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Wicki M, Brown DJ, Gurman PM, Raoul J, Legarra A, Swan AA. Combined genomic evaluation of Merino and Dohne Merino Australian sheep populations. Genet Sel Evol 2024; 56:69. [PMID: 39350072 PMCID: PMC11440750 DOI: 10.1186/s12711-024-00934-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 09/03/2024] [Indexed: 10/04/2024] Open
Abstract
BACKGROUND The Dohne Merino sheep was introduced to Australia from South Africa in the 1990s. It was primarily used in crosses with the Merino breed sheep to improve on attributes such as reproduction and carcass composition. Since then, this breed has continued to expand in Australia but the number of genotyped and phenotyped purebred individuals remains low, calling into question the accuracy of genomic selection. The Australian Merino, on the other hand, has a substantial reference population in a separate genomic evaluation (MERINOSELECT). Combining these resources could fast track the impact of genomic selection on the smaller breed, but the efficacy of this needs to be investigated. This study was based on a dataset of 53,663 genotypes and more than 2 million phenotypes. Its main objectives were (1) to characterize the genetic structure of Merino and Dohne Merino breeds, (2) to investigate the utility of combining their evaluations in terms of quality of predictions, and (3) to compare several methods of genetic grouping. We used the 'LR-method' (Linear Regression) for these assessments. RESULTS We found very low Fst values (below 0.048) between the different Merino lines and Dohne breed considered in our study, indicating very low genetic differentiation. Principal component analysis revealed three distinct groups, identified as purebred Merino, purebred Dohne, and crossbred animals. Considering the whole population in the reference led to the best quality of predictions and the largest increase in accuracy (from 'LR-method') from pedigree to genomic-based evaluations: 0.18, 0.14 and 0.16 for yearling fibre diameter (YFD), yearling greasy fleece weight (YGFW) and yearling liveweight (YWT), respectively. Combined genomic evaluations showed higher accuracies than the evaluation based on the Dohne reference only (accuracies increased by 0.16, 0.06 and 0.07 for YFD, YGFW, and YWT, respectively). For the combined genomic evaluations, metafounder models were more accurate than Unknown Parent Groups models (accuracies increased by 0.04, 0.04 and 0.06 for YFD, YGFW and YWT, respectively). CONCLUSIONS We found promising results for the future transition of the Dohne breed from pedigree to genomic selection. A combined genomic evaluation, with the MERINOSELECT evaluation in addition to using metafounders, is expected to enhance the quality of genomic predictions for the Dohne Merino breed.
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Affiliation(s)
- Marine Wicki
- INRAE, INP, UMR 1388 GenPhySE, 31326, Castanet-Tolosan, France.
- Institut de l'Elevage, 31321, Castanet-Tolosan, France.
| | - Daniel J Brown
- AGBU, A Joint Venture of NSW Department of Primary Industries and University of New-England, Armidale, Australia
| | - Phillip M Gurman
- AGBU, A Joint Venture of NSW Department of Primary Industries and University of New-England, Armidale, Australia
| | - Jérôme Raoul
- INRAE, INP, UMR 1388 GenPhySE, 31326, Castanet-Tolosan, France
- Institut de l'Elevage, 31321, Castanet-Tolosan, France
| | | | - Andrew A Swan
- AGBU, A Joint Venture of NSW Department of Primary Industries and University of New-England, Armidale, Australia
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Barani S, Miraie Ashtiani SR, Nejati Javaremi A, Khansefid M, Esfandyari H. Optimizing purebred selection to improve crossbred performance. Front Genet 2024; 15:1384973. [PMID: 39381139 PMCID: PMC11458422 DOI: 10.3389/fgene.2024.1384973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Accepted: 08/29/2024] [Indexed: 10/10/2024] Open
Abstract
Crossbreeding is a widely adopted practice in the livestock industry, leveraging the advantages of heterosis and breed complementarity. The prediction of Crossbred Performance (CP) often relies on Purebred Performance (PB) due to limited crossbred data availability. However, the effective selection of purebred parents for enhancing CP depends on non-additive genetic effects and environmental factors. These factors are encapsulated in the genetic correlation between crossbred and purebred populations (r p c ). In this study, a two-way crossbreeding simulation was employed to investigate various strategies for integrating data from purebred and crossbred populations. The goal was to identify optimal models that maximize CP across different levels ofr p c . Different scenarios involving the selection of genotyped individuals from purebred and crossbred populations were explored using ssGBLUP (single-step Genomic Best Linear Unbiased Prediction) and ssGBLUP-MF (ssGBLUP with metafounders) models. The findings revealed an increase in prediction accuracy across all scenarios asr p c values increased. Notably, in the scenario incorporating genotypes from both purebred parent breeds and their crossbreds, both ssGBLUP and ssGBLUP-MF models exhibited nearly identical predictive accuracy. This scenario achieved maximum accuracy whenr p c was less than 0.5. However, atr p c = 0.8, ssGBLUP, which exclusively included sire breed genotypes in the training set, achieved the highest overall prediction accuracy at 73.2%. In comparison, the BLUP-UPG (BLUP with unknown parent group) model demonstrated lower accuracy than ssGBLUP and ssGBLUP-MF across allr p c levels. Although ssGBLUP and ssGBLUP-MF did not demonstrate a definitive trend in their respective scenarios, the prediction ability for CP increased when incorporating both crossbred and purebred population genotypes at lower levels of r p c . Furthermore, whenr p c was high, utilizing paternal genotype for CP predictions emerged as the most effective strategy. Predicted dispersion remained relatively similar in all scenarios, indicating a slight underestimation of breeding values. Overall, ther p c value emerged as a critical factor in predicting CP based on purebred data. However, the optimal model to maximize CP depends on the factors influencingr p c . Consequently, ongoing research aims to develop models that optimize purebred selection, further enhancing CP.
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Affiliation(s)
- Somayeh Barani
- Department of Animal Science, Faculty of Agriculture, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Sayed Reza Miraie Ashtiani
- Department of Animal Science, Faculty of Agriculture, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Ardeshir Nejati Javaremi
- Department of Animal Science, Faculty of Agriculture, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Majid Khansefid
- Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
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López-Correa RD, Legarra A, Aguilar I. Modeling missing pedigree with metafounders and validating single-step genomic predictions in a small dairy cattle population with a great influence of foreign genetics. J Dairy Sci 2024; 107:4685-4692. [PMID: 38310956 DOI: 10.3168/jds.2023-23732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 12/22/2023] [Indexed: 02/06/2024]
Abstract
Genetic improvement in small countries rely heavily on foreign genetics. In an importing country such as Uruguay, consideration of unknown parent groups (UPG) for foreign sires is essential. However, the use of UPG in genomic model evaluations may lead to bias in genomic estimated breeding values (GEBV). The objective of this study was to study different models including UPG or metafounders (MF) in the Uruguayan Holstein evaluation and to analyze bias, dispersion, and accuracy of GEBV predictions in BLUP and single-step genomic BLUP (ssGBLUP). A gamma matrix (Γ) was estimated either by using base allele population frequencies obtained by bounded linear regression (MFbounded), or by using 2 values to design Γ (i.e., a single value for the diagonal and a different value for the off-diagonal [MFrobust]). Both Γ estimators performed well in terms of GEBV predictions, but MFbounded was the best option. There is, however, some bias whose origin was not completely understood. UPG or MF seem to model correctly genetic progress for unknown parents except for the very first groups (earlier time period). As for validation bulls, bias was observed across all models, whereas for validation cows it was only observed with UPG in BLUP. Overdispersion was found in all models, but it was mostly detected in validation bulls. Ratio of accuracies indicated that ssGBLUP gave better predictions than BLUP.
