Acta Vet. Brno 2021, 90: 145-154

https://doi.org/10.2754/avb202190020145

Boosting the potential of cattle breeding using molecular biology, genetics, and bioinformatics approaches – a review

Monika Čechová, Michaela Andrlíková

Veterinary Research Institute, Central European Institute of Technology (CEITEC), Genetics and Reproductive Biotechnologies, Brno, Czech Republic

Received September 22, 2020
Accepted May 26, 2021

References

1. Anton I, Húth B, Füller I, Gábor G, Holló G, Zsolnai A 2018: Effect of single-nucleotide polymorphisms on the breeding value of fertility and breeding value of beef in Hungarian Simmental cattle. Acta Vet Hung 66: 215-225 <https://doi.org/10.1556/004.2018.020>
2. Bach A 2012: Ruminant Nutrition Symposium: Optimizing performance of the offspring: Nourishing and managing the dam and postnatal calf for optimal lactation, reproduction, and immunity. J Anim Sci 90: 1835-1845 <https://doi.org/10.2527/jas.2011-4516>
3. Bell BR, McDaniel BT, Robison OW 1985: Effects of cytoplasmic inheritance on production traits of dairy cattle. J Dairy Sci 68: 2038-2051 <https://doi.org/10.3168/jds.S0022-0302(85)81066-3>
4. Berry DP, Wall E, Pryce JE 2014: Genetics and genomics of reproductive performance in dairy and beef cattle. Animal 8 Suppl 1: 105-121 <https://doi.org/10.1017/S1751731114000743>
5. Bickhart DM, McClure JC, Schnabel RD, Rosen BD, Medrano JF, Smith TPL 2020: Symposium review: Advances in sequencing technology herald a new frontier in cattle genomics and genome-enabled selection. J Dairy Sci 103: 5278-5290 <https://doi.org/10.3168/jds.2019-17693>
6. Boichard D, Chung H, Dassonneville R, David X, Eggen A, Fritz S, Gietzen KJ, Hayes BJ, Lawley CT Sonstegard TS 2012: Design of a bovine low-density SNP array optimized for imputation. PloS one 7: e34130 <https://doi.org/10.1371/journal.pone.0034130>
7. Bungartz L, Lucas-Hahn A, Rath D, Niemann H 1995: Collection of oocytes from cattle via follicular aspiration aided by ultrasound with or without gonadotropin pretreatment and in different reproductive stages. Theriogenology 43: 667-675 <https://doi.org/10.1016/0093-691X(94)00072-3>
8. Capitan A, Michot P, Baur A, Saintilan R, Hozé C, Valour D, Guillaume F, Boichon D, Barbat A, Boichard D, Schibler L Fritz S 2014: Genetic tools to improve reproduction traits in dairy cattle. Reprod Fertil Dev 27: 14-21 <https://doi.org/10.1071/RD14379>
9. Carlson DF, Lancto CA, Zang B, Kim ES, Walton M, Oldeschulte D, Seabury C, Sonstegard TS, Fahrenkrug SC 2016: Production of hornless dairy cattle from genome-edited cell lines. Nat Biotechnol 34: 479-481 <https://doi.org/10.1038/nbt.3560>
10. Carthy TR, Ryan DP, Fitzgerald AM, Evans RD, Berry DP 2016: Genetic relationships between detailed reproductive traits and performance traits in Holstein-Friesian dairy cattle. J Dairy Sci 99: 1286-1297 <https://doi.org/10.3168/jds.2015-9825>
11. Castro RJ 2016: Mitochondrial replacement therapy: the UK and US regulatory landscapes. J Law Biosci 3: 726-735 <https://doi.org/10.1093/jlb/lsw051>
12. Chen Z, Hagen DE, Wang J, Elsik CG, Ji T, Siqueira LG, Hansen PJ Rivera RM 2016: Global assessment of imprinted gene expression in the bovine conceptus by next generation sequencing. Epigenetics 11: 501-516 <https://doi.org/10.1080/15592294.2016.1184805>
