畜牧兽医学报 ›› 2024, Vol. 55 ›› Issue (5): 1827-1841.doi: 10.11843/j.issn.0366-6964.2024.05.002
张元旭1,2, 李竟1,2, 王泽昭2, 陈燕2, 徐凌洋2, 张路培2, 高雪2, 高会江2, 李俊雅2, 朱波2*, 郭鹏1*
收稿日期:
2023-10-13
出版日期:
2024-05-23
发布日期:
2024-05-27
通讯作者:
朱波,主要从事肉牛分子数量遗传学研究,E-mail:zhubo@caas.cn;郭鹏,主要从事并行全基因组选择技术研究,E-mail:super_guopeng@163.com
作者简介:
张元旭(2000-),男,山东临沂人,硕士生,主要从事机器学习全基因组选择研究,E-mail:zyx1251865935@163.com
基金资助:
ZHANG Yuanxu1,2, LI Jing1,2, WANG Zezhao2, CHEN Yan2, XU Lingyang2, ZHANG Lupei2, GAO Xue2, GAO Huijiang2, LI Junya2, ZHU Bo2*, GUO Peng1*
Received:
2023-10-13
Online:
2024-05-23
Published:
2024-05-27
摘要: 遗传评估软件在动物领域的应用极大地提高了育种工作效率。随着基因组测序技术不断完善和人工智能技术的兴起,动物遗传评估软件也得到了快速的发展。本文首先介绍了常规育种和基因组育种在动物育种领域的应用,然后重点回顾了GBLUP方法、贝叶斯方法和机器学习以及深度学习方法的全基因组遗传评估软件的特点和发展历史,最后展望了计算机软件在动物遗传评估育种中的未来发展趋势,旨为动物育种领域的研究人员提供相关遗传评估软件的参考。
中图分类号:
张元旭, 李竟, 王泽昭, 陈燕, 徐凌洋, 张路培, 高雪, 高会江, 李俊雅, 朱波, 郭鹏. 动物遗传评估软件研究进展[J]. 畜牧兽医学报, 2024, 55(5): 1827-1841.
ZHANG Yuanxu, LI Jing, WANG Zezhao, CHEN Yan, XU Lingyang, ZHANG Lupei, GAO Xue, GAO Huijiang, LI Junya, ZHU Bo, GUO Peng. Advances in Animal Genetic Evaluation Software[J]. Acta Veterinaria et Zootechnica Sinica, 2024, 55(5): 1827-1841.
[1] HENDERSON C R.Best linear unbiased estimation and prediction under a selection model[J].Biometrics,1975, 31(2): 423-447. [2] GIANOLA D,ROSA G J M.One hundred years of statistical developments in animal breeding[J].Annu Rev Anim Biosci, 2015, 3:19-56. [3] WIGGANS G R,CARRILLO J A.Genomic selection in United States dairy cattle[J].Front Genet,2022,13:994466. [4] SARAVANAN K A,PANIGRAHI M,KUMAR H,et al.Progress and future perspectives of livestock genomics in India:a mini review[J].Anim Biotechnol,2023,34(6):1979-1987. [5] VANRADEN P M.Efficient methods to compute genomic predictions[J].J Dairy Sci,2008,91(11): 4414-4423. [6] YANG J,BENYAMIN B,MCEVOY B P,et al.Common SNPs explain a large proportion of the heritability for human height[J].Nat Genet,2010,42(7):565-569. [7] BOLDMAN K G,KRIESE L A,VAN VLECK L D,et al.A manual for use of MTDFREML:a set of programs to obtain estimates of variances and covariances[R].Washington:U.S. Department of Agriculture,Agricultural Research Service,1995. [8] MEYER K.Restricted maximum likelihood to estimate variance components for animal models with several random effects using a derivative-free algorithm[J].Genet Sel Evol,1989,21(3):317. [9] GRASER H U,SMITH S P,TIER B.A derivative-free approach for estimating variance components in animal models by restricted maximum likelihood[J].J Anim Sci,1987,64(5):1362-1370. [10] MISZTAL I,TSURUTA S,STRABEL T,et al.BLUPF90 and related programs (BGF90)[C]//Proceedings of the 7th World Congress on Genetics Applied to Livestock Production.Montpellier, 2002. [11] YANG J,LEE S H,GODDARD M E,et al.GCTA:a tool for genome-wide complex trait analysis[J]. Am J Hum Genet,2011, 88(1):76-82. [12] MADSEN P,JENSEN J.DMU:a user’s guide.A package for analysing multivariate mixed models[M]. Denmark:DJF,2008. [13] MEYER K.WOMBAT—A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML)[J].J Zhejiang Univ Sci B,2007,8(11):815-821. [14] YIN L L,ZHANG H H,TANG Z S,et al.HIBLUP:an integration of statistical models on the BLUP framework for efficient genetic evaluation using big genomic data[J].Nucleic Acids Res,2023,51(8): 3501-3512. [15] DEMPSTER A P,LAIRD N M,RUBIN D B.Maximum likelihood from incomplete data via the EM algorithm[J].J Roy Stat Soc Ser B,1977,39(1):1-22. [16] GILMOUR A