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Affiliation(s)
- R D López-Correa
- Universidad de la República, Facultad de Agronomía, 12900 Montevideo, Uruguay; Universidad de la República, Facultad de Veterinaria, 13000 Montevideo, Uruguay.
| | - A Legarra
- Council on Dairy Cattle Breeding, Bowie, MD 20716
| | - I Aguilar
- Instituto Nacional de Investigación Agropecuaria, 90100 Montevideo, Uruguay
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Himmelbauer J, Schwarzenbacher H, Fuerst C, Fuerst-Waltl B. Exploring unknown parent groups and metafounders in single-step genomic BLUP: Insights from a simulated cattle population. J Dairy Sci 2024:S0022-0302(24)00950-0. [PMID: 38908687 DOI: 10.3168/jds.2024-24891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 05/17/2024] [Indexed: 06/24/2024]
Abstract
This study explores how the metafounder (MF) concept enhances genetic evaluations in dairy cattle populations using single-step genomic best linear unbiased prediction (ssGBLUP). By improving the consideration of relationships among founder populations, MF ensures accurate alignment of pedigree and genomic relationships. The research aims to propose a method for grouping MF based on genotypic information, assess different approaches for estimating the gamma matrix, and compare unknown parent groups (UPG) and MF methodologies across various scenarios, including those with low and high pedigree completeness based on a simulated dairy cattle population. In the scenario where unknown ancestors are rare, the impact of UPG or MF on breeding values is minimal but MF still performs slightly better compared with UPG. The scenario with lower genotyping rates and more unknown parents shows significant differences in evaluations with and without UPG and also compared with MF. The study shows that ssGBLUP evaluations where UPG are considered via Quaas-Pollak-transformation in the pedigree-based and genomic relationship matrix (UPG_fullQP) results in double counting and subsequently in a pronounced bias and overdispersion. Another focus is on the estimation of the gamma matrix, emphasizing the importance of crossbred genotypes for accuracy. Challenges emerge in classifying animals into subpopulations and further into MF or UPG, but the method used in this study, which is based on genotypes, results in predictions which are comparable to those obtained using the true subpopulations for the assignment. Estimated validation results using the linear regression method confirm the superior performance of MF evaluations, although differences compared with true validations are smaller. Notably, UPG_fullQP's extreme bias is less evident in routine validation statistics.
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Affiliation(s)
- Judith Himmelbauer
- ZuchtData EDV-Dienstleistungen GmbH, Dresdner Straße 89/B1/18, 1200 Vienna, Austria; University of Natural Resources and Life Sciences, Vienna, Gregor-Mendel Str. 33, 1180 Vienna, Austria.
| | | | - Christian Fuerst
- ZuchtData EDV-Dienstleistungen GmbH, Dresdner Straße 89/B1/18, 1200 Vienna, Austria
| | - Birgit Fuerst-Waltl
- University of Natural Resources and Life Sciences, Vienna, Gregor-Mendel Str. 33, 1180 Vienna, Austria
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5
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Melo TP, Zwirtes AK, Silva AA, Lázaro SF, Oliveira HR, Silveira KR, Santos JCG, Andrade WBF, Kluska S, Evangelho LA, Oliveira HN, Tonhati H. Unknown parent groups and truncated pedigree in single-step genomic evaluations of Murrah buffaloes. J Dairy Sci 2024:S0022-0302(24)00847-6. [PMID: 38825116 DOI: 10.3168/jds.2023-24608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 04/16/2024] [Indexed: 06/04/2024]
Abstract
Missing pedigree may produce bias in genomic evaluations. Thus, strategies to deal with this problem have been proposed as using unknown parent groups (UPG) or truncated pedigrees. The aim of this study was to investigate the impact of modeling missing pedigree under ssGBLUP evaluations for productive and reproductive traits in dairy buffalos using different approaches: 1) traditional BLUP without UPG (BLUP), 2) traditional BLUP including UPG (BLUP/UPG), 3) ssGBLUP without UPG (ssGBLUP), 4) ssGBLUP including UPG in the A and A22 matrices (ssGBLUP/A_UPG), 5) ssGBLUP including UPG in all elements of the H matrix (ssGBLUP/H_UPG), 6) BLUP with pedigree truncation for the last 3 generations (BLUP/truncated), and 7) ssGBLUP with pedigree truncation for the last 3 generations (ssGBLUP/ truncated). UPGs were not used in the scenarios with truncated pedigree. A total of 3,717, 4,126 and 3,823 records of the first lactation for accumulated 305 d milk yield (MY), age at first calving (AFC) and lactation length (LL), respectively were used. Accuracies ranged from 0.27 for LL (BLUP) to 0.46 for MY (BLUP), bias ranged from -0.62 for MY (ssGBLUP) to 0.0002 for AFC (BLUP/truncated), and dispersion ranged from 0.88 for MY (BLUP/ A_UPG) to 1.13 for LL (BLUP). Genetic trend showed genetic gains for all traits across 20 years of selection and the impact of including either genomic information, UPG or pedigree truncation under GEBV accuracies ranged among the evaluated traits. Overall, methods using UPGs, truncation pedigree and genomic information exhibited potential to improve GEBV accuracies, bias and dispersion for all traits compared with other methods. Truncated scenarios promoted high genetic gains. In small populations with few genotyped animals, combining truncated pedigree or UPG with genomic information is a feasible approach to deal with missing pedigrees.
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Affiliation(s)
- T P Melo
- Departament of Animal Science, Federal University of Santa Maria (UFSM), Santa Maria, 97105-900, Rio Grande do Sul, Brazil.
| | - A K Zwirtes
- Departament of Animal Science, Federal University of Santa Maria (UFSM), Santa Maria, 97105-900, Rio Grande do Sul, Brazil
| | - A A Silva
- Departament of Animal Science, Sao Paulo State University (UNESP), Jaboticabal 14884-900, Sao Paulo, Brazil
| | - S F Lázaro
- Department of Animal Biosciences, University of Guelph, Guelph, N1G 1Y2, Ontario, Canada
| | - H R Oliveira
- Departament of Animal Sciences, Purdue University, West Lafayette, 47906, Indiana, USA
| | - K R Silveira
- Departament of Animal Science, Sao Paulo State University (UNESP), Jaboticabal 14884-900, Sao Paulo, Brazil
| | - J C G Santos
- Departament of Animal Science, Sao Paulo State University (UNESP), Jaboticabal 14884-900, Sao Paulo, Brazil
| | - W B F Andrade
- Departament of Animal Science, Sao Paulo State University (UNESP), Jaboticabal 14884-900, Sao Paulo, Brazil
| | - S Kluska
- Brazilian Association of Girolando Breeder's
| | - L A Evangelho
- Departament of Animal Science, Federal University of Santa Maria (UFSM), Santa Maria, 97105-900, Rio Grande do Sul, Brazil
| | - H N Oliveira
- Departament of Animal Science, Sao Paulo State University (UNESP), Jaboticabal 14884-900, Sao Paulo, Brazil
| | - H Tonhati
- Departament of Animal Science, Sao Paulo State University (UNESP), Jaboticabal 14884-900, Sao Paulo, Brazil
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Zoda A, Ogawa S, Kagawa R, Tsukahara H, Obinata R, Urakawa M, Oono Y. Single-Step Genomic Prediction of Superovulatory Response Traits in Japanese Black Donor Cows. BIOLOGY 2023; 12:biology12050718. [PMID: 37237533 DOI: 10.3390/biology12050718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023]
Abstract
We assessed the performance of single-step genomic prediction of breeding values for superovulatory response traits in Japanese Black donor cows. A total of 25,332 records of the total number of embryos and oocytes (TNE) and the number of good embryos (NGE) per flush for 1874 Japanese Black donor cows were collected during 2008 and 2022. Genotype information on 36,426 autosomal single-nucleotide polymorphisms (SNPs) for 575 out of the 1,874 cows was used. Breeding values were predicted exploiting a two-trait repeatability animal model. Two genetic relationship matrices were used, one based on pedigree information (A matrix) and the other considering both pedigree and SNP marker genotype information (H matrix). Estimated heritabilities of TNE and NGE were 0.18 and 0.11, respectively, when using the H matrix, which were both slightly lower than when using the A matrix (0.26 for TNE and 0.16 for NGE). Estimated genetic correlations between the traits were 0.61 and 0.66 when using H and A matrices, respectively. When the variance components were the same in breeding value prediction, the mean reliability was greater when using the H matrix than when using the A matrix. This advantage seems more prominent for cows with low reliability when using the A matrix. The results imply that introducing single-step genomic prediction could boost the rate of genetic improvement of superovulatory response traits, but efforts should be made to maintain genetic diversity when performing selection.
