Integration of genomic selection and assisted embryo technologies for precision cattle breeding: opportunities, challenges and global perspectives

DOI: https://doi.org/10.58292/CT.v18.14424
Keywords: genomic selection;, embryo transfer;, in vitro embryo production;, ovum pick-up;, genomic breeding values;, precision livestock breeding.

Abstract

Modern cattle breeding increasingly integrates genomic selection (GS) with assisted reproductive technologies (ARTs) to accelerate genetic gain and enhance herd productivity. This review examines the synergistic application of GS and embryo-based technologies in dairy and beef cattle, and evaluates their potential to support precision breeding strategies. Relevant literature on genomic evaluation, embryo technologies and embryo-based breeding programs was synthesized to summarize current developments, opportunities, challenges, and global adoption patterns. ARTs, including artificial insemination, multiple ovulation and embryo transfer, in vitro embryo production, ovum pick-up, cryopreservation, and embryo biopsy, facilitate rapid multiplication of elite genetics and enable preimplantation selection. When combined with GS, these technologies allow estimation of genomic breeding values at early developmental stages, thereby reducing generation intervals, increasing selection intensity and improving the rate of genetic gain. Integrated breeding programs can also support targeted improvement of economically important traits (e.g. fertility, disease resistance, feed efficiency, production performance). Despite these advantages, practical implementation requires consideration of economic costs, technical expertise, infrastructure, recipient availability, and strategies to manage inbreeding and maintain genetic diversity. Global adoption varies considerably; highly integrated genomic and embryo-based breeding systems are well established in some countries (e.g. US, Australia) whereas emerging applications in regions including India, China, Brazil and Pakistan reflect varying levels of technological capacity and industry development. Emerging and developing approaches, including genome editing, artificial intelligence-assisted embryo evaluation and noninvasive embryo selection, together with continued improvements in established genomic prediction systems, are expected to further enhance precision breeding. Overall, integration of GS and ARTs represents a transformative approach for accelerating sustainable genetic improvement in cattle while supporting efficient and responsible breeding programs.

Downloads

Download data is not yet available.

References


1.
Fleming A, Abdalla EA, Maltecca C, et al: Invited review: reproductive and genomic technologies to optimize breeding strategies for genetic progress in dairy cattle. Arch Anim Breed 2018;61:43–57. doi: 10.5194/aab-61-43-2018

2.
Spangler ML, Berry DP, Kuehn LA: Leveraging data from commercial cattle for genetic improvement- an international perspective. J Anim Sci 2025;103:skaf291. doi: 10.1093/jas/skaf291

3.
Van Vleck LD: Selection when traits have different genetic and phenotypic variances in different environments. J Dairy Sci 1987;70:337–344. doi: 10.3168/jds.S0022-0302(87)80015-2

4.
Mrode R: Best linear unbiased prediction of breeding value: univariate models with one random effect. In: Mrode RA, Thompson R: editors. Models for the Prediction of Animal Breeding Values. Oxfordshire, UK; CABI Publishing: 2014. p. 34–60. doi: 10.1079/9781780643915.0034

5.
VanRaden PM, Sullivan PG: International genomic evaluation methods for dairy cattle. Genet Sel Evol 2010;42:7. doi: 10.1186/1297-9686-42-7

6.
Goddard ME, Hayes BJ: Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat Rev Genet 2009;10:381–391. doi: 10.1038/nrg2575

7.
Moore SG, Hasler JF. A 100-year review: reproductive technologies in dairy science. J Dairy Sci 2017;100:10314–10331. doi: 10.3168/jds.2017-13138

8.
Hansen PJ, Block J: Towards an embryocentric world: the current and potential uses of embryo technologies in dairy production. Reprod Fertil Dev 2004;16:1–14. doi: 10.1071/RD03070

9.
Kasinathan P, Wei H, Xiang T, et al: Acceleration of genetic gain in cattle by reduction of generation interval. Sci Rep 2015;5:8674. doi: 10.1038/srep08674

10.
Meuwissen THE, Hayes BJ, Goddard ME: Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001;157:1819–1829. doi: 10.1093/genetics/157.4.1819

11.
Fujii T: Genomic evaluation and gene expression analysis of preimplantation embryos for enhancing cattle breeding and in vitro embryo production. J Reprod Dev 2026;72:590–596. doi: 10.1262/jrd.2026-067

12.
Hayes BJ, Bowman PJ, Chamberlain AJ, et al: Genomic selection in dairy cattle: progress and challenges. J Dairy Sci 2009;92:433–443. doi: 10.3168/jds.2008-1508

13.
Van Eenennaam AL, Drake DJ: Where in vitro embryo production technology is headed: genetic selection and efficiency improvements. Theriogenology 2012;78:1701–1713. doi: 10.1016/j.theriogenology.2012.07.002

14.
Gutierrez-Reinoso MA, Aponte PM, Garcia-Herreros M: Genomic analysis, progress and future perspectives in dairy cattle selection: a review. Animals (Basel) 2021;11:599. doi: 10.3390/ani11030599

