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Qu J, Liu G, Zheng H, Wang X, Zhang H, Gou X, Xu S, Xue J. Deciphering the Genetic Basis of Kernel Composition in a Maize Association Panel. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:20182-20193. [PMID: 39196892 DOI: 10.1021/acs.jafc.4c04683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2024]
Abstract
The primary objective in contemporary maize breeding is to pursue high quality alongside high yield. Deciphering the genetic basis of natural variation in starch, protein, oil, and fiber contents is essential for manipulating kernel composition, thereby enhancing the kernel quality and meeting growing demands. Here, we identified 12 to 88 statistically significant loci associated with kernel composition traits through a genome-wide association study (GWAS) using a panel of 212 diverse inbred lines. A regional association study pinpointed numerous causal candidate genes at these loci. Coexpression and protein-protein interaction network analyses of candidate genes revealed several causal genes directly or indirectly involved in the metabolic processes related to kernel composition traits. Subsequent mutant experiment revealed that nonsense mutations in ZmTIFY12 affect starch, protein, and fiber content, whereas nonsense mutations in ZmTT12 affect starch, protein, and oil content. These findings provide valuable guidance for improving kernel quality in maize breeding efforts.
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Affiliation(s)
- Jianzhou Qu
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning 110866, China
- The Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, Yangling, Shaanxi 712100, China
- Maize Engineering & Technology Research Centre, College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Gengyu Liu
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning 110866, China
| | - Hongyun Zheng
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning 110866, China
| | - Xiaoyue Wang
- The Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, Yangling, Shaanxi 712100, China
- Maize Engineering & Technology Research Centre, College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Hao Zhang
- The Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, Yangling, Shaanxi 712100, China
- Maize Engineering & Technology Research Centre, College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiaonan Gou
- The Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, Yangling, Shaanxi 712100, China
- Maize Engineering & Technology Research Centre, College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Shutu Xu
- The Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, Yangling, Shaanxi 712100, China
- Maize Engineering & Technology Research Centre, College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Jiquan Xue
- The Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, Yangling, Shaanxi 712100, China
- Maize Engineering & Technology Research Centre, College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
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Katral A, Hossain F, Zunjare RU, Ragi S, Kasana RK, Duo H, Gopinath I, Mehta BK, Guleria SK, Thimmegowda V, Vasudev S, Kumar B, Karjagi CG, Pandey S, Neeraja CN, Yadava DK, Muthusamy V. Maize genotypes with favourable dgat1-2 and fatb alleles possess stable high kernel oil and better fatty acid health and nutritive indices. Int J Biol Macromol 2024; 278:134848. [PMID: 39168197 DOI: 10.1016/j.ijbiomac.2024.134848] [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: 03/17/2024] [Revised: 08/16/2024] [Accepted: 08/16/2024] [Indexed: 08/23/2024]
Abstract
Diverse uses of maize oil attracted various stakeholders, including food, feed, and bioenergy, highlighting the increased demand for sustainable production. Here, 48 diverse sub-tropical maize genotypes varying for dgat1-2 and fatb genes governing oil attributes, were evaluated in three diverse locations to assess trends of oil content, fatty acid (FA) profile, the effect of environment on oil attributes, the impact of different gene combinations and determine FA health and nutritional properties. The genotypes revealed wide variation in oil content (OC: 3.4-6.8 %) and FA compositional traits, namely palmitic (PA, 11.3-24.1 %), oleic (OA, 21.5-42.7 %), linoleic (LA, 36.6-61.7 %), and linolenic (ALA, 0.7-2.3 %) acids. Double-mutants with both favourable alleles (dd/ff) exhibited 51.6 % higher oil, 33.2 % higher OA, and 30.2 % reduced PA compared to wild-types (d+d+/f+f+) across locations. These double-mutants had lower saturated FA (12.2 %), and higher unsaturated FA (87.0 %), indicating reduced susceptibility to autooxidation, with lower atherogenicity (0.14), thrombogenicity (0.27) and peroxidisability (48.15), higher cholesterolemic index (7.16), optimum oxidability (5.27) and higher nutritive-value-index (3.35) compared to d+d+/f+f+, making them promising for significant health and nutritional benefits. Locally adapted stable novel double-mutants with high-oil and better FA properties identified here can expedite the maize breeding programs, meeting production demands and addressing long-standing challenges for breeders.
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Affiliation(s)
| | - Firoz Hossain
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | | | - Shridhar Ragi
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | | | - Hriipulou Duo
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | | | - Brijesh K Mehta
- ICAR-Indian Grassland and Fodder Research Institute, Jhansi, India
| | - Satish K Guleria
- CSK-Himachal Pradesh Krishi Vishvavidyalaya, Bajaura, Himachal Pradesh, India
| | | | - Sujata Vasudev
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | | | | | - Sushil Pandey
- ICAR-National Bureau of Plant Genetic Resource, New Delhi, India
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Ni X, Huffaker A, Schmelz EA, Xu W, Williams WP, Guo B, Li X, Huang F. Field Evaluation of Experimental Maize Hybrids for Resistance to the Fall Armyworm (Lepidoptera: Noctuidae) in a Warm Temperate Climate. INSECTS 2024; 15:289. [PMID: 38667419 PMCID: PMC11050381 DOI: 10.3390/insects15040289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 03/22/2024] [Accepted: 04/07/2024] [Indexed: 04/28/2024]
Abstract
The polyphagous fall armyworm (FAW), Spodoptera frugiperda, has become an invasive pest worldwide in recent years. To develop maize germplasm with multiple pest resistance and understand genetic inheritance, 12 experimental hybrids (six pairs of reciprocal crosses) with diverse genetic backgrounds and four commercial checks were examined for FAW resistance in 2013 and 2014. The experiment utilized a randomized complete block design with four replications as the block factor. FAW injury on maize plants was assessed at 7 and 14 d after the artificial infestation at the V6 stage, and predatory arthropod taxa and abundance on maize seedlings were recorded 7 d after the infestation. Spodoptera frugiperda resistance varied significantly among the 16 hybrids. Two reciprocal crosses ('FAW1430' × 'Oh43' and 'CML333' × 'NC358') showed the least FAW injury. Eleven arthropod predators [i.e., six coleopterans, three hemipterans, earwigs (dermapterans), and spiders (or arachnids)] were also recorded; the two most common predators were the pink spotted ladybeetle, Coleomegilla maculata, and the insidious flower (or minute pirate) bug, Orius spp. Predator abundance was not correlated to FAW injury but varied greatly between 2013 and 2014. Principal component analysis demonstrated that, when compared with FAW resistant (or Bt-transgenic) checks ('DKC69-71', 'DKC67-88', and 'P31P42'), five pairs of the reciprocal crosses had moderate FAW resistance, whereas a pair of reciprocal crosses ('NC350' × 'NC358' and NC358 × NC350) showed the same FAW susceptibility as the non-Bt susceptible check 'DKC69-72'. Both parents contributed similarly to FAW resistance, or no maternal/cytoplasmic effect was detected in the experimental hybrids.
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Affiliation(s)
- Xinzhi Ni
- United States Department of Agriculture-Agricultural Research Service, Crop Genetics and Breeding Research Unit, Tifton, GA 31793, USA;
| | - Alisa Huffaker
- Division of Biological Science, University of California-San Diego, La Jolla, CA 92093, USA; (A.H.); (E.A.S.)
| | - Eric A. Schmelz
- Division of Biological Science, University of California-San Diego, La Jolla, CA 92093, USA; (A.H.); (E.A.S.)
| | - Wenwei Xu
- Agricultural Research & Extension Center, Texas A&M AgriLife Research, Texas A&M University System, Lubbock, TX 79403, USA;
| | - W. Paul Williams
- United States Department of Agriculture-Agricultural Research Service, Corn Host Plant Resistance Research Unit, Mississippi State, MS 39762, USA;
| | - Baozhu Guo
- United States Department of Agriculture-Agricultural Research Service, Crop Genetics and Breeding Research Unit, Tifton, GA 31793, USA;
| | - Xianchun Li
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA;
| | - Fangneng Huang
- Department of Entomology, Louisiana State University Agricultural Center, Baton Rouge, LA 70803, USA;
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Dermail A, Mitchell M, Foster T, Fakude M, Chen YR, Suriharn K, Frei UK, Lübberstedt T. Haploid identification in maize. FRONTIERS IN PLANT SCIENCE 2024; 15:1378421. [PMID: 38708398 PMCID: PMC11067884 DOI: 10.3389/fpls.2024.1378421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 04/08/2024] [Indexed: 05/07/2024]
Abstract
Doubled haploid (DH) line production through in vivo maternal haploid induction is widely adopted in maize breeding programs. The established protocol for DH production includes four steps namely in vivo maternal haploid induction, haploid identification, genome doubling of haploid, and self-fertilization of doubled haploids. Since modern haploid inducers still produce relatively small portion of haploids among undesirable hybrid kernels, haploid identification is typically laborious, costly, and time-consuming, making this step the second foremost in the DH technique. This manuscript reviews numerous methods for haploid identification from different approaches including the innate differences in haploids and diploids, biomarkers integrated in haploid inducers, and automated seed sorting. The phenotypic differentiation, genetic basis, advantages, and limitations of each biomarker system are highlighted. Several approaches of automated seed sorting from different research groups are also discussed regarding the platform or instrument used, sorting time, accuracy, advantages, limitations, and challenges before they go through commercialization. The past haploid selection was focusing on finding the distinguishable marker systems with the key to effectiveness. The current haploid selection is adopting multiple reliable biomarker systems with the key to efficiency while seeking the possibility for automation. Fully automated high-throughput haploid sorting would be promising in near future with the key to robustness with retaining the feasible level of accuracy. The system that can meet between three major constraints (time, workforce, and budget) and the sorting scale would be the best option.
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Affiliation(s)
- Abil Dermail
- Department of Agronomy, Faculty of Agriculture, Khon Kaen University, Khon Kaen, Thailand
| | - Mariah Mitchell
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Tyler Foster
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Mercy Fakude
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Yu-Ru Chen
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Khundej Suriharn
- Department of Agronomy, Faculty of Agriculture, Khon Kaen University, Khon Kaen, Thailand
- Plant Breeding Research Center for Sustainable Agriculture, Faculty of Agriculture, Khon Kaen University, Khon Kaen, Thailand
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Zhang J, Yue Y, Hu M, Yi F, Chen J, Lai J, Xin B. Dynamic transcriptome landscape of maize pericarp development. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 117:1574-1591. [PMID: 37970738 DOI: 10.1111/tpj.16548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 10/09/2023] [Accepted: 11/05/2023] [Indexed: 11/17/2023]
Abstract
As a maternal tissue, the pericarp supports and protects for other components of seed, such as embryo and endosperm. Despite the importance of maize pericarp in seed, the genome-wide transcriptome pattern throughout maize pericarp development has not been well characterized. Here, we developed RNA-seq transcriptome atlas of B73 maize pericarp development based on 21 samples from 5 days before fertilization (DBP5) to 32 days after fertilization (DAP32). A total of 25 346 genes were detected in programming pericarp development, including 1887 transcription factors (TFs). Together with pericarp morphological changes, the global clustering of gene expression revealed four developmental stages: undeveloped, thickening, expansion and strengthening. Coexpression analysis provided further insights on key regulators in functional transition of four developmental stages. Combined with non-seed, embryo, endosperm, and nucellus transcriptome data, we identified 598 pericarp-specific genes, including 75 TFs, which could elucidate key mechanisms and regulatory networks of pericarp development. Cell wall related genes were identified that reflected their crucial role in the maize pericarp structure building. In addition, key maternal proteases or TFs related with programmed cell death (PCD) were proposed, suggesting PCD in the maize pericarp was mediated by vacuolar processing enzymes (VPE), and jasmonic acid (JA) and ethylene-related pathways. The dynamic transcriptome atlas provides a valuable resource for unraveling the genetic control of maize pericarp development.
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Affiliation(s)
- Jihong Zhang
- State Key Laboratory of Plant Physiology and Biochemistry & National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, P. R. China
| | - Yang Yue
- State Key Laboratory of Plant Physiology and Biochemistry & National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, P. R. China
| | - Mingjian Hu
- State Key Laboratory of Plant Physiology and Biochemistry & National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, P. R. China
| | - Fei Yi
- Engineering Research Center of Plant Growth Regulator, Ministry of Education & College of Agronomy and Biotechnology, China Agricultural University, Beijing, P. R. China
| | - Jian Chen
- State Key Laboratory of Plant Physiology and Biochemistry & National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, P. R. China
- Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, Beijing, 100193, P. R. China
| | - Jinsheng Lai
- State Key Laboratory of Plant Physiology and Biochemistry & National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, P. R. China
- Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, Beijing, 100193, P. R. China
| | - Beibei Xin
- State Key Laboratory of Plant Physiology and Biochemistry & National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, P. R. China
- Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, Beijing, 100193, P. R. China
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Kamal NM, Gorafi YSA, Tomemori H, Kim JS, Elhadi GMI, Tsujimoto H. Genetic variation for grain nutritional profile and yield potential in sorghum and the possibility of selection for drought tolerance under irrigated conditions. BMC Genomics 2023; 24:515. [PMID: 37660014 PMCID: PMC10474746 DOI: 10.1186/s12864-023-09613-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 08/22/2023] [Indexed: 09/04/2023] Open
Abstract
BACKGROUND Increasing grain nutritional value in sorghum (Sorghum bicolor) is a paramount breeding objective, as is increasing drought resistance (DR), because sorghum is grown mainly in drought-prone areas. The genetic basis of grain nutritional traits remains largely unknown. Marker-assisted selection using significant loci identified through genome-wide association study (GWAS) shows potential for selecting desirable traits in crops. This study assessed natural variation available in sorghum accessions from around the globe to identify novel genes or genomic regions with potential for improving grain nutritional value, and to study associations between DR traits and grain weight and nutritional composition. RESULTS We dissected the genetic architecture of grain nutritional composition, protein content, thousand-kernel weight (TKW), and plant height (PH) in sorghum through GWAS of 163 unique African and Asian accessions under irrigated and post-flowering drought conditions. Several QTLs were detected. Some were significantly associated with DR, TKW, PH, protein, and Zn, Mn, and Ca contents. Genomic regions on chromosomes 1, 2, 4, 8, 9, and 10 were associated with TKW, nutritional, and DR traits; colocalization patterns of these markers indicate potential for simultaneous improvement of these traits. In African accessions, markers associated with TKW were mapped to six regions also associated with protein, Zn, Ca, Mn, Na, and DR, suggesting the potential for simultaneous selection for higher grain nutrition and TKW. Our results indicate that it may be possible to select for increased DR on the basis of grain nutrition and weight potential. CONCLUSIONS This study provides a valuable resource for selecting landraces for use in plant breeding programs and for identifying loci that may contribute to grain nutrition and weight with the hope of producing cultivars that combine improved yield traits, nutrition, and DR.
