Across-breed; Genetic diversity; Local breeds; Reference population; Stratification
Abstract :
[en] Currently, enhancing the collaboration between related breeds is of main importance to increase the competitivity and the sustainability of local breeds. One type of collaboration is the development of an across-breed reference population that will allow a better management of local breeds. For this purpose, the genomic relatedness between the local target breed and possible breeds to be included in the reference population should be estimated. In Europe, there are several local red-pied cattle breeds that would benefit from this kind of collaboration. However, how different red-pied cattle breeds from the Benelux are related to each other and can collaborate is still unclear. The objectives of this study were therefore: (1) to estimate the level of inbreeding of the East Belgian Red and White (EBRW), the Red-Pied of the Ösling (RPO) and Dutch red-pied cattle breeds; (2) to determine the genomic relatedness of several red-pied cattle breeds, with a special focus on two endangered breeds: the EBRW and the RPO, and (3) based on the second objective, to detect animals from other breeds that were genomically close enough to be considered as advantageous in the creation of an across-breed reference population of EBRW or RPO. The estimated inbreeding levels based on runs of homozygosity were relatively low for almost all the studied breeds and especially for the EBRW and RPO. This would imply that inbreeding is currently not an issue in these two endangered breeds and that their sustainability is not threatened by their level of inbreeding. The results from the principal component analysis, the phylogenetic tree and the clustering all highlighted that the EBRW and RPO breeds were included in the genomic continuum of the studied red-pied cattle breeds and can be therefore considered as genomically close to Dutch red-pied cattle breeds, highlighting the possibility of a collaboration between these breeds. Especially, EBRW animals were closely related to Deep Red and Improved Red animals while, to a lesser extent, the RPO animals were closely related to the Meuse-Rhine-Yssel breed. Based on these results, we could use distance measures, based either on the principal component analysis or clustering, to detect animals from Dutch breeds that were genomically closest to the EBRW or RPO breeds. This will finally allow the building of an across-breed reference population for EBRW or RPO for further genomic evaluations, considering these genomically closest animals from other breeds.
Disciplines :
Animal production & animal husbandry
Author, co-author :
Wilmot, Hélène ; Université de Liège - ULiège > TERRA Research Centre > Ingénierie des productions animales et nutrition
Druet, Tom ; Université de Liège - ULiège > Département des sciences de la vie
Hulsegge, I ; Animal Breeding and Genomics, Wageningen University & Research, Droevendaalsesteeg 1, 6700AH Wageningen, the Netherlands, Centre for Genetic Resources, The Netherlands, Wageningen University & Research, Droevendaalsesteeg 1, 6700AH Wageningen, the Netherlands
Gengler, Nicolas ; Université de Liège - ULiège > TERRA Research Centre > Ingénierie des productions animales et nutrition
Calus, M P L; Animal Breeding and Genomics, Wageningen University & Research, Droevendaalsesteeg 1, 6700AH Wageningen, the Netherlands. Electronic address: mario.calus@wur.nl
Language :
English
Title :
Estimation of inbreeding, between-breed genomic relatedness and definition of sub-populations in red-pied cattle breeds.
Alemu, S.W., Kadri, N.K., Harland, C., Faux, P., Charlier, C., Caballero, A., Druet, T., An evaluation of inbreeding measures using a whole-genome sequenced cattle pedigree. Heredity 126 (2021), 410–423, 10.1038/s41437-020-00383-9.
Alexander, D.H., Novembre, J., Lange, K., Fast model-based estimation of ancestry in unrelated individuals. Genome Research 19 (2009), 1655–1664, 10.1101/gr.094052.109.
Bertrand, A.R., Kadri, N.K., Flori, L., Gautier, M., Druet, T., RZooRoH: An R package to characterize individual genomic autozygosity and identify homozygous-by-descent segments. Methods in Ecology and Evolution 10 (2019), 860–866, 10.1111/2041-210X.13167.
Bosse, M., Megens, H.J., Derks, M.F.L., de Cara, Á.M.R., Groenen, M.A.M., Deleterious alleles in the context of domestication, inbreeding, and selection. Evolutionary Applications 12 (2019), 6–17, 10.1111/eva.12691.
Browning, S.R., Browning, B.L., Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. American Journal of Human Genetics 81 (2007), 1084–1097, 10.1086/521987.
Chang, C.C., Chow, C.C., Tellier, L.C.A.M., Vattikuti, S., Purcell, S.M., Lee, J.J., Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 4, 2015, 7, 10.1186/s13742-015-0047-8.
Danecek, P., Bonfield, J.K., Liddle, J., Marshall, J., Ohan, V., Pollard, M.O., Whitwham, A., Keane, T., McCarthy, S.A., Davies, R.M., Li, H., Twelve years of SAMtools and BCFtools. GigaScience, 10, 2021, 8, 10.1093/gigascience/giab008.