15.
Rauw WM, Kanis E, Noordhuizen-Stassen EN, et al: Undesirable side effects of selection for high production efficiency in farm animals: a review. Livest Prod Sci 1998;56:15–33. doi: 10.1016/S0301-6226(98)00147-X

16.
Hasler JF: Overview of commercial embryo transfer in cattle. Theriogenology 2001;56:1323–1340. doi: 10.1016/S0093-691X(01)00679-5

17.
Rizos D, Gutierrez-Adan A, Perez-Garnelo S, et al: Consequences of in vitro culture conditions on embryo development and quality. Theriogenology 2002;57:5–16. doi: 10.1111/j.1439-0531.2008.01230.x

18.
Galli C, Duchi R, Crotti G, et al: Bovine embryo technologies. Theriogenology 2003;59:599–616. doi: 10.1016/s0093-691x(02)01243-8

19.
Wheeler MB, Rutledge JJ, Fischer-Brown A, et al: Application of sexed semen technology to in vitro embryo production in cattle. Theriogenology 2006;65:219–227. doi: 10.1016/j.theriogenology.2005.09.032

20.
Leme LO, Carvalho JO, Mendes CM, et al: Impact of sperm sex sorting on sperm quality and in vitro embryo production in bovine. Anim Reprod Sci 2024;270:107604. doi: 10.1016/j.anireprosci.2024.107604

21.
Valente RS, Marsico TV, Sudano MJ: Basic and applied features in the cryopreservation progress of bovine embryos. Anim Reprod Sci 2022;239:106970. doi: 10.1016/j.anireprosci.2022.106970

22.
Ferré LB, Kjelland ME, Taiyeb AM, et al: Recent progress in bovine in vitro-derived embryo cryotolerance: Impact of in vitro culture systems, advances in cryopreservation and future considerations. Reprod Domest Anim 2020;55:659–676. doi: 10.1111/rda.13667

23.
Kasimanickam R, Kasimanickam V, Kastelic J, et al: Effects of twice daily compared with split-time estrous detection on pregnancy percentage in recipient beef cows. Anim Reprod Sci 2020;219:106526. doi: 10.1016/j.anireprosci.2020.106526

24.
Barnes M, Kasimanickam R, Kasimanickam V: Effect of subclinical endometritis and flunixin meglumine administration on pregnancy in embryo recipient beef cows. Theriogenology 2023;201:76–82. doi: 10.1016/j.theriogenology.2023.02.020

25.
Mullaart E, Wells D: Embryo biopsies for genomic selection. In: Niemann H, Wrenzycki C: editors. Animal Biotechnology 2. New York, USA; Cham: Springer: 2018. p. 81–94. doi: 10.1007/978-3-319-92348-2_5

26.
Silvestri G, Canedo-Ribeiro C, Serrano-Albal M, et al: Preimplantation genetic testing for aneuploidy improves live birth rates with in vitro produced bovine embryos: a blind retrospective study. Cells 2021;10:2284. doi: 10.3390/cells10092284

27.
Fujii T, Naito A, Hirayama H, et al: Potential of preimplantation genomic selection for carcass traits in Japanese Black cattle. J Reprod Dev 2019;65:251–258. doi: 10.1262/jrd.2019-009

28.
Doultani S, Layek SS, Gohil T, et al: Selecting bovine embryos using genomics tools: sex determination and genetic merit assessment in developing cattle embryos. Reprod Fertil Dev 2023;36:202–203. doi: 10.1071/RDv36n2Ab101

29.
Oliveira CS, Silva MVGBD, Quintão CC, et al: Imputation accuracy for genomic selection using embryo biopsy samples in Gir. Reprod Biol 2023;23:100765. doi: 10.1016/j.repbio.2023.100765

30.
Shojaei Saadi HA, Vigneault C, Sargolzaei M, et al: Impact of whole-genome amplification on the reliability of pre-transfer cattle embryo breeding value estimates. BMC Genomics. 2014;15:889. doi: 10.1186/1471-2164-15-889

31.
Cenariu M, Pall E, Cernea C, Groza I: Evaluation of bovine embryo biopsy techniques according to their ability to preserve embryo viability. J Biomed Biotechnol 2012;2012:541384. doi: 10.1155/2012/541384

32.
VanRaden PM: Efficient methods to compute genomic predictions. J Dairy Sci 2008;91:4414–4423. doi: 10.3168/jds.2007-0980

33.
Miller S: Genomic selection in beef cattle creates additional opportunities for embryo technologies to meet industry needs. Reprod Fertil Dev 2022;35:98–105. doi: 10.1071/RD22233

34.
Thomasen JR, Willam A, Egger-Danner C, et al: Reproductive technologies combine well with genomic selection in dairy breeding programs. J Dairy Sci 2016;99:1331–1340. doi: 10.3168/jds.2015-9437

35.
Sonesson AK, Woolliams JA, Meuwissen TH: Genomic selection requires genomic control of inbreeding. Genet Sel Evol 2012;44:27. doi: 10.1186/1297-9686-44-27

36.
Bai Y: Editorial: enhancing livestock breeding through advanced genetic tools and phenotyping systems. Front Genet 2025;16:1617113. doi: 10.3389/fgene.2025.1617113

37.
Humblot P, Le Bourhis D, Fritz S, et al: Reproductive technologies and genomic selection in cattle. Vet Med Int 2010;2010:192787. doi: 10.4061/2010/192787

38.
Kadarmideen HN, Mazzoni G, Watanabe YF, et al: Genomic selection of in vitro produced and somatic cell nuclear transfer embryos for rapid genetic improvement in cattle production. Anim Reprod Sci 2015;12:8.