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Affiliation(s)
- Nasrein Mohamed Kamal
- Arid Land Research Center, Tottori University, Tottori, 680-0001, Japan.
- Agricultural Research Corporation, PO Box 126, Wad Medani, Sudan.
| | - Yasir Serag Alnor Gorafi
- Agricultural Research Corporation, PO Box 126, Wad Medani, Sudan
- International Platform for Dryland Research and Education, Tottori University, Tottori, Japan
| | - Hisashi Tomemori
- Arid Land Research Center, Tottori University, Tottori, 680-0001, Japan
| | - June-Sik Kim
- RIKEN Center for Sustainable Resource Science, Yokohama, 230-0045, Japan
- Institute of Plant Science and Resources, Okayama University, Kurashiki, 710-0046, Japan
| | | | - Hisashi Tsujimoto
- Arid Land Research Center, Tottori University, Tottori, 680-0001, Japan.
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Zhang R, Wu H, Li Y, Huang Z, Yin Z, Yang CX, Du ZQ. GWLD: an R package for genome-wide linkage disequilibrium analysis. G3 (BETHESDA, MD.) 2023; 13:jkad154. [PMID: 37431944 PMCID: PMC10468308 DOI: 10.1093/g3journal/jkad154] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/14/2023] [Accepted: 06/26/2023] [Indexed: 07/12/2023]
Abstract
Linkage disequilibrium (LD) analysis is fundamental to the investigation of the genetic architecture of complex traits (e.g. human disease, animal and plant breeding) and population structure and evolution dynamics. However, until now, studies primarily focus on LD status between genetic variants located on the same chromosome. Moreover, genome (re)sequencing produces unprecedented numbers of genetic variants, and fast LD computation becomes a challenge. Here, we have developed GWLD, a parallelized and generalized tool designed for the rapid genome-wide calculation of LD values, including conventional D/D', r2, and (reduced) mutual information (MI and RMI) measures. LD between genetic variants within and across chromosomes can be rapidly computed and visualized in either an R package or a standalone C++ software package. To evaluate the accuracy and speed of LD calculation, we conducted comparisons using 4 real datasets. Interchromosomal LD patterns observed potentially reflect levels of selection intensity across different species. Both versions of GWLD, the R package (https://github.com/Rong-Zh/GWLD/GWLD-R) and the standalone C++ software (https://github.com/Rong-Zh/GWLD/GWLD-C++), are freely available on GitHub.
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Affiliation(s)
- Rong Zhang
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
| | - Huaxuan Wu
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
| | - Yasai Li
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
| | - Zehang Huang
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
| | - Zongjun Yin
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Cai-Xia Yang
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
| | - Zhi-Qiang Du
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
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Khan N, Zhang J, Islam S, Appels R, Dell B. Wheat Water-Soluble Carbohydrate Remobilisation under Water Deficit by 1-FEH w3. Curr Issues Mol Biol 2023; 45:6634-6650. [PMID: 37623238 PMCID: PMC10453044 DOI: 10.3390/cimb45080419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/03/2023] [Accepted: 08/08/2023] [Indexed: 08/26/2023] Open
Abstract
Fructan 1-exohydrolase (1-FEH) is one of the major enzymes in water-soluble carbohydrate (WSC) remobilisation for grains in wheat. We investigated the functional role of 1-FEH w1, w2, and w3 isoforms in WSC remobilisation under post-anthesis water deficit using mutation lines derived from the Australian wheat variety Chara. F1 seeds, developed by backcrossing the 1-FEH w1, w2, and w3 mutation lines with Chara, were genotyped using the Infinium 90K SNP iSelect platform to characterise the mutated region. Putative deletions were identified in FEH mutation lines encompassing the FEH genomic regions. Mapping analysis demonstrated that mutations affected significantly longer regions than the target FEH gene regions. Functional roles of the non-target genes were carried out utilising bioinformatics and confirmed that the non-target genes were unlikely to confound the effects considered to be due to the influence of 1-FEH gene functions. Glasshouse experiments revealed that the 1-FEH w3 mutation line had a slower degradation and remobilisation of fructans than the 1-FEH w2 and w1 mutation lines and Chara, which reduced grain filling and grain yield. Thus, 1-FEH w3 plays a vital role in reducing yield loss under drought. This insight into the distinct role of the 1-FEH isoforms provides new gene targets for water-deficit-tolerant wheat breeding.
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Affiliation(s)
- Nusrat Khan
- Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, 90 South Street, Murdoch, WA 6163, Australia; (N.K.); (J.Z.); (S.I.)
- Department of Plant Sciences, North Dakota State University, Fargo, ND 58102, USA
| | - Jingjuan Zhang
- Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, 90 South Street, Murdoch, WA 6163, Australia; (N.K.); (J.Z.); (S.I.)
| | - Shahidul Islam
- Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, 90 South Street, Murdoch, WA 6163, Australia; (N.K.); (J.Z.); (S.I.)
- Department of Plant Sciences, North Dakota State University, Fargo, ND 58102, USA
| | - Rudi Appels
- Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia;
| | - Bernard Dell
- Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, 90 South Street, Murdoch, WA 6163, Australia; (N.K.); (J.Z.); (S.I.)
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Jiang F, Liu L, Li Z, Bi Y, Yin X, Guo R, Wang J, Zhang Y, Shaw RK, Fan X. Identification of Candidate QTLs and Genes for Ear Diameter by Multi-Parent Population in Maize. Genes (Basel) 2023; 14:1305. [PMID: 37372485 DOI: 10.3390/genes14061305] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/06/2023] [Accepted: 06/17/2023] [Indexed: 06/29/2023] Open
Abstract
Ear diameter (ED) is a critical component of grain yield (GY) in maize (Zea mays L.). Studying the genetic basis of ED in maize is of great significance in enhancing maize GY. Against this backdrop, this study was framed to (1) map the ED-related quantitative trait locus (QTL) and SNPs associated with ED; and (2) identify putative functional genes that may affect ED in maize. To accomplish this, an elite maize inbred line, Ye107, which belongs to the Reid heterotic group, was used as a common parent and crossed with seven elite inbred lines from three different heterotic groups (Suwan1, Reid, and nonReid) that exhibited abundant genetic variation in ED. This led to the construction of a multi-parent population consisting of 1215 F7 recombinant inbred lines (F7RILs). A genome-wide association study (GWAS) and linkage analysis were then conducted for the multi-parent population using 264,694 high-quality SNPs generated via the genotyping-by-sequencing method. Our study identified a total of 11 SNPs that were significantly associated with ED through the GWAS, and three QTLs were revealed by the linkage analysis for ED. The major QTL on chromosome 1 was co-identified in the region by the GWAS at SNP_143985532. SNP_143985532, located upstream of the Zm00001d030559 gene, encodes a callose synthase that is expressed in various tissues, with the highest expression level in the maize ear primordium. Haplotype analysis indicated that the haplotype B (allele AA) of Zm00001d030559 was positively correlated with ED. The candidate genes and SNPs identified in this study provide crucial insights for future studies on the genetic mechanism of maize ED formation, cloning of ED-related genes, and genetic improvement of ED. These results may help develop important genetic resources for enhancing maize yield through marker-assisted breeding.
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Affiliation(s)
- Fuyan Jiang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Li Liu
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Ziwei Li
- Yunnan Dehong Dai and Jingpo Nationality Institute of Agricultural Sciences, Mangshi 678400, China
| | - Yaqi Bi
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Xingfu Yin
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Ruijia Guo
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Jing Wang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Yudong Zhang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Ranjan Kumar Shaw
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Xingming Fan
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
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Ljubičić N, Popović V, Kostić M, Pajić M, Buđen M, Gligorević K, Dražić M, Bižić M, Crnojević V. Multivariate Interaction Analysis of Zea mays L. Genotypes Growth Productivity in Different Environmental Conditions. PLANTS (BASEL, SWITZERLAND) 2023; 12:plants12112165. [PMID: 37299146 DOI: 10.3390/plants12112165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 05/11/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023]
Abstract
Evaluating maize genotypes under different conditions is important for identifying which genotypes combine stability with high yield potential. The aim of this study was to assess stability and the effect of the genotype-environment interaction (GEI) on the grain yield traits of four maize genotypes grown in field trials; one control trial without nitrogen, and three applying different levels of nitrogen (0, 70, 140, and 210 kg ha-1, respectively). Across two growing seasons, both the phenotypic variability and GEI for yield traits over four maize genotypes (P0725, P9889, P9757 and P9074) grown in four different fertilization treatments were studied. The additive main effects and multiplicative interaction (AMMI) models were used to estimate the GEI. The results revealed that genotype and environmental effects, such as the GEI effect, significantly influenced yield, as well as revealing that maize genotypes responded differently to different conditions and fertilization measures. An analysis of the GEI using the IPCA (interaction principal components) analysis method showed the statistical significance of the first source of variation, IPCA1. As the main component, IPCA1 explained 74.6% of GEI variation in maize yield. Genotype G3, with a mean grain yield of 10.6 t ha-1, was found to be the most stable and adaptable to all environments in both seasons, while genotype G1 was found to be unstable, following its specific adaptation to the environments.
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Affiliation(s)
- Nataša Ljubičić
- BioSense Institute, University of Novi Sad, 21000 Novi Sad, Serbia
| | - Vera Popović
- Institute of Field and Vegetable Crops, 21000 Novi Sad, Serbia
- Faculty of Agriculture, University of Bijeljina, 76300 Bijeljina, Bosnia and Herzegovina
| | - Marko Kostić
- Faculty of Agriculture, University of Novi Sad, 21000 Novi Sad, Serbia
| | - Miloš Pajić
- Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11000 Belgrade, Serbia
| | - Maša Buđen
- BioSense Institute, University of Novi Sad, 21000 Novi Sad, Serbia
| | - Kosta Gligorević
- Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11000 Belgrade, Serbia
| | - Milan Dražić
- Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11000 Belgrade, Serbia
| | - Milica Bižić
- Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11000 Belgrade, Serbia
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11
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Bocianowski J, Tomkowiak A, Bocianowska M, Sobiech A. The Use of DArTseq Technology to Identify Markers Related to the Heterosis Effects in Selected Traits in Maize. Curr Issues Mol Biol 2023; 45:2644-2660. [PMID: 37185697 PMCID: PMC10136425 DOI: 10.3390/cimb45040173] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/18/2023] [Accepted: 03/21/2023] [Indexed: 05/17/2023] Open
Abstract
Spectacular scientific advances in the area of molecular biology and the development of modern biotechnological tools have had a significant impact on the development of maize heterosis breeding. One technology based on next-generation sequencing is DArTseq. The plant material used for the research consisted of 13 hybrids resulting from the crossing of inbred maize lines. A two-year field experiment was established at two Polish breeding stations: Smolice and Łagiewniki. Nine quantitative traits were observed: cob length, cob diameter, core length, core diameter, number of rows of grain, number of grains in a row, mass of grain from the cob, weight of one thousand grains, and yield. The isolated DNA was subjected to DArTseq genotyping. Association mapping was performed using a method based on the mixed linear model. A total of 81602 molecular markers (28571 SNPs and 53031 SilicoDArTs) were obtained as a result of next-generation sequencing. Out of 81602, 15409 (13850 SNPs and 1559 SilicoDArTs) were selected for association analysis. The 105 molecular markers (8 SNPs and 97 SilicoDArTs) were associated with the heterosis effect of at least one trait in at least one environment. A total of 186 effects were observed. The number of statistically significant relationships between the molecular marker and heterosis effect varied from 8 (for cob length) and 9 (for yield) to 42 (for the number of rows of grain). Of particular note were three markers (2490222, 2548691 and 7058267), which were significant in 17, 8 and 6 cases, respectively. Two of them (2490222 and 7058267) were associated with the heterosis effects of yield in three of the four environments.
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Affiliation(s)
- Jan Bocianowski
- Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637 Poznan, Poland
| | - Agnieszka Tomkowiak
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznan, Poland
| | - Marianna Bocianowska
- Faculty of Chemical Technology, Poznań University of Technology, Piotrowo 3A, 60-965 Poznan, Poland
| | - Aleksandra Sobiech
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznan, Poland
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12
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Khaipho-Burch M, Ferebee T, Giri A, Ramstein G, Monier B, Yi E, Romay MC, Buckler ES. Elucidating the patterns of pleiotropy and its biological relevance in maize. PLoS Genet 2023; 19:e1010664. [PMID: 36943844 PMCID: PMC10030035 DOI: 10.1371/journal.pgen.1010664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/09/2023] [Indexed: 03/23/2023] Open
Abstract
Pleiotropy-when a single gene controls two or more seemingly unrelated traits-has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of biological pleiotropy across all maize phenotypes is largely unknown. Here, we investigate the extent to which biological pleiotropy impacts phenotypes within maize using GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56-32.3% of intervals show some degree of pleiotropy. We then assess the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide and large effect variants, leaving the prevalence of detectable pleiotropy relatively low.
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Affiliation(s)
| | - Taylor Ferebee
- Department of Computational Biology, Cornell University, Ithaca, New York
| | - Anju Giri
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
| | - Guillaume Ramstein
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Brandon Monier
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
| | - Emily Yi
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
| | - M Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
| | - Edward S Buckler
- Section of Plant Breeding and Genetics, Cornell University, Ithaca, New York
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
- USDA-ARS, Ithaca, New York, United States of America
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13
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Wan W, Wu Y, Hu D, Ye F, Wu X, Qi X, Liang H, Zhou H, Xue J, Xu S, Zhang X. Genome-wide association analysis of kernel nutritional quality in two natural maize populations. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:18. [PMID: 37313300 PMCID: PMC10248675 DOI: 10.1007/s11032-023-01360-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 02/05/2023] [Indexed: 06/15/2023]
Abstract
As one of the three staple crops, nutritional traits in maize are important for human and animal nutrition. Grain quality-related traits are closely related to grain commercial value. Understanding the genetic basis of quality-related traits in maize would be helpful for breeding high-quality maize varieties. In this study, two association panels (AM122 and AM180) were subjected to genome-wide association analysis of grain quality-related traits, including protein content, oil content, starch content, and fiber content. In total, 98 SNPs (P < 1 × 10-4) were identified to be significantly associated with these four grain quality-related traits. By integrating two sets of public transcriptome data, 31 genes located in 200 kb regions flanking the associated SNP showed high expression during kernel development and were differentially expressed in two maize inbred lines, KA225 and KB035, with significantly different quality. These genes might regulate maize grain quality by participating in plant hormone processes, autophagy processes, and others. All these results could provide important reference information for breeding high‑quality maize varieties. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01360-w.
