Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-22T16:48:46.002Z Has data issue: false hasContentIssue false
  • English
  • Français

A review of traditional and machine learning methods applied to animal breeding

Part of: Big Data

Published online by Cambridge University Press:  18 December 2019

Shadi Nayeri ,
Mehdi Sargolzaei  and
Dan Tulpan Open the ORCID record for Dan Tulpan [Opens in a new window]
Shadi Nayeri
Affiliation:
Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
Mehdi Sargolzaei
Affiliation:
Select Sires Inc., Plain City, Ohio, 43064, USA Department of Pathobiology, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
Dan Tulpan*
Affiliation:
Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
*
Author for correspondence: Dan Tulpan, Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, N1G 2W1, Canada. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The current livestock management landscape is transitioning to a high-throughput digital era where large amounts of information captured by systems of electro-optical, acoustical, mechanical, and biosensors is stored and analyzed on a daily and hourly basis, and actionable decisions are made based on quantitative and qualitative analytic results. While traditional animal breeding prediction methods have been used with great success until recently, the deluge of information starts to create a computational and storage bottleneck that could lead to negative long-term impacts on herd management strategies if not handled properly. A plethora of machine learning approaches, successfully used in various industrial and scientific applications, made their way in the mainstream approaches for livestock breeding techniques, and current results show that such methods have the potential to match or surpass the traditional approaches, while most of the time they are more scalable from a computational and storage perspective. This article provides a succinct view on what traditional and novel prediction methods are currently used in the livestock breeding field, how successful they are, and how the future of the field looks in the new digital agriculture era.

Keywords

Animal breedinganimal healthmachine learningpredictionregression
Type
Review Article
Information
Animal Health Research Reviews , Volume 20 , Issue 1 , June 2019 , pp. 31 - 46
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdel-Azim, G and Freeman, AE (2002) Superiority of QTL-assisted selection in dairy cattle breeding schemes. Journal of Dairy Science 85, 1869–1880.CrossRefGoogle ScholarPubMed
Abo-Ismail, MK, Brito, LF, Miller, SP, Sargolzaei, M, Grossi, DA, Moore, SS, Plastow, G, Stothard, P, Nayeri, S, and Schenkel, FS (2017) Genome-wide association studies and genomic prediction of breeding values for calving performance and body conformation traits in Holstein cattle. Genetics Selection Evolution 49, 82.CrossRefGoogle ScholarPubMed
Aguilar, I, Misztal, I, Johnson, DL, 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 ScholarPubMed
Akanno, EC, Chen, L, Abo-Ismail, MK, Crowley, JJ, Wang, Z, Li, C, Basarab, JA, MacNeil, MS, Plastow, GS (2018) Genome-wide association scan for heterotic quantitative trait loci in multi-breed and crossbred beef cattle. Genetics Selection Evolution 50, 48.CrossRefGoogle ScholarPubMed
Alados, I, Mellado, JA, Ramos, F and Alados-Arboledas, L (2004) Estimating UV erythemal irradiance by means of neural networks. Photochemistry and Photobiology 80, 351–358.CrossRefGoogle ScholarPubMed
Andersson, L (2001) Genetic dissection of phenotypic diversity in farm animals. Nature Reviews Genetics 2, 130–138.CrossRefGoogle ScholarPubMed
Ashari, A, Paryudi, I and Min, A (2013) Performance comparison between naïve Bayes, decision tree and k-nearest neighbor in searching alternative design in an energy simulation tool. International Journal of Advanced Computer Science and Applications 4, 33–39.CrossRefGoogle Scholar