39.
Loi P, Toschi P, Zacchini F, et al: Synergies between assisted reproduction technologies and functional genomics. Genet Sel Evol 2016;48:53. doi: 10.1186/s12711-016-0231-z

40.
Xu Y, Li P, Zou C, et al: Enhancing genetic gain in the era of molecular breeding. J Exp Bot 2017;68:2641–2666. doi: 10.1093/jxb/erx135

41.
Lohuis MM: Potential benefits of bovine embryo-manipulation technologies to genetic improvement programs. Theriogenology 1995;43:51–60. doi: 10.1016/0093-691X(94)00016-N

42.
Granleese T, Clark SA, Swan AA, et al: Increased genetic gains in sheep, beef and dairy breeding programs from using female reproductive technologies combined with optimal contribution selection and genomic breeding values. Genet Sel Evol 2015;47:70. doi: 10.1186/s12711-015-0151-3

43.
Clark SA, Kinghorn BP, Hickey JM, et al: The effect of genomic information on optimal contribution selection in livestock breeding programs. Genet Sel Evol 2013;45:44. doi: 10.1186/1297-9686-45-44

44.
Crowe AD, Lonergan P, Butler ST: Invited review: use of assisted reproduction techniques to accelerate genetic gain and increase value of beef production in dairy herds. J Dairy Sci 2021;104:12189–12206. doi: 10.3168/jds.2021-20281

45.
Bouquet A, Sørensen AC, Juga J: Genomic selection strategies to optimize the use of multiple ovulation and embryo transfer schemes in dairy cattle breeding programs. Livest Sci 2015;174:18–25. doi: 10.1016/j.livsci.2015.01.014

46.
Pryce JE, Hayes BJ, Goddard ME: Novel strategies to minimize progeny inbreeding while maximizing genetic gain using genomic information. J Dairy Sci 2012;95:377–388. doi: 10.3168/jds.2011-4254

47.
Seabury CM, Smith JL, Wilson ML, et al: Genome-wide association and genomic prediction for a reproductive index summarizing fertility outcomes in U.S. Holsteins. G3 (Bethesda) 2023;13:jkad043. doi: 10.1093/g3journal/jkad043

48.
Melo DB, Bruno RGS, Bisinotto RS, et al: Association of genomic predictions for fertility traits with reproductive outcomes in dairy cows under 2 estrus detection and timed artificial insemination programs. J Dairy Sci 2025;108:2060–2069. doi: 10.3168/jds.2024-25499

49.
Norman HD, Hutchison JL, Miller RH: Use of sexed semen and its effect on conception rate, calf sex, dystocia, and stillbirth of Holsteins in the United States. J Dairy Sci 2010;93:3880–3890. doi: 10.3168/jds.2009-2781

50.
Goddard ME, Hayes BJ: Genomic selection. J Anim Breed Genet 2007;124:323–330. doi: 10.1111/j.1439-0388.2007.00702.x

51.
Chalupková M, Moravčíková N, Halvoník A, et al: Genetic and genomic evaluation of health traits in cattle breeding: a review. Acta Fytotechn Zootechn 2024;27:209–215. doi: 10.15414/afz.2024.27.03.209-215

52.
Nguyen TTT, Bowman PJ, Haile-Mariam M, et al: Genomic selection for tolerance to heat stress in Australian dairy cattle. J Dairy Sci 2016;99:2849–2862. doi: 10.3168/jds.2015-9685

53.
Berry DP, Crowley JJ: Cell biology symposium: genetics of feed efficiency in dairy and beef cattle. J Anim Sci 2013;91:1594–1613. doi: 10.2527/jas.2012-5862

54.
Menchaca A: Assisted reproductive technologies (ART) and genome editing to support a sustainable livestock. Anim Reprod 2023;20:e20230074. doi: 10.1590/1984-3143-AR2023-0074

55.
Singh U, Deb R, Alyethodi RR, et al: Genetic features of reproductive traits in bovine and buffalo: lessons from bovine to buffalo. Front Genet 2021;12:617128. doi: 10.3389/fgene.2021.617128

56.
Hu G, Do DN, Gray J, et al: Selection for favorable health traits: a potential approach to cope with diseases in farm animals. Animals (Basel) 2020;10:1717. doi: 10.3390/ani10091717