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Affiliation(s)
- Atsushi Zoda
- Research and Development Group, Zen-noh Embryo Transfer Center, Kamishihoro 080-1407, Japan
| | - Shinichiro Ogawa
- Division of Meat Animal and Poultry Research, Institute of Livestock and Grassland Science, National Agriculture and Food Research Organization (NARO), Tsukuba 305-0901, Japan
| | - Rino Kagawa
- Research and Development Group, Zen-noh Embryo Transfer Center, Kamishihoro 080-1407, Japan
| | - Hayato Tsukahara
- Research and Development Group, Zen-noh Embryo Transfer Center, Kamishihoro 080-1407, Japan
| | - Rui Obinata
- Research and Development Group, Zen-noh Embryo Transfer Center, Kamishihoro 080-1407, Japan
| | - Manami Urakawa
- Research and Development Group, Zen-noh Embryo Transfer Center, Kamishihoro 080-1407, Japan
| | - Yoshio Oono
- Research and Development Group, Zen-noh Embryo Transfer Center, Kamishihoro 080-1407, Japan
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7
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Esrafili Taze Kand Mohammaddiyeh M, Rafat SA, Shodja J, Javanmard A, Esfandyari H. Selective genotyping to implement genomic selection in beef cattle breeding. Front Genet 2023; 14:1083106. [PMID: 37007975 PMCID: PMC10064214 DOI: 10.3389/fgene.2023.1083106] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 02/28/2023] [Indexed: 03/19/2023] Open
Abstract
Genomic selection (GS) plays an essential role in livestock genetic improvement programs. In dairy cattle, the method is already a recognized tool to estimate the breeding values of young animals and reduce generation intervals. Due to the different breeding structures of beef cattle, the implementation of GS is still a challenge and has been adopted to a much lesser extent than dairy cattle. This study aimed to evaluate genotyping strategies in terms of prediction accuracy as the first step in the implementation of GS in beef while some restrictions were assumed for the availability of phenotypic and genomic information. For this purpose, a multi-breed population of beef cattle was simulated by imitating the practical system of beef cattle genetic evaluation. Four genotyping scenarios were compared to traditional pedigree-based evaluation. Results showed an improvement in prediction accuracy, albeit a limited number of animals being genotyped (i.e., 3% of total animals in genetic evaluation). The comparison of genotyping scenarios revealed that selective genotyping should be on animals from both ancestral and younger generations. In addition, as genetic evaluation in practice covers traits that are expressed in either sex, it is recommended that genotyping covers animals from both sexes.
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Affiliation(s)
| | - Seyed Abbas Rafat
- Department of Animal Sciences, University of Tabriz, Tabriz, Iran
- *Correspondence: Maryam Esrafili Taze Kand Mohammaddiyeh, ; Seyed Abbas Rafat,
| | - Jalil Shodja
- Department of Animal Sciences, University of Tabriz, Tabriz, Iran
| | - Arash Javanmard
- Department of Animal Sciences, University of Tabriz, Tabriz, Iran
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Genomic evaluation of commercial herds with different pedigree structures using the single-step genomic BLUP in Nelore cattle. Trop Anim Health Prod 2023; 55:95. [PMID: 36810697 DOI: 10.1007/s11250-023-03508-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 02/11/2023] [Indexed: 02/23/2023]
Abstract
The aim of this work was to evaluate the impact of applying genomic information in pedigree uncertainty situations on genetic evaluations for growth- and cow productivity-related traits in Nelore commercial herds. Records for accumulated cow productivity (ACP) and adjusted weight at 450 days of age (W450) were used, as well as genotypes of registered and commercial herd animals, genotyped with the Clarifide Nelore 3.1 panel (~29,000 SNPs). The genetic values for commercial and registered populations were estimated using different approaches that included (ssGBLUP) or did not include genomic information (BLUP), with different pedigree structures. Different scenarios were tested, varying the proportion of young animals with unknown sires (0, 25, 50, 75, and 100%), and unknown maternal grandsires (0, 25, 50, 75, and 100%). The prediction accuracies and abilities were calculated. The estimated breeding value accuracies decreased as the proportion of unknown sires and maternal grandsires increased. The genomic estimated breeding value accuracy using the ssGBLUP was higher in scenarios with a lower proportion of known pedigree when compared to the BLUP methodology. The results obtained with the ssGBLUP showed that it is possible to obtain reliable direct and indirect predictions for young animals from commercial herds without pedigree structure.
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Kudinov AA, Koivula M, Aamand GP, Strandén I, Mäntysaari EA. Single-step genomic BLUP with many metafounders. Front Genet 2022; 13:1012205. [DOI: 10.3389/fgene.2022.1012205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 10/31/2022] [Indexed: 11/23/2022] Open
Abstract
Single-step genomic BLUP (ssGBLUP) model for routine genomic prediction of breeding values is developed intensively for many dairy cattle populations. Compatibility between the genomic (G) and the pedigree (A) relationship matrices remains an important challenge required in ssGBLUP. The compatibility relates to the amount of missing pedigree information. There are two prevailing approaches to account for the incomplete pedigree information: unknown parent groups (UPG) and metafounders (MF). unknown parent groups have been used routinely in pedigree-based evaluations to account for the differences in genetic level between groups of animals with missing parents. The MF approach is an extension of the UPG approach. The MF approach defines MF which are related pseudo-individuals. The MF approach needs a Γ matrix of the size number of MF to describe relationships between MF. The UPG and MF can be the same. However, the challenge in the MF approach is the estimation of Γ having many MF, typically needed in dairy cattle. In our study, we present an approach to fit the same amount of MF as UPG in ssGBLUP with Woodbury matrix identity (ssGTBLUP). We used 305-day milk, protein, and fat yield data from the DFS (Denmark, Finland, Sweden) Red Dairy cattle population. The pedigree had more than 6 million animals of which 207,475 were genotyped. We constructed the preliminary gamma matrix (Γpre) with 29 MF which was expanded to 148 MF by a covariance function (Γ148). The quality of the extrapolation of the Γpre matrix was studied by comparing average off-diagonal elements between breed groups. On average relationships among MF in Γ148 were 1.8% higher than in Γpre. The use of Γ148 increased the correlation between the G and A matrices by 0.13 and 0.11 for the diagonal and off-diagonal elements, respectively. [G]EBV were predicted using the ssGTBLUP and Pedigree-BLUP models with the MF and UPG. The prediction reliabilities were slightly higher for the ssGTBLUP model using MF than UPG. The ssGBLUP MF model showed less overprediction compared to other models.
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Callister AN, Bermann M, Elms S, Bradshaw BP, Lourenco D, Brawner JT. Accounting for population structure in genomic predictions of Eucalyptus globulus. G3 GENES|GENOMES|GENETICS 2022; 12:6654591. [PMID: 35920792 PMCID: PMC9434241 DOI: 10.1093/g3journal/jkac180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 06/29/2022] [Indexed: 12/02/2022]
Abstract
Genetic groups have been widely adopted in tree breeding to account for provenance effects within pedigree-derived relationship matrices. However, provenances or genetic groups have not yet been incorporated into single-step genomic BLUP (“HBLUP”) analyses of tree populations. To quantify the impact of accounting for population structure in Eucalyptus globulus, we used HBLUP to compare breeding value predictions from models excluding base population effects and models including either fixed genetic groups or the marker-derived proxies, also known as metafounders. Full-sib families from 2 separate breeding populations were evaluated across 13 sites in the “Green Triangle” region of Australia. Gamma matrices (Γ) describing similarities among metafounders reflected the geographic distribution of populations and the origins of 2 land races were identified. Diagonal elements of Γ provided population diversity or allelic covariation estimates between 0.24 and 0.56. Genetic group solutions were strongly correlated with metafounder solutions across models and metafounder effects influenced the genetic solutions of base population parents. The accuracy, stability, dispersion, and bias of model solutions were compared using the linear regression method. Addition of genomic information increased accuracy from 0.41 to 0.47 and stability from 0.68 to 0.71, while increasing bias slightly. Dispersion was within 0.10 of the ideal value (1.0) for all models. Although inclusion of metafounders did not strongly affect accuracy or stability and had mixed effects on bias, we nevertheless recommend the incorporation of metafounders in prediction models to represent the hierarchical genetic population structure of recently domesticated populations.
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Affiliation(s)
| | - Matias Bermann
- Department of Animal and Dairy Science, University of Georgia , Athens, GA 30602, USA
| | - Stephen Elms
- HVP Plantations , Churchill, VIC 3842, Australia
| | - Ben P Bradshaw
- Australian Bluegum Plantations , Albany, WA 6330, Australia
| | - Daniela Lourenco
- Department of Animal and Dairy Science, University of Georgia , Athens, GA 30602, USA
| | - Jeremy T Brawner
- Department of Plant Pathology, University of Florida , Gainesville, FL 32611, USA
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Belay TK, Eikje LS, Gjuvsland AB, Nordbø Ø, Tribout T, Meuwissen T. Correcting for base-population differences and unknown parent groups in single-step genomic predictions of Norwegian Red Cattle. J Anim Sci 2022; 100:6618053. [PMID: 35752161 PMCID: PMC9467032 DOI: 10.1093/jas/skac227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 06/23/2022] [Indexed: 11/15/2022] Open
Abstract
Bias and inflation in genomic evaluation with the single-step methods have been reported in several studies. Incompatibility between the base-populations of the pedigree-based and the genomic relationship matrix (G) could be a reason for these biases. Inappropriate ways of accounting for missing parents could be another reason for biases in genetic evaluations with or without genomic information. To handle these problems, we fitted and evaluated a fixed covariate (J) that contains ones for genotyped animals and zeros for unrelated non-genotyped animals, or pedigree-based regression coefficients for related non-genotyped animals. We also evaluated alternative ways of fitting the J covariate together with genetic groups on biases and stability of breeding value estimates, and of including it into G as a random effect. In a whole vs. partial data set comparison, four scenarios were investigated for the partial data: genotypes missing, phenotypes missing, both genotypes and phenotypes missing, and pedigree missing. Fitting J either as fixed or random reduced level-bias and inflation and increased stability of genomic predictions as compared to the basic model where neither J nor genetic groups were fitted. In most models, genomic predictions were largely biased for scenarios with missing genotype and phenotype information. The biases were reduced for models which combined group and J effects. Models with these corrected group covariates performed better than the recently published model where genetic groups were encapsulated and fitted as random via the Quaas and Pollak transformation. In our Norwegian Red cattle data, a model which combined group and J regression coefficients was preferred because it showed least bias and highest stability of genomic predictions across the scenarios.