Doublet, A.-C., Croiseau, P., Fritz, S., Michenet, A., Hozé, C., Danchin-Burge, C., Laloë, D., Restoux, G., The impact of genomic selection on genetic diversity and genetic gain in three French dairy cattle breeds. Genetics Selection Evolution, 51, 2019, 52, 10.1186/s12711-019-0495-1.
Druet, T., Gautier, M., A model-based approach to characterize individual inbreeding at both global and local genomic scales. Molecular Ecology 26 (2017), 5820–5841, 10.1111/mec.14324.
Druet, T., Gautier, M., A hidden Markov model to estimate homozygous-by-descent probabilities associated with nested layers of ancestors. Theoretical Population Biology 145 (2022), 38–51, 10.1016/j.tpb.2022.03.001.
Ferenčaković, M., Sölkner, J., Curik, I., Estimating autozygosity from high-throughput information: Effects of SNP density and genotyping errors. Genetics Selection Evolution, 45, 2013, 42, 10.1186/1297-9686-45-42.
François, L., Wijnrocx, K., Colinet, F.G., Gengler, N., Hulsegge, B., Windig, J.J., Buys, N., Janssens, S., Genomics of a revived breed: Case study of the Belgian campine cattle. PLoS ONE, 12, 2017, e0175916.
Gengler, N., Wilmot, H., 2022. Rotbunte Rassen: Genetische und geschichtliche Betrachtung. Proceedings of the Kongress zur Zucht und Erhaltung alter und bedrohter einheimischer Nutztierrassen, 26–28 September 2022, Bonn, Germany, 11 pages.
Goddard, M., Genomic selection: Prediction of accuracy and maximisation of long term response. Genetica 136 (2009), 245–257, 10.1007/s10709-008-9308-0.
Gómez-Romano, F., Villanueva, B., Rodríguez De Cara, M.Á., Fernández, J., Maintaining genetic diversity using molecular coancestry: The effect of marker density and effective population size. Genetics Selection Evolution, 45, 2013, 38, 10.1186/1297-9686-45-38.
Hiemstra, S.J., de Haas, Y., Mäki-Tanila, A., Gandini, G., 2010. Local cattle breeds in Europe. Wageningen Academic Publishers, Wageningen, The Netherlands. Doi: 10.3920/978-90-8686-697-7.
Hozé, C., Fritz, S., Phocas, F., Boichard, D., Ducrocq, V., Croiseau, P., Efficiency of multi-breed genomic selection for dairy cattle breeds with different sizes of reference population. Journal of Dairy Science 97 (2014), 3918–3929, 10.3168/jds.2013-7761.
Lawson, D.J., Hellenthal, G., Myers, S., Falush, D., Inference of population structure using dense haplotype data. PLoS Genetics, 8, 2012, e1002453.
Lawson, D.J., van Dorp, L., Falush, D., A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots. Nature Communications, 9, 2018, 3258, 10.1038/s41467-018-05257-7.
Lê, S., Josse, F., Husson, F., FactoMineR: An R Package for Multivariate Analysis. Journal of Statistical Software 25 (2008), 1–18, 10.18637/jss.v025.i01.
Lencz, T., Lambert, C., DeRosse, P., Burdick, K.E., Morgan, T.V., Kane, J.M., Kucherlapati, R., Malhotra, A.K., Runs of homozygosity reveal highly penetrant recessive loci in schizophrenia. Proceedings of the National Academy of Sciences of the United States of America 104 (2007), 19942–19947, 10.1073/pnas.0710021104.
Leroy, G., Inbreeding depression in livestock species: Review and meta-analysis. Animal Genetics 45 (2014), 618–628, 10.1111/age.12178.
Marjanovic, J., Calus, M.P.L., Factors affecting accuracy of estimated effective number of chromosome segments for numerically small breeds. Journal of Animal Breeding and Genetics 138 (2021), 151–160, 10.1111/jbg.12512.
Marjanovic, J., Hulsegge, B., Calus, M.P.L., Relatedness between numerically small Dutch Red dairy cattle populations and possibilities for multibreed genomic prediction. Journal of Dairy Science 104 (2021), 4498–4506, 10.3168/jds.2020-19573.
Medugorac, I., Medugorac, A., Russ, I., Veit-Kensch, C.E., Taberlet, P., Luntz, B., Mix, H.M., Förster, M., Genetic diversity of European cattle breeds highlights the conservation value of traditional unselected breeds with high effective population size. Molecular Ecology 18 (2009), 3394–3410, 10.1111/j.1365-294X.2009.04286.x.
Meuwissen, T., Hayes, B., Goddard, M., Genomic selection: A paradigm shift in animal breeding. Animal Frontiers 6 (2016), 6–14, 10.2527/af.2016-0002.
