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Zhao H, Khansefid M, Lin Z, Hayden MJ. Genetic Gain and Inbreeding in Different Simulated Genomic Selection Schemes for Grain Yield and Oil Content in Safflower. PLANTS (BASEL, SWITZERLAND) 2024; 13:1577. [PMID: 38891385 PMCID: PMC11174797 DOI: 10.3390/plants13111577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 06/21/2024]
Abstract
Safflower (Carthamus tinctorius L.) is a multipurpose minor crop consumed by developed and developing nations around the world with limited research funding and genetic resources. Genomic selection (GS) is an effective modern breeding tool that can help to fast-track the genetic diversity preserved in genebank collections to facilitate rapid and efficient germplasm improvement and variety development. In the present study, we simulated four GS strategies to compare genetic gains and inbreeding during breeding cycles in a safflower recurrent selection breeding program targeting grain yield (GY) and seed oil content (OL). We observed positive genetic gains over cycles in all four GS strategies, where the first cycle delivered the largest genetic gain. Single-trait GS strategies had the greatest gain for the target trait but had very limited genetic improvement for the other trait. Simultaneous selection for GY and OL via indices indicated higher gains for both traits than crossing between the two single-trait independent culling strategies. The multi-trait GS strategy with mating relationship control (GS_GY + OL + Rel) resulted in a lower inbreeding coefficeint but a similar gain compared to that of the GS_GY + OL (without inbreeding control) strategy after a few cycles. Our findings lay the foundation for future safflower GS breeding.
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Affiliation(s)
- Huanhuan Zhao
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia;
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
| | - Majid Khansefid
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia;
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
| | - Zibei Lin
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
| | - Matthew J. Hayden
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia;
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
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Ali F, Arif MAR, Ali A, Nadeem MA, Aksoy E, Bakhsh A, Khan SU, Kurt C, Tekdal D, Ilyas MK, Hameed A, Chung YS, Baloch FS. Genome-wide association studies identifies genetic loci related to fatty acid and branched-chain amino acid metabolism and histone modifications under varying nitrogen treatments in safflower ( Carthamus tinctorius). FUNCTIONAL PLANT BIOLOGY : FPB 2024; 51:FP23310. [PMID: 38683936 DOI: 10.1071/fp23310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 04/09/2024] [Indexed: 05/02/2024]
Abstract
Effective identification and usage of genetic variation are prerequisites for developing nutrient-efficient cultivars. A collection of 94 safflower (Carthamus tinctorius ) genotypes (G) was investigated for important morphological and photosynthetic traits at four nitrogen (N) treatments. We found significant variation for all the studied traits except chlorophyll b (chl b ) among safflower genotypes, nitrogen treatments and G×N interaction. The examined traits showed a 2.82-50.00% increase in response to N application. Biological yield (BY) reflected a significantly positive correlation with fresh shoot weight (FSW), root length (RL), fresh root weight (FRW) and number of leaves (NOL), while a significantly positive correlation was also observed among carotenoids (C), chlorophyll a (chl a ), chl b and total chlorophyll content (CT) under all treatments. Superior genotypes with respect to plant height (PH), FSW, NOL, RL, FRW and BY were clustered into Group 3, while genotypes with better mean performance regarding chl a , chl b C and CT were clustered into Group 2 as observed in principal component analysis. The identified eight best-performing genotypes could be useful to develop improved nitrogen efficient cultivars. Genome-wide association analysis resulted in 32 marker-trait associations (MTAs) under four treatments. Markers namely DArT-45481731 , DArT-17812864 , DArT-15670279 and DArT-45482737 were found consistent. Protein-protein interaction networks of loci associated with MTAs were related to fatty acid and branched-chain amino acid metabolism and histone modifications.