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Affiliation(s)
- Wenting Wan
- Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, College of Agronomy, Northwest A&F University, Yangling, 712100 Shaanxi China
- Maize Engineering Technology Research Centre, Yangling, 712100 Shaanxi China
| | - Ying Wu
- Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, College of Agronomy, Northwest A&F University, Yangling, 712100 Shaanxi China
- Maize Engineering Technology Research Centre, Yangling, 712100 Shaanxi China
| | - Die Hu
- Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, College of Agronomy, Northwest A&F University, Yangling, 712100 Shaanxi China
- Maize Engineering Technology Research Centre, Yangling, 712100 Shaanxi China
| | - Fan Ye
- Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, College of Agronomy, Northwest A&F University, Yangling, 712100 Shaanxi China
- Maize Engineering Technology Research Centre, Yangling, 712100 Shaanxi China
| | - Xiaopeng Wu
- Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, College of Agronomy, Northwest A&F University, Yangling, 712100 Shaanxi China
- Maize Engineering Technology Research Centre, Yangling, 712100 Shaanxi China
| | - Xingyue Qi
- Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, College of Agronomy, Northwest A&F University, Yangling, 712100 Shaanxi China
- Maize Engineering Technology Research Centre, Yangling, 712100 Shaanxi China
| | - Hangyu Liang
- Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, College of Agronomy, Northwest A&F University, Yangling, 712100 Shaanxi China
- Maize Engineering Technology Research Centre, Yangling, 712100 Shaanxi China
| | - Haiyang Zhou
- Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, College of Agronomy, Northwest A&F University, Yangling, 712100 Shaanxi China
| | - Jiquan Xue
- Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, College of Agronomy, Northwest A&F University, Yangling, 712100 Shaanxi China
- Maize Engineering Technology Research Centre, Yangling, 712100 Shaanxi China
| | - Shutu Xu
- Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, College of Agronomy, Northwest A&F University, Yangling, 712100 Shaanxi China
- Maize Engineering Technology Research Centre, Yangling, 712100 Shaanxi China
| | - Xinghua Zhang
- Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, College of Agronomy, Northwest A&F University, Yangling, 712100 Shaanxi China
- Maize Engineering Technology Research Centre, Yangling, 712100 Shaanxi China
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14
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Diers BW, Specht JE, Graef GL, Song Q, Rainey KM, Ramasubramanian V, Liu X, Myers CL, Stupar RM, An YQC, Beavis WD. Genetic architecture of protein and oil content in soybean seed and meal. THE PLANT GENOME 2023; 16:e20308. [PMID: 36744727 DOI: 10.1002/tpg2.20308] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/09/2023] [Indexed: 05/10/2023]
Abstract
Soybean is grown primarily for the protein and oil extracted from its seed and its value is influenced by these components. The objective of this study was to map marker-trait associations (MTAs) for the concentration of seed protein, oil, and meal protein using the soybean nested association mapping (SoyNAM) population. The composition traits were evaluated on seed harvested from over 5000 inbred lines of the SoyNAM population grown in 10 field locations across 3 years. Estimated heritabilities were at least 0.85 for all three traits. The genotyping of lines with single nucleotide polymorphism markers resulted in the identification of 107 MTAs for the three traits. When MTAs for the three traits that mapped within 5 cM intervals were binned together, the MTAs were mapped to 64 intervals on 19 of the 20 soybean chromosomes. The majority of the MTA effects were small and of the 107 MTAs, 37 were for protein content, 39 for meal protein, and 31 for oil content. For cases where a protein and oil MTAs mapped to the same interval, most (94%) significant effects were opposite for the two traits, consistent with the negative correlation between these traits. A coexpression analysis identified candidate genes linked to MTAs and 18 candidate genes were identified. The large number of small effect MTAs for the composition traits suggest that genomic prediction would be more effective in improving these traits than marker-assisted selection.
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Affiliation(s)
- Brian W Diers
- Department of Crop Sciences, University of Illinois, Urbana, IL, USA
| | - James E Specht
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, USA
| | - George L Graef
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, USA
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD, USA
| | | | | | - Xiaotong Liu
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, MN, USA
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, USA
| | - Robert M Stupar
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, USA
| | - Yong-Qiang Charles An
- USDA-ARS Plant Genetic Research Unit at Donald Danforth Plant Science Center, St. Louis, MO, USA
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15
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Guo X, Ge Z, Wang M, Zhao M, Pei Y, Song X. Genome-wide association study of quality traits and starch pasting properties of maize kernels. BMC Genomics 2023; 24:59. [PMID: 36732681 PMCID: PMC9893588 DOI: 10.1186/s12864-022-09031-4] [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: 07/08/2022] [Accepted: 11/21/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Starch are the main nutritional components of maize (Zea mays L.), and starch pasting properties are widely used as essential indicators for quality estimation. Based on the previous studies, various genes related to pasting properties have been identified in maize. However, the loci underlying variations in starch pasting properties in maize inbred lines remain to be identified. RESULTS To investigate the genetic architecture of these traits, the starch pasting properties were examined based on 292 maize inbred lines, which were genotyped with the MaizeSNP50 BeadChip composed of 55,126 evenly spaced, random SNPs. A genome-wide association study (GWAS) implemented in the software package FarmCPU was employed to identify genomic loci for the starch pasting properties. 48 SNPs were found to be associated with pasting properties. Moreover, 37 candidate genes were correlated with pasting properties. Among the candidate genes, GRMZM2G143646 and GRMZM2G166407 were associated with breakdown and final viscosity significantly, and both genes encode PPR (Pentatricopeptide repeat) protein. We used GWAS to explore candidate genes of maize starch pasting properties in this study. The identified candidate genes will be useful for further understanding of the genetic architecture of starch pasting properties in maize. CONCLUSION This study showed a complex regulation network about maize quality trait and starch pasting properties. It may provide some useful markers for marker assisted selection and a basis for cloning the genes behind these SNPs.
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Affiliation(s)
- Xinmei Guo
- grid.412608.90000 0000 9526 6338College of Agronomy, Qingdao Agricultural University, Qingdao, 266109 China
| | - Zhaopeng Ge
- grid.412608.90000 0000 9526 6338College of Agronomy, Qingdao Agricultural University, Qingdao, 266109 China
| | - Ming Wang
- grid.412608.90000 0000 9526 6338College of Agronomy, Qingdao Agricultural University, Qingdao, 266109 China
| | - Meiai Zhao
- grid.412608.90000 0000 9526 6338College of Life Sciences, Qingdao Agricultural University, Qingdao, 266109 China
| | - Yuhe Pei
- grid.412608.90000 0000 9526 6338College of Agronomy, Qingdao Agricultural University, Qingdao, 266109 China
| | - Xiyun Song
- grid.412608.90000 0000 9526 6338College of Agronomy, Qingdao Agricultural University, Qingdao, 266109 China
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16
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Wang Y, Zhao J, Chen Q, Zheng K, Deng X, Gao W, Pei W, Geng S, Deng Y, Li C, Chen Q, Qu Y. Quantitative trait locus mapping and identification of candidate genes for resistance to Verticillium wilt in four recombinant inbred line populations of Gossypium hirsutum. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 327:111562. [PMID: 36509244 DOI: 10.1016/j.plantsci.2022.111562] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 12/01/2022] [Accepted: 12/06/2022] [Indexed: 05/16/2023]
Abstract
Improving resistance to Verticillium wilt is of great significance for achieving high and stable yields of Upland cotton (Gossypium hirsutum). To deeply understand the genetic basis of cotton resistance to Verticillium wilt, Verticillium wilt-resistant Upland Lumianyan 28 and four Verticillium wilt-susceptible Acala cotton cultivars were used to create four recombinant inbred line (RIL) populations of 469 families through nested hybridization. Phenotypic data collected in five stressful environments were used to select resistant and sensitive lines and create a mixed pool of extreme phenotypes for BSA-seq. A total of 8 QTLs associated with Verticillium wilt resistance were identified on 4 chromosomes, of which qVW-A12-5 was detected simultaneously in the RIL populations and in one of the RIL populations and was identified for the first time. According to the sequence comparison and transcriptome analysis of candidate genes in the QTL interval between parents and pools, 4 genes were identified in the qVW-A12-5 interval. qRT-PCR of parental and phenotypically extreme lines revealed that Gh_CPR30 was induced by and may be a candidate gene for resistance to Verticillium wilt in G. hirsutum. Furthermore, VIGS technology revealed that the disease severity index (DSI) of the Gh_CPR30-silenced plants was significantly higher than that of the control. These results indicate that the Gh_CPR30 gene plays an important role in the resistance of G. hirsutum to Verticillium wilt, and the study provides a molecular basis for analyzing the molecular mechanism underlying G. hirsutum resistance to Verticillium wilt.
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Affiliation(s)
- Yuxiang Wang
- Engineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, China
| | - Jieyin Zhao
- Engineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, China
| | - Qin Chen
- Engineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, China
| | - Kai Zheng
- Engineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, China
| | - Xiaojuan Deng
- Engineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, China
| | - Wenju Gao
- Engineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, China
| | - Wenfeng Pei
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, China
| | - Shiwei Geng
- Engineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, China
| | - Yahui Deng
- Engineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, China
| | - Chunping Li
- Institute of Cash Crops, Xinjiang Academy of Agricultural Sciences, Urumqi 830052, China
| | - Quanjia Chen
- Engineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, China
| | - Yanying Qu
- Engineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, China.
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17
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Sa KJ, Park H, Jang SJ, Lee JK. Association Mapping of Amylose Content in Maize RIL Population Using SSR and SNP Markers. PLANTS (BASEL, SWITZERLAND) 2023; 12:239. [PMID: 36678952 PMCID: PMC9865990 DOI: 10.3390/plants12020239] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/27/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
The ratio of amylose to amylopectin in maize kernel starch is important for the appearance, structure, and quality of food products and processing. This study aimed to identify quantitative trait loci (QTLs) controlling amylose content in maize through association mapping with simple sequence repeat (SSR) and single-nucleotide polymorphism (SNP) markers. The average value of amylose content for an 80-recombinant-inbred-line (RIL) population was 8.8 ± 0.7%, ranging from 2.1 to 15.9%. We used two different analyses-Q + K and PCA + K mixed linear models (MLMs)-and found 38 (35 SNP and 3 SSR) and 32 (29 SNP and 3 SSR) marker-trait associations (MTAs) associated with amylose content. A total of 34 (31 SNP and 3 SSR) and 28 (25 SNP and 3 SSR) MTAs were confirmed in the Q + K and PCA + K MLMs, respectively. This study detected some candidate genes for amylose content, such as GRMZM2G118690-encoding BBR/BPC transcription factor, which is used for the control of seed development and is associated with the amylose content of rice. GRMZM5G830776-encoding SNARE-interacting protein (KEULE) and the uncharacterized marker PUT-163a-18172151-1376 were significant with higher R2 value in two difference methods. GRMZM2G092296 were also significantly associated with amylose content in this study. This study focused on amylose content using a RIL population derived from dent and waxy inbred lines using molecular markers. Future studies would be of benefit for investigating the physical linkage between starch synthesis genes using SNP and SSR markers, which would help to build a more detailed genetic map and provide new insights into gene regulation of agriculturally important traits.
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Affiliation(s)
- Kyu Jin Sa
- Department of Applied Plant Sciences, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Hyeon Park
- Department of Applied Plant Sciences, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
- Interdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - So Jung Jang
- Department of Applied Plant Sciences, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
- Interdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Ju Kyong Lee
- Department of Applied Plant Sciences, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
- Interdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon 24341, Republic of Korea
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18
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González-Rodríguez T, Pérez-Limón S, Peniche-Pavía H, Rellán-Álvarez R, Sawers RJH, Winkler R. Genetic mapping of maize metabolites using high-throughput mass profiling. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 326:111530. [PMID: 36368482 DOI: 10.1016/j.plantsci.2022.111530] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
Plant metabolites are the basis of human nutrition and have biological relevance in ecology. Farmers selected plants with favorable characteristics since prehistoric times and improved the cultivars, but without knowledge of underlying mechanisms. Understanding the genetic basis of metabolite production can facilitate the successful breeding of plants with augmented nutritional value. To identify genetic factors related to the metabolic composition in maize, we generated mass profiles of 198 recombinant inbred lines (RILs) and their parents (B73 and Mo17) using direct-injection electrospray ionization mass spectrometry (DLI-ESI MS). Mass profiling allowed the correct clustering of samples according to genotype. We quantified 71 mass features from grains and 236 mass features from leaf extracts. For the corresponding ions, we identified tissue-specific metabolic 'Quantitative Trait Loci' (mQTLs) distributed across the maize genome. These genetic regions could regulate multiple metabolite biosynthesis pathways. Our findings demonstrate that DLI-ESI MS has sufficient analytical resolution to map mQTLs. These identified genetic loci will be helpful in metabolite-focused maize breeding. Mass profiling is a powerful tool for detecting mQTLs in maize and enables the high-throughput screening of loci responsible for metabolite biosynthesis.
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Affiliation(s)
- Tzitziki González-Rodríguez
- Center for Research and Advanced Studies (CINVESTAV) Irapuato, Department of Biotechnology and Biochemistry, Mexico
| | - Sergio Pérez-Limón
- The Pennsylvania State University, Department of Plant Science, State College, PA, USA
| | - Héctor Peniche-Pavía
- Center for Research and Advanced Studies (CINVESTAV) Irapuato, Department of Biotechnology and Biochemistry, Mexico
| | - Rubén Rellán-Álvarez
- North Carolina State University, Department of Molecular and Structural Biochemistry, USA; Unidad de Genómica Avanzada (UGA) - Laboratorio Nacional de Genómica para la Biodiversidad (LANGEBIO), Km. 9.6 Libramiento Norte Carr. Irapuato-León, 36824 Irapuato Gto, Mexico
| | - Ruairidh J H Sawers
- The Pennsylvania State University, Department of Plant Science, State College, PA, USA; Unidad de Genómica Avanzada (UGA) - Laboratorio Nacional de Genómica para la Biodiversidad (LANGEBIO), Km. 9.6 Libramiento Norte Carr. Irapuato-León, 36824 Irapuato Gto, Mexico
| | - Robert Winkler
- Center for Research and Advanced Studies (CINVESTAV) Irapuato, Department of Biotechnology and Biochemistry, Mexico; Unidad de Genómica Avanzada (UGA) - Laboratorio Nacional de Genómica para la Biodiversidad (LANGEBIO), Km. 9.6 Libramiento Norte Carr. Irapuato-León, 36824 Irapuato Gto, Mexico.
