1
|
Rajput P, Urfan M, Sharma S, Hakla HR, Nandan B, Das R, Roychowdhury R, Chowdhary SP. Natural variation in root traits identifies significant SNPs and candidate genes for phosphate deficiency tolerance in Zea mays L. PHYSIOLOGIA PLANTARUM 2024; 176:e14396. [PMID: 38887929 DOI: 10.1111/ppl.14396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 05/08/2024] [Accepted: 05/30/2024] [Indexed: 06/20/2024]
Abstract
Phosphorus (P) is a crucial macronutrient required for normal plant growth. Its effective uptake from the soil is a trait of agronomic importance. Natural variation in maize (339 accessions) root traits, namely root length and number of primary, seminal, and crown roots, root and shoot phosphate (Pi) contents, and root-to-shoot Pi translocation (root: shoot Pi) under normal (control, 40 ppm) and low phosphate (LP, 1 ppm) conditions, were used for genome-wide association studies (GWAS). The Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) model of GWAS provided 23 single nucleotide polymorphisms (SNPs) and 12 relevant candidate genes putatively linked with root Pi, root: shoot Pi, and crown root number (CRN) under LP. The DNA-protein interaction analysis of Zm00001d002842, Zm00001d002837, Zm00001d002843 for root Pi, and Zm00001d044312, Zm00001d045550, Zm00001d025915, Zm00001d044313, Zm00001d051842 for root: shoot Pi, and Zm00001d031561, Zm00001d001803, and Zm00001d001804 for CRN showed the presence of potential binding sites of key transcription factors like MYB62, bZIP11, ARF4, ARF7, ARF10 and ARF16 known for induction/suppression of phosphate starvation response (PHR). The in-silico RNA-seq analysis revealed up or down-regulation of candidate genes along with key transcription factors of PHR, while Uniprot analysis provided genetic relatedness. Candidate genes that may play a role in P uptake and root-to-shoot Pi translocation under LP are proposed using common PHR signaling components like MYB62, ARF4, ARF7, ARF10, ARF16, and bZIP11 to induce changes in root growth in maize. Candidate genes may be used to improve low P tolerance in maize using the CRISPR strategy.
Collapse
Affiliation(s)
- Prakriti Rajput
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu, India
| | - Mohammad Urfan
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu, India
| | - Shubham Sharma
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu, India
| | - Haroon Rashid Hakla
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu, India
| | - Brij Nandan
- Agronomy Division, SKUAST-JAMMU, Union Territory of Jammu & Kashmir, India
| | - Ranjan Das
- Department of Crop Physiology, Assam Agricultural University, Jorhat, Assam, India
| | - Rajib Roychowdhury
- Department of Plant Pathology and Weed Research, Institute of Plant Protection, Agricultural Research Organization (ARO) - Volcani Institute, Rishon Lezion, Israel
| | | |
Collapse
|
2
|
Zhang D, Zhao R, Xian G, Kou Y, Ma W. A new model construction based on the knowledge graph for mining elite polyphenotype genes in crops. FRONTIERS IN PLANT SCIENCE 2024; 15:1361716. [PMID: 38571713 PMCID: PMC10987776 DOI: 10.3389/fpls.2024.1361716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 03/04/2024] [Indexed: 04/05/2024]
Abstract
Identifying polyphenotype genes that simultaneously regulate important agronomic traits (e.g., plant height, yield, and disease resistance) is critical for developing novel high-quality crop varieties. Predicting the associations between genes and traits requires the organization and analysis of multi-dimensional scientific data. The existing methods for establishing the relationships between genomic data and phenotypic data can only elucidate the associations between genes and individual traits. However, there are relatively few methods for detecting elite polyphenotype genes. In this study, a knowledge graph for traits regulating-genes was constructed by collecting data from the PubMed database and eight other databases related to the staple food crops rice, maize, and wheat as well as the model plant Arabidopsis thaliana. On the basis of the knowledge graph, a model for predicting traits regulating-genes was constructed by combining the data attributes of the gene nodes and the topological relationship attributes of the gene nodes. Additionally, a scoring method for predicting the genes regulating specific traits was developed to screen for elite polyphenotype genes. A total of 125,591 nodes and 547,224 semantic relationships were included in the knowledge graph. The accuracy of the knowledge graph-based model for predicting traits regulating-genes was 0.89, the precision rate was 0.91, the recall rate was 0.96, and the F1 value was 0.94. Moreover, 4,447 polyphenotype genes for 31 trait combinations were identified, among which the rice polyphenotype gene IPA1 and the A. thaliana polyphenotype gene CUC2 were verified via a literature search. Furthermore, the wheat gene TraesCS5A02G275900 was revealed as a potential polyphenotype gene that will need to be further characterized. Meanwhile, the result of venn diagram analysis between the polyphenotype gene datasets (consists of genes that are predicted by our model) and the transcriptome gene datasets (consists of genes that were differential expression in response to disease, drought or salt) showed approximately 70% and 54% polyphenotype genes were identified in the transcriptome datasets of Arabidopsis and rice, respectively. The application of the model driven by knowledge graph for predicting traits regulating-genes represents a novel method for detecting elite polyphenotype genes.
