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Wang X, Zhang Z, Du H, Pfeiffer C, Mészáros G, Ding X. Predictive ability of multi-population genomic prediction methods of phenotypes for reproduction traits in Chinese and Austrian pigs. Genet Sel Evol 2024; 56:49. [PMID: 38926647 PMCID: PMC11201905 DOI: 10.1186/s12711-024-00915-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/30/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Multi-population genomic prediction can rapidly expand the size of the reference population and improve genomic prediction ability. Machine learning (ML) algorithms have shown advantages in single-population genomic prediction of phenotypes. However, few studies have explored the effectiveness of ML methods for multi-population genomic prediction. RESULTS In this study, 3720 Yorkshire pigs from Austria and four breeding farms in China were used, and single-trait genomic best linear unbiased prediction (ST-GBLUP), multitrait GBLUP (MT-GBLUP), Bayesian Horseshoe (BayesHE), and three ML methods (support vector regression (SVR), kernel ridge regression (KRR) and AdaBoost.R2) were compared to explore the optimal method for joint genomic prediction of phenotypes of Chinese and Austrian pigs through 10 replicates of fivefold cross-validation. In this study, we tested the performance of different methods in two scenarios: (i) including only one Austrian population and one Chinese pig population that were genetically linked based on principal component analysis (PCA) (designated as the "two-population scenario") and (ii) adding reference populations that are unrelated based on PCA to the above two populations (designated as the "multi-population scenario"). Our results show that, the use of MT-GBLUP in the two-population scenario resulted in an improvement of 7.1% in predictive ability compared to ST-GBLUP, while the use of SVR and KKR yielded improvements in predictive ability of 4.5 and 5.3%, respectively, compared to MT-GBLUP. SVR and KRR also yielded lower mean square errors (MSE) in most population and trait combinations. In the multi-population scenario, improvements in predictive ability of 29.7, 24.4 and 11.1% were obtained compared to ST-GBLUP when using, respectively, SVR, KRR, and AdaBoost.R2. However, compared to MT-GBLUP, the potential of ML methods to improve predictive ability was not demonstrated. CONCLUSIONS Our study demonstrates that ML algorithms can achieve better prediction performance than multitrait GBLUP models in multi-population genomic prediction of phenotypes when the populations have similar genetic backgrounds; however, when reference populations that are unrelated based on PCA are added, the ML methods did not show a benefit. When the number of populations increased, only MT-GBLUP improved predictive ability in both validation populations, while the other methods showed improvement in only one population.
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Affiliation(s)
- Xue Wang
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture and Rural Affairs, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Zipeng Zhang
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture and Rural Affairs, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Hehe Du
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture and Rural Affairs, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | | | - Gábor Mészáros
- University of Natural Resources and Life Sciences, Vienna, Austria
| | - Xiangdong Ding
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture and Rural Affairs, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China.
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Yan X, Li J, He L, Chen O, Wang N, Wang S, Wang X, Wang Z, Su R. Accuracy of Genomic prediction for fleece traits in Inner Mongolia Cashmere goats. BMC Genomics 2024; 25:349. [PMID: 38589806 PMCID: PMC11000370 DOI: 10.1186/s12864-024-10249-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 03/22/2024] [Indexed: 04/10/2024] Open
Abstract
The fleece traits are important economic traits of goats. With the reduction of sequencing and genotyping cost and the improvement of related technologies, genomic selection for goats has become possible. The research collect pedigree, phenotype and genotype information of 2299 Inner Mongolia Cashmere goats (IMCGs) individuals. We estimate fixed effects, and compare the estimates of variance components, heritability and genomic predictive ability of fleece traits in IMCGs when using the pedigree based Best Linear Unbiased Prediction (ABLUP), Genomic BLUP (GBLUP) or single-step GBLUP (ssGBLUP). The fleece traits considered are cashmere production (CP), cashmere diameter (CD), cashmere length (CL) and fiber length (FL). It was found that year of production, sex, herd and individual ages had highly significant effects on the four fleece traits (P < 0.01). All of these factors should be considered when the genetic parameters of fleece traits in IMCGs are evaluated. The heritabilities of FL, CL, CP and CD with ABLUP, GBLUP and ssGBLUP methods were 0.26 ~ 0.31, 0.05 ~ 0.08, 0.15 ~ 0.20 and 0.22 ~ 0.28, respectively. Therefore, it can be inferred that the genetic progress of CL is relatively slow. The predictive ability of fleece traits in IMCGs with GBLUP (56.18% to 69.06%) and ssGBLUP methods (66.82% to 73.70%) was significantly higher than that of ABLUP (36.73% to 41.25%). For the ssGBLUP method is significantly (29% ~ 33%) higher than that with ABLUP, and which is slightly (4% ~ 14%) higher than that of GBLUP. The ssGBLUP will be as an superiors method for using genomic selection of fleece traits in Inner Mongolia Cashmere goats.