R,THOMPSON R,CULLIS B R.Average information REML:an efficient algorithm for variance parameter estimation in linear mixed models[J].Biometrics,1995,51(4):1440-1450. [17] GILMOUR A R,GOGEL B J,CULLIS B R,et al.ASReml user guide release 1.0[R].VSN International,Hemel Hempstead,2002. [18] MISZTAL I.Comparison of software packages in animal breeding[C]//Proceedings of the 5th World Congress on Genetics Applied to Livestock Production.Guelph,Canada,1994. [19] THOMPSON R,MÄNTYSAARI E A.Prospects for statistical methods in dairy cattle breeding[C]//Proceedings of the Computational Cattle Breeding’99 Workshop.Tuusula,Finland, 1999:71-78. [20] MÄNTYSAARI E,VAN VLECK L D.Restricted maximum likelihood estimates of variance components from multitrait sire models with large number of fixed effects[J].J Anim Breed Genet,1989,106(1-6):409-422. [21] MISZTAL I.Reliable computing in estimation of variance components[J].J Anim Breed Genet,2008, 125(6): 363-370. [22] VAN TASSELL C P,VAN VLECK L D.Multiple-trait Gibbs sampler for animal models:flexible programs for Bayesian and likelihood-based (co) variance component inference[J].J Anim Sci,1996, 74(11):2586-2597. [23] MEUWISSEN T H E,HAYES B J,GODDARD M E.Prediction of total genetic value using genome-wide dense marker maps[J].Genetics,2001,157(4):1819-1829. [24] GUINAN F L,WIGGANS G R,NORMAN H D,et al.Changes in genetic trends in US dairy cattle since the implementation of genomic selection[J].J Dairy Sci,2023,106(2):1110-1129. [25] BRITO L F,BEDERE N,DOUHARD F,et al.Review:genetic selection of high-yielding dairy cattle toward sustainable farming systems in a rapidly changing world[J].Animal,2021,15 Suppl 1:100292. [26] YÁÑEZ J M,BARRÍA A,LÓPEZ M E,et al.Genome-wide association and genomic selection in aquaculture[J].Rev Aquac,2023,15(2):645-675. [27] TAN X D,LIU R R,LI W,et al.Assessment the effect of genomic selection and detection of selective signature in broilers[J].Poult Sci,2022,101(6):101856. [28] NEL C L,VAN DER WERF J H J,RAUW W M,et al.Challenges and strategies for genetic selection of sheep better adapted to harsh environments[J].Anim Front,2023,13(5):43-52. [29] SELL-KUBIAK E,KNOL E F,LOPES M.Evaluation of the phenotypic and genomic background of variability based on litter size of Large White pigs[J].Genet Sel Evol,2022,54(1):1. [30] MA H R,LI H W,GE F,et al.Improving genomic predictions in multi-breed cattle populations:a comparative analysis of BayesR and GBLUP models[J].Genes,2024,15(2):253. [31] NICOLAZZI E L,BIFFANI S,BISCARINI F,et al.Software solutions for the livestock genomics SNP array revolution[J].Anim Genet,2015,46(4):343-353. [32] PURCELL S,NEALE B,TODD-BROWN K,et al.PLINK:a tool set for whole-genome association and population-based linkage analyses[J].Am J Hum Genet,2007,81(3):559-575. [33] CHANG C C,CHOW C C,TELLIER L C A M,et al.Second-generation PLINK:rising to the challenge of larger and richer datasets[J].GigaScience,2015,4(1):7. [34] GROENEVELD E,LICHTENBERG H.TheSNPpit—a high performance database system for managing large scale SNP data[J].PLoS One,2016,11(10):e0164043. [35] JIANG J C,JIANG L,ZHOU B,et al.Snat:a SNP annotation tool for bovine by integrating various sources of genomic information[J].BMC Genet,2011,12(1):85. [36] NICOLAZZI E L,PICCIOLINI M,STROZZI F,et al.SNPchiMp:a database to disentangle the SNPchip jungle in bovine livestock[J].BMC Genomics,2014,15(1):123. [37] NICOLAZZI E