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Feng X, Guan H, Wen Y, Zhou H, Xing X, Li Y, Zheng D, Wang Q, Zhang W, Xiong H, Hu Y, Jia L, Luo S, Zhang X, Guo W, Wu F, Xu J, Liu Y, Lu Y. Profiling the selected hotspots for ear traits in two maize-teosinte populations. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:74. [PMID: 38451289 DOI: 10.1007/s00122-024-04554-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 01/12/2024] [Indexed: 03/08/2024]
Abstract
KEY MESSAGE Eight selected hotspots related to ear traits were identified from two maize-teosinte populations. Throughout the history of maize cultivation, ear-related traits have been selected. However, little is known about the specific genes involved in shaping these traits from their origins in the wild progenitor, teosinte, to the characteristics observed in modern maize. In this study, five ear traits (kernel row number [KRN], ear length [EL], kernel number per row [KNR], cob diameter [CD], and ear diameter [ED]) were investigated, and eight quantitative trait loci (QTL) hotspots were identified in two maize-teosinte populations. Notably, our findings revealed a significant enrichment of genes showing a selection signature and expressed in the ear in qbdCD1.1, qbdCD5.1, qbpCD2.1, qbdED1.1, qbpEL1.1, qbpEL5.1, qbdKNR1.1, and qbdKNR10.1, suggesting that these eight QTL are selected hotspots involved in shaping the maize ear. By combining the results of the QTL analysis with data from previous genome-wide association study (GWAS) involving two natural panels, we identified eight candidate selected genes related to KRN, KNR, CD, and ED. Among these, considering their expression pattern and sequence variation, Zm00001d025111, encoding a WD40/YVTN protein, was proposed as a positive regulator of KNR. This study presents a framework for understanding the genomic distribution of selected loci crucial in determining ear-related traits.
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Affiliation(s)
- Xuanjun Feng
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Wenjiang, 611130, Sichuan, China
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Huarui Guan
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Ying Wen
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Hanmei Zhou
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Xiaobin Xing
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Yinzhi Li
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Dan Zheng
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Qingjun Wang
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Weixiao Zhang
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Hao Xiong
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Yue Hu
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Li Jia
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Shuang Luo
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Xuemei Zhang
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Wei Guo
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Fengkai Wu
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Jie Xu
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Yaxi Liu
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China
| | - Yanli Lu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Wenjiang, 611130, Sichuan, China.
- Maize Research Institute, Sichuan Agricultural University, Wenjiang, 611130, Sichuan, China.
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Liu M, Zhang Y, Shaw RK, Zhang X, Li J, Li L, Li S, Adnan M, Jiang F, Bi Y, Yin X, Fan X. Genome-Wide Association Study and Prediction of Tassel Weight of Tropical Maize Germplasm in Multi-Parent Population. Int J Mol Sci 2024; 25:1756. [PMID: 38339032 PMCID: PMC10855296 DOI: 10.3390/ijms25031756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 01/20/2024] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
Tassel weight (TW) is a crucial agronomic trait that significantly affects pollen supply and grain yield development in maize breeding. To improve maize yield and develop new varieties, a comprehensive understanding of the genetic mechanisms underlying tassel weight is essential. In this study, tropical maize inbred lines, namely CML312, CML373, CML444, and YML46, were selected as female parents and crossed with the elite maize inbred line Ye107, which served as the common male parent, to develop a multi-parent population comprising four F8 recombinant inbred line (RIL) subpopulations. Using 6616 high-quality single nucleotide polymorphism (SNP) markers, we conducted genome-wide association analysis (GWAS) and genomic selection (GS) on 642 F8 RILs in four subpopulations across three different environments. Through GWAS, we identified 16 SNPs that were significantly associated with TW, encompassing two stable loci expressed across multiple environments. Furthermore, within the candidate regions of these SNPs, we discovered four novel candidate genes related to TW, namely Zm00001d044362, Zm00001d011048, Zm00001d011049, and Zm00001d031173 distributed on chromosomes 1, 3, and 8, which have not been previously reported. These genes are involved in processes such as signal transduction, growth and development, protein splicing, and pollen development, all of which play crucial roles in inflorescence meristem development, directly affecting TW. The co-localized SNP, S8_137379725, on chromosome 8 was situated within a 16.569 kb long terminal repeat retrotransposon (LTR-RT), located 22.819 kb upstream and 26.428 kb downstream of the candidate genes (Zm00001d011048 and Zm00001d011049). When comparing three distinct GS models, the BayesB model demonstrated the highest accuracy in predicting TW. This study establishes the theoretical foundation for future research into the genetic mechanisms underlying maize TW and the efficient breeding of high-yielding varieties with desired tassel weight through GS.
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Affiliation(s)
- Meichen Liu
- School of Agriculture, Yunnan University, Kunming 650500, China; (M.L.); (X.Z.); (J.L.); (L.L.); (S.L.)
| | - Yudong Zhang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Ranjan K. Shaw
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Xingjie Zhang
- School of Agriculture, Yunnan University, Kunming 650500, China; (M.L.); (X.Z.); (J.L.); (L.L.); (S.L.)
| | - Jinfeng Li
- School of Agriculture, Yunnan University, Kunming 650500, China; (M.L.); (X.Z.); (J.L.); (L.L.); (S.L.)
| | - Linzhuo Li
- School of Agriculture, Yunnan University, Kunming 650500, China; (M.L.); (X.Z.); (J.L.); (L.L.); (S.L.)
| | - Shaoxiong Li
- School of Agriculture, Yunnan University, Kunming 650500, China; (M.L.); (X.Z.); (J.L.); (L.L.); (S.L.)
| | - Muhammad Adnan
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Fuyan Jiang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Yaqi Bi
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Xingfu Yin
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Xingming Fan
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
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Dong Y, Li G, Zhang X, Feng Z, Li T, Li Z, Xu S, Xu S, Liu W, Xue J. Genome-Wide Association Study for Maize Hybrid Performance in a Typical Breeder Population. Int J Mol Sci 2024; 25:1190. [PMID: 38256265 PMCID: PMC10816832 DOI: 10.3390/ijms25021190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/14/2024] [Accepted: 01/16/2024] [Indexed: 01/24/2024] Open
Abstract
Maize is one of the major crops that has demonstrated success in the utilization of heterosis. Developing high-yield hybrids is a crucial part of plant breeding to secure global food demand. In this study, we conducted a genome-wide association study (GWAS) for 10 agronomic traits using a typical breeder population comprised 442 single-cross hybrids by evaluating additive, dominance, and epistatic effects. A total of 49 significant single nucleotide polymorphisms (SNPs) and 69 significant pairs of epistasis were identified, explaining 26.2% to 64.3% of the phenotypic variation across the 10 traits. The enrichment of favorable genotypes is significantly correlated to the corresponding phenotype. In the confident region of the associated site, 532 protein-coding genes were discovered. Among these genes, the Zm00001d044211 candidate gene was found to negatively regulate starch synthesis and potentially impact yield. This typical breeding population provided a valuable resource for dissecting the genetic architecture of yield-related traits. We proposed a novel mating strategy to increase the GWAS efficiency without utilizing more resources. Finally, we analyzed the enrichment of favorable alleles in the Shaan A and Shaan B groups, as well as in each inbred line. Our breeding practice led to consistent results. Not only does this study demonstrate the feasibility of GWAS in F1 hybrid populations, it also provides a valuable basis for further molecular biology and breeding research.
