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Li HF, Wang JT, Zhao Q, Zhang YM. BLUPmrMLM: A Fast mrMLM Algorithm in Genome-wide Association Studies. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae020. [PMID: 39348630 DOI: 10.1093/gpbjnl/qzae020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 12/13/2023] [Accepted: 01/10/2024] [Indexed: 10/02/2024]
Abstract
Multilocus genome-wide association study has become the state-of-the-art tool for dissecting the genetic architecture of complex and multiomic traits. However, most existing multilocus methods require relatively long computational time when analyzing large datasets. To address this issue, in this study, we proposed a fast mrMLM method, namely, best linear unbiased prediction multilocus random-SNP-effect mixed linear model (BLUPmrMLM). First, genome-wide single-marker scanning in mrMLM was replaced by vectorized Wald tests based on the best linear unbiased prediction (BLUP) values of marker effects and their variances in BLUPmrMLM. Then, adaptive best subset selection (ABESS) was used to identify potentially associated markers on each chromosome to reduce computational time when estimating marker effects via empirical Bayes. Finally, shared memory and parallel computing schemes were used to reduce the computational time. In simulation studies, BLUPmrMLM outperformed GEMMA, EMMAX, mrMLM, and FarmCPU as well as the control method (BLUPmrMLM with ABESS removed), in terms of computational time, power, accuracy for estimating quantitative trait nucleotide positions and effects, false positive rate, false discovery rate, false negative rate, and F1 score. In the reanalysis of two large rice datasets, BLUPmrMLM significantly reduced the computational time and identified more previously reported genes, compared with the aforementioned methods. This study provides an excellent multilocus model method for the analysis of large-scale and multiomic datasets. The software mrMLM v5.1 is available at BioCode (https://ngdc.cncb.ac.cn/biocode/tool/BT007388) or GitHub (https://github.com/YuanmingZhang65/mrMLM).
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Affiliation(s)
- Hong-Fu Li
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Jing-Tian Wang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Qiong Zhao
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuan-Ming Zhang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
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Osorio-Guarin JA, Higgins J, Toloza-Moreno DL, Di Palma F, Enriquez Valencia AL, Riveros Munévar F, De Vega JJ, Yockteng R. Genome-wide association analyses using multilocus models on bananas (Musa spp.) reveal candidate genes related to morphology, fruit quality, and yield. G3 (BETHESDA, MD.) 2024; 14:jkae108. [PMID: 38775627 PMCID: PMC11304972 DOI: 10.1093/g3journal/jkae108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 05/17/2024] [Indexed: 08/09/2024]
Abstract
Bananas (Musa spp.) are an essential fruit worldwide and rank as the fourth most significant food crop for addressing malnutrition due to their rich nutrients and starch content. The potential of their genetic diversity remains untapped due to limited molecular breeding tools. Our study examined a phenotypically diverse group of 124 accessions from the Colombian Musaceae Collection conserved in AGROSAVIA. We assessed 12 traits categorized into morphology, fruit quality, and yield, alongside sequence data. Our sequencing efforts provided valuable insights, with an average depth of about 7× per accession, resulting in 187,133 single-nucleotide polymorphisms (SNPs) against Musa acuminata (A genome) and 220,451 against Musa balbisiana (B genome). Population structure analysis grouped samples into four and five clusters based on the reference genome. By using different association models, we identified marker-trait associations (MTAs). The mixed linear model revealed four MTAs, while the Bayesian-information and linkage-disequilibrium iteratively nested keyway and fixed and random model for circulating probability unification models identified 82 and 70 MTAs, respectively. We identified 38 and 40 candidate genes in linkage proximity to significant MTAs for the A genome and B genome, respectively. Our findings provide insights into the genetic underpinnings of morphology, fruit quality, and yield. Once validated, the SNP markers and candidate genes can potentially drive advancements in genomic-guided breeding strategies to enhance banana crop improvement.
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Affiliation(s)
- Jaime Andrés Osorio-Guarin
- Centro de Investigación Tibaitatá, Corporación Colombiana de Investigación Agropecuaria, AGROSAVIA, Km 14 vía Mosquera, Cundinamarca 250047, Colombia
| | - Janet Higgins
- Earlham Institute, Norwich Research Park, NR4 7UZ Norwich, UK
| | - Deisy Lisseth Toloza-Moreno
- Centro de Investigación Tibaitatá, Corporación Colombiana de Investigación Agropecuaria, AGROSAVIA, Km 14 vía Mosquera, Cundinamarca 250047, Colombia
| | | | - Ayda Lilia Enriquez Valencia
- Centro de Investigación Palmira, Corporación Colombiana de Investigación Agropecuaria, AGROSAVIA, Palmira, Valle del Cauca 763533, Colombia
| | - Fernando Riveros Munévar
- Facultad de Psicología y Ciencias del Comportamiento, Universidad de La Sabana, Chía, Cundinamarca 250001, Colombia
| | - José J De Vega
- Earlham Institute, Norwich Research Park, NR4 7UZ Norwich, UK
| | - Roxana Yockteng
- Centro de Investigación Tibaitatá, Corporación Colombiana de Investigación Agropecuaria, AGROSAVIA, Km 14 vía Mosquera, Cundinamarca 250047, Colombia
- Institut de Systématique, Evolution, Biodiversité-UMR-CNRS 7205, Muséum National d´Histoire Naturelle, Paris, Ile 75005, France
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Hu X, Yasir M, Zhuo Y, Cai Y, Ren X, Rong J. Genomic insights into glume pubescence in durum wheat: GWAS and haplotype analysis implicates TdELD1-1A as a candidate gene. Gene 2024; 909:148309. [PMID: 38417687 DOI: 10.1016/j.gene.2024.148309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 02/19/2024] [Accepted: 02/21/2024] [Indexed: 03/01/2024]
Abstract
Glume pubescence is an important morphological trait for the characterization of wheat cultivars. It shows tolerance to biotic and abiotic stresses to some extent. Hg1 (formerly named Hg) locus on chromosome 1AS controls glume pubescence in wheat. Its genetic analysis, fine-mapping and candidate gene analysis have been widely studied recently, however, the cloning of Hg1 has not yet been reported. Here, we conducted a GWAS between a dense panel of 171,103 SNPs and glume pubescence (Gp) in a durum wheat population of 145 lines, and further analyzed the candidate genes of Hg1 combined with the gene expression, functional annotation, and haplotype analysis. As a results, TRITD0Uv1G104670 (TdELD1-1A), encoding glycosyltransferase-like ELD1/KOBITO 1, was detected as the most promising candidate gene of Hg1 for glume pubescence in durum wheat. Our findings not only contribute to a deeper understanding of its cloning and functional validation but also underscore the significance of accurate genome sequences and annotations. Additionally, our study highlights the relevance of unanchored sequences in chrUn and the application of bioinformatics analysis for gene discovery in durum wheat.
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Affiliation(s)
- Xin Hu
- The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Lin'an, Hangzhou 311300, Zhejiang, China
| | - Muhammad Yasir
- The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Lin'an, Hangzhou 311300, Zhejiang, China
| | - Yujie Zhuo
- The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Lin'an, Hangzhou 311300, Zhejiang, China
| | - Yijing Cai
- The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Lin'an, Hangzhou 311300, Zhejiang, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Xifeng Ren
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China.
| | - Junkang Rong
- The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Lin'an, Hangzhou 311300, Zhejiang, China.
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Wang JT, Chang XY, Zhao Q, Zhang YM. FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study. Brief Bioinform 2024; 25:bbae290. [PMID: 38888457 PMCID: PMC11184901 DOI: 10.1093/bib/bbae290] [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: 01/18/2024] [Revised: 05/19/2024] [Accepted: 06/09/2024] [Indexed: 06/20/2024] Open
Abstract
Large sample datasets have been regarded as the primary basis for innovative discoveries and the solution to missing heritability in genome-wide association studies. However, their computational complexity cannot consider all comprehensive effects and all polygenic backgrounds, which reduces the effectiveness of large datasets. To address these challenges, we included all effects and polygenic backgrounds in a mixed logistic model for binary traits and compressed four variance components into two. The compressed model combined three computational algorithms to develop an innovative method, called FastBiCmrMLM, for large data analysis. These algorithms were tailored to sample size, computational speed, and reduced memory requirements. To mine additional genes, linkage disequilibrium markers were replaced by bin-based haplotypes, which are analyzed by FastBiCmrMLM, named FastBiCmrMLM-Hap. Simulation studies highlighted the superiority of FastBiCmrMLM over GMMAT, SAIGE and fastGWA-GLMM in identifying dominant, small α (allele substitution effect), and rare variants. In the UK Biobank-scale dataset, we demonstrated that FastBiCmrMLM could detect variants as small as 0.03% and with α ≈ 0. In re-analyses of seven diseases in the WTCCC datasets, 29 candidate genes, with both functional and TWAS evidence, around 36 variants identified only by the new methods, strongly validated the new methods. These methods offer a new way to decipher the genetic architecture of binary traits and address the challenges outlined above.
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Affiliation(s)
| | | | | | - Yuan-Ming Zhang
- Corresponding author. College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China. Tel.: +086-13505161564; E-mail:
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Su J, Zeng J, Wang S, Zhang X, Zhao L, Wen S, Zhang F, Jiang J, Chen F. Multi-locus genome-wide association studies reveal the dynamic genetic architecture of flowering time in chrysanthemum. PLANT CELL REPORTS 2024; 43:84. [PMID: 38448703 DOI: 10.1007/s00299-024-03172-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 02/07/2024] [Indexed: 03/08/2024]
Abstract
KEY MESSAGE The dynamic genetic architecture of flowering time in chrysanthemum was elucidated by GWAS. Thirty-six known genes and 14 candidate genes were identified around the stable QTNs and QEIs, among which ERF-1 was highlighted. Flowering time (FT) adaptation is one of the major breeding goals in chrysanthemum, a multipurpose ornamental plant. In order to reveal the dynamic genetic architecture of FT in chrysanthemum, phenotype investigation of ten FT-related traits was conducted on 169 entries in 2 environments. The broad-sense heritability of five non-conditional FT traits, i.e., budding (FBD), visible coloring (VC), early opening (EO), full-bloom (OF) and decay period (DP), ranged from 56.93 to 84.26%, which were higher than that of the five derived conditional FT traits (38.51-75.13%). The phenotypic variation coefficients of OF_EO and DP_OF were relatively large ranging from 30.59 to 36.17%. Based on 375,865 SNPs, the compressed variance component mixed linear model 3VmrMLM was applied for a multi-locus genome-wide association study (GWAS). As a result, 313 quantitative trait nucleotides (QTNs) were identified for the non-conditional FT traits in single-environment analysis, while 119 QTNs and 67 QTN-by-environment interactions (QEIs) were identified in multi-environment analysis. As for the conditional traits, 343 QTNs were detected in single-environment analysis, and 119 QTNs and 83 QEIs were identified in multi- environment analysis. Among the genes around stable QTNs and QEIs, 36 were orthologs of known FT genes in Arabidopsis and other plants; 14 candidates were mined by combining the transcriptomics data and functional annotation, including ERF-1, ACA10, and FOP1. Furthermore, the haplotype analysis of ERF-1 revealed six elite accessions with extreme FBD. Our findings contribute to the understanding of dynamic genetic architecture of FT and provide valuable resources for future chrysanthemum molecular breeding programs.
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Affiliation(s)
- Jiangshuo Su
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No.1, Nanjing, 210095, Jiangsu Province, China
| | - Junwei Zeng
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No.1, Nanjing, 210095, Jiangsu Province, China
| | - Siyue Wang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No.1, Nanjing, 210095, Jiangsu Province, China
| | - Xuefeng Zhang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No.1, Nanjing, 210095, Jiangsu Province, China
| | - Limin Zhao
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No.1, Nanjing, 210095, Jiangsu Province, China
| | - Shiyun Wen
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No.1, Nanjing, 210095, Jiangsu Province, China
| | - Fei Zhang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No.1, Nanjing, 210095, Jiangsu Province, China
- Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing, 210014, China
| | - Jiafu Jiang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No.1, Nanjing, 210095, Jiangsu Province, China
- Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing, 210014, China
| | - Fadi Chen
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No.1, Nanjing, 210095, Jiangsu Province, China.
- Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing, 210014, China.
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Sachdeva S, Singh R, Maurya A, Singh VK, Singh UM, Kumar A, Singh GP. New insights into QTNs and potential candidate genes governing rice yield via a multi-model genome-wide association study. BMC PLANT BIOLOGY 2024; 24:124. [PMID: 38373874 PMCID: PMC10877931 DOI: 10.1186/s12870-024-04810-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 02/08/2024] [Indexed: 02/21/2024]
Abstract
BACKGROUND Rice (Oryza sativa L.) is one of the globally important staple food crops, and yield-related traits are prerequisites for improved breeding efficiency in rice. Here, we used six different genome-wide association study (GWAS) models for 198 accessions, with 553,229 single nucleotide markers (SNPs) to identify the quantitative trait nucleotides (QTNs) and candidate genes (CGs) governing rice yield. RESULTS Amongst the 73 different QTNs in total, 24 were co-localized with already reported QTLs or loci in previous mapping studies. We obtained fifteen significant QTNs, pathway analysis revealed 10 potential candidates within 100kb of these QTNs that are predicted to govern plant height, days to flowering, and plot yield in rice. Based on their superior allelic information in 20 elite and 6 inferior genotypes, we found a higher percentage of superior alleles in the elite genotypes in comparison to inferior genotypes. Further, we implemented expression analysis and enrichment analysis enabling the identification of 73 candidate genes and 25 homologues of Arabidopsis, 19 of which might regulate rice yield traits. Of these candidate genes, 40 CGs were found to be enriched in 60 GO terms of the studied traits for instance, positive regulator metabolic process (GO:0010929), intracellular part (GO:0031090), and nucleic acid binding (GO:0090079). Haplotype and phenotypic variation analysis confirmed that LOC_OS09G15770, LOC_OS02G36710 and LOC_OS02G17520 are key candidates associated with rice yield. CONCLUSIONS Overall, we foresee that the QTNs, putative candidates elucidated in the study could summarize the polygenic regulatory networks controlling rice yield and be useful for breeding high-yielding varieties.
