1
|
Gore D, Okeno T, Muasya T, Mburu J. Improved response to selection in dairy goat breeding programme through reproductive technology and genomic selection in the tropics. Small Rumin Res 2021. [DOI: 10.1016/j.smallrumres.2021.106397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
2
|
Fuerst-Waltl B, Fuerst C, Obritzhauser W, Egger-Danner C. Sustainable breeding objectives and possible selection response: Finding the balance between economics and breeders’ preferences. J Dairy Sci 2016; 99:9796-9809. [DOI: 10.3168/jds.2016-11095] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Accepted: 08/09/2016] [Indexed: 11/19/2022]
|
3
|
Pryce JE, Parker Gaddis KL, Koeck A, Bastin C, Abdelsayed M, Gengler N, Miglior F, Heringstad B, Egger-Danner C, Stock KF, Bradley AJ, Cole JB. Invited review: Opportunities for genetic improvement of metabolic diseases. J Dairy Sci 2016; 99:6855-6873. [PMID: 27372587 DOI: 10.3168/jds.2016-10854] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Accepted: 05/26/2016] [Indexed: 02/01/2023]
Abstract
Metabolic disorders are disturbances to one or more of the metabolic processes in dairy cattle. Dysfunction of any of these processes is associated with the manifestation of metabolic diseases or disorders. In this review, data recording, incidences, genetic parameters, predictors, and status of genetic evaluations were examined for (1) ketosis, (2) displaced abomasum, (3) milk fever, and (4) tetany, as these are the most prevalent metabolic diseases where published genetic parameters are available. The reported incidences of clinical cases of metabolic disorders are generally low (less than 10% of cows are recorded as having a metabolic disease per herd per year or parity/lactation). Heritability estimates are also low and are typically less than 5%. Genetic correlations between metabolic traits are mainly positive, indicating that selection to improve one of these diseases is likely to have a positive effect on the others. Furthermore, there may also be opportunities to select for general disease resistance in terms of metabolic stability. Although there is inconsistency in published genetic correlation estimates between milk yield and metabolic traits, selection for milk yield may be expected to lead to a deterioration in metabolic disorders. Under-recording and difficulty in diagnosing subclinical cases are among the reasons why interest is growing in using easily measurable predictors of metabolic diseases, either recorded on-farm by using sensors and milk tests or off-farm using data collected from routine milk recording. Some countries have already initiated genetic evaluations of metabolic disease traits and currently most of these use clinical observations of disease. However, there are opportunities to use clinical diseases in addition to predictor traits and genomic information to strengthen genetic evaluations for metabolic health in the future.
Collapse
Affiliation(s)
- J E Pryce
- Department of Economic Developments, Jobs, Transport and Resources and La Trobe University, Agribio, 5 Ring Road, Bundoora, VIC 3083, Australia.