57.
Schefers JM, Weigel KA: Genomic selection in dairy cattle: integration of DNA testing into breeding programs. Anim Front 2012;2:4–9. doi: 10.2527/af.2011-0032

58.
Demetrio DGB, Benedetti E, Demetrio CGB, et al: How can we improve embryo production and pregnancy outcomes of Holstein embryos produced in vitro? (12 years of practical results at a California dairy farm). Anim Reprod 2020;17: e20200053. doi: 10.1590/1984-3143-AR2020-0053

59.
Mueller ML, Van Eenennaam AL: Synergistic power of genomic selection, assisted reproductive technologies, and gene editing to drive genetic improvement of cattle. CABI Agric Biosci 2022;3:13. doi: 10.1186/s43170-022-00080-z

60.
Mansour HAEH: Integration of assisted reproductive technologies and artificial intelligence for optimizing fertility and genetic selection in livestock production. Discov Appl Sci 2025;7:1143. doi: 10.1007/s42452-025-07380-9

61.
Zhou F, Chen X, Liu S, et al: The clinical application and challenges of preimplantation genetic testing. Front Genet 2025;16:1599088. doi: 10.3389/fgene.2025.1599088

62.
McKimmie C, Forutan M, Tajet HM, et al: Impact of implementing female genomic selection and the use of sex-selected semen technology on genetic gain in a dairy herd in New Zealand. Int J Mol. Sci. 2025;26:990. doi: 10.3390/ijms26030990

63.
Seno LO, Guidolin DGF, Aspilcueta-Borquis RR, et al: Genomic selection in dairy cattle simulated populations. J Dairy Res 2018;85:125–132. doi: 10.1017/S0022029918000304

64.
Zheng X, Wang T, Niu Q, et al: Evaluation of linear programming and optimal contribution selection approaches for long-term selection on beef cattle breeding. Biology (Basel) 2023;12:1157. doi: 10.3390/biology12091157

65.
Cornelissen MAMC, Mullaart E, Van der Linde C, et al: Estimating variance components and breeding values for number of oocytes and number of embryos in dairy cattle using a single-step genomic evaluation. J Dairy Sci 2017;100:4698–4705. doi: 10.3168/jds.2016-12075

66.
Pontes JH, Nonato-Junior I, Sanches BV, et al: Comparison of embryo yield and pregnancy rate between in vivo and in vitro methods in the same Nelore (Bos indicus) donor cows. Theriogenology 2009;7:690–697. doi: 10.1016/j.theriogenology.2008.09.031

67.
Lamb GC, Fontes PLP, Oosthuizen N: In vitro fertilization (IVF) versus multiple ovulation embryo transfer (MOET): Making the decision to use one or both. In: Proceedings, Applied Reproductive Strategies in Beef Cattle; 2019 Aug 20-21; Knoxville, TN. p. 233. Available from: https://beefrepro.org/wp-content/uploads/2020/09/14-Lamb-C.pdf [cited 13 March 2026].

68.
Salek F, Guest A, Johnson C, et al: Factors affecting the success of ovum pick-up, in vitro production and cryopreservation of embryos in cattle. Animals (Basel) 2025;15:344. doi: 10.3390/ani15030344

69.
Zhang J, Shen H, Zhang Z, et al: Optimisation of laser-assisted biopsy parameters in bovine embryos. Reprod Domest Anim 2026;61:e70184. doi: 10.1111/rda.70184

70.
Kelly M, John T: The Economics of Breeding to Improve Eating Quality of Beef using Tenderness Marker Panels. North Sydney, NSW, Australia; The University of Queensland: 2014. Available from: https://www.mla.com.au/research-and-development/reports/2014/the-cost-analysis-of-breeding-to-improve-eating-quality-of-beef-using-genetic-marker-panels [cited 13 March 2026].

71.
Khatkar MS, Moser G, Hayes BJ, et al: Strategies and utility of imputed SNP genotypes for genomic analysis in dairy cattle. BMC Genomics 2012;13:538. doi: 10.1186/1471-2164-13-538

72.
Tsuruta S, Lourenco DAL, Masuda Y, et al: Reducing computational cost of large-scale genomic evaluation by using indirect genomic prediction. JDS Commun 2021;2:356–360. doi: 10.3168/jdsc.2021-0097

73.
VanRaden PM, Cole JB, Parker Gaddis KL: Net Merit as a Measure of Lifetime Profit: 2018 Revision. USDA-AIP Research Report NM$7 (5-18). USA: USDA; 2018. Available from: https://aipl.arsusda.gov/reference/nmcalc-2018.htm [cited 8 September 2019].