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Grants
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
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Affiliation(s)
- Supriya Sachdeva
- Division of Genomic Resources, ICAR-NBPGR, Pusa, New Delhi, India
| | - Rakesh Singh
- Division of Genomic Resources, ICAR-NBPGR, Pusa, New Delhi, India.
| | - Avantika Maurya
- Division of Genomic Resources, ICAR-NBPGR, Pusa, New Delhi, India
| | - Vikas K Singh
- International Rice Research Institute (IRRI), South Asia Hub, ICRISAT, Hyderabad, India
| | - Uma Maheshwar Singh
- International Rice Research Institute (IRRI), South Asia Regional Centre (ISARC), Varanasi, India
| | - Arvind Kumar
- International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Telangana, India
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Liu Z, Li H, Wang X, Zhang Y, Gou Z, Zhao X, Ren H, Wen Z, Li Y, Yu L, Gao H, Wang D, Qi X, Qiu L. QTL for yield per plant under water deficit and well-watered conditions and drought susceptibility index in soybean ( Glycine max (L.) Merr.). BIOTECHNOL BIOTEC EQ 2023. [DOI: 10.1080/13102818.2022.2155569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Affiliation(s)
- Zhangxiong Liu
- National Key Facility for Gene Resources and Genetic Improvement/Key Laboratory of Crop Germplasm Utilization, Ministry of Agriculture/Center of Crop Germplasm Resource, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, PR China
| | - Huihui Li
- National Key Facility for Gene Resources and Genetic Improvement/Key Laboratory of Crop Germplasm Utilization, Ministry of Agriculture/Center of Crop Germplasm Resource, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, PR China
| | - Xingrong Wang
- Laboratory of Crop Germplasm Resource, Institute of Crop Sciences, Gansu Academy of Agricultural Sciences, Lanzhou, Gansu, PR China
| | - Yanjun Zhang
- Laboratory of Crop Germplasm Resource, Institute of Crop Sciences, Gansu Academy of Agricultural Sciences, Lanzhou, Gansu, PR China
| | - Zuowang Gou
- Laboratory of Crop Germplasm Resource, Institute of Crop Sciences, Gansu Academy of Agricultural Sciences, Lanzhou, Gansu, PR China
| | - Xingzhen Zhao
- National Key Facility for Gene Resources and Genetic Improvement/Key Laboratory of Crop Germplasm Utilization, Ministry of Agriculture/Center of Crop Germplasm Resource, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, PR China
| | - Honglei Ren
- Laboratory of Disease Resistance Breeding, Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Haerbin, Heilongjiang, PR China
| | - Zixiang Wen
- Department of Plant, Soil and Microbial Sciences, College of Agriculture & Natural Resources, Michigan State University, East Lansing, MI, USA
| | - Yinghui Li
- National Key Facility for Gene Resources and Genetic Improvement/Key Laboratory of Crop Germplasm Utilization, Ministry of Agriculture/Center of Crop Germplasm Resource, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, PR China
| | - Lili Yu
- National Key Facility for Gene Resources and Genetic Improvement/Key Laboratory of Crop Germplasm Utilization, Ministry of Agriculture/Center of Crop Germplasm Resource, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, PR China
| | - Huawei Gao
- National Key Facility for Gene Resources and Genetic Improvement/Key Laboratory of Crop Germplasm Utilization, Ministry of Agriculture/Center of Crop Germplasm Resource, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, PR China
| | - Dechun Wang
- Department of Plant, Soil and Microbial Sciences, College of Agriculture & Natural Resources, Michigan State University, East Lansing, MI, USA
| | - Xusheng Qi
- Laboratory of Crop Germplasm Resource, Institute of Crop Sciences, Gansu Academy of Agricultural Sciences, Lanzhou, Gansu, PR China
| | - Lijuan Qiu
- National Key Facility for Gene Resources and Genetic Improvement/Key Laboratory of Crop Germplasm Utilization, Ministry of Agriculture/Center of Crop Germplasm Resource, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, PR China
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Zhang YM, Jia Z, Dunwell JM. Editorial: The applications of new multi-locus GWAS methodologies in the genetic dissection of complex traits, volume II. FRONTIERS IN PLANT SCIENCE 2023; 14:1340767. [PMID: 38146269 PMCID: PMC10749431 DOI: 10.3389/fpls.2023.1340767] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 11/27/2023] [Indexed: 12/27/2023]
Affiliation(s)
- Yuan-Ming Zhang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Zhenyu Jia
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
| | - Jim M. Dunwell
- School of Agriculture, Policy and Development, University of Reading, Reading, United Kingdom
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Elakhdar A, Slaski JJ, Kubo T, Hamwieh A, Hernandez Ramirez G, Beattie AD, Capo-chichi LJ. Genome-wide association analysis provides insights into the genetic basis of photosynthetic responses to low-temperature stress in spring barley. FRONTIERS IN PLANT SCIENCE 2023; 14:1159016. [PMID: 37346141 PMCID: PMC10279893 DOI: 10.3389/fpls.2023.1159016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 05/04/2023] [Indexed: 06/23/2023]
Abstract
Low-temperature stress (LTS) is among the major abiotic stresses affecting the geographical distribution and productivity of the most important crops. Understanding the genetic basis of photosynthetic variation under cold stress is necessary for developing more climate-resilient barley cultivars. To that end, we investigated the ability of chlorophyll fluorescence parameters (FVFM, and FVF0) to respond to changes in the maximum quantum yield of Photosystem II photochemistry as an indicator of photosynthetic energy. A panel of 96 barley spring cultivars from different breeding zones of Canada was evaluated for chlorophyll fluorescence-related traits under cold acclimation and freeze shock stresses at different times. Genome-wide association studies (GWAS) were performed using a mixed linear model (MLM). We identified three major and putative genomic regions harboring 52 significant quantitative trait nucleotides (QTNs) on chromosomes 1H, 3H, and 6H for low-temperature tolerance. Functional annotation indicated several QTNs were either within the known or close to genes that play important roles in the photosynthetic metabolites such as abscisic acid (ABA) signaling, hydrolase activity, protein kinase, and transduction of environmental signal transduction at the posttranslational modification levels. These outcomes revealed that barley plants modified their gene expression profile in response to decreasing temperatures resulting in physiological and biochemical modifications. Cold tolerance could influence a long-term adaption of barley in many parts of the world. Since the degree and frequency of LTS vary considerably among production sites. Hence, these results could shed light on potential approaches for improving barley productivity under low-temperature stress.
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Affiliation(s)
- Ammar Elakhdar
- Field Crops Research Institute, Agricultural Research Center, Giza, Egypt
- Institute of Genetic Resources, Faculty of Agriculture, Kyushu University, Fukuoka, Japan
| | - Jan J. Slaski
- Bio Industrial Services Division, InnoTech Alberta Inc., Vegreville, AB, Canada
| | - Takahiko Kubo
- Institute of Genetic Resources, Faculty of Agriculture, Kyushu University, Fukuoka, Japan
| | - Aladdin Hamwieh
- International Center for Agriculture Research in the Dry Areas (ICARDA), Giza, Egypt
| | - Guillermo Hernandez Ramirez
- Department of Renewable Resources, Faculty of Agriculture, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada
| | - Aaron D. Beattie
- Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ludovic J.A. Capo-chichi
- Department of Renewable Resources, Faculty of Agriculture, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada
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Jiang H, Lv S, Zhou C, Qu S, Liu F, Sun H, Zhao X, Han Y. Identification of QTL, QTL-by-environment interactions, and their candidate genes for resistance HG Type 0 and HG Type 1.2.3.5.7 in soybean using 3VmrMLM. FRONTIERS IN PLANT SCIENCE 2023; 14:1177345. [PMID: 37152131 PMCID: PMC10162016 DOI: 10.3389/fpls.2023.1177345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 03/31/2023] [Indexed: 05/09/2023]
Abstract
Introduction Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is an important disease affecting soybean yield in the world. Potential SCN-related QTLs and QTL-by-environment interactions (QEIs) have been used in SCN-resistant breeding. Methods In this study, a compressed variance component mixed model, 3VmrMLM, in genome-wide association studies was used to detect QTLs and QEIs for resistance to SCN HG Type 0 and HG Type 1.2.3.5.7 in 156 different soybean cultivars materials. Results and discussion The results showed that 53 QTLs were detected in single environment analysis; 36 QTLs and 9 QEIs were detected in multi-environment analysis. Based on the statistical screening of the obtained QTLs, we obtained 10 novel QTLs and one QEI which were different from the previous studies. Based on previous studies, we identified 101 known genes around the significant/suggested QTLs and QEIs. Furthermore, used the transcriptome data of SCN-resistant (Dongnong L-10) and SCN-susceptible (Suinong 14) cultivars, 10 candidate genes related to SCN resistance were identified and verified by Quantitative real time polymerase chain reaction (qRT-PCR) analysis. Haplotype difference analysis showed that Glyma.03G005600 was associated with SCN HG Type 0 and HG Type 1.2.3.5.7 resistance and had a haplotype beneficial to multi-SCN-race resistance. These results provide a new idea for accelerating SCN disease resistance breeding.
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Affiliation(s)
- Haipeng Jiang
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Suchen Lv
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Changjun Zhou
- Daqing Branch, Heilongjiang Academy of Agricultural Science, Daqing, China
| | - Shuo Qu
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Fang Liu
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Haowen Sun
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Xue Zhao
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
- *Correspondence: Yingpeng Han, ; Xue Zhao,
| | - Yingpeng Han
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
- *Correspondence: Yingpeng Han, ; Xue Zhao,
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11
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Alamin M, Sultana MH, Lou X, Jin W, Xu H. Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS. PLANTS (BASEL, SWITZERLAND) 2022; 11:3277. [PMID: 36501317 PMCID: PMC9739826 DOI: 10.3390/plants11233277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/23/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. Recently more attention has been paid to pleiotropy, multi-trait, gene-gene interaction, gene-environment interaction, and multi-locus methods with the growing availability of large-scale GWAS data and relevant phenotype samples. In this review, we have demonstrated all possible LMMs-based methods available in the literature for GWAS. We briefly discuss the different LMM methods, software packages, and available open-source applications in GWAS. Then, we include the advantages and weaknesses of the LMMs in GWAS. Finally, we discuss the future perspective and conclusion. The present review paper would be helpful to the researchers for selecting appropriate LMM models and methods quickly for GWAS data analysis and would benefit the scientific society.
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Affiliation(s)
- Md. Alamin
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | | | - Xiangyang Lou
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Wenfei Jin
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Haiming Xu
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
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12
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Hong H, Li M, Chen Y, Wang H, Wang J, Guo B, Gao H, Ren H, Yuan M, Han Y, Qiu L. Genome-wide association studies for soybean epicotyl length in two environments using 3VmrMLM. FRONTIERS IN PLANT SCIENCE 2022; 13:1033120. [PMID: 36452100 PMCID: PMC9704727 DOI: 10.3389/fpls.2022.1033120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/04/2022] [Indexed: 06/17/2023]
Abstract
Germination of soybean seed is the imminent vital process after sowing. The status of plumular axis and radicle determine whether soybean seed can emerge normally. Epicotyl, an organ between cotyledons and first functional leaves, is essential for soybean seed germination, seedling growth and early morphogenesis. Epicotyl length (EL) is a quantitative trait controlled by multiple genes/QTLs. Here, the present study analyzes the phenotypic diversity and genetic basis of EL using 951 soybean improved cultivars and landraces from Asia, America, Europe and Africa. 3VmrMLM was used to analyze the associations between EL in 2016 and 2020 and 1,639,846 SNPs for the identification of QTNs and QTN-by-environment interactions (QEIs)".A total of 180 QTNs and QEIs associated with EL were detected. Among them, 74 QTNs (ELS_Q) and 16 QEIs (ELS_QE) were identified to be associated with ELS (epicotyl length of single plant emergence), and 60 QTNs (ELT_Q) and 30 QEIs (ELT_QE) were identified to be associated with ELT (epicotyl length of three seedlings). Based on transcript abundance analysis, GO (Gene Ontology) enrichment and haplotype analysis, ten candidate genes were predicted within nine genic SNPs located in introns, upstream or downstream, which were supposed to be directly or indirectly involved in the process of seed germination and seedling development., Of 10 candidate genes, two of them (Glyma.04G122400 and Glyma.18G183600) could possibly affect epicotyl length elongation. These results indicate the genetic basis of EL and provides a valuable basis for specific functional studies of epicotyl traits.