13. Chenoweth PJ, McPherson FJ 2016: Bull breeding soundness, semen evaluation and cattle productivity. Anim Reprod Sci 169: 32-36 <https://doi.org/10.1016/j.anireprosci.2016.03.001>
14. Colleau J-J, Tual K, de Preaumont H, Regaldo D 2009: A mating method accounting for inbreeding and multi-trait selection in dairy cattle populations. Genet Sel Evol 41: 7 <https://doi.org/10.1186/1297-9686-41-7>
15. Davis SR, Spelman RJ, Littlejohn MD 2017: Breeding and Genetics Symposium: Breeding heat tolerant dairy cattle: the case for introgression of the “slick” prolactin receptor variant into Bos taurus dairy breeds. J Anim Sci 95: 1788-1800
16. de Sousa RV, da Silva Cardoso CR, Butzke G, Dode MAN, Rumpf R, Franco MM 2016: Biopsy of bovine embryos produced in vivo and in vitro does not affect pregnancy rates. Theriogenology 90: 25-31 <https://doi.org/10.1016/j.theriogenology.2016.11.003>
17. De Vries A 2017: Economic trade-offs between genetic improvement and longevity in dairy cattle. J Dairy Sci 100: 4184-4192 <https://doi.org/10.3168/jds.2016-11847>
18. Diskin MG, Waters SM, Parr MH, Kenny DA 2016: Pregnancy losses in cattle: potential for improvement. Reprod Fertil Dev 28: 83-93 <https://doi.org/10.1071/RD15366>
19. Fonseca PAS, Dos Santos FC, Lam S, Suárez-Vega A, Miglior F, Schenkel FS, Diniz LAF, Id-Lahoucine S, Carvalho MRS, Cánovas A 2018: Genetic mechanisms underlying spermatic and testicular traits within and among cattle breeds: systematic review and prioritization of GWAS results. J Anim Sci 96: 4978-4999 <https://doi.org/10.1093/jas/sky073.023>
20. Gao Y, Wu H, Wang Y, Liu X, Chen L, Li Q, Cui C, Liu X, Zhang J, Zhang Y 2017: Single Cas9 nickase induced generation of NRAMP1 knockin cattle with reduced off-target effects. Genome Biol 18: 13 <https://doi.org/10.1186/s13059-016-1144-4>
21. García-Ruiz A, Cole JB, VanRaden PM, Wiggans GR, Ruiz-López FJ, Van Tassell CP 2016: Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proc Natl Acad Sci USA 113: 3995-4004 <https://doi.org/10.1073/pnas.1519061113>
22. Garner DL, Seidel GE 2008: History of commercializing sexed semen for cattle. Theriogenology 69: 886-895 <https://doi.org/10.1016/j.theriogenology.2008.01.006>
23. Goddard ME, Hayes BJ 2007: Genomic selection. J Anim Breed Genet 124: 323-330 <https://doi.org/10.1111/j.1439-0388.2007.00702.x>
24. Goossens K, Van Soom A, Van Poucke M, Vandaele L, Vandesompele J, Van Zeveren A, Peelman LJ 2007: Identification and expression analysis of genes associated with bovine blastocyst formation. BMC Dev Biol 7: 64 <https://doi.org/10.1186/1471-213X-7-64>
25. Graf A, Krebs S, Heininen-Brown M, Zakhartchenko V, Blum H Wolf E 2014: Genome activation in bovine embryos: review of the literature and new insights from RNA sequencing experiments. Anim Reprod Sci 149: 46-58 <https://doi.org/10.1016/j.anireprosci.2014.05.016>
26. Griffin DK, JTurner K, Silvestri G, Smith C, Dobson G, Black DH, Sinclair KD, Handyside AH 2019: The use of Karyomapping for genomic evaluation and PGT-A of preimplantation cattle embryos: the first live-born calves. Reprod. Biomed. Online 38: 54-55
