Multiple-trait multiple-country genetic evaluation of Holstein bulls for female fertility and milk production traits

Nilforooshan, Mohammad Ali; Jakobsen, Jette; Fikse, W.F.; Berglund, B.; Jorjani, H.

doi:10.1017/s1751731114000895

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…The method of Sullivan and Wilton (2001) for the derivation of multi-trait independent EDC factors is presented in Appendix 2. Despite many studies performed on MT-MACE (Schaeffer, 2001;Sullivan and Wilton, 2001;Sullivan et al, 2005;Mark and Sullivan, 2006;Nilforooshan et al, 2010aNilforooshan et al, , 2014, it has never been officially adopted for IGE.…”

Section: Multiple Trait Per Country Macementioning

confidence: 99%

“…Mäntysaari et al (2010) proposed a validation procedure for national genomic evaluations in GMACE. This procedure is well-explained and tested by Nilforooshan et al (2010b).…”

Section: Genomic Macementioning

confidence: 99%

“…Validation of NGE with emphasis on genomic preselection and empirical estimates of its bias is necessary. Methods are already in place for this (Mäntysaari et al, 2010;Nilforooshan et al, 2010b;Tyrisevä et al, 2018); however, further research is necessary. The extent of genomic preselection in breeds other than Holstein-Friesian does not seem to be at a level to rule out MACE for those breeds.…”

Section: Future Of Mace and Igementioning

confidence: 99%

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Invited review: A quarter of a century—International genetic evaluation of dairy sires using MACE methodology

Nilforooshan

Jorjani

2022

Journal of Dairy Science

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For the past few decades, the international exchange of genetic materials has accelerated. This acceleration has been more substantial for dairy cattle compared with other species. The industry faced the need to put international genetic evaluation (IGE) systems in place. The Interbull Centre has been conducting IGE for various dairy cattle breeds and traits. This study reviews the past and the current status of IGE for dairy cattle, emphasizing the most prominent and wellestablished method of IGE, namely multiple acrosscountry evaluation (MACE), and the challenges that should be addressed in the future of IGE. The first IGE methods were simple conversion equations. Only a limited number of common bulls between pairs of countries were considered. These bulls were a biased sample of highly selected animals, with their daughters under preferential treatment in the importing countries. Genetic relationships among animals were not considered either. The MACE method was the first IGE method based on mixed-model theory that could handle genotype by environment interaction (G × E) between countries. The G × E between countries is handled by treating the same trait in different countries as different traits, with genetic correlations less than unity between the traits. The G × E between countries is not solely due to different genetic expressions in different environments (countries), but is also attributable to different units or ways of measuring the trait, data editing, and statistical approaches and models used in different countries. The MACE method also considers different genetic means, genetic groups for unknown parents, heterogeneous genetic and residual variances among countries, and heterogeneous residual variances (precision weights for observations) within countries. Other IGE methods that came after MACE are rooted in MACE. The genomic revolution of the industry created new needs and opportunities. However, an unwanted aspect of it was genomic preselection bias. Genomic preselection causes directional information loss from pre-culled animals (bias) in statistical models for genetic and genomic evaluations, and preselected progeny of a mating are no longer a random sample of possible progeny from that mating. National genetic evaluations without genotypes are input to MACE, and biases in national evaluations are propagated internationally through MACE. Genomic preselection for the Holstein breed is a source of concern for introducing bias to MACE, especially when genomic preselection is practiced intensively in the population. However, MACE continues to be useful for other breeds, among other species, or for non-IGE purposes. Future methods will need to make optimum use of genomic information and be free of genomic preselection bias.

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Section: Multiple Trait Per Country Macementioning

confidence: 99%

“…Mäntysaari et al (2010) proposed a validation procedure for national genomic evaluations in GMACE. This procedure is well-explained and tested by Nilforooshan et al (2010b).…”

Section: Genomic Macementioning

confidence: 99%

Section: Future Of Mace and Igementioning

confidence: 99%

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Invited review: A quarter of a century—International genetic evaluation of dairy sires using MACE methodology

Nilforooshan

Jorjani

2022

Journal of Dairy Science

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“…The first and classical approach is to consider performance in the different environments as different, potentially correlated, traits. This approach is used in across-country genetic evaluations (Schaeffer, 1994;Nilforooshan et al, 2010Nilforooshan et al, , 2014 and yields environment (country)-specific predictions of breeding values. The extent of G × E is quantified by means of the genetic correlations across environments.…”