74.
Boler R, Kasimanickam R: Ovum pick up, in vitro embryo production, and embryo transfer as value-added theriogenology service in miniature Hereford cattle operation. Clinical Theriogenology 2024;16:10528. doi: 10.58292/CT.v16.10528

75.
Hansen PJ: The incompletely fulfilled promise of embryo transfer in cattle-why aren’t pregnancy rates greater and what can we do about it? J Anim Sci 2020;98:skaa288. doi: 10.1093/jas/skaa288

76.
Kasimanickam R, Kasimanickam V: Impact of nutrition on embryo production in cattle: mechanistic insights. Animals (Basel) 2026;16:892. doi: 10.3390/ani16060892

77.
Velazquez MA: Nutritional strategies to promote bovine oocyte quality for in vitro embryo production: do they really work? Vet Sci 2023;10:604. doi: 10.3390/vetsci10100604

78.
Santos JEP, Cerri RLA, Sartori R: Nutritional management of the donor cow. Theriogenology 2008;69:88–97. doi: 10.1016/j.theriogenology.2007.09.010

79.
Bae JW, Jo JH, Uwamahoro C, et al: Biochemical predictors of pregnancy in cattle: enhancing recipient selection for embryo transfer efficiency. Theriogenology 2026;252:117774. doi: 10.1016/j.theriogenology.2025.117774

80.
Ponsart C, Salas Cortes L, Jeanguyot N, et al: Le sexage des embryons a le vent en poupe. Bull Tech Insémin Artif 2008;129:30–31.

81.
Bouwman AC, Mullaart E: Screening of in vitro-produced cattle embryos to assess incidence and characteristics of unbalanced chromosomal aberrations. JDS Commun 2023;4:101–105. doi: 10.3168/jdsc.2022-0275

82.
Jaton C, Schenkel FS, Chud TCS, et al: Genetic and genomic analyses of embryo production in dairy cattle. Reprod Fertil Dev 2019;32:50–55. doi: 10.1071/RD19275

83.
Simopoulou M, Sfakianoudis K, Giannelou P, et al: Discarding IVF embryos: reporting on global practices. J Assist Reprod Genet 2019;36:2447–2457. doi: 10.1007/s10815-019-01592-w

84.
De Vries A. Economic value of pregnancy in dairy cattle. J Dairy Sci 2006;89:3876–3885. doi: 10.3168/jds.S0022-0302(06)72430-4

85.
Sartori R, Balistrieri M, Silva LOE, et al: Pregnancy loss in cattle with emphasis on embryo transfer programs. Anim Reprod 2025;22:e20250045. doi: 10.1590/1984-3143-AR2025-0045

86.
Ealy AD, Wooldridge LK, McCoski SR: Post-transfer consequences of in vitro-produced embryos in cattle. J Anim Sci 2019;97:2555–2568. doi: 10.1093/jas/skz116

87.
Rizos D, Ward F, Duffy P, et al: Consequences of bovine oocyte maturation, fertilization or early embryo development in vitro versus in vivo: implications for blastocyst yield and blastocyst quality. Mol Reprod Dev 2002;61:234–248. doi: 10.1002/mrd.1153

88.
Kathambi EK, Sonstegard TS, Larsen PA: Cross-breeding, advanced reproductive technologies, and genetic selection in twelve dairy production systems in Africa. Animal 2025;19:101424. doi: 10.1016/j.animal.2025.101424

89.
Tenghe AMM, Bouwman AC, Berglund B, et al: Genome-wide association study for endocrine fertility traits using single nucleotide polymorphism arrays and sequence variants in dairy cattle. J Dairy Sci 2016;99:5470–5485. doi: 10.3168/jds.2015-10533

90.
Tenhunen SR: Balancing genetic gain and diversity in dairy cattle breeding schemes in the genomics era. PhD Dissertation. Aarhus, Denmark; Aarhus University: 2025.

91.
Maskal JM, Pedrosa VB, Rojas de Oliveira H, et al: A comprehensive meta-analysis of genetic parameters for resilience and productivity indicator traits in Holstein cattle. J Dairy Sci 2024;107:3062–3079. doi: 10.3168/jds.2023-23668

92.
Hallerman E, Bredlau J, Camargo LSA, et al: Enabling regulatory policy globally will promote realization of the potential of animal biotechnology. CABI Agric Biosci 2024;5:25. doi: 10.1186/s43170-024-00225-6

93.
Nuffield Council on Bioethics: Genome Editing and Farmed Animal Breeding: Social and Ethical Issues. London, UK; Nuffield Council on Bioethics: 2021. Available from: https://cdn.nuffieldbioethics.org/wp-content/uploads/Genome-editing-and-farmed-animal-breeding-FINAL-WEB-PDF.pdf [cited 13 March 2026].