27. Hayes BJ, Daetwyler HD 2019: 1000 bull genomes project to map simple and complex genetic traits in cattle: applications and outcomes. Annual review of animal biosciences 7: 89-102 <https://doi.org/10.1146/annurev-animal-020518-115024>
28. Herbert M, Turnbull D 2018: Progress in mitochondrial replacement therapies. Nat Rev Mol Cell Biol 19: 71-72 <https://doi.org/10.1038/nrm.2018.3>
29. Hill GE, Havird JC, Sloan DB, Burton RS, Greening C, Dowling DK 2019: Assessing the fitness consequences of mitonuclear interactions in natural populations. Biol Rev Camb Philos Soc 94: 1089-1104 <https://doi.org/10.1111/brv.12493>
30. Hornak M, Kubicek D, Broz P, Hulinska P, Hanzalova K, Griffin D, Machatkova M, Rubes J 2016: Aneuploidy detection and mtDNA quantification in bovine embryos with different cleavage onset using a next-generation sequencing-based protocol. Cytogenet Genome Res 150: 60-67 <https://doi.org/10.1159/000452923>
31. Howard JT, Pryce JE, Baes C, Maltecca C 2017: Invited review: Inbreeding in the genomics era: Inbreeding, inbreeding depression, and management of genomic variability. J Dairy Sci 100: 6009-6024 <https://doi.org/10.3168/jds.2017-12787>
32. Hsu PD, Lander ES, Zhang F 2014: Development and applications of CRISPR-Cas9 for genome engineering. Cell 157: 1262-1278 <https://doi.org/10.1016/j.cell.2014.05.010>
33. Jami E, White BA, Mizrahi I 2014: Potential role of the bovine rumen microbiome in modulating milk composition and feed efficiency. PLoS One 9: e85423 <https://doi.org/10.1371/journal.pone.0085423>
34. Jiang Z, Sun J, Dong H, Luo O, Zheng X, Obergfell C, Tang Y, Bi J, O’Neill R, Ruan Y, Chen J, Tian XC 2014: Transcriptional profiles of bovine in vivo pre-implantation development. BMC Genomics 15: 756 <https://doi.org/10.1186/1471-2164-15-756>
35. Kaniyamattam K, Block J, Hansen PJ, De Vries A 2017: Comparison between an exclusive in vitro–produced embryo transfer system and artificial insemination for genetic, technical, and financial herd performance. J Dairy Sci 100: 5729-5745 <https://doi.org/10.3168/jds.2016-11979>
36. Karakaya E, Yilmazbas-Mecitoglu G, Keskin A, Alkan A, Tasdemir U, Santos JE Gumen A 2014: Fertility in dairy cows after artificial insemination using sex-sorted sperm or conventional semen. Reprod Domest Anim 49: 333-337 <https://doi.org/10.1111/rda.12280>
37. Kasinathan P, Wei H, Xiang T, Molina JA, Metzger J, Broek D, Kasinathan S, Faber DC, Allan MF 2015: Acceleration of genetic gain in cattle by reduction of generation interval. Sci Rep 5: 8674 <https://doi.org/10.1038/srep08674>
38. Kiser JN, Keuter EM, Seabury CM, Neupane M, Moraes JGN, Dalton J, Burns GW, Spencer TE, Neibergs HL 2019: Validation of 46 loci associated with female fertility traits in cattle. BMC Genomics 20: 576 <https://doi.org/10.1186/s12864-019-5935-3>
39. Lamb HJ, Ross EM, Nguyen LT, Lyons RE, Moore SS, Hayes BJ 2020: Characterization of the poll allele in Brahman cattle using long-read Oxford Nanopore sequencing. J Anim Sci 98: skaa127 <https://doi.org/10.1093/jas/skaa127>
