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Li W, Li W, Song Z, Gao Z, Xie K, Wang Y, Wang B, Hu J, Zhang Q, Ning C, Wang D, Fan X. Marker Density and Models to Improve the Accuracy of Genomic Selection for Growth and Slaughter Traits in Meat Rabbits. Genes (Basel) 2024; 15:454. [PMID: 38674388 PMCID: PMC11050255 DOI: 10.3390/genes15040454] [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: 03/11/2024] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
The selection and breeding of good meat rabbit breeds are fundamental to their industrial development, and genomic selection (GS) can employ genomic information to make up for the shortcomings of traditional phenotype-based breeding methods. For the practical implementation of GS in meat rabbit breeding, it is necessary to assess different marker densities and GS models. Here, we obtained low-coverage whole-genome sequencing (lcWGS) data from 1515 meat rabbits (including parent herd and half-sibling offspring). The specific objectives were (1) to derive a baseline for heritability estimates and genomic predictions based on randomly selected marker densities and (2) to assess the accuracy of genomic predictions for single- and multiple-trait linear mixed models. We found that a marker density of 50 K can be used as a baseline for heritability estimation and genomic prediction. For GS, the multi-trait genomic best linear unbiased prediction (GBLUP) model results in more accurate predictions for virtually all traits compared to the single-trait model, with improvements greater than 15% for all of them, which may be attributed to the use of information on genetically related traits. In addition, we discovered a positive correlation between the performance of the multi-trait GBLUP and the genetic correlation between the traits. We anticipate that this approach will provide solutions for GS, as well as optimize breeding programs, in meat rabbits.
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Affiliation(s)
- Wenjie Li
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
- Department of Animal Genetics and Breeding, University of Anhui Agricultural, Hefei 230031, China
| | - Wenqiang Li
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Zichen Song
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Zihao Gao
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Kerui Xie
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Yubing Wang
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Bo Wang
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Jiaqing Hu
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Qin Zhang
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Chao Ning
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Dan Wang
- Key Laboratory of Efficient Utilization of Non-Grain Feed Resources (Co-Construction by Ministry and Province), College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Ministry of Agriculture and Rural Affairs, Taian 271000, China
| | - Xinzhong Fan
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
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Mir ZA, Chandra T, Saharan A, Budhlakoti N, Mishra DC, Saharan MS, Mir RR, Singh AK, Sharma S, Vikas VK, Kumar S. Recent advances on genome-wide association studies (GWAS) and genomic selection (GS); prospects for Fusarium head blight research in Durum wheat. Mol Biol Rep 2023; 50:3885-3901. [PMID: 36826681 DOI: 10.1007/s11033-023-08309-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 01/26/2023] [Indexed: 02/25/2023]
Abstract
PURPOSE Wheat is an important cereal crop that is cultivated in different parts of the world. The biotic stresses are the major concerns in wheat-growing nations and are responsible for production loss globally. The change in climate dynamics makes the pathogen more virulent in foothills and tropical regions. There is growing concern about FHB in major wheat-growing nations, and until now, there has been no known potential source of resistance identified in wheat germplasm. The plant pathogen interaction activates the cascade of pathways, genes, TFs, and resistance genes. Pathogenesis-related genes' role in disease resistance is functionally validated in different plant systems. Similarly, Genomewide association Studies (GWAS) and Genomic selection (GS) are promising tools and have led to the discovery of resistance genes, genomic regions, and novel markers. Fusarium graminearum produces deoxynivalenol (DON) mycotoxins in wheat kernels, affecting wheat productivity globally. Modern technology now allows for detecting and managing DON toxin to reduce the risk to humans and animals. This review offers a comprehensive overview of the roles played by GWAS and Genomic selection (GS) in the identification of new genes, genetic variants, molecular markers and DON toxin management strategies. METHODS The review offers a comprehensive and in-depth analysis of the function of Fusarium graminearum virulence factors in Durum wheat. The role of GWAS and GS for Fusarium Head Blight (FHB) resistance has been well described. This paper provides a comprehensive description of the various statistical models that are used in GWAS and GS. In this review, we look at how different detection methods have been used to analyze and manage DON toxin exposure. RESULTS This review highlights the role of virulent genes in Fusarium disease establishment. The role of genome-based selection offers the identification of novel QTLs in resistant wheat germplasm. The role of GWAS and GS selection has minimized the use of population development through breeding technology. Here, we also emphasized the function of recent technological developments in minimizing the impact of DON toxins and their implications for food safety.
