.); (D.N.D.)
- Correspondence:
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VanRaden PM, Tooker ME, Chud TCS, Norman HD, Megonigal JH, Haagen IW, Wiggans GR. Genomic predictions for crossbred dairy cattle. J Dairy Sci 2019; 103:1620-1631. [PMID: 31837783 DOI: 10.3168/jds.2019-16634] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 10/14/2019] [Indexed: 01/14/2023]
Abstract
Genomic evaluations are useful for crossbred as well as purebred populations when selection is applied to commercial herds. Dairy farmers had already spent more than $1 million to genotype over 32,000 crossbred animals before US genomic evaluations became available for those animals. Thus, new tools were needed to provide accurate genomic predictions for crossbreds. Genotypes for crossbreds are imputed more accurately when the imputation reference population includes purebreds. Therefore, genotypes of 6,296 crossbred animals were imputed from lower-density chips by including either 3,119 ancestors or 834,367 genotyped animals in the reference population. Crossbreds in the imputation study included 733 Jersey × Holstein F1 animals, 55 Brown Swiss × Holstein F1 animals, 2,300 Holstein backcrosses, 2,026 Jersey backcrosses, 27 Brown Swiss backcrosses, and 502 other crossbreds of various breed combinations. Another 653 animals appeared to be purebreds that owners had miscoded as a different breed. Genomic breed composition was estimated from 60,671 markers using the known breed identities for purebred, progeny-tested Holstein, Jersey, Brown Swiss, Ayrshire, and Guernsey bulls as the 5 traits (breed fractions) to be predicted. Estimates of breed composition were adjusted so that no percentages were negative or exceeded 100%, and breed percentages summed to 100%. Another adjustment set percentages above 93.5% equal to 100%, and the resulting value was termed breed base representation (BBR). Larger percentages of missing alleles were imputed by using a crossbred reference population rather than only the closest purebred reference population. Crossbred predictions were averages of genomic predictions computed using marker effects for each pure breed, which were weighted by the animal's BBR. Marker and polygenic effects were estimated separately for each breed on the all-breed scale instead of within-breed scales. For crossbreds, genomic predictions weighted by BBR were more accurate than the average of parents' breeding values and slightly more accurate than predictions using only the predominant breed. For purebreds, single-trait predictions using only within-breed data were as accurate as multi-trait predictions with allele effects in different breeds treated as correlated effects. Crossbred genomic predicted transmitting abilities were implemented by the Council on Dairy Cattle Breeding in April 2019 and will aid producers in managing their breeding programs and selecting replacement heifers.
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Affiliation(s)
- P M VanRaden
- USDA, Agricultural Research Service, Animal Genomics and Improvement Laboratory, Beltsville, MD 20705-2350.
| | - M E Tooker
- USDA, Agricultural Research Service, Animal Genomics and Improvement Laboratory, Beltsville, MD 20705-2350
| | - T C S Chud
- Departamento de Ciências Exatas, Universidade Estadual Paulista (Unesp), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, São Paulo CEP 14884-900, Brazil
| | - H D Norman
- Council on Dairy Cattle Breeding, Bowie, MD 20716
| | | | - I W Haagen
- Council on Dairy Cattle Breeding, Bowie, MD 20716
| | - G R Wiggans
- Council on Dairy Cattle Breeding, Bowie, MD 20716
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11
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Thomasen JR, Liu H, Sørensen AC. Genotyping more cows increases genetic gain and reduces rate of true inbreeding in a dairy cattle breeding scheme using female reproductive technologies. J Dairy Sci 2019; 103:597-606. [PMID: 31733861 DOI: 10.3168/jds.2019-16974] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 09/23/2019] [Indexed: 12/26/2022]
Abstract
Both small dairy cattle populations and dairy cattle populations with a low level of linkage disequilibrium (LD) suffer from low reliability of genomic prediction. In this study, we investigated whether adding more genotyped cows to the reference population influences the rate of genetic gain and rate of inbreeding by affecting the reliability. A standard breeding program with a large reference population and high LD, which mimicked a breeding program for Danish Holstein population, was simulated as a reference. A Danish Jersey population with a small reference population and high LD and a Red Dairy Cattle population with a large reference population and low LD were also simulated. Two additional breeding programs were simulated for Danish Jersey and Red Dairy Cattle populations, where 2,000 additional genotyped cows were included in the population for genomic selection. All 5 simulated breeding programs were initiated by a founder population to generate LD resembling the real LD pattern, followed by a 20-yr conventional progeny-testing scheme with 1,000 or 10,000 genotyped progeny-tested bulls and a 10-yr genomic selection scheme with or without 2,000 additional genotyped cows. Evaluation criteria were annual monetary genetic gain and rate of true inbreeding. Our results showed that adding more genotyped cows to the reference in dairy cattle populations has the potential to increase genetic gain and reduce the rate of inbreeding, regardless of reference population size and level of LD. However, it is still not possible to reach the same genetic gain as in the simulated Danish Holstein population with either a small reference population or low LD. Our results also showed that in a small reference population with high LD, it is difficult to manage inbreeding because of lower accuracy compared with the simulated Danish Holstein population and a smaller number of relevant families to select from. Therefore, breeding strategies need to be chosen to match population size and structure. The rate of true inbreeding is always underestimated by pedigree inbreeding and even more in genomic breeding programs, indicating that some forms of genome-wide inbreeding, instead of pedigree-based inbreeding, should be used to monitor inbreeding when genomic selection is implemented.