40. Lee RSF, Peterson AJ, Donnison MJ, Ravelich S, Ledgard AM, Li N, Oliver JE, Miller AL, Tucker FC, Breier B, Wells DN 2004: Cloned cattle fetuses with the same nuclear genetics are more variable than contemporary half-siblings resulting from artificial insemination and exhibit fetal and placental growth deregulation even in the first trimester. Biol Reprod 70: 1-11 <https://doi.org/10.1095/biolreprod.103.020982>
41. Legarra A, Aguilar I, Misztal I 2009: A relationship matrix including full pedigree and genomic information. J Dairy Sci 92: 4656-4663 <https://doi.org/10.3168/jds.2009-2061>
42. Li F, Li C, Chen Y, Liu J, Zhang C, Irving B, Fitzsimmons C, Plastow G, Guan LL 2019: Host genetics influence the rumen microbiota and heritable rumen microbial features associate with feed efficiency in cattle. Microbiome 7: 92 <https://doi.org/10.1186/s40168-019-0699-1>
43. Lopez H, Satter LD, Wiltbank MC 2004: Relationship between level of milk production and estrous behavior of lactating dairy cows. Anim Reprod Sci 81: 209-223 <https://doi.org/10.1016/j.anireprosci.2003.10.009>
44. Lund MS, de Roos APW, de Vries AG, Druet T, Ducrocq V, Fritz S, Guillaume F, Guldbrandtsen B, Liu Z, Reents R 2011: A common reference population from four European Holstein populations increases reliability of genomic predictions. Genet Sel Evol 43: 43 <https://doi.org/10.1186/1297-9686-43-43>
45. Mäki-Tanila A, Webster L 2019: Heritability, SNP, inbreeding, dairy cattle, genomic selection—and other keywords. J Anim Breed Genet 136: 1-2 <https://doi.org/10.1111/jbg.12377>
46. Malmuthuge N, Guan LL 2017: Understanding the gut microbiome of dairy calves: Opportunities to improve early-life gut health. J Dairy Sci 100: 5996-6005 <https://doi.org/10.3168/jds.2016-12239>
47. Marshall K, Gibson JP, Mwai O, Mwacharo JM, Haile A, Getachew T, Mrode R, Kemp SJ 2019: Livestock genomics for developing countries–african examples in practice. Front Genet 10: 297 <https://doi.org/10.3389/fgene.2019.00297>
48. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM Daly MJ 2019: Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet 51: 584-591 <https://doi.org/10.1038/s41588-019-0379-x>
49. McConnachie E, Hötzel MJ, Robbins JA, Shriver A, Weary DM, von Keyserlingk MAG 2019: Public attitudes towards genetically modified polled cattle. PLoS One 14: e0216542 <https://doi.org/10.1371/journal.pone.0216542>
50. McFarlane GR, Salvesen HA, Sternberg A, Lillico SG 2019: On-farm livestock genome editing using cutting edge reproductive technologies. Front Sustain Food Syst 3: 106 <https://doi.org/10.3389/fsufs.2019.00106>
51. McLean Z, Oback B, Laible G 2020: Embryo-mediated genome editing for accelerated genetic improvement of livestock. Front Agric Sci Eng 7: 148-160 <https://doi.org/10.15302/J-FASE-2019305>
52. Miller KA, Bibber-Krueger V, Drouillard JS 2013: Orally dosing steers with Lactipro (Megasphaera elsdenii) decreases the quantity of roughages fed during finishing. Kans Agric Exp Stn Res Rep 0: 55-58
53. Mueller ML, Cole JB, Sonstegard TS, Van Eenennaam AL 2019: Comparison of gene editing versus conventional breeding to introgress the POLLED allele into the US dairy cattle population. J Dairy Sci 102: 4215-4226 <https://doi.org/10.3168/jds.2018-15892>
54. Newton JE, Hayes BJ, Pryce JE 2018: The cost-benefit of genomic testing of heifers and using sexed semen in pasture-based dairy herds. J Dairy Sci 101: 6159-6173 <https://doi.org/10.3168/jds.2017-13476>