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Affiliation(s)
- Zahoor Ahmad Mir
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, 110012, India
| | - Tilak Chandra
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India
| | - Anurag Saharan
- ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Neeraj Budhlakoti
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India
| | - D C Mishra
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India
| | - M S Saharan
- ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-Kashmir), Srinagar, Jammu Kashmir, 190025, India
| | - Amit Kumar Singh
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, 110012, India
| | - Soumya Sharma
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India
| | - V K Vikas
- ICAR- Indian Agricultural Research Institute, Regional Station, Wellington, The Nilgiris, Tamilnadu, 643231, India.
| | - Sundeep Kumar
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, 110012, India.
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Xu Y, Zhang X, Li H, Zheng H, Zhang J, Olsen MS, Varshney RK, Prasanna BM, Qian Q. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. MOLECULAR PLANT 2022; 15:1664-1695. [PMID: 36081348 DOI: 10.1016/j.molp.2022.09.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 05/12/2023]
Abstract
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; CIMMYT-China Tropical Maize Research Center, School of Food Science and Engineering, Foshan University, Foshan, Guangdong 528231, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China.
| | - Xingping Zhang
- Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China
| | - Huihui Li
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, Hainan 572024, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Jianan Zhang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang, Hebei 050035, China
| | - Michael S Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Rajeev K Varshney
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Australia
| | - Boddupalli M Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Qian Qian
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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Ning C, Xie K, Huang J, Di Y, Wang Y, Yang A, Hu J, Zhang Q, Wang D, Fan X. Marker density and statistical model designs to increase accuracy of genomic selection for wool traits in Angora rabbits. Front Genet 2022; 13:968712. [PMID: 36118881 PMCID: PMC9478554 DOI: 10.3389/fgene.2022.968712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/17/2022] [Indexed: 11/13/2022] Open
Abstract
The Angora rabbit, a well-known breed for fiber production, has been undergoing traditional breeding programs relying mainly on phenotypes. Genomic selection (GS) uses genomic information and promises to accelerate genetic gain. Practically, to implement GS in Angora rabbit breeding, it is necessary to evaluate different marker densities and GS models to develop suitable strategies for an optimized breeding pipeline. Considering a lack in microarray, low-coverage sequencing combined with genotype imputation was used to boost the number of SNPs across the rabbit genome. Here, in a population of 629 Angora rabbits, a total of 18,577,154 high-quality SNPs were imputed (imputation accuracy above 98%) based on low-coverage sequencing of 3.84X genomic coverage, and wool traits and body weight were measured at 70, 140 and 210 days of age. From the original markers, 0.5K, 1K, 3K, 5K, 10K, 50K, 100K, 500K, 1M and 2M were randomly selected and evaluated, resulting in 50K markers as the baseline for the heritability estimation and genomic prediction. Comparing to the GS performance of single-trait models, the prediction accuracy of nearly all traits could be improved by multi-trait models, which might because multiple-trait models used information from genetically correlated traits. Furthermore, we observed high significant negative correlation between the increased prediction accuracy from single-trait to multiple-trait models and estimated heritability. The results indicated that low-heritability traits could borrow more information from correlated traits and hence achieve higher prediction accuracy. The research first reported heritability estimation in rabbits by using genome-wide markers, and provided 50K as an optimal marker density for further microarray design, genetic evaluation and genomic selection in Angora rabbits. We expect that the work could provide strategies for GS in early selection, and optimize breeding programs in rabbits.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Dan Wang
- *Correspondence: Dan Wang, ; Xinzhong Fan,
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Mbebi AJ, Breitler JC, Bordeaux M, Sulpice R, McHale M, Tong H, Toniutti L, Castillo JA, Bertrand B, Nikoloski Z. A comparative analysis of genomic and phenomic predictions of growth-related traits in 3-way coffee hybrids. G3 GENES|GENOMES|GENETICS 2022; 12:6632664. [PMID: 35792875 PMCID: PMC9434219 DOI: 10.1093/g3journal/jkac170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 06/14/2022] [Indexed: 11/14/2022]
Abstract
Abstract
Genomic prediction has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction models that can account, in part, for this challenge. Here, we compare and contrast genomic prediction and phenomic prediction models for 3 growth-related traits, namely, leaf count, tree height, and trunk diameter, from 2 coffee 3-way hybrid populations exposed to a series of treatment-inducing environmental conditions. The models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors. This comparative analysis demonstrates that the best-performing phenomic prediction models show higher predictability than the best genomic prediction models for the considered traits and environments in the vast majority of comparisons within 3-way hybrid populations. In addition, we show that phenomic prediction models are transferrable between conditions but to a lower extent between populations and we conclude that chlorophyll a fluorescence data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.