Bayes, T (1763) LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes FR S. communicated by Mr. Price, in a letter to John Canton, A. M. F. R. S. Philosophical Transactions of the Royal Society of London 53, 370–418.Google Scholar
Ben-Hur, A, Horn, D, Siegelmann, HT and Vapnik, V (2001) Support vector clustering. Journal of Machine Learning Research 2, 125–137.Google Scholar
Berlinet, A and Thomas-Agnan, C (2004) Theory. In Reproducing Kernel Hilbert Spaces in Probability and Statistics. Boston, MA: Springer US, pp. 1–54. doi: 10.1007/978-1-4419-9096-9_1.CrossRefGoogle Scholar
Bovine HapMap Consortium TBH, Gibbs, RA, Taylor, JF, Van Tassell, CP, Barendse, W, Eversole, KA, Gill, CA, Green, RD, Hamernik, DL, Kappes, SM, Lien, S, Matukumalli, LK, McEwan, JC, Nazareth, LV, Schnabel, RD, Weinstock, GM, Wheeler, DA, Ajmone-Marsan, P, Boettcher, PJ, Caetano, AR, Garcia, JF, Hanotte, O, Mariani, P, Skow, LC, Sonstegard, TS, Williams, JL, Diallo, B, Hailemariam, L, Martinez, ML, Morris, CA, Silva, LO, Spelman, RJ, Mulatu, W, Zhao, K, Abbey, CA, Agaba, M, Araujo, FR, Bunch, RJ, Burton, J, Gorni, C, Olivier, H, Harrison, BE, Luff, B, Machado, MA, Mwakaya, J, Plastow, G, Sim, W, Smith, T, Thomas, MB, Valentini, A, Williams, P, Womack, J, Woolliams, JA, Liu, Y, Qin, X, Worley, KC, Gao, C, Jiang, H, Moore, SS, Ren, Y, Song, XZ, Bustamante, CD, Hernandez, RD, Muzny, DM, Patil, S, San Lucas, A, Fu, Q, Kent, MP, Vega, R, Matukumalli, A, McWilliam, S, Sclep, G, Bryc, K, Choi, J, Gao, H, Grefenstette, JJ, Murdoch, B, Stella, A, Villa-Angulo, R, Wright, M, Aerts, J, Jann, O, Negrini, R, Goddard, ME, Hayes, BJ, Bradley, DG, Barbosa da Silva, M, Lau, LP, Liu, GE, Lynn, DJ, Panzitta, F and Dodds, KG (2009) Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science (New York, N.Y.) 324, 528–532.Google ScholarPubMed
Breiman, L (1996) Bagging predictors. Machine Learning 24, 123–140.CrossRefGoogle Scholar
Breiman, L (2001) Random forests. Machine Learning 45, 5–32.CrossRefGoogle Scholar
Campos, G, Hickey, JM, Pong-Wong, R, Daetwyler, HD and Calus, MPL (2013) Whole-Genome regression and prediction methods applied to plant and animal breeding. Genetics 193, 327.CrossRefGoogle Scholar
Cardoso, FF, Gomes, CCG, Sollero, BP, Oliveira, MM, Roso, VM, Piccoli, ML, Higa, RH, Yokoo, MJ, Caetano, AR and Aguilar, I (2015) Genomic prediction for tick resistance in Braford and Hereford cattle1. Journal of Animal Science 93, 2693–2705.CrossRefGoogle Scholar
Chen, T and Guestrin, C (2016) Xgboost: a scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, pp. 785–794.CrossRefGoogle Scholar
Chen, Z, Yao, Y, Ma, P, Wang, Q and Pan, Y (2018) Haplotype-based genome-wide association study identifies loci and candidate genes for milk yield in Holsteins. PLoS One 13, e0192695.Google ScholarPubMed
Clark, P and Niblett, T (1989) The CN2 induction algorithm. Machine Learning 3, 261–283.CrossRefGoogle Scholar
Cole, JB, Wiggans, GR, Ma, L, Sonstegard, TS, Lawlor, TJ Jr, Crooker, BA, Van Tassell, CP, Yang, J, Wang, S, Matukumalli, LK and Da, Y (2011) Genome-wide association analysis of thirty-one production, health, reproduction and body conformation traits in contemporary U.S. Holstein cows. BMC Genomics 12, 408.CrossRefGoogle ScholarPubMed
Collard, BL, Boettcher, PJ, Dekkers, JCM, Petitclerc, D and Schaeffer, LR (2000) Relationships between energy balance and health traits of dairy cattle in early lactation. Journal of Dairy Science 83, 2683–2690.CrossRefGoogle ScholarPubMed
Daetwyler, HD, Pong-Wong, R, Villanueva, B and Woolliams, JA (2010) The impact of genetic architecture on genome-wide evaluation methods. Genetics 185, 1021–1031.CrossRefGoogle ScholarPubMed