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Affiliation(s)
- Yuan Dong
- Key Laboratory of Biology and Genetic Breeding of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling 712100, China
| | - Guoliang Li
- National Maize Improvement Center of China, Key Laboratory of Crop Heterosis and Utilization (MOE), China Agricultural University, Beijing 100193, China
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466 Seeland, Germany
| | - Xinghua Zhang
- Key Laboratory of Biology and Genetic Breeding of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling 712100, China
| | - Zhiqian Feng
- Key Laboratory of Biology and Genetic Breeding of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling 712100, China
| | - Ting Li
- Key Laboratory of Biology and Genetic Breeding of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling 712100, China
| | - Zhoushuai Li
- Key Laboratory of Biology and Genetic Breeding of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling 712100, China
| | - Shizhong Xu
- Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
| | - Shutu Xu
- Key Laboratory of Biology and Genetic Breeding of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling 712100, China
| | - Wenxin Liu
- National Maize Improvement Center of China, Key Laboratory of Crop Heterosis and Utilization (MOE), China Agricultural University, Beijing 100193, China
| | - Jiquan Xue
- Key Laboratory of Biology and Genetic Breeding of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling 712100, China
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Qian F, Jing J, Zhang Z, Chen S, Sang Z, Li W. GWAS and Meta-QTL Analysis of Yield-Related Ear Traits in Maize. PLANTS (BASEL, SWITZERLAND) 2023; 12:3806. [PMID: 38005703 PMCID: PMC10674677 DOI: 10.3390/plants12223806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/31/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023]
Abstract
Maize ear traits are an important component of yield, and the genetic basis of ear traits facilitates further yield improvement. In this study, a panel of 580 maize inbred lines were used as the study material, eight ear-related traits were measured through three years of planting, and whole genome sequencing was performed using the maize 40 K breeding chip based on genotyping by targeted sequencing (GBTS) technology. Five models were used to conduct a genome-wide association study (GWAS) on best linear unbiased estimate (BLUE) of ear traits to find the best model. The FarmCPU (Fixed and random model Circulating Probability Unification) model was the best model for this study; a total of 104 significant single nucleotide polymorphisms (SNPs) were detected, and 10 co-location SNPs were detected simultaneously in more than two environments. Through gene function annotation and prediction, a total of nine genes were identified as potentially associated with ear traits. Moreover, a total of 760 quantitative trait loci (QTL) associated with yield-related traits reported in 37 different articles were collected. Using the collected 760 QTL for meta-QTL analysis, a total of 41 MQTL (meta-QTL) associated with yield-related traits were identified, and 19 MQTL detected yield-related ear trait functional genes and candidate genes that have been reported in maize. Five significant SNPs detected by GWAS were located within these MQTL intervals, and another three significant SNPs were close to MQTL (less than 1 Mb). The results provide a theoretical reference for the analysis of the genetic basis of ear-related traits and the improvement of maize yield.
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Affiliation(s)
- Fu Qian
- Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, China; (F.Q.); (Z.Z.); (S.C.)
- The Key Laboratory of Oasis Eco-Agriculture, College of Agriculture, Shihezi University, Shihezi 832003, China;
| | - Jianguo Jing
- The Key Laboratory of Oasis Eco-Agriculture, College of Agriculture, Shihezi University, Shihezi 832003, China;
| | - Zhanqin Zhang
- Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, China; (F.Q.); (Z.Z.); (S.C.)
| | - Shubin Chen
- Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, China; (F.Q.); (Z.Z.); (S.C.)
| | - Zhiqin Sang
- Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, China; (F.Q.); (Z.Z.); (S.C.)
| | - Weihua Li
- The Key Laboratory of Oasis Eco-Agriculture, College of Agriculture, Shihezi University, Shihezi 832003, China;
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Dong Z, Wang Y, Bao J, Li Y, Yin Z, Long Y, Wan X. The Genetic Structures and Molecular Mechanisms Underlying Ear Traits in Maize ( Zea mays L.). Cells 2023; 12:1900. [PMID: 37508564 PMCID: PMC10378120 DOI: 10.3390/cells12141900] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Maize (Zea mays L.) is one of the world's staple food crops. In order to feed the growing world population, improving maize yield is a top priority for breeding programs. Ear traits are important determinants of maize yield, and are mostly quantitatively inherited. To date, many studies relating to the genetic and molecular dissection of ear traits have been performed; therefore, we explored the genetic loci of the ear traits that were previously discovered in the genome-wide association study (GWAS) and quantitative trait locus (QTL) mapping studies, and refined 153 QTL and 85 quantitative trait nucleotide (QTN) clusters. Next, we shortlisted 19 common intervals (CIs) that can be detected simultaneously by both QTL mapping and GWAS, and 40 CIs that have pleiotropic effects on ear traits. Further, we predicted the best possible candidate genes from 71 QTL and 25 QTN clusters that could be valuable for maize yield improvement.
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Affiliation(s)
- Zhenying Dong
- Research Institute of Biology and Agriculture, Shunde Innovation School, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China; (Z.D.)
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
| | - Yanbo Wang
- Research Institute of Biology and Agriculture, Shunde Innovation School, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China; (Z.D.)
| | - Jianxi Bao
- Research Institute of Biology and Agriculture, Shunde Innovation School, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China; (Z.D.)
| | - Ya’nan Li
- Research Institute of Biology and Agriculture, Shunde Innovation School, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China; (Z.D.)
| | - Zechao Yin
- Research Institute of Biology and Agriculture, Shunde Innovation School, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China; (Z.D.)
| | - Yan Long
- Research Institute of Biology and Agriculture, Shunde Innovation School, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China; (Z.D.)
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
| | - Xiangyuan Wan
- Research Institute of Biology and Agriculture, Shunde Innovation School, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China; (Z.D.)
- Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
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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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8
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Wang Y, Bi Y, Jiang F, Shaw RK, Sun J, Hu C, Guo R, Fan X. Mapping and Functional Analysis of QTL for Kernel Number per Row in Tropical and Temperate-Tropical Introgression Lines of Maize ( Zea mays L.). Curr Issues Mol Biol 2023; 45:4416-4430. [PMID: 37232750 DOI: 10.3390/cimb45050281] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 05/10/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023] Open
Abstract
Kernel number per row (KNR) is an essential component of maize (Zea mays L.) grain yield (GY), and understanding its genetic mechanism is crucial to improve GY. In this study, two F7 recombinant inbred line (RIL) populations were created using a temperate-tropical introgression line TML418 and a tropical inbred line CML312 as female parents and a backbone maize inbred line Ye107 as the common male parent. Bi-parental quantitative trait locus (QTL) mapping and genome-wide association analysis (GWAS) were then performed on 399 lines of the two maize RIL populations for KNR in two different environments using 4118 validated single nucleotide polymorphism (SNP) markers. This study aimed to: (1) detect molecular markers and/or the genomic regions associated with KNR; (2) identify the candidate genes controlling KNR; and (3) analyze whether the candidate genes are useful in improving GY. The authors reported a total of 7 QTLs tightly linked to KNR through bi-parental QTL mapping and identified 21 SNPs significantly associated with KNR through GWAS. Among these, a highly confident locus qKNR7-1 was detected at two locations, Dehong and Baoshan, with both mapping approaches. At this locus, three novel candidate genes (Zm00001d022202, Zm00001d022168, Zm00001d022169) were identified to be associated with KNR. These candidate genes were primarily involved in the processes related to compound metabolism, biosynthesis, protein modification, degradation, and denaturation, all of which were related to the inflorescence development affecting KNR. These three candidate genes were not reported previously and are considered new candidate genes for KNR. The progeny of the hybrid Ye107 × TML418 exhibited strong heterosis for KNR, which the authors believe might be related to qKNR7-1. This study provides a theoretical foundation for future research on the genetic mechanism underlying KNR in maize and the use of heterotic patterns to develop high-yielding hybrids.