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Affiliation(s)
- Tesfaye K Belay
- Department of animal and aquacultural Sciences, Norwegian University of Life Sciences, NMBU, Norway
| | | | | | | | - Thierry Tribout
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, JouyenJosas, France
| | - Theo Meuwissen
- Department of animal and aquacultural Sciences, Norwegian University of Life Sciences, NMBU, Norway
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Strandén I, Aamand GP, Mäntysaari EA. Single-step genomic BLUP with genetic groups and automatic adjustment for allele coding. Genet Sel Evol 2022; 54:38. [PMID: 35655157 PMCID: PMC9164359 DOI: 10.1186/s12711-022-00721-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 04/13/2022] [Indexed: 11/10/2022] Open
Abstract
Background Genomic estimated breeding values (GEBV) by single-step genomic BLUP (ssGBLUP) are affected by the centering of marker information used. The use of a fixed effect called J factor will lead to GEBV that are unaffected by the centering used. We extended the use of a single J factor to a group of J factors. Results J factor(s) are usually included in mixed model equations (MME) as regression effects but a transformation similar to that regularly used for genetic groups can be applied to obtain a simpler MME, which is sparser than the original MME and does not need computation of the J factors. When the J factor is based on the same structure as the genetic groups, then MME can be transformed such that coefficients for the genetic groups no longer include information from the genomic relationship matrix. We illustrate the use of J factors in the analysis of a Red dairy cattle data set for fertility. Conclusions The GEBV from these analyses confirmed the theoretical derivations that show that the resulting GEBV are allele coding independent when a J factor is used. Transformed MME led to faster computing time than the original regression-based MME.
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Affiliation(s)
- Ismo Strandén
- Natural Resources Institute Finland (Luke), Jokioinen, Finland.
| | - Gert P Aamand
- Nordic Cattle Genetic Evaluation (NAV), Aarhus, Denmark
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Masuda Y, VanRaden PM, Tsuruta S, Lourenco DAL, Misztal I. Invited review: Unknown-parent groups and metafounders in single-step genomic BLUP. J Dairy Sci 2021; 105:923-939. [PMID: 34799109 DOI: 10.3168/jds.2021-20293] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 09/26/2021] [Indexed: 11/19/2022]
Abstract
Single-step genomic BLUP (ssGBLUP) is a method for genomic prediction that integrates matrices of pedigree (A) and genomic (G) relationships into a single unified additive relationship matrix whose inverse is incorporated into a set of mixed model equations (MME) to compute genomic predictions. Pedigree information in dairy cattle is often incomplete. Missing pedigree potentially causes biases and inflation in genomic estimated breeding values (GEBV) obtained with ssGBLUP. Three major issues are associated with missing pedigree in ssGBLUP, namely biased predictions by selection, missing inbreeding in pedigree relationships, and incompatibility between G and A in level and scale. These issues can be solved using a proper model for unknown-parent groups (UPG). The theory behind the use of UPG is well established for pedigree BLUP, but not for ssGBLUP. This study reviews the development of the UPG model in pedigree BLUP, the properties of UPG models in ssGBLUP, and the effect of UPG on genetic trends and genomic predictions. Similarities and differences between UPG and metafounder (MF) models, a generalized UPG model, are also reviewed. A UPG model (QP) derived using a transformation of the MME has a good convergence behavior. However, with insufficient data, the QP model may yield biased genetic trends and may underestimate UPG. The QP model can be altered by removing the genomic relationships linking GEBV and UPG effects from MME. This altered QP model exhibits less bias in genetic trends and less inflation in genomic predictions than the QP model, especially with large data sets. Recently, a new model, which encapsulates the UPG equations into the pedigree relationships for genotyped animals, was proposed in simulated purebred populations. The MF model is a comprehensive solution to the missing pedigree issue. This model can be a choice for multibreed or crossbred evaluations if the data set allows the estimation of a reasonable relationship matrix for MF. Missing pedigree influences genetic trends, but its effect on the predictability of genetic merit for genotyped animals should be negligible when many proven bulls are genotyped. The SNP effects can be back-solved using GEBV from older genotyped animals, and these predicted SNP effects can be used to calculate GEBV for young-genotyped animals with missing parents.
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Affiliation(s)
- Yutaka Masuda
- Department of Animal and Dairy Science, University of Georgia, Athens 30602.
| | - Paul M VanRaden
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, US Department of Agriculture, Beltsville, MD 20705
| | - Shogo Tsuruta
- Department of Animal and Dairy Science, University of Georgia, Athens 30602
| | | | - Ignacy Misztal
- Department of Animal and Dairy Science, University of Georgia, Athens 30602
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Fu C, Ostersen T, Christensen OF, Xiang T. Single-step genomic evaluation with metafounders for feed conversion ratio and average daily gain in Danish Landrace and Yorkshire pigs. Genet Sel Evol 2021; 53:79. [PMID: 34620083 PMCID: PMC8499570 DOI: 10.1186/s12711-021-00670-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 09/02/2021] [Indexed: 11/17/2022] Open
Abstract
Background The single-step genomic best linear unbiased prediction (SSGBLUP) method is a popular approach for genetic evaluation with high-density genotype data. To solve the problem that pedigree and genomic relationship matrices refer to different base populations, a single-step genomic method with metafounders (MF-SSGBLUP) was put forward. The aim of this study was to compare the predictive ability and bias of genomic evaluations obtained with MF-SSGBLUP and standard SSGBLUP. We examined feed conversion ratio (FCR) and average daily gain (ADG) in DanBred Landrace (LL) and Yorkshire (YY) pigs using both univariate and bivariate models, as well as the optimal weighting factors (ω), which represent the proportions of the genetic variance not captured by markers, for ADG and FCR in SSGBLUP and MF-SSGBLUP. Results In general, SSGBLUP and MF-SSGBLUP showed similar predictive abilities and bias of genomic estimated breeding values (GEBV). In the LL population, the predictive ability for ADG reached 0.36 using uni- or bi-variate SSGBLUP or MF-SSGBLUP, while the predictive ability for FCR was highest (0.20) for the bivariate model using MF-SSGBLUP, but differences between analyses were very small. In the YY population, predictive ability for ADG was similar for the four analyses (up to 0.35), while the predictive ability for FCR was highest (0.36) for the uni- and bi-variate MF-SSGBLUP analyses. SSGBLUP and MF-SSGBLUP exhibited nearly the same bias. In general, the bivariate models had lower bias than the univariate models. In the LL population, the optimal ω for ADG was ~ 0.2 in the univariate or bivariate models using SSGBLUP or MF-SSGBLUP, and the optimal ω for FCR was 0.70 and 0.55 for SSGBLUP and MF-SSGBLUP, respectively. In the YY population, the optimal ω ranged from 0.25 to 0. 35 for ADG across the four analyses and from 0.10 to 0.30 for FCR. Conclusions Our results indicate that MF-SSGBLUP performed slightly better than SSGBLUP for genomic evaluation. There was little difference in the optimal weighting factors (ω) between SSGBLUP and MF-SSGBLUP. Overall, the bivariate model using MF-SSGBLUP is recommended for single-step genomic evaluation of ADG and FCR in DanBred Landrace and Yorkshire pigs.
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Affiliation(s)
- Chuanke Fu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, 430070, China
| | - Tage Ostersen
- SEGES, Danish Agriculture & Food Council F.m.b.A., Agro Food Park 15, 8200, Aarhus N, Denmark
| | - Ole F Christensen
- Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Alle 20, 8830, Tjele, Denmark
| | - Tao Xiang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, 430070, China.