Daetwyler, HD, Capitan, A, Pausch, H, Stothard, P, van Binsbergen, R, Brøndum, RF, Liao, X, Djari, A, Rodriguez, SC, Grohs, C, Esquerré, D, Bouchez, O, Rossignol, M-N, Klopp, C, Rocha, D, Fritz, S, Eggen, A, Bowman, PJ, Coote, D, Chamberlain, AJ, Anderson, C, VanTassell, CP, Hulsegge, I, Goddard, ME, Guldbrandtsen, B, Lund, MS, Veerkamp, RF, Boichard, DA, Fries, R and Hayes, BJ (2014) Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nature Genetics 46, 858–865.CrossRefGoogle ScholarPubMed
Dekkers, JCM (2004) Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons. Journal of Animal Science 82(E-Suppl), E313–E328.Google ScholarPubMed
Dekkers, JCM (2012) Application of genomics tools to animal breeding. Current Genomics 13, 207.CrossRefGoogle ScholarPubMed
Dekkers, JCM and Hospital, F (2002) The use of molecular genetics in the improvement of agricultural populations. Nature Reviews Genetics 3, 22–32.CrossRefGoogle ScholarPubMed
Efron, B and Tibshirani, R (1994) An Introduction to the Bootstrap. Boca Raton/London, UK: Chapman & Hall.Google Scholar
Ehret, A, Hochstuhl, D, Krattenmacher, N, Tetens, J, Klein, M, Gronwald, W and Thaller, G (2015) Short communication: use of genomic and metabolic information as well as milk performance records for prediction of subclinical ketosis risk via artificial neural networks. Journal of Dairy Science 98, 322–329.CrossRefGoogle ScholarPubMed
Erbe, M, Hayes, BJ, Matukumalli, LK, Goswami, S, Bowman, PJ, Reich, CM, Mason, BA and Goddard, ME (2012) Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. Journal of Dairy Science 95, 4114–4129.CrossRefGoogle ScholarPubMed
Falconer, DS, Douglas, S and Mackay, TFC (1996) Introduction to Quantitative Genetics. Harlow, Essex, England: Burnt Mill, Longman.Google Scholar
Frank, E, Trigg, L, Holmes, G and Witten, IH (2000) Technical note: naive Bayes for regression. Machine Learning 41, 5–25.CrossRefGoogle Scholar
Freund, Y and Schapire, RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55, 119–139.CrossRefGoogle Scholar
Gianola, D, de los Campos, G, Hill, WG, Manfredi, E and Fernando, R (2009) Additive genetic variability and the Bayesian alphabet. Genetics 183, 347–363.CrossRefGoogle ScholarPubMed
Gianola, D, Okut, H, Weigel, KA and Rosa, GJ (2011) Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat. BMC Genetics 12, 87.CrossRefGoogle ScholarPubMed
Gianola, D, Hayes, BJ, Goddard, ME, Sorensen, D and Calus, MPL (2013) Priors in whole-genome regression: the Bayesian alphabet returns. Genetics 194, 573–596.CrossRefGoogle ScholarPubMed
Gianola, D, Weigel, KA, Krämer, N, Stella, A and Schön, C-C (2014) Enhancing genome-enabled prediction by bagging genomic BLUP. PLoS One 9, e91693.CrossRefGoogle ScholarPubMed
Glazier, AM, Nadeau, JH and Aitman, TJ (2002) Finding genes that underlie Complex traits. Science 298, 2345–2349.CrossRefGoogle ScholarPubMed
González-Recio, O and Forni, S (2011) Genome-wide prediction of discrete traits using Bayesian regressions and machine learning. Genetics Selection Evolution 43, 7.CrossRefGoogle ScholarPubMed
González-Recio, Oscar, Gianola, D, Long, N, Weigel, KA, Rosa, GJM and Avendaño, S (2008) Nonparametric methods for incorporating genomic information into genetic evaluations: an application to mortality in broilers. Genetics 178, 2305–2313.CrossRefGoogle ScholarPubMed
González-Recio, O, Jiménez-Montero, JA and Alenda, R (2013) The gradient boosting algorithm and random boosting for genome-assisted evaluation in large data sets. Journal of Dairy Science 96, 614–624.CrossRefGoogle ScholarPubMed
González-Recio, O, Rosa, GJM and Gianola, D (2014) Machine learning methods and predictive ability metrics for genome-wide prediction of complex traits. Livestock Science 166, 217–231.CrossRefGoogle Scholar
Habier, D, Fernando, RL and Dekkers, JCM (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177, 2389–2397.CrossRefGoogle ScholarPubMed
