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Farooq MA, Gao S, Hassan MA, Huang Z, Rasheed A, Hearne S, Prasanna B, Li X, Li H. Artificial intelligence in plant breeding. Trends Genet 2024:S0168-9525(24)00167-7. [PMID: 39117482 DOI: 10.1016/j.tig.2024.07.001] [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: 04/30/2024] [Revised: 07/06/2024] [Accepted: 07/12/2024] [Indexed: 08/10/2024]
Abstract
Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.
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Affiliation(s)
- Muhammad Amjad Farooq
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Shang Gao
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Muhammad Adeel Hassan
- Adaptive Cropping Systems Laboratory, Beltsville Agricultural Research Center, US Department of Agriculture, Beltsville, MD 20705, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, USA
| | - Zhangping Huang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Awais Rasheed
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Sarah Hearne
- CIMMYT, KM 45 Carretera Mexico-Veracruz, El Batan, Texcoco 56237, Mexico
| | - Boddupalli Prasanna
- CIMMYT, International Centre for Research in Agroforestry (ICRAF) House, Nairobi 00100, Kenya
| | - Xinhai Li
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China
| | - Huihui Li
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China.
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Jones D, Fornarelli R, Derbyshire M, Gibberd M, Barker K, Hane J. The pursuit of genetic gain in agricultural crops through the application of machine-learning to genomic prediction. Front Genet 2023; 14:1186782. [PMID: 37614817 PMCID: PMC10443705 DOI: 10.3389/fgene.2023.1186782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/24/2023] [Indexed: 08/25/2023] Open
Abstract
Current practice in agriculture applies genomic prediction to assist crop breeding in the analysis of genetic marker data. Genomic selection methods typically use linear mixed models, but using machine-learning may provide further potential for improved selection accuracy, or may provide additional information. Here we describe SelectML, an automated pipeline for testing and comparing the performance of a range of linear mixed model and machine-learning-based genomic selection methods. We demonstrate the use of SelectML on an in silico-generated marker dataset which simulated a randomly-sampled (mixed) and an unevenly-sampled (unbalanced) population, comparing the relative performance of various methods included in SelectML on the two datasets. Although machine-learning based methods performed similarly overall to linear mixed models, they performed worse on the mixed dataset and marginally better on the unbalanced dataset, being more affected than linear mixed models by the imposed sampling bias. SelectML can assist in the training, comparison, and selection of genomic selection models, and is available from https://github.com/darcyabjones/selectml.
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Affiliation(s)
- Darcy Jones
- Centre for Crop and Disease Management, Curtin University, Perth, WA, Australia
| | - Roberta Fornarelli
- Centre for Crop and Disease Management, Curtin University, Perth, WA, Australia
- Curtin Institute for Computation, Curtin University, Perth, WA, Australia
| | - Mark Derbyshire
- Centre for Crop and Disease Management, Curtin University, Perth, WA, Australia
| | - Mark Gibberd
- Centre for Crop and Disease Management, Curtin University, Perth, WA, Australia
| | - Kathryn Barker
- Curtin Institute for Computation, Curtin University, Perth, WA, Australia
| | - James Hane
- Centre for Crop and Disease Management, Curtin University, Perth, WA, Australia
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Zhuang Z, Wu J, Qiu Y, Ruan D, Ding R, Xu C, Zhou S, Zhang Y, Liu Y, Ma F, Yang J, Sun Y, Zheng E, Yang M, Cai G, Yang J, Wu Z. Improving the accuracy of genomic prediction for meat quality traits using whole genome sequence data in pigs. J Anim Sci Biotechnol 2023; 14:67. [PMID: 37161604 PMCID: PMC10170792 DOI: 10.1186/s40104-023-00863-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/05/2023] [Indexed: 05/11/2023] Open
Abstract
BACKGROUND Pork quality can directly affect customer purchase tendency and meat quality traits have become valuable in modern pork production. However, genetic improvement has been slow due to high phenotyping costs. In this study, whole genome sequence (WGS) data was used to evaluate the prediction accuracy of genomic best linear unbiased prediction (GBLUP) for meat quality in large-scale crossbred commercial pigs. RESULTS We produced WGS data (18,695,907 SNPs and 2,106,902 INDELs exceed quality control) from 1,469 sequenced Duroc × (Landrace × Yorkshire) pigs and developed a reference panel for meat quality including meat color score, marbling score, L* (lightness), a* (redness), and b* (yellowness) of genomic prediction. The prediction accuracy was defined as the Pearson correlation coefficient between adjusted phenotypes and genomic estimated breeding values in the validation population. Using different marker density panels derived from WGS data, accuracy differed substantially among meat quality traits, varied from 0.08 to 0.47. Results showed that MultiBLUP outperform GBLUP and yielded accuracy increases ranging from 17.39% to 75%. We optimized the marker density and found medium- and high-density marker panels are beneficial for the estimation of heritability for meat quality. Moreover, we conducted genotype imputation from 50K chip to WGS level in the same population and found average concordance rate to exceed 95% and r2 = 0.81. CONCLUSIONS Overall, estimation of heritability for meat quality traits can benefit from the use of WGS data. This study showed the superiority of using WGS data to genetically improve pork quality in genomic prediction.