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19
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Zhang X, Wang M, Guan H, Wen H, Zhang C, Dai C, Wang J, Pan B, Li J, Liao H. Genetic dissection of QTLs for oil content in four maize DH populations. FRONTIERS IN PLANT SCIENCE 2023; 14:1174985. [PMID: 37123853 PMCID: PMC10130369 DOI: 10.3389/fpls.2023.1174985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 03/28/2023] [Indexed: 05/03/2023]
Abstract
Oil is one of the main components in maize kernels. Increasing the total oil content (TOC) is favorable to optimize feeding requirement by improving maize quality. To better understand the genetic basis of TOC, quantitative trait loci (QTL) in four double haploid (DH) populations were explored. TOC exhibited continuously and approximately normal distribution in the four populations. The moderate to high broad-sense heritability (67.00-86.60%) indicated that the majority of TOC variations are controlled by genetic factors. A total of 16 QTLs were identified across all chromosomes in a range of 3.49-30.84% in term of phenotypic variation explained. Among them, six QTLs were identified as the major QTLs that explained phenotypic variation larger than 10%. Especially, qOC-1-3 and qOC-2-3 on chromosome 9 were recognized as the largest effect QTLs with 30.84% and 21.74% of phenotypic variance, respectively. Seventeen well-known genes involved in fatty acid metabolic pathway located within QTL intervals. These QTLs will enhance our understanding of the genetic basis of TOC in maize and offer prospective routes to clone candidate genes regulating TOC for breeding program to cultivate maize varieties with the better grain quality.
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Affiliation(s)
- Xiaolei Zhang
- Quality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences, Harbin, Heilongjiang, China
| | - Min Wang
- National Maize Improvement Center of China, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Haitao Guan
- Quality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences, Harbin, Heilongjiang, China
| | - Hongtao Wen
- Quality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences, Harbin, Heilongjiang, China
| | | | - Changjun Dai
- Quality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences, Harbin, Heilongjiang, China
| | - Jing Wang
- Quality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences, Harbin, Heilongjiang, China
| | - Bo Pan
- Quality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences, Harbin, Heilongjiang, China
| | - Jialei Li
- Food Processing Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, Heilongjiang, China
| | - Hui Liao
- Quality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences, Harbin, Heilongjiang, China
- *Correspondence: Hui Liao,
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20
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Lu X, Zhou Z, Wang Y, Wang R, Hao Z, Li M, Zhang D, Yong H, Han J, Wang Z, Weng J, Zhou Y, Li X. Genetic basis of maize kernel protein content revealed by high-density bin mapping using recombinant inbred lines. FRONTIERS IN PLANT SCIENCE 2022; 13:1045854. [PMID: 36589123 PMCID: PMC9798238 DOI: 10.3389/fpls.2022.1045854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Maize with a high kernel protein content (PC) is desirable for human food and livestock fodder. However, improvements in its PC have been hampered by a lack of desirable molecular markers. To identify quantitative trait loci (QTL) and candidate genes for kernel PC, we employed a genotyping-by-sequencing strategy to construct a high-resolution linkage map with 6,433 bin markers for 275 recombinant inbred lines (RILs) derived from a high-PC female Ji846 and low-PC male Ye3189. The total genetic distance covered by the linkage map was 2180.93 cM, and the average distance between adjacent markers was 0.32 cM, with a physical distance of approximately 0.37 Mb. Using this linkage map, 11 QTLs affecting kernel PC were identified, including qPC7 and qPC2-2, which were identified in at least two environments. For the qPC2-2 locus, a marker named IndelPC2-2 was developed with closely linked polymorphisms in both parents, and when tested in 30 high and 30 low PC inbred lines, it showed significant differences (P = 1.9E-03). To identify the candidate genes for this locus, transcriptome sequencing data and PC best linear unbiased estimates (BLUE) for 348 inbred lines were combined, and the expression levels of the four genes were correlated with PC. Among the four genes, Zm00001d002625, which encodes an S-adenosyl-L-methionine-dependent methyltransferase superfamily protein, showed significantly different expression levels between two RIL parents in the endosperm and is speculated to be a potential candidate gene for qPC2-2. This study will contribute to further research on the mechanisms underlying the regulation of maize PC, while also providing a genetic basis for marker-assisted selection in the future.
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Affiliation(s)
- Xin Lu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Zhiqiang Zhou
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yunhe Wang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
- College of Agriculture, Northeast Agricultural University, Harbin, Heilongjiang, China
| | - Ruiqi Wang
- College of Agriculture, Northeast Agricultural University, Harbin, Heilongjiang, China
| | - Zhuanfang Hao
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mingshun Li
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Degui Zhang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hongjun Yong
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jienan Han
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Zhenhua Wang
- College of Agriculture, Northeast Agricultural University, Harbin, Heilongjiang, China
| | - Jianfeng Weng
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yu Zhou
- College of Agriculture, Northeast Agricultural University, Harbin, Heilongjiang, China
| | - Xinhai Li
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
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21
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Ndlovu N, Spillane C, McKeown PC, Cairns JE, Das B, Gowda M. Genome-wide association studies of grain yield and quality traits under optimum and low-nitrogen stress in tropical maize (Zea mays L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:4351-4370. [PMID: 36131140 PMCID: PMC9734216 DOI: 10.1007/s00122-022-04224-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 09/14/2022] [Indexed: 06/15/2023]
Abstract
KEY MESSAGE Genome-wide association study (GWAS) demonstrated that multiple genomic regions influence grain quality traits under nitrogen-starved soils. Using genomic prediction, genetic gains can be improved through selection for grain quality traits. Soils in sub-Saharan Africa are nitrogen deficient due to low fertilizer use and inadequate soil fertility management practices. This has resulted in a significant yield gap for the major staple crop maize, which is undermining nutritional security and livelihood sustainability across the region. Dissecting the genetic basis of grain protein, starch and oil content under nitrogen-starved soils can increase our understanding of the governing genetic systems and improve the efficacy of future breeding schemes. An association mapping panel of 410 inbred lines and four bi-parental populations were evaluated in field trials in Kenya and South Africa under optimum and low nitrogen conditions and genotyped with 259,798 SNP markers. Genetic correlations demonstrated that these populations may be utilized to select higher performing lines under low nitrogen stress. Furthermore, genotypic, environmental and GxE variations in nitrogen-starved soils were found to be significant for oil content. Broad sense heritabilities ranged from moderate (0.18) to high (0.86). Under low nitrogen stress, GWAS identified 42 SNPs linked to grain quality traits. These significant SNPs were associated with 51 putative candidate genes. Linkage mapping identified multiple QTLs for the grain quality traits. Under low nitrogen conditions, average prediction accuracies across the studied genotypes were higher for oil content (0.78) and lower for grain yield (0.08). Our findings indicate that grain quality traits are polygenic and that using genomic selection in maize breeding can improve genetic gain. Furthermore, the identified genomic regions and SNP markers can be utilized for selection to improve maize grain quality traits.
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Affiliation(s)
- Noel Ndlovu
- Plant & AgriBiosciences Research Centre, Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4, Ireland
| | - Charles Spillane
- Plant & AgriBiosciences Research Centre, Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4, Ireland.
| | - Peter C McKeown
- Plant & AgriBiosciences Research Centre, Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4, Ireland
| | - Jill E Cairns
- International Maize and Wheat Improvement Center (CIMMYT), P.O. Box MP163, Harare, Zimbabwe
| | - Biswanath Das
- International Maize and Wheat Improvement Center (CIMMYT), P.O. Box 1041-00621, Nairobi, Kenya
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), P.O. Box 1041-00621, Nairobi, Kenya.
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22
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Hu Y, Han Z, Shen W, Jia Y, He L, Si Z, Wang Q, Fang L, Du X, Zhang T. Identification of candidate genes in cotton associated with specific seed traits and their initial functional characterization in Arabidopsis. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 112:800-811. [PMID: 36121755 DOI: 10.1111/tpj.15982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 09/05/2022] [Accepted: 09/13/2022] [Indexed: 06/15/2023]
Abstract
Oilseed crops are used to produce vegetable oil to satisfy the requirements of humans and livestock. Cotton (Gossypium spp.) is of great economic value because it is used as both an important textile commodity and a nutrient-rich resource. Cottonseed oil is rich in polyunsaturated fatty acids and does not contain trans fatty acids; hence, it is considered a healthy vegetable oil. However, research on the genetic basis for cottonseed protein content, oil production, and fatty acid composition is lacking. Here, we investigated the protein content, oil content, and fatty acid composition in terms of oleic acid (C18:1) and linoleic acid (C18:2) in mature cottonseeds from 318 Gossypium hirsutum accessions. Moreover, we examined the dynamic change of protein content and lipid composition including palmitic acid (C16:0), stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2), and linolenic acid (C18:3) in developing seeds from 258 accessions at 10 and 20 days post-anthesis. Then, we conducted a genome-wide association study and identified 152 trait-associated loci and 64 candidate genes responsible for protein and oil-related contents in mature cottonseeds and ovules. Finally, six candidate genes were experimentally validated to be involved in the regulation of fatty acid biosynthesis through heterologous expression in Arabidopsis. These results comprise a solid foundation for expanding our understanding of lipid biosynthesis in cotton, which will help breeders manipulate protein and oil contents to make it a fully developed 'fiber, food, and oil crop'.
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Affiliation(s)
- Yan Hu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Zegang Han
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Weijuan Shen
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Yinhua Jia
- Institute of cotton Research, Chinese Academy of Agricultural Sciences (CAAS), State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Anyang, China
| | - Lu He
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Zhanfeng Si
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Qiong Wang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Lei Fang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Xiongming Du
- Institute of cotton Research, Chinese Academy of Agricultural Sciences (CAAS), State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Anyang, China
| | - Tianzhen Zhang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
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23
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Mural RV, Sun G, Grzybowski M, Tross MC, Jin H, Smith C, Newton L, Andorf CM, Woodhouse MR, Thompson AM, Sigmon B, Schnable JC. Association mapping across a multitude of traits collected in diverse environments in maize. Gigascience 2022; 11:giac080. [PMID: 35997208 PMCID: PMC9396454 DOI: 10.1093/gigascience/giac080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/25/2022] [Indexed: 11/14/2022] Open
Abstract
Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data-18M markers-from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction.
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Affiliation(s)
- Ravi V Mural
- Center for Plant Science Innovation, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
| | - Guangchao Sun
- Center for Plant Science Innovation, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
| | - Marcin Grzybowski
- Center for Plant Science Innovation, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
| | - Michael C Tross
- Center for Plant Science Innovation, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
| | - Hongyu Jin
- Center for Plant Science Innovation, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
| | - Christine Smith
- Center for Plant Science Innovation, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
| | - Linsey Newton
- Department of Plant Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Carson M Andorf
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50010, USA
- Department of Computer Science, Iowa State University, Ames, IA 50011, USA
| | | | - Addie M Thompson
- Department of Plant Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Brandi Sigmon
- Department of Plant Pathology, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
| | - James C Schnable
- Center for Plant Science Innovation, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
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24
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Fei X, Wang Y, Zheng Y, Shen X, E L, Ding J, Lai J, Song W, Zhao H. Identification of two new QTLs of maize (Zea mays L.) underlying kernel row number using the HNAU-NAM1 population. BMC Genomics 2022; 23:593. [PMID: 35971070 PMCID: PMC9380338 DOI: 10.1186/s12864-022-08793-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 07/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Maize kernel row number (KRN) is one of the most important yield traits and has changed greatly during maize domestication and selection. Elucidating the genetic basis of KRN will be helpful to improve grain yield in maize. RESULTS Here, we measured KRN in four environments using a nested association mapping (NAM) population named HNAU-NAM1 with 1,617 recombinant inbred lines (RILs) that were derived from 12 maize inbred lines with a common parent, GEMS41. Then, five consensus quantitative trait loci (QTLs) distributing on four chromosomes were identified in at least three environments along with the best linear unbiased prediction (BLUP) values by the joint linkage mapping (JLM) method. These QTLs were further validated by the separate linkage mapping (SLM) and genome-wide association study (GWAS) methods. Three KRN genes cloned through the QTL assay were found in three of the five consensus QTLs, including qKRN1.1, qKRN2.1 and qKRN4.1. Two new QTLs of KRN, qKRN4.2 and qKRN9.1, were also identified. On the basis of public RNA-seq and genome annotation data, five genes highly expressed in ear tissue were considered candidate genes contributing to KRN. CONCLUSIONS This study carried out a comprehensive analysis of the genetic architecture of KRN by using a new NAM population under multiple environments. The present results provide solid information for understanding the genetic components underlying KRN and candidate genes in qKRN4.2 and qKRN9.1. Single-nucleotide polymorphisms (SNPs) closely linked to qKRN4.2 and qKRN9.1 could be used to improve inbred yield during molecular breeding in maize.
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Affiliation(s)
- Xiaohong Fei
- State Key Laboratory of Plant Physiology and Biochemistry, China Agricultural University, Beijing, 100193, People's Republic of China
- Department of Plant Genetics and Breeding, National Maize Improvement Center, China Agricultural University, Beijing, 100193, People's Republic of China
- Longping Agriculture Science Co. Ltd, Beijing, 100004, People's Republic of China
| | - Yifei Wang
- State Key Laboratory of Plant Physiology and Biochemistry, China Agricultural University, Beijing, 100193, People's Republic of China
- Department of Plant Genetics and Breeding, National Maize Improvement Center, China Agricultural University, Beijing, 100193, People's Republic of China
| | - Yunxiao Zheng
- State Key Laboratory of Plant Physiology and Biochemistry, China Agricultural University, Beijing, 100193, People's Republic of China
- Department of Plant Genetics and Breeding, National Maize Improvement Center, China Agricultural University, Beijing, 100193, People's Republic of China
| | - Xiaomeng Shen
- State Key Laboratory of Plant Physiology and Biochemistry, China Agricultural University, Beijing, 100193, People's Republic of China
- Department of Plant Genetics and Breeding, National Maize Improvement Center, China Agricultural University, Beijing, 100193, People's Republic of China
| | - Lizhu E
- State Key Laboratory of Plant Physiology and Biochemistry, China Agricultural University, Beijing, 100193, People's Republic of China
- Department of Plant Genetics and Breeding, National Maize Improvement Center, China Agricultural University, Beijing, 100193, People's Republic of China
| | - Junqiang Ding
- State Key Laboratory of Wheat and Maize Crop Science and Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou, 450046, People's Republic of China
| | - Jinsheng Lai
- State Key Laboratory of Plant Physiology and Biochemistry, China Agricultural University, Beijing, 100193, People's Republic of China
- Department of Plant Genetics and Breeding, National Maize Improvement Center, China Agricultural University, Beijing, 100193, People's Republic of China
| | - Weibin Song
- State Key Laboratory of Plant Physiology and Biochemistry, China Agricultural University, Beijing, 100193, People's Republic of China.