Habier, David, Fernando, RL, Kizilkaya, K and Garrick, DJ (2011) Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 12, 186.CrossRefGoogle ScholarPubMed
Hayes, M (2013) Algorithms to Resolve Large-Scale and Complex Structural Variants in the Human Genome (PhD thesis). Case Western Reserve University, Electrical Engineering and Computer Science.Google Scholar
Hayes, BJ, Bowman, PJ, Chamberlain, AC, Verbyla, K and Goddard, ME (2009) Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genetics Selection Evolution 41, 51.CrossRefGoogle ScholarPubMed
Hempstalk, K, McParland, S and Berry, DP (2015) Machine learning algorithms for the prediction of conception success to a given insemination in lactating dairy cows. Journal of Dairy Science 98, 5262–5273.CrossRefGoogle ScholarPubMed
Henderson, CR (1985) Best linear unbiased prediction of nonadditive genetic merits in noninbred populations. Journal of Animal Science 60, 111–117.CrossRefGoogle Scholar
Hidalgo, A, Zouari, F, Knijn, H and van der Beek, S (2018) Prediction of postpartum diseases of dairy cattle using machine learning. Proceedings of the World Congress on Genetics Applied to Livestock Production. World Congress on Genetics Applied to Livestock Production. p. 104. Available at http://www.wcgalp.org/proceedings/2018/prediction-postpartum-diseases-dairy-cattle-using-machine-learning (Accessed 3 May 2019).Google Scholar
Hoggart, CJ, Whittaker, JC, De Iorio, M and Balding, DJ (2008) Simultaneous analysis of All SNPs in genome-wide and Re-sequencing association studies. PLoS Genetics 4, e1000130.CrossRefGoogle ScholarPubMed
Iheshiulor, OOM, Woolliams, JA, Svendsen, M, Solberg, T and Meuwissen, THE (2017) Simultaneous fitting of genomic-BLUP and Bayes-C components in a genomic prediction model. Genetics Selection Evolution 49, 63.CrossRefGoogle Scholar
Kennedy, BW, Quinton, M and van Arendonk, JAM (1992) Estimation of effects of single genes on quantitative traits. Journal of Animal Science 70, 2000–2012.CrossRefGoogle ScholarPubMed
Kingsford, C and Salzberg, SL (2008) What are decision trees? Nature Biotechnology 26, 1011–1013.CrossRefGoogle ScholarPubMed
Kizilkaya, K, Tait, RG, Garrick, DJ, Fernando, RL and Reecy, JM (2011) Whole genome analysis of infectious bovine keratoconjunctivitis in Angus cattle using Bayesian threshold models. BMC Proceedings 5 (Suppl 4), S22.CrossRefGoogle ScholarPubMed
Kohavi, R and Kohavi, R (1995) A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. 1137–1143. Available at https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.529 (Accessed 19 June 2019).Google Scholar
LeCun, Y, Bengio, Y and Hinton, G (2015) Deep learning. Nature 521, 436–444.CrossRefGoogle ScholarPubMed
Li, H, Su, G, Jiang, L and Bao, Z (2017) An efficient unified model for genome-wide association studies and genomic selection. Genetics Selection Evolution 49, 64.CrossRefGoogle ScholarPubMed
Li, B, Zhang, N, Wang, Y.-G, George, AW, Reverter, A and Li, Y (2018 a) Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods. Frontiers in Genetics 9, 237–256.CrossRefGoogle ScholarPubMed
Li, Y, Raidan, FSS, Li, B, Vitezica, ZG and Reverter, A (2018 b) Using Random Forests as a prescreening tool for genomic prediction: impact of subsets of SNPs on prediction accuracy of total genetic values. Proceedings of the 11th World Congress on Genetics Applied to Livestock Production (WCGALP). p. 248. Available at http://www.wcgalp.org/system/files/proceedings/2018/using-random-forests-prescreening-tool-genomic-prediction-impact-subsets-snps-prediction-accuracy.pdf (Accessed 19 June 2019).Google Scholar
Long, N, Gianola, D, Rosa, GJM, Weigel, KA and Avendaño, S (2007) Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers. Journal of Animal Breeding and Genetics 124, 377–389.CrossRefGoogle ScholarPubMed