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Affiliation(s)
- Huilong Hong
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
- Institute of Crop Science, National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI) Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mei Li
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Yijie Chen
- College of Agriculture, Yangtze University, Jingzhou, China
| | - Haorang Wang
- Jiangsu Xuhuai Regional Institute of Agricultural Sciences, Xuzhou, China
| | - Jun Wang
- College of Agriculture, Yangtze University, Jingzhou, China
| | - Bingfu Guo
- Nanchang Branch of National Center of Oil crops Improvement, Jiangxi Province Key Laboratory of Oil crops Biology, Crops Research Institute of Jiangxi Academy of Agricultural Sciences, Nanchang, China
| | - Huawei Gao
- Institute of Crop Science, National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI) Chinese Academy of Agricultural Sciences, Beijing, China
| | - Honglei Ren
- Soybean Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Ming Yuan
- Qiqihar Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihar, China
| | - Yingpeng Han
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, China
| | - Lijuan Qiu
- Institute of Crop Science, National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI) Chinese Academy of Agricultural Sciences, Beijing, China
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13
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Zhang J, Wang S, Wu X, Han L, Wang Y, Wen Y. Identification of QTNs, QTN-by-environment interactions and genes for yield-related traits in rice using 3VmrMLM. FRONTIERS IN PLANT SCIENCE 2022; 13:995609. [PMID: 36325550 PMCID: PMC9618716 DOI: 10.3389/fpls.2022.995609] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
Rice, which supports more than half the population worldwide, is one of the most important food crops. Thus, potential yield-related quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs) have been used to develop efficient rice breeding strategies. In this study, a compressed variance component mixed model, 3VmrMLM, in genome-wide association studies was used to detect QTNs for eight yield-related traits of 413 rice accessions with 44,000 single nucleotide polymorphisms. These traits include florets per panicle, panicle fertility, panicle length, panicle number per plant, plant height, primary panicle branch number, seed number per panicle, and flowering time. Meanwhile, QTNs and QEIs were identified for flowering times in three different environments and five subpopulations. In the detections, a total of 7~23 QTNs were detected for each trait, including the three single-environment flowering time traits. In the detection of QEIs for flowering time in the three environments, 21 QTNs and 13 QEIs were identified. In the five subpopulation analyses, 3~9 QTNs and 2~4 QEIs were detected for each subpopulation. Based on previous studies, we identified 87 known genes around the significant/suggested QTNs and QEIs, such as LOC_Os06g06750 (OsMADS5) and LOC_Os07g47330 (FZP). Further differential expression analysis and functional enrichment analysis identified 30 candidate genes. Of these candidate genes, 27 genes had high expression in specific tissues, and 19 of these 27 genes were homologous to known genes in Arabidopsis. Haplotype difference analysis revealed that LOC_Os04g53210 and LOC_Os07g42440 are possibly associated with yield, and LOC_Os04g53210 may be useful around a QEI for flowering time. These results provide insights for future breeding for high quality and yield in rice.
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Affiliation(s)
- Jin Zhang
- College of Science, Nanjing Agricultural University, Nanjing, China
- Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Shengmeng Wang
- College of Science, Nanjing Agricultural University, Nanjing, China
| | - Xinyi Wu
- College of Science, Nanjing Agricultural University, Nanjing, China
| | - Le Han
- College of Science, Nanjing Agricultural University, Nanjing, China
| | - Yuan Wang
- College of Science, Nanjing Agricultural University, Nanjing, China
| | - Yangjun Wen
- College of Science, Nanjing Agricultural University, Nanjing, China
- Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
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14
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Li L, Wu X, Chen J, Wang S, Wan Y, Ji H, Wen Y, Zhang J. Genetic Dissection of Epistatic Interactions Contributing Yield-Related Agronomic Traits in Rice Using the Compressed Mixed Model. PLANTS 2022; 11:plants11192504. [PMID: 36235370 PMCID: PMC9571936 DOI: 10.3390/plants11192504] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/09/2022] [Accepted: 09/19/2022] [Indexed: 11/26/2022]
Abstract
Rice (Oryza sativa) is one of the most important cereal crops in the world, and yield-related agronomic traits, including plant height (PH), panicle length (PL), and protein content (PC), are prerequisites for attaining the desired yield and quality in breeding programs. Meanwhile, the main effects and epistatic effects of quantitative trait nucleotides (QTNs) are all important genetic components for yield-related quantitative traits. In this study, we conducted genome-wide association studies (GWAS) for 413 rice germplasm resources, with 36,901 single nucleotide polymorphisms (SNPs), to identify QTNs, QTN-by-QTN interaction (QQI), and their candidate genes, using a multi-locus compressed variance component mixed model, 3VmrMLM. As a result, two significant QTNs and 56 paired QQIs were detected, amongst 5219 genes of these QTNs, and 26 genes were identified as the yield-related confirmed genes, such as LCRN1, OsSPL3, and OsVOZ1 for PH, and LOG and QsBZR1 for PL. To reveal the substantial contributions related to the variation of yield-related agronomic traits in rice, we further implemented an enrichment analysis and expression analysis. As the results showed, 114 genes, nearly all significant QQIs, were involved in 37 GO terms; for example, the macromolecule metabolic process (GO:0043170), intracellular part (GO:0044424), and binding (GO:0005488). It was revealed that most of the QQIs and the candidate genes were significantly involved in the biological process, molecular function, and cellular component of the target traits. The demonstrated genetic interactions play a critical role in yield-related agronomic traits of rice, and such epistatic interactions contributed to large portions of the missing heritability in GWAS. These results help us to understand the genetic basis underlying the inheritance of the three yield-related agronomic traits and provide implications for rice improvement.
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Affiliation(s)
- Ling Li
- College of Science, Nanjing Agricultural University, Nanjing 210095, China
| | - Xinyi Wu
- College of Science, Nanjing Agricultural University, Nanjing 210095, China
| | - Juncong Chen
- College of Finance, Nanjing Agricultural University, Nanjing 210095, China
| | - Shengmeng Wang
- College of Science, Nanjing Agricultural University, Nanjing 210095, China
| | - Yuxuan Wan
- School of Business Administration, Jiangxi University of Finance and Economics, Nanchang 330013, China
| | - Hanbing Ji
- College of Science, Nanjing Agricultural University, Nanjing 210095, China
| | - Yangjun Wen
- College of Science, Nanjing Agricultural University, Nanjing 210095, China
- Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
- Correspondence: (Y.W.); (J.Z.)
| | - Jin Zhang
- College of Science, Nanjing Agricultural University, Nanjing 210095, China
- Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
- Correspondence: (Y.W.); (J.Z.)
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15
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Hu X, Zuo J. Population Genomics and Haplotype Analysis in Bread Wheat Identify a Gene Regulating Glume Pubescence. FRONTIERS IN PLANT SCIENCE 2022; 13:897772. [PMID: 35909788 PMCID: PMC9328021 DOI: 10.3389/fpls.2022.897772] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
Glume hairiness or pubescence is an important morphological trait with high heritability to distinguish/characterize wheat and is related to the resistance to biotic and abiotic stresses. Hg1 (formerly named Hg) on chromosome arm 1AS controlled glume hairiness in wheat. Its genetic analysis and mapping have been widely studied, yet more useful and accurate information for fine mapping of Hg1 and identification of its candidate gene is lacking. The cloning of this gene has not yet been reported for the large complex wheat genome. Here, we performed a GWAS between SNP markers and glume pubescence (Gp) in a wheat population with 352 lines and further demonstrated the gene expression and haplotype analysis approach for isolating the Hg1 gene. One gene, TraesCSU02G143200 (TaELD1-1A), encoding glycosyltransferase-like ELD1/KOBITO 1, was identified as the most promising candidate gene of Hg1. The gene annotation, expression pattern, function SNP variation, haplotype analysis, and co-expression analysis in floral organ (spike) development indicated that it is likely to be involved in the regulation of glume pubescence. Our study demonstrates the importance of high-quality reference genomes and annotation information, as well as bioinformatics analysis, for gene cloning in wheat.
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Affiliation(s)
- Xin Hu
- The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Hangzhou, China
| | - Jianfang Zuo
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
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16
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Ishwarya Lakshmi VG, Sreedhar M, JhansiLakshmi V, Gireesh C, Rathod S, Bohar R, Deshpande S, Laavanya R, Kiranmayee KNSU, Siddi S, Vanisri S. Development and Validation of Diagnostic KASP Markers for Brown Planthopper Resistance in Rice. Front Genet 2022; 13:914131. [PMID: 35899197 PMCID: PMC9309266 DOI: 10.3389/fgene.2022.914131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 06/06/2022] [Indexed: 11/13/2022] Open
Abstract
Rice (Oryza sativa L.) is an important source of nutrition for the world's burgeoning population that often faces yield loss due to infestation by the brown planthopper (BPH, Nilaparvata lugens (Stål)). The development of rice cultivars with BPH resistance is one of the crucial precedences in rice breeding programs. Recent progress in high-throughput SNP-based genotyping technology has made it possible to develop markers linked to the BPH more quickly than ever before. With this view, a genome-wide association study was undertaken for deriving marker-trait associations with BPH damage scores and SNPs from genotyping-by-sequencing data of 391 multi-parent advanced generation inter-cross (MAGIC) lines. A total of 23 significant SNPs involved in stress resistance pathways were selected from a general linear model along with 31 SNPs reported from a FarmCPU model in previous studies. Of these 54 SNPs, 20 were selected in such a way to cover 13 stress-related genes. Kompetitive allele-specific PCR (KASP) assays were designed for the 20 selected SNPs and were subsequently used in validating the genotypes that were identified, six SNPs, viz, snpOS00912, snpOS00915, snpOS00922, snpOS00923, snpOS00927, and snpOS00929 as efficient in distinguishing the genotypes into BPH-resistant and susceptible clusters. Bph17 and Bph32 genes that are highly effective against the biotype 4 of the BPH have been validated by gene specific SNPs with favorable alleles in M201, M272, M344, RathuHeenati, and RathuHeenati accession. These identified genotypes could be useful as donors for transferring BPH resistance into popular varieties with marker-assisted selection using these diagnostic SNPs. The resistant lines and the significant SNPs unearthed from our study can be useful in developing BPH-resistant varieties after validating them in biparental populations with the potential usefulness of SNPs as causal markers.
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Affiliation(s)
- V. G. Ishwarya Lakshmi
- Department of Genetics and Plant Breeding, College of Agriculture, Professor Jayashankar Telangana State Agricultural University (PJTSAU), Hyderabad, India
| | - M. Sreedhar
- Administrative Office, PJTSAU, Hyderabad, India
| | | | - C. Gireesh
- ICAR-Indian Institute of Rice Research (IIRR), Hyderabad, India
| | - Santosha Rathod
- ICAR-Indian Institute of Rice Research (IIRR), Hyderabad, India
| | - Rajaguru Bohar
- CGIAR Excellence in Breeding (EiB), CIMMYT-ICRISAT, Hyderabad, India
| | - Santosh Deshpande
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
| | - R. Laavanya
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
| | | | - Sreedhar Siddi
- Agricultural Research Station, PJTSAU, Peddapalli, India
| | - S. Vanisri
- Institute of Biotechnology, PJTSAU, Hyderabad, India
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17
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Cao Y, Jia S, Chen L, Zeng S, Zhao T, Karikari B. Identification of major genomic regions for soybean seed weight by genome-wide association study. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:38. [PMID: 37313505 PMCID: PMC10248628 DOI: 10.1007/s11032-022-01310-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/12/2022] [Indexed: 06/15/2023]
Abstract
The hundred-seed weight (HSW) is an important yield component and one of the principal breeding traits in soybean. More than 250 quantitative trait loci (QTL) for soybean HSW have been identified. However, most of them have a large genomic region or are environmentally sensitive, which provide limited information for improving the phenotype in marker-assisted selection (MAS) and identifying the candidate genes. Here, we utilized 281 soybean accessions with 58,112 single nucleotide polymorphisms (SNPs) to dissect the genetic basis of HSW in across years in the northern Shaanxi province of China through one single-locus (SL) and three multi-locus (ML) genome-wide association study (GWAS) models. As a result, one hundred and fifty-four SNPs were detected to be significantly associated with HSW in at least one environment via SL-GWAS model, and 27 of these 154 SNPs were detected in all (three) environments and located within 7 linkage disequilibrium (LD) block regions with the distance of each block ranged from 40 to 610 Kb. A total of 15 quantitative trait nucleotides (QTNs) were identified by three ML-GWAS models. Combined with the results of different GWAS models, the 7 LD block regions associated with HSW detected by SL-GWAS model could be verified directly or indirectly by the results of ML-GWAS models. Eleven candidate genes underlying the stable loci that may regulate seed weight in soybean were predicted. The significantly associated SNPs and the stable loci as well as predicted candidate genes may be of great importance for marker-assisted breeding, polymerization breeding, and gene discovery for HSW in soybean. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01310-y.
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Affiliation(s)
- Yongce Cao
- Shaanxi Key Laboratory of Chinese Jujube, College of Life Science, Yan’an University, Yan’an, Shaanxi, 716000 China
| | - Shihao Jia
- Shaanxi Key Laboratory of Chinese Jujube, College of Life Science, Yan’an University, Yan’an, Shaanxi, 716000 China
| | - Liuxing Chen
- Shaanxi Key Laboratory of Chinese Jujube, College of Life Science, Yan’an University, Yan’an, Shaanxi, 716000 China
| | - Shunan Zeng
- Shaanxi Key Laboratory of Chinese Jujube, College of Life Science, Yan’an University, Yan’an, Shaanxi, 716000 China
| | - Tuanjie Zhao
- Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, National Center for Soybean Improvement, National Key Laboratory for Crop Genetics and Germplasm Enhancement, Soybean Research Institute of Nanjing Agricultural University, Nanjing, 210095 Jiangsu China
| | - Benjamin Karikari
- Department of Crop Science, Faculty of Agriculture, Food and Consumer Sciences, University for Development Studies, 00233 Tamale, Ghana
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18
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Ravikiran KT, Gopala Krishnan S, Abhijith KP, Bollinedi H, Nagarajan M, Vinod KK, Bhowmick PK, Pal M, Ellur RK, Singh AK. Genome-Wide Association Mapping Reveals Novel Putative Gene Candidates Governing Reproductive Stage Heat Stress Tolerance in Rice. Front Genet 2022; 13:876522. [PMID: 35734422 PMCID: PMC9208292 DOI: 10.3389/fgene.2022.876522] [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: 02/15/2022] [Accepted: 03/25/2022] [Indexed: 11/14/2022] Open
Abstract
Temperature rise predicted for the future will severely affect rice productivity because the crop is highly sensitive to heat stress at the reproductive stage. Breeding tolerant varieties is an economically viable option to combat heat stress, for which the knowledge of target genomic regions associated with the reproductive stage heat stress tolerance (RSHT) is essential. A set of 192 rice genotypes of diverse origins were evaluated under natural field conditions through staggered sowings for RSHT using two surrogate traits, spikelet fertility and grain yield, which showed significant reduction under heat stress. These genotypes were genotyped using a 50 k SNP array, and the association analysis identified 10 quantitative trait nucleotides (QTNs) for grain yield, of which one QTN (qHTGY8.1) was consistent across the different models used. Only two out of 10 MTAs coincided with the previously reported QTLs, making the remaing eight novel. A total of 22 QTNs were observed for spikelet fertility, among which qHTSF5.1 was consistently found across three models. Of the QTNs identified, seven coincided with previous reports, while the remaining QTNs were new. The genes near the QTNs were found associated with the protein–protein interaction, protein ubiquitination, stress signal transduction, and so forth, qualifying them to be putative for RSHT. An in silico expression analysis revealed the predominant expression of genes identified for spikelet fertility in reproductive organs. Further validation of the biological relevance of QTNs in conferring heat stress tolerance will enable their utilization in improving the reproductive stage heat stress tolerance in rice.