94.
Jones HE, Wilson PB: Progress and opportunities through use of genomics in animal production. Trends Genet 2022;38:1228–1252. doi: 10.1016/j.tig.2022.06.014

95.
van Marle-Köster E, Visser C: Unintended consequences of selection for increased production on the health and welfare of livestock. Arch. Anim. Breed 2021;64:177–185. doi: 10.5194/aab-64-177-2021

96.
Doublet AC, Restoux G, Fritz S, et al: Intensified use of reproductive technologies and reduced dimensions of breeding schemes put genetic diversity at risk in dairy cattle breeds. Animals 2020;10:1903. doi: 10.3390/ani10101903

97.
Bérodier M, Berg P, Meuwissen T, et al: Improved dairy cattle mating plans at herd level using genomic information. Animal 2021;15:100016. doi: 10.1016/j.animal.2020.100016

98.
Vries A, Kaniyamattam K: A review of simulation analyses of economics and genetics for the use of in-vitro produced embryos and artificial insemination in dairy herds. Anim Reprod 2020;17:e20200020. doi: 10.1590/1984-3143-AR2020-0020

99.
Baruselli PS, de Souza AH, de Sá MF, et al: Genetic market in cattle (Bull, AI, FTAI, MOET and IVP): financial payback based on reproductive efficiency in beef and dairy herds in Brazil. Anim Reprod 2018;15:247–255. doi: 10.21451/1984-3143-AR2018-0091

100.
Xu Y, Song L, Wei Z, et al: Application of gene editing technology in cattle genetic breeding. Agric Commun 2025;3:100086. doi: 10.1016/j.agrcom.2025.100086

101.
Bishop TF, Van Eenennaam AL: Genome editing approaches to augment livestock breeding programs. J Exp Biol 2020;223(Suppl_1):jeb207159. doi: 10.1242/jeb.207159

102.
Passamonti MM, Somenzi E, Barbato M, et al: The quest for genes involved in adaptation to climate change in ruminant livestock. Animals 2021;11:2833. doi: 10.3390/ani11102833

103.
Sonstegard TS, Flórez JM, Garcia JF, et al: Commercial perspectives: genome editing as a breeding tool for health and well-being in dairy cattle. JDS Commun 2024;5:767–771. doi: 10.3168/jdsc.2023-0481

104.
Mueller ML, Cole JB, Sonstegard TS, et al: Comparison of gene editing versus conventional breeding to introgress the POLLED allele into the US dairy cattle population. J Dairy Sci 2019;102:4215–4226. doi: 10.3168/jds.2018-15532

105.
Aponte PFC, Carneiro PLS, Araujo AC, et al: Investigating the genomic background of calving-related traits in Canadian Jersey cattle. J Dairy Sci 2024;107:11195–11213. doi: 10.3168/jds.2024-24768

106.
Wang X, Shi S, Ali Khan MY, et al: Improving the accuracy of genomic prediction in dairy cattle using the biologically annotated neural networks framework. J Anim Sci Biotechno. 2024;15:87. doi: 10.1186/s40104-024-01044-1

107.
Hay EH: Machine learning for the genomic prediction of growth traits in a composite beef cattle population. Animals 2024; 14:3014. doi: 10.3390/ani14203014

108.
Takeda M, Inoue K, Oyama H, et al: Exploring the size of reference population for expected accuracy of genomic prediction using simulated and real data in Japanese Black cattle. BMC Genomics 2021;22:799. doi: 10.1186/s12864-021-08121-z

109.
Chafai N, Hayah I, Houaga I, et al: A review of machine learning models applied to genomic prediction in animal breeding. Front Genet 2023;14:1150596. doi: 10.3389/fgene.2023.1150596

110.
Xiang R, Breen EJ, Bolormaa S, et al: Integrating extensive functional annotations and multiomics of cattle enhances climate resilience prediction and mapping. Proc Natl Acad Sci USA 2025;122:e2514736122. doi: 10.1073/pnas.2514736122

111.
Gao Z, Lu Y, Chong Y, et al: Beef Cattle Genome Project: advances in genome sequencing, assembly, and functional genes discovery. Int J Mol Sci 2024;25:7147. doi: 10.3390/ijms25137147

112.
Wang Y, Cui X, Chen Z: Innovations in cattle breeding technology: prospects in the era of gene editing. Animals (Basel) 2025;15:1364. doi: 10.3390/ani1510136

113.
Ghoreishifar M, Macleod IM, Chamberlain AJ, et al: An integrative approach to prioritize candidate causal genes for complex traits in cattle. PLoS Genet 2025;21:e1011492. doi: 10.1371/journal.pgen.1011492

114.
Rabel RAC, Marchioretto PV, Bangert EA, et al: Pre-implantation bovine embryo evaluation--from optics to omics and beyond. Animals (Basel) 2023;13:2102. doi: 10.3390/ani13132102

115.
Magata F: Time-lapse monitoring technologies for the selection of bovine in vitro fertilized embryos with high implantation potential. J Reprod Dev 2023;69:57–64. doi: 10.1262/jrd.2022-131

116.
Džermeikaitė K, Šidlauskaitė M, Antanaitis R, et al: Enhancing genomic selection in dairy cattle through artificial intelligence: integrating advanced phenotyping and predictive models to advance health, climate resilience, and sustainability. Dairy (Basel) 2025;6:50. doi: 10.3390/dairy6050050

117.
Wells C, Hayden C, Rea M, et al: Advancing bovine embryo evaluation: machine learning to assess embryos in routine embryo transfer practice. J IVF-Worldw 2025;3:45–57. doi: 10.46989/001c.141131