Lynch, M and Walsh, B (1998) Genetics and Analysis of Quantitative Traits. Sinauer: Cary, NC, USA. Available at https://global.oup.com/ushe/product/genetics-and-analysis-of-quantitative-traits-9780878934812?cc=us&lang=en& (Accessed 28 May 2019).Google Scholar
Madsen, P and Jensen, J (2013) A User's Guide to DMU A Package for Analysing Multivariate Mixed Models. Available at http://dmu.agrsci.dk (Accessed 19 June 2019).Google Scholar
Mai, MD, Sahana, G, Christiansen, FB and Guldbrandtsen, B (2010) A genome-wide association study for milk production traits in Danish Jersey cattle using a 50K single nucleotide polymorphism chip1. Journal of Animal Science 88, 3522–3528.CrossRefGoogle Scholar
Mammadova, N and Keskin, I (2013) Application of the support vector machine to predict subclinical mastitis in dairy cattle. The Scientific World Journal 2013, 603897.CrossRefGoogle ScholarPubMed
Manton, JH and Amblard, P-O (2014) A Primer on Reproducing Kernel Hilbert Spaces. Available at http://arxiv.org/abs/1408.0952 (Accessed 18 June 2019).Google Scholar
Matthews, BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA) – Protein Structure 405, 442–451.CrossRefGoogle ScholarPubMed
Matukumalli, LK, Lawley, CT, Schnabel, RD, Taylor, JF, Allan, MF, Heaton, MP, O'Connell, J, Moore, SS, Smith, TP, Sonstegard, TS and Van Tassell, CP (2009) Development and characterization of a high density SNP genotyping assay for cattle. PLoS One 4, e5350.CrossRefGoogle ScholarPubMed
Metz, CE (1978) Basic principles of ROC analysis. Seminars in Nuclear Medicine 8, 283–298. Available at http://www.ncbi.nlm.nih.gov/pubmed/112681 (Accessed 17 July 2019).CrossRefGoogle ScholarPubMed
Meuwissen, THE and Goddard, ME (2007) Multipoint identity-by-descent prediction using dense markers to map quantitative trait loci and estimate effective population size. Genetics 176, 2551–2560.CrossRefGoogle ScholarPubMed
Meuwissen, TH, Hayes, BJ and Goddard, ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829. Available at http://www.ncbi.nlm.nih.gov/pubmed/11290733 (Accessed 28 May 2019).Google ScholarPubMed
Meuwissen, Theo HE, Solberg, TR, Shepherd, R and Woolliams, JA (2009) A fast algorithm for BayesB type of prediction of genome-wide estimates of genetic value. Genetics Selection Evolution 41, 2.CrossRefGoogle ScholarPubMed
Mikshowsky, AA, Gianola, D and Weigel, KA (2017) Assessing genomic prediction accuracy for Holstein sires using bootstrap aggregation sampling and leave-one-out cross validation. Journal of Dairy Science 100, 453–464.CrossRefGoogle ScholarPubMed
Misztal, I (2016) Inexpensive computation of the inverse of the genomic relationship matrix in populations with small effective population size. Genetics 202, 401–409.CrossRefGoogle ScholarPubMed
Misztal, I, Legarra, A and Aguilar, I (2009) Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. Journal of Dairy Science 92, 4648–4655.CrossRefGoogle ScholarPubMed
Misztal, I, Aggrey, SE and Muir, WM (2013) Experiences with a single-step genome evaluation1. Poultry Science 92, 2530–2534.CrossRefGoogle Scholar
Naderi, S, Yin, T and König, S (2016) Random forest estimation of genomic breeding values for disease susceptibility over different disease incidences and genomic architectures in simulated cow calibration groups. Journal of Dairy Science 99, 7261–7273.CrossRefGoogle ScholarPubMed
Nascimento, M, Alexandre Peternelli, L, Damião Cruz, C, Carolina Campana Nascimento, A, de Paula Ferreira, R, Lopes Bhering, L and Césio Salgado, C (2013) Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes, Crop Breeding and Applied Biotechnology. Vol. 13. Available at http://www.det.ufv.br/~moyses/links.php. (Accessed 19 June 2019).CrossRefGoogle Scholar