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Affiliation(s)
- Xiaochun Yan
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia Autonomous Region, 010018, China
| | - Jinquan Li
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia Autonomous Region, 010018, China
- Inner Mongolia Key Laboratory of Sheep & Goat Genetics Breeding and Reproduction, Hohhot, Inner Mongolia Autonomous Region, 010018, China
- Key Laboratory Of Mutton Sheep & Goat Genetics And Breeding, Ministry of Agriculture And Rural Affairs, Hohhot, Inner Mongolia Autonomous Region, 010018, China
- Engineering Research Centre for Goat Genetics and Breeding, Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia Autonomous Region, 010018, China
| | - Libing He
- Inner Mongolia Jinlai Livestock Technology Co., Ltd, Hohhot, Inner Mongolia Autonomous Region, 010018, China
| | - Oljibilig Chen
- Inner Mongolia Yiwei White Cashmere Goat Co., Ltd, Ordos, Inner Mongolia Autonomous Region, 010018, China
| | - Na Wang
- Inner Mongolia Yiwei White Cashmere Goat Co., Ltd, Ordos, Inner Mongolia Autonomous Region, 010018, China
| | - Shuai Wang
- Inner Mongolia Yiwei White Cashmere Goat Co., Ltd, Ordos, Inner Mongolia Autonomous Region, 010018, China
| | - Xiuyan Wang
- Livestock Improvement Center of Alxa Left Banner, Alxa League, Inner Mongolia Autonomous Region, 75000, China
| | - Zhiying Wang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia Autonomous Region, 010018, China.
| | - Rui Su
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia Autonomous Region, 010018, China.
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Martins FB, Aono AH, Moraes ADCL, Ferreira RCU, Vilela MDM, Pessoa-Filho M, Rodrigues-Motta M, Simeão RM, de Souza AP. Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits in Urochloa ruziziensis. FRONTIERS IN PLANT SCIENCE 2023; 14:1303417. [PMID: 38148869 PMCID: PMC10749977 DOI: 10.3389/fpls.2023.1303417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 11/15/2023] [Indexed: 12/28/2023]
Abstract
Tropical forage grasses, particularly those belonging to the Urochloa genus, play a crucial role in cattle production and serve as the main food source for animals in tropical and subtropical regions. The majority of these species are apomictic and tetraploid, highlighting the significance of U. ruziziensis, a sexual diploid species that can be tetraploidized for use in interspecific crosses with apomictic species. As a means to support breeding programs, our study investigates the feasibility of genome-wide family prediction in U. ruziziensis families to predict agronomic traits. Fifty half-sibling families were assessed for green matter yield, dry matter yield, regrowth capacity, leaf dry matter, and stem dry matter across different clippings established in contrasting seasons with varying available water capacity. Genotyping was performed using a genotyping-by-sequencing approach based on DNA samples from family pools. In addition to conventional genomic prediction methods, machine learning and feature selection algorithms were employed to reduce the necessary number of markers for prediction and enhance predictive accuracy across phenotypes. To explore the regulation of agronomic traits, our study evaluated the significance of selected markers for prediction using a tree-based approach, potentially linking these regions to quantitative trait loci (QTLs). In a multiomic approach, genes from the species transcriptome were mapped and correlated to those markers. A gene coexpression network was modeled with gene expression estimates from a diverse set of U. ruziziensis genotypes, enabling a comprehensive investigation of molecular mechanisms associated with these regions. The heritabilities of the evaluated traits ranged from 0.44 to 0.92. A total of 28,106 filtered SNPs were used to predict phenotypic measurements, achieving a mean predictive ability of 0.762. By employing feature selection techniques, we could reduce the dimensionality of SNP datasets, revealing potential genotype-phenotype associations. The functional annotation of genes near these markers revealed associations with auxin transport and biosynthesis of lignin, flavonol, and folic acid. Further exploration with the gene coexpression network uncovered associations with DNA metabolism, stress response, and circadian rhythm. These genes and regions represent important targets for expanding our understanding of the metabolic regulation of agronomic traits and offer valuable insights applicable to species breeding. Our work represents an innovative contribution to molecular breeding techniques for tropical forages, presenting a viable marker-assisted breeding approach and identifying target regions for future molecular studies on these agronomic traits.