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Affiliation(s)
- K T Ravikiran
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - S Gopala Krishnan
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - K P Abhijith
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - H Bollinedi
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - M Nagarajan
- Rice Breeding and Genetics Research Centre, ICAR-IARI, Aduthurai, India
| | - K K Vinod
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - P K Bhowmick
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Madan Pal
- Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - R K Ellur
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - A K Singh
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
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19
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Li P, Li G, Zhang YW, Zuo JF, Liu JY, Zhang YM. A combinatorial strategy to identify various types of QTLs for quantitative traits using extreme phenotype individuals in an F 2 population. PLANT COMMUNICATIONS 2022; 3:100319. [PMID: 35576159 PMCID: PMC9251438 DOI: 10.1016/j.xplc.2022.100319] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 03/07/2022] [Accepted: 03/22/2022] [Indexed: 06/09/2023]
Abstract
Theoretical and applied studies demonstrate the difficulty of detecting extremely over-dominant and small-effect genes for quantitative traits via bulked segregant analysis (BSA) in an F2 population. To address this issue, we proposed an integrated strategy for mapping various types of quantitative trait loci (QTLs) for quantitative traits via a combination of BSA and whole-genome sequencing. In this strategy, the numbers of read counts of marker alleles in two extreme pools were used to predict the numbers of read counts of marker genotypes. These observed and predicted numbers were used to construct a new statistic, Gw, for detecting quantitative trait genes (QTGs), and the method was named dQTG-seq1. This method was significantly better than existing BSA methods. If the goal was to identify extremely over-dominant and small-effect genes, another reserved DNA/RNA sample from each extreme phenotype F2 plant was sequenced, and the observed numbers of marker alleles and genotypes were used to calculate Gw to detect QTGs; this method was named dQTG-seq2. In simulated and real rice dataset analyses, dQTG-seq2 could identify many more extremely over-dominant and small-effect genes than BSA and QTL mapping methods. dQTG-seq2 may be extended to other heterogeneous mapping populations. The significance threshold of Gw in this study was determined by permutation experiments. In addition, a handbook for the R software dQTG.seq, which is available at https://cran.r-project.org/web/packages/dQTG.seq/index.html, has been provided in the supplemental materials for the users' convenience. This study provides a new strategy for identifying all types of QTLs for quantitative traits in an F2 population.
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Affiliation(s)
- Pei Li
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Guo Li
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ya-Wen Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Jian-Fang Zuo
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Jin-Yang Liu
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
| | - Yuan-Ming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
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20
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Aoun M, Chen X, Somo M, Xu SS, Li X, Elias EM. Novel stripe rust all-stage resistance loci identified in a worldwide collection of durum wheat using genome-wide association mapping. THE PLANT GENOME 2021; 14:e20136. [PMID: 34609797 DOI: 10.1002/tpg2.20136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 06/24/2021] [Indexed: 06/13/2023]
Abstract
Durumwheat [Triticum turgidum L. ssp. durum (Desf.)] production is constrained by fungal diseases including stripe rust caused by Puccinia striiformis Westend. f. sp. tritici Erikss. (Pst). Continuous mining of germplasm for the discovery and deployment of stripe rust resistance (Yr) genes is needed to counter the impact of this disease. In this study, we evaluated a worldwide collection of 432 durum wheat accessions to seven U.S. Pst races that carry diverse virulence and avirulence combinations on wheat Yr genes. We found that 47-82% of the durum wheat accessions were susceptible to each of the tested Pst races. A total of 32 accessions were resistant to all seven races. Genome-wide association studies (GWAS) using over 97,000 single-nucleotide polymorphism markers generated from genotyping-by-sequencing of 364 accessions identified 56 quantitative trait loci (QTL) associated with all-stage stripe rust resistance located on all 14 durum wheat chromosomes. Six of these QTL were associated with resistance to 2-4 Pst races, and none were associated with resistance to all seven races. The remaining 50 QTL were race specific. Eighteen of the 56 identified QTL had relatively large effects against at least one of the races. A map-based comparison of the discovered QTL in this study with previously published Yr genes and QTL showed that 29 were previously identified, whereas the remaining 27 QTL appeared to be novel. This study reports effective sources of stripe rust resistance to contemporary races in the United States and shows that this durum wheat collection is abundant in novel resistance loci that can be transferred into adapted durum cultivars.
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Affiliation(s)
- Meriem Aoun
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, USA
| | - Xianming Chen
- Wheat Health, Genetics, and Quality Research Unit, USDA-ARS, Pullman, WA, USA
| | - Mohamed Somo
- Dep. of Plant Breeding and Genetics, Cornell Univ., Ithaca, NY, USA
| | - Steven S Xu
- USDA-ARS, Cereal Crops Research Unit, Fargo, ND, USA
| | - Xuehui Li
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, USA
| | - Elias M Elias
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, USA
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21
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Xiao J, Zhou Y, He S, Ren WL. An Efficient Score Test Integrated with Empirical Bayes for Genome-Wide Association Studies. Front Genet 2021; 12:742752. [PMID: 34659362 PMCID: PMC8517403 DOI: 10.3389/fgene.2021.742752] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 09/13/2021] [Indexed: 11/30/2022] Open
Abstract
Many methods used in multi-locus genome-wide association studies (GWAS) have been developed to improve statistical power. However, most existing multi-locus methods are not quicker than single-locus methods. To address this concern, we proposed a fast score test integrated with Empirical Bayes (ScoreEB) for multi-locus GWAS. Firstly, a score test was conducted for each single nucleotide polymorphism (SNP) under a linear mixed model (LMM) framework, taking into account the genetic relatedness and population structure. Then, all of the potentially associated SNPs were selected with a less stringent criterion. Finally, Empirical Bayes in a multi-locus model was performed for all of the selected SNPs to identify the true quantitative trait nucleotide (QTN). Our new method ScoreEB adopts the similar strategy of multi-locus random-SNP-effect mixed linear model (mrMLM) and fast multi-locus random-SNP-effect EMMA (FASTmrEMMA), and the only difference is that we use the score test to select all the potentially associated markers. Monte Carlo simulation studies demonstrate that ScoreEB significantly improved the computational efficiency compared with the popular methods mrMLM, FASTmrEMMA, iterative modified-sure independence screening EM-Bayesian lasso (ISIS EM-BLASSO), hybrid of restricted and penalized maximum likelihood (HRePML) and genome-wide efficient mixed model association (GEMMA). In addition, ScoreEB remained accurate in QTN effect estimation and effectively controlled false positive rate. Subsequently, ScoreEB was applied to re-analyze quantitative traits in plants and animals. The results show that ScoreEB not only can detect previously reported genes, but also can mine new genes.
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Affiliation(s)
- Jing Xiao
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China
| | - Yang Zhou
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China
| | - Shu He
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China
| | - Wen-Long Ren
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China
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22
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Khan SU, Saeed S, Khan MHU, Fan C, Ahmar S, Arriagada O, Shahzad R, Branca F, Mora-Poblete F. Advances and Challenges for QTL Analysis and GWAS in the Plant-Breeding of High-Yielding: A Focus on Rapeseed. Biomolecules 2021; 11:1516. [PMID: 34680149 PMCID: PMC8533950 DOI: 10.3390/biom11101516] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 10/07/2021] [Accepted: 10/11/2021] [Indexed: 12/15/2022] Open
Abstract
Yield is one of the most important agronomic traits for the breeding of rapeseed (Brassica napus L), but its genetic dissection for the formation of high yield remains enigmatic, given the rapid population growth. In the present review, we review the discovery of major loci underlying important agronomic traits and the recent advancement in the selection of complex traits. Further, we discuss the benchmark summary of high-throughput techniques for the high-resolution genetic breeding of rapeseed. Biparental linkage analysis and association mapping have become powerful strategies to comprehend the genetic architecture of complex agronomic traits in crops. The generation of improved crop varieties, especially rapeseed, is greatly urged to enhance yield productivity. In this sense, the whole-genome sequencing of rapeseed has become achievable to clone and identify quantitative trait loci (QTLs). Moreover, the generation of high-throughput sequencing and genotyping techniques has significantly enhanced the precision of QTL mapping and genome-wide association study (GWAS) methodologies. Furthermore, this study demonstrates the first attempt to identify novel QTLs of yield-related traits, specifically focusing on ovule number per pod (ON). We also highlight the recent breakthrough concerning single-locus-GWAS (SL-GWAS) and multi-locus GWAS (ML-GWAS), which aim to enhance the potential and robust control of GWAS for improved complex traits.
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Affiliation(s)
- Shahid Ullah Khan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; (S.U.K.); (S.S.); (M.H.U.K.)
| | - Sumbul Saeed
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; (S.U.K.); (S.S.); (M.H.U.K.)
| | - Muhammad Hafeez Ullah Khan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; (S.U.K.); (S.S.); (M.H.U.K.)
| | - Chuchuan Fan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; (S.U.K.); (S.S.); (M.H.U.K.)
| | - Sunny Ahmar
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3465548, Chile;
| | - Osvin Arriagada
- Departamento de Ciencias Vegetales, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile;
| | - Raheel Shahzad
- Department of Biotechnology, Faculty of Science & Technology, Universitas Muhammadiyah Bandung, Bandung 40614, Indonesia;
| | - Ferdinando Branca
- Department of Agriculture, Food and Environment (Di3A), University of Catania, 95123 Catania, Italy;
| | - Freddy Mora-Poblete
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3465548, Chile;
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23
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Zhong H, Liu S, Sun T, Kong W, Deng X, Peng Z, Li Y. Multi-locus genome-wide association studies for five yield-related traits in rice. BMC PLANT BIOLOGY 2021; 21:364. [PMID: 34376143 PMCID: PMC8353822 DOI: 10.1186/s12870-021-03146-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 07/27/2021] [Indexed: 05/14/2023]
Abstract
BACKGROUND Improving the overall production of rice with high quality is a major target of breeders. Mining potential yield-related loci have been geared towards developing efficient rice breeding strategies. In this study, one single-locus genome-wide association studies (SL-GWAS) method (MLM) in conjunction with five multi-locus genome-wide association studies (ML-GWAS) approaches (mrMLM, FASTmrMLM, pLARmEB, pKWmEB, and ISIS EM-BLASSO) were conducted in a panel consisting of 529 rice core varieties with 607,201 SNPs. RESULTS A total of 152, 106, 12, 111, and 64 SNPs were detected by the MLM model associated with the five yield-related traits, namely grain length (GL), grain width (GW), grain thickness (GT), thousand-grain weight (TGW), and yield per plant (YPP), respectively. Furthermore, 74 significant quantitative trait nucleotides (QTNs) were presented across at least two ML-GWAS methods to be associated with the above five traits successively. Finally, 20 common QTNs were simultaneously discovered by both SL-GWAS and ML-GWAS methods. Based on genome annotation, gene expression analysis, and previous studies, two candidate key genes (LOC_Os09g02830 and LOC_Os07g31450) were characterized to affect GW and TGW, separately. CONCLUSIONS These outcomes will provide an indication for breeding high-yielding rice varieties in the immediate future.
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Affiliation(s)
- Hua Zhong
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan, People's Republic of China, 430072
| | - Shuai Liu
- Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology, Mississippi State University, Starkville, MS, 39762, USA
| | - Tong Sun
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan, People's Republic of China, 430072
| | - Weilong Kong
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan, People's Republic of China, 430072
| | - Xiaoxiao Deng
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan, People's Republic of China, 430072
| | - Zhaohua Peng
- Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology, Mississippi State University, Starkville, MS, 39762, USA
| | - Yangsheng Li
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan, People's Republic of China, 430072.
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24
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Aoun M, Carter AH, Ward BP, Morris CF. Genome-wide association mapping of the 'super-soft' kernel texture in white winter wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:2547-2559. [PMID: 34052883 DOI: 10.1007/s00122-021-03841-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 04/20/2021] [Indexed: 06/12/2023]
Abstract
The novel super-soft kernel phenotype has the potential to improve wheat processing and flour quality. We identified genomic regions associated with this kernel texture in white winter wheat. Grain hardness is a key determinant of wheat milling and baking quality. The recently discovered 'super-soft' kernel phenotype has the potential to improve wheat processing and flour quality. However, the genetic basis underlying the super-soft trait in wheat is not yet well understood. In this study, we investigated the phenotypic and genotypic structure of the super-soft trait in a collection of 172 advanced soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the USA. This collection had a continuous distribution for grain hardness index (single-kernel characterization system). Ten super-soft genotypes showed hardness index ≤ 12 including the cultivar Jasper. Over 98,000 SNP markers from genotyping-by-sequencing were used for association mapping (GWAS). The GWAS identified 20 significant markers associated with grain hardness. These significant SNPs corresponded to seven QTL on chromosomes 2B, 3A, 3B, 5A, 6B,7A, and one unaligned chromosome. Two of these QTL, QSKhard.wql-3A and QSKhard.wql-5A, had large effects and distinguished between the normal soft and the super-soft classes. QSKhard.wql-3A and QSKhard.wql-5A reduced the hardness index by 11.7 and 13.1 on average, respectively. The remaining QTL had small effects and reduced grain hardness within the normal soft range. QSKhard.wql-2B, QSKhard.wql-3A, QSKhard.wql-3B, and QSKhard.wql-6B were not previously reported to be in genomic regions of grain hardness-related genes/QTL. The identified super-soft genotypes as well as the SNPs associated with lower grain hardness will be useful to assist breeding for this grain texture trait.