Meyermans, R., Gorssen, W., Buys, N., Janssens, S., How to study runs of homozygosity using plink? A guide for analyzing medium density snp data in livestock and pet species. BMC Genomics, 21, 2020, 94, 10.1186/s12864-020-6463-x.
Purcell, S., Chang, C., 2019. PLINK v1.9. Retrieved on 28 March 2022 from www.cog-genomics.org/plink/1.9/.
Purfield, D.C., Berry, D.P., McParland, S., Bradley, D.G., Runs of homozygosity and population history in cattle. BMC Genetics, 13, 2012, 70, 10.1186/1471-2156-13-70.
R Core Team, 2022. R: A language and environment for statistical computing. (3.6.3). R Foundation for Statistical Computing, Vienna, Austria.
R Studio Team, 2022. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA, USA.
Rezende, F.M., Haile-Mariam, M., Pryce, J.E., Peñagaricano, F., Across-country genomic prediction of bull fertility in Jersey dairy cattle. Journal of Dairy Science 103 (2020), 11618–11627, 10.3168/jds.2020-18910.
Rosen, B.D., Bickhart, D.M., Schnabel, R.D., Koren, S., Elsik, C.G., Tseng, E., Rowan, T.N., Low, W.Y., Zimin, A., Couldrey, C., Hall, R., Li, W., Rhie, A., Ghurye, J., McKay, S.D., Thibaud-Nissen, F., Hoffman, J., Murdoch, B.M., Snelling, W.M., MacDaneld, T.G., Hammond, J.A., Schwartz, J.C., Nadolo, W., Hagen, D.E., Dreischer, C., Schultheiss, S.J., Schroeder, S.G., Phillippy, A.M., Cole, J.B., Van Tassell, C.P., Liu, G., Smith, T.P.L., Medrano, J.F., De novo assembly of the cattle reference genome with single-molecule sequencing. GigaScience, 9, 2020, 21, 10.1093/gigascience/giaa021.
Schliep, K., Potts, A.J., Morrison, D.A., Grimm, G.W., Intertwining phylogenetic trees and networks. Methods in Ecology and Evolution 8 (2017), 1212–1220, 10.1111/2041-210X.12760.
Schmidtmann, C., Schönherz, A., Guldbrandtsen, B., Marjanovic, J., Calus, M., Hinrichs, D., Thaller, G., Assessing the genetic background and genomic relatedness of red cattle populations originating from Northern Europe. Genetics Selection Evolution, 53, 2021, 23, 10.1186/s12711-021-00613-6.
Schnabel, R.D., 2019. ARS-UCD1.2 Cow Genome Assembly: Mapping of all existing variants. Retrieved on 28 March 2022 from https://www.animalgenome.org/repository/cattle/UMC_bovine_coordinates/.
Slagboom, M., Milkevych, V., Liu, H., Thomasen, J.R., Kargo, M., Schmidtmann, C., Conservation of local Red cattle breeds by collaboration with a mainstream Red dairy cattle breed. Livestock Science, 260, 2022, 104936, 10.1016/j.livsci.2022.104936.
Solé, M., Gori, A.S., Faux, P., Bertrand, A., Farnir, F., Gautier, M., Druet, T., Age-based partitioning of individual genomic inbreeding levels in Belgian Blue cattle. Genetics Selection Evolution, 49, 2017, 92, 10.1186/s12711-017-0370-x.
van Breukelen, A.E., Doekes, H.P., Windig, J.J., Oldenbroek, K., Characterization of genetic diversity conserved in the gene bank for dutch cattle breeds. Diversity, 11, 2019, 229, 10.3390/d11120229.
Vereniging Het Brandrode Rund, 2022. Geschiedenis. Retrieved on 14 December 2022 from https://www.hetbrandroderund.nl/geschiedenis/.
Weir, B.S., Cockerham, C.C., Estimating F-statistics for the analysis of population structure. Evolution 38 (1984), 1358–1370, 10.2307/2408641.
Wellmann, R., Bennewitz, J., Key genetic parameters for population management. Frontiers in Genetics, 10, 2019, 667, 10.3389/fgene.2019.00667.
Wientjes, Y.C.J., Veerkamp, R.F., Calus, M.P.L., The effect of linkage disequilibrium and family relationships on the reliability of genomic prediction. Genetics 193 (2013), 621–631, 10.1534/genetics.112.146290.
Wientjes, Y.C.J., Bijma, P., Veerkamp, R.F., Calus, M.P.L., An equation to predict the accuracy of genomic values by combining data from multiple traits, populations, or environments. Genetics 202 (2016), 799–823, 10.1534/genetics.115.183269.
Wilmot, H., Bormann, J., Soyeurt, H., Hubin, X., Glorieux, G., Mayeres, P., Bertozzi, C., Gengler, N., Development of a genomic tool for breed assignment by comparison of different classification models - Application to three local cattle breeds. Journal of Animal Breeding and Genetics 139 (2022), 40–61, 10.1111/jbg.12643.