- Department of Plant Genetics and Breeding, National Maize Improvement Center, China Agricultural University, Beijing, 100193, People's Republic of China.
| | - Haiming Zhao
- State Key Laboratory of Plant Physiology and Biochemistry, China Agricultural University, Beijing, 100193, People's Republic of China.
- Department of Plant Genetics and Breeding, National Maize Improvement Center, China Agricultural University, Beijing, 100193, People's Republic of China.
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25
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Gong P, Demuynck K, De Block J, Aesaert S, Coussens G, Pauwels L, Inzé D, Nelissen H. Modulation of the DA1 pathway in maize shows that translatability of information from Arabidopsis to crops is complex. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2022; 321:111295. [PMID: 35696903 DOI: 10.1016/j.plantsci.2022.111295] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/15/2022] [Accepted: 04/16/2022] [Indexed: 06/15/2023]
Abstract
Modern agriculture is struggling to meet the increasing food, silage and raw material demands due to the rapid growth of population and climate change. In Arabidopsis, DA1 and DAR1 are proteases that negatively regulate cell proliferation and control organ size. DA1 and DAR1 are activated by ubiquitination catalyzed by the E3 ligase BIG BROTHER (BB). Here, we characterized the DA1, DAR1 and BB gene families in maize and analyzed whether perturbation of these genes regulates organ size similar to what was observed in Arabidopsis. We generated da1_dar1a_dar1b triple CRISPR maize mutants and bb1_bb2 double mutants. Detailed phenotypic analysis showed that the size of leaf, stem, cob, and seed was not consistently enlarged in these mutants. Also overexpression of a dominant-negative DA1R333K allele, resembling the da1-1 allele of Arabidopsis which has larger leaves and seeds, did not alter the maize phenotype. The mild negative effects on plant height of the DA1R333K_bb1_bb2 mutant indicate that the genes in the DA1 pathway may control organ size in maize, albeit less obvious than in Arabidopsis.
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Affiliation(s)
- Pan Gong
- Ghent University, Department of Plant Biotechnology and Bioinformatics, 9052 Ghent, Belgium; VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Kirin Demuynck
- Ghent University, Department of Plant Biotechnology and Bioinformatics, 9052 Ghent, Belgium; VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Jolien De Block
- Ghent University, Department of Plant Biotechnology and Bioinformatics, 9052 Ghent, Belgium; VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Stijn Aesaert
- Ghent University, Department of Plant Biotechnology and Bioinformatics, 9052 Ghent, Belgium; VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Griet Coussens
- Ghent University, Department of Plant Biotechnology and Bioinformatics, 9052 Ghent, Belgium; VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Laurens Pauwels
- Ghent University, Department of Plant Biotechnology and Bioinformatics, 9052 Ghent, Belgium; VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Dirk Inzé
- Ghent University, Department of Plant Biotechnology and Bioinformatics, 9052 Ghent, Belgium; VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Hilde Nelissen
- Ghent University, Department of Plant Biotechnology and Bioinformatics, 9052 Ghent, Belgium; VIB Center for Plant Systems Biology, 9052 Ghent, Belgium.
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26
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Hill MJ, Penning BW, McCann MC, Carpita NC. COMPILE: a GWAS computational pipeline for gene discovery in complex genomes. BMC PLANT BIOLOGY 2022; 22:315. [PMID: 35778686 PMCID: PMC9250234 DOI: 10.1186/s12870-022-03668-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 05/16/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Genome-Wide Association Studies (GWAS) are used to identify genes and alleles that contribute to quantitative traits in large and genetically diverse populations. However, traits with complex genetic architectures create an enormous computational load for discovery of candidate genes with acceptable statistical certainty. We developed a streamlined computational pipeline for GWAS (COMPILE) to accelerate identification and annotation of candidate maize genes associated with a quantitative trait, and then matches maize genes to their closest rice and Arabidopsis homologs by sequence similarity. RESULTS COMPILE executed GWAS using a Mixed Linear Model that incorporated, without compression, recent advancements in population structure control, then linked significant Quantitative Trait Loci (QTL) to candidate genes and RNA regulatory elements contained in any genome. COMPILE was validated using published data to identify QTL associated with the traits of α-tocopherol biosynthesis and flowering time, and identified published candidate genes as well as additional genes and non-coding RNAs. We then applied COMPILE to 274 genotypes of the maize Goodman Association Panel to identify candidate loci contributing to resistance of maize stems to penetration by larvae of the European Corn Borer (Ostrinia nubilalis). Candidate genes included those that encode a gene of unknown function, WRKY and MYB-like transcriptional factors, receptor-kinase signaling, riboflavin synthesis, nucleotide-sugar interconversion, and prolyl hydroxylation. Expression of the gene of unknown function has been associated with pathogen stress in maize and in rice homologs closest in sequence identity. CONCLUSIONS The relative speed of data analysis using COMPILE allowed comparison of population size and compression. Limitations in population size and diversity are major constraints for a trait and are not overcome by increasing marker density. COMPILE is customizable and is readily adaptable for application to species with robust genomic and proteome databases.
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Affiliation(s)
- Matthew J Hill
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, Indiana, 47907, USA
- Present address: Whitehead Institute for Biomedical Research, 455 Main Street, Cambridge, MA, 02142, USA
- Present address: Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Bryan W Penning
- USDA-ARS Corn, Soybean and Wheat Quality Research Unit, Wooster, OH, 44691, USA
| | - Maureen C McCann
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907, USA
- Present address: Biosciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, USA
| | - Nicholas C Carpita
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, Indiana, 47907, USA.
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907, USA.
- Present address: Biosciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, USA.
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27
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Li C, Guan H, Jing X, Li Y, Wang B, Li Y, Liu X, Zhang D, Liu C, Xie X, Zhao H, Wang Y, Liu J, Zhang P, Hu G, Li G, Li S, Sun D, Wang X, Shi Y, Song Y, Jiao C, Ross-Ibarra J, Li Y, Wang T, Wang H. Genomic insights into historical improvement of heterotic groups during modern hybrid maize breeding. NATURE PLANTS 2022; 8:750-763. [PMID: 35851624 DOI: 10.1038/s41477-022-01190-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 06/07/2022] [Indexed: 06/15/2023]
Abstract
Single-cross maize hybrids display superior heterosis and are produced from crossing two parental inbred lines belonging to genetically different heterotic groups. Here we assembled 1,604 historically utilized maize inbred lines belonging to various female heterotic groups (FHGs) and male heterotic groups (MHGs), and conducted phenotyping and genomic sequencing analyses. We found that the FHGs and MHGs have undergone both convergent and divergent changes for different sets of agronomic traits. Using genome-wide selection scans and association analyses, we identified a large number of candidate genes that contributed to the improvement of agronomic traits of the FHGs and MHGs. Moreover, we observed increased genetic differentiation between the FHGs and MHGs across the breeding eras, and we found a positive correlation between increasing heterozygosity levels in the differentiated genes and heterosis in hybrids. Furthermore, we validated the function of two selected genes and a differentiated gene. This study provides insights into the genomic basis of modern hybrid maize breeding.
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Affiliation(s)
- Chunhui Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Honghui Guan
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xin Jing
- Novogene Bioinformatics Institute, Beijing, China
| | - Yaoyao Li
- Guangdong Laboratory for Lingnan Modern Agriculture, State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, South China Agricultural University, Guangzhou, China
| | - Baobao Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yongxiang Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xuyang Liu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Dengfeng Zhang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Cheng Liu
- Institute of Food Crops, Xinjiang Academy of Agricultural Sciences, Urumqi, China
| | - Xiaoqing Xie
- Institute of Food Crops, Xinjiang Academy of Agricultural Sciences, Urumqi, China
| | - Haiyan Zhao
- Institute of Maize Research, Liaoning Academy of Agricultural Sciences, Shenyang, China
| | - Yanbo Wang
- Institute of Maize Research, Liaoning Academy of Agricultural Sciences, Shenyang, China
| | - Jingbao Liu
- Institute of Cereal Crops, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Panpan Zhang
- Institute of Cereal Crops, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Guanghui Hu
- Institute of Maize Research, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Guoliang Li
- Institute of Maize Research, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Suiyan Li
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Dequan Sun
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Xiaoming Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yunsu Shi
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yanchun Song
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | | | - Jeffrey Ross-Ibarra
- Department of Evolution and Ecology, University of California, Davis, CA, USA.
- Center for Population Biology and Genome Center, University of California, Davis, CA, USA.
| | - Yu Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Tianyu Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Haiyang Wang
- Guangdong Laboratory for Lingnan Modern Agriculture, State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, South China Agricultural University, Guangzhou, China.
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28
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Fernandes SB, Casstevens TM, Bradbury PJ, Lipka AE. A multi-trait multi-locus stepwise approach for conducting GWAS on correlated traits. THE PLANT GENOME 2022; 15:e20200. [PMID: 35307964 DOI: 10.1002/tpg2.20200] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 02/14/2022] [Indexed: 06/14/2023]
Abstract
The ability to accurately quantify the simultaneous effect of multiple genomic loci on multiple traits is now possible due to current and emerging high-throughput genotyping and phenotyping technologies. To date, most efforts to quantify these genotype-to-phenotype relationships have focused on either multi-trait models that test a single marker at a time or multi-locus models that quantify associations with a single trait. Therefore, the purpose of this study was to compare the performance of a multi-trait, multi-locus stepwise (MSTEP) model selection procedure we developed to (a) a commonly used multi-trait single-locus model and (b) a univariate multi-locus model. We used real marker data in maize (Zea mays L.) and soybean (Glycine max L.) to simulate multiple traits controlled by various combinations of pleiotropic and nonpleiotropic quantitative trait nucleotides (QTNs). In general, we found that both multi-trait models outperformed the univariate multi-locus model, especially when analyzing a trait of low heritability. For traits controlled by either a combination of pleiotropic and nonpleiotropic QTNs or a large number of QTNs (i.e., 50), our MSTEP model often outperformed at least one of the two alternative models. When applied to the analysis of two tocochromanol-related traits in maize grain, MSTEP identified the same peak-associated marker that has been reported in a previous study. We therefore conclude that MSTEP is a useful addition to the suite of statistical models that are commonly used to gain insight into the genetic architecture of agronomically important traits.
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Affiliation(s)
- Samuel B Fernandes
- Dep. of Crop Sciences, Univ. of Illinois Urbana-Champaign, Urbana, IL, USA
| | | | | | - Alexander E Lipka
- Dep. of Crop Sciences, Univ. of Illinois Urbana-Champaign, Urbana, IL, USA
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29
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Tandukar Z, Chopra R, Frels K, Heim B, Marks MD, Anderson JA. Genetic dissection of seed characteristics in field pennycress via genome-wide association mapping studies. THE PLANT GENOME 2022; 15:e20211. [PMID: 35484973 DOI: 10.1002/tpg2.20211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 03/14/2022] [Indexed: 06/14/2023]
Abstract
Field pennycress (Thlaspi arvense L.) is a new winter annual cash cover crop with high oil content and seed yield, excellent winter hardiness, early maturation, and resistance to most pests and diseases. It provides living cover on fallow croplands between summer seasons, and in doing so reduces nutrient leaching into water sources, mitigates soil erosion, and suppresses weed growth. The first ever genome-wide association study (GWAS) was conducted on a pennycress diversity panel to identify marker trait associations with important seed size and composition related traits. The entire population was phenotyped in three total environments over 2 yr, and seed area, length, width, thousand grain weight, total oil, and total protein were measured post-harvest with specialized high-throughput imaging and near-infrared spectroscopy. Basic unbiased linear prediction values were calculated for each trait. Seed size traits tended to have higher entry mean reliabilities (0.76-0.79) compared with oil content (0.51) and protein content (0.37). Genotyping-by-sequencing identified 33,606 high quality genome-wide single nucleotide polymorphism (SNPs) that were coupled with phenotypic data to perform GWAS for seed area, length, width, thousand grain weight, total oil, and total protein content. Fifty-nine total marker-trait associations were identified revealing genomic regions controlling each trait. The significant SNPs explained 0.06-0.18% of the total variance for that trait in our population. A list of candidate genes was identified based on their functional annotations and characterization in other species. Our results confirm that GWAS is an efficient strategy to identify significant marker-trait associations that can be incorporated into marker-assisted selection pipelines to accelerate pennycress breeding progress.
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Affiliation(s)
- Zenith Tandukar
- Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, Saint Paul, MN, USA
| | - Ratan Chopra
- Dep. of Plant and Microbial Biology, Univ. of Minnesota, Saint Paul, MN, USA
| | - Katherine Frels
- Dep. of Agronomy and Horticulture, Univ. of Nebraska, Lincoln, NE, USA
| | - Brett Heim
- Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, Saint Paul, MN, USA
| | - M David Marks
- Dep. of Plant and Microbial Biology, Univ. of Minnesota, Saint Paul, MN, USA
| | - James A Anderson
- Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, Saint Paul, MN, USA
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30
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Murphy MD, Fernandes SB, Morota G, Lipka AE. Assessment of two statistical approaches for variance genome-wide association studies in plants. Heredity (Edinb) 2022; 129:93-102. [PMID: 35538221 PMCID: PMC9338250 DOI: 10.1038/s41437-022-00541-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 04/28/2022] [Accepted: 04/29/2022] [Indexed: 11/09/2022] Open
Abstract
Genomic loci that control the variance of agronomically important traits are increasingly important due to the profusion of unpredictable environments arising from climate change. The ability to identify such variance-controlling loci in association studies will be critical for future breeding efforts. Two statistical approaches that have already been used in the variance genome-wide association study (vGWAS) paradigm are the Brown-Forsythe test (BFT) and the double generalized linear model (DGLM). To ensure that these approaches are deployed as effectively as possible, it is critical to study the factors that influence their ability to identify variance-controlling loci. We used genome-wide marker data in maize (Zea mays L.) and Arabidopsis thaliana to simulate traits controlled by epistasis, genotype by environment (GxE) interactions, and variance quantitative trait nucleotides (vQTNs). We then quantified true and false positive detection rates of the BFT and DGLM across all simulated traits. We also conducted a vGWAS using both the BFT and DGLM on plant height in a maize diversity panel. The observed true positive detection rates at the maximum sample size considered (N = 2815) suggest that both of these vGWAS approaches are capable of identifying epistasis and GxE for sufficiently large sample sizes. We also noted that the DGLM decisively outperformed the BFT for simulated traits controlled by vQTNs at sample sizes of N = 500. Although we conclude that there are still certain aspects of vGWAS approaches that need further refinement, this study suggests that the BFT and DGLM are capable of identifying variance-controlling loci in current state-of-the-art plant or agronomic data sets.