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Affiliation(s)
| | - H Liu
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, DK-8830, Tjele, Denmark.
| | - A C Sørensen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, DK-8830, Tjele, Denmark
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Santos DJA, Cole JB, Lawlor TJ, VanRaden PM, Tonhati H, Ma L. Variance of gametic diversity and its application in selection programs. J Dairy Sci 2019; 102:5279-5294. [PMID: 30981488 DOI: 10.3168/jds.2018-15971] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Accepted: 02/27/2019] [Indexed: 11/19/2022]
Abstract
The variance of gametic diversity ( σgamete2) can be used to find individuals that more likely produce progeny with extreme breeding values. The aim of this study was to obtain this variance for individuals from routine genomic evaluations, and to apply gametic variance in a selection criterion in conjunction with breeding values to improve genetic progress. An analytical approach was developed to estimate σgamete2 by the sum of binomial variances of all individual quantitative trait loci across the genome. Simulation was used to verify the predictability of this variance in a range of scenarios. The accuracy of prediction ranged from 0.49 to 0.85, depending on the scenario and model used. Compared with sequence data, SNP data are sufficient for estimating σgamete2 Results also suggested that markers with low minor allele frequency and the covariance between markers should be included in the estimation. To incorporate σgamete2 into selective breeding programs, we proposed a new index, relative predicted transmitting ability, which better utilizes the genetic potential of individuals than traditional predicted transmitting ability. Simulation with a small genome showed an additional genetic gain of up to 16% in 10 generations, depending on the number of quantitative trait loci and selection intensity. Finally, we applied σgamete2 to the US genomic evaluations for Holstein and Jersey cattle. As expected, the DGAT1 gene had a strong effect on the estimation of σgamete2 for several production traits. However, inbreeding had a small impact on gametic variability, with greater effect for more polygenic traits. In conclusion, gametic variance, a potentially important parameter for selection programs, can be easily computed and is useful for improving genetic progress and controlling genetic diversity.
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Affiliation(s)
- D J A Santos
- Department of Animal and Avian Sciences, University of Maryland, College Park 20742; Departamento de Zootecinia, Universidade Estadual Paulista, Jaboticabal, 14884-900, Brazil.
| | - J B Cole
- Henry A. Wallace Beltsville Agricultural Research Center, Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350
| | - T J Lawlor
- Holstein Association USA, Brattleboro, VT 05302-0808
| | - P M VanRaden
- Henry A. Wallace Beltsville Agricultural Research Center, Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350
| | - H Tonhati
- Departamento de Zootecinia, Universidade Estadual Paulista, Jaboticabal, 14884-900, Brazil
| | - L Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park 20742.
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13
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Toosi A, Fernando RL, Dekkers JCM. Genome-wide mapping of quantitative trait loci in admixed populations using mixed linear model and Bayesian multiple regression analysis. Genet Sel Evol 2018; 50:32. [PMID: 29914353 PMCID: PMC6006859 DOI: 10.1186/s12711-018-0402-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Accepted: 06/01/2018] [Indexed: 12/18/2022] Open
Abstract
Background Population stratification and cryptic relationships have been the main sources of excessive false-positives and false-negatives in population-based association studies. Many methods have been developed to model these confounding factors and minimize their impact on the results of genome-wide association studies. In most of these methods, a two-stage approach is applied where: (1) methods are used to determine if there is a population structure in the sample dataset and (2) the effects of population structure are corrected either by modeling it or by running a separate analysis within each sub-population. The objective of this study was to evaluate the impact of population structure on the accuracy and power of genome-wide association studies using a Bayesian multiple regression method. Methods We conducted a genome-wide association study in a stochastically simulated admixed population. The genome was composed of six chromosomes, each with 1000 markers. Fifteen segregating quantitative trait loci contributed to the genetic variation of a quantitative trait with heritability of 0.30. The impact of genetic relationships and breed composition (BC) on three analysis methods were evaluated: single marker simple regression (SMR), single marker mixed linear model (MLM) and Bayesian multiple-regression analysis (BMR). Each method was fitted with and without BC. Accuracy, power, false-positive rate and the positive predictive value of each method were calculated and used for comparison. Results SMR and BMR, both without BC, were ranked as the worst and the best performing approaches, respectively. Our results showed that, while explicit modeling of genetic relationships and BC is essential for models SMR and MLM, BMR can disregard them and yet result in a higher power without compromising its false-positive rate. Conclusions This study showed that the Bayesian multiple-regression analysis is robust to population structure and to relationships among study subjects and performs better than a single marker mixed linear model approach.