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Affiliation(s)
- Yuling Wang
- Institute of Resource Plants, Yunnan University, Kunming 650504, China
| | - Yaqi Bi
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Fuyan Jiang
- 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
| | - Jiachen Sun
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650500, China
| | - Can Hu
- Institute of Resource Plants, Yunnan University, Kunming 650504, China
| | - Ruijia Guo
- 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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Li K, Tassinari A, Giuliani S, Rosignoli S, Urbany C, Tuberosa R, Salvi S. QTL mapping identifies novel major loci for kernel row number-associated ear fasciation, ear prolificacy and tillering in maize ( Zea mays L.). FRONTIERS IN PLANT SCIENCE 2023; 13:1017983. [PMID: 36704171 PMCID: PMC9871824 DOI: 10.3389/fpls.2022.1017983] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 12/14/2022] [Indexed: 05/31/2023]
Abstract
Maize ear fasciation originates from excessive or abnormal proliferation of the ear meristem and usually manifests as flattened multiple-tipped ear and/or disordered kernel arrangement. Ear prolificacy expresses as multiple ears per plant or per node. Both ear fasciation and prolificacy can affect grain yield. The genetic control of the two traits was studied using two recombinant inbred line populations (B73 × Lo1016 and Lo964 × Lo1016) with Lo1016 and Lo964 as donors of ear fasciation and prolificacy, respectively. Ear fasciation-related traits, number of kernel rows (KRN), ear prolificacy and number of tillers were phenotyped in multi-year field experiments. Ear fasciation traits and KRN showed relatively high heritability (h 2 > 0.5) except ratio of ear diameters. For all ear fasciation-related traits, fasciation level positively correlated with KRN (0.30 ≤ r ≤ 0.68). Prolificacy and tillering were not correlated and their h 2 ranged from 0.41 to 0.78. QTL mapping identified four QTLs for ear fasciation, on chromosomes 1 (two QTLs), 5 and 7, the latter two overlapping with QTLs for number of kernel rows. Notably, at these QTLs, the Lo1016 alleles increased both ear fasciation and KRN across populations, thus showing potential breeding applicability. Four and five non-overlapping QTLs were mapped for ear prolificacy and tillering, respectively. Two ear fasciation QTLs, qFas1.2 and qFas7, overlapped with fasciation QTLs mapped in other studies and spanned compact plant2 and ramosa1 candidate genes. Our study identified novel ear fasciation loci and alleles positively affecting grain yield components, and ear prolificacy and tillering loci which are unexpectedly still segregating in elite maize materials, contributing useful information for genomics-assisted breeding programs.
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Affiliation(s)
- Kai Li
- Department of Agricultural and Food Sciences (DISTAL), University of Bologna, Bologna, Italy
| | - Alberto Tassinari
- Department of Agricultural and Food Sciences (DISTAL), University of Bologna, Bologna, Italy
| | - Silvia Giuliani
- Department of Agricultural and Food Sciences (DISTAL), University of Bologna, Bologna, Italy
| | - Serena Rosignoli
- Department of Agricultural and Food Sciences (DISTAL), University of Bologna, Bologna, Italy
| | | | - Roberto Tuberosa
- Department of Agricultural and Food Sciences (DISTAL), University of Bologna, Bologna, Italy
| | - Silvio Salvi
- Department of Agricultural and Food Sciences (DISTAL), University of Bologna, Bologna, Italy
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10
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Zeng T, Meng Z, Yue R, Lu S, Li W, Li W, Meng H, Sun Q. Genome wide association analysis for yield related traits in maize. BMC PLANT BIOLOGY 2022; 22:449. [PMID: 36127632 PMCID: PMC9490995 DOI: 10.1186/s12870-022-03812-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Understanding the genetic basis of yield related traits contributes to the improvement of grain yield in maize. RESULTS Using 291 excellent maize inbred lines as materials, six yield related traits of maize, including grain yield per plant (GYP), grain length (GL), grain width (GW), kernel number per row (KNR), 100 kernel weight (HKW) and tassel branch number (TBN) were investigated in Jinan, in 2017, 2018 and 2019. The average values of three environments were taken as the phenotypic data of yield related traits, and they were statistically analyzed. Based on 38,683 high-quality SNP markers in the whole genome of the association panel, the MLM with PCA model was used for genome-wide association analysis (GWAS) to obtain 59 significantly associated SNP sites. Moreover, 59 significantly associated SNPs (P < 0.0001) referring to GYP, GL, GW, KNR, HKW and TBN, of which 14 SNPs located in yield related QTLs/QTNs previously reported. A total of 66 candidate genes were identified based on the 59 significantly associated SNPs, of which 58 had functional annotation. CONCLUSIONS Using genome-wide association analysis strategy to identify genetic loci related to maize yield, a total of 59 significantly associated SNP were detected. Those results aid in our understanding of the genetic architecture of maize yield and provide useful SNPs for genetic improvement of maize.
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Affiliation(s)
- Tingru Zeng
- Maize Institute, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Zhaodong Meng
- Maize Institute, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Runqing Yue
- Maize Institute, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Shouping Lu
- Maize Institute, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Wenlan Li
- Maize Institute, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Wencai Li
- Maize Institute, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Hong Meng
- Maize Institute, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Qi Sun
- Maize Institute, Shandong Academy of Agricultural Sciences, Jinan, China
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11
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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] [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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12
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Genetic Architecture of Grain Yield-Related Traits in Sorghum and Maize. Int J Mol Sci 2022; 23:ijms23052405. [PMID: 35269548 PMCID: PMC8909957 DOI: 10.3390/ijms23052405] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/06/2022] [Accepted: 02/18/2022] [Indexed: 02/08/2023] Open
Abstract
Grain size, grain number per panicle, and grain weight are crucial determinants of yield-related traits in cereals. Understanding the genetic basis of grain yield-related traits has been the main research object and nodal in crop science. Sorghum and maize, as very close C4 crops with high photosynthetic rates, stress tolerance and large biomass characteristics, are extensively used to produce food, feed, and biofuels worldwide. In this review, we comprehensively summarize a large number of quantitative trait loci (QTLs) associated with grain yield in sorghum and maize. We placed great emphasis on discussing 22 fine-mapped QTLs and 30 functionally characterized genes, which greatly hinders our deep understanding at the molecular mechanism level. This review provides a general overview of the comprehensive findings on grain yield QTLs and discusses the emerging trend in molecular marker-assisted breeding with these QTLs.
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13
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Liu L, Lindsay PL, Jackson D. Next Generation Cereal Crop Yield Enhancement: From Knowledge of Inflorescence Development to Practical Engineering by Genome Editing. Int J Mol Sci 2021; 22:5167. [PMID: 34068350 PMCID: PMC8153303 DOI: 10.3390/ijms22105167] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 05/06/2021] [Accepted: 05/10/2021] [Indexed: 11/16/2022] Open
Abstract
Artificial domestication and improvement of the majority of crops began approximately 10,000 years ago, in different parts of the world, to achieve high productivity, good quality, and widespread adaptability. It was initiated from a phenotype-based selection by local farmers and developed to current biotechnology-based breeding to feed over 7 billion people. For most cereal crops, yield relates to grain production, which could be enhanced by increasing grain number and weight. Grain number is typically determined during inflorescence development. Many mutants and genes for inflorescence development have already been characterized in cereal crops. Therefore, optimization of such genes could fine-tune yield-related traits, such as grain number. With the rapidly advancing genome-editing technologies and understanding of yield-related traits, knowledge-driven breeding by design is becoming a reality. This review introduces knowledge about inflorescence yield-related traits in cereal crops, focusing on rice, maize, and wheat. Next, emerging genome-editing technologies and recent studies that apply this technology to engineer crop yield improvement by targeting inflorescence development are reviewed. These approaches promise to usher in a new era of breeding practice.
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Affiliation(s)
| | | | - David Jackson
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; (L.L.); (P.L.L.)
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14
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Han X, Qin Y, Sandrine AMN, Qiu F. Fine mapping of qKRN8, a QTL for maize kernel row number, and prediction of the candidate gene. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:3139-3150. [PMID: 32857170 DOI: 10.1007/s00122-020-03660-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 08/03/2020] [Indexed: 06/11/2023]
Abstract
KEY MESSAGE: qKRN8, a major QTL for kernel row number in maize, was fine mapped to an interval of ~ 520 kb on chromosome 8 and the key candidate gene was identified via expression analysis. Kernel row number (KRN) is one of the most important yield-influencing traits and is closely associated with female inflorescence development in maize (Zea mays L.). In this study, an F2:3 population derived from a cross between V54 (low KRN line) and Lian87 (high KRN line) was used to map quantitative trait loci (QTLs) conferring KRN in maize. We identified 12 QTLs for KRN in four environments, each explaining 1.40-14.95% of phenotypic variance. Among these, one novel major QTL (named qKRN8) was mapped to bin 8.03 in all four environments, explaining 8.79-14.95% of phenotypic variation. By combining map-based cloning with progeny testing of recombinants, we ultimately mapped qKRN8 to an ~ 520 kb genomic interval, harboring six putative candidate genes. Among them, one candidate gene showed contrasted expression level in immature ears of the near-isogenic lines qKRN8Lian87 and qKRN8V54. These findings should facilitate molecular breeding for KRN and the further identification of the polymorphism underlying this QTL.
