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Navea IP, Maung PP, Yang S, Han JH, Jing W, Shin NH, Zhang W, Chin JH. A meta-QTL analysis highlights genomic hotspots associated with phosphorus use efficiency in rice ( Oryza sativa L.). FRONTIERS IN PLANT SCIENCE 2023; 14:1226297. [PMID: 37662146 PMCID: PMC10471825 DOI: 10.3389/fpls.2023.1226297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 07/31/2023] [Indexed: 09/05/2023]
Abstract
Phosphorus use efficiency (PUE) is a complex trait, governed by many minor quantitative trait loci (QTLs) with small effects. Advances in molecular marker technology have led to the identification of QTLs underlying PUE. However, their practical use in breeding programs remains challenging due to the unstable effects in different genetic backgrounds and environments, interaction with soil status, and linkage drag. Here, we compiled PUE QTL information from 16 independent studies. A total of 192 QTLs were subjected to meta-QTL (MQTL) analysis and were projected into a high-density SNP consensus map. A total of 60 MQTLs, with significantly reduced number of initial QTLs and confidence intervals (CI), were identified across the rice genome. Candidate gene (CG) mining was carried out for the 38 MQTLs supported by multiple QTLs from at least two independent studies. Genes related to amino and organic acid transport and auxin response were found to be abundant in the MQTLs linked to PUE. CGs were cross validated using a root transcriptome database (RiceXPro) and haplotype analysis. This led to the identification of the eight CGs (OsARF8, OsSPX-MFS3, OsRING141, OsMIOX, HsfC2b, OsFER2, OsWRKY64, and OsYUCCA11) modulating PUE. Potential donors for superior PUE CG haplotypes were identified through haplotype analysis. The distribution of superior haplotypes varied among subspecies being mostly found in indica but were largely scarce in japonica. Our study offers an insight on the complex genetic networks that modulate PUE in rice. The MQTLs, CGs, and superior CG haplotypes identified in our study are useful in the combination of beneficial alleles for PUE in rice.
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Affiliation(s)
- Ian Paul Navea
- Food Crops Molecular Breeding Laboratory, Department of Integrative Biological Sciences and Industry, Sejong University, Seoul, Republic of Korea
- Convergence Research Center for Natural Products, Sejong University, Seoul, Republic of Korea
| | - Phyu Phyu Maung
- Food Crops Molecular Breeding Laboratory, Department of Integrative Biological Sciences and Industry, Sejong University, Seoul, Republic of Korea
- Convergence Research Center for Natural Products, Sejong University, Seoul, Republic of Korea
| | - Shiyi Yang
- College of Life Sciences, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, China
| | - Jae-Hyuk Han
- Food Crops Molecular Breeding Laboratory, Department of Integrative Biological Sciences and Industry, Sejong University, Seoul, Republic of Korea
- The International Rice Research Institute-Korea Office, National Institute of Crop Science, Rural Development Administration, Iseo-myeon, Republic of Korea
| | - Wen Jing
- College of Life Sciences, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, China
| | - Na-Hyun Shin
- Food Crops Molecular Breeding Laboratory, Department of Integrative Biological Sciences and Industry, Sejong University, Seoul, Republic of Korea
- Convergence Research Center for Natural Products, Sejong University, Seoul, Republic of Korea
| | - Wenhua Zhang
- College of Life Sciences, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, China
| | - Joong Hyoun Chin
- Food Crops Molecular Breeding Laboratory, Department of Integrative Biological Sciences and Industry, Sejong University, Seoul, Republic of Korea
- Convergence Research Center for Natural Products, Sejong University, Seoul, Republic of Korea
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Khaipho-Burch M, Ferebee T, Giri A, Ramstein G, Monier B, Yi E, Romay MC, Buckler ES. Elucidating the patterns of pleiotropy and its biological relevance in maize. PLoS Genet 2023; 19:e1010664. [PMID: 36943844 PMCID: PMC10030035 DOI: 10.1371/journal.pgen.1010664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/09/2023] [Indexed: 03/23/2023] Open
Abstract
Pleiotropy-when a single gene controls two or more seemingly unrelated traits-has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of biological pleiotropy across all maize phenotypes is largely unknown. Here, we investigate the extent to which biological pleiotropy impacts phenotypes within maize using GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56-32.3% of intervals show some degree of pleiotropy. We then assess the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide and large effect variants, leaving the prevalence of detectable pleiotropy relatively low.