Collapse
Affiliation(s)
- Dandan Zhang
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ruixue Zhao
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agricultural Integration Publishing Knowledge Mining and Knowledge Service, National Press and Publication Administration, Beijing, China
| | - Guojian Xian
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yuantao Kou
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agricultural Integration Publishing Knowledge Mining and Knowledge Service, National Press and Publication Administration, Beijing, China
| | - Weilu Ma
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
| |
Collapse
|
3
|
Zhang X, Sun J, Zhang Y, Li J, Liu M, Li L, Li S, Wang T, Shaw RK, Jiang F, Fan X. Hotspot Regions of Quantitative Trait Loci and Candidate Genes for Ear-Related Traits in Maize: A Literature Review. Genes (Basel) 2023; 15:15. [PMID: 38275597 PMCID: PMC10815758 DOI: 10.3390/genes15010015] [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: 11/10/2023] [Revised: 12/12/2023] [Accepted: 12/16/2023] [Indexed: 01/27/2024] Open
Abstract
In this study, hotspot regions, QTL clusters, and candidate genes for eight ear-related traits of maize (ear length, ear diameter, kernel row number, kernel number per row, kernel length, kernel width, kernel thickness, and 100-kernel weight) were summarized and analyzed over the past three decades. This review aims to (1) comprehensively summarize and analyze previous studies on QTLs associated with these eight ear-related traits and identify hotspot bin regions located on maize chromosomes and key candidate genes associated with the ear-related traits and (2) compile major and stable QTLs and QTL clusters from various mapping populations and mapping methods and techniques providing valuable insights for fine mapping, gene cloning, and breeding for high-yield and high-quality maize. Previous research has demonstrated that QTLs for ear-related traits are distributed across all ten chromosomes in maize, and the phenotypic variation explained by a single QTL ranged from 0.40% to 36.76%. In total, 23 QTL hotspot bins for ear-related traits were identified across all ten chromosomes. The most prominent hotspot region is bin 4.08 on chromosome 4 with 15 QTLs related to eight ear-related traits. Additionally, this study identified 48 candidate genes associated with ear-related traits. Out of these, five have been cloned and validated, while twenty-eight candidate genes located in the QTL hotspots were defined by this study. This review offers a deeper understanding of the advancements in QTL mapping and the identification of key candidates associated with eight ear-related traits. These insights will undoubtedly assist maize breeders in formulating strategies to develop higher-yield maize varieties, contributing to global food security.
Collapse
Affiliation(s)
- Xingjie Zhang
- School of Agriculture, Yunnan University, Kunming 650500, China; (X.Z.); (J.L.); (M.L.); (L.L.); (S.L.)
| | - Jiachen Sun
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China; (J.S.); (T.W.)
| | - Yudong Zhang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (F.J.)
| | - Jinfeng Li
- School of Agriculture, Yunnan University, Kunming 650500, China; (X.Z.); (J.L.); (M.L.); (L.L.); (S.L.)
| | - Meichen Liu
- School of Agriculture, Yunnan University, Kunming 650500, China; (X.Z.); (J.L.); (M.L.); (L.L.); (S.L.)
| | - Linzhuo Li
- School of Agriculture, Yunnan University, Kunming 650500, China; (X.Z.); (J.L.); (M.L.); (L.L.); (S.L.)
| | - Shaoxiong Li
- School of Agriculture, Yunnan University, Kunming 650500, China; (X.Z.); (J.L.); (M.L.); (L.L.); (S.L.)
| | - Tingzhao Wang
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China; (J.S.); (T.W.)
| | - Ranjan Kumar Shaw
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (F.J.)
| | - Fuyan Jiang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (F.J.)
| | - Xingming Fan
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (F.J.)
| |
Collapse
|
4
|
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.
Collapse
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;
| |
Collapse
|