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Affiliation(s)
- Felipe Bitencourt Martins
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Campinas, São Paulo, Brazil
| | - Alexandre Hild Aono
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Campinas, São Paulo, Brazil
| | - Aline da Costa Lima Moraes
- Department of Plant Biology, Biology Institute, University of Campinas (UNICAMP), Campinas, São Paulo, Brazil
| | | | | | - Marco Pessoa-Filho
- Embrapa Cerrados, Brazilian Agricultural Research Corporation, Brasília, Brazil
| | | | - Rosangela Maria Simeão
- Embrapa Gado de Corte, Brazilian Agricultural Research Corporation, Campo Grande, Mato Grosso, Brazil
| | - Anete Pereira de Souza
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Campinas, São Paulo, Brazil
- Department of Plant Biology, Biology Institute, University of Campinas (UNICAMP), Campinas, São Paulo, Brazil
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Chafai N, Hayah I, Houaga I, Badaoui B. A review of machine learning models applied to genomic prediction in animal breeding. Front Genet 2023; 14:1150596. [PMID: 37745853 PMCID: PMC10516561 DOI: 10.3389/fgene.2023.1150596] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 08/22/2023] [Indexed: 09/26/2023] Open
Abstract
The advent of modern genotyping technologies has revolutionized genomic selection in animal breeding. Large marker datasets have shown several drawbacks for traditional genomic prediction methods in terms of flexibility, accuracy, and computational power. Recently, the application of machine learning models in animal breeding has gained a lot of interest due to their tremendous flexibility and their ability to capture patterns in large noisy datasets. Here, we present a general overview of a handful of machine learning algorithms and their application in genomic prediction to provide a meta-picture of their performance in genomic estimated breeding values estimation, genotype imputation, and feature selection. Finally, we discuss a potential adoption of machine learning models in genomic prediction in developing countries. The results of the reviewed studies showed that machine learning models have indeed performed well in fitting large noisy data sets and modeling minor nonadditive effects in some of the studies. However, sometimes conventional methods outperformed machine learning models, which confirms that there's no universal method for genomic prediction. In summary, machine learning models have great potential for extracting patterns from single nucleotide polymorphism datasets. Nonetheless, the level of their adoption in animal breeding is still low due to data limitations, complex genetic interactions, a lack of standardization and reproducibility, and the lack of interpretability of machine learning models when trained with biological data. Consequently, there is no remarkable outperformance of machine learning methods compared to traditional methods in genomic prediction. Therefore, more research should be conducted to discover new insights that could enhance livestock breeding programs.
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Affiliation(s)
- Narjice Chafai
- Laboratory of Biodiversity, Ecology, and Genome, Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco
| | - Ichrak Hayah
- Laboratory of Biodiversity, Ecology, and Genome, Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco
| | - Isidore Houaga
- Centre for Tropical Livestock Genetics and Health, The Roslin Institute, Royal (Dick) School of Veterinary Medicine, The University of Edinburgh, Edinburgh, United Kingdom
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom
| | - Bouabid Badaoui
- Laboratory of Biodiversity, Ecology, and Genome, Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco
- African Sustainable Agriculture Research Institute (ASARI), Mohammed VI Polytechnic University (UM6P), Laayoune, Morocco
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Xia X, Qu K, Wang Y, Sinding MHS, Wang F, Hanif Q, Ahmed Z, Lenstra JA, Han J, Lei C, Chen N. Global dispersal and adaptive evolution of domestic cattle: a genomic perspective. STRESS BIOLOGY 2023; 3:8. [PMID: 37676580 PMCID: PMC10441868 DOI: 10.1007/s44154-023-00085-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 03/26/2023] [Indexed: 09/08/2023]
Abstract
Domestic cattle have spread across the globe and inhabit variable and unpredictable environments. They have been exposed to a plethora of selective pressures and have adapted to a variety of local ecological and management conditions, including UV exposure, diseases, and stall-feeding systems. These selective pressures have resulted in unique and important phenotypic and genetic differences among modern cattle breeds/populations. Ongoing efforts to sequence the genomes of local and commercial cattle breeds/populations, along with the growing availability of ancient bovid DNA data, have significantly advanced our understanding of the genomic architecture, recent evolution of complex traits, common diseases, and local adaptation in cattle. Here, we review the origin and spread of domestic cattle and illustrate the environmental adaptations of local cattle breeds/populations.