55. Norris AL, Lee SS, Greenlees KJ, Tadesse DA, Miller MF, Lombardi HA 2020: Template plasmid integration in germline genome-edited cattle. Nat Biotechnol 38: 163-164 <https://doi.org/10.1038/s41587-019-0394-6>
56. Norton T, Berckmans D 2017: Developing precision livestock farming tools for precision dairy farming. Anim Front 7: 18-23 <https://doi.org/10.2527/af.2017.0104>
57. O’Hara E, Neves ALA, Song Y, Guan LL 2020: The role of the gut microbiome in cattle production and health: Driver or passenger. Annu Rev Anim Biosci 8: 199-220 <https://doi.org/10.1146/annurev-animal-021419-083952>
58. Oliveira HR, Brito LF, Lourenco DAL, Silva FF, Jamrozik J, Schaeffer LR, Schenkel FS 2019: Invited review: Advances and applications of random regression models: From quantitative genetics to genomics. J Dairy Sci 102: 7664-7683 <https://doi.org/10.3168/jds.2019-16265>
59. Ross PJ, Cibelli JB 2010: Bovine somatic cell nuclear transfer. Methods Mol Biol 636: 155-177 <https://doi.org/10.1007/978-1-60761-691-7_10>
60. Scheper C, Wensch-Dorendorf M, Yin T, Dressel H, Swalve H, König S 2016: Evaluation of breeding strategies for polledness in dairy cattle using a newly developed simulation framework for quantitative and Mendelian traits. Genet Sel Evol 48: 1-11 <https://doi.org/10.1186/s12711-016-0228-7>
61. Schutz MM, Freeman AE, Lindberg GL, Koehler CM, Beitz DC 1994: The effect of mitochondrial DNA on milk production and health of dairy cattle. Livest Prod Sci 37: 283-295 <https://doi.org/10.1016/0301-6226(94)90123-6>
62. Seidel GE 2009: Brief introduction to whole-genome selection in cattle using single nucleotide polymorphisms. Reprod Fert Develop 22: 138-144 <https://doi.org/10.1071/RD09220>
63. Sejian V, Hyder I, Ezeji T, Lakritz J, Bhatta R, Ravindra JP, Prasad CS, Lal R 2015: Global warming: Role of livestock. In Springer Publisher (Ed.): Climate Change Impact on Livestock: Adaptation and Mitigation, pp. 141-169
64. Srirattana K, John JCS 2017: Manipulating the mitochondrial genome to enhance cattle embryo development. G3: Genes Genom Genet 7: 2065-2080 <https://doi.org/10.1534/g3.117.042655>
65. Strandén I, Kantanen J, Russo I-RM, Orozco-terWengel P, Bruford MW 2019: Genomic selection strategies for breeding adaptation and production in dairy cattle under climate change. Heredity 123: 307-317 <https://doi.org/10.1038/s41437-019-0207-1>
66. Suh TK, Schenk JL, Seidel GE 2005: High pressure flow cytometric sorting damages sperm. Theriogenology 64: 1035-1048 <https://doi.org/10.1016/j.theriogenology.2005.02.002>
67. Taylor JF, Taylor KH, Decker JE 2016: Holsteins are the genomic selection poster cows. Proc Natl Acad Sci USA 113: 7690-7692 <https://doi.org/10.1073/pnas.1608144113>
68. Taylor JF, Schnabel RD, Sutovsky P 2018: Review: Genomics of bull fertility. Animal 12: 172-183 <https://doi.org/10.1017/S1751731118000599>
69. Tian C, Gregersen PK, Seldin MF 2008: Accounting for ancestry: population substructure and genome-wide association studies. Human Mol Genet 17: 143-150 <https://doi.org/10.1093/hmg/ddn268>
