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Genomic prediction and genetic correlations estimated for milk production and fatty acid traits in Walloon Holstein cattle using random regression models
Published online by Cambridge University Press: 05 September 2022
Abstract
The aims of this study were to: (1) estimate genetic correlation for milk production traits (milk, fat and protein yields and fat and protein contents) and fatty acids (FA: C16:0, C18:1 cis-9, LCFA, SFA, and UFA) over days in milk, (2) investigate the performance of genomic predictions using single-step GBLUP (ssGBLUP) based on random regression models (RRM), and (3) identify the optimal scaling and weighting factors to be used in the construction of the H matrix. A total of 302 684 test-day records of 63.875 first lactation Walloon Holstein cows were used. Positive genetic correlations were found between milk yield and fat and protein yield (rg from 0.46 to 0.85) and between fat yield and milk FA (rg from 0.17 to 0.47). On the other hand, negative correlations were estimated between fat and protein contents (rg from −0.22 to −0.59), between milk yield and milk FA (rg from −0.22 to −0.62), and between protein yield and milk FA (rg from −0.11 to −0.19). The selection for high fat content increases milk FA throughout lactation (rg from 0.61 to 0.98). The test-day ssGBLUP approach showed considerably higher prediction reliability than the parent average for all milk production and FA traits, even when no scaling and weighting factors were used in the H matrix. The highest validation reliabilities (r2 from 0.09 to 0.38) and less biased predictions (b1 from 0.76 to 0.92) were obtained using the optimal parameters (i.e., ω = 0.7 and α = 0.6) for the genomic evaluation of milk production traits. For milk FA, the optimal parameters were ω = 0.6 and α = 0.6. However, biased predictions were still observed (b1 from 0.32 to 0.81). The findings suggest that using ssGBLUP based on RRM is feasible for the genomic prediction of daily milk production and FA traits in Walloon Holstein dairy cattle.
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- Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of Hannah Dairy Research Foundation
Footnotes
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Present address: Council on Dairy Cattle Breeding – CDCB, Bowie, Maryland, USA.
References
Aguilar, I, Misztal, I, Johnson D, L, Legarra, A, Tsuruta, S and Lawlor, TJ (2010) Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science 93, 743–752.CrossRefGoogle Scholar
Aguilar, I, Misztal, I, Tsuruta, S and Legarra, A (2014) PREGSF90 – POSTGSF90 : Computational Tools for the Implementation of Single-step Genomic Selection and Genome-wide … Proceedings, 10th World Congress of Genetics Applied to Livestock Production PREGSF90, 0–3.Google Scholar
Barber, MC, Clegg, RA, Travers, MT and Vernon, RG (1997) Lipid metabolism in the lactating mammary gland. Biochimica et Biophysica Acta – Lipids and Lipid Metabolism 1347, 101–126.CrossRefGoogle ScholarPubMed
Bastin, C, Gengler, N and Soyeurt, H (2011) Phenotypic and genetic variability of production traits and milk fatty acid contents across days in milk for Walloon Holstein first-parity cows. Journal of Dairy Science 94, 4152–4163.CrossRefGoogle ScholarPubMed
Chilliard, Y, Ferlay, A, Mansbridge, RM and Doreau, M (2000) Ruminant milk fat plasticity: nutritional control of saturated, polyunsaturated, trans and conjugated fatty acids. Annales de Zootechnie 49, 181–205.CrossRefGoogle Scholar
Christensen, OF and Lund, MS (2010) Genomic relationship matrix when some animals are not genotyped Genomic prediction models. Genetics Selection Evolution 42, 1–8.CrossRefGoogle ScholarPubMed
Colinet, FG, Vandenplas, J, Vanderick, S, Hammami, H, Mota, RR, Gillon, A, Hubin, X, Bertozzi, C and Gengler, N (2017) Bayesian single-step genomic evaluations combining local and foreign information in Walloon Holsteins. Animal 12, 898–905.CrossRefGoogle ScholarPubMed
Druet, T, Jaffrézic, F, Boichard, D and Ducrocq, V (2003) Modeling lactation curves and estimation of genetic parameters for first lactation test-day records of French Holstein cows. Journal of Dairy Science 86, 2480–2490.CrossRefGoogle ScholarPubMed
Fleming, A, Schenkel, FS, Malchiodi, F, Ali, RA, Mallard, B, Sargolzaei, M, Jamrozik, J, Johnston, J and Miglior, F (2018) Genetic correlations of mid-infrared-predicted milk fatty acid groups with milk production traits. Journal of Dairy Science 101, 4295–4306.CrossRefGoogle ScholarPubMed
Freitas, PHF, Oliveira, HR, Silva, FF, Fleming, A, Miglior, F, Schenkel, FS and Brito, LF (2020) Genomic analyses for predicted milk fatty acid composition throughout lactation in North American Holstein cattle. Journal of Dairy Science 103, 6318–6331.CrossRefGoogle ScholarPubMed
Gao, H, Christensen, OF, Madsen, P, Nielsen, US, Zhang, Y, Lund, MS and Su, G (2012) Comparison on genomic predictions using three GBLUP methods and two single-step blending methods in the Nordic Holstein population. Genetics Selection Evolution 44, 8.CrossRefGoogle ScholarPubMed