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Affiliation(s)
- Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Jie Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Yibin Qiu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Donglin Ruan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Rongrong Ding
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Cineng Xu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Shenping Zhou
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Yuling Zhang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Yiyi Liu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Fucai Ma
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Jifei Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Ying Sun
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Enqin Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Ming Yang
- College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China.
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China.
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu, 527400, China.
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Mota LFM, Santos SWB, Júnior GAF, Bresolin T, Mercadante MEZ, Silva JAV, Cyrillo JNSG, Monteiro FM, Carvalheiro R, Albuquerque LG. Meta-analysis across Nellore cattle populations identifies common metabolic mechanisms that regulate feed efficiency-related traits. BMC Genomics 2022; 23:424. [PMID: 35672696 PMCID: PMC9172108 DOI: 10.1186/s12864-022-08671-w] [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: 11/01/2021] [Accepted: 05/03/2022] [Indexed: 11/28/2022] Open
Abstract
Background Feed efficiency (FE) related traits play a key role in the economy and sustainability of beef cattle production systems. The accurate knowledge of the physiologic background for FE-related traits can help the development of more efficient selection strategies for them. Hence, multi-trait weighted GWAS (MTwGWAS) and meta-analyze were used to find genomic regions associated with average daily gain (ADG), dry matter intake (DMI), feed conversion ratio (FCR), feed efficiency (FE), and residual feed intake (RFI). The FE-related traits and genomic information belong to two breeding programs that perform the FE test at different ages: post-weaning (1,024 animals IZ population) and post-yearling (918 animals for the QLT population). Results The meta-analyze MTwGWAS identified 14 genomic regions (-log10(p -value) > 5) regions mapped on BTA 1, 2, 3, 4, 7, 8, 11, 14, 15, 18, 21, and 29. These regions explained a large proportion of the total genetic variance for FE-related traits across-population ranging from 20% (FCR) to 36% (DMI) in the IZ population and from 22% (RFI) to 28% (ADG) in the QLT population. Relevant candidate genes within these regions (LIPE, LPL, IGF1R, IGF1, IGFBP5, IGF2, INS, INSR, LEPR, LEPROT, POMC, NPY, AGRP, TGFB1, GHSR, JAK1, LYN, MOS, PLAG1, CHCD7, LCAT, and PLA2G15) highlighted that the physiological mechanisms related to neuropeptides and the metabolic signals controlling the body's energy balance are responsible for leading to greater feed efficiency. Integrated meta-analysis results and functional pathway enrichment analysis highlighted the major effect of biological functions linked to energy, lipid metabolism, and hormone signaling that mediates the effects of peptide signals in the hypothalamus and whole-body energy homeostasis affecting the genetic control of FE-related traits in Nellore cattle. Conclusions Genes and pathways associated with common signals for feed efficiency-related traits provide better knowledge about regions with biological relevance in physiological mechanisms associated with differences in energy metabolism and hypothalamus signaling. These pleiotropic regions would support the selection for feed efficiency-related traits, incorporating and pondering causal variations assigning prior weights in genomic selection approaches. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08671-w.
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Affiliation(s)
- Lucio F M Mota
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal - SP, São Paulo, 14884-900, Brazil.
| | - Samuel W B Santos
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal - SP, São Paulo, 14884-900, Brazil
| | - Gerardo A Fernandes Júnior
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal - SP, São Paulo, 14884-900, Brazil
| | - Tiago Bresolin
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal - SP, São Paulo, 14884-900, Brazil
| | - Maria E Z Mercadante
- Institute of Animal Science, Beef Cattle Research Center, Sertãozinho - SP, São Paulo, 14174-000, Brazil.,National Council for Science and Technological Development, Brasilia - DF, 71605-001, Brazil
| | - Josineudson A V Silva
- National Council for Science and Technological Development, Brasilia - DF, 71605-001, Brazil.,School of Veterinary Medicine and Animal Science, São Paulo State University (UNESP), Botucatu - SP, 18618-681, Brazil
| | - Joslaine N S G Cyrillo
- Institute of Animal Science, Beef Cattle Research Center, Sertãozinho - SP, São Paulo, 14174-000, Brazil
| | - Fábio M Monteiro
- Institute of Animal Science, Beef Cattle Research Center, Sertãozinho - SP, São Paulo, 14174-000, Brazil
| | - Roberto Carvalheiro
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal - SP, São Paulo, 14884-900, Brazil.,National Council for Science and Technological Development, Brasilia - DF, 71605-001, Brazil
| | - Lucia G Albuquerque
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal - SP, São Paulo, 14884-900, Brazil. .,National Council for Science and Technological Development, Brasilia - DF, 71605-001, Brazil.
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