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Affiliation(s)
- Ali Toosi
- Cobb-Vantress Inc., 4703 US HWY 412 E, Siloam Springs, AR, 72761, USA.
| | - Rohan L Fernando
- Department of Animal Science, Iowa State University, Ames, IA, 50010, USA
| | - Jack C M Dekkers
- Department of Animal Science, Iowa State University, Ames, IA, 50010, USA
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14
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Oliveira Júnior GA, Chud TCS, Ventura RV, Garrick DJ, Cole JB, Munari DP, Ferraz JBS, Mullart E, DeNise S, Smith S, da Silva MVGB. Genotype imputation in a tropical crossbred dairy cattle population. J Dairy Sci 2017; 100:9623-9634. [PMID: 28987572 DOI: 10.3168/jds.2017-12732] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Accepted: 08/16/2017] [Indexed: 11/19/2022]
Abstract
The objective of this study was to investigate different strategies for genotype imputation in a population of crossbred Girolando (Gyr × Holstein) dairy cattle. The data set consisted of 478 Girolando, 583 Gyr, and 1,198 Holstein sires genotyped at high density with the Illumina BovineHD (Illumina, San Diego, CA) panel, which includes ∼777K markers. The accuracy of imputation from low (20K) and medium densities (50K and 70K) to the HD panel density and from low to 50K density were investigated. Seven scenarios using different reference populations (RPop) considering Girolando, Gyr, and Holstein breeds separately or combinations of animals of these breeds were tested for imputing genotypes of 166 randomly chosen Girolando animals. The population genotype imputation were performed using FImpute. Imputation accuracy was measured as the correlation between observed and imputed genotypes (CORR) and also as the proportion of genotypes that were imputed correctly (CR). This is the first paper on imputation accuracy in a Girolando population. The sample-specific imputation accuracies ranged from 0.38 to 0.97 (CORR) and from 0.49 to 0.96 (CR) imputing from low and medium densities to HD, and 0.41 to 0.95 (CORR) and from 0.50 to 0.94 (CR) for imputation from 20K to 50K. The CORRanim exceeded 0.96 (for 50K and 70K panels) when only Girolando animals were included in RPop (S1). We found smaller CORRanim when Gyr (S2) was used instead of Holstein (S3) as RPop. The same behavior was observed between S4 (Gyr + Girolando) and S5 (Holstein + Girolando) because the target animals were more related to the Holstein population than to the Gyr population. The highest imputation accuracies were observed for scenarios including Girolando animals in the reference population, whereas using only Gyr animals resulted in low imputation accuracies, suggesting that the haplotypes segregating in the Girolando population had a greater effect on accuracy than the purebred haplotypes. All chromosomes had similar imputation accuracies (CORRsnp) within each scenario. Crossbred animals (Girolando) must be included in the reference population to provide the best imputation accuracies.