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Callister AN, Bradshaw BP, Elms S, Gillies RAW, Sasse JM, Brawner JT. Single-step genomic BLUP enables joint analysis of disconnected breeding programs: an example with Eucalyptus globulus Labill. G3-GENES GENOMES GENETICS 2021; 11:6322958. [PMID: 34568915 PMCID: PMC8473980 DOI: 10.1093/g3journal/jkab253] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 07/08/2021] [Indexed: 11/13/2022]
Abstract
Single-step GBLUP (HBLUP) efficiently combines genomic, pedigree, and phenotypic information for holistic genetic analyses of disjunct breeding populations. We combined data from two independent multigenerational Eucalyptus globulus breeding populations to provide direct comparisons across the programs and indirect predictions in environments where pedigreed families had not been evaluated. Despite few known pedigree connections between the programs, genomic relationships provided the connectivity required to create a unified relationship matrix, H, which was used to compare pedigree-based and HBLUP models. Stem volume data from 48 sites spread across three regions of southern Australia and wood quality data across 20 sites provided comparisons of model accuracy. Genotyping proved valuable for correcting pedigree errors and HBLUP more precisely defines relationships within and among populations, with relationships among the genotyped individuals used to connect the pedigrees of the two programs. Cryptic relationships among the native range populations provided evidence of population structure and evidence of the origin of landrace populations. HBLUP across programs improved the prediction accuracy of parents and genotyped individuals and enabled breeding value predictions to be directly compared and inferred in regions where little to no testing has been undertaken. The impact of incorporating genetic groups in the estimation of H will further align traditional genetic evaluation pipelines with approaches that incorporate marker-derived relationships into prediction models.
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Affiliation(s)
| | - Ben P Bradshaw
- Australian Bluegum Plantations, Albany, WA 6330, Australia
| | | | | | - Joanna M Sasse
- Sassafras Group Pty Ltd, Yarraville, VIC 3013, Australia
| | - Jeremy T Brawner
- Plant Pathology, University of Florida, Gainesville, FL 32611, USA
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Kluska S, Masuda Y, Ferraz JBS, Tsuruta S, Eler JP, Baldi F, Lourenco D. Metafounders May Reduce Bias in Composite Cattle Genomic Predictions. Front Genet 2021; 12:678587. [PMID: 34490031 PMCID: PMC8417888 DOI: 10.3389/fgene.2021.678587] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 07/15/2021] [Indexed: 11/13/2022] Open
Abstract
Metafounders are pseudo-individuals that act as proxies for animals in base populations. When metafounders are used, individuals from different breeds can be related through pedigree, improving the compatibility between genomic and pedigree relationships. The aim of this study was to investigate the use of metafounders and unknown parent groups (UPGs) for the genomic evaluation of a composite beef cattle population. Phenotypes were available for scrotal circumference at 14 months of age (SC14), post weaning gain (PWG), weaning weight (WW), and birth weight (BW). The pedigree included 680,551 animals, of which 1,899 were genotyped for or imputed to around 30,000 single-nucleotide polymorphisms (SNPs). Evaluations were performed based on pedigree (BLUP), pedigree with UPGs (BLUP_UPG), pedigree with metafounders (BLUP_MF), single-step genomic BLUP (ssGBLUP), ssGBLUP with UPGs for genomic and pedigree relationship matrices (ssGBLUP_UPG) or only for the pedigree relationship matrix (ssGBLUP_UPGA), and ssGBLUP with metafounders (ssGBLUP_MF). Each evaluation considered either four or 10 groups that were assigned based on breed of founders and intermediate crosses. To evaluate model performance, we used a validation method based on linear regression statistics to obtain accuracy, stability, dispersion, and bias of (genomic) estimated breeding value [(G)EBV]. Overall, relationships within and among metafounders were stronger in the scenario with 10 metafounders. Accuracy was greater for models with genomic information than for BLUP. Also, the stability of (G)EBVs was greater when genomic information was taken into account. Overall, pedigree-based methods showed lower inflation/deflation (regression coefficients close to 1.0) for SC14, WWM, and BWD traits. The level of inflation/deflation for genomic models was small and trait-dependent. Compared with regular ssGBLUP, ssGBLUP_MF4 displayed regression coefficient closer to one SC14, PWG, WWM, and BWD. Genomic models with metafounders seemed to be slightly more stable than models with UPGs based on higher similarity of results with different numbers of groups. Further, metafounders can help to reduce bias in genomic evaluations of composite beef cattle populations without reducing the stability of GEBVs.
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Affiliation(s)
- Sabrina Kluska
- Departamento de Zootecnia, Universidade Estadual Paulista Júlio de Mesquita Filho, Jaboticabal, Brazil.,Department of Animal and Dairy Science, University of Georgia, Athens, GA, United States
| | - Yutaka Masuda
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, United States
| | | | - Shogo Tsuruta
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, United States
| | - Joanir Pereira Eler
- Departamento de Medicina Veterinaìria, Universidade de São Paulo, Pirassununga, Brazil
| | - Fernando Baldi
- Departamento de Zootecnia, Universidade Estadual Paulista Júlio de Mesquita Filho, Jaboticabal, Brazil
| | - Daniela Lourenco
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, United States
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17
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Tonussi RL, Londoño-Gil M, de Oliveira Silva RM, Magalhães AFB, Amorim ST, Kluska S, Espigolan R, Peripolli E, Pereira ASC, Lôbo RB, Aguilar I, Lourenço DAL, Baldi F. Accuracy of genomic breeding values and predictive ability for postweaning liveweight and age at first calving in a Nellore cattle population with missing sire information. Trop Anim Health Prod 2021; 53:432. [PMID: 34373940 DOI: 10.1007/s11250-021-02879-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 07/30/2021] [Indexed: 11/30/2022]
Abstract
The multiple sire system (MSS) is a common mating scheme in extensive beef production systems. However, MSS does not allow paternity identification and lead to inaccurate genetic predictions. The objective of this study was to investigate the implementation of single-step genomic BLUP (ssGBLUP) in different scenarios of uncertain paternity in the evaluation for 450-day adjusted liveweight (W450) and age at first calving (AFC) in a Nellore cattle population. To estimate the variance components using BLUP and ssGBLUP, the relationship matrix (A) with different proportions of animals with missing sires (MS) (scenarios 0, 25, 50, 75, and 100% of MS) was created. The genotyped animals with MS were randomly chosen, and ten replicates were performed for each scenario and trait. Five groups of animals were evaluated in each scenario: PHE, all animals with phenotypic records in the population; SIR, proven sires; GEN, genotyped animals; YNG, young animals without phenotypes and progeny; and YNGEN, young genotyped animals. The additive genetic variance decreased for both traits as the proportion of MS increased in the population when using the regular REML. When using the ssGBLUP, accuracies ranged from 0.13 to 0.47 for W450 and from 0.10 to 0.25 for AFC. For both traits, the prediction ability of the direct genomic value (DGV) decreased as the percentage of MS increased. These results emphasize that indirect prediction via DGV of young animals is more accurate when the SNP effects are derived from ssGBLUP with a reference population with known sires. The ssGBLUP could be applied in situations of uncertain paternity, especially when selecting young animals. This methodology is shown to be accurate, mainly in scenarios with a high percentage of MS.
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Affiliation(s)
- Rafael Lara Tonussi
- Grupo de Melhoramento Animal, Faculdade de Ciências Agrárias E Veterinárias, Universidade Estadual Paulista Júlio de Mesquita Filho, Jaboticabal, SP, CEP 14884-900, Brazil
| | - Marisol Londoño-Gil
- Grupo de Melhoramento Animal, Faculdade de Ciências Agrárias E Veterinárias, Universidade Estadual Paulista Júlio de Mesquita Filho, Jaboticabal, SP, CEP 14884-900, Brazil.