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Affiliation(s)
- Alain J Mbebi
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam , Potsdam-Golm 14476, Germany
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology , Potsdam-Golm 14476, Germany
| | - Jean-Christophe Breitler
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Montpellier 34398, France
| | - Mélanie Bordeaux
- Fundación Nicafrance , Finca La Cumplida Km. 147 Carretera Matagalpa - La Dalia, 3 Km al Noreste, Matagalpa, Nicaragua
| | - Ronan Sulpice
- National University Ireland Galway, Plant Systems Biology Laboratory, Ryan Institute, School of Natural Sciences , Galway H91 TK33, Ireland
| | - Marcus McHale
- National University Ireland Galway, Plant Systems Biology Laboratory, Ryan Institute, School of Natural Sciences , Galway H91 TK33, Ireland
| | - Hao Tong
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam , Potsdam-Golm 14476, Germany
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology , Potsdam-Golm 14476, Germany
- Center for Plant Systems Biology and Biotechnology , Plovdiv 4000, Bulgaria
| | - Lucile Toniutti
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Montpellier 34398, France
| | - Jonny Alonso Castillo
- Fundación Nicafrance , Finca La Cumplida Km. 147 Carretera Matagalpa - La Dalia, 3 Km al Noreste, Matagalpa, Nicaragua
| | - Benoît Bertrand
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Montpellier 34398, France
| | - Zoran Nikoloski
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam , Potsdam-Golm 14476, Germany
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology , Potsdam-Golm 14476, Germany
- Center for Plant Systems Biology and Biotechnology , Plovdiv 4000, Bulgaria
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Parihar AK, Kumar J, Gupta DS, Lamichaney A, Naik SJ S, Singh AK, Dixit GP, Gupta S, Toklu F. Genomics Enabled Breeding Strategies for Major Biotic Stresses in Pea ( Pisum sativum L.). FRONTIERS IN PLANT SCIENCE 2022; 13:861191. [PMID: 35665148 PMCID: PMC9158573 DOI: 10.3389/fpls.2022.861191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 03/28/2022] [Indexed: 06/15/2023]
Abstract
Pea (Pisum sativum L.) is one of the most important and productive cool season pulse crops grown throughout the world. Biotic stresses are the crucial constraints in harnessing the potential productivity of pea and warrant dedicated research and developmental efforts to utilize omics resources and advanced breeding techniques to assist rapid and timely development of high-yielding multiple stress-tolerant-resistant varieties. Recently, the pea researcher's community has made notable achievements in conventional and molecular breeding to accelerate its genetic gain. Several quantitative trait loci (QTLs) or markers associated with genes controlling resistance for fusarium wilt, fusarium root rot, powdery mildew, ascochyta blight, rust, common root rot, broomrape, pea enation, and pea seed borne mosaic virus are available for the marker-assisted breeding. The advanced genomic tools such as the availability of comprehensive genetic maps and linked reliable DNA markers hold great promise toward the introgression of resistance genes from different sources to speed up the genetic gain in pea. This review provides a brief account of the achievements made in the recent past regarding genetic and genomic resources' development, inheritance of genes controlling various biotic stress responses and genes controlling pathogenesis in disease causing organisms, genes/QTLs mapping, and transcriptomic and proteomic advances. Moreover, the emerging new breeding approaches such as transgenics, genome editing, genomic selection, epigenetic breeding, and speed breeding hold great promise to transform pea breeding. Overall, the judicious amalgamation of conventional and modern omics-enabled breeding strategies will augment the genetic gain and could hasten the development of biotic stress-resistant cultivars to sustain pea production under changing climate. The present review encompasses at one platform the research accomplishment made so far in pea improvement with respect to major biotic stresses and the way forward to enhance pea productivity through advanced genomic tools and technologies.