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Affiliation(s)
- Matthew D Murphy
- Department of Crop Sciences, University of Illinois Urbana-Champaign, 1102 S Goodwin Ave, Urbana, IL, 61801, USA
| | - Samuel B Fernandes
- Department of Crop Sciences, University of Illinois Urbana-Champaign, 1102 S Goodwin Ave, Urbana, IL, 61801, USA
| | - Gota Morota
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, 175 West Campus Drive, Blacksburg, VA, 24061, USA
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois Urbana-Champaign, 1102 S Goodwin Ave, Urbana, IL, 61801, USA.
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31
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Li D, Dutta S, Roy V. Model Based Screening Embedded Bayesian Variable Selection for Ultra-high Dimensional Settings. J Comput Graph Stat 2022. [DOI: 10.1080/10618600.2022.2074428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Dongjin Li
- Department of Statistics, Iowa State University, Ames, IA 50010
| | - Somak Dutta
- Department of Statistics, Iowa State University, Ames, IA 50010
| | - Vivekananda Roy
- Department of Statistics, Iowa State University, Ames, IA 50010
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32
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Variability in changes of acrylamide precursors during nixtamalization for masa production. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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33
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Shrestha V, Yobi A, Slaten ML, Chan YO, Holden S, Gyawali A, Flint-Garcia S, Lipka AE, Angelovici R. Multiomics approach reveals a role of translational machinery in shaping maize kernel amino acid composition. PLANT PHYSIOLOGY 2022; 188:111-133. [PMID: 34618082 PMCID: PMC8774818 DOI: 10.1093/plphys/kiab390] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 07/21/2021] [Indexed: 06/13/2023]
Abstract
Maize (Zea mays) seeds are a good source of protein, despite being deficient in several essential amino acids. However, eliminating the highly abundant but poorly balanced seed storage proteins has revealed that the regulation of seed amino acids is complex and does not rely on only a handful of proteins. In this study, we used two complementary omics-based approaches to shed light on the genes and biological processes that underlie the regulation of seed amino acid composition. We first conducted a genome-wide association study to identify candidate genes involved in the natural variation of seed protein-bound amino acids. We then used weighted gene correlation network analysis to associate protein expression with seed amino acid composition dynamics during kernel development and maturation. We found that almost half of the proteome was significantly reduced during kernel development and maturation, including several translational machinery components such as ribosomal proteins, which strongly suggests translational reprogramming. The reduction was significantly associated with a decrease in several amino acids, including lysine and methionine, pointing to their role in shaping the seed amino acid composition. When we compared the candidate gene lists generated from both approaches, we found a nonrandom overlap of 80 genes. A functional analysis of these genes showed a tight interconnected cluster dominated by translational machinery genes, especially ribosomal proteins, further supporting the role of translation dynamics in shaping seed amino acid composition. These findings strongly suggest that seed biofortification strategies that target the translation machinery dynamics should be considered and explored further.
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Affiliation(s)
- Vivek Shrestha
- Division of Biological Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri 65211, USA
| | - Abou Yobi
- Division of Biological Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri 65211, USA
| | - Marianne L Slaten
- Division of Biological Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri 65211, USA
| | - Yen On Chan
- Division of Biological Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri 65211, USA
| | - Samuel Holden
- Division of Biological Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri 65211, USA
| | - Abiskar Gyawali
- Division of Biological Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri 65211, USA
| | - Sherry Flint-Garcia
- U.S. Department of Agriculture-Agricultural Research Service, Columbia, Missouri 65211, USA
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois, Urbana, Illinois 61801, USA
| | - Ruthie Angelovici
- Division of Biological Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri 65211, USA
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34
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Zhang X, Wang M, Zhang C, Dai C, Guan H, Zhang R. Genetic dissection of QTLs for starch content in four maize DH populations. FRONTIERS IN PLANT SCIENCE 2022; 13:950664. [PMID: 36275573 PMCID: PMC9583244 DOI: 10.3389/fpls.2022.950664] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 05/17/2023]
Abstract
Starch is the principal carbohydrate source in maize kernels. Understanding the genetic basis of starch content (SC) benefits greatly in improving maize yield and optimizing end-use quality. Here, four double haploid (DH) populations were generated and were used to identify quantitative trait loci (QTLs) associated with SC. The phenotype of SC exhibited continuous and approximate normal distribution in each population. A total of 13 QTLs for SC in maize kernels was detected in a range of 3.65-16.18% of phenotypic variation explained (PVE). Among those, only some partly overlapped with QTLs previously known to be related to SC. Meanwhile, 12 genes involved in starch synthesis and metabolism located within QTLs were identified in this study. These QTLs will lay the foundation to explore candidate genes regulating SC in maize kernel and facilitate the application of molecular marker-assisted selection for a breeding program to cultivate maize varieties with a deal of grain quality.
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Affiliation(s)
- Xiaolei Zhang
- Quality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Min Wang
- Institute of Advanced Agricultural Technology, Qilu Normal University, Jinan, China
| | | | - Changjun Dai
- Quality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Haitao Guan
- Quality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Ruiying Zhang
- Quality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences, Harbin, China
- *Correspondence: Ruiying Zhang
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35
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Hu S, Wang M, Zhang X, Chen W, Song X, Fu X, Fang H, Xu J, Xiao Y, Li Y, Bai G, Li J, Yang X. Genetic basis of kernel starch content decoded in a maize multi-parent population. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:2192-2205. [PMID: 34077617 PMCID: PMC8541773 DOI: 10.1111/pbi.13645] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/20/2021] [Accepted: 05/31/2021] [Indexed: 05/25/2023]
Abstract
Starch is the most abundant storage carbohydrate in maize kernels and provides calories for humans and other animals as well as raw materials for various industrial applications. Decoding the genetic basis of natural variation in kernel starch content is needed to manipulate starch quantity and quality via molecular breeding to meet future needs. Here, we identified 50 unique single quantitative trait loci (QTLs) for starch content with 18 novel QTLs via single linkage mapping, joint linkage mapping and a genome-wide association study in a multi-parent population containing six recombinant inbred line populations. Only five QTLs explained over 10% of phenotypic variation in single populations. In addition to a few large-effect and many small-effect additive QTLs, limited pairs of epistatic QTLs also contributed to the genetic basis of the variation in kernel starch content. A regional association study identified five non-starch-pathway genes that were the causal candidate genes underlying the identified QTLs for starch content. The pathway-driven analysis identified ZmTPS9, which encodes a trehalose-6-phosphate synthase in the trehalose pathway, as the causal gene for the QTL qSTA4-2, which was detected by all three statistical analyses. Knockout of ZmTPS9 increased kernel starch content and, in turn, kernel weight in maize, suggesting potential applications for ZmTPS9 in maize starch and yield improvement. These findings extend our knowledge about the genetic basis of starch content in maize kernels and provide valuable information for maize genetic improvement of starch quantity and quality.
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Affiliation(s)
- Shuting Hu
- State Key Laboratory of Plant Physiology and BiochemistryNational Maize Improvement Center of ChinaMOA Key Lab of Maize BiologyChina Agricultural UniversityBeijingChina
| | - Min Wang
- State Key Laboratory of Plant Physiology and BiochemistryNational Maize Improvement Center of ChinaMOA Key Lab of Maize BiologyChina Agricultural UniversityBeijingChina
| | - Xuan Zhang
- State Key Laboratory of Plant Physiology and BiochemistryNational Maize Improvement Center of ChinaMOA Key Lab of Maize BiologyChina Agricultural UniversityBeijingChina
| | - Wenkang Chen
- State Key Laboratory of Plant Physiology and BiochemistryNational Maize Improvement Center of ChinaMOA Key Lab of Maize BiologyChina Agricultural UniversityBeijingChina
| | - Xinran Song
- State Key Laboratory of Plant Physiology and BiochemistryNational Maize Improvement Center of ChinaMOA Key Lab of Maize BiologyChina Agricultural UniversityBeijingChina
- Agronomy CollegeXinjiang Agricultural UniversityUrumqiChina
| | - Xiuyi Fu
- State Key Laboratory of Plant Physiology and BiochemistryNational Maize Improvement Center of ChinaMOA Key Lab of Maize BiologyChina Agricultural UniversityBeijingChina
- Maize Research CenterBeijing Academy of Agriculture & Forestry Sciences (BAAFS)BeijingChina
| | - Hui Fang
- State Key Laboratory of Plant Physiology and BiochemistryNational Maize Improvement Center of ChinaMOA Key Lab of Maize BiologyChina Agricultural UniversityBeijingChina
| | - Jing Xu
- State Key Laboratory of Plant Physiology and BiochemistryNational Maize Improvement Center of ChinaMOA Key Lab of Maize BiologyChina Agricultural UniversityBeijingChina
| | - Yingni Xiao
- State Key Laboratory of Plant Physiology and BiochemistryNational Maize Improvement Center of ChinaMOA Key Lab of Maize BiologyChina Agricultural UniversityBeijingChina
- Crop Research InstituteGuangdong Academy of Agricultural SciencesKey Laboratory of Crops Genetics and Improvement of Guangdong ProvinceGuangzhouChina
| | - Yaru Li
- State Key Laboratory of Plant Physiology and BiochemistryNational Maize Improvement Center of ChinaMOA Key Lab of Maize BiologyChina Agricultural UniversityBeijingChina
| | - Guanghong Bai
- Agronomy CollegeXinjiang Agricultural UniversityUrumqiChina
| | - Jiansheng Li
- State Key Laboratory of Plant Physiology and BiochemistryNational Maize Improvement Center of ChinaMOA Key Lab of Maize BiologyChina Agricultural UniversityBeijingChina
| | - Xiaohong Yang
- State Key Laboratory of Plant Physiology and BiochemistryNational Maize Improvement Center of ChinaMOA Key Lab of Maize BiologyChina Agricultural UniversityBeijingChina
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36
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Renk JS, Gilbert AM, Hattery TJ, O'Connor CH, Monnahan PJ, Anderson N, Waters AJ, Eickholt DP, Flint-Garcia SA, Yandeau-Nelson MD, Hirsch CN. Genetic control of kernel compositional variation in a maize diversity panel. THE PLANT GENOME 2021; 14:e20115. [PMID: 34197039 DOI: 10.1002/tpg2.20115] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 05/07/2021] [Indexed: 06/13/2023]
Abstract
Maize (Zea mays L.) is a multi-purpose row crop grown worldwide, which, over time, has often been bred for increased yield at the detriment of lower composition grain quality. Some knowledge of the genetic factors that affect quality traits has been discovered through the study of classical maize mutants; however, much of the underlying genetic control of these traits and the interaction between these traits remains unknown. To better understand variation that exists for grain compositional traits in maize, we evaluated 501 diverse temperate maize inbred lines in five unique environments and predicted 16 compositional traits (e.g., carbohydrates, protein, and starch) based on the output of near-infrared (NIR) spectroscopy. Phenotypic analysis found substantial variation for compositional traits and the majority of variation was explained by genetic and environmental factors. Correlations and trade-offs among traits in different maize types (e.g., dent, sweetcorn, and popcorn) were explored, and significant differences and meaningful correlations were detected. In total, 22.9-71.0% of the phenotypic variation across these traits could be explained using 2,386,666 single nucleotide polymorphism (SNP) markers generated from whole-genome resequencing data. A genome-wide association study (GWAS) was conducted using these same markers and found 72 statistically significant SNPs for 11 compositional traits. This study provides valuable insights in the phenotypic variation and genetic control underlying compositional traits that can be used in breeding programs for improving maize grain quality.
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Affiliation(s)
- Jonathan S Renk
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Amanda M Gilbert
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Travis J Hattery
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, 50011, USA
| | - Christine H O'Connor
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN, 55108, USA
| | - Patrick J Monnahan
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN, 55108, USA
| | | | | | | | - Sherry A Flint-Garcia
- United States Department of Agriculture, Agricultural Research Service, Columbia, MO, 65211, USA
| | - Marna D Yandeau-Nelson
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, 50011, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
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37
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Joshi V, Nimmakayala P, Song Q, Abburi V, Natarajan P, Levi A, Crosby K, Reddy UK. Genome-wide association study and population structure analysis of seed-bound amino acids and total protein in watermelon. PeerJ 2021; 9:e12343. [PMID: 34722000 PMCID: PMC8533027 DOI: 10.7717/peerj.12343] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 09/28/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Watermelon seeds are a powerhouse of value-added traits such as proteins, free amino acids, vitamins, and essential minerals, offering a paleo-friendly dietary option. Despite the availability of substantial genetic variation, there is no sufficient information on the natural variation in seed-bound amino acids or proteins across the watermelon germplasm. This study aimed to analyze the natural variation in watermelon seed amino acids and total protein and explore underpinning genetic loci by genome-wide association study (GWAS). METHODS The study evaluated the distribution of seed-bound free amino acids and total protein in 211 watermelon accessions of Citrullus spp, including 154 of Citrullus lanatus, 54 of Citrullus mucosospermus (egusi) and three of Citrullus amarus. We used the GWAS approach to associate seed phenotypes with 11,456 single nucleotide polymorphisms (SNPs) generated by genotyping-by-sequencing (GBS). RESULTS Our results demonstrate a significant natural variation in different free amino acids and total protein content across accessions and geographic regions. The accessions with high protein content and proportion of essential amino acids warrant its use for value-added benefits in the food and feed industries via biofortification. The GWAS analysis identified 188 SNPs coinciding with 167 candidate genes associated with watermelon seed-bound amino acids and total protein. Clustering of SNPs associated with individual amino acids found by principal component analysis was independent of the speciation or cultivar groups and was not selected during the domestication of sweet watermelon. The identified candidate genes were involved in metabolic pathways associated with amino acid metabolism, such as Argininosuccinate synthase, explaining 7% of the variation in arginine content, which validate their functional relevance and potential for marker-assisted analysis selection. This study provides a platform for exploring potential gene loci involved in seed-bound amino acids metabolism, useful in genetic analysis and development of watermelon varieties with superior seed nutritional values.