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Affiliation(s)
- Xuesong Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, No. 1, Shizishan Street, Hongshan District, Wuhan, 430070, People's Republic of China
| | - Yao Qin
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, No. 1, Shizishan Street, Hongshan District, Wuhan, 430070, People's Republic of China
| | - Ada Menie Nelly Sandrine
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, No. 1, Shizishan Street, Hongshan District, Wuhan, 430070, People's Republic of China
| | - Fazhan Qiu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, No. 1, Shizishan Street, Hongshan District, Wuhan, 430070, People's Republic of China.
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15
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An Y, Chen L, Li YX, Li C, Shi Y, Zhang D, Li Y, Wang T. Genome-wide association studies and whole-genome prediction reveal the genetic architecture of KRN in maize. BMC PLANT BIOLOGY 2020; 20:490. [PMID: 33109077 PMCID: PMC7590725 DOI: 10.1186/s12870-020-02676-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 09/24/2020] [Indexed: 05/21/2023]
Abstract
BACKGROUND Kernel row number (KRN) is an important trait for the domestication and improvement of maize. Exploring the genetic basis of KRN has great research significance and can provide valuable information for molecular assisted selection. RESULTS In this study, one single-locus method (MLM) and six multilocus methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB and ISIS EM-BLASSO) of genome-wide association studies (GWASs) were used to identify significant quantitative trait nucleotides (QTNs) for KRN in an association panel including 639 maize inbred lines that were genotyped by the MaizeSNP50 BeadChip. In three phenotyping environments and with best linear unbiased prediction (BLUP) values, the seven GWAS methods revealed different numbers of KRN-associated QTNs, ranging from 11 to 177. Based on these results, seven important regions for KRN located on chromosomes 1, 2, 3, 5, 9, and 10 were identified by at least three methods and in at least two environments. Moreover, 49 genes from the seven regions were expressed in different maize tissues. Among the 49 genes, ARF29 (Zm00001d026540, encoding auxin response factor 29) and CKO4 (Zm00001d043293, encoding cytokinin oxidase protein) were significantly related to KRN, based on expression analysis and candidate gene association mapping. Whole-genome prediction (WGP) of KRN was also performed, and we found that the KRN-associated tagSNPs achieved a high prediction accuracy. The best strategy was to integrate all of the KRN-associated tagSNPs identified by all GWAS models. CONCLUSIONS These results aid in our understanding of the genetic architecture of KRN and provide useful information for genomic selection for KRN in maize breeding.
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Affiliation(s)
- Yixin An
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Lin Chen
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Yong-Xiang Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Chunhui Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Yunsu Shi
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Dengfeng Zhang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Yu Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Tianyu Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
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16
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Zhang X, Guan Z, Li Z, Liu P, Ma L, Zhang Y, Pan L, He S, Zhang Y, Li P, Ge F, Zou C, He Y, Gao S, Pan G, Shen Y. A combination of linkage mapping and GWAS brings new elements on the genetic basis of yield-related traits in maize across multiple environments. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:2881-2895. [PMID: 32594266 DOI: 10.1007/s00122-020-03639-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 06/18/2020] [Indexed: 05/05/2023]
Abstract
Using GWAS and QTL mapping identified 100 QTL and 138 SNPs, which control yield-related traits in maize. The candidate gene GRMZM2G098557 was further validated to regulate ear row number by using a segregation population. Understanding the genetic basis of yield-related traits contributes to the improvement of grain yield in maize. This study used an inter-mated B73 × Mo17 (IBM) Syn10 doubled-haploid (DH) population and an association panel to identify the genetic loci responsible for nine yield-related traits in maize. Using quantitative trait loci (QTL) mapping, 100 QTL influencing these traits were detected across different environments in the IBM Syn10 DH population, with 25 co-detected in multiple environments. Using a genome-wide association study (GWAS), 138 single-nucleotide polymorphisms (SNPs) were identified as correlated with these traits (P < 2.04E-06) in the association panel. Twenty-one pleiotropic QTL/SNPs were identified to control different traits in both populations. A combination of QTL mapping and GWAS uncovered eight significant SNPs (PZE-101097575, PZE-103169263, ZM011204-0763, PZE-104044017, PZE-104123110, SYN8062, PZE-108060911, and PZE-102043341) that were co-located within seven QTL confidence intervals. According to the eight co-localized SNPs by the two populations, 52 candidate genes were identified, among which the ear row number (ERN)-associated SNP SYN8062 was closely linked to SBP-transcription factor 7 (GRMZM2G098557). Several SBP-transcription factors were previously demonstrated to modulate maize ERN. We then validated the phenotypic effects of SYN8062 in the IBM Syn10 DH population, indicating that the ERN of the lines with the A-allele in SYN8062 was significantly (P < 0.05) larger than that of the lines with the G-allele in SYN8062 in each environment. These findings provide valuable information for understanding the genetic mechanisms of maize grain yield formation and for improving molecular marker-assisted selection for the high-yield breeding of maize.
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Affiliation(s)
- Xiaoxiang Zhang
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Zhongrong Guan
- Chongqing Yudongnan Academy of Agricultural Sciences, Chongqing, 408000, China
| | - Zhaoling Li
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Peng Liu
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Langlang Ma
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yinchao Zhang
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Lang Pan
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Shijiang He
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yanling Zhang
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Peng Li
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Fei Ge
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Chaoying Zou
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yongcong He
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Shibin Gao
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, 611130, China
| | - Guangtang Pan
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yaou Shen
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, 611130, China.
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17
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Li Z, Liu P, Zhang X, Zhang Y, Ma L, Liu M, Guan Z, Zhang Y, Li P, Zou C, He Y, Gao S, Pan G, Shen Y. Genome-wide association studies and QTL mapping uncover the genetic architecture of ear tip-barrenness in maize. PHYSIOLOGIA PLANTARUM 2020; 170:27-39. [PMID: 32175598 DOI: 10.1111/ppl.13087] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 03/08/2020] [Accepted: 03/13/2020] [Indexed: 06/10/2023]
Abstract
Ear tip-barrenness (ETB) phenotype threatens crop yield, because it reduces the kernel number per ear. The genetic basis of ETB in maize remains largely unknown. Herein, a genome-wide association study (GWAS) and quantitative trait loci (QTL) mapping were jointly applied to identify the significant genetic loci interrelated with ETB. Six significant SNPs were detected at a stringent P-value threshold (1.95 × 10-6 ). Additionally, four environment-stable SNPs were co-detected across a single environment and best linear unbiased prediction (BLUP) model at a less stringent P-value threshold (1 × 10-4 ). The above 10 SNPs were closely linked to 6 candidate genes, which mainly involved seed development, photosynthesis and ethylene response. Moreover, the ratio of superior allele at each significant SNP ranged from 0 to 83.33% in 30 investigated maize elite lines. QTL mapping identified 14 QTL with phenotypic variation explained (PVE) ranging from 3.64 to 7.09%, of which one QTL (qETB2-1) was repeatedly identified in two environments. Combined analysis of GWAS and QTL mapping showed that one SNP (PZE-102175229, chromosome 2: 217 66 Mb) was located in the QTL (qETB2-2, chromosome 2: 215 90-217 82 Mb). Eighteen gene models situated in the linkage disequilibrium (LD) region of the co-localized SNP were further used to evaluate their correlation with ETB by candidate gene association analysis. Two superior haplotypes and two superior alleles were detected among 74 lines for Zm00001d007195, Zm00001d007197 and Zm00001d007201. These results provide more information for clarifying the molecular mechanism of ETB and for speeding up the genetic improvement of maize varieties.