| | | | - Ana Fabrícia Braga Magalhães
- Grupo de Melhoramento Animal, Faculdade de Ciências Agrárias E Veterinárias, Universidade Estadual Paulista Júlio de Mesquita Filho, Jaboticabal, SP, CEP 14884-900, Brazil
| | - Sabrina Thaise Amorim
- Grupo de Melhoramento Animal, Faculdade de Ciências Agrárias E Veterinárias, Universidade Estadual Paulista Júlio de Mesquita Filho, Jaboticabal, SP, CEP 14884-900, Brazil
| | - Sabrina Kluska
- Grupo de Melhoramento Animal, Faculdade de Ciências Agrárias E Veterinárias, Universidade Estadual Paulista Júlio de Mesquita Filho, Jaboticabal, SP, CEP 14884-900, Brazil
| | - Rafael Espigolan
- Grupo de Melhoramento Animal, Faculdade de Ciências Agrárias E Veterinárias, Universidade Estadual Paulista Júlio de Mesquita Filho, Jaboticabal, SP, CEP 14884-900, Brazil
| | - Elisa Peripolli
- Grupo de Melhoramento Animal, Faculdade de Ciências Agrárias E Veterinárias, Universidade Estadual Paulista Júlio de Mesquita Filho, Jaboticabal, SP, CEP 14884-900, Brazil
| | | | - Raysildo Barbosa Lôbo
- Associação Nacional de Criadores E Pesquisadores (ANCP), Ribeirão Preto, SP, CEP 14020-230, Brazil
| | - Ignácio Aguilar
- Instituto Nacional de Pesquisa Agropecuária (INIA), CEP 90200, Las Brujas, Uruguay
| | | | - Fernando Baldi
- Grupo de Melhoramento Animal, Faculdade de Ciências Agrárias E Veterinárias, Universidade Estadual Paulista Júlio de Mesquita Filho, Jaboticabal, SP, CEP 14884-900, Brazil
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18
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Bermann M, Lourenco D, Breen V, Hawken R, Brito Lopes F, Misztal I. Modeling genetic differences of combined broiler chicken populations in single-step GBLUP. J Anim Sci 2021; 99:6154135. [PMID: 33649764 PMCID: PMC8355479 DOI: 10.1093/jas/skab056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 02/17/2021] [Indexed: 11/13/2022] Open
Abstract
The introduction of animals from a different environment or population is a common practice in commercial livestock populations. In this study, we modeled the inclusion of a group of external birds into a local broiler chicken population for the purpose of genomic evaluations. The pedigree was composed of 242,413 birds and genotypes were available for 107,216 birds. A five-trait model that included one growth, two yield, and two efficiency traits was used for the analyses. The strategies to model the introduction of external birds were to include a fixed effect representing the origin of parents and to use unknown parent groups (UPG) or metafounders (MF). Genomic estimated breeding values (GEBV) were obtained with single-step GBLUP using the Algorithm for Proven and Young. Bias, dispersion, and accuracy of GEBV for the validation birds, that is, from the most recent generation, were computed. The bias and dispersion were estimated with the linear regression (LR) method,whereas accuracy was estimated by the LR method and predictive ability. When fixed UPG were fit without estimated inbreeding, the model did not converge. In contrast, models with fixed UPG and estimated inbreeding or random UPG converged and resulted in similar GEBV. The inclusion of an extra fixed effect in the model made the GEBV unbiased and reduced the inflation. Genomic predictions with MF were slightly biased and inflated due to the unbalanced number of observations assigned to each metafounder. When combining local and external populations, the greatest accuracy can be obtained by adding an extra fixed effect to account for the origin of parents plus UPG with estimated inbreeding or random UPG. To estimate the accuracy, the LR method is more consistent among scenarios, whereas the predictive ability greatly depends on the model specification.
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Affiliation(s)
- Matias Bermann
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
| | - Daniela Lourenco
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
| | - Vivian Breen
- Cobb-Vantress Inc., Siloam Springs, AR 72761, USA
| | | | | | - Ignacy Misztal
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
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19
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Vandenplas J, Eding H, Calus MP. Technical note: Genetic groups in single-step single nucleotide polymorphism best linear unbiased predictor. J Dairy Sci 2021; 104:3298-3303. [DOI: 10.3168/jds.2020-19460] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 10/02/2020] [Indexed: 11/19/2022]
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Gutierrez-Reinoso MA, Aponte PM, Garcia-Herreros M. Genomic Analysis, Progress and Future Perspectives in Dairy Cattle Selection: A Review. Animals (Basel) 2021; 11:599. [PMID: 33668747 PMCID: PMC7996307 DOI: 10.3390/ani11030599] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 11/23/2020] [Accepted: 11/24/2020] [Indexed: 12/16/2022] Open
Abstract
Genomics comprises a set of current and valuable technologies implemented as selection tools in dairy cattle commercial breeding programs. The intensive progeny testing for production and reproductive traits based on genomic breeding values (GEBVs) has been crucial to increasing dairy cattle productivity. The knowledge of key genes and haplotypes, including their regulation mechanisms, as markers for productivity traits, may improve the strategies on the present and future for dairy cattle selection. Genome-wide association studies (GWAS) such as quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), or single-step genomic best linear unbiased prediction (ssGBLUP) methods have already been included in global dairy programs for the estimation of marker-assisted selection-derived effects. The increase in genetic progress based on genomic predicting accuracy has also contributed to the understanding of genetic effects in dairy cattle offspring. However, the crossing within inbred-lines critically increased homozygosis with accumulated negative effects of inbreeding like a decline in reproductive performance. Thus, inaccurate-biased estimations based on empirical-conventional models of dairy production systems face an increased risk of providing suboptimal results derived from errors in the selection of candidates of high genetic merit-based just on low-heritability phenotypic traits. This extends the generation intervals and increases costs due to the significant reduction of genetic gains. The remarkable progress of genomic prediction increases the accurate selection of superior candidates. The scope of the present review is to summarize and discuss the advances and challenges of genomic tools for dairy cattle selection for optimizing breeding programs and controlling negative inbreeding depression effects on productivity and consequently, achieving economic-effective advances in food production efficiency. Particular attention is given to the potential genomic selection-derived results to facilitate precision management on modern dairy farms, including an overview of novel genome editing methodologies as perspectives toward the future.
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Affiliation(s)
- Miguel A. Gutierrez-Reinoso
- Facultad de Ciencias Agropecuarias y Recursos Naturales, Carrera de Medicina Veterinaria, Universidad Técnica de Cotopaxi (UTC), Latacunga 05-0150, Ecuador
- Laboratorio de Biotecnología Animal, Departamento de Ciencia Animal, Facultad de Ciencias Veterinarias, Universidad de Concepción (UdeC), Chillán 3780000, Chile
| | - Pedro M. Aponte
- Colegio de Ciencias Biológicas y Ambientales (COCIBA), Universidad San Francisco de Quito (USFQ), Quito 170157, Ecuador
- Campus Cumbayá, Instituto de Investigaciones en Biomedicina “One-health”, Universidad San Francisco de Quito (USFQ), Quito 170157, Ecuador
| | - Manuel Garcia-Herreros
- Instituto Nacional de Investigação Agrária e Veterinária (INIAV), 2005-048 Santarém, Portugal
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21
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Masuda Y, Tsuruta S, Bermann M, Bradford HL, Misztal I. Comparison of models for missing pedigree in single-step genomic prediction. J Anim Sci 2021; 99:6119644. [PMID: 33493284 DOI: 10.1093/jas/skab019] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 01/20/2021] [Indexed: 11/14/2022] Open
Abstract
Pedigree information is often missing for some animals in a breeding program. Unknown-parent groups (UPGs) are assigned to the missing parents to avoid biased genetic evaluations. Although the use of UPGs is well established for the pedigree model, it is unclear how UPGs are integrated into the inverse of the unified relationship matrix (H-inverse) required for single-step genomic best linear unbiased prediction. A generalization of the UPG model is the metafounder (MF) model. The objectives of this study were to derive 3 H-inverses and to compare genetic trends among models with UPG and MF H-inverses using a simulated purebred population. All inverses were derived using the joint density function of the random breeding values and genetic groups. The breeding values of genotyped animals (u2) were assumed to be adjusted for UPG effects (g) using matrix Q2 as u2∗=u2+Q2g before incorporating genomic information. The Quaas-Pollak-transformed (QP) H-inverse was derived using a joint density function of u2∗ and g updated with genomic information and assuming nonzero cov(u2∗,g'). The modified QP (altered) H-inverse also assumes that the genomic information updates u2∗ and g, but cov(u2∗,g')=0. The UPG-encapsulated (EUPG) H-inverse assumed genomic information updates the distribution of u2∗. The EUPG H-inverse had the same structure as the MF H-inverse. Fifty percent of the genotyped females in the simulation had a missing dam, and missing parents were replaced with UPGs by generation. The simulation study indicated that u2∗ and g in models using the QP and altered H-inverses may be inseparable leading to potential biases in genetic trends. Models using the EUPG and MF H-inverses showed no genetic trend biases. These 2 H-inverses yielded the same genomic EBV (GEBV). The predictive ability and inflation of GEBVs from young genotyped animals were nearly identical among models using the QP, altered, EUPG, and MF H-inverses. Although the choice of H-inverse in real applications with enough data may not result in biased genetic trends, the EUPG and MF H-inverses are to be preferred because of theoretical justification and possibility to reduce biases.