| | - K L Parker Gaddis
- Department of Animal Sciences, University of Florida, Gainesville 32611
| | - A Koeck
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - C Bastin
- Agriculture, Bio-engineering and Chemistry Department, Gembloux Agro-Bio Tech, University of Liège, B-5030 Gembloux, Belgium
| | - M Abdelsayed
- Holstein Australia, 24-36 Camberwell Road, Hawthorn East, Victoria, 3122, Australia
| | - N Gengler
- Agriculture, Bio-engineering and Chemistry Department, Gembloux Agro-Bio Tech, University of Liège, B-5030 Gembloux, Belgium
| | - F Miglior
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G 2W1, Canada; Canadian Dairy Network, Guelph, ON, N1K 1E5, Canada
| | - B Heringstad
- Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, PO Box 5003, NO-1432 Ås, Norway
| | - C Egger-Danner
- ZuchtData EDV-Dienstleistungen GmbH, Dresdner Str. 89/19, A-1200 Vienna, Austria
| | - K F Stock
- Vereinigte Informationssysteme Tierhaltung w.V. (vit), Heinrich-Schroeder-Weg 1, D-27283 Verden, Germany
| | - A J Bradley
- University of Nottingham, School of Veterinary Medicine and Science, Sutton Bonington Campus, Sutton Bonington, Leicestershire, LE12 5RD, United Kingdom, and; Quality Milk Management Services Ltd., Cedar Barn, Easton Hill, Easton, Wells, Somerset, BA5 1EY, United Kingdom
| | - J B Cole
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705
| |
Collapse
|
4
|
Naderi S, Yin T, König S. Random forest estimation of genomic breeding values for disease susceptibility over different disease incidences and genomic architectures in simulated cow calibration groups. J Dairy Sci 2016; 99:7261-7273. [PMID: 27344385 DOI: 10.3168/jds.2016-10887] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2016] [Accepted: 05/23/2016] [Indexed: 11/19/2022]
Abstract
A simulation study was conducted to investigate the performance of random forest (RF) and genomic BLUP (GBLUP) for genomic predictions of binary disease traits based on cow calibration groups. Training and testing sets were modified in different scenarios according to disease incidence, the quantitative-genetic background of the trait (h(2)=0.30 and h(2)=0.10), and the genomic architecture [725 quantitative trait loci (QTL) and 290 QTL, populations with high and low levels of linkage disequilibrium (LD)]. For all scenarios, 10,005 SNP (depicting a low-density 10K SNP chip) and 50,025 SNP (depicting a 50K SNP chip) were evenly spaced along 29 chromosomes. Training and testing sets included 20,000 cows (4,000 sick, 16,000 healthy, disease incidence 20%) from the last 2 generations. Initially, 4,000 sick cows were assigned to the testing set, and the remaining 16,000 healthy cows represented the training set. In the ongoing allocation schemes, the number of sick cows in the training set increased stepwise by moving 10% of the sick animals from the testing set to the training set, and vice versa. The size of the training and testing sets was kept constant. Evaluation criteria for both GBLUP and RF were the correlations between genomic breeding values and true breeding values (prediction accuracy), and the area under the receiving operating characteristic curve (AUROC). Prediction accuracy and AUROC increased for both methods and all scenarios as increasing percentages of sick cows were allocated to the training set. Highest prediction accuracies were observed for disease incidences in training sets that reflected the population disease incidence of 0.20. For this allocation scheme, the largest prediction accuracies of 0.53 for RF and of 0.51 for GBLUP, and the largest AUROC of 0.66 for RF and of 0.64 for GBLUP, were achieved using 50,025 SNP, a heritability of 0.30, and 725 QTL. Heritability decreases from 0.30 to 0.10 and QTL reduction from 725 to 290 were associated with decreasing prediction accuracy and decreasing AUROC for all scenarios. This decrease was more pronounced for RF. Also, the increase of LD had stronger effect on RF results than on GBLUP results. The highest prediction accuracy from the low LD scenario was 0.30 from RF and 0.36 from GBLUP, and increased to 0.39 for both methods in the high LD population. Random forest successfully identified important SNP in close map distance to QTL explaining a high proportion of the phenotypic trait variations.
Collapse
Affiliation(s)
- S Naderi
- Department of Animal Breeding, University of Kassel, 37213 Witzenhausen, Germany
| | - T Yin
- Department of Animal Breeding, University of Kassel, 37213 Witzenhausen, Germany
| | - S König
- Department of Animal Breeding, University of Kassel, 37213 Witzenhausen, Germany.