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Affiliation(s)
- Meriem Aoun
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA
| | - Arron H Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA
| | - Brian P Ward
- USDA-ARS Plant Science Research Campus, Raleigh, NC, 27695, USA
- Department of Horticulture and Crop Science, Ohio State University, Wooster, OH, 44691, USA
| | - Craig F Morris
- USDA-ARS Western Wheat Quality Laboratory, E-202 Food Quality Building, Washington State University, Pullman, WA, 99164-6394, USA.
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25
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Han X, Xu ZR, Zhou L, Han CY, Zhang YM. Identification of QTNs and their candidate genes for flowering time and plant height in soybean using multi-locus genome-wide association studies. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:39. [PMID: 37309439 PMCID: PMC10236079 DOI: 10.1007/s11032-021-01230-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 05/06/2021] [Indexed: 06/14/2023]
Abstract
Flowering time (FT) and plant height (PH) are important agronomic traits in soybean. However, their genetic foundations are not fully understood. Thus, in this study, a total of 106,013 single nucleotide polymorphisms in 286 soybean accessions were used to associate with the first and full FT (FT1 and FT2) and PH in 4 environments and their BLUP values using 6 multi-locus genome-wide association study methods. As a result, 38, 43, and 27 stable quantitative trait nucleotides (QTNs) were identified, respectively, for FT1, FT2, and PH across at least 3 methods and/or environments. Among these QTNs for FT1, FT2, and PH, 31, 36, and 21 were found to have significant phenotype differences across 2 alleles; 22, 18, and 13 were consistent with the corresponding loci in previous studies; 13 and 8 genes, with more than average expression level, around 64 FT and 27 PH QTNs were predicted as their corresponding candidate genes. Among these candidate genes, GmPRR3b, and GmGIa for FT, and GmTFL1b for PH were known, while some were new, e.g., GmPHYA4, GmVRN5, GmFPA, and GmSPA1 for FT, and Glyma.02g300200, GmFPA, and Glyma.13g339800 for PH. All the validated QTNs were used to design the best cross-combinations in 2 FT directions. In each FT direction, the best 5 cross-combinations were predicted, such as Heihe 54 × Qincha 1 for early FT, and Yingdejiadou × Wuhuabayuehuang for late FT. This study provides solid foundations for genetic basis, molecular biology, and breeding by design of soybean FT and PH. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01230-3.
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Affiliation(s)
- Xu Han
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070 China
| | - Zhuo-Ran Xu
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070 China
| | - Ling Zhou
- Institute of Crop Germplasm and Biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014 China
| | - Chun-Yu Han
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070 China
| | - Yuan-Ming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070 China
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26
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Gaikpa DS, Kessel B, Presterl T, Ouzunova M, Galiano-Carneiro AL, Mayer M, Melchinger AE, Schön CC, Miedaner T. Exploiting genetic diversity in two European maize landraces for improving Gibberella ear rot resistance using genomic tools. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:793-805. [PMID: 33274402 PMCID: PMC7925457 DOI: 10.1007/s00122-020-03731-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 11/13/2020] [Indexed: 06/12/2023]
Abstract
KEY MESSAGE High genetic variation in two European maize landraces can be harnessed to improve Gibberella ear rot resistance by integrated genomic tools. Fusarium graminearum (Fg) causes Gibberella ear rot (GER) in maize leading to yield reduction and contamination of grains with several mycotoxins. This study aimed to elucidate the molecular basis of GER resistance among 500 doubled haploid lines derived from two European maize landraces, "Kemater Landmais Gelb" (KE) and "Petkuser Ferdinand Rot" (PE). The two landraces were analyzed individually using genome-wide association studies and genomic selection (GS). The lines were genotyped with a 600-k maize array and phenotyped for GER severity, days to silking, plant height, and seed-set in four environments using artificial infection with a highly aggressive Fg isolate. High genotypic variances and broad-sense heritabilities were found for all traits. Genotype-environment interaction was important throughout. The phenotypic (r) and genotypic ([Formula: see text]) correlations between GER severity and three agronomic traits were low (r = - 0.27 to 0.20; [Formula: see text]= - 0.32 to 0.22). For GER severity, eight QTLs were detected in KE jointly explaining 34% of the genetic variance. In PE, no significant QTLs for GER severity were detected. No common QTLs were found between GER severity and the three agronomic traits. The mean prediction accuracies ([Formula: see text]) of weighted GS (wRR-BLUP) were higher than [Formula: see text] of marker-assisted selection (MAS) and unweighted GS (RR-BLUP) for GER severity. Using KE as the training set and PE as the validation set resulted in very low [Formula: see text] that could be improved by using fixed marker effects in the GS model.
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Affiliation(s)
| | - Bettina Kessel
- Kleinwanzlebener Saatzucht (KWS) KWS SAAT SE & Co. KGaA, Einbeck, Germany
| | - Thomas Presterl
- Kleinwanzlebener Saatzucht (KWS) KWS SAAT SE & Co. KGaA, Einbeck, Germany
| | - Milena Ouzunova
- Kleinwanzlebener Saatzucht (KWS) KWS SAAT SE & Co. KGaA, Einbeck, Germany
| | | | - Manfred Mayer
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Albrecht E Melchinger
- Institute of Plant Breeding, Population Genetics and Seed Science, University of Hohenheim, Stuttgart, Germany
| | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Thomas Miedaner
- State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany.
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27
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Li S, Zhang C, Yang D, Lu M, Qian Y, Jin F, Liu X, Wang Y, Liu W, Li X. Detection of QTNs for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus GWAS. Sci Rep 2021; 11:1764. [PMID: 33469070 PMCID: PMC7815807 DOI: 10.1038/s41598-020-80391-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Accepted: 12/21/2020] [Indexed: 11/10/2022] Open
Abstract
Maize is China’s largest grain crop. Mechanical grain harvesting is the key technology in maize production, and the kernel moisture concentration (KMC) is the main controlling factor in mechanical maize harvesting in China. The kernel dehydration rate (KDR) is closely related to the KMC. Thus, it is important to conduct genome-wide association studies (GWAS) of the KMC and KDR in maize, detect relevant quantitative trait nucleotides (QTNs), and mine relevant candidate genes. Here, 132 maize inbred lines were used to measure the KMC every 5 days from 10 to 40 days after pollination (DAP) in order to calculate the KDR. These lines were genotyped using a maize 55K single-nucleotide polymorphism array. QTNs for the KMC and KDR were detected based on five methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, and ISIS EM-BLASSO) in the package mrMLM. A total of 334 significant QTNs were found for both the KMC and KDR, including 175 QTNs unique to the KMC and 178 QTNs unique to the KDR; 116 and 58 QTNs were detected among the 334 QTNs by two and more than two methods, respectively; and 9 and 5 QTNs among 58 QTNs were detected in 2 and 3 years, respectively. A significant enrichment in cellular component was revealed by Gene Ontology enrichment analysis of candidate genes in the intervals adjacent to the 14 QTNs and this category contained five genes. The information provided in this study may be useful for further mining of genes associated with the KMC and KDR in maize.
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Affiliation(s)
- Shufang Li
- Crop Germplasm Resources Institute, Jilin Academy of Agricultural Sciences, Kemaoxi Street 303, Gongzhuling, 136100, Jilin Province, China
| | - Chunxiao Zhang
- Crop Germplasm Resources Institute, Jilin Academy of Agricultural Sciences, Kemaoxi Street 303, Gongzhuling, 136100, Jilin Province, China
| | - Deguang Yang
- College of Agronomy, Northeast Agricultural University, Harbin, 150030, China
| | - Ming Lu
- Maize Research Institute, Jilin Academy of Agricultural Sciences, Gongzhuling, 136100, China
| | - Yiliang Qian
- Maize Research Center, Anhui Academy of Agricultural Science, Hefei, 230001, China
| | - Fengxue Jin
- Crop Germplasm Resources Institute, Jilin Academy of Agricultural Sciences, Kemaoxi Street 303, Gongzhuling, 136100, Jilin Province, China
| | - Xueyan Liu
- Crop Germplasm Resources Institute, Jilin Academy of Agricultural Sciences, Kemaoxi Street 303, Gongzhuling, 136100, Jilin Province, China
| | - Yu Wang
- Gongzhuling Meteorological Bureau, Gongzhuling, 136100, China
| | - Wenguo Liu
- Maize Research Institute, Jilin Academy of Agricultural Sciences, Gongzhuling, 136100, China.
| | - Xiaohui Li
- Crop Germplasm Resources Institute, Jilin Academy of Agricultural Sciences, Kemaoxi Street 303, Gongzhuling, 136100, Jilin Province, China.
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28
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Bu S, Wu W, Zhang YM. A Multi-Locus Association Model Framework for Nested Association Mapping With Discriminating QTL Effects in Various Subpopulations. Front Genet 2021; 11:590012. [PMID: 33537057 PMCID: PMC7848182 DOI: 10.3389/fgene.2020.590012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 12/04/2020] [Indexed: 11/13/2022] Open
Abstract
Nested association mapping (NAM) has been an invaluable approach for plant genetics community and can dissect the genetic architecture of complex traits. As the most popular NAM analysis strategy, joint multifamily mapping can combine all information from diverse genetic backgrounds and increase population size. However, it is influenced by the genetic heterogeneity of quantitative trait locus (QTL) across various subpopulations. Multi-locus association mapping has been proven to be powerful in many cases of QTL mapping and genome-wide association studies. Therefore, we developed a multi-locus association model of multiple families in the NAM population, which could discriminate the effects of QTLs in all subpopulations. A series of simulations with a real maize NAM genomic data were implemented. The results demonstrated that the new method improves the statistical power in QTL detection and the accuracy in QTL effect estimation. The new approach, along with single-family linkage mapping, was used to identify QTLs for three flowering time traits in the maize NAM population. As a result, most QTLs detected in single family linkage mapping were identified by the new method. In addition, the new method also mapped some new QTLs with small effects, although their functions need to be identified in the future.
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Affiliation(s)
- Suhong Bu
- College of Agriculture, South China Agricultural University, Guangzhou, China
- Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Weiren Wu
- Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Yuan-Ming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
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29
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Lin Y, Zhou K, Hu H, Jiang X, Yu S, Wang Q, Li C, Ma J, Chen G, Yang Z, Liu Y. Multi-Locus Genome-Wide Association Study of Four Yield-Related Traits in Chinese Wheat Landraces. FRONTIERS IN PLANT SCIENCE 2021; 12:665122. [PMID: 34484253 PMCID: PMC8415402 DOI: 10.3389/fpls.2021.665122] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 07/20/2021] [Indexed: 05/13/2023]
Abstract
Wheat (Triticum aestivum L.) is one of the most important crops in the world. Here, four yield-related traits, namely, spike length, spikelets number, tillers number, and thousand-kernel weight, were evaluated in 272 Chinese wheat landraces in multiple environments. Five multi-locus genome-wide association studies (FASTmrEMMA, ISIS EN-BLASSO, mrMLM, pKWmEB, and pLARmEB) were performed using 172,711 single-nucleotide polymorphisms (SNPs) to identify yield-related quantitative trait loci (QTL). A total of 27 robust QTL were identified by more than three models. Nine of these QTL were consistent with those in previous studies. The remaining 18 QTL may be novel. We identified a major QTL, QTkw.sicau-4B, with up to 18.78% of phenotypic variation explained. The developed kompetitive allele-specific polymerase chain reaction marker for QTkw.sicau-4B was validated in two recombinant inbred line populations with an average phenotypic difference of 16.07%. After combined homologous function annotation and expression analysis, TraesCS4B01G272300 was the most likely candidate gene for QTkw.sicau-4B. Our findings provide new insights into the genetic basis of yield-related traits and offer valuable QTL to breed wheat cultivars via marker-assisted selection.
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Affiliation(s)
- Yu Lin
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Kunyu Zhou
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Haiyan Hu
- School of Life Science and Technology, Henan Institute of Science and Technology, Xinxiang, China
| | - Xiaojun Jiang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Shifan Yu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Qing Wang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Caixia Li
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Jian Ma
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Guangdeng Chen
- College of Resources, Sichuan Agricultural University, Chengdu, China
| | - Zisong Yang
- College of Resources and Environment, Aba Teachers University, Wenchuan, China
| | - Yaxi Liu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, China
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
- *Correspondence: Yaxi Liu, , orcid.org/0000-0001-6814-7218
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Zhang YW, Tamba CL, Wen YJ, Li P, Ren WL, Ni YL, Gao J, Zhang YM. mrMLM v4.0.2: An R Platform for Multi-locus Genome-wide Association Studies. GENOMICS PROTEOMICS & BIOINFORMATICS 2020; 18:481-487. [PMID: 33346083 PMCID: PMC8242264 DOI: 10.1016/j.gpb.2020.06.006] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 03/27/2020] [Accepted: 09/08/2020] [Indexed: 12/01/2022]
Abstract
Previous studies have reported that some important loci are missed in single-locus genome-wide association studies (GWAS), especially because of the large phenotypic error in field experiments. To solve this issue, multi-locus GWAS methods have been recommended. However, only a few software packages for multi-locus GWAS are available. Therefore, we developed an R software named mrMLM v4.0.2. This software integrates mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO methods developed by our lab. There are four components in mrMLM v4.0.2, including dataset input, parameter setting, software running, and result output. The fread function in data.table is used to quickly read datasets, especially big datasets, and the doParallel package is used to conduct parallel computation using multiple CPUs. In addition, the graphical user interface software mrMLM.GUI v4.0.2, built upon Shiny, is also available. To confirm the correctness of the aforementioned programs, all the methods in mrMLM v4.0.2 and three widely-used methods were used to analyze real and simulated datasets. The results confirm the superior performance of mrMLM v4.0.2 to other methods currently available. False positive rates are effectively controlled, albeit with a less stringent significance threshold. mrMLM v4.0.2 is publicly available at BioCode (https://bigd.big.ac.cn/biocode/tools/BT007077) or R (https://cran.r-project.org/web/packages/mrMLM.GUI/index.html) as an open-source software.