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Affiliation(s)
- Ashok Kumar Parihar
- Crop Improvement Division, ICAR-Indian Institute of Pulses Research (ICAR-IIPR), Kanpur, India
| | - Jitendra Kumar
- Crop Improvement Division, ICAR-Indian Institute of Pulses Research (ICAR-IIPR), Kanpur, India
| | - Debjyoti Sen Gupta
- Crop Improvement Division, ICAR-Indian Institute of Pulses Research (ICAR-IIPR), Kanpur, India
| | - Amrit Lamichaney
- Crop Improvement Division, ICAR-Indian Institute of Pulses Research (ICAR-IIPR), Kanpur, India
| | - Satheesh Naik SJ
- Crop Improvement Division, ICAR-Indian Institute of Pulses Research (ICAR-IIPR), Kanpur, India
| | - Anil K. Singh
- Crop Improvement Division, ICAR-Indian Institute of Pulses Research (ICAR-IIPR), Kanpur, India
| | - Girish P. Dixit
- All India Coordinated Research Project on Chickpea, ICAR-IIPR, Kanpur, India
| | - Sanjeev Gupta
- Indian Council of Agricultural Research, New Delhi, India
| | - Faruk Toklu
- Department of Field Crops, Faculty of Agricultural, Cukurova University, Adana, Turkey
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Genome-Enabled Prediction Methods Based on Machine Learning. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2467:189-218. [PMID: 35451777 DOI: 10.1007/978-1-0716-2205-6_7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Growth of artificial intelligence and machine learning (ML) methodology has been explosive in recent years. In this class of procedures, computers get knowledge from sets of experiences and provide forecasts or classification. In genome-wide based prediction (GWP), many ML studies have been carried out. This chapter provides a description of main semiparametric and nonparametric algorithms used in GWP in animals and plants. Thirty-four ML comparative studies conducted in the last decade were used to develop a meta-analysis through a Thurstonian model, to evaluate algorithms with the best predictive qualities. It was found that some kernel, Bayesian, and ensemble methods displayed greater robustness and predictive ability. However, the type of study and data distribution must be considered in order to choose the most appropriate model for a given problem.
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Fathoni A, Boonkum W, Chankitisakul V, Duangjinda M. An Appropriate Genetic Approach for Improving Reproductive Traits in Crossbred Thai-Holstein Cattle under Heat Stress Conditions. Vet Sci 2022; 9:vetsci9040163. [PMID: 35448661 PMCID: PMC9031002 DOI: 10.3390/vetsci9040163] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/19/2022] [Accepted: 03/26/2022] [Indexed: 01/16/2023] Open
Abstract
Thailand is a tropical country affected by global climate change and has high temperatures and humidity that cause heat stress in livestock. A temperature−humidity index (THI) is required to assess and evaluate heat stress levels in livestock. One of the livestock types in Thailand experiencing heat stress due to extreme climate change is crossbred dairy cattle. Genetic evaluations of heat tolerance in dairy cattle have been carried out for reproductive traits. Heritability values for reproductive traits are generally low (<0.10) because environmental factors heavily influence them. Consequently, genetic improvement for these traits would be slow compared to production traits. Positive and negative genetic correlations were found between reproductive traits and reproductive traits and yield traits. Several selection methods for reproductive traits have been introduced, i.e., the traditional method, marker-assisted selection (MAS), and genomic selection (GS). GS is the most promising technique and provides accurate results with a high genetic gain. Single-step genomic BLUP (ssGBLUP) has higher accuracy than the multi-step equivalent for fertility traits or low-heritability traits.