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Affiliation(s)
| | - Taylor Ferebee
- Department of Computational Biology, Cornell University, Ithaca, New York
| | - Anju Giri
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
| | - Guillaume Ramstein
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Brandon Monier
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
| | - Emily Yi
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
| | - M Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
| | - Edward S Buckler
- Section of Plant Breeding and Genetics, Cornell University, Ithaca, New York
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
- USDA-ARS, Ithaca, New York, United States of America
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Aloryi KD, Okpala NE, Amo A, Bello SF, Akaba S, Tian X. A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice. FRONTIERS IN PLANT SCIENCE 2022; 13:1035851. [PMID: 36466247 PMCID: PMC9709451 DOI: 10.3389/fpls.2022.1035851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 10/19/2022] [Indexed: 06/17/2023]
Abstract
Improving grain yield potential in rice is an important step toward addressing global food security challenges. The meta-QTL analysis offers stable and robust QTLs irrespective of the genetic background of mapping populations and phenotype environment and effectively narrows confidence intervals (CI) for candidate gene (CG) mining and marker-assisted selection improvement. To achieve these aims, a comprehensive bibliographic search for grain yield traits (spikelet fertility, number of grains per panicle, panicles number per plant, and 1000-grain weight) QTLs was conducted, and 462 QTLs were retrieved from 47 independent QTL research published between 2002 and 2022. QTL projection was performed using a reference map with a cumulative length of 2,945.67 cM, and MQTL analysis was conducted on 313 QTLs. Consequently, a total of 62 MQTLs were identified with reduced mean CI (up to 3.40 fold) compared to the mean CI of original QTLs. However, 10 of these MQTLs harbored at least six of the initial QTLs from diverse genetic backgrounds and environments and were considered the most stable and robust MQTLs. Also, MQTLs were compared with GWAS studies and resulted in the identification of 16 common significant loci modulating the evaluated traits. Gene annotation, gene ontology (GO) enrichment, and RNA-seq analyses of chromosome regions of the stable MQTLs detected 52 potential CGs including those that have been cloned in previous studies. These genes encode proteins known to be involved in regulating grain yield including cytochrome P450, zinc fingers, MADs-box, AP2/ERF domain, F-box, ubiquitin ligase domain protein, homeobox domain, DEAD-box ATP domain, and U-box domain. This study provides the framework for molecular dissection of grain yield in rice. Moreover, the MQTLs and CGs identified could be useful for fine mapping, gene cloning, and marker-assisted selection to improve rice productivity.
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Affiliation(s)
- Kelvin Dodzi Aloryi
- Hubei Collaborative Innovation Centre for Grain Industry, College of Agriculture, Yangtze University, Jingzhou, China
| | - Nnaemeka Emmanuel Okpala
- Hubei Collaborative Innovation Centre for Grain Industry, College of Agriculture, Yangtze University, Jingzhou, China
| | - Aduragbemi Amo
- Institute of Plant Breeding, Genetics and Genomics University of Georgia, Athens, GA, United States
| | - Semiu Folaniyi Bello
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
| | - Selorm Akaba
- School of Agriculture, University of Cape Coast, Cape Coast, Ghana
| | - Xiaohai Tian
- Hubei Collaborative Innovation Centre for Grain Industry, College of Agriculture, Yangtze University, Jingzhou, China
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Sandhu N, Pruthi G, Prakash Raigar O, Singh MP, Phagna K, Kumar A, Sethi M, Singh J, Ade PA, Saini DK. Meta-QTL Analysis in Rice and Cross-Genome Talk of the Genomic Regions Controlling Nitrogen Use Efficiency in Cereal Crops Revealing Phylogenetic Relationship. Front Genet 2021; 12:807210. [PMID: 34992638 PMCID: PMC8724540 DOI: 10.3389/fgene.2021.807210] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
The phenomenal increase in the use of nitrogenous fertilizers coupled with poor nitrogen use efficiency is among the most important threats to the environment, economic, and social health. During the last 2 decades, a number of genomic regions associated with nitrogen use efficiency (NUE) and related traits have been reported by different research groups, but none of the stable and major effect QTL have been utilized in the marker-assisted introgression/pyramiding program. Compiling the data available in the literature could be very useful in identifying stable and major effect genomic regions associated with the root and NUE-related trait improving the rice grain yield. In the present study, we performed meta-QTL analysis on 1,330 QTL from 29 studies published in the past 2 decades. A total of 76 MQTL with a stable effect over different genetic backgrounds and environments were identified. The significant reduction in the confidence interval of the MQTL compared to the initial QTL resulted in the identification of annotated and putative candidate genes related to the traits considered in the present study. A hot spot region associated with correlated traits on chr 1, 4, and 8 and candidate genes associated with nitrate transporters, nitrogen content, and ammonium uptake on chromosomes 2, 4, 6, and 8 have been identified. The identified MQTL, putative candidate genes, and their orthologues were validated on our previous studies conducted on rice and wheat. The research-based interventions such as improving nitrogen use efficiency via identification of major genomic regions and candidate genes can be a plausible, simple, and low-cost solution to address the challenges of the crop improvement program.
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Affiliation(s)
| | | | | | | | - Kanika Phagna
- Indian Institute of Science Education and Research, Berhampur, India
| | - Aman Kumar
- Punjab Agricultural University, Ludhiana, India
| | - Mehak Sethi
- Punjab Agricultural University, Ludhiana, India
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