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Affiliation(s)
- Ya-Wen Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Cox Lwaka Tamba
- Department of Mathematics, Egerton University, Egerton 536-20115, Kenya
| | - Yang-Jun Wen
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Pei Li
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Wen-Long Ren
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong 226019, China
| | - Yuan-Li Ni
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Jun Gao
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuan-Ming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
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Genome-Wide Association Study (GWAS) for Resistance to Sclerotinia sclerotiorum in Common Bean. Genes (Basel) 2020; 11:genes11121496. [PMID: 33322730 PMCID: PMC7764677 DOI: 10.3390/genes11121496] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 12/03/2020] [Accepted: 12/10/2020] [Indexed: 12/25/2022] Open
Abstract
White mold (WM) is a devastating fungal disease affecting common bean (Phaseolus vulgaris L.). In this research, a genome-wide association study (GWAS) for WM resistance was conducted using 294 lines of the Spanish diversity panel. One single-locus method and six multi-locus methods were used in the GWAS. Response to this fungus showed a continuous distribution, and 28 lines were identified as potential resistance sources, including lines of Andean and Mesoamerican origin, as well as intermediate lines between the two gene pools. Twenty-two significant associations were identified, which were organized into 15 quantitative trait intervals (QTIs) located on chromosomes Pv01, Pv02, Pv03, Pv04, Pv08, and Pv09. Seven of these QTIs were identified for the first time, whereas eight corresponded to chromosome regions previously identified in the WM resistance. In all, 468 genes were annotated in these regions, 61 of which were proposed potential candidate genes for WM resistance, based on their function related to the three main defense stages on the host: recognition (22), signal transduction (8), and defense response (31). Results obtained from this work will contribute to a better understanding of the complex quantitative resistance to WM in common bean and reveal information of significance for future breeding programs.
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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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Satturu V, Vattikuti JL, J DS, Kumar A, Singh RK, M SP, Zaw H, Jubay ML, Satish L, Rathore A, Mulinti S, Lakshmi VG I, Fiyaz R. A, Chakraborty A, Thirunavukkarasu N. Multiple Genome Wide Association Mapping Models Identify Quantitative Trait Nucleotides for Brown Planthopper ( Nilaparvata lugens) Resistance in MAGIC Indica Population of Rice. Vaccines (Basel) 2020; 8:vaccines8040608. [PMID: 33066559 PMCID: PMC7712083 DOI: 10.3390/vaccines8040608] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 10/07/2020] [Accepted: 10/08/2020] [Indexed: 02/06/2023] Open
Abstract
Brown planthopper (BPH), one of the most important pests of the rice (Oryza sativa) crop, becomes catastrophic under severe infestations and causes up to 60% yield loss. The highly disastrous BPH biotype in the Indian sub-continent is Biotype 4, which also known as the South Asian Biotype. Though many resistance genes were mapped until now, the utility of the resistance genes in the breeding programs is limited due to the breakdown of resistance and emergence of new biotypes. Hence, to identify the resistance genes for this economically important pest, we have used a multi-parent advanced generation intercross (MAGIC) panel consisting of 391 lines developed from eight indica founder parents. The panel was phenotyped at the controlled conditions for two consecutive years. A set of 27,041 cured polymorphic single nucleotide polymorphism (SNPs) and across-year phenotypic data were used for the identification of marker–trait associations. Genome-wide association analysis was performed to find out consistent associations by employing four single and two multi-locus models. Sixty-one SNPs were consistently detected by all six models. A set of 190 significant marker-associations identified by fixed and random model circulating probability unification (FarmCPU) were considered for searching resistance candidate genes. The highest number of annotated genes were found in chromosome 6 followed by 5 and 1. Ninety-two annotated genes identified across chromosomes of which 13 genes are associated BPH resistance including NB-ARC (nucleotide binding in APAF-1, R gene products, and CED-4) domain-containing protein, NHL repeat-containing protein, LRR containing protein, and WRKY70. The significant SNPs and resistant lines identified from our study could be used for an accelerated breeding program to develop new BPH resistant cultivars.
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Affiliation(s)
- Vanisri Satturu
- Institute of Biotechnology, Professor Jayashankar Telangana State Agricultural University, Rajendranagar, Hyderabad 500030, India; (D.S.J.); (I.L.V.)
- Correspondence: ; Tel.: +91-8186945838
| | - Jhansi Lakshmi Vattikuti
- Entomology, Pathology and Plant breeding Division, Indian Institute of Rice Research (ICAR-IIRR), Rajendranagar, Hyderabad 500030, India; (J.L.V.); (S.P.M.); (A.F.R.)
| | - Durga Sai J
- Institute of Biotechnology, Professor Jayashankar Telangana State Agricultural University, Rajendranagar, Hyderabad 500030, India; (D.S.J.); (I.L.V.)
| | - Arvind Kumar
- Plant Breeding Division, International Rice Research Institute (IRRI)-South Asia Hub (SAH), Patancheru, Hyderabad 502324, India;
| | - Rakesh Kumar Singh
- Plant Breeding Division, International Rice Research Institute (IRRI), Metro Manila 1226, Philippines; (R.K.S.); (H.Z.); (M.L.J.)
- Program Leader and Principal Scientist (Plant Breeding), Crop Diversification and Genetics, International Center for Biosaline Agriculture, Academic City, Dubai 14660, UAE
| | - Srinivas Prasad M
- Entomology, Pathology and Plant breeding Division, Indian Institute of Rice Research (ICAR-IIRR), Rajendranagar, Hyderabad 500030, India; (J.L.V.); (S.P.M.); (A.F.R.)
| | - Hein Zaw
- Plant Breeding Division, International Rice Research Institute (IRRI), Metro Manila 1226, Philippines; (R.K.S.); (H.Z.); (M.L.J.)
- Department of Agriculture, Plant Biotechnology Center, Shwe Nanthar, Mingalardon Township, Yangon 11021, Myanmar
| | - Mona Liza Jubay
- Plant Breeding Division, International Rice Research Institute (IRRI), Metro Manila 1226, Philippines; (R.K.S.); (H.Z.); (M.L.J.)
| | - Lakkakula Satish
- Department of Biotechnology Engineering, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel;
| | - Abhishek Rathore
- Agriculture Statistics Division, International Crops Research for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad 502324, India;
| | - Sreedhar Mulinti
- MFPI-Quality Control Lab, Professor Jayashankar Telangana State Agricultural University, Rajendranagar, Hyderabad 500030, India;
| | - Ishwarya Lakshmi VG
- Institute of Biotechnology, Professor Jayashankar Telangana State Agricultural University, Rajendranagar, Hyderabad 500030, India; (D.S.J.); (I.L.V.)
| | - Abdul Fiyaz R.
- Institute of Biotechnology, Professor Jayashankar Telangana State Agricultural University, Rajendranagar, Hyderabad 500030, India; (D.S.J.); (I.L.V.)
| | - Animikha Chakraborty
- Plant Breeding Division, Indian Institute of Millets Research (ICAR-IIMR), Rajendranagar, Hyderabad 500030, India; (A.C.); (N.T.)
| | - Nepolean Thirunavukkarasu
- Plant Breeding Division, Indian Institute of Millets Research (ICAR-IIMR), Rajendranagar, Hyderabad 500030, India; (A.C.); (N.T.)
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Ramzan F, Gültas M, Bertram H, Cavero D, Schmitt AO. Combining Random Forests and a Signal Detection Method Leads to the Robust Detection of Genotype-Phenotype Associations. Genes (Basel) 2020; 11:E892. [PMID: 32764260 PMCID: PMC7465705 DOI: 10.3390/genes11080892] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 07/28/2020] [Accepted: 08/03/2020] [Indexed: 12/21/2022] Open
Abstract
Genome wide association studies (GWAS) are a well established methodology to identify genomic variants and genes that are responsible for traits of interest in all branches of the life sciences. Despite the long time this methodology has had to mature the reliable detection of genotype-phenotype associations is still a challenge for many quantitative traits mainly because of the large number of genomic loci with weak individual effects on the trait under investigation. Thus, it can be hypothesized that many genomic variants that have a small, however real, effect remain unnoticed in many GWAS approaches. Here, we propose a two-step procedure to address this problem. In a first step, cubic splines are fitted to the test statistic values and genomic regions with spline-peaks that are higher than expected by chance are considered as quantitative trait loci (QTL). Then the SNPs in these QTLs are prioritized with respect to the strength of their association with the phenotype using a Random Forests approach. As a case study, we apply our procedure to real data sets and find trustworthy numbers of, partially novel, genomic variants and genes involved in various egg quality traits.
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Affiliation(s)
- Faisal Ramzan
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany; (F.R.); (M.G.); (H.B.)
- Department of Animal Breeding and Genetics, University of Agriculture Faisalabad, 38000 Faisalabad, Pakistan
| | - Mehmet Gültas
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany; (F.R.); (M.G.); (H.B.)
- Center for Integrated Breeding Research (CiBreed), Albrecht-Thaer-Weg 3, Georg-August University, 37075 Göttingen, Germany
| | - Hendrik Bertram
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany; (F.R.); (M.G.); (H.B.)
| | | | - Armin Otto Schmitt
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany; (F.R.); (M.G.); (H.B.)
- Center for Integrated Breeding Research (CiBreed), Albrecht-Thaer-Weg 3, Georg-August University, 37075 Göttingen, Germany
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Li S, Zhang C, Lu M, Yang D, Qian Y, Yue Y, Zhang Z, Jin F, Wang M, Liu X, Liu W, Li X. QTL mapping and GWAS for field kernel water content and kernel dehydration rate before physiological maturity in maize. Sci Rep 2020; 10:13114. [PMID: 32753586 PMCID: PMC7403598 DOI: 10.1038/s41598-020-69890-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 07/20/2020] [Indexed: 11/09/2022] Open
Abstract
Kernel water content (KWC) and kernel dehydration rate (KDR) are two main factors affecting maize seed quality and have a decisive influence on the mechanical harvest. It is of great importance to map and mine candidate genes related to KWCs and KDRs before physiological maturity in maize. 120 double-haploid (DH) lines constructed from Si287 with low KWC and JiA512 with high KWC were used as the mapping population. KWCs were measured every 5 days from 10 to 40 days after pollination, and KDRs were calculated. A total of 1702 SNP markers were used to construct a linkage map, with a total length of 1,309.02 cM and an average map distance of 0.77 cM. 10 quantitative trait loci (QTLs) and 27 quantitative trait nucleotides (QTNs) were detected by genome-wide composite interval mapping (GCIM) and multi-locus random-SNP-effect mixed linear model (mrMLM), respectively. One and two QTL hotspot regions were found on Chromosome 3 and 7, respectively. Analysis of the Gene Ontology showed that 2 GO terms of biological processes (BP) were significantly enriched (P ≤ 0.05) and 6 candidate genes were obtained. This study provides theoretical support for marker-assisted breeding of mechanical harvest variety in maize.
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Affiliation(s)
- Shufang Li
- Crop Germplasm Resources Institute, Jilin Academy of Agricultural Sciences, Kemaoxi Street 303, Gongzhuling, 136100, Jilin Province, China
| | - Chunxiao Zhang
- Crop Germplasm Resources Institute, Jilin Academy of Agricultural Sciences, Kemaoxi Street 303, Gongzhuling, 136100, Jilin Province, China
| | - Ming Lu
- Maize Research Institute, Jilin Academy of Agricultural Sciences, Gongzhuling, 136100, China
| | - Deguang Yang
- College of Agronomy, Northeast Agricultural University, Harbin, 150030, China
| | - Yiliang Qian
- Maize Research Center, Anhui Academy of Agricultural Science, Hefei, 230001, China
| | - Yaohai Yue
- Maize Research Institute, Jilin Academy of Agricultural Sciences, Gongzhuling, 136100, China
| | - Zhijun Zhang
- Maize Research Institute, Jilin Academy of Agricultural Sciences, Gongzhuling, 136100, China
| | - Fengxue Jin
- Crop Germplasm Resources Institute, Jilin Academy of Agricultural Sciences, Kemaoxi Street 303, Gongzhuling, 136100, Jilin Province, China
| | - Min Wang
- Maize Research Institute, Jilin Academy of Agricultural Sciences, Gongzhuling, 136100, China
| | - Xueyan Liu
- Crop Germplasm Resources Institute, Jilin Academy of Agricultural Sciences, Kemaoxi Street 303, Gongzhuling, 136100, Jilin Province, China
| | - Wenguo Liu
- Maize Research Institute, Jilin Academy of Agricultural Sciences, Gongzhuling, 136100, China.
| | - Xiaohui Li
- Crop Germplasm Resources Institute, Jilin Academy of Agricultural Sciences, Kemaoxi Street 303, Gongzhuling, 136100, Jilin Province, China.
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Zhang YW, Tamba CL, Wen YJ, Li P, Ren WL, Ni YL, Gao J, Zhang YM. mrMLM v4.0.2: An R Platform for Multi-locus Genome-wide Association Studies. GENOMICS, PROTEOMICS & BIOINFORMATICS 2020; 18:481-487. [PMID: 33346083 DOI: 10.1101/2020.03.04.976464] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 03/27/2020] [Accepted: 09/08/2020] [Indexed: 05/22/2023]
Abstract
Previous studies have reported that some important loci are missed in single-locus genome-wide association studies (GWAS), especially because of the large phenotypic error in field experiments. To solve this issue, multi-locus GWAS methods have been recommended. However, only a few software packages for multi-locus GWAS are available. Therefore, we developed an R software named mrMLM v4.0.2. This software integrates mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO methods developed by our lab. There are four components in mrMLM v4.0.2, including dataset input, parameter setting, software running, and result output. The fread function in data.table is used to quickly read datasets, especially big datasets, and the doParallel package is used to conduct parallel computation using multiple CPUs. In addition, the graphical user interface software mrMLM.GUI v4.0.2, built upon Shiny, is also available. To confirm the correctness of the aforementioned programs, all the methods in mrMLM v4.0.2 and three widely-used methods were used to analyze real and simulated datasets. The results confirm the superior performance of mrMLM v4.0.2 to other methods currently available. False positive rates are effectively controlled, albeit with a less stringent significance threshold. mrMLM v4.0.2 is publicly available at BioCode (https://bigd.big.ac.cn/biocode/tools/BT007077) or R (https://cran.r-project.org/web/packages/mrMLM.GUI/index.html) as an open-source software.