70. Tubman LM, Brink Z, Suh TK, Seidel GE 2004: Characteristics of calves produced with sperm sexed by flow cytometry/cell sorting. J Anim Sci 82: 1029-1036 <https://doi.org/10.2527/2004.8241029x>
71. Tutt DAR, Passaro C, Whitworth DJ, Holland MK 2020: Laser assisted blastomere extrusion biopsy of in vitro produced cattle embryos-A potential high throughput, minimally invasive approach for sampling pre-morula and morula stage embryos. Anim Reprod Sci 219: 106546 <https://doi.org/10.1016/j.anireprosci.2020.106546>
72. Urrego R, Rodriguez-Osorio N, Niemann H 2014: Epigenetic disorders and altered gene expression after use of Assisted Reproductive Technologies in domestic cattle. Epigenetics 9: 803-815 <https://doi.org/10.4161/epi.28711>
73. VanRaden PM, Sanders AH, Tooker ME, Miller RH, Norman HD, Kuhn MT, Wiggans GR 2004: Development of a national genetic evaluation for cow fertility. J Dairy Sci 87: 2285-2292 <https://doi.org/10.3168/jds.S0022-0302(04)70049-1>
74. Vishwanath R, Moreno JF 2018: Review: Semen sexing - current state of the art with emphasis on bovine species. Animal 12: 85-96 <https://doi.org/10.1017/S1751731118000496>
75. Wang J, Xiang H, Liu L, Kong M, Yin T, Zhao X 2017: Mitochondrial haplotypes influence metabolic traits across bovine inter-and intra-species cybrids. Sci Rep 7: 1-9
76. Wei S, Weiss ZR, Gaur P, Forman E, Williams Z 2018: Rapid preimplantation genetic screening using a handheld, nanopore-based DNA sequencer. Fertil Steril 110: 910-916. e2 <https://doi.org/10.1016/j.fertnstert.2018.06.014>
77. Wilmut I, Young L, DeSousa P, King T 2000: New opportunities in animal breeding and production - an introductory remark. Anim Reprod Sci 60-61: 5-14 <https://doi.org/10.1016/S0378-4320(00)00142-1>
78. Wolff JN, Ladoukakis ED, Enríquez JA, Dowling DK 2014: Mitonuclear interactions: evolutionary consequences over multiple biological scales. Philos Trans R Soc Lond B Biol Sci 369: 20130443 <https://doi.org/10.1098/rstb.2013.0443>
79. Xue M-Y, Sun H-Z, Wu X-H, Liu J-X, Guan LL 2020: Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance. Microbiome 8: 1-19
80. Yáñez-Ruiz DR, Abecia L, Newbold CJ 2015: Manipulating rumen microbiome and fermentation through interventions during early life: a review. Front Microbiol 6: 1133 <https://doi.org/10.3389/fmicb.2015.01133>
81. Young J, Skarlupka JH, Cox MS, Resende RT, Fischer A, Kalscheur KF, McClure JC, Cole JB, Suen G, Bickhart DM 2020: Validating the use of bovine buccal sampling as a proxy for the rumen microbiota using a time course and random forest classification approach. Appl Environ Microbiol 86: e00861-20 <https://doi.org/10.1128/AEM.00861-20>
82. Yue X-P, Dechow C, Liu W-S 2015: A limited number of Y chromosome lineages is present in North American Holsteins. J Dairy Sci 98: 2738-2745 <https://doi.org/10.3168/jds.2014-8601>
83. Yum SY, Youn KY, Choi WJ, Jang G 2018: Development of genome engineering technologies in cattle: from random to specific. J Anim Sci Biotechnol 9: 16 <https://doi.org/10.1186/s40104-018-0232-6>
84. Zaidi AA, Makova KD 2019: Investigating mitonuclear interactions in human admixed populations. Nat Ecol Evol 3: 213-222 <https://doi.org/10.1038/s41559-018-0766-1>
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