118.
Pereira JMV, Ferreira FC, Marcondes MI: Effects of beef semen and beef embryo strategies on profitability: economics of using beef semen and beef in vitro–produced embryo transfer in Jersey herds. J Dairy Sci 2024;107:9491–9503. doi: 10.3168/jds.2023-24530

119.
Holden SA, Butler ST: Review: applications and benefits of sexed semen in dairy and beef herds. Anim 2018;12:s97–103. doi: 10.1017/S1751731118000721

120.
Berry DP, Amer PR, Evans RD, et al: A breeding index to rank beef bulls for use on dairy females to maximize profit. J Dairy Sci 2019;102:10056–10072. doi: 10.3168/jds.2019-16912

121.
Hozé C, Baur A, Fritz S, et al: Prediction of gametic variance and its use in bovine breeding programs. In: Proceedings of the 12th World Congress on Genetics Applied to Livestock Production. Rotterdam; Wageningen Academic Publishers: 2022. p. 1837–1840. doi: 10.3920/978-90-8686-940-4_441

122.
González-Rodríguez N, Martínez-Rodero I, Scherzer J, et al: Vitrification and in-straw warming do not affect pregnancy rates of biopsied bovine embryos. Theriogenology 2022;191:221–230. doi: 10.1016/j.theriogenology.2022.07.021

123.
Leroy G, Danchin-Burge C, Verrier E, et al: Impact of the use of cryobank samples in a selected cattle breed: a simulation study. Genet Sel Evol 2011;43:36. doi: 10.1186/1297-9686-43-36

124.
Mota RR, Brito LF, Berry DP, et al: Editorial: beef on dairy: the use of a simple tool to improve both cattle production systems. Front Genet 2022;13:813949. doi: 10.3389/fgene.2022.813949

125.
Basiel BL, Felix TL: Board invited review: crossbreeding beef × dairy cattle for the modern beef production system. Transl Anim Sci 2022;6:txac025. doi: 10.1093/tas/txac025

126.
Boichard D, Ducrocq V, Croiseau P, et al: Genomic selection in domestic animals: principles, applications and perspectives. CR Biol 2016;339:274–277. doi: 10.1016/j.crvi.2016.04.007

127.
Wiggans GR, Carrillo JA: Genomic selection in United States dairy cattle. Front Genet 2022;13:994466. doi: 10.3389/fgene.2022.994466

128.
Schaeffer CH: Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet 2006;123:218–223. doi: 10.1111/j.1439-0388.2006.00595.x

129.
Mikkola M, Desmet KLJ, Kommisrud E, et al: Recent advancements to increase success in assisted reproductive technologies in cattle. Anim Reprod 2024;21:e20240031. doi: 10.1590/1984-3143-AR2024-0031

130.
Van Eenennaam AL: Current and future uses of genetic improvement technologies in livestock breeding programs. Anim Front 2025;15:80–90. doi: 10.1093/af/vfae042

131.
Lund MS, de Roos APW, de Vries AG, et al: A common reference population from four European Holstein populations increases reliability of genomic predictions. Genet Sel Evol 2011;43:43. doi: 10.1186/1297-9686-43-43

132.
VanRaden PM, Van Tassell CP, Wiggans GR, et al: Invited review: reliability of genomic predictions for North American Holstein bulls. J Dairy Sci 2009;92:16–24. doi: 10.3168/jds.2008-1514

133.
Andersson L, Archibald A, Bottema C, et al: Coordinated international action to accelerate genome-to-phenome with FAANG, the Functional Annotation of Animal Genomes project. Genome Biol 2015;16:57. doi: 10.1186/s13059-015-0622-4

134.
Pryce JE, Gredler B, Bolormaa S, et al: Short communication: genomic selection using a multi-breed, across-country reference population. J Dairy Sci 2011;94:2625–2630. doi: 10.3168/jds.2010-3719

135.
Prabhakar P, Bharti SK, Kumar P, et al: Challenges and strategies for sustainable dairy farming in India: a review. Int J Curr Microbiol Appl Sci 2020;(Special Issue 11):4065–4071.

136.
Sahana VN, Gowane GR, Nayee N, et al: Shared SNP effects across breeds increase the genomic prediction accuracy for numerically small breeds. Sci Rep 2025;15:31421. doi: 10.1038/s41598-025-15733-y

137.
Ministry of Fisheries, Animal Husbandry & Dairying. Azadi Ka Amrit Mahotsav: Rashtriya Gokul Mission: 2023. Available from: https://www.pib.gov.in/PressReleasePage.aspx?PRID=2099675 [cited 12 March 2026].

138.
Ministry of Fisheries, Animal Husbandry & Dairying. Azadi Ka Amrit Mahotsav: New Technologies in Animal Husbandry and Dairy. 2023. Available from: https://www.pib.gov.in/PressReleseDetailm.aspx?PRID=2149835 [cited 12 March 2026].

139.
Admin. Genomic Selection in Indian Cattle and Buffalo: A Game-Changer for Smallholder Dairy Farmers. 2025. Available from: https://baif.org.in/genomic-selection [cited 12 March 2026].