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Affiliation(s)
- Ya-Wen Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Cox Lwaka Tamba
- Department of Mathematics, Egerton University, Egerton 536-20115, Kenya
| | - Yang-Jun Wen
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Pei Li
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Wen-Long Ren
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong 226019, China
| | - Yuan-Li Ni
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Jun Gao
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuan-Ming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
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Yang Y, Chai Y, Zhang X, Lu S, Zhao Z, Wei D, Chen L, Hu YG. Multi-Locus GWAS of Quality Traits in Bread Wheat: Mining More Candidate Genes and Possible Regulatory Network. FRONTIERS IN PLANT SCIENCE 2020; 11:1091. [PMID: 32849679 PMCID: PMC7411135 DOI: 10.3389/fpls.2020.01091] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 07/02/2020] [Indexed: 05/20/2023]
Abstract
In wheat breeding, improved quality traits, including grain quality and dough rheological properties, have long been a critical goal. To understand the genetic basis of key quality traits of wheat, two single-locus and five multi-locus GWAS models were performed for six grain quality traits and three dough rheological properties based on 19, 254 SNPs in 267 bread wheat accessions. As a result, 299 quantitative trait nucleotides (QTNs) within 105 regions were identified to be associated with these quality traits in four environments. Of which, 40 core QTN regions were stably detected in at least three environments, 19 of which were novel. Compared with the previous studies, these novel QTN regions explained smaller phenotypic variation, which verified the advantages of the multi-locus GWAS models in detecting important small effect QTNs associated with complex traits. After characterization of the function and expression in-depth, 67 core candidate genes involved in protein/sugar synthesis, histone modification and the regulation of transcription factor were observed to be associated with the formation of grain quality, which showed that multi-level regulations influenced wheat grain quality. Finally, a preliminary network of gene regulation that may affect wheat quality formation was inferred. This study verified the power and reliability of multi-locus GWAS methods in wheat quality trait research, and increased the understanding of wheat quality formation mechanisms. The detected QTN regions and candidate genes in this study could be further used for gene cloning and marker-assisted selection in high-quality breeding of bread wheat.
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Affiliation(s)
- Yang Yang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Yongmao Chai
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Xuan Zhang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Shan Lu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Zhangchen Zhao
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Di Wei
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Liang Chen
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Yin-Gang Hu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
- Institute of Water Saving Agriculture in Arid Regions of China, Northwest A&F University, Yangling, China
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Genetic Basis of Maize Resistance to Multiple Insect Pests: Integrated Genome-Wide Comparative Mapping and Candidate Gene Prioritization. Genes (Basel) 2020; 11:genes11060689. [PMID: 32599710 PMCID: PMC7349181 DOI: 10.3390/genes11060689] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 05/30/2020] [Accepted: 06/01/2020] [Indexed: 01/01/2023] Open
Abstract
Several species of herbivores feed on maize in field and storage setups, making the development of multiple insect resistance a critical breeding target. In this study, an association mapping panel of 341 tropical maize lines was evaluated in three field environments for resistance to fall armyworm (FAW), whilst bulked grains were subjected to a maize weevil (MW) bioassay and genotyped with Diversity Array Technology's single nucleotide polymorphisms (SNPs) markers. A multi-locus genome-wide association study (GWAS) revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits on all 10 maize chromosomes, of which, 47 and 31 were discovered at stringent Bonferroni genome-wide significance levels of 0.05 and 0.01, respectively, and located within or close to multiple insect resistance genomic regions (MIRGRs) concerning FAW, SB, and MW. Sixteen QTNs influenced multiple traits, of which, six were associated with resistance to both FAW and MW, suggesting a pleiotropic genetic control. Functional prioritization of candidate genes (CGs) located within 10-30 kb of the QTNs revealed 64 putative GWAS-based CGs (GbCGs) showing evidence of involvement in plant defense mechanisms. Only one GbCG was associated with each of the five of the six combined resistance QTNs, thus reinforcing the pleiotropy hypothesis. In addition, through in silico co-functional network inferences, an additional 107 network-based CGs (NbCGs), biologically connected to the 64 GbCGs, and differentially expressed under biotic or abiotic stress, were revealed within MIRGRs. The provided multiple insect resistance physical map should contribute to the development of combined insect resistance in maize.
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Kaler AS, Abdel-Haleem H, Fritschi FB, Gillman JD, Ray JD, Smith JR, Purcell LC. Genome-Wide Association Mapping of Dark Green Color Index using a Diverse Panel of Soybean Accessions. Sci Rep 2020; 10:5166. [PMID: 32198467 PMCID: PMC7083947 DOI: 10.1038/s41598-020-62034-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Accepted: 03/06/2020] [Indexed: 11/14/2022] Open
Abstract
Nitrogen (N) plays a key role in plants because it is a major component of RuBisCO and chlorophyll. Hence, N is central to both the dark and light reactions of photosynthesis. Genotypic variation in canopy greenness provides insights into the variation of N and chlorophyll concentration, photosynthesis rates, and N2 fixation in legumes. The objective of this study was to identify significant loci associated with the intensity of greenness of the soybean [Glycine max (L.) Merr.] canopy as determined by the Dark Green Color Index (DGCI). A panel of 200 maturity group IV accessions was phenotyped for canopy greenness using DGCI in three environments. Association mapping identified 45 SNPs that were significantly (P ≤ 0.0003) associated with DGCI in three environments, and 16 significant SNPs associated with DGCI averaged across all environments. These SNPs likely tagged 43 putative loci. Out of these 45 SNPs, eight were present in more than one environment. Among the identified loci, 21 were located in regions previously reported for N traits and ureide concentration. Putative loci that were coincident with previously reported genomic regions may be important resources for pyramiding favorable alleles for improved N and chlorophyll concentrations, photosynthesis rates, and N2 fixation in soybean.
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Affiliation(s)
- Avjinder S Kaler
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, 72704, USA
| | - Hussein Abdel-Haleem
- USDA-ARS, U.S. Arid Land Agricultural Research Center, 21881 North Cardon Lane, Maricopa, AZ, 85138, USA
| | - Felix B Fritschi
- Division of Plant Sciences, Univ. of Missouri, Columbia, MO, 65211, USA
| | - Jason D Gillman
- Plant Genetic Research Unit, USDA-ARS, University of Missouri, Columbia, MO, 65211, USA
| | - Jeffery D Ray
- Crop Genetics Research Unit, USDA-ARS, 141 Experimental Station Road, Stoneville, MS, 38776, USA
| | - James R Smith
- Crop Genetics Research Unit, USDA-ARS, 141 Experimental Station Road, Stoneville, MS, 38776, USA
| | - Larry C Purcell
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, 72704, USA.
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Genome-Wide Composite Interval Mapping (GCIM) of Expressional Quantitative Trait Loci in Backcross Population. Methods Mol Biol 2020; 2082:63-71. [PMID: 31849008 DOI: 10.1007/978-1-0716-0026-9_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
One of the most remarkable findings in expressional quantitative trait locus (eQTL) mapping is that trans (distal) eQTL has small effect. The widely used approaches have a low power in the detection of small-effect eQTL. To overcome this issue, we integrate polygenic background control with multi-locus genetic model to develop genome-wide composite interval mapping (GCIM). This chapter covers the GCIM procedure in a backcross or doubled haploid populations. We describe the genetic model, parameter estimation, multi-locus genetic model, hypothesis tests, and software. Finally, some issues related to the GCIM method are discussed.
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41
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Wu P, Wang K, Yang Q, Zhou J, Chen D, Liu Y, Ma J, Tang Q, Jin L, Xiao W, Lou P, Jiang A, Jiang Y, Zhu L, Li M, Li X, Tang G. Whole-genome re-sequencing association study for direct genetic effects and social genetic effects of six growth traits in Large White pigs. Sci Rep 2019; 9:9667. [PMID: 31273229 PMCID: PMC6609718 DOI: 10.1038/s41598-019-45919-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 06/20/2019] [Indexed: 12/23/2022] Open
Abstract
Socially affected traits are affected by direct genetic effects (DGE) and social genetic effects (SGE). DGE and SGE of an individual directly quantify the genetic influence of its own phenotypes and the phenotypes of other individuals, respectively. In the current study, a total of 3,276 Large White pigs from different pens were used, and each pen contained 10 piglets. DGE and SGE were estimated for six socially affected traits, and then a GWAS was conducted to identify SNPs associated with DGE and SGE. Based on the whole-genome re-sequencing, 40 Large White pigs were genotyped and 10,501,384 high quality SNPs were retained for single-locus and multi-locus GWAS. For single-locus GWAS, a total of 54 SNPs associated with DGE and 33 SNPs with SGE exceeded the threshold (P < 5.00E-07) were detected for six growth traits. Of these, 22 SNPs with pleiotropic effects were shared by DGE and SGE. For multi-locus GWAS, a total of 72 and 110 putative QTNs were detected for DGE and SGE, respectively. Of these, 5 SNPs with pleiotropic effects were shared by DGE and SGE. It is noteworthy that 2 SNPs (SSC8: 16438396 for DGE and SSC17: 9697454 for SGE) were detected in single-locus and multi-locus GWAS. Furthermore, 15 positional candidate genes shared by SGE and DGE were identified because of their roles in behaviour, health and disease. Identification of genetic variants and candidate genes for DGE and SGE for socially affected traits will provide a new insight to understand the genetic architecture of socially affected traits in pigs.
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Affiliation(s)
- Pingxian Wu
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Kai Wang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Qiang Yang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Jie Zhou
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Dejuan Chen
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Yihui Liu
- Sichuan Animal Husbandry Station, Chengdu, 610041, Sichuan, China
| | - Jideng Ma
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Qianzi Tang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Long Jin
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Weihang Xiao
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Pinger Lou
- Zhejiang Tianpeng Group Co., Ltd., Jiangshan, 324111, Zhejiang, China
| | - Anan Jiang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Yanzhi Jiang
- College of Life Science, Sichuan Agricultural University, Yaan, 625014, Sichuan, China
| | - Li Zhu
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Mingzhou Li
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Xuewei Li
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Guoqing Tang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.
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42
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Wei J, Xie W, Li R, Wang S, Qu H, Ma R, Zhou X, Jia Z. Analysis of trait heritability in functionally partitioned rice genomes. Heredity (Edinb) 2019; 124:485-498. [PMID: 31253955 DOI: 10.1038/s41437-019-0244-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Revised: 06/05/2019] [Accepted: 06/08/2019] [Indexed: 01/10/2023] Open
Abstract
Knowledge of the genetic architecture of importantly agronomical traits can speed up genetic improvement in cultivated rice (Oryza sativa L.). Many recent investigations have leveraged genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs), associated with agronomic traits in various rice populations. The reported trait-relevant SNPs appear to be arbitrarily distributed along the genome, including genic and nongenic regions. Whether the SNPs in different genomic regions play different roles in trait heritability and which region is more responsible for phenotypic variation remains opaque. We analyzed a natural rice population of 524 accessions with 3,616,597 SNPs to compare the genetic contributions of functionally distinct genomic regions for five agronomic traits, i.e., yield, heading date, plant height, grain length, and grain width. An analysis of heritability in the functionally partitioned rice genome showed that regulatory or intergenic regions account for the most trait heritability. A close look at the trait-associated SNPs (TASs) indicated that the majority of the TASs are located in nongenic regions, and the genetic effects of the TASs in nongenic regions are generally greater than those in genic regions. We further compared the predictabilities using the genetic variants from genic regions with those using nongenic regions. The results revealed that nongenic regions play a more important role than genic regions in trait heritability in rice, which is consistent with findings in humans and maize. This conclusion not only offers clues for basic research to disclose genetics behind these agronomic traits, but also provides a new perspective to facilitate genomic selection in rice.
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Affiliation(s)
- Julong Wei
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu, China.,Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Weibo Xie
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Ruidong Li
- Department of Botany & Plant Sciences, University of California (Riverside), Riverside, CA, USA
| | - Shibo Wang
- Department of Botany & Plant Sciences, University of California (Riverside), Riverside, CA, USA
| | - Han Qu
- Department of Botany & Plant Sciences, University of California (Riverside), Riverside, CA, USA
| | - Renyuan Ma
- Department of Botany & Plant Sciences, University of California (Riverside), Riverside, CA, USA.,Department of Mathematics, Bowdoin College, Brunswick, ME, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Zhenyu Jia
- Department of Botany & Plant Sciences, University of California (Riverside), Riverside, CA, USA.
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43
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Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R. Methodological implementation of mixed linear models in multi-locus genome-wide association studies. Brief Bioinform 2019; 19:700-712. [PMID: 28158525 PMCID: PMC6054291 DOI: 10.1093/bib/bbw145] [Citation(s) in RCA: 190] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Indexed: 12/01/2022] Open
Abstract
The mixed linear model has been widely used in genome-wide association studies (GWAS), but its application to multi-locus GWAS analysis has not been explored and assessed. Here, we implemented a fast multi-locus random-SNP-effect EMMA (FASTmrEMMA) model for GWAS. The model is built on random single nucleotide polymorphism (SNP) effects and a new algorithm. This algorithm whitens the covariance matrix of the polygenic matrix K and environmental noise, and specifies the number of nonzero eigenvalues as one. The model first chooses all putative quantitative trait nucleotides (QTNs) with ≤ 0.005 P-values and then includes them in a multi-locus model for true QTN detection. Owing to the multi-locus feature, the Bonferroni correction is replaced by a less stringent selection criterion. Results from analyses of both simulated and real data showed that FASTmrEMMA is more powerful in QTN detection and model fit, has less bias in QTN effect estimation and requires a less running time than existing single- and multi-locus methods, such as empirical Bayes, settlement of mixed linear model under progressively exclusive relationship (SUPER), efficient mixed model association (EMMA), compressed MLM (CMLM) and enriched CMLM (ECMLM). FASTmrEMMA provides an alternative for multi-locus GWAS.