L,CAPRERA A,NAZZICARI N,et al.SNPchiMp v.3:integrating and standardizing single nucleotide polymorphism data for livestock species[J].BMC Genomics,2015,16(1):283. [38] GONDRO C,PORTO-NETO L R,LEE S H.SNPQC-an R pipeline for quality control of illumina SNP genotyping array data[J].Anim Genet,2014,45(5):758-761. [39] GRUENEBERG A,DE LOS CAMPOS G.BGData-A suite of R packages for genomic analysis with big data[J].G3 Genes|Genomes|Genetics,2019,9(5):1377-1383. [40] DE JONG M J,DE JONG J F,HOELZEL A R,et al.SambaR:an R package for fast,easy and reproducible population-genetic analyses of biallelic SNP data sets[J].Mol Ecol Resour,2021,21(4): 1369-1379. [41] DING X,ZHANG Z,LI X,et al.Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows[J].J Dairy Sci,2013,96(8):5315-5323. [42] LIANG M,AN B X,DENG T Y,et al.Incorporating genome-wide and transcriptome-wide association studies to identify genetic elements of longissimus dorsi muscle in Huaxi cattle[J].Front Genet,2023,13:982433. [43] WANG X Y,RAN X Q,NIU X,et al.Whole-genome sequence analysis reveals selection signatures for important economic traits in Xiang pigs[J].Sci Rep,2022,12(1):11823. [44] MIAO M X,WU J R,CAI F J,et al.A modified memetic algorithm with an application to gene selection in a sheep body weight study[J].Animals,2022,12(2):201. [45] HABIER D,FERNANDO R L,DEKKERS J C M.The impact of genetic relationship information on genome-assisted breeding values[J].Genetics,2007,177(4):2389-2397. [46] LEE H S,KIM Y,LEE D H,et al.Comparison of accuracy of breeding value for cow from three methods in Hanwoo (Korean cattle) population[J].J Anim Sci Technol,2023,65(4):720-734. [47] LEGARRA A,AGUILAR I,MISZTAL I.A relationship matrix including full pedigree and genomic information[J].J Dairy Sci,2009,92(9):4656-4663. [48] ZHANG Z,OBER U,ERBE M,et al.Improving the accuracy of whole genome prediction for complex traits using the results of genome wide association studies[J].PLoS One,2014,9(3):e93017. [49] MEHER P K,KUMAR A,PRADHAN S K.Genomic selection using Bayesian methods:models, software,and application[M]//WANI S H,KUMAR A.Genomics of Cereal Crops.New York:Humana, 2022: 259-269. [50] MA Y,ZHOU X.Genetic prediction of complex traits with polygenic scores:a statistical review[J].Trends Genet,2021,37(11):995-1011. [51] ZENG J,DE VLAMING R,WU Y,et al.Signatures of negative selection in the genetic architecture of human complex traits[J].Nat Genet,2018,50(5):746-753. [52] LEGARRA A,RICARD A,FILANGI O.GS3:genomic selection-Gibbs sampling-gauss Seidel (and BayesCπ and Bayesian lasso)[R].Paris,France:INRA,2010. [53] PÉREZ P,DE LOS CAMPOS G.Genome-wide regression and prediction with the BGLR statistical package[J].Genetics, 2014,198(2):483-495. [54] BOERNER V,TIER B.BESSiE:a software for linear model BLUP and Bayesian MCMC analysis of large-scale genomic data[J].Genet Sel Evol,2016,48:63. [55] GUO G,ZHAO F P,WANG Y C,et al.Comparison of single-trait and multiple-trait genomic prediction models[J].BMC Genet,2014,15:30. [56] BUDHLAKOTI N,MISHRA D C,RAI A,et al.A comparative study of single-trait and multi-trait genomic selection[J].J Comput Biol,2019,26(10):1100-1112. [57] AYALEW W,ALIY M,NEGUSSIE E.Estimation of genetic parameters of the productive and reproductive traits in Ethiopian Holstein using multi-trait models[J].Asian-Australas J Anim Sci,2017,30(11):1550-1556. [58] SRIVASTAVA S,LOPEZ B I,DE LAS HERAS-SALDANA