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Affiliation(s)
- Gerson A Oliveira Júnior
- Departamento de Medicina Veterinária, Universidade de São Paulo (USP), Faculdade de Zootecnia e Engenharia de Alimentos, Pirassununga, SP, 13635-900, Brazil
| | - Tatiane C S Chud
- Departamento de Ciências Exatas, Universidade Estadual Paulista (Unesp), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, SP, 14884-900, Brazil
| | - Ricardo V Ventura
- Beef Improvement Opportunities, Guelph, ON N1K1E5, Canada; Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON N1G2W1, Canada
| | - Dorian J Garrick
- Department of Animal Science, Iowa State University, Ames 50011-3150
| | - John B Cole
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, 20705-2350
| | - Danísio P Munari
- Departamento de Ciências Exatas, Universidade Estadual Paulista (Unesp), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, SP, 14884-900, Brazil
| | - José B S Ferraz
- Departamento de Medicina Veterinária, Universidade de São Paulo (USP), Faculdade de Zootecnia e Engenharia de Alimentos, Pirassununga, SP, 13635-900, Brazil
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15
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Farah MM, Swan AA, Fortes MRS, Fonseca R, Moore SS, Kelly MJ. Accuracy of genomic selection for age at puberty in a multi-breed population of tropically adapted beef cattle. Anim Genet 2015; 47:3-11. [PMID: 26490440 DOI: 10.1111/age.12362] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/17/2015] [Indexed: 12/25/2022]
Abstract
Genomic selection is becoming a standard tool in livestock breeding programs, particularly for traits that are hard to measure. Accuracy of genomic selection can be improved by increasing the quantity and quality of data and potentially by improving analytical methods. Adding genotypes and phenotypes from additional breeds or crosses often improves the accuracy of genomic predictions but requires specific methodology. A model was developed to incorporate breed composition estimated from genotypes into genomic selection models. This method was applied to age at puberty data in female beef cattle (as estimated from age at first observation of a corpus luteum) from a mix of Brahman and Tropical Composite beef cattle. In this dataset, the new model incorporating breed composition did not increase the accuracy of genomic selection. However, the breeding values exhibited slightly less bias (as assessed by deviation of regression of phenotype on genomic breeding values from the expected value of 1). Adding additional Brahman animals to the Tropical Composite analysis increased the accuracy of genomic predictions and did not affect the accuracy of the Brahman predictions.
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Affiliation(s)
- M M Farah
- Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, Jaboticabal, SP, 14884-900, Brazil
| | - A A Swan
- Animal Genetics and Breeding Unit, University of New England, Armidale, NSW, 2351, Australia
| | - M R S Fortes
- Queensland Alliance for Agriculture and Food Innovation, Centre for Animal Science, The University of Queensland, Brisbane, Qld, 4072, Australia
| | - R Fonseca
- Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, Jaboticabal, SP, 14884-900, Brazil
| | - S S Moore
- Queensland Alliance for Agriculture and Food Innovation, Centre for Animal Science, The University of Queensland, Brisbane, Qld, 4072, Australia
| | - M J Kelly
- Queensland Alliance for Agriculture and Food Innovation, Centre for Animal Science, The University of Queensland, Brisbane, Qld, 4072, Australia
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16
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Graham CF, Glenn TC, McArthur AG, Boreham DR, Kieran T, Lance S, Manzon RG, Martino JA, Pierson T, Rogers SM, Wilson JY, Somers CM. Impacts of degraded
DNA
on restriction enzyme associated
DNA
sequencing (
RADS
eq). Mol Ecol Resour 2015; 15:1304-15. [DOI: 10.1111/1755-0998.12404] [Citation(s) in RCA: 84] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2014] [Revised: 03/04/2015] [Accepted: 03/06/2015] [Indexed: 12/01/2022]
Affiliation(s)
- Carly F. Graham
- Department of Biology University of Regina Regina Saskatchewan S4S 0A2 Canada
| | - Travis C. Glenn
- College of Public Health University of Georgia Athens GA 30602 USA
| | - Andrew G. McArthur
- M.G. DeGroote Institute for Infectious Disease Research Department of Biochemistry and Biomedical Sciences DeGroote School of Medicine McMaster University 1280 Main Street West Hamilton Ontario L8S 4K1 Canada
| | - Douglas R. Boreham
- Medical Sciences Northern Ontario School of Medicine Greater Sudbury Ontario P0M Canada
| | - Troy Kieran
- College of Public Health University of Georgia Athens GA 30602 USA
| | - Stacey Lance
- Savannah River Ecology Laboratory University of Georgia Athens GA 30602 USA
| | - Richard G. Manzon
- Department of Biology University of Regina Regina Saskatchewan S4S 0A2 Canada
| | - Jessica A. Martino
- Department of Biology University of Regina Regina Saskatchewan S4S 0A2 Canada
| | - Todd Pierson
- College of Public Health University of Georgia Athens GA 30602 USA
| | - Sean M. Rogers
- Department of Biological Sciences University of Calgary Calgary Alberta T2N 1N4 Canada
| | - Joanna Y. Wilson
- Department of Biology McMaster University Hamilton Ontario L8S 4M1 Canada
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17