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Affiliation(s)
- Zhaoling Li
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Peng Liu
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Xiaoxiang Zhang
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yinchao Zhang
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Langlang Ma
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Min Liu
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Zhongrong Guan
- Chongqing Yudongnan Academy of Agricultural Sciences, Mustard Tuber Research Center, Chongqing, 408000, China
| | - Yanling Zhang
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Peng Li
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Chaoying Zou
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yongcong He
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Shibin Gao
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Crop Germplasm Innovation and Variety Design Team, Chengdu, 611130, China
| | - Guangtang Pan
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yaou Shen
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Crop Germplasm Innovation and Variety Design Team, Chengdu, 611130, China
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18
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Brauner PC, Müller D, Molenaar WS, Melchinger AE. Genomic prediction with multiple biparental families. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:133-147. [PMID: 31595337 DOI: 10.1007/s00122-019-03445-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 09/18/2019] [Indexed: 06/10/2023]
Abstract
For genomic prediction within biparental families using multiple biparental families, combined training sets comprising full-sibs from the same family and half-sib families are recommended to reach high and robust prediction accuracy, whereas inclusion of unrelated families is risky and can have negative effects. In recycling breeding, where elite inbreds are recombined to generate new source material, genomic and phenotypic information from lines of numerous biparental families (BPFs) is commonly available for genomic prediction (GP). For each BPF with a large number of candidates in the prediction set (PS), the training set (TS) can be composed of lines from the same full-sib family or multiple related and unrelated families to increase the TS size. GP was applied to BPFs generated in silico and from two published experiments to evaluate the prediction accuracy ([Formula: see text]) of different TS compositions. We compared [Formula: see text] for individual pairs of BPFs using as TS either full-sib, half-sib, or unrelated BPFs. While full-sibs yielded highly positive [Formula: see text] and half-sibs also mostly positive [Formula: see text] values, unrelated families had often negative [Formula: see text], and including these families in a combined TS reduced [Formula: see text]. By simulations, we demonstrated that optimized TS compositions exist, yielding 5-10% higher [Formula: see text] than the TS including all available BPFs. However, identification of poorly predictive families and finding the optimal TS composition with various quantitative-genetic parameters estimated from available data was not successful. Therefore, we suggest omitting unrelated families and combining in the TS full-sib and few half-sib families produced by specific mating designs, with a medium number (~ 50) of genotypes per family. This helps in balancing high [Formula: see text] in GP with a sufficient effective population size of the entire breeding program for securing high short- and long-term selection progress.
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Affiliation(s)
- Pedro C Brauner
- Institute of Plant Breeding, Seed Sciences and Population Genetics, University of Hohenheim, Fruwirthstraße 21, 70599, Stuttgart, Germany
| | - Dominik Müller
- Institute of Plant Breeding, Seed Sciences and Population Genetics, University of Hohenheim, Fruwirthstraße 21, 70599, Stuttgart, Germany
| | - Willem S Molenaar
- Institute of Plant Breeding, Seed Sciences and Population Genetics, University of Hohenheim, Fruwirthstraße 21, 70599, Stuttgart, Germany
| | - Albrecht E Melchinger
- Institute of Plant Breeding, Seed Sciences and Population Genetics, University of Hohenheim, Fruwirthstraße 21, 70599, Stuttgart, Germany.
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19
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Combined GWAS and QTL analysis for dissecting the genetic architecture of kernel test weight in maize. Mol Genet Genomics 2019; 295:409-420. [PMID: 31807910 DOI: 10.1007/s00438-019-01631-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 11/25/2019] [Indexed: 12/16/2022]
Abstract
Kernel weight in a unit volume is referred to as kernel test weight (KTW) that directly reflects maize (Zea mays L.) grain quality. In this study, an inter-mated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population and an association panel were used to identify loci responsible for KTW of maize across multiple environments. A total of 18 significant KTW-related single-nucleotide polymorphisms (SNPs) were identified using genome-wide association study (GWAS); they were closely linked to 12 candidate genes. In the IBM Syn10 DH population, linkage analysis detected 19 common quantitative trait loci (QTL), five of which were repeatedly detected among multiple environments. Several verified genes that regulate maize seed development were found in the confidence intervals of the mapped QTL and the LD regions of GWAS, such as ZmYUC1, BAP2, ZmTCRR-1, dek36 and ZmSWEET4c. Combined QTL mapping and GWAS identified one significant SNP that was co-identified in the both populations. Based on the co-localized SNP across the both populations, 17 candidate genes were identified. Of them, Zm00001d044075, Zm00001d044086, and Zm00001d044081 were further identified by candidate gene association study for KTW. Zm00001d044081 encodes homeobox-leucine zipper protein ATHB-4, which has been demonstrated to control apical embryo development in Arabidopsis. Our findings provided insights into the mechanism underlying maize KTW and contributed to the application of molecular-assisted selection of high KTW breeding in maize.
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20
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Nie N, Ding X, Chen L, Wu X, An Y, Li C, Song Y, Zhang D, Liu Z, Wang T, Li Y, Li YX, Shi Y. Characterization and fine mapping of qkrnw4, a major QTL controlling kernel row number in maize. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:3321-3331. [PMID: 31555888 DOI: 10.1007/s00122-019-03427-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2019] [Accepted: 09/12/2019] [Indexed: 06/10/2023]
Abstract
A major QTL controlling kernel row number, qkrnw4, was identified by combining linkage analysis and association mapping. Within qkrnw4, on the basis of its expression and bioinformatics analysis, Zm00001d052910 was supposed to be the candidate gene for kernel row number. Kernel row number (KRN) is an important yield-related trait that affects kernel number in maize. Understanding the genetic basis of KRN is important for increasing maize yields. In the present study, by the use of a near-isogenic line (NIL) that has a B73 background and that consistently displays a low KRN across environments, qkrnw4, a major quantitative trait locus (QTL) associated with KRN within a yield trait-related QTL hotspot in bin 4.08, was finely mapped to an ~ 33-kb interval. Regional association analysis of a nested association mapping population comprising 5000 recombinant inbred lines revealed Zm00001d052910, which encodes a protein with an unknown function, as the important candidate gene responsible for qkrnw4. Different expression levels of this candidate gene in immature ears were detected between the NIL and its recurrent parent. Moreover, the expression of several auxin-related genes was consistent with that of the candidate gene. Furthermore, the potential associations of this candidate gene with well-known inflorescence-related genes were discussed. The results of this study provide important information for the genetic elucidation of KRN variation in maize.
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Affiliation(s)
- Ningning Nie
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xiaoyu Ding
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- College of Agronomy and Biotechnology, Southwest University, Chongqing, 400715, China
| | - Lin Chen
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xun Wu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yixin An
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Chunhui Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yanchun Song
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Dengfeng Zhang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Zhizhai Liu
- College of Agronomy and Biotechnology, Southwest University, Chongqing, 400715, China
| | - Tianyu Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yu Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yong-Xiang Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Yunsu Shi
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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21
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Shen X, Zhao R, Liu L, Zhu C, Li M, Du H, Zhang Z. Identification of a candidate gene underlying qKRN5b for kernel row number in Zea mays L. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:3439-3448. [PMID: 31612262 DOI: 10.1007/s00122-019-03436-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 09/17/2019] [Indexed: 06/10/2023]
Abstract
A quantitative trait locus for kernel row number, qKRN5, was dissected into two tightly linked loci, qKRN5a and qKRN5b. Fine mapping, comparative analysis of nucleotide sequences and gene expression established the endonuclease/exonuclease/phosphatase family protein-encoding gene Zm00001d013603 as a causal gene of qKRN5b. Maize grain yield is determined by agronomically important traits that are controlled by interactions among and between genes and environmental factors. Considerable efforts have been made to identify major quantitative trait loci (QTLs) for yield-related traits; however, few were previously isolated and characterized in maize. In this study, we divided a QTL for kernel row number (KRN), qKRN5, into two tightly linked loci, qKRN5a and qKRN5b, using advanced backcross populations derived from near-isogenic lines. KRN was greater in individuals that were homozygous for the NX531 allele, which showed coupling-phase linkage. The major QTL qKRN5b had an additive effect of approximately one kernel row. Furthermore, fine mapping narrowed qKRN5b within a 147.2-kb region. The upstream sequence Zm00001d013603 and its expression in the ear inflorescence showed obvious differences between qKRN5b near-isogenic lines. In situ hybridization located Zm00001d013603 on the primordia of the spikelet pair meristems and spikelet meristems, but not in the inflorescence meristem, which indicates a role in regulating the initiation of reproductive axillary meristems of ear inflorescences. Expression analysis and nucleotide sequence alignment revealed that Zm00001d013603, which encodes an endonuclease/exonuclease/phosphatase family protein that hydrolyzes phosphatidyl inositol diphosphates, is the causal gene of qKRN5b. These results provide insight into the genetic basis of KRN and have potential value for enhancing maize grain yield.