S,et al.Estimation of genetic parameters by single-trait and multi-trait models for carcass traits in Hanwoo cattle[J].Animals,2019, 9(12):1061. [59] CLARK S A,VAN DER WERF J.Genomic best linear unbiased prediction (gBLUP) for the estimation of genomic breeding values[M]//GONDRO C,VAN DER WERF J,HAYES B.Genome-Wide Association Studies and Genomic Prediction.Totowa:Humana Press,2013:321-330. [60] LEE S H,VAN DER WERF J H J.MTG2:an efficient algorithm for multivariate linear mixed model analysis based on genomic information[J].Bioinformatics,2016,32(9):1420-1422. [61] FERNANDO R L,GARRICK D J.GenSel—User manual for a portfolio of genomic selection related analyses[R].Ames: Animal Breeding and Genetics,2008. [62] ENDELMAN J B.Ridge regression and other kernels for genomic selection with R package rrBLUP[J].Plant Genome, 2011, 4(3):250-255. [63] COVARRUBIAS-PAZARAN G.Genome-assisted prediction of quantitative traits using the R package sommer[J].PLoS One,2016,11(6):e0156744. [64] HERRERA-OJEDA J B,PARRA-BRACAMONTE G M,LÓPEZ-VILLALOBOS N,et al.Bivariate analysis for the improvement of genetic evaluations with incomplete records in Charolais cattle[J].Rev MVZ Córdoba,2021,26(2):e2128. [65] SILALAHI P,CHEN Y C.Estimation of genetic parameters for litter traits in Taiwan Duroc,landrace,and yorkshire pigs[J].Trop Anim Sci J,2023,46(3):280-286. [66] LIU H H,ZHOU Z K,HU J,et al.Genetic variations for egg internal quality of ducks revealed by genome-wide association study[J].Anim Genet,2021,52(4):536-541. [67] NEL C,GURMAN P,SWAN A,et al.Including genomic information in the genetic evaluation of production and reproduction traits in South African Merino sheep[J].J Anim Breed Genet,2024,141(1): 65-82. [68] XU L,NIU Q H,CHEN Y,et al.Validation of the prediction accuracy for 13 traits in Chinese Simmental beef cattle using a preselected low-density SNP panel[J].Animals,2021,11(7):1890. [69] SHIRZEYLI F H,JOEZY-SHEKALGORABI S,AMINAFSHAR M,et al.The estimation of genetic parameters and genetic trends for growth traits in Markhoz goats[J].Small Rumin Res,2023,218: 106886. [70] YADAV N,MUKHERJEE S,MUKHERJEE A.Comparative genetic analysis of frequentist and Bayesian approach for reproduction,production and life time traits showing favourable association of age at first calving in Tharparkar cattle[J].Anim Biosci,2023,36(12):1806-1820. [71] YANG R F,XU Z Q,WANG Q,et al.Genome-wide association study and genomic prediction for growth traits in yellow-plumage chicken using genotyping-by-sequencing[J].Genet Sel Evol,2021, 53(1):82. [72] AYTEKINİ, DOĞAN Ş,ODACI Ö,et al.Estimation of variance components for birth and weaning weights in Holstein-Friesian calves by using WOMBAT software[J].Selcuk J Agric Food Sci,2019, 33(2):88-93. [73] YU H W,RAZA S H A,ALMOHAIMEED H M,et al.The body weight heritability and the effect of non-genetic factors on the body measurement traits in Qinchuan cattle[J].Anim Biotechnol,2023, 34(9):4387-4393. [74] GILMOUR A R.Echidna mixed model software[C]//Proceedings of the World Congress on Genetics Applied to Livestock Production.Auckland,2018:995. [75] ASHRAFIAN A,KASHAN N E J,ABBASI M A,et al.Study of persistency of lactation and survival of Iranian Holstein dairy cattle using random regression model[J].Can J Anim Sci,2023,103(4): 406-415. [76] VANDENPLAS J,VEERKAMP R F,CALUS M P L,et al.MiXBLUP 3.0-software for large genomic evaluations in animal breeding