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Affiliation(s)
- Yutaka Masuda
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
| | - Shogo Tsuruta
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
| | - Matias Bermann
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
| | - Heather L Bradford
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Ignacy Misztal
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
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22
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Junqueira VS, Lopes PS, Lourenco D, Silva FFE, Cardoso FF. Applying the Metafounders Approach for Genomic Evaluation in a Multibreed Beef Cattle Population. Front Genet 2021; 11:556399. [PMID: 33424914 PMCID: PMC7793833 DOI: 10.3389/fgene.2020.556399] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 10/29/2020] [Indexed: 11/23/2022] Open
Abstract
Pedigree information is incomplete by nature and commonly not well-established because many of the genetic ties are not known a priori or can be wrong. The genomic era brought new opportunities to assess relationships between individuals. However, when pedigree and genomic information are used simultaneously, which is the case of single-step genomic BLUP (ssGBLUP), defining the genetic base is still a challenge. One alternative to overcome this challenge is to use metafounders, which are pseudo-individuals that describe the genetic relationship between the base population individuals. The purpose of this study was to evaluate the impact of metafounders on the estimation of breeding values for tick resistance under ssGBLUP for a multibreed population composed by Hereford, Braford, and Zebu animals. Three different scenarios were studied: pedigree-based model (BLUP), ssGBLUP, and ssGBLUP with metafounders (ssGBLUPm). In ssGBLUPm, a total of four different metafounders based on breed of origin (i.e., Hereford, Braford, Zebu, and unknown) were included for the animals with missing parents. The relationship coefficient between metafounders was in average 0.54 (ranging from 0.34 to 0.96) suggesting an overlap between ancestor populations. The estimates of metafounder relationships indicate that Hereford and Zebu breeds have a possible common ancestral relationship. Inbreeding coefficients calculated following the metafounder approach had less negative values, suggesting that ancestral populations were large enough and that gametes inherited from the historical population were not identical. Variance components were estimated based on ssGBLUPm, ssGBLUP, and BLUP, but the values from ssGBLUPm were scaled to provide a fair comparison with estimates from the other two models. In general, additive, residual, and phenotypic variance components in the Hereford population were smaller than in Braford across different models. The addition of genomic information increased heritability for Hereford, possibly because of improved genetic relationships. As expected, genomic models had greater predictive ability, with an additional gain for ssGBLUPm over ssGBLUP. The increase in predictive ability was greater for Herefords. Our results show the potential of using metafounders to increase accuracy of GEBV, and therefore, the rate of genetic gain in beef cattle populations with partial levels of missing pedigree information.
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Affiliation(s)
- Vinícius Silva Junqueira
- Departamento de Zootecnia, Universidade Federal de Viçosa, Viçosa, Brazil.,Breeding Research Department, Bayer Crop Science, Uberlândia, Brazil
| | - Paulo Sávio Lopes
- Departamento de Zootecnia, Universidade Federal de Viçosa, Viçosa, Brazil
| | - Daniela Lourenco
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, United States
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Meyer K. Impact of missing pedigrees in single-step genomic evaluation. ANIMAL PRODUCTION SCIENCE 2021. [DOI: 10.1071/an21045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Context
A common problem in mixed model-based genetic evaluation schemes for livestock is that cohorts of animals differ systematically in mean genetic merit, for example, due to missing pedigree. This can be modelled by fitting genetic groups. Single-step genomic evaluation (ssGBLUP) combining information from genotyped and non-genotyped individuals has become routine, but little is known of the effects of unknown parents in this context.
Aims
To investigate the effects of missing pedigrees on accuracy and bias of predicted breeding values for ssGBLUP analyses.
Methods
A simulation study was used to examine alternative ways to account for genetic groups in ssGBLUP, for multi-generation data with strong selection and rapidly increasing numbers of genotyped animals in the most recent generations.
Key results
Results demonstrated that missing pedigrees can markedly impair predicted breeding values. With selection, alignment of genomic and pedigree relationship matrices is essential when fitting unknown parent groups (UPG). Genomic relationships are complete; that is, they ‘automatically’ reference the genomic base, which typically differs from the genetic base for pedigreed animals. This can lead to biased comparisons between genotyped and non-genotyped animals with unknown parents when the two categories of animals are assigned to the same UPG. Allocating genotyped individuals to a separate UPG across all generations for each strain or breed was shown to be a simple and effective way to reduce misalignment bias. In contrast, fitting metafounders modified pedigree-based relationships to account for ancestral genomic relationships and inbreeding rather than the genomic relationship matrix. Thus, no bias due to different types of animals assigned to the same metafounders was apparent. Overall, fitting metafounders yielded slightly higher correlations between true and predicted breeding values than did UPG models, which assume genetic groups to be unrelated.
Conclusions
Missing pedigrees are more problematic with ssGBLUP than for analyses considering pedigree-based relationships only. UPG models with separation of genotyped and non-genotyped individuals and analyses fitting metafounders yielded comparable predictions of breeding values in terms of accuracy and bias.
Implications
A previously unidentified incompatibility between alignment of founder populations and assignment of genotyped and non-genotyped animals to the same UPG has been reported. Implementation of the proposed strategy to reduce ‘double counting’ is straightforward and can improve results of ssGBLUP analyses.
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Bermann M, Lourenco D, Misztal I. Technical note: Automatic scaling in single-step genomic BLUP. J Dairy Sci 2020; 104:2027-2031. [PMID: 33309381 DOI: 10.3168/jds.2020-18969] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 09/16/2020] [Indexed: 11/19/2022]
Abstract
Single-step genomic BLUP (ssGBLUP) requires compatibility between genomic and pedigree relationships for unbiased and accurate predictions. Scaling the genomic relationship matrix (G) to have the same averages as the pedigree relationship matrix (i.e., scaling by averages) is one way to ensure compatibility. This requires computing both relationship matrices, calculating averages, and changing G, whereas only the inverses of those matrices are needed in the mixed model equations. Therefore, the compatibility process can add extra computing burden. In the single-step Bayesian regression, the scaling is done by including a mean (μg) as a fixed effect in the model. The parameter μg can be interpreted as the average of the breeding values of the genotyped animals. In this study, such scaling, called automatic, was implemented in ssGBLUP via Quaas-Pollak transformation of the inverse of the relationship matrix used in ssGBLUP (H), which combines the inverses of the pedigree and genomic relationship matrices. Comparisons involved a simulated data set, and the genomic relationship matrix was computed using different allele frequencies either from the current population (i.e., realized allele frequencies), equal among all the loci, or from the base population. For all of the scenarios, we computed bias [defined as the average difference between true breeding values (TBV) and genomic estimated breeding values (GEBV)], accuracy (defined as the correlation between TBV and GEBV), and dispersion (defined as the regression coefficient of GEBV on TBV). With no scaling, the bias expressed in terms of genetic standard deviations was 0.86, 0.64, and 0.58 with realized, equal, and base population allele frequencies, respectively. With scaling by averages, which is currently used in ssGBLUP, bias was 0.07, 0.08, and 0.03, respectively. With automatic scaling, bias was 0.18 regardless of allele frequencies. Accuracies were similar among scaling methods, but about 0.1 lower in the scenario without scaling. The GEBV were more inflated without any scaling, whereas the automatic scaling performed similarly to the scaling by averages. The average dispersion for those methods was 0.94. When μg was treated as random, with the variance equal to differences between pedigree and genomic relationships, the bias was the same as with the scaling by averages. The automatic scaling is biased, especially when μg is treated as a fixed effect. The bias may be small in real data with fewer generations, when traits are undergoing weak selection, or when the number of genotyped animals is large.
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Affiliation(s)
- M Bermann
- Department of Animal and Dairy Science, University of Georgia, Athens, 30602.
| | - D Lourenco
- Department of Animal and Dairy Science, University of Georgia, Athens, 30602
| | - I Misztal
- Department of Animal and Dairy Science, University of Georgia, Athens, 30602
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Tsuruta S, Lawlor TJ, Lourenco DAL, Misztal I. Bias in genomic predictions by mating practices for linear type traits in a large-scale genomic evaluation. J Dairy Sci 2020; 104:662-677. [PMID: 33162076 DOI: 10.3168/jds.2020-18668] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 08/19/2020] [Indexed: 12/17/2022]
Abstract
The objective of this study was to clarify how bias in genomic predictions is created by investigating a relationship among selection intensity, a change in heritability (Δh2), and assortative mating (ASM). A change in heritability, resulting from selection, reflects the impact that the Bulmer effect has on the reduction in between-family variation, whereas assortative mating impacts the within-family variance or Mendelian sampling variation. A partial data set up to 2014, including 841K genotyped animals, was used to calculate genomic predictions with a single-step genomic model for 18 linear type traits in US Holsteins. A full data set up to 2018, including 2.3 million genotyped animals, was used to calculate benchmark genomic predictions. Inbreeding and unknown parent groups for missing parents of animals were included in the model. Genomic evaluation was performed using 2 different genetic parameters: those estimated 14 yr ago, which have been used in the national genetic evaluation for linear type traits in the United States, and those newly estimated with recent records from 2015 to 2018 and those corresponding pedigrees. Genetic trends for 18 type traits were estimated for bulls with daughters and cows with phenotypes in 2018. Based on selection intensity and mating decisions, these traits can be categorized into 3 groups: (a) high directional selection, (b) moderate selection, and (c) intermediate optimum selection. The first 2 categories can be explained by positive assortative mating, and the last can be explained by negative assortative or disassortative mating. Genetic progress was defined by genetic gain per year based on average standardized genomic predictions for cows from 2000 to 2014. Traits with more genetic progress tended to have more "inflated" genomic predictions (i.e., "inflation" means here that genomic predictions are larger in absolute values than expected, whereas "deflation" means smaller than expected). Heritability estimates for 14 out of 18 traits declined in the last 16 yr, and Δh2 ranged from -0.09 to 0.04. Traits with a greater decline in heritability tended to have more deflated genomic predictions. Biases (inflation or deflation) in genomic predictions were not improved by using the latest genetic parameters, implying that bias in genomic predictions due to preselection was not substantial for a large-scale genomic evaluation. Moreover, the strong selection intensity was not fully responsible for bias in genomic predictions. The directional selection can decrease heritability; however, positive assortative mating, which was strongly associated with large genetic gains, could minimize the decline in heritability for a trait under strong selection and could affect bias in genomic predictions.