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Affiliation(s)
- Xiaomeng Shen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430030, China
| | - Ran Zhao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430030, China
| | - Lei Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430030, China
| | - Can Zhu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430030, China
| | - Manfei Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430030, China
| | - Hewei Du
- Hubei Collaborative Innovation Center for Grain Crops, Yangtze University, Jingzhou, 434025, China
| | - Zuxin Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430030, China.
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22
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Empirical Comparisons of Different Statistical Models To Identify and Validate Kernel Row Number-Associated Variants from Structured Multi-parent Mapping Populations of Maize. G3-GENES GENOMES GENETICS 2018; 8:3567-3575. [PMID: 30213868 PMCID: PMC6222574 DOI: 10.1534/g3.118.200636] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Advances in next generation sequencing technologies and statistical approaches enable genome-wide dissection of phenotypic traits via genome-wide association studies (GWAS). Although multiple statistical approaches for conducting GWAS are available, the power and cross-validation rates of many approaches have been mostly tested using simulated data. Empirical comparisons of single variant (SV) and multi-variant (MV) GWAS approaches have not been conducted to test if a single approach or a combination of SV and MV is effective, through identification and cross-validation of trait-associated loci. In this study, kernel row number (KRN) data were collected from a set of 6,230 entries derived from the Nested Association Mapping (NAM) population and related populations. Three different types of GWAS analyses were performed: 1) single-variant (SV), 2) stepwise regression (STR) and 3) a Bayesian-based multi-variant (BMV) model. Using SV, STR, and BMV models, 257, 300, and 442 KRN-associated variants (KAVs) were identified in the initial GWAS analyses. Of these, 231 KAVs were subjected to genetic validation using three unrelated populations that were not included in the initial GWAS. Genetic validation results suggest that the three GWAS approaches are complementary. Interestingly, KAVs in low recombination regions were more likely to exhibit associations in independent populations than KAVs in recombinationally active regions, probably as a consequence of linkage disequilibrium. The KAVs identified in this study have the potential to enhance our understanding of the genetic basis of ear development.
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23
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Fletcher JC. The CLV-WUS Stem Cell Signaling Pathway: A Roadmap to Crop Yield Optimization. PLANTS 2018; 7:plants7040087. [PMID: 30347700 PMCID: PMC6313860 DOI: 10.3390/plants7040087] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 10/09/2018] [Accepted: 10/10/2018] [Indexed: 12/22/2022]
Abstract
The shoot apical meristem at the growing shoot tip acts a stem cell reservoir that provides cells to generate the entire above-ground architecture of higher plants. Many agronomic plant yield traits such as tiller number, flower number, fruit number, and kernel row number are therefore defined by the activity of the shoot apical meristem and its derivatives, the floral meristems. Studies in the model plant Arabidopsis thaliana demonstrated that a molecular negative feedback loop called the CLAVATA (CLV)-WUSCHEL (WUS) pathway regulates stem cell maintenance in shoot and floral meristems. CLV-WUS pathway components are associated with quantitative trait loci (QTL) for yield traits in crop plants such as oilseed, tomato, rice, and maize, and may have played a role in crop domestication. The conservation of these pathway components across the plant kingdom provides an opportunity to use cutting edge techniques such as genome editing to enhance yield traits in a wide variety of agricultural plant species.
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Affiliation(s)
- Jennifer C Fletcher
- Plant Gene Expression Center, United States Department of Agriculture-Agricultural Research Service, Albany, CA 94710, USA.
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA.
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24
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Je BI, Xu F, Wu Q, Liu L, Meeley R, Gallagher JP, Corcilius L, Payne RJ, Bartlett ME, Jackson D. The CLAVATA receptor FASCIATED EAR2 responds to distinct CLE peptides by signaling through two downstream effectors. eLife 2018; 7:35673. [PMID: 29543153 DOI: 10.7554/elife.35673.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 02/21/2018] [Indexed: 05/29/2023] Open
Abstract
Meristems contain groups of indeterminate stem cells, which are maintained by a feedback loop between CLAVATA (CLV) and WUSCHEL (WUS) signaling. CLV signaling involves the secretion of the CLV3 peptide and its perception by a number of Leucine-Rich-Repeat (LRR) receptors, including the receptor-like kinase CLV1 and the receptor-like protein CLV2 coupled with the CORYNE (CRN) pseudokinase. CLV2, and its maize ortholog FASCIATED EAR2 (FEA2) appear to function in signaling by CLV3 and several related CLV3/EMBRYO-SURROUNDING REGION (CLE) peptide ligands. Nevertheless, how signaling specificity is achieved remains unknown. Here we show that FEA2 transmits signaling from two distinct CLE peptides, the maize CLV3 ortholog ZmCLE7 and ZmFON2-LIKE CLE PROTEIN1 (ZmFCP1) through two different candidate downstream effectors, the alpha subunit of the maize heterotrimeric G protein COMPACT PLANT2 (CT2), and ZmCRN. Our data provide a novel framework to understand how diverse signaling peptides can activate different downstream pathways through common receptor proteins.
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Affiliation(s)
- Byoung Il Je
- Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
- Department of Horticultural Bioscience, College of Natural Resource and Life Science, Pusan National University, Miryang, Republic of Korea
| | - Fang Xu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
| | - Qingyu Wu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
| | - Lei Liu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
| | - Robert Meeley
- DuPont Pioneer, Agricultural Biotechnology, Johnston, United States
| | | | | | | | | | - David Jackson
- Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
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25
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Je BI, Xu F, Wu Q, Liu L, Meeley R, Gallagher JP, Corcilius L, Payne RJ, Bartlett ME, Jackson D. The CLAVATA receptor FASCIATED EAR2 responds to distinct CLE peptides by signaling through two downstream effectors. eLife 2018. [PMID: 29543153 PMCID: PMC5854466 DOI: 10.7554/elife.35673] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Meristems contain groups of indeterminate stem cells, which are maintained by a feedback loop between CLAVATA (CLV) and WUSCHEL (WUS) signaling. CLV signaling involves the secretion of the CLV3 peptide and its perception by a number of Leucine-Rich-Repeat (LRR) receptors, including the receptor-like kinase CLV1 and the receptor-like protein CLV2 coupled with the CORYNE (CRN) pseudokinase. CLV2, and its maize ortholog FASCIATED EAR2 (FEA2) appear to function in signaling by CLV3 and several related CLV3/EMBRYO-SURROUNDING REGION (CLE) peptide ligands. Nevertheless, how signaling specificity is achieved remains unknown. Here we show that FEA2 transmits signaling from two distinct CLE peptides, the maize CLV3 ortholog ZmCLE7 and ZmFON2-LIKE CLE PROTEIN1 (ZmFCP1) through two different candidate downstream effectors, the alpha subunit of the maize heterotrimeric G protein COMPACT PLANT2 (CT2), and ZmCRN. Our data provide a novel framework to understand how diverse signaling peptides can activate different downstream pathways through common receptor proteins.