140.
Saravanan KA, Panigrahi M, Kumar H, et al: Progress and future perspectives of livestock genomics in India: a mini review. Anim Biotechnol 2023;34:1979–1987. doi: 10.1080/10495398.2022.2056046

141.
Burrow HM, Mrode R, Mwai AO, et al: Challenges and opportunities in applying genomic selection to ruminants owned by smallholder farmers. Agriculture 2021;11:1172. doi: 10.3390/agriculture11111172

142.
Li X, Wang S, Huang J, et al: Improving the accuracy of genomic prediction in Chinese Holstein cattle by using one-step blending. Genet Sel Evol 2014;46:66. doi: 10.1186/s12711-014-0066-4

143.
Wang W, Li L, Chen Y, et al: Whole-genome resequencing and genetic diversity of five indigenous cattle breeds from China. Sci Data 2026;13:282. doi: 10.1038/s41597-026-06610-y

144.
Liu D, Xu Z, Zhao W, et al: Genetic parameters and genome-wide association for milk production traits and somatic cell score in different lactation stages of Shanghai Holstein population. Front Genet 2022;13:940650. doi: 10.3389/fgene.2022.940650

145.
Liu Y, Liu S, Sheng H, et al: Revolutionizing cattle breeding: gene editing advancements for enhancing economic traits. Gene 2024;927:148595. doi: 10.1016/j.gene.2024.148595

146.
Madalena FE, Peixoto MGCD, Gibson J: Dairy cattle genetics and its applications in Brazil. Livest Res Rural Dev 2012;24:97.

147.
McManus C, Albuquerque LG, Dias LT, et al: Livestock breeding, conservation and genomics in Brazil: who, when, where, and what. Livest Sci 2024;281:105429. doi: 10.1016/j.livsci.2024.105429

148.
Marchioretto PV, Rabel RAC, Allen CA, et al: Development of genetically improved tropical-adapted dairy cattle. Anim Front 2023;13:7–15. doi: 10.1093/af/vfad050

149.
Santos JB, Salvarani FM: Impacts of climate change on cattle health and production in the Brazilian Amazon biome. Ruminants 2025;5:58. doi: 10.3390/ruminants5040058

150.
Johnston DJ, Tier B, Graser HU: Beef cattle breeding in Australia with genomics: opportunities and needs. Anim Prod Sci 2012;52:100–106. doi: 10.1071/AN11116

151.
Scott BA, Haile-Mariam M, Cocks BG, et al: How genomic selection has increased rates of genetic gain and inbreeding in the Australian national herd, genomic information nucleus, and bulls. J Dairy Sci 2021;104:11832–11849. doi: 10.3168/jds.2021-20326

152.
Burnham R: Optimising Beef Genetic Selection in Northern Australia. Nuffield Scholar Report. 2019. Available from: https://www.nuffieldscholar.org/sites/default/files/2021-02/Rebecca%20Burnham%20Nuffield%20Report_FINAL%20reviewed2_0.pdf [cited 13 May 2026].

153.
Muner RD, Moaeen-Ud-Din M, Saleem AH, et al: Genomic selection, gene editing, and reproductive biotechnology: a triad for the improvement of native buffalo breeds in a developing country perspective like Pakistan. Trop Anim Health Prod 2025;57:102. doi: 10.1007/s11250-025-04352-4

154.
Islam S, Reddy UK, Natarajan P, et al: Population demographic history and population structure for Pakistani Nili-Ravi breeding bulls based on SNP genotyping to identify genomic regions associated with male effects for milk yield and body weight. PLoS One 2020;15:e0242500. doi: 10.1371/journal.pone.0242500

155.
National Food Security and Research, Ministry of National Food Security, Pakistan. Pakistan Launches Historic Public-Private Genomics Venture to Revolutionize Food Security and Biotechnology. 2025. Available from: https://www.nfsr.gov.pk [cited 12 March 2026].

156.
Bai Z, Ma W, Ma L, et al: China’s livestock transition: driving forces, impacts, and consequences. Sci Adv 2018;4:eaar8534. doi: 10.1126/sciadv.aar8534

157.
ANI. India, Brazil seal landmark animal genomics partnership to transform global dairy future: a strategic South-South alliance for global food security. ANI News: 2025. Available from: https://www.aninews.in [cited 12 March 2026].
Published
2026-11-02
How to Cite
Kasimanickam , R. (2026). Integration of genomic selection and assisted embryo technologies for precision cattle breeding: opportunities, challenges and global perspectives. Clinical Theriogenology, 18. https://doi.org/10.58292/CT.v18.14424
Issue
Vol. 18 (2026): Special Issue in progress: Advances and Clinical Applications of Embryo Transfer and Assisted Reproductive Technologies in Domestic and Exotic Animals
Section
Review Reports
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Authors retain copyright of their work, with first publication rights granted to Clinical Theriogenology. Read more about copyright and licensing here.