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Affiliation(s)
- Vijay Joshi
- Department of Horticultural Sciences, Texas A&M University, Uvalde, Texas, United States
- Texas A&M AgriLife Research and Extension Center, Uvalde, Texas, United States
| | - Padma Nimmakayala
- Department of Biology, Gus R. Douglass Institute, West Virginia State University, Institute, Charleston, West Virginia, United States
| | - Qiushuo Song
- Department of Horticultural Sciences, Texas A&M University, Uvalde, Texas, United States
| | - Venkata Abburi
- Department of Biology, Gus R. Douglass Institute, West Virginia State University, Institute, Charleston, West Virginia, United States
| | - Purushothaman Natarajan
- Department of Biology, Gus R. Douglass Institute, West Virginia State University, Institute, Charleston, West Virginia, United States
| | - Amnon Levi
- Vegetable Laboratory, USDA-ARS, Charleston, South Carolina, United States
| | - Kevin Crosby
- Department of Horticultural Sciences, Texas A&M University, Uvalde, Texas, United States
| | - Umesh K. Reddy
- Department of Biology, Gus R. Douglass Institute, West Virginia State University, Institute, Charleston, West Virginia, United States
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38
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Haplotype associated RNA expression (HARE) improves prediction of complex traits in maize. PLoS Genet 2021; 17:e1009568. [PMID: 34606492 PMCID: PMC8516254 DOI: 10.1371/journal.pgen.1009568] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 10/14/2021] [Accepted: 09/07/2021] [Indexed: 11/19/2022] Open
Abstract
Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for predicting many complex traits. However, it usually cannot capture the combination of alleles in haplotypes and it has generated little insight about the biological function of polymorphisms. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism. Therefore, HARE estimates were more transferrable across different tissues and populations compared to measured transcript expression. We also showed that HARE estimates captured one-third of the variation in gene expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels–a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel)–for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues, whereas accuracy by HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations. Genomic marker data is widely used in the prediction of many traits. However, prediction has been primarily carried out within populations and without explicit modeling of RNA or protein expression. In this study, we explored the prediction of field traits within and across populations using estimated RNA expression attributable to only the DNA sequence around a gene. We showed that the estimated RNA expression was more transferable across populations and tissues than measured RNA expression. We improved prediction of field traits up to 15% using estimated gene expression as compared to observed expression or gene sequence alone. Overall, these findings indicate that structural and functional information in the gene sequence is highly transferable.
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Fang H, Fu X, Ge H, Zhang A, Shan T, Wang Y, Li P, Wang B. Genetic basis of maize kernel oil-related traits revealed by high-density SNP markers in a recombinant inbred line population. BMC PLANT BIOLOGY 2021; 21:344. [PMID: 34289812 PMCID: PMC8293480 DOI: 10.1186/s12870-021-03089-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 06/04/2021] [Indexed: 05/17/2023]
Abstract
BACKGROUND Maize (Zea mays ssp. mays) is the most abundantly cultivated and highly valued food commodity in the world. Oil from maize kernels is highly nutritious and important for the diet and health of humans, and it can be used as a source of bioenergy. A better understanding of genetic basis for maize kernel oil can help improve the oil content and quality when applied in breeding. RESULTS In this study, a KUI3/SC55 recombinant inbred line (RIL) population, consisting of 180 individuals was constructed from a cross between inbred lines KUI3 and SC55. We phenotyped 19 oil-related traits and subsequently dissected the genetic architecture of oil-related traits in maize kernels based on a high-density genetic map. In total, 62 quantitative trait loci (QTLs), with 2 to 5 QTLs per trait, were detected in the KUI3/SC55 RIL population. Each QTL accounted for 6.7% (qSTOL1) to 31.02% (qBELI6) of phenotypic variation and the total phenotypic variation explained (PVE) of all detected QTLs for each trait ranged from 12.5% (OIL) to 52.5% (C16:0/C16:1). Of all these identified QTLs, only 5 were major QTLs located in three genomic regions on chromosome 6 and 9. In addition, two pairs of epistatic QTLs with additive effects were detected and they explained 3.3 and 2.4% of the phenotypic variation, respectively. Colocalization with a previous GWAS on oil-related traits, identified 19 genes. Of these genes, two important candidate genes, GRMZM2G101515 and GRMZM2G022558, were further verified to be associated with C20:0/C22:0 and C18:0/C20:0, respectively, according to a gene-based association analysis. The first gene encodes a kinase-related protein with unknown function, while the second gene encodes fatty acid elongase 2 (fae2) and directly participates in the biosynthesis of very long chain fatty acids in Arabidopsis. CONCLUSIONS Our results provide insights on the genetic basis of oil-related traits and a theoretical basis for improving maize quality by marker-assisted selection.
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Affiliation(s)
- Hui Fang
- Ministry of Agricultural Scientific Observing and Experimental Station of Maize in Plain Area of Southern Region, School of Life Sciences, Nantong University, Nantong, 226019, People's Republic of China
| | - Xiuyi Fu
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture and Forestry Sciences (BAAFS), Shuguang Garden Middle Road No. 9, Beijing, 100097, China
| | - Hanqiu Ge
- Ministry of Agricultural Scientific Observing and Experimental Station of Maize in Plain Area of Southern Region, School of Life Sciences, Nantong University, Nantong, 226019, People's Republic of China
| | - Aixia Zhang
- Ministry of Agricultural Scientific Observing and Experimental Station of Maize in Plain Area of Southern Region, School of Life Sciences, Nantong University, Nantong, 226019, People's Republic of China
| | - Tingyu Shan
- Ministry of Agricultural Scientific Observing and Experimental Station of Maize in Plain Area of Southern Region, School of Life Sciences, Nantong University, Nantong, 226019, People's Republic of China
| | - Yuandong Wang
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture and Forestry Sciences (BAAFS), Shuguang Garden Middle Road No. 9, Beijing, 100097, China.
| | - Ping Li
- Ministry of Agricultural Scientific Observing and Experimental Station of Maize in Plain Area of Southern Region, School of Life Sciences, Nantong University, Nantong, 226019, People's Republic of China.
- Nantong Bear Seeds Company, Nantong, 226009, People's Republic of China.
| | - Baohua Wang
- Ministry of Agricultural Scientific Observing and Experimental Station of Maize in Plain Area of Southern Region, School of Life Sciences, Nantong University, Nantong, 226019, People's Republic of China.
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Medeiros DB, Brotman Y, Fernie AR. The utility of metabolomics as a tool to inform maize biology. PLANT COMMUNICATIONS 2021; 2:100187. [PMID: 34327322 PMCID: PMC8299083 DOI: 10.1016/j.xplc.2021.100187] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 03/26/2021] [Accepted: 04/19/2021] [Indexed: 05/04/2023]
Abstract
With the rise of high-throughput omics tools and the importance of maize and its products as food and bioethanol, maize metabolism has been extensively explored. Modern maize is still rich in genetic and phenotypic variation, yielding a wide range of structurally and functionally diverse metabolites. The maize metabolome is also incredibly dynamic in terms of topology and subcellular compartmentalization. In this review, we examine a broad range of studies that cover recent developments in maize metabolism. Particular attention is given to current methodologies and to the use of metabolomics as a tool to define biosynthetic pathways and address biological questions. We also touch upon the use of metabolomics to understand maize natural variation and evolution, with a special focus on research that has used metabolite-based genome-wide association studies (mGWASs).
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Affiliation(s)
- David B. Medeiros
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Yariv Brotman
- Department of Life Sciences, Ben-Gurion University of the Negev, Beersheva, Israel
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Chidzanga C, Fleury D, Baumann U, Mullan D, Watanabe S, Kalambettu P, Pontre R, Edwards J, Forrest K, Wong D, Langridge P, Chalmers K, Garcia M. Development of an Australian Bread Wheat Nested Association Mapping Population, a New Genetic Diversity Resource for Breeding under Dry and Hot Climates. Int J Mol Sci 2021; 22:4348. [PMID: 33919411 PMCID: PMC8122485 DOI: 10.3390/ijms22094348] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 04/16/2021] [Accepted: 04/20/2021] [Indexed: 12/20/2022] Open
Abstract
Genetic diversity, knowledge of the genetic architecture of the traits of interest and efficient means of transferring the desired genetic diversity into the relevant genetic background are prerequisites for plant breeding. Exotic germplasm is a rich source of genetic diversity; however, they harbor undesirable traits that limit their suitability for modern agriculture. Nested association mapping (NAM) populations are valuable genetic resources that enable incorporation of genetic diversity, dissection of complex traits and providing germplasm to breeding programs. We developed the OzNAM by crossing and backcrossing 73 diverse exotic parents to two Australian elite varieties Gladius and Scout. The NAM parents were genotyped using the iSelect wheat 90K Infinium SNP array, and the progeny were genotyped using a custom targeted genotyping-by-sequencing assay based on molecular inversion probes designed to target 12,179 SNPs chosen from the iSelect wheat 90K Infinium SNP array of the parents. In total, 3535 BC1F4:6 RILs from 125 families with 21 to 76 lines per family were genotyped and we found 4964 polymorphic and multi-allelic haplotype markers that spanned the whole genome. A subset of 530 lines from 28 families were evaluated in multi-environment trials over three years. To demonstrate the utility of the population in QTL mapping, we chose to map QTL for maturity and plant height using the RTM-GWAS approach and we identified novel and known QTL for maturity and plant height.
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Affiliation(s)
- Charity Chidzanga
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia; (C.C.); (D.F.); (U.B.); (S.W.); (P.K.); (P.L.); (K.C.)
- ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia; (D.M.); (J.E.)
| | - Delphine Fleury
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia; (C.C.); (D.F.); (U.B.); (S.W.); (P.K.); (P.L.); (K.C.)
- ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia; (D.M.); (J.E.)
| | - Ute Baumann
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia; (C.C.); (D.F.); (U.B.); (S.W.); (P.K.); (P.L.); (K.C.)
- ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia; (D.M.); (J.E.)
| | - Dan Mullan
- ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia; (D.M.); (J.E.)
- Intergrain 19 Ambitious Link, Bibra Lake, WA 6163, Australia;
| | - Sayuri Watanabe
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia; (C.C.); (D.F.); (U.B.); (S.W.); (P.K.); (P.L.); (K.C.)
- ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia; (D.M.); (J.E.)
| | - Priyanka Kalambettu
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia; (C.C.); (D.F.); (U.B.); (S.W.); (P.K.); (P.L.); (K.C.)
- ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia; (D.M.); (J.E.)
| | - Robert Pontre
- Intergrain 19 Ambitious Link, Bibra Lake, WA 6163, Australia;
| | - James Edwards
- ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia; (D.M.); (J.E.)
- Australian Grain Technologies, 20 Leitch Rd, Roseworthy, SA 5371, Australia
| | - Kerrie Forrest
- Genomics & Cell Sciences, Agriculture Victoria Research, Department of Jobs, Precincts and Regions, Agribio, 5 Ring Rd, Bundoora, VIC 3083, Australia; (K.F.); (D.W.)
| | - Debbie Wong
- Genomics & Cell Sciences, Agriculture Victoria Research, Department of Jobs, Precincts and Regions, Agribio, 5 Ring Rd, Bundoora, VIC 3083, Australia; (K.F.); (D.W.)
| | - Peter Langridge
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia; (C.C.); (D.F.); (U.B.); (S.W.); (P.K.); (P.L.); (K.C.)
| | - Ken Chalmers
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia; (C.C.); (D.F.); (U.B.); (S.W.); (P.K.); (P.L.); (K.C.)
| | - Melissa Garcia
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia; (C.C.); (D.F.); (U.B.); (S.W.); (P.K.); (P.L.); (K.C.)
- ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia; (D.M.); (J.E.)
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Shen C, Chen K, Cui Y, Chen J, Mi X, Zhu S, Zhu Y, Ali J, Ye G, Li Z, Xu J. QTL Mapping and Favorable Allele Mining of Nitrogen Deficiency Tolerance Using an Interconnected Breeding Population in Rice. Front Genet 2021; 12:616428. [PMID: 33889173 PMCID: PMC8056011 DOI: 10.3389/fgene.2021.616428] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 03/04/2021] [Indexed: 02/04/2023] Open
Abstract
Nitrogen is one of the most important nutrients for rice growth and development. Breeding of nitrogen deficiency tolerance (NDT) variety is considered to be the most economic measure to solve the constrain of low nitrogen stress on grain yield in rice. An interconnected breeding (IB) population of 497 lines developed using Huanghuazhan (HHZ) as the recurrent parent and eight elite lines as the donor parents were tested for five traits including grain yield, biomass, harvest index, thousand grain weight, and spikelet fertility under two nitrogen treatments in three growing seasons. Association analysis using 7,388 bins generated by sequencing identified a total of 14, 14, and 12 QTLs for the five traits under low nitrogen (LN), normal nitrogen (NN), and LN/NN conditions, respectively, across three seasons. Favorable alleles were dissected for the 40 QTLs at the 10 NDT regions, and OM1723 was considered as the most important parent with the highest frequency of favorable alleles contributing to NDT-related traits. Six superior lines all showed significantly higher GY in LN environments and similar GY under NN environments except for H10. Substitution mapping using near-isogenic introgression lines delimited the qTGW2-1, which was identified on chromosome 2 under LN, NN, and LN/NN conditions into two QTLs, which were located in the two regions of about 200 and 350 kb with different favorable alleles. The bins 16, 1301, 1465, 1486, 3464, and 6249 harbored the QTLs for NDT detected in this study, and the QTLs/genes previously identified for NDT or nitrogen use efficiency (NUE) could be used for enhancing NDT and NUE by marker-assisted selection (MAS).