programs[M]//VEERKAMP R F,DE HAAS Y.Proceedings of 12th World Congress on Genetics Applied to Livestock Production.Wageningen Academic Publishers,2022: 1498-1501. [77] LIU Z T,ALKHODER H.Application of a single-step SNP BLUP model to conformation traits of German Holsteins[J]. Interbull Bull,2021(56):30-40. [78] LIU S Y,YAO T X,CHEN D,et al.Genomic prediction in pigs using data from a commercial crossbred population:insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding system[J].Genet Sel Evol,2023,55(1):21. [79] DAWOOD M,KRAMER L M,SHABBIR M I,et al.Genome-wide association study for fatty acid composition in American angus cattle[J].Animals,2021,11(8):2424. [80] KHALTABADI FARAHANI A H,MOHAMMADI H,MORADI M H,et al.Genomic-wide association study for egg weight-related traits in Rhode Island Red breed using Bayesian methods[J].Anim Prod Res,2022,11(3):41-53. [81] FERNÁNDEZ J,VILLANUEVA B,TORO M A.Optimum mating designs for exploiting dominance in genomic selection schemes for aquaculture species[J].Genet Sel Evol,2021,53(1):14. [82] RIOS A C H,NASNER S L C,LONDOÑO-GIL M,et al.Genome-wide association study for reproduction traits in Colombian Creole Blanco Orejinegro cattle[J].Trop Anim Health Prod,2023, 55(6):429. [83] SHAN X X,XU T F,MA Z Y,et al.Genome-wide association improves genomic selection for ammonia tolerance in the orange-spotted grouper (Epinephelus coioides)[J]. Aquaculture, 2021, 533:736214. [84] LI H,WU X L,TAIT R G Jr,et al.Genome-wide association study of milk production traits in a crossbred dairy sheep population using three statistical models[J].Anim Genet,2020,51(4):624-628. [85] MARTIN P,TAUSSAT S,VINET A,et al.Genetic parameters and genome-wide association study regarding feed efficiency and slaughter traits in Charolais cows[J].J Anim Sci,2019,97(9):3684-3698. [86] HONG J K,JEONG Y D,CHO E S,et al.A genome-wide association study of social genetic effects in Landrace pigs[J].Asian-Australas J Anim Sci,2018,31(6):784-790. [87] BITARAF SANI M,ZARE HAROFTE J,BANABAZI M H,et al.Genomic prediction for growth using a low-density SNP panel in dromedary camels[J].Sci Rep,2021,11(1):7675. [88] BRITO LOPES F,MAGNABOSCO C U,PASSAFARO T L,et al.Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks[J].J Anim Breed Genet,2020,137(5):438-448. [89] AL-MAMUN H A,KIM S,PARK B H,et al.Prediction of genomic breeding values of primal cut weights in Korean Hanwoo cattle from different growth and carcass traits[C]//Proceedings of the 22nd Association for the Advancement of Animal Breeding and Genetics.Townsville,2017:187-190. [90] SAMARAWEERA A M,BOERNER V,CYRIL H W,et al.Genetic parameters for milk yield in imported Jersey and Jersey-Friesian cows using daily milk records in Sri Lanka[J].Asian-Australas J Anim Sci,2020,33(11):1741-1754. [91] CAI W T,HU J,FAN W L,et al.Strategies to improve genomic predictions for 35 duck carcass traits in an F2 population[J].J Anim Sci Biotechnol,2023,14(1):74. [92] SMITH J L,WILSON M L,NILSON S M,et al.Genome-wide association and genotype by environment interactions for growth traits in U.S. Red Angus cattle[J].BMC Genomics,2022, 23(1):517. [93] PÉREZ-ENCISO M.Animal breeding learning from machine learning[EB/OL].2017. https://digital.csic.es/bitstream/10261/247945/1/animallearn.pdf. [94] NAYERI S,SARGOLZAEI M,TULPAN D.A review of traditional and machine learning methods applied to animal breeding[J].Anim Health Res