Genomic selection and its significance in Indian dairying
203 / 292
Authors
-
Vikas Vohra
Vikas Vohra
Principal Scientist
Animal Genetics and Breeding Division
ICAR-National Dairy Research Institute, Karnal – 132001 (Haryana) India
Email: vohravikas@gmail.com
Keywords:
Accuracy, Cattle, Dairy, Genomic, Methods, Selection, SNP chipsAbstract
Genomic selection is now a reality particularly in dairy cattle. The strategy for genomic selection is based on whole genome SNP marker effects. The method was first used in the year 2008 in cattle and since then frequent improvements have been proposed. Genomic selection strategy is still an emerging field and is a naïve science in India. In near future with the improvement in the field of data analysis, it is expected to soon transform the existing methodologies used in genomic selection. The present article is an attempt to review the concepts of genomic selection in dairy animals, current methods used for genomic evaluations and their accuracy, and discusses the relevance and practical limitation in implementation of genomic selection under Indian dairying scenario.
References
Aguilar I, Misztal I, Johnson DL, Legarra A, Tsuruta S, 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
Baruch E and Weller JI (2008) Incorporation of discrete genotypes effects for multiple genes into animal model evaluations when only a small fraction of the population has been genotyped. Journal of Dairy Science, 91:4365-4371
Boichard D, Ducrocq V, Croiseau P, Fritz S (2016) Genomic selection in domestic animals: Principles, applications and perspectives. Comptes Rendus Biologies 339(7): 274-277
Cornelissen MAMC, Mulder HA (2017) Estimating variance components and breeding values for number of oocytes and number of embryos in dairy cattle using a single-step genomic evaluation. Journal of Dairy Science 100(6):4698-4705
Georges M, Nielsen D, Mackinnon M, Mishra A, Okimoto R, Pasquino AT, Sargeant LS, Sorensen A, Steele MR, Zhao X (1995) Mapping quantitative trait loci controlling milk production in dairy cattle by exploiting progeny testing. Genetics 139: 907-920
Goddard ME, Hayes BJ (2009) Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Review Genetics 10: 381-391
Habier D, Fernando RL, Dekkers JCM (2009) Genomic selection using low density marker panels. Genetics 182: 343-353
Harris BL, Johnson DL,Spelman RJ (2008) Genomic selection in New Zealand and the implications for national genetic evaluation. Proc. Interbull Meeting, Niagara Falls, NY. Interbull, Uppsala, SwedenÂ
Hayes BJ,Visscher PM, McPartlan HC, Goddard ME (2003) Novel multilocus measure of linkage disequilibrium to estimate past effective population size. Genome Research, 13: 635-643.
Hayes BJ, Bowman PJ, Chamberlain AC, Verbyla K, Goddard ME (2009) Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genetics Selection Evolution 41:51. https://doi.org/10.1186/1297-9686-41-51
Henderson CR (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31(2): 423–447
Iamartino D, Nicolazzi EL, Van Tassell, CP, Reecy JM, Fritz-Waters ER, Koltes JE, Biffani S, Sonstegard T, Schroeder SG, Ajmone-Marsan P, Negrini R, Pasquariello R, Ramelli P, Coletta A, Garcia JF, Ali A, Ramunno L, Cosenza G, De Oliveira DA, Drummond MG, Bastianetto E, Davassi A, Pirani A, Brew F, Williams JL (2017) Design and validation of a 90K SNP genotyping assay for the water buffalo (Bubalus bubalis). PLoS One. 12(10): e0185220
International Buffalo Federation. Proceedings of the 9th World Buffalo Congress 25-28 April 2010–Buenos Aires, ARGENTINA http://internationalbuffalofed.org/world-meeting/9th-world-buffalo-congress/
Ismael A, Lovendahl P, Fogh A, Lund MS, Su G (2017) Improving genetic evaluation using a multitrait single-step genomic model for ability to resume cycling after calving, measured by activity tags in Holstein cows. Journal of Dairy Science 100: 8188-8196