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Affiliation(s)
- Byoung Il Je
- Cold Spring Harbor Laboratory, Cold Spring Harbor, United States.,Department of Horticultural Bioscience, College of Natural Resource and Life Science, Pusan National University, Miryang, Republic of Korea
| | - Fang Xu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
| | - Qingyu Wu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
| | - Lei Liu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
| | - Robert Meeley
- DuPont Pioneer, Agricultural Biotechnology, Johnston, United States
| | | | | | | | | | - David Jackson
- Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
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26
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Li M, Zhong W, Yang F, Zhang Z. Genetic and Molecular Mechanisms of Quantitative Trait Loci Controlling Maize Inflorescence Architecture. PLANT & CELL PHYSIOLOGY 2018; 59:448-457. [PMID: 29420811 DOI: 10.1093/pcp/pcy022] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Accepted: 01/22/2018] [Indexed: 05/04/2023]
Abstract
The establishment of inflorescence architecture is critical for the reproduction of flowering plant species. The maize plant generates two types of inflorescences, the tassel and the ear, and their architectures have a large effect on grain yield and yield-related traits that are genetically controlled by quantitative trait loci (QTLs). Since ear and tassel architecture are deeply affected by the activity of inflorescence meristems, key QTLs and genes regulating meristematic activity have important impacts on inflorescence development and show great potential for optimizing grain yield. Isolation of yield trait-related QTLs is challenging, but these QTLs have direct application in maize breeding. Additionally, characterization and functional dissection of QTLs can provide genetic and molecular knowledge of quantitative variation in inflorescence architecture. In this review, we summarize currently identified QTLs responsible for the establishment of ear and tassel architecture and discuss the potential genetic control of four ear-related and four tassel-related traits. In recent years, several inflorescence architecture-related QTLs have been characterized at the gene level. We review the mechanisms of these characterized QTLs.
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Affiliation(s)
- Manfei Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Wanshun Zhong
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Fang Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Zuxin Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, PR China
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27
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Xiao Y, Liu H, Wu L, Warburton M, Yan J. Genome-wide Association Studies in Maize: Praise and Stargaze. MOLECULAR PLANT 2017; 10:359-374. [PMID: 28039028 DOI: 10.1016/j.molp.2016.12.008] [Citation(s) in RCA: 208] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2016] [Revised: 12/02/2016] [Accepted: 12/20/2016] [Indexed: 05/18/2023]
Abstract
Genome-wide association study (GWAS) has become a widely accepted strategy for decoding genotype-phenotype associations in many species thanks to advances in next-generation sequencing (NGS) technologies. Maize is an ideal crop for GWAS and significant progress has been made in the last decade. This review summarizes current GWAS efforts in maize functional genomics research and discusses future prospects in the omics era. The general goal of GWAS is to link genotypic variations to corresponding differences in phenotype using the most appropriate statistical model in a given population. The current review also presents perspectives for optimizing GWAS design and analysis. GWAS analysis of data from RNA, protein, and metabolite-based omics studies is discussed, along with new models and new population designs that will identify causes of phenotypic variation that have been hidden to date. The joint and continuous efforts of the whole community will enhance our understanding of maize quantitative traits and boost crop molecular breeding designs.
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Affiliation(s)
- Yingjie Xiao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Haijun Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Liuji Wu
- Synergetic Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China
| | - Marilyn Warburton
- United States of Department of Agriculture, Agricultural Research Service, Corn Host Plant Resistance Research Unit, Box 9555, MS 39762, Mississippi, USA
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
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28
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Jittham O, Fu X, Xu J, Chander S, Li J, Yang X. Genetic dissection of carotenoids in maize kernels using high-density single nucleotide polymorphism markers in a recombinant inbred line population. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.cj.2016.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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29
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Guan H, Ali F, Pan Q. Dissection of Recombination Attributes for Multiple Maize Populations Using a Common SNP Assay. FRONTIERS IN PLANT SCIENCE 2017; 8:2063. [PMID: 29250099 PMCID: PMC5714861 DOI: 10.3389/fpls.2017.02063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2017] [Accepted: 11/17/2017] [Indexed: 05/16/2023]
Abstract
Recombination is a vital characteristic for quantitative trait loci mapping and breeding to enhance the yield potential of maize. However, recombination characteristics in globally used segregating populations have never been evaluated at similar genetic marker densities. This study aimed to divulge the characteristics of recombination events, recombinant chromosomal segments, and recombination frequency for four dissimilar populations. These populations were doubled haploid (DH), recombination inbred line (RIL), intermated B73xMo17 (IBM), and multi-parent advanced generation inter-cross (MAGIC), using the Illumina MaizeSNP50 BeadChip to provide markers. Our results revealed that the average number of recombination events was 16, 41, 72, and 86 per line in DH, RIL, IBM, and MAGIC populations, respectively. Accordingly, the average length of recombinant chromosomal segments was 84.8, 47.3, 29.2, and 20.4 Mb in DH, RIL, IBM, and MAGIC populations, respectively. Furtherly, the recombination frequency varied in different genomic regions and population types [DH (0-12.7 cM/Mb), RIL (0-15.5 cM/Mb), IBM (0-24.1 cM/Mb), MAGIC (0-42.3 cM/Mb)]. Utilizing different sub-sets of lines, the recombination bin number and size were analyzed in each population. Additionally, different sub-sets of markers and lines were employed to estimate the recombination bin number and size via formulas for relationship in these populations. The relationship between recombination events and recombination bin length was also examined. Our results contribute to determining the most suitable number of genetic markers, lines in each population, and population type for successful mapping and breeding.
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Affiliation(s)
- Haiying Guan
- Maize Research Institute, Shandong Academy of Agricultural Sciences, Jinan, China
- National Engineering Laboratory of Wheat and Maize, Jinan, China
- Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-Huai River Plain, Ministry of Agriculture, Jinan, China
| | - Farhan Ali
- Cereal Crops Research Institute, Nowshera, Pakistan
| | - Qingchun Pan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- *Correspondence: Qingchun Pan,
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30
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Li K, Wang H, Hu X, Liu Z, Wu Y, Huang C. Genome-Wide Association Study Reveals the Genetic Basis of Stalk Cell Wall Components in Maize. PLoS One 2016; 11:e0158906. [PMID: 27479588 PMCID: PMC4968789 DOI: 10.1371/journal.pone.0158906] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 06/23/2016] [Indexed: 12/03/2022] Open
Abstract
Lignin, cellulose and hemicellulose are the three main components of the plant cell wall and can impact stalk quality by affecting cell wall structure and strength. In this study, we evaluated the lignin (LIG), cellulose (CEL) and hemicellulose (HC) contents in maize using an association mapping panel that included 368 inbred lines in seven environments. A genome-wide association study using approximately 0.56 million SNPs with a minor allele frequency of 0.05 identified 22, 18 and 24 loci significantly associated with LIG, CEL and HC at P < 1.0×10−4, respectively. The allelic variation of each significant association contributed 4 to 7% of the phenotypic variation. Candidate genes identified by GWAS mainly encode enzymes involved in cell wall metabolism, transcription factors, protein kinase and protein related to other biological processes. Among the association signals, six candidate genes had pleiotropic effects on lignin and cellulose content. These results provide valuable information for better understanding the genetic basis of stalk cell wall components in maize.