Section: Introductionmentioning

confidence: 99%

Genotype by environment (climate) interaction improves genomic prediction for production traits in US Holstein cattle

Tiezzi

Campos

Gaddis

et al. 2017

Journal of Dairy Science

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Genotype by environment interaction (G × E) in dairy cattle productive traits has been shown to exist, but current genetic evaluation methods do not take this component into account. As several environmental descriptors (e.g., climate, farming system) are known to vary within the United States, not accounting for the G × E could lead to reranking of bulls and loss in genetic gain. Using test-day records on milk yield, somatic cell score, fat, and protein percentage from all over the United States, we computed within herd-year-season daughter yield deviations for 1,087 Holstein bulls and regressed them on genetic and environmental information to estimate variance components and to assess prediction accuracy. Genomic information was obtained from a 50k SNP marker panel. Environmental effect inputs included herd (160 levels), geographical region (7 levels), geographical location (2 variables), climate information (7 variables), and management conditions of the herds (16 total variables divided in 4 subgroups). For each set of environmental descriptors, environmental, genomic, and G × E components were sequentially fitted. Variance components estimates confirmed the presence of G × E on milk yield, with its effect being larger than main genetic effect and the environmental effect for some models. Conversely, G × E was moderate for somatic cell score and small for milk composition. Genotype by environment interaction, when included, partially eroded the genomic effect (as compared with the models where G × E was not included), suggesting that the genomic variance could at least in part be attributed to G × E not appropriately accounted for. Model predictive ability was assessed using 3 cross-validation schemes (new bulls, incomplete progeny test, and new environmental conditions), and performance was compared with a reference model including only the main genomic effect. In each scenario, at least 1 of the models including G × E was able to perform better than the reference model, although it was not possible to find the overall best-performing model that included the same set of environmental descriptors. In general, the methodology used is promising in accounting for G × E in genomic predictions, but challenges exist in identifying a unique set of covariates capable of describing the entire variety of environments.

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“…Grass-based milk has also been shown to contain higher levels of 'good' fats, such as conjugated linoleic acid and omega 3 fatty acids, and antioxidants like carotene (Dhiman et al 1999). Realising that animals should not be bred solely to maximise production levels, but instead towards an optimum level within a given production system (Rauw et al 1998), breeding programmes have incorporated many other traits, notably improved fertility (Nilforooshan et al 2014). Recently, a system to determine breeding values for the immune responsiveness of cattle has been developed (Mallard et al 2015).…”

Section: Holistic Incorporation Of Animal Resistance and Resilience Tmentioning

confidence: 99%

Control of helminth ruminant infections by 2030

Charlier²,

et al. 2018

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Helminth infections have large negative impacts on production efficiency in ruminant farming systems worldwide, and their effective management is essential if livestock production is to increase to meet future human needs for dietary protein. The control of helminths relies heavily on routine use of chemotherapeutics, but this approach is unsustainable as resistance to anthelmintic drugs is widespread and increasing. At the same time, infection patterns are being altered by changes in climate, land-use and farming practices. Future farms will need to adopt more efficient, robust and sustainable control methods, integrating ongoing scientific advances. Here, we present a vision of helminth control in farmed ruminants by 2030, bringing to bear progress in: (1) diagnostic tools, (2) innovative control approaches based on vaccines and selective breeding, (3) anthelmintics, by sustainable use of existing products and potentially new compounds, and (4) rational integration of future control practices. In this review, we identify the technical advances that we believe will place new tools in the hands of animal health decision makers in 2030, to enhance their options for control and allow them to achieve a more integrated and sustainable approach to helminth control in support of animal welfare and production.

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Multiple-trait multiple-country genetic evaluation of Holstein bulls for female fertility and milk production traits

Cited by 4 publications

References 13 publications

Invited review: A quarter of a century—International genetic evaluation of dairy sires using MACE methodology

Invited review: A quarter of a century—International genetic evaluation of dairy sires using MACE methodology

Genotype by environment (climate) interaction improves genomic prediction for production traits in US Holstein cattle

Control of helminth ruminant infections by 2030

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