Junior GAF, Roza GJM, Valente BD, Carvalheiro R, Baldi F, Garcia DA, Gordo DGM, Espigolan R, Takada L, Tonussi RL, Andrade WBF, Magalhaes AFB, Chardulo LAL, Tonhati H, Albuquerque LG (2016). Genomic prediction of breeding values for carcass traits in Nellore cattle. Genetics Selection Evolution 48:7 DOI 10.1186/s12711-016-0188-y
Kramer M (2013) Genomic breeding value estimation for novel functional traits in Brown Swiss cattle. Doctoral Thesis. Georg-August-.University, Gottingen, Germany
Li X, Wang S, Huang J, Li L, Zhang Q, Ding X (2014) Improving the accuracy of genomic prediction in Chinese Holstein cattle by using one-step blending. Genetics Selection Evolution 46:66. https://doi.org/10.1186/s12711-014-0066-4
Lourenco DAL, Misztal I, Tsuruta S, Aguilar I, Ezra E, Ron M, Shirak A, Weller JI (2014) Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses. Journal of Dairy Science 97:1742-1752
Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157: 1819-1829
Moser G, Tier B, Crump RE, Khatkar MS, Raadsma HW (2009) A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers. Genetics Selection Evolution 41:56. https://doi.org/10.1186/1297-9686-41-56
Moser G, Khatkar MS, Hayes BJ, Raadsma HW (2010) Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers. Genetics Selection Evolution 42: 37. https://doi.org/10.1186/1297-9686-42-37
Piccoli ML, Brito LF, Braccini J, Brito FV, Cardoso FF, Cobuci JA, Sargolzaei M, Schenkel FS (2018) A comprehensive comparison between single-and two-step GBLUP methods in a simulated beef cattle population. Canadian Journal of Animal Science 98(3):565-575
Pintus, MA, Gaspa G, Nicolazzi EL, Vicario D, Rossoni A, Ajmone-Marsan P, Nardone A, Dimauro C, Macciotta NPP (2012)Prediction of genomic breeding values for dairy traits in Italian Brown and Simmental bulls using a principal component approach. Journal of Dairy Science 95: 3390–3400
Ruiz-Lopez FJ, Garcia-Ruiz A, Cole JB, Van Raden PM, Wiggans GR, Van Tassell CP (2018) Impact of genomic selection on genetic gain of Net Merit of US dairy cattle. World Congress of Genetics Applied in Livestock Production. Auckland, New Zealand, Feb. 11-16, Vol. Electron. Poster Sess.–Biol. & Species–Bovine (dairy) 2:710
Smaragdov MG (2013) Genomic selection for milk in cattle. The practical application over 5 years. Russian Journal of Genetics 49:1089-1097
Su G, Madsen P, Nielsen US, Mantysaari EA, Aamand GP, Christensen OF, Lund MS (2012) Genomic prediction for Nordic Red cattle using one-step and selection index blending. Journal of Dairy Science 95:909-917
VanRaden PM, VanTassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS (2009) Invited review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92: 16–24
Vazquez AI, Rosa GJM, Weigel KA, Campos G, Gianola D, Allison DB (2010) Predictive ability of subsets of single nucleotide polymorphisms with and without parent average in US Holsteins, Journal of Dairy Science 93: 5942-5949
Wang CL, Ma Pei-Pie, Zhang Zhe, Ding XD, Liu Jian-Feng, Fu WX, Weng ZQ, Zhang Q (2011) Comparison of five methods for genomic breeding value estimation for the common dataset of the 15th QTL-MAS Workshop. Proceedings of 15th European workshop on QTL mapping and marker assisted selection (QTLMAS)
Wang H, Misztal I, Aguilar I, Legarra A, Muir WM (2012) Genome-wide association mapping including phenotypes from relatives without genotypes. Genet Res Camb 94:73-83
Weigel KA, de los Campos G, Gonzalez-Recio O, Naya H, Wu XL Long N, Rosa GJ, Gianola D (2009) Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers. Journal of Dairy Science 92: 5248–5257
Weller Joel Ira. (2016) Genomic selection in animals. John Wiley and Sons, New Jersey
Zhang Z, Ding X, Liu J, Zhang Q, de Koning DJ (2011) Accuracy of genomic prediction using low-density marker panels. Journal of Dairy Science 94: 3642–3650
http://www.apeda.gov.in/apedawebsite/SubHead_Products/Buffalo_Meat.htm
www.dahd.nic.in
www.fao.org /stat/
www.ovita.co.nz/what-we-do/ovita-snp-chip/what-is-whole-genome-selection