do Nascimento, AV, da Romero, ÂRS, Utsunomiya, YT, Utsunomiya, ATH, Cardoso, DF, Neves, HHR, Carvalheiro, R, Garcia, JF and Grisolia, AB (2018) Genome-wide association study using haplotype alleles for the evaluation of reproductive traits in Nellore cattle. PLoS One 13, e0201876.CrossRefGoogle Scholar
Natekin, A and Knoll, A (2013) Gradient boosting machines, a tutorial. Frontiers in Neurorobotics 7, 21.CrossRefGoogle ScholarPubMed
National Genomic Evaluations Info –Interbull Centre (2019) Available at https://interbull.org/ib/nationalgenoforms (Accessed 28 May 2019).Google Scholar
Neves, HH, Carvalheiro, R and Queiroz, SA (2012) A comparison of statistical methods for genomic selection in a mice population. BMC Genetics 13, 100.CrossRefGoogle Scholar
Ødegård, J and Meuwissen, TH (2014) Identity-by-descent genomic selection using selective and sparse genotyping. Genetics Selection Evolution 46, 3.CrossRefGoogle ScholarPubMed
Ødegård, J and Meuwissen, THE (2015) Identity-by-descent genomic selection using selective and sparse genotyping for binary traits. Genetics, Selection, Evolution: GSE 47, 8.CrossRefGoogle ScholarPubMed
Pérez-Enciso, M (2017) Animal breeding learning from machine learning. Journal of Animal Breeding and Genetics 134, 85–86.CrossRefGoogle ScholarPubMed
Peters, SO, Kizilkaya, K, Garrick, DJ, Fernando, RL, Reecy, JM, Weaber, RL, Silver, GA and Thomas, MG (2012) Bayesian genome-wide association analysis of growth and yearling ultrasound measures of carcass traits in Brangus heifers. Journal of Animal Science 90, 3398–3409.CrossRefGoogle ScholarPubMed
Pryce, JE, Goddard, ME, Raadsma, HW and Hayes, BJ (2010) Deterministic models of breeding scheme designs that incorporate genomic selection. Journal of Dairy Science 93, 5455–5466.CrossRefGoogle ScholarPubMed
Richardson, IW, Berry, DP, Wiencko, HL, Higgins, IM, More, SJ, McClure, J, Lynn, DJ and Bradley, DG (2016) A genome-wide association study for genetic susceptibility to Mycobacterium bovis infection in dairy cattle identifies a susceptibility QTL on chromosome 23. Genetics Selection Evolution 48, 19.CrossRefGoogle ScholarPubMed
Rodriguez, JJ, Kuncheva, LI and Alonso, CJ (2006) Rotation forest: anew classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1619–1630.CrossRefGoogle Scholar
Rumelhart, DE, Hinton, GE and Williams, RJ (1986) Learning representations by back-propagating errors. Nature 323, 533–536.CrossRefGoogle Scholar
Schaeffer, LR (2006) Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics 123, 218–223.CrossRefGoogle ScholarPubMed
Schmid, M and Bennewitz, J (2017) Invited review: genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs. Archives Animal Breeding 60, 335–346.CrossRefGoogle Scholar
Schork, AJ, Thompson, WK, Pham, P, Torkamani, A, Roddey, JC, Sullivan, PF, Kelsoe, JR, O'Donovan, MC, Furberg, H, The Tobacco and Genetics Consortium, The Bipolar Disorder Psychiatric Genomics Consortium, The Schizophrenia Psychiatric Genomics Consortium, Schork, NJ, Andreassen, OA and Dale, AM (2013) All SNPs are not created equal: genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated SNPs. PLoS Genetics 9, e1003449.CrossRefGoogle Scholar
Shahinfar, S, Page, D, Guenther, J, Cabrera, V, Fricke, P and Weigel, K (2014) Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms. Journal of Dairy Science 97, 731–742.CrossRefGoogle ScholarPubMed
Silva, GN, Tomaz, RS, Sant'Anna, I de C, Nascimento, M, Bhering, LL and Cruz, CD (2014) Neural networks for predicting breeding values and genetic gains. Scientia Agricola 71, 494–498.CrossRefGoogle Scholar
Spelman, RJ, Coppieters, W, Grisart, B, Blott, S and Georges, M (2001) Review of QTL mapping in the New Zealand and Dutch dairy cattle populations. Proceedings of Advances in Animal Breeding Genetics AAABG, pp. 11–16. Available at http://www.livestocklibrary.com.au/handle/1234/5514 (Accessed 28 May 2019).Google Scholar