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Affiliation(s)
- Fawad Ali
- School of Breeding and Multiplication (Sanya Institute of Breeding and Multiplication), School of Tropical Agriculture and Forestry Hainan University, Sanya 572025, Hai-nan, China; and Department of Botany, University of Baltistan Skardu, Gilgil Baltistan, 16100, Pakistan
| | - Mian A R Arif
- Nuclear Institute for Agriculture and Biology, Faisalabad, Pakistan
| | - Arif Ali
- Department of Plant Sciences, Quaid-I-Azam University, Islamabad, 45320, Pakistan
| | - Muhammad A Nadeem
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas 58140, Turkey
| | - Emre Aksoy
- Department of Biological Sciences, Middle East Technical University, Ankara, Turkey
| | - Allah Bakhsh
- Centre of Excellence in Molecular Biology, University of the Punjab, Lahore, Pakistan
| | - Shahid U Khan
- Integrative Science Center of Germplasm Creation in Western China (CHONGQING) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, 400715, China; and Women Medical and Dental College, Khyber Medical University, Peshawar, KPK, 22020, Pakistan
| | - Cemal Kurt
- Department of Field Crops, Faculty of Agriculture, University of Çukurova, Adana, Turkey
| | - Dilek Tekdal
- Faculty of Science, Department of Biotechnology, Mersin University, 33343, Yenisehir, Mersin, Turkey
| | - Muhammad K Ilyas
- National Agricultural Research Centre, Park Road, Islamabad 45500, Pakistan
| | - Amjad Hameed
- Nuclear Institute for Agriculture and Biology, Faisalabad, Pakistan
| | - Yong S Chung
- Department of Plant Resources and Environment, Jeju National University, Jeju 63243, Republic of Korea
| | - Faheem S Baloch
- Faculty of Science, Department of Biotechnology, Mersin University, 33343, Yenisehir, Mersin, Turkey
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Zhao H, Lin Z, Khansefid M, Tibbits JF, Hayden MJ. Genomic prediction and selection response for grain yield in safflower. Front Genet 2023; 14:1129433. [PMID: 37051598 PMCID: PMC10083426 DOI: 10.3389/fgene.2023.1129433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/13/2023] [Indexed: 03/29/2023] Open
Abstract
In plant breeding programs, multiple traits are recorded in each trial, and the traits are often correlated. Correlated traits can be incorporated into genomic selection models, especially for traits with low heritability, to improve prediction accuracy. In this study, we investigated the genetic correlation between important agronomic traits in safflower. We observed the moderate genetic correlations between grain yield (GY) and plant height (PH, 0.272–0.531), and low correlations between grain yield and days to flowering (DF, −0.157–0.201). A 4%–20% prediction accuracy improvement for grain yield was achieved when plant height was included in both training and validation sets with multivariate models. We further explored the selection responses for grain yield by selecting the top 20% of lines based on different selection indices. Selection responses for grain yield varied across sites. Simultaneous selection for grain yield and seed oil content (OL) showed positive gains across all sites with equal weights for both grain yield and oil content. Combining g×E interaction into genomic selection (GS) led to more balanced selection responses across sites. In conclusion, genomic selection is a valuable breeding tool for breeding high grain yield, oil content, and highly adaptable safflower varieties.
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Affiliation(s)
- Huanhuan Zhao
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- *Correspondence: Huanhuan Zhao,
| | - Zibei Lin
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Majid Khansefid
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Josquin F. Tibbits
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Matthew J. Hayden
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
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Han X, Tang Q, Xu L, Guan Z, Tu J, Yi B, Liu K, Yao X, Lu S, Guo L. Genome-wide detection of genotype environment interactions for flowering time in Brassica napus. FRONTIERS IN PLANT SCIENCE 2022; 13:1065766. [PMID: 36479520 PMCID: PMC9721451 DOI: 10.3389/fpls.2022.1065766] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 10/31/2022] [Indexed: 06/17/2023]
Abstract
Flowering time is strongly related to the environment, while the genotype-by-environment interaction study for flowering time is lacking in Brassica napus. Here, a total of 11,700,689 single nucleotide polymorphisms in 490 B. napus accessions were used to associate with the flowering time and related climatic index in eight environments using a compressed variance-component mixed model, 3VmrMLM. As a result, 19 stable main-effect quantitative trait nucleotides (QTNs) and 32 QTN-by-environment interactions (QEIs) for flowering time were detected. Four windows of daily average temperature and precipitation were found to be climatic factors highly correlated with flowering time. Ten main-effect QTNs were found to be associated with these flowering-time-related climatic indexes. Using differentially expressed gene (DEG) analysis in semi-winter and spring oilseed rapes, 5,850 and 5,511 DEGs were found to be significantly expressed before and after vernalization. Twelve and 14 DEGs, including 7 and 9 known homologs in Arabidopsis, were found to be candidate genes for stable QTNs and QEIs for flowering time, respectively. Five DEGs were found to be candidate genes for main-effect QTNs for flowering-time-related climatic index. These candidate genes, such as BnaFLCs, BnaFTs, BnaA02.VIN3, and BnaC09.PRR7, were further validated by the haplotype, selective sweep, and co-expression networks analysis. The candidate genes identified in this study will be helpful to breed B. napus varieties adapted to particular environments with optimized flowering time.
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Affiliation(s)
- Xu Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Qingqing Tang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Liping Xu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Zhilin Guan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Jinxing Tu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Bin Yi
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Kede Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Xuan Yao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Shaoping Lu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Liang Guo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
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