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Affiliation(s)
- Yang-Jun Wen
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Hanwen Zhang
- Applied Science, University of British Columbia, Columbia, Canada
| | - Yuan-Li Ni
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Bo Huang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Jin Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Jian-Ying Feng
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Shi-Bo Wang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Jim M Dunwell
- School of Agriculture, Policy and Development, University of Reading, Berkshire, UK
| | - Yuan-Ming Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.,College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Rongling Wu
- Public Health Sciences and Statistics and Center for Statistical Genetics, Pennsylvania State University, Hershey, PA, USA.,Center for Computational Biology, Beijing Forestry University, Beijing, China
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44
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Zhang YM, Jia Z, Dunwell JM. Editorial: The Applications of New Multi-Locus GWAS Methodologies in the Genetic Dissection of Complex Traits. FRONTIERS IN PLANT SCIENCE 2019; 10:100. [PMID: 30804969 DOI: 10.3389/978-2-88945-834-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 01/22/2019] [Indexed: 05/22/2023]
Affiliation(s)
- Yuan-Ming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Zhenyu Jia
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
| | - Jim M Dunwell
- School of Agriculture, Policy and Development, University of Reading, Reading, United Kingdom
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45
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Zhang YM, Jia Z, Dunwell JM. Editorial: The Applications of New Multi-Locus GWAS Methodologies in the Genetic Dissection of Complex Traits. FRONTIERS IN PLANT SCIENCE 2019; 10:100. [PMID: 30804969 PMCID: PMC6378272 DOI: 10.3389/fpls.2019.00100] [Citation(s) in RCA: 102] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 01/22/2019] [Indexed: 05/04/2023]
Affiliation(s)
- Yuan-Ming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
- *Correspondence: Yuan-Ming Zhang
| | - Zhenyu Jia
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
| | - Jim M. Dunwell
- School of Agriculture, Policy and Development, University of Reading, Reading, United Kingdom
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46
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Lü H, Yang Y, Li H, Liu Q, Zhang J, Yin J, Chu S, Zhang X, Yu K, Lv L, Chen X, Zhang D. Genome-Wide Association Studies of Photosynthetic Traits Related to Phosphorus Efficiency in Soybean. FRONTIERS IN PLANT SCIENCE 2018; 9:1226. [PMID: 30210514 PMCID: PMC6122521 DOI: 10.3389/fpls.2018.01226] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 07/31/2018] [Indexed: 05/20/2023]
Abstract
Photosynthesis is the basis of plant growth and development, and is seriously affected by low phosphorus (P) stress. However, few studies have reported for the genetic foundation of photosynthetic response to low P stress in soybean. To address this issue, 219 soybean accessions were genotyped by 292,035 high-quality single nucleotide polymorphisms (SNPs) and phenotyped under normal and low P conditions in 2015 and 2016. These datasets were used to identify quantitative trait nucleotides (QTNs) for photosynthesis-related traits using mrMLM, ISIS EM-BLASSO, pLARmEB, FASTmrMLM, FASTmrEMMA, and pKWmEB methods. As a result, 159 QTNs within 31 genomic regions were found to be associated with four photosynthesis-related traits under different P stress conditions. Among the 31 associated regions, five (q7-2, q8-1, q9, q13-1, and q20-2) were detected commonly under both normal and low P conditions, indicating the insensitivity of these candidate genes to low P stress; five were detected only under normal P condition, indicating the sensitivity of these candidate genes to low P stress; six were detected only under low P condition, indicating the tolerantness of these candidate genes to low P stress; 20 were reported in previous studies. Around the 159 QTNs, 52 candidate genes were mined. These results provide the important information for marker-assisted breeding in soybean and further reveal the basis for the application of P tolerance to photosynthetic capacity.
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Affiliation(s)
- Haiyan Lü
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, China
| | - Yuming Yang
- National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Haiwang Li
- College of Food Science and Technology, Henan University of Technology, Zhengzhou, China
| | - Qijia Liu
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, China
| | - Jianjun Zhang
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, China
| | - Junyi Yin
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, China
| | - Shanshan Chu
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, China
| | - Xiangqian Zhang
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, China
| | - Kaiye Yu
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, China
| | - Lingling Lv
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, China
| | - Xi Chen
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, China
| | - Dan Zhang
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, China
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47
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Song J, Li J, Sun J, Hu T, Wu A, Liu S, Wang W, Ma D, Zhao M. Genome-Wide Association Mapping for Cold Tolerance in a Core Collection of Rice ( Oryza sativa L.) Landraces by Using High-Density Single Nucleotide Polymorphism Markers From Specific-Locus Amplified Fragment Sequencing. FRONTIERS IN PLANT SCIENCE 2018; 9:875. [PMID: 30013584 PMCID: PMC6036282 DOI: 10.3389/fpls.2018.00875] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 06/05/2018] [Indexed: 05/02/2023]
Abstract
Understanding the genetic mechanism of cold tolerance in rice is important to mine elite genes from rice landraces and breed excellent cultivars for this trait. In this study, a genome-wide association study (GWAS) was performed using high-density single nucleotide polymorphisms (SNPs) obtained using specific-locus amplified fragment sequencing (SLAF-seq) technology from a core collection of landraces of rice. A total of 67,511 SNPs obtained from 116,643 SLAF tags were used for genotyping the 150 accessions of rice landraces in the Ting's rice core collection. A compressed mixed liner model was used to perform GWAS by using the high-density SNPs for cold tolerance in rice landraces at the seedling stage. A total of 26 SNPs were found to be significantly (P < 1.48 × 10-7) associated with cold tolerance, which could explained phenotypic variations ranging from 26 to 33%. Among them, two quantitative trait loci (QTLs) were mapped closely to the previously cloned/mapped genes or QTLs for cold tolerance. A newly identified QTL for cold tolerance in rice was further characterized by sequencing, real time-polymerase chain reaction, and bioinformatics analyses. One candidate gene, i.e., Os01g0620100, showed different gene expression levels between the cold tolerant and sensitive landraces under cold stress. We found the difference of coding amino acid in Os01g0620100 between cold tolerant and sensitive landraces caused by polymorphism within the coding domain sequence. In addition, the prediction of Os01g0620100 protein revealed a WD40 domain that was frequently found in cold tolerant landraces. Therefore, we speculated that Os01g0620100 was highly important for the response to cold stress in rice. These results indicated that rice landraces are important sources for investigating rice cold tolerance, and the mapping results might provide important information to breed cold-tolerant rice cultivars by using marker-assisted selection.
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Affiliation(s)
- Jiayu Song
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
| | - Jinqun Li
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, South China Agricultural University, Guangzhou, China
- Department of Plant Breeding and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Jian Sun
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
| | - Tao Hu
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
| | - Aiting Wu
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
| | - Sitong Liu
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
| | - Wenjia Wang
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
| | - Dianrong Ma
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
| | - Minghui Zhao
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
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48
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Ren WL, Wen YJ, Dunwell JM, Zhang YM. pKWmEB: integration of Kruskal-Wallis test with empirical Bayes under polygenic background control for multi-locus genome-wide association study. Heredity (Edinb) 2018; 120:208-218. [PMID: 29234158 PMCID: PMC5836593 DOI: 10.1038/s41437-017-0007-4] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Revised: 08/30/2017] [Accepted: 09/06/2017] [Indexed: 11/08/2022] Open
Abstract
Although nonparametric methods in genome-wide association studies (GWAS) are robust in quantitative trait nucleotide (QTN) detection, the absence of polygenic background control in single-marker association in genome-wide scans results in a high false positive rate. To overcome this issue, we proposed an integrated nonparametric method for multi-locus GWAS. First, a new model transformation was used to whiten the covariance matrix of polygenic matrix K and environmental noise. Using the transferred model, Kruskal-Wallis test along with least angle regression was then used to select all the markers that were potentially associated with the trait. Finally, all the selected markers were placed into multi-locus model, these effects were estimated by empirical Bayes, and all the nonzero effects were further identified by a likelihood ratio test for true QTN detection. This method, named pKWmEB, was validated by a series of Monte Carlo simulation studies. As a result, pKWmEB effectively controlled false positive rate, although a less stringent significance criterion was adopted. More importantly, pKWmEB retained the high power of Kruskal-Wallis test, and provided QTN effect estimates. To further validate pKWmEB, we re-analyzed four flowering time related traits in Arabidopsis thaliana, and detected some previously reported genes that were not identified by the other methods.
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Affiliation(s)
- Wen-Long Ren
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095, China
- Statistical Genomics Lab, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yang-Jun Wen
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095, China
- Statistical Genomics Lab, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jim M Dunwell
- School of Agriculture, Policy and Development, University of Reading, Reading, RG6 6AR, UK
| | - Yuan-Ming Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095, China.
- Statistical Genomics Lab, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.
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49
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Ma L, Liu M, Yan Y, Qing C, Zhang X, Zhang Y, Long Y, Wang L, Pan L, Zou C, Li Z, Wang Y, Peng H, Pan G, Jiang Z, Shen Y. Genetic Dissection of Maize Embryonic Callus Regenerative Capacity Using Multi-Locus Genome-Wide Association Studies. FRONTIERS IN PLANT SCIENCE 2018; 9:561. [PMID: 29755499 PMCID: PMC5933171 DOI: 10.3389/fpls.2018.00561] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Accepted: 04/10/2018] [Indexed: 05/04/2023]
Abstract
The regenerative capacity of the embryonic callus, a complex quantitative trait, is one of the main limiting factors for maize transformation. This trait was decomposed into five traits, namely, green callus rate (GCR), callus differentiating rate (CDR), callus plantlet number (CPN), callus rooting rate (CRR), and callus browning rate (CBR). To dissect the genetic foundation of maize transformation, in this study multi-locus genome-wide association studies (GWAS) for the five traits were performed in a population of 144 inbred lines genotyped with 43,427 SNPs. Using the phenotypic values in three environments and best linear unbiased prediction (BLUP) values, as a result, a total of 127, 56, 160, and 130 significant quantitative trait nucleotides (QTNs) were identified by mrMLM, FASTmrEMMA, ISIS EM-BLASSO, and pLARmEB, respectively. Of these QTNs, 63 QTNs were commonly detected, including 15 across multiple environments and 58 across multiple methods. Allele distribution analysis showed that the proportion of superior alleles for 36 QTNs was <50% in 31 elite inbred lines. Meanwhile, these superior alleles had obviously additive effect on the regenerative capacity. This indicates that the regenerative capacity-related traits can be improved by proper integration of the superior alleles using marker-assisted selection. Moreover, a total of 40 candidate genes were found based on these common QTNs. Some annotated genes were previously reported to relate with auxin transport, cell fate, seed germination, or embryo development, especially, GRMZM2G108933 (WOX2) was found to promote maize transgenic embryonic callus regeneration. These identified candidate genes will contribute to a further understanding of the genetic foundation of maize embryonic callus regeneration.
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Affiliation(s)
- Langlang Ma
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Min Liu
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Yuanyuan Yan
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Chunyan Qing
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Xiaoling Zhang
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Yanling Zhang
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Yun Long
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Lei Wang
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Lang Pan
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Chaoying Zou
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Zhaoling Li
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Yanli Wang
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Huanwei Peng
- Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu, China
| | - Guangtang Pan
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Zhou Jiang
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Yaou Shen
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, China
- *Correspondence: Yaou Shen
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Hu X, Zuo J, Wang J, Liu L, Sun G, Li C, Ren X, Sun D. Multi-Locus Genome-Wide Association Studies for 14 Main Agronomic Traits in Barley. FRONTIERS IN PLANT SCIENCE 2018; 9:1683. [PMID: 30524459 PMCID: PMC6257129 DOI: 10.3389/fpls.2018.01683] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 10/29/2018] [Indexed: 05/02/2023]
Abstract
The agronomic traits, including morphological and yield component traits, are important in barley breeding programs. In order to reveal the genetic foundation of agronomic traits of interest, in this study 122 doubled haploid lines from a cross between cultivars "Huaai 11" (six-rowed and dwarf) and "Huadamai 6" (two-rowed) were genotyped by 9680 SNPs and phenotyped 14 agronomic traits in 3 years, and the two datasets were used to conduct multi-locus genome-wide association studies. As a result, 913 quantitative trait nucleotides (QTNs) were identified by five multi-locus GWAS methods to be associated with the above 14 traits and their best linear unbiased predictions. Among these QTNs and their adjacent genes, 39 QTNs (or QTN clusters) were repeatedly detected in various environments and methods, and 10 candidate genes were identified from gene annotation. Nineteen QTNs and two genes (sdw1/denso and Vrs1) were previously reported, and eight candidate genes need to be further validated. The Vrs1 gene, controlling the number of rows in the spike, was found to be associated with spikelet number of main spike, spikelet number per plant, grain number per plant, grain number per spike, and 1,000 grain weight in multiple environments and by multi-locus GWAS methods. Therefore, the above results evidenced the feasibility and reliability of genome-wide association studies in doubled haploid population, and the QTNs and their candidate genes detected in this study are useful for marker-assisted selection breeding, gene cloning, and functional identification in barley.
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Affiliation(s)
- Xin Hu
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
- Guiyang College of Traditional Chinese Medicine, Guiyang, China
| | - Jianfang Zuo
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Jibin Wang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Lipan Liu
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Genlou Sun
- Biology Department, Saint Mary's University, Halifax, NS, Canada
| | - Chengdao Li
- School of Veterinary and Life Sciences, Murdoch University, Murdoch, WA, Australia
- Hubei Collaborative Innovation Center for Grain Industry, Jingzhou, China
| | - Xifeng Ren
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
- Xifeng Ren
| | - Dongfa Sun
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
- Hubei Collaborative Innovation Center for Grain Industry, Jingzhou, China
- *Correspondence: Dongfa Sun
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