| |
Collapse
|
5
|
Egger-Danner C, Cole JB, Pryce JE, Gengler N, Heringstad B, Bradley A, Stock KF. Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits. Animal 2015; 9:191-207. [PMID: 25387784 PMCID: PMC4299537 DOI: 10.1017/s1751731114002614] [Citation(s) in RCA: 145] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2014] [Accepted: 09/11/2014] [Indexed: 12/26/2022] Open
Abstract
For several decades, breeding goals in dairy cattle focussed on increased milk production. However, many functional traits have negative genetic correlations with milk yield, and reductions in genetic merit for health and fitness have been observed. Herd management has been challenged to compensate for these effects and to balance fertility, udder health and metabolic diseases against increased production to maximize profit without compromising welfare. Functional traits, such as direct information on cow health, have also become more important because of growing concern about animal well-being and consumer demands for healthy and natural products. There are major concerns about the impact of drugs used in veterinary medicine on the spread of antibiotic-resistant strains of bacteria that can negatively impact human health. Sustainability and efficiency are also increasingly important because of the growing competition for high-quality, plant-based sources of energy and protein. Disruptions to global environments because of climate change may encourage yet more emphasis on these traits. To be successful, it is vital that there be a balance between the effort required for data recording and subsequent benefits. The motivation of farmers and other stakeholders involved in documentation and recording is essential to ensure good data quality. To keep labour costs reasonable, existing data sources should be used as much as possible. Examples include the use of milk composition data to provide additional information about the metabolic status or energy balance of the animals. Recent advances in the use of mid-infrared spectroscopy to measure milk have shown considerable promise, and may provide cost-effective alternative phenotypes for difficult or expensive-to-measure traits, such as feed efficiency. There are other valuable data sources in countries that have compulsory documentation of veterinary treatments and drug use. Additional sources of data outside of the farm include, for example, slaughter houses (meat composition and quality) and veterinary labs (specific pathogens, viral loads). At the farm level, many data are available from automated and semi-automated milking and management systems. Electronic devices measuring physiological status or activity parameters can be used to predict events such as oestrus, and also behavioural traits. Challenges concerning the predictive biology of indicator traits or standardization need to be solved. To develop effective selection programmes for new traits, the development of large databases is necessary so that high-reliability breeding values can be estimated. For expensive-to-record traits, extensive phenotyping in combination with genotyping of females is a possibility.
Collapse
Affiliation(s)
- C. Egger-Danner
- ZuchtData EDV-Dienstleistungen GmbH, Dresdner Str.
89/19, A-1200 Vienna, Austria
| | - J. B. Cole
- Animal Genomics and Improvement Laboratory,
ARS, USDA, 10300 Baltimore
Avenue, Beltsville, MD 20705-2350,
USA
| | - J. E. Pryce
- Department of Environment and Primary Industries, La
Trobe University, Agribio, 5 Ring
Road, Bundoora, Victoria 3083,
Australia
| | - N. Gengler
- University of Liège, Gembloux Agro-Bio Tech
(GxABT), Animal Science Unit, Passage des
Déportés 2, B-5030 Gembloux, Belgium
| | - B. Heringstad
- Department of Animal and Aquacultural Sciences,
Norwegian University of Life Sciences, PO Box
5003, N-1432 Ås, Norway
| | - A. Bradley
- Quality Milk Management Services Ltd, Cedar
Barn, Easton Hill, Easton,
Wells, Somerset, BA5
1EY, UK
- University of Nottingham, School of Veterinary
Medicine and Science, Sutton Bonington Campus,
Sutton Bonington, Leicestershire,
LE12 5RD, UK
| | - K. F. Stock
- Vereinigte Informationssysteme Tierhaltung w.V. (vit),
Heideweg 1, D-27283 Verden,
Germany
| |
Collapse
|
6
|
Egger-Danner C, Schwarzenbacher H, Willam A. Short communication: Genotyping of cows to speed up availability of genomic estimated breeding values for direct health traits in Austrian Fleckvieh (Simmental) cattle--genetic and economic aspects. J Dairy Sci 2014; 97:4552-6. [PMID: 24835973 DOI: 10.3168/jds.2013-7661] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2013] [Accepted: 03/26/2014] [Indexed: 11/19/2022]
Abstract
The aim of this study was to quantify the impact of genotyping cows with reliable phenotypes for direct health traits on annual monetary genetic gain (AMGG) and discounted profit. The calculations were based on a deterministic approach using ZPLAN software (University of Hohenheim, Stuttgart, Germany). It was assumed that increases in reliability of the total merit index (TMI) of 5, 15, and 25 percentage points were achieved through genotyping 5,000, 25,000, and 50,000 cows, respectively. Costs for phenotyping, genotyping, and genomic estimated breeding values vary between €150 and €20 per cow. The gain in genotyping cows for traits with medium to high heritability is more than for direct health traits with low heritability. The AMGG is increased by 1.5% if the reliability of TMI is 5 percentage points higher (i.e., 5,000 cows genotyped) and 6.53% higher AMGG can be expected when the reliability of TMI is increased by 25 percentage points (i.e., 50,000 cows genotyped). The discounted profit depends not only on the costs of genotyping but also on the population size. This study indicates that genotyping cows with reliable phenotypes is feasible to speed up the availability of genomic estimated breeding values for direct health traits. But, because of the huge amount of valid phenotypes and genotypes needed to establish an efficient genomic evaluation, it is likely that financial constraints will be the main limiting factor for implementation into breeding program such as Fleckvieh Austria.