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Affiliation(s)
- S Tsuruta
- Animal and Dairy Science Department, University of Georgia, Athens 30602.
| | - T J Lawlor
- Holstein Association USA Inc., Brattleboro, VT 05301
| | - D A L Lourenco
- Animal and Dairy Science Department, University of Georgia, Athens 30602
| | - I Misztal
- Animal and Dairy Science Department, University of Georgia, Athens 30602
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Garcia ALS, Masuda Y, Tsuruta S, Miller S, Misztal I, Lourenco D. Indirect predictions with a large number of genotyped animals using the algorithm for proven and young. J Anim Sci 2020; 98:5831156. [PMID: 32374831 PMCID: PMC7263398 DOI: 10.1093/jas/skaa154] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 04/30/2020] [Indexed: 11/21/2022] Open
Abstract
Reliable single-nucleotide polymorphisms (SNP) effects from genomic best linear unbiased prediction BLUP (GBLUP) and single-step GBLUP (ssGBLUP) are needed to calculate indirect predictions (IP) for young genotyped animals and animals not included in official evaluations. Obtaining reliable SNP effects and IP requires a minimum number of animals and when a large number of genotyped animals are available, the algorithm for proven and young (APY) may be needed. Thus, the objectives of this study were to evaluate IP with an increasingly larger number of genotyped animals and to determine the minimum number of animals needed to compute reliable SNP effects and IP. Genotypes and phenotypes for birth weight, weaning weight, and postweaning gain were provided by the American Angus Association. The number of animals with phenotypes was more than 3.8 million. Genotyped animals were assigned to three cumulative year-classes: born until 2013 (N = 114,937), born until 2014 (N = 183,847), and born until 2015 (N = 280,506). A three-trait model was fitted using the APY algorithm with 19,021 core animals under two scenarios: 1) core 2013 (random sample of animals born until 2013) used for all year-classes and 2) core 2014 (random sample of animals born until 2014) used for year-class 2014 and core 2015 (random sample of animals born until 2015) used for year-class 2015. GBLUP used phenotypes from genotyped animals only, whereas ssGBLUP used all available phenotypes. SNP effects were predicted using genomic estimated breeding values (GEBV) from either all genotyped animals or only core animals. The correlations between GEBV from GBLUP and IP obtained using SNP effects from core 2013 were ≥0.99 for animals born in 2013 but as low as 0.07 for animals born in 2014 and 2015. Conversely, the correlations between GEBV from ssGBLUP and IP were ≥0.99 for animals born in all years. IP predictive abilities computed with GEBV from ssGBLUP and SNP predictions based on only core animals were as high as those based on all genotyped animals. The correlations between GEBV and IP from ssGBLUP were ≥0.76, ≥0.90, and ≥0.98 when SNP effects were computed using 2k, 5k, and 15k core animals. Suitable IP based on GEBV from GBLUP can be obtained when SNP predictions are based on an appropriate number of core animals, but a considerable decline in IP accuracy can occur in subsequent years. Conversely, IP from ssGBLUP based on large numbers of phenotypes from non-genotyped animals have persistent accuracy over time.
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Affiliation(s)
- Andre L S Garcia
- Department of Animal and Dairy Science, University of Georgia, Athens, GA
| | - Yutaka Masuda
- Department of Animal and Dairy Science, University of Georgia, Athens, GA
| | - Shogo Tsuruta
- Department of Animal and Dairy Science, University of Georgia, Athens, GA
| | | | - Ignacy Misztal
- Department of Animal and Dairy Science, University of Georgia, Athens, GA
| | - Daniela Lourenco
- Department of Animal and Dairy Science, University of Georgia, Athens, GA
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Macedo FL, Christensen OF, Astruc JM, Aguilar I, Masuda Y, Legarra A. Bias and accuracy of dairy sheep evaluations using BLUP and SSGBLUP with metafounders and unknown parent groups. Genet Sel Evol 2020; 52:47. [PMID: 32787772 PMCID: PMC7425573 DOI: 10.1186/s12711-020-00567-1] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 08/04/2020] [Indexed: 11/29/2022] Open
Abstract
Background Bias has been reported in genetic or genomic evaluations of several species. Common biases are systematic differences between averages of estimated and true breeding values, and their over- or under-dispersion. In addition, comparing accuracies of pedigree versus genomic predictions is a difficult task. This work proposes to analyse biases and accuracies in the genetic evaluation of milk yield in Manech Tête Rousse dairy sheep, over several years, by testing five models and using the estimators of the linear regression method. We tested models with and without genomic information [best linear unbiased prediction (BLUP) and single-step genomic BLUP (SSGBLUP)] and using three strategies to handle missing pedigree [unknown parent groups (UPG), UPG with QP transformation in the \documentclass[12pt]{minimal}
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\begin{document}$${\mathbf{H}}$$\end{document}H matrix (EUPG) and metafounders (MF)]. Methods We compared estimated breeding values (EBV) of selected rams at birth with the EBV of the same rams obtained each year from the first daughters with phenotypes up to 2017. We compared within and across models. Finally, we compared EBV at birth of the rams with and without genomic information. Results Within models, bias and over-dispersion were small (bias: 0.20 to 0.40 genetic standard deviations; slope of the dispersion: 0.95 to 0.99) except for model SSGBLUP-EUPG that presented an important over-dispersion (0.87). The estimates of accuracies confirm that the addition of genomic information increases the accuracy of EBV in young rams. The smallest bias was observed with BLUP-MF and SSGBLUP-MF. When we estimated dispersion by comparing a model with no markers to models with markers, SSGBLUP-MF showed a value close to 1, indicating that there was no problem in dispersion, whereas SSGBLUP-EUPG and SSGBLUP-UPG showed a significant under-dispersion. Another important observation was the heterogeneous behaviour of the estimates over time, which suggests that a single check could be insufficient to make a good analysis of genetic/genomic evaluations. Conclusions The addition of genomic information increases the accuracy of EBV of young rams in Manech Tête Rousse. In this population that has missing pedigrees, the use of UPG and EUPG in SSGBLUP produced bias, whereas MF yielded unbiased estimates, and we recommend its use. We also recommend assessing biases and accuracies using multiple truncation points, since these statistics are subject to random variation across years.
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Affiliation(s)
- Fernando L Macedo
- GenPhySE, INRAE, 31326, Castanet Tolosan, France. .,Facultad de Veterinaria, UdelaR, A. Lasplaces 1620, Montevideo, Uruguay.
| | - Ole F Christensen
- Center for Quantitative Genetics and Genomics, Blichers Allé 20, 8830, Tjele, Denmark
| | | | - Ignacio Aguilar
- Instituto Nacional de Investigación Agropecuaria, Montevideo, Uruguay
| | - Yutaka Masuda
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
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Kudinov A, Mäntysaari E, Aamand G, Uimari P, Strandén I. Metafounder approach for single-step genomic evaluations of Red Dairy cattle. J Dairy Sci 2020; 103:6299-6310. [DOI: 10.3168/jds.2019-17483] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 03/09/2020] [Indexed: 01/01/2023]
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Granado-Tajada I, Legarra A, Ugarte E. Exploring the inclusion of genomic information and metafounders in Latxa dairy sheep genetic evaluations. J Dairy Sci 2020; 103:6346-6353. [DOI: 10.3168/jds.2019-18033] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 02/25/2020] [Indexed: 11/19/2022]
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