Stothard, P, Choi, J-W, Basu, U, Sumner-Thomson, JM, Meng, Y, Liao, X and Moore, SS (2011) Whole genome resequencing of black Angus and Holstein cattle for SNP and CNV discovery. BMC Genomics 12, 559.CrossRefGoogle ScholarPubMed
Strandén, I and Garrick, DJ (2009) Technical note: derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit. Journal of Dairy Science 92, 2971–2975.CrossRefGoogle ScholarPubMed
Sun, X, Fernando, RL, Garrick, DJ and Dekkers, JCM (2011) An iterative approach for efficient calculation of breeding values and genome-wide association analysis using weighted genomic BLUP. Journal of Animal Science 89(E-Suppl. 2), 28.Google Scholar
Tin Kam Ho, (1995) Random decision forests. Proceedings of 3rd International Conference on Document Analysis and Recognition, Vol. 1. IEEE Computer Society Press, pp. 278–282. doi: 10.1109/ICDAR.1995.598994.CrossRefGoogle Scholar
Valente, TS, Baldi, F, Sant'Anna, AC, Albuquerque, LG and Paranhos da Costa, MJR (2016) Genome-wide association study between single nucleotide polymorphisms and flight speed in Nellore cattle. PLoS One 11, e0156956.CrossRefGoogle ScholarPubMed
VanRaden, PM (2008) Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.CrossRefGoogle ScholarPubMed
VanRaden, PM, Van Tassell, CP, Wiggans, GR, Sonstegard, TS, Schnabel, RD, Taylor, JF and Schenkel, FS (2009) Invited review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 16–24.CrossRefGoogle ScholarPubMed
Veerkamp, RF, Verbyla, KL, Mulder, HA and Calus, MPL (2010) Simultaneous QTL detection and genomic breeding value estimation using high density SNP chips. BMC Proceedings 4 (Suppl 1), S9. Available at http://www.ncbi.nlm.nih.gov/pubmed/20380763 (Accessed 29 May 2019).CrossRefGoogle ScholarPubMed
Verbyla, KLARA L, Hayes, BJ, Bowman, PJ and Goddard, ME (2009) Accuracy of genomic selection using stochastic search variable selection in Australian Holstein Friesian dairy cattle. Genetics Research 91, 307–311.CrossRefGoogle ScholarPubMed
Verbyla, Klara L, Bowman, PJ, Hayes, BJ and Goddard, ME (2010) Sensitivity of genomic selection to using different prior distributions. BMC Proceedings 4, S5.CrossRefGoogle Scholar
Vitezica, ZG, Varona, L and Legarra, A (2013) On the additive and dominant variance and covariance of individuals within the genomic selection scope. Genetics 195, 1223–1230.CrossRefGoogle ScholarPubMed
Weller, JI, Kashi, Y and Soller, M (1990) Power of daughter and granddaughter designs for determining linkage between marker loci and quantitative trait loci in dairy cattle. Journal of Dairy Science 73, 2525–2537.CrossRefGoogle ScholarPubMed
Wu, Y, Fan, H, Wang, Y, Zhang, L, Gao, X, Chen, Y, Li, J, Ren, HY and Gao, H (2014) Genome-wide association studies using haplotypes and individual SNPs in Simmental cattle. PLoS One 9, e109330.Google ScholarPubMed
Yao, C, Zhu, X and Weigel, KA (2016) Semi-supervised learning for genomic prediction of novel traits with small reference populations: an application to residual feed intake in dairy cattle. Genetics Selection Evolution 48, 84.CrossRefGoogle ScholarPubMed
Yu, X and Meuwissen, TH (2011) Using the Pareto principle in genome-wide breeding value estimation. Genetics Selection Evolution 43, 35.CrossRefGoogle ScholarPubMed
Yu, J, Pressoir, G, Briggs, WH, Vroh Bi, I, Yamasaki, M, Doebley, JF, McMullen, MD, Gaut, BS, Nielsen, DM, Holland, JB, Kresovich, S and Buckler, ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics 38, 203–208.CrossRefGoogle ScholarPubMed
Zhu, X and Goldberg, AB (2009) Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 3, 1–130.CrossRefGoogle Scholar
Zhu, B, Zhu, M, Jiang, J, Niu, H, Wang, Y, Wu, Y, Xu, L, Chen, Y, Zhang, L, Gao, X, Gao, H, Liu, J and Li, J (2016) The impact of variable degrees of freedom and scale parameters in Bayesian methods for genomic prediction in Chinese simmental beef cattle. PLoS One 11, e0154118.Google ScholarPubMed