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Affiliation(s)
- Congcong Shen
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.,Institute of Crop Sciences, National Key Facility for Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Kai Chen
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yanru Cui
- College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Jiantao Chen
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Xuefei Mi
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Shuangbin Zhu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yajun Zhu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Jauhar Ali
- International Rice Research Institute, Los Baños, Philippines
| | - Guoyou Ye
- International Rice Research Institute, Los Baños, Philippines
| | - Zhikang Li
- Institute of Crop Sciences, National Key Facility for Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jianlong Xu
- Institute of Crop Sciences, National Key Facility for Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, China.,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
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Ullah S, Randhawa IAS, Trethowan R. Genome-wide association study of multiple traits linked to heat tolerance in emmer-derived hexaploid wheat genotypes. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:29. [PMID: 37309354 PMCID: PMC10236052 DOI: 10.1007/s11032-021-01222-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 03/17/2021] [Indexed: 06/13/2023]
Abstract
Heat stress tolerance in plants is a complex trait controlled by multiple genes of minor effect which are influenced by the environment and this makes breeding and selection complicated. Emmer wheat (Triticum dicoccon Schrank) carries valuable diversity that can be used to improve the heat tolerance of modern bread wheat. A diverse set of emmer-based genotypes was developed by crossing emmer wheat with hexaploid wheat. These materials, along with their hexaploid recurrent parents and commercial cultivars, were evaluated at optimum (E1) and heat stressed (E2) sowing times in the field for three consecutive years (2014-2016). The material was genotyped using the Infinium iSelect SNP 90K SNP Assay. The phenotypic data were combined across years within each sowing time and best linear unbiased estimators calculated for each genotype in each environment. These estimates were used for GWAS analysis. Significant phenotypic and genotypic variation was observed for all traits. A total of 125 and 142 marker-trait associations (MTAs) were identified in E1 and E2, respectively. The highest number of MTAs were observed on the A genome (106), followed by the B (105) and D (56) genomes. MTAs with pleiotropic effects within and across the environments were observed. Many of the MTAs found were reported previously for various traits, and a few significant MTAs under heat stress were new and linked to emmer genome. Genomic regions identified on chromosomes 2B and 3A had a significant positive impact on grain yield under stress with a 7% allelic effect. Genomic regions on chromosomes 1A and 4B contributed 11% and 9% of the variation for thousand kernel weight (TKW) under heat stress respectively. Following fine mapping, these regions could be used for marker-assisted selection to improve heat tolerance in wheat. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01222-3.
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Affiliation(s)
- Smi Ullah
- School of Life and Environmental Sciences, Plant Breeding Institute and Sydney Institute of Agriculture, The University of Sydney, Narrabri, New South Wales 2390 Australia
| | - Imtiaz A. S. Randhawa
- School of Veterinary Science, The University of Queensland, Gatton, Queensland 4343 Australia
| | - Richard Trethowan
- School of Life and Environmental Sciences, Plant Breeding Institute and Sydney Institute of Agriculture, The University of Sydney, Narrabri, New South Wales 2390 Australia
- School of Life and Environmental Sciences, Plant Breeding Institute and Sydney Institute of Agriculture, The University of Sydney, Cobbitty, New South Wales 2570 Australia
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Mbebi AJ, Tong H, Nikoloski Z. L2,1-norm regularized multivariate regression model with applications to genomic prediction. Bioinformatics 2021; 37:2896-2904. [PMID: 33774677 PMCID: PMC8479665 DOI: 10.1093/bioinformatics/btab212] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 03/16/2021] [Accepted: 03/26/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Genomic selection (GS) is currently deemed the most effective approach to speed up breeding of agricultural varieties. It has been recognized that consideration of multiple traits in GS can improve accuracy of prediction for traits of low heritability. However, since GS forgoes statistical testing with the idea of improving predictions, it does not facilitate mechanistic understanding of the contribution of particular single nucleotide polymorphisms (SNP). RESULTS Here, we propose a L2,1-norm regularized multivariate regression model and devise a fast and efficient iterative optimization algorithm, called L2,1-joint, applicable in multi-trait GS. The usage of the L2,1-norm facilitates variable selection in a penalized multivariate regression that considers the relation between individuals, when the number of SNPs is much larger than the number of individuals. The capacity for variable selection allows us to define master regulators that can be used in a multi-trait GS setting to dissect the genetic architecture of the analyzed traits. Our comparative analyses demonstrate that the proposed model is a favorable candidate compared to existing state-of-the-art approaches. Prediction and variable selection with datasets from Brassica napus, wheat and Arabidopsis thaliana diversity panels are conducted to further showcase the performance of the proposed model. AVAILABILITY AND IMPLEMENTATION : The model is implemented using R programming language and the code is freely available from https://github.com/alainmbebi/L21-norm-GS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alain J Mbebi
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany,Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam-Golm, Germany
| | - Hao Tong
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany,Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam-Golm, Germany,Center for Plant Systems Biology and Biotechnology, Ruski 139, 4000 Tsentar, Plovdiv, Bulgaria
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany,Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam-Golm, Germany,Center for Plant Systems Biology and Biotechnology, Ruski 139, 4000 Tsentar, Plovdiv, Bulgaria,To whom correspondence should be addressed.
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Wang R, Dutta S, Roy V. A note on marginal correlation based screening. Stat Anal Data Min 2021. [DOI: 10.1002/sam.11491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Run Wang
- Department of Statistics Iowa State University Iowa USA
| | - Somak Dutta
- Department of Statistics Iowa State University Iowa USA
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Baslam M, Mitsui T, Sueyoshi K, Ohyama T. Recent Advances in Carbon and Nitrogen Metabolism in C3 Plants. Int J Mol Sci 2020; 22:E318. [PMID: 33396811 PMCID: PMC7795015 DOI: 10.3390/ijms22010318] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/23/2020] [Accepted: 12/23/2020] [Indexed: 12/19/2022] Open
Abstract
C and N are the most important essential elements constituting organic compounds in plants. The shoots and roots depend on each other by exchanging C and N through the xylem and phloem transport systems. Complex mechanisms regulate C and N metabolism to optimize plant growth, agricultural crop production, and maintenance of the agroecosystem. In this paper, we cover the recent advances in understanding C and N metabolism, regulation, and transport in plants, as well as their underlying molecular mechanisms. Special emphasis is given to the mechanisms of starch metabolism in plastids and the changes in responses to environmental stress that were previously overlooked, since these changes provide an essential store of C that fuels plant metabolism and growth. We present general insights into the system biology approaches that have expanded our understanding of core biological questions related to C and N metabolism. Finally, this review synthesizes recent advances in our understanding of the trade-off concept that links C and N status to the plant's response to microorganisms.
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Affiliation(s)
- Marouane Baslam
- Laboratory of Biochemistry, Faculty of Agriculture, Niigata University, Niigata 950-2181, Japan; (M.B.); (T.M.)
| | - Toshiaki Mitsui
- Laboratory of Biochemistry, Faculty of Agriculture, Niigata University, Niigata 950-2181, Japan; (M.B.); (T.M.)
- Department of Life and Food Sciences, Graduate School of Science and Technology, Niigata University, Niigata 950-2181, Japan;
| | - Kuni Sueyoshi
- Department of Life and Food Sciences, Graduate School of Science and Technology, Niigata University, Niigata 950-2181, Japan;
| | - Takuji Ohyama
- Department of Life and Food Sciences, Graduate School of Science and Technology, Niigata University, Niigata 950-2181, Japan;
- Faculty of Applied Biosciences, Tokyo University of Agriculture, Tokyo 156-8502, Japan
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Bohra A, Chand Jha U, Godwin ID, Kumar Varshney R. Genomic interventions for sustainable agriculture. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:2388-2405. [PMID: 32875704 PMCID: PMC7680532 DOI: 10.1111/pbi.13472] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 07/21/2020] [Accepted: 08/16/2020] [Indexed: 05/05/2023]
Abstract
Agricultural production faces a Herculean challenge to feed the increasing global population. Food production systems need to deliver more with finite land and water resources while exerting the least negative influence on the ecosystem. The unpredictability of climate change and consequent changes in pests/pathogens dynamics aggravate the enormity of the challenge. Crop improvement has made significant contributions towards food security, and breeding climate-smart cultivars are considered the most sustainable way to accelerate food production. However, a fundamental change is needed in the conventional breeding framework in order to respond adequately to the growing food demands. Progress in genomics has provided new concepts and tools that hold promise to make plant breeding procedures more precise and efficient. For instance, reference genome assemblies in combination with germplasm sequencing delineate breeding targets that could contribute to securing future food supply. In this review, we highlight key breakthroughs in plant genome sequencing and explain how the presence of these genome resources in combination with gene editing techniques has revolutionized the procedures of trait discovery and manipulation. Adoption of new approaches such as speed breeding, genomic selection and haplotype-based breeding could overcome several limitations of conventional breeding. We advocate that strengthening varietal release and seed distribution systems will play a more determining role in delivering genetic gains at farmer's field. A holistic approach outlined here would be crucial to deliver steady stream of climate-smart crop cultivars for sustainable agriculture.
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Affiliation(s)
- Abhishek Bohra
- ICAR‐Indian Institute of Pulses Research (IIPR)KanpurIndia
| | - Uday Chand Jha
- ICAR‐Indian Institute of Pulses Research (IIPR)KanpurIndia
| | - Ian D. Godwin
- Centre for Crop ScienceQueensland Alliance for Agriculture and Food Innovation (QAAFI)The University of QueenslandBrisbaneQldAustralia
| | - Rajeev Kumar Varshney
- International Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
- The UWA Institute of AgricultureThe University of Western AustraliaPerthAustralia
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Fernandes SB, Lipka AE. simplePHENOTYPES: SIMulation of pleiotropic, linked and epistatic phenotypes. BMC Bioinformatics 2020; 21:491. [PMID: 33129253 PMCID: PMC7603745 DOI: 10.1186/s12859-020-03804-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 10/08/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Advances in genotyping and phenotyping techniques have enabled the acquisition of a great amount of data. Consequently, there is an interest in multivariate statistical analyses that identify genomic regions likely to contain causal mutations affecting multiple traits (i.e., pleiotropy). As the demand for multivariate analyses increases, it is imperative that optimal tools are available to assess their performance. To facilitate the testing and validation of these multivariate approaches, we developed simplePHENOTYPES, an R/CRAN package that simulates pleiotropy, partial pleiotropy, and spurious pleiotropy in a wide range of genetic architectures, including additive, dominance and epistatic models. RESULTS We illustrate simplePHENOTYPES' ability to simulate thousands of phenotypes in less than one minute. We then provide two vignettes illustrating how to simulate sets of correlated traits in simplePHENOTYPES. Finally, we demonstrate the use of results from simplePHENOTYPES in a standard GWAS software, as well as the equivalence of simulated phenotypes from simplePHENOTYPES and other packages with similar capabilities. CONCLUSIONS simplePHENOTYPES is a R/CRAN package that makes it possible to simulate multiple traits controlled by loci with varying degrees of pleiotropy. Its ability to interface with both commonly-used marker data formats and downstream quantitative genetics software and packages should facilitate a rigorous assessment of both existing and emerging statistical GWAS and GS approaches. simplePHENOTYPES is also available at https://github.com/samuelbfernandes/simplePHENOTYPES .
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Affiliation(s)
- Samuel B Fernandes
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, 61801, USA
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, 61801, USA.
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Maize Introgression Library Provides Evidence for the Involvement of liguleless1 in Resistance to Northern Leaf Blight. G3-GENES GENOMES GENETICS 2020; 10:3611-3622. [PMID: 32816917 PMCID: PMC7534436 DOI: 10.1534/g3.120.401500] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Plant disease resistance is largely governed by complex genetic architecture. In maize, few disease resistance loci have been characterized. Near-isogenic lines are a powerful genetic tool to dissect quantitative trait loci. We analyzed an introgression library of maize (Zea mays) near-isogenic lines, termed a nested near-isogenic line library for resistance to northern leaf blight caused by the fungal pathogen Setosphaeria turcica The population was comprised of 412 BC5F4 near-isogenic lines that originated from 18 diverse donor parents and a common recurrent parent, B73. Single nucleotide polymorphisms identified through genotyping by sequencing were used to define introgressions and for association analysis. Near-isogenic lines that conferred resistance and susceptibility to northern leaf blight were comprised of introgressions that overlapped known northern leaf blight quantitative trait loci. Genome-wide association analysis and stepwise regression further resolved five quantitative trait loci regions, and implicated several candidate genes, including Liguleless1, a key determinant of leaf architecture in cereals. Two independently-derived mutant alleles of liguleless1 inoculated with S. turcica showed enhanced susceptibility to northern leaf blight. In the maize nested association mapping population, leaf angle was positively correlated with resistance to northern leaf blight in five recombinant inbred line populations, and negatively correlated with northern leaf blight in four recombinant inbred line populations. This study demonstrates the power of an introgression library combined with high density marker coverage to resolve quantitative trait loci. Furthermore, the role of liguleless1 in leaf architecture and in resistance to northern leaf blight has important applications in crop improvement.
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Li H, Wang M, Li W, He L, Zhou Y, Zhu J, Che R, Warburton ML, Yang X, Yan J. Genetic variants and underlying mechanisms influencing variance heterogeneity in maize. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 103:1089-1102. [PMID: 32344461 DOI: 10.1111/tpj.14786] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 04/04/2020] [Accepted: 04/20/2020] [Indexed: 06/11/2023]
Abstract
Traditional genetic studies focus on identifying genetic variants associated with the mean difference in a quantitative trait. Because genetic variants also influence phenotypic variation via heterogeneity, we conducted a variance-heterogeneity genome-wide association study to examine the contribution of variance heterogeneity to oil-related quantitative traits. We identified 79 unique variance-controlling single nucleotide polymorphisms (vSNPs) from the sequences of 77 candidate variance-heterogeneity genes for 21 oil-related traits using the Levene test (P < 1.0 × 10-5 ). About 30% of the candidate genes encode enzymes that work in lipid metabolic pathways, most of which define clear expression variance quantitative trait loci. Of the vSNPs specifically associated with the genetic variance heterogeneity of oil concentration, 89% can be explained by additional linked mean-effects genetic variants. Furthermore, we demonstrated that gene × gene interactions play important roles in the formation of variance heterogeneity for fatty acid compositional traits. The interaction pattern was validated for one gene pair (GRMZM2G035341 and GRMZM2G152328) using yeast two-hybrid and bimolecular fluorescent complementation analyses. Our findings have implications for uncovering the genetic basis of hidden additive genetic effects and epistatic interaction effects, and we indicate opportunities to stabilize efficient breeding and selection of high-oil maize (Zea mays L.).
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Affiliation(s)
- Hui Li
- School of Biological Science and Technology, University of Jinan, Jinan, 250022, China
| | - Min Wang
- Key Laboratory of Crop Genomics and Genetic Improvement, National Maize Improvement Center of China, China Agricultural University, Beijing, 100083, China
| | - Weijun Li
- School of Biological Science and Technology, University of Jinan, Jinan, 250022, China
| | - Linlin He
- School of Biological Science and Technology, University of Jinan, Jinan, 250022, China
| | - Yuanyuan Zhou
- School of Biological Science and Technology, University of Jinan, Jinan, 250022, China
| | - Jiantang Zhu
- School of Biological Science and Technology, University of Jinan, Jinan, 250022, China
| | - Ronghui Che
- School of Biological Science and Technology, University of Jinan, Jinan, 250022, China
| | - Marilyn L Warburton
- USDA ARS Corn Host Plant Resistance Research Unit, Mississippi State, MS, 39759, USA
| | - Xiaohong Yang
- Key Laboratory of Crop Genomics and Genetic Improvement, National Maize Improvement Center of China, China Agricultural University, Beijing, 100083, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
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