Gebreyesus, G, Bovenhuis, H, Lund, MS, Poulsen, NA, Sun, D and Buitenhuis, B (2019) Reliability of genomic prediction for milk fatty acid composition by using a multi-population reference and incorporating GWAS results. Genetics Selection Evolution 51, 16.CrossRefGoogle ScholarPubMed
Gross, J, van Dorland, HA, Bruckmaier, RM and Schwarz, FJ (2011) Milk fatty acid profile related to energy balance in dairy cows. Journal of Dairy Research 78, 479–488.CrossRefGoogle ScholarPubMed
Guarini, AR, Lourenco, DAL, Brito, LF, Sargolzaei, M, Baes, CF, Miglior, F, Misztal, I and Schenkel, FS (2018) Comparison of genomic predictions for lowly heritable traits using multi-step and single-step genomic best linear unbiased predictor in Holstein cattle. Journal of Dairy Science 101, 8076–8086.CrossRefGoogle ScholarPubMed
Hanuš, O, Samková, E, Křížová, L, Hasoňová, L and Kala, R (2018) Role of fatty acids in milk fat and the influence of selected factors on their variability-A review. Molecules 23, 1636.CrossRefGoogle ScholarPubMed
Haug, A, Høstmark, AT and Harstad, OM (2007) Bovine milk in human nutrition – a review. Lipids in Health and Disease 6, 25.CrossRefGoogle ScholarPubMed
Jorjong, S, van Knegsel, ATM, Verwaeren, J, Lahoz, MV, Bruckmaier, RM, De Baets, B, Kemp, B and Fievez, V (2014) Milk fatty acids as possible biomarkers to early diagnose elevated concentrations of blood plasma nonesterified fatty acids in dairy cows. Journal of Dairy Science 97, 7054–7064.CrossRefGoogle ScholarPubMed
Koivula, M, Strandén, I, Pösö, J, Aamand, GP and Mäntysaari, EA (2015) Single-step genomic evaluation using multitrait random regression model and test-day data. Journal of Dairy Science 98, 2775–2784.CrossRefGoogle ScholarPubMed
Legarra, A, Christensen, OF, Aguilar, I and Misztal, I (2014) Single step, a general approach for genomic selection. Livestock Science 166, 54–65.CrossRefGoogle Scholar
Loften, JR, Linn, JG, Drackley, JK, Jenkins, TC, Soderholm, CG and Kertz, AF (2014) Invited review: palmitic and stearic acid metabolism in lactating dairy cows. Journal of Dairy Science 97, 4661–4674.CrossRefGoogle ScholarPubMed
Lourenco, D, Legarra, A, Tsuruta, S, Masuda, Y, Aguilar, I and Misztal, I (2020) Single-step genomic evaluations from theory to practice: using SNP chips and sequence data in BLUPF90. Genes 11, 790.CrossRefGoogle ScholarPubMed
Mäntysaari, E, Liu, Z and VanRaden, PM (2010) Interbull validation test for genomic evaluations. Interbull Bulletin 51, 17–22.Google Scholar
Martini, JWR, Schrauf, MF, Garcia-Baccino, CA, Pimentel, ECG, Munilla, S, Rogberg-Munõz, A, Cantet, RJC, Reimer, C, Gao, N, Wimmer, V and Simianer, H (2018) The effect of the H − 1 scaling factors τ and ω on the structure of H in the single-step procedure. Genetics Selection Evolution 50, 16.CrossRefGoogle Scholar
Masuda, Y, Misztal, I, Tsuruta, S, Legarra, A, Aguilar, I, Lourenco, DAL, Fragomeni, BO and Lawlor, TJ (2016) Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals. Journal of Dairy Science 99, 1968–1974.CrossRefGoogle ScholarPubMed
Miglior, F, Fleming, A, Malchiodi, F, Brito, LF, Martin, P and Baes, CF (2017) A 100-year review: identification and genetic selection of economically important traits in dairy cattle. Journal of Dairy Science 100, 10251–10271.CrossRefGoogle ScholarPubMed
Misztal, I, Tsuruta, S, Strabel, T, Auvray, B, Druet, T and Lee, DH (2002) Blupf90 and Related Programs (Bgf90), 2001–2002.Google Scholar
Misztal, I, Bradford, HL, Lourenco, DAL, Tsuruta, S, Masuda, Y and Legarra, A (2017) Studies on inflation of GEBV in single-step GBLUP for type. Interbull Bulletin.Google Scholar
Oliveira, HR, Lourenco, DAL, Masuda, Y, Misztal, I, Tsuruta, S, Jamrozik, J, Brito, LF, Silva, FF and Schenkel, FS (2019) Application of single-step genomic evaluation using multiple-trait random regression test-day models in dairy cattle. Journal of Dairy Science 102, 2365–2377.CrossRefGoogle ScholarPubMed
Paiva, JT, Mota, RR, Lopes, PS, Hammami, H, Vanderick, S, Oliveira, HR, Veroneze, R, Silva, FF and Gengler, N (2022) Random regression test-day models to describe milk production and fatty acid traits in first lactation Walloon Holstein cows. Journal of Animal Breeding and Genetics. Journal of Animal Breeding and Genetics 139, 398–413.CrossRefGoogle ScholarPubMed
Penasa, M, Tiezzi, F, Gottardo, P, Cassandro, M and De Marchi, M (2015) Genetics of milk fatty acid groups predicted during routine data recording in Holstein dairy cattle. Livestock Science 173, 9–13.CrossRefGoogle Scholar
Tsuruta, S, Misztal, I, Aguilar, I and Lawlor, TJ (2011) Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. Journal of Dairy Science 94, 4198–4204.CrossRefGoogle ScholarPubMed
Tzompa-Sosa, DA, van Aken, GA, van Hooijdonk, ACM and van Valenberg, HJF (2014) Influence of C16:0 and long-chain saturated fatty acids on normal variation of bovine milk fat triacylglycerol structure. Journal of Dairy Science 97, 4542–4551.CrossRefGoogle ScholarPubMed
VanRaden, PM (2008) Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.CrossRefGoogle ScholarPubMed
Paiva et al. supplementary material
Tables S1 and S2
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