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Affiliation(s)
- Akhmad Fathoni
- Department of Animal Science, Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002, Thailand; (A.F.); (W.B.); (V.C.)
- Department of Animal Breeding and Reproduction, Faculty of Animal Science, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
| | - Wuttigrai Boonkum
- Department of Animal Science, Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002, Thailand; (A.F.); (W.B.); (V.C.)
- Network Center for Animal Breeding and OMICS Research, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Vibuntita Chankitisakul
- Department of Animal Science, Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002, Thailand; (A.F.); (W.B.); (V.C.)
- Network Center for Animal Breeding and OMICS Research, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Monchai Duangjinda
- Department of Animal Science, Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002, Thailand; (A.F.); (W.B.); (V.C.)
- Network Center for Animal Breeding and OMICS Research, Khon Kaen University, Khon Kaen 40002, Thailand
- Correspondence: ; Tel.: +66-81-872-4207
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Budhlakoti N, Kushwaha AK, Rai A, Chaturvedi KK, Kumar A, Pradhan AK, Kumar U, Kumar RR, Juliana P, Mishra DC, Kumar S. Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops. Front Genet 2022; 13:832153. [PMID: 35222548 PMCID: PMC8864149 DOI: 10.3389/fgene.2022.832153] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 01/10/2022] [Indexed: 12/17/2022] Open
Abstract
Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.
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Affiliation(s)
- Neeraj Budhlakoti
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Anil Rai
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - K K Chaturvedi
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anuj Kumar
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Uttam Kumar
- Borlaug Institute for South Asia (BISA), Ludhiana, India
| | | | | | - D C Mishra
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sundeep Kumar
- ICAR- National Bureau of Plant Genetic Resources, New Delhi, India
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Hao H, Li Z, Leng C, Lu C, Luo H, Liu Y, Wu X, Liu Z, Shang L, Jing HC. Sorghum breeding in the genomic era: opportunities and challenges. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1899-1924. [PMID: 33655424 PMCID: PMC7924314 DOI: 10.1007/s00122-021-03789-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 02/05/2021] [Indexed: 05/04/2023]
Abstract
The importance and potential of the multi-purpose crop sorghum in global food security have not yet been fully exploited, and the integration of the state-of-art genomics and high-throughput technologies into breeding practice is required. Sorghum, a historically vital staple food source and currently the fifth most important major cereal, is emerging as a crop with diverse end-uses as food, feed, fuel and forage and a model for functional genetics and genomics of tropical grasses. Rapid development in high-throughput experimental and data processing technologies has significantly speeded up sorghum genomic researches in the past few years. The genomes of three sorghum lines are available, thousands of genetic stocks accessible and various genetic populations, including NAM, MAGIC, and mutagenised populations released. Functional and comparative genomics have elucidated key genetic loci and genes controlling agronomical and adaptive traits. However, the knowledge gained has far away from being translated into real breeding practices. We argue that the way forward is to take a genome-based approach for tailored designing of sorghum as a multi-functional crop combining excellent agricultural traits for various end uses. In this review, we update the new concepts and innovation systems in crop breeding and summarise recent advances in sorghum genomic researches, especially the genome-wide dissection of variations in genes and alleles for agronomically important traits. Future directions and opportunities for sorghum breeding are highlighted to stimulate discussion amongst sorghum academic and industrial communities.
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Affiliation(s)
- Huaiqing Hao
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
| | - Zhigang Li
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Chuanyuan Leng
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Cheng Lu
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hong Luo
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Yuanming Liu
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaoyuan Wu
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Zhiquan Liu
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Li Shang
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Hai-Chun Jing
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
- Engineering Laboratory for Grass-based Livestock Husbandry, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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