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Affiliation(s)
- Xiaoting Xia
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Kaixing Qu
- Academy of Science and Technology, Chuxiong Normal University, Chuxiong, 675000, China
| | - Yan Wang
- Qingdao Municipal Bureau of Agriculture and Rural Affairs, Qingdao, 266000, China
| | - Mikkel-Holger S Sinding
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, 1350, Denmark
| | - Fuwen Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Quratulain Hanif
- National Institute for Biotechnology and Genetic Engineering, Faisalabad, Pakistan
| | - Zulfiqar Ahmed
- Faculty of Veterinary and Animal Sciences, University of Poonch Rawalakot, Azad Jammu and Kashmir, 12350, Pakistan
| | - Johannes A Lenstra
- Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Jianlin Han
- Livestock Genetic Program, International Livestock Research Institute (ILRI), Nairobi, 00100, Kenya
- CAAS-ILRI Joint Laboratory On Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China
| | - Chuzhao Lei
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China.
| | - Ningbo Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China.
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Nishio M, Inoue K, Arakawa A, Ichinoseki K, Kobayashi E, Okamura T, Fukuzawa Y, Ogawa S, Taniguchi M, Oe M, Takeda M, Kamata T, Konno M, Takagi M, Sekiya M, Matsuzawa T, Inoue Y, Watanabe A, Kobayashi H, Shibata E, Ohtani A, Yazaki R, Nakashima R, Ishii K. Application of linear and machine learning models to genomic prediction of fatty acid composition in Japanese Black cattle. Anim Sci J 2023; 94:e13883. [PMID: 37909231 DOI: 10.1111/asj.13883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/29/2023] [Accepted: 09/15/2023] [Indexed: 11/02/2023]
Abstract
We collected 3180 records of oleic acid (C18:1) and monounsaturated fatty acid (MUFA) measured using gas chromatography (GC) and 6960 records of C18:1 and MUFA measured using near-infrared spectroscopy (NIRS) in intermuscular fat samples of Japanese Black cattle. We compared genomic prediction performance for four linear models (genomic best linear unbiased prediction [GBLUP], kinship-adjusted multiple loci [KAML], BayesC, and BayesLASSO) and five machine learning models (Gaussian kernel [GK], deep kernel [DK], random forest [RF], extreme gradient boost [XGB], and convolutional neural network [CNN]). For GC-based C18:1 and MUFA, KAML showed the highest accuracies, followed by BayesC, XGB, DK, GK, and BayesLASSO, with more than 6% gain of accuracy by KAML over GBLUP. Meanwhile, DK had the highest prediction accuracy for NIRS-based C18:1 and MUFA, but the difference in accuracies between DK and KAML was slight. For all traits, accuracies of RF and CNN were lower than those of GBLUP. The KAML extends GBLUP methods, of which marker effects are weighted, and involves only additive genetic effects; whereas machine learning methods capture non-additive genetic effects. Thus, KAML is the most suitable method for breeding of fatty acid composition in Japanese Black cattle.
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Affiliation(s)
- Motohide Nishio
- Institute of Livestock and Grassland Science, NARO, Tsukuba, Japan
| | - Keiichi Inoue
- National Livestock Breeding Center, Fukushima, Japan
- University of Miyazaki, Miyazaki, Japan
| | - Aisaku Arakawa
- Institute of Livestock and Grassland Science, NARO, Tsukuba, Japan
| | | | - Eiji Kobayashi
- Institute of Livestock and Grassland Science, NARO, Tsukuba, Japan
| | | | - Yo Fukuzawa
- Institute of Livestock and Grassland Science, NARO, Tsukuba, Japan
| | - Shinichiro Ogawa
- Institute of Livestock and Grassland Science, NARO, Tsukuba, Japan
| | | | - Mika Oe
- Institute of Livestock and Grassland Science, NARO, Tsukuba, Japan
| | | | - Takehiro Kamata
- Aomori Prefectural Industrial Technology Research Center, Tsugaru, Japan
| | - Masaru Konno
- Iwate Agricultural Research Center Animal Industry Research Institute, Takizawa, Japan
| | - Michihiro Takagi
- Miyagi Prefecture Animal Industry Experiment Station, Osaki, Japan
| | - Mario Sekiya
- Akita Prefectural Livestock Experiment Station, Daisen, Japan
| | - Tamotsu Matsuzawa
- Livestock Research Centre, Fukushima Agricultural Technology Centre, Fukushima, Japan
| | - Yoshinobu Inoue
- Tottori Prefectural Livestock Research Center, Tottori, Japan
| | | | - Hiroshi Kobayashi
- Institute of Animal Production Okayama Prefectural Technology Center for Agriculture, Forestry and Fisheries, Misaki, Japan
| | - Eri Shibata