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Odegård J, Moen T, Santi N, Korsvoll SA, Kjøglum S, Meuwissen THE. Genomic prediction in an admixed population of Atlantic salmon (Salmo salar). Front Genet 2014; 5:402. [PMID: 25484890 PMCID: PMC4240172 DOI: 10.3389/fgene.2014.00402] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Accepted: 10/31/2014] [Indexed: 12/30/2022] Open
Abstract
Reliability of genomic selection (GS) models was tested in an admixed population of Atlantic salmon, originating from crossing of several wild subpopulations. The models included ordinary genomic BLUP models (GBLUP), using genome-wide SNP markers of varying densities (1–220 k), a genomic identity-by-descent model (IBD-GS), using linkage analysis of sparse genome-wide markers, as well as a classical pedigree-based model. Reliabilities of the models were compared through 5-fold cross-validation. The traits studied were salmon lice (Lepeophtheirus salmonis) resistance (LR), measured as (log) density on the skin and fillet color (FC), with respective estimated heritabilities of 0.14 and 0.43. All genomic models outperformed the classical pedigree-based model, for both traits and at all marker densities. However, the relative improvement differed considerably between traits, models and marker densities. For the highly heritable FC, the IBD-GS had similar reliability as GBLUP at high marker densities (>22 k). In contrast, for the lowly heritable LR, IBD-GS was clearly inferior to GBLUP, irrespective of marker density. Hence, GBLUP was robust to marker density for the lowly heritable LR, but sensitive to marker density for the highly heritable FC. We hypothesize that this phenomenon may be explained by historical admixture of different founder populations, expected to reduce short-range lice density (LD) and induce long-range LD. The relative importance of LD/relationship information is expected to decrease/increase with increasing heritability of the trait. Still, using the ordinary GBLUP, the typical long-range LD of an admixed population may be effectively captured by sparse markers, while efficient utilization of relationship information may require denser markers (e.g., 22 k or more).
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Affiliation(s)
| | - Thomas Moen
- Breeding and Genetics, AquaGen AS Trondheim, Norway
| | - Nina Santi
- Research and Development, AquaGen AS Trondheim, Norway
| | | | | | - Theo H E Meuwissen
- Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences Aas, Norway
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19
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Thomasen JR, Sørensen AC, Lund MS, Guldbrandtsen B. Adding cows to the reference population makes a small dairy population competitive. J Dairy Sci 2014; 97:5822-32. [PMID: 24996280 DOI: 10.3168/jds.2014-7906] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Accepted: 05/23/2014] [Indexed: 12/25/2022]
Abstract
Small dairy breeds are challenged by low reliabilities of genomic prediction. Therefore, we evaluated the effect of including cows in the reference population for small dairy cattle populations with a limited number of sires in the reference population. Using detailed simulations, 2 types of scenarios for maintaining and updating the reference population over a period of 15yr were investigated: a turbo scheme exclusively using genotyped young bulls and a hybrid scheme with mixed use of genotyped young bulls and progeny-tested bulls. Two types of modifications were investigated: (1) number of progeny-tested bulls per year was tested at 6 levels: 15, 40, 60, 100, 250, and 500; and (2) each year, 2,000 first-lactation cows were randomly selected from the cow population for genotyping or, alternatively, an additional 2,000 first-lactation cows were randomly selected and typed in the first 2yr. The effects were evaluated in the 2 main breeding schemes. The breeding schemes were chosen to mimic options for the Danish Jersey cattle population. Evaluation criteria were annual monetary genetic gain, rate of inbreeding, reliability of genomic predictions, and variance of response. Inclusion of cows in the reference population increased monetary genetic gain and decreased the rate of inbreeding. The increase in genetic gain was larger for the turbo schemes with shorter generation intervals. The variance of response was generally higher in turbo schemes than in schemes using progeny-tested bulls. However, the risk was reduced by adding cows to the reference population. The annual genetic gain and the reliability of genomic predictions were slightly higher with more cows in the reference population. Inclusion of cows in the reference population is a rapid way to increase reliabilities of genomic predictions and hence increase genetic gain in a small population. An economic evaluation shows that genotyping of cows is a profitable investment.
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Affiliation(s)
- J R Thomasen
- VikingGenetics, DK 8860, Assentoft, Denmark; Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, DK-8830, Tjele, Denmark.
| | - A C Sørensen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, DK-8830, Tjele, Denmark
| | - M S Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, DK-8830, Tjele, Denmark
| | - B Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, DK-8830, Tjele, Denmark
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Thomasen J, Egger-Danner C, Willam A, Guldbrandtsen B, Lund M, Sørensen A. Genomic selection strategies in a small dairy cattle population evaluated for genetic gain and profit. J Dairy Sci 2014; 97:458-70. [DOI: 10.3168/jds.2013-6599] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2013] [Accepted: 09/30/2013] [Indexed: 12/24/2022]
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