Collapse
Affiliation(s)
- C Egger-Danner
- ZuchtData EDV-Dienstleistungen GmbH, Dresdner Str. 89/19, 1200 Vienna, Austria.
| | - H Schwarzenbacher
- ZuchtData EDV-Dienstleistungen GmbH, Dresdner Str. 89/19, 1200 Vienna, Austria
| | - A Willam
- Division of Livestock Sciences, Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences Vienna, Gregor-Mendel-Str. 33, 1180 Vienna, Austria
| |
Collapse
|
7
|
Shumbusho F, Raoul J, Astruc JM, Palhiere I, Elsen JM. Potential benefits of genomic selection on genetic gain of small ruminant breeding programs. J Anim Sci 2013; 91:3644-57. [PMID: 23736059 DOI: 10.2527/jas.2012-6205] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
In conventional small ruminant breeding programs, only pedigree and phenotype records are used to make selection decisions but prospects of including genomic information are now under consideration. The objective of this study was to assess the potential benefits of genomic selection on the genetic gain in French sheep and goat breeding designs of today. Traditional and genomic scenarios were modeled with deterministic methods for 3 breeding programs. The models included decisional variables related to male selection candidates, progeny testing capacity, and economic weights that were optimized to maximize annual genetic gain (AGG) of i) a meat sheep breeding program that improved a meat trait of heritability (h(2)) = 0.30 and a maternal trait of h(2) = 0.09 and ii) dairy sheep and goat breeding programs that improved a milk trait of h(2) = 0.30. Values of ±0.20 of genetic correlation between meat and maternal traits were considered to study their effects on AGG. The Bulmer effect was accounted for and the results presented here are the averages of AGG after 10 generations of selection. Results showed that current traditional breeding programs provide an AGG of 0.095 genetic standard deviation (σa) for meat and 0.061 σa for maternal trait in meat breed and 0.147 σa and 0.120 σa in sheep and goat dairy breeds, respectively. By optimizing decisional variables, the AGG with traditional selection methods increased to 0.139 σa for meat and 0.096 σa for maternal traits in meat breeding programs and to 0.174 σa and 0.183 σa in dairy sheep and goat breeding programs, respectively. With a medium-sized reference population (nref) of 2,000 individuals, the best genomic scenarios gave an AGG that was 17.9% greater than with traditional selection methods with optimized values of decisional variables for combined meat and maternal traits in meat sheep, 51.7% in dairy sheep, and 26.2% in dairy goats. The superiority of genomic schemes increased with the size of the reference population and genomic selection gave the best results when nref > 1,000 individuals for dairy breeds and nref > 2,000 individuals for meat breed. Genetic correlation between meat and maternal traits had a large impact on the genetic gain of both traits. Changes in AGG due to correlation were greatest for low heritable maternal traits. As a general rule, AGG was increased both by optimizing selection designs and including genomic information.
Collapse
Affiliation(s)
- F Shumbusho
- Institut de l'Elevage, F-31321 Castanet-Tolosan, France.
| | | | | | | | | |
Collapse
|
8
|
|