- Hiroshima Prefectural Technology Research Institute, Livestock Technology Research Center, Shobara, Japan
| | - Akihumi Ohtani
- Yamaguchi Prefectural Agriculture and Forestry General Technology Center, Mine, Japan
| | - Ryu Yazaki
- Oita Prefectural Agriculture, Forestry, and Fisheries Research Center, Takeda, Japan
| | - Ryotaro Nakashima
- Cattle Breeding Development Institute of Kagoshima Prefecture, Soo, Japan
| | - Kazuo Ishii
- Institute of Livestock and Grassland Science, NARO, Tsukuba, Japan
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Wang X, Shi S, Wang G, Luo W, Wei X, Qiu A, Luo F, Ding X. Using machine learning to improve the accuracy of genomic prediction of reproduction traits in pigs. J Anim Sci Biotechnol 2022; 13:60. [PMID: 35578371 PMCID: PMC9112588 DOI: 10.1186/s40104-022-00708-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 03/13/2022] [Indexed: 12/02/2022] Open
Abstract
Background Recently, machine learning (ML) has become attractive in genomic prediction, but its superiority in genomic prediction over conventional (ss) GBLUP methods and the choice of optimal ML methods need to be investigated. Results In this study, 2566 Chinese Yorkshire pigs with reproduction trait records were genotyped with the GenoBaits Porcine SNP 50 K and PorcineSNP50 panels. Four ML methods, including support vector regression (SVR), kernel ridge regression (KRR), random forest (RF) and Adaboost.R2 were implemented. Through 20 replicates of fivefold cross-validation (CV) and one prediction for younger individuals, the utility of ML methods in genomic prediction was explored. In CV, compared with genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP) and the Bayesian method BayesHE, ML methods significantly outperformed these conventional methods. ML methods improved the genomic prediction accuracy of GBLUP, ssGBLUP, and BayesHE by 19.3%, 15.0% and 20.8%, respectively. In addition, ML methods yielded smaller mean squared error (MSE) and mean absolute error (MAE) in all scenarios. ssGBLUP yielded an improvement of 3.8% on average in accuracy compared to that of GBLUP, and the accuracy of BayesHE was close to that of GBLUP. In genomic prediction of younger individuals, RF and Adaboost.R2_KRR performed better than GBLUP and BayesHE, while ssGBLUP performed comparably with RF, and ssGBLUP yielded slightly higher accuracy and lower MSE than Adaboost.R2_KRR in the prediction of total number of piglets born, while for number of piglets born alive, Adaboost.R2_KRR performed significantly better than ssGBLUP. Among ML methods, Adaboost.R2_KRR consistently performed well in our study. Our findings also demonstrated that optimal hyperparameters are useful for ML methods. After tuning hyperparameters in CV and in predicting genomic outcomes of younger individuals, the average improvement was 14.3% and 21.8% over those using default hyperparameters, respectively. Conclusion Our findings demonstrated that ML methods had better overall prediction performance than conventional genomic selection methods, and could be new options for genomic prediction. Among ML methods, Adaboost.R2_KRR consistently performed well in our study, and tuning hyperparameters is necessary for ML methods. The optimal hyperparameters depend on the character of traits, datasets etc. Supplementary Information The online version contains supplementary material available at 10.1186/s40104-022-00708-0.
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Affiliation(s)
- Xue Wang
- Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture and Rural Affairs, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Shaolei Shi
- Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture and Rural Affairs, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Guijiang Wang
- Hebei Province Animal Husbandry and Improved Breeds Work Station, Shijiazhuang, Hebei, China
| | - Wenxue Luo
- Hebei Province Animal Husbandry and Improved Breeds Work Station, Shijiazhuang, Hebei, China
| | - Xia Wei
- Zhangjiakou Dahao Heshan New Agricultural Development Co., Ltd, Zhangjiakou, Hebei, China
| | - Ao Qiu
- Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture and Rural Affairs, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Fei Luo
- Hebei Province Animal Husbandry and Improved Breeds Work Station, Shijiazhuang, Hebei, China
| | - Xiangdong Ding
- Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture and Rural Affairs, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China.
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