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Affiliation(s)
- Kun Li
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Hongwu Wang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xiaojiao Hu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Zhifang Liu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yujin Wu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Changling Huang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- * E-mail:
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31
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Huo D, Ning Q, Shen X, Liu L, Zhang Z. QTL Mapping of Kernel Number-Related Traits and Validation of One Major QTL for Ear Length in Maize. PLoS One 2016; 11:e0155506. [PMID: 27176215 PMCID: PMC4866764 DOI: 10.1371/journal.pone.0155506] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2015] [Accepted: 04/30/2016] [Indexed: 12/02/2022] Open
Abstract
The kernel number is a grain yield component and an important maize breeding goal. Ear length, kernel number per row and ear row number are highly correlated with the kernel number per ear, which eventually determines the ear weight and grain yield. In this study, two sets of F2:3 families developed from two bi-parental crosses sharing one inbred line were used to identify quantitative trait loci (QTL) for four kernel number-related traits: ear length, kernel number per row, ear row number and ear weight. A total of 39 QTLs for the four traits were identified in the two populations. The phenotypic variance explained by a single QTL ranged from 0.4% to 29.5%. Additionally, 14 overlapping QTLs formed 5 QTL clusters on chromosomes 1, 4, 5, 7, and 10. Intriguingly, six QTLs for ear length and kernel number per row overlapped in a region on chromosome 1. This region was designated qEL1.10 and was validated as being simultaneously responsible for ear length, kernel number per row and ear weight in a near isogenic line-derived population, suggesting that qEL1.10 was a pleiotropic QTL with large effects. Furthermore, the performance of hybrids generated by crossing 6 elite inbred lines with two near isogenic lines at qEL1.10 showed the breeding value of qEL1.10 for the improvement of the kernel number and grain yield of maize hybrids. This study provides a basis for further fine mapping, molecular marker-aided breeding and functional studies of kernel number-related traits in maize.
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Affiliation(s)
- Dongao Huo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, P. R. China
| | - Qiang Ning
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, P. R. China
| | - Xiaomeng Shen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, P. R. China
| | - Lei Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, P. R. China
| | - Zuxin Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, P. R. China
- Hubei Collaborative Innovation Center for Grain Crops, Jingzhou 434025, P. R. China
- * E-mail:
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Liu H, Wang X, Wei B, Wang Y, Liu Y, Zhang J, Hu Y, Yu G, Li J, Xu Z, Huang Y. Characterization of Genome-Wide Variation in Four-Row Wax, a Waxy Maize Landrace with a Reduced Kernel Row Phenotype. FRONTIERS IN PLANT SCIENCE 2016; 7:667. [PMID: 27242868 PMCID: PMC4870249 DOI: 10.3389/fpls.2016.00667] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Accepted: 05/01/2016] [Indexed: 05/10/2023]
Abstract
In southwest China, some maize landraces have long been isolated geographically, and have phenotypes that differ from those of widely grown cultivars. These landraces may harbor rich genetic variation responsible for those phenotypes. Four-row Wax is one such landrace, with four rows of kernels on the cob. We resequenced the genome of Four-row Wax, obtaining 50.46 Gb sequence at 21.87× coverage, then identified and characterized 3,252,194 SNPs, 213,181 short InDels (1-5 bp) and 39,631 structural variations (greater than 5 bp). Of those, 312,511 (9.6%) SNPs were novel compared to the most detailed haplotype map (HapMap) SNP database of maize. Characterization of variations in reported kernel row number (KRN) related genes and KRN QTL regions revealed potential causal mutations in fea2, td1, kn1, and te1. Genome-wide comparisons revealed abundant genetic variations in Four-row Wax, which may be associated with environmental adaptation. The sequence and SNP variations described here enrich genetic resources of maize, and provide guidance into study of seed numbers for crop yield improvement.
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Affiliation(s)
- Hanmei Liu
- College of Life Science, Sichuan Agricultural UniversityYa’an, China
| | - Xuewen Wang
- Germplasm Bank of Wild Species, Kunming Institute of Botany, Chinese Academy of Sciences, KunmingChina
| | - Bin Wei
- Maize Research Institute, Sichuan Agricultural University, ChengduChina
| | - Yongbin Wang
- Maize Research Institute, Sichuan Agricultural University, ChengduChina
| | - Yinghong Liu
- Maize Research Institute, Sichuan Agricultural University, ChengduChina
| | - Junjie Zhang
- College of Life Science, Sichuan Agricultural UniversityYa’an, China
| | - Yufeng Hu
- College of Agronomy, Sichuan Agricultural University, ChengduChina
| | - Guowu Yu
- College of Agronomy, Sichuan Agricultural University, ChengduChina
| | - Jian Li
- Seed Station of Xishuangbanna, JinghongChina
| | - Zhanbin Xu
- Seed Station of Xishuangbanna, JinghongChina
| | - Yubi Huang
- Maize Research Institute, Sichuan Agricultural University, ChengduChina
- College of Agronomy, Sichuan Agricultural University, ChengduChina
- *Correspondence: Yubi Huang,
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Liu L, Du Y, Shen X, Li M, Sun W, Huang J, Liu Z, Tao Y, Zheng Y, Yan J, Zhang Z. KRN4 Controls Quantitative Variation in Maize Kernel Row Number. PLoS Genet 2015; 11:e1005670. [PMID: 26575831 PMCID: PMC4648495 DOI: 10.1371/journal.pgen.1005670] [Citation(s) in RCA: 93] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Accepted: 10/23/2015] [Indexed: 12/27/2022] Open
Abstract
Kernel row number (KRN) is an important component of yield during the domestication and improvement of maize and controlled by quantitative trait loci (QTL). Here, we fine-mapped a major KRN QTL, KRN4, which can enhance grain productivity by increasing KRN per ear. We found that a ~3-Kb intergenic region about 60 Kb downstream from the SBP-box gene Unbranched3 (UB3) was responsible for quantitative variation in KRN by regulating the level of UB3 expression. Within the 3-Kb region, the 1.2-Kb Presence-Absence variant was found to be strongly associated with quantitative variation in KRN in diverse maize inbred lines, and our results suggest that this 1.2-Kb transposon-containing insertion is likely responsible for increased KRN. A previously identified A/G SNP (S35, also known as Ser220Asn) in UB3 was also found to be significantly associated with KRN in our association-mapping panel. Although no visible genetic effect of S35 alone could be detected in our linkage mapping population, it was found to genetically interact with the 1.2-Kb PAV to modulate KRN. The KRN4 was under strong selection during maize domestication and the favorable allele for the 1.2-Kb PAV and S35 has been significantly enriched in modern maize improvement process. The favorable haplotype (Hap1) of 1.2-Kb-PAV-S35 was selected during temperate maize improvement, but is still rare in tropical and subtropical maize germplasm. The dissection of the KRN4 locus improves our understanding of the genetic basis of quantitative variation in complex traits in maize. Maize (Zea mays L.) is one of the world's most important sources of calories for humans. With an expanding global population, the demands for maize-derived food, feed, and fuel are rapidly increasing. To meet these needs, geneticists and breeders are facing the challenge of enhancing grain yield through genetic improvement of maize germplasm. Understanding the genetic basis of grain yield is necessary to guide breeding efforts towards the development of high-yielding hybrids. Kernel row number (KRN) in maize is one of the most important yield components and a significant breeding target. Over the last few decades, many genes that determine inflorescence development and architecture have been identified and characterized. The formation of kernel rows is an integral part of the development of the female inflorescence in maize. Nevertheless, the genetic basis and molecular regulation of quantitative variation in KRN is poorly understood. This study provides experimental evidence for the hypothesis that variation in intergenic regions can regulate quantitative variation of important grain yield-related traits, and also provides tools for improving KRN in maize.
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Affiliation(s)
- Lei Liu
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan Hubei, People's Republic of China
| | - Yanfang Du
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan Hubei, People's Republic of China
| | - Xiaomeng Shen
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan Hubei, People's Republic of China
| | - Manfei Li
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan Hubei, People's Republic of China
| | - Wei Sun
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan Hubei, People's Republic of China
| | - Juan Huang
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan Hubei, People's Republic of China
| | - Zhijie Liu
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan Hubei, People's Republic of China
| | - Yongsheng Tao
- College of Agronomy, Hebei Agricultural University, Baoding Hebei, People's Republic of China
| | - Yonglian Zheng
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan Hubei, People's Republic of China
| | - Jianbing Yan
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan Hubei, People's Republic of China
| | - Zuxin Zhang
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan Hubei, People's Republic of China
- Hubei Collaborative Innovation Center for Grain Crops, Jingzhou Hubei, People's Republic of China
- * E-mail:
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