Rev,2019,20(1):31-46. [95] WESTHUES C C,SIMIANER H,BEISSINGER T M.learnMET:an R package to apply machine learning methods for genomic prediction using multi-environment trial data[J].G3 Genes|Genomes|Genetics,2022,12(11):jkac226. [96] HASELBECK F,JOHN M,GRIMM D G.easyPheno:an easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization[J].Bioinform Adv,2023,3(1): vbad035. [97] CHARMET G,TRAN L G,AUZANNEAU J,et al.BWGS:A R package for genomic selection and its application to a wheat breeding programme[J].PLoS One,2020,15(4):e0222733. [98] MA W L,QIU Z X,SONG J,et al.A deep convolutional neural network approach for predicting phenotypes from genotypes[J].Planta,2018,248(5):1307-1318. [99] ZENG S,MAO Z T,REN Y J,et al.G2PDeep:a web-based deep-learning framework for quantitative phenotype prediction and discovery of genomic markers[J].Nucleic Acids Res,2021,49(W1): W228-W236. [100] SHARAF T,WILLIAMS T,CHEHADE A,et al.BLNN:an R package for training neural networks using Bayesian inference[J]. SoftwareX,2020,11:100432. [101] NEU D A,LAHANN J,FETTKE P.A systematic literature review on state-of-the-art deep learning methods for process prediction[J].Artif Intell Rev,2022,55(2):801-827. [102] GREENWELL B,BOEHMKE B,CUNNINGHAM J,et al.Gbm:generalized boosted regression models[R].R Package Version 2.1.8,2020. [103] VU N T,PHUC T H,OANH K T P,et al.Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms[J].G3 Genes|Genomes|Genetics, 2022,12(1):jkab361. [104] SRIVASTAVA S,LOPEZ B I,KUMAR H,et al.Prediction of hanwoo cattle phenotypes from genotypes using machine learning methods[J].Animals,2021,11(7):2066. [105] LIANG M,MIAO J,WANG X Q,et al.Application of ensemble learning to genomic selection in Chinese simmental beef cattle[J].J Anim Breed Genet,2021,138(3):291-299. [106] REINOSO-PELÁEZ E L,GIANOLA D,GONZÁLEZ-RECIO O.Genome-enabled prediction methods based on machine learning[M]//AHMADI N,BARTHOLOMÉ.Genomic Prediction of Complex Traits.New York:Humana,2022:189-218. [107] HABIER D,TETENS J,SEEFRIED F R,et al.The impact of genetic relationship information on genomic breeding values in German Holstein cattle[J].Genet Sel Evol,2010,42(1):5. [108] HABIER D,FERNANDO R L,KIZILKAYA K,et al.Extension of the Bayesian alphabet for genomic selection[J].BMC Bioinformatics,2011,12:186. [109] CHAMBERS R L,STEEL D G,WANG S,et al.Maximum likelihood estimation for sample surveys[M]. Boca Raton:CRC Press,2012. [110] HARVILLE D A.Maximum likelihood approaches to variance component estimation and to related problems[J].J Am Stat Assoc,1977,72(358):320-338. [111] MISZTAL I,PEREZ-ENCISO M.Sparse matrix inversion for restricted maximum likelihood estimation of variance components by expectation-maximization[J].J Dairy Sci,1993,76(5): 1479-1483. [112] WAHEED A,KHAN M S.Computing genetic parameters and breeding values in Nili-Ravi buffaloes-experiencing wombat and ASREML software[J].Pak J Zool,2009(9):587-591. [113] CADENA-MENESES J A,CASTILLO-MORALES A.Comparación de diferentes métodos para la estimación de componentes de varianza[J].Agrociencia,2002,36(6):713-723. [114] PÉREZ-ENCISO M,ZINGARETTI L M.A guide on deep learning for complex trait genomic prediction[J]. Genes (Basel),2019,10(7):553. [115] ABDOLLAHI-ARPANAHI R,GIANOLA D,PEÑAGARICANO F.Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes[J].Genet Sel Evol,2020,52(1): 12. |
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