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Mane RS, Prasad BD, Sahni S, Quaiyum Z, Sharma VK. Biotechnological studies towards improvement of finger millet using multi-omics approaches. Funct Integr Genomics 2024; 24:148. [PMID: 39218842 DOI: 10.1007/s10142-024-01438-4] [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: 04/19/2024] [Revised: 08/22/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024]
Abstract
A plethora of studies have uncovered numerous important genes with agricultural significance in staple crops. However, when it comes to orphan crops like minor millet, genomic research lags significantly behind that of major crops. This situation has promoted a focus on exploring research opportunities in minor millets, particularly in finger millet, using cutting-edge methods. Finger millet, a coarse cereal known for its exceptional nutritional content and ability to withstand environmental stresses represents a promising climate-smart and nutritional crop in the battle against escalating environmental challenges. The existing traditional improvement programs for finger millet are insufficient to address global hunger effectively. The lack of utilization of high-throughput platforms, genome editing, haplotype breeding, and advanced breeding approaches hinders the systematic multi-omics studies on finger millet, which are essential for pinpointing crucial genes related to agronomically important and various stress responses. The growing environmental uncertainties have widened the gap between the anticipated and real progress in crop improvement. To overcome these challenges a combination of cutting-edge multi-omics techniques such as high-throughput sequencing, speed breeding, mutational breeding, haplotype-based breeding, genomic selection, high-throughput phenotyping, pangenomics, genome editing, and more along with integration of deep learning and artificial intelligence technologies are essential to accelerate research efforts in finger millet. The scarcity of multi-omics approaches in finger millet leaves breeders with limited modern tools for crop enhancement. Therefore, leveraging datasets from previous studies could prove effective in implementing the necessary multi-omics interventions to enrich the genetic resource in finger millet.
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Affiliation(s)
- Rushikesh Sanjay Mane
- Department of AB and MB, CBS&H, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur, Bihar, 848125, India
| | - Bishun Deo Prasad
- Department of AB and MB, CBS&H, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur, Bihar, 848125, India.
| | - Sangita Sahni
- Department of Plant Pathology, TCA Dholi, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur, Bihar, 848125, India
| | - Zeba Quaiyum
- Department of AB and MB, CBS&H, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur, Bihar, 848125, India
| | - V K Sharma
- Department of AB and MB, CBS&H, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur, Bihar, 848125, India
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Li D, Wang Q, Tian Y, Lyv X, Zhang H, Hong H, Gao H, Li YF, Zhao C, Wang J, Wang R, Yang J, Liu B, Schnable PS, Schnable JC, Li YH, Qiu LJ. TWAS facilitates gene-scale trait genetic dissection through gene expression, structural variations, and alternative splicing in soybean. PLANT COMMUNICATIONS 2024:101010. [PMID: 38918950 DOI: 10.1016/j.xplc.2024.101010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 05/15/2024] [Accepted: 06/23/2024] [Indexed: 06/27/2024]
Abstract
A genome-wide association study (GWAS) identifies trait-associated loci, but identifying the causal genes can be a bottleneck, due in part to slow decay of linkage disequilibrium (LD). A transcriptome-wide association study (TWAS) addresses this issue by identifying gene expression-phenotype associations or integrating gene expression quantitative trait loci with GWAS results. Here, we used self-pollinated soybean (Glycine max [L.] Merr.) as a model to evaluate the application of TWAS to the genetic dissection of traits in plant species with slow LD decay. We generated RNA sequencing data for a soybean diversity panel and identified the genetic expression regulation of 29 286 soybean genes. Different TWAS solutions were less affected by LD and were robust to the source of expression, identifing known genes related to traits from different tissues and developmental stages. The novel pod-color gene L2 was identified via TWAS and functionally validated by genome editing. By introducing a new exon proportion feature, we significantly improved the detection of expression variations that resulted from structural variations and alternative splicing. As a result, the genes identified through our TWAS approach exhibited a diverse range of causal variations, including SNPs, insertions or deletions, gene fusion, copy number variations, and alternative splicing. Using this approach, we identified genes associated with flowering time, including both previously known genes and novel genes that had not previously been linked to this trait, providing insights complementary to those from GWAS. In summary, this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.
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Affiliation(s)
- Delin Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Qi Wang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China; College of Agriculture, Northeast Agricultural University, Harbin 150030, China
| | - Yu Tian
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xiangguang Lyv
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Hao Zhang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Huilong Hong
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Huawei Gao
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China
| | - Yan-Fei Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Chaosen Zhao
- Crops Research Institute of Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China
| | - Jiajun Wang
- Soybean Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
| | - Ruizhen Wang
- Crops Research Institute of Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China
| | - Jinliang Yang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Bin Liu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | | | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.
| | - Ying-Hui Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
| | - Li-Juan Qiu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
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Chien PS, Chen PH, Lee CR, Chiou TJ. Transcriptome-wide association study coupled with eQTL analysis reveals the genetic connection between gene expression and flowering time in Arabidopsis. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:5653-5666. [PMID: 37419660 DOI: 10.1093/jxb/erad262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 07/06/2023] [Indexed: 07/09/2023]
Abstract
Genome-wide association study (GWAS) has improved our understanding of complex traits, but challenges exist in distinguishing causation versus association caused by linkage disequilibrium. Instead, transcriptome-wide association studies (TWAS) detect direct associations between expression levels and phenotypic variations, providing an opportunity to better prioritize candidate genes. To assess the feasibility of TWAS, we investigated the association between transcriptomes, genomes, and various traits in Arabidopsis, including flowering time. The associated genes formerly known to regulate growth allometry or metabolite production were first identified by TWAS. Next, for flowering time, six TWAS-newly identified genes were functionally validated. Analysis of the expression quantitative trait locus (eQTL) further revealed a trans-regulatory hotspot affecting the expression of several TWAS-identified genes. The hotspot covers the FRIGIDA (FRI) gene body, which possesses multiple haplotypes differentially affecting the expression of downstream genes, such as FLOWERING LOCUS C (FLC) and SUPPRESSOR OF OVEREXPRESSION OF CO 1 (SOC1). We also revealed multiple independent paths towards the loss of function of FRI in natural accessions. Altogether, this study demonstrates the potential of combining TWAS with eQTL analysis to identify important regulatory modules of FRI-FLC-SOC1 for quantitative traits in natural populations.
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Affiliation(s)
- Pei-Shan Chien
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
| | - Pin-Hua Chen
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
| | - Cheng-Ruei Lee
- Institute of Ecology and Evolutionary Biology, National Taiwan University, Taipei, Taiwan
- Institute of Plant Biology, National Taiwan University, Taipei, Taiwan
| | - Tzyy-Jen Chiou
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
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Zhao T, Wu H, Wang X, Zhao Y, Wang L, Pan J, Mei H, Han J, Wang S, Lu K, Li M, Gao M, Cao Z, Zhang H, Wan K, Li J, Fang L, Zhang T, Guan X. Integration of eQTL and machine learning to dissect causal genes with pleiotropic effects in genetic regulation networks of seed cotton yield. Cell Rep 2023; 42:113111. [PMID: 37676770 DOI: 10.1016/j.celrep.2023.113111] [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: 02/27/2023] [Revised: 06/19/2023] [Accepted: 08/24/2023] [Indexed: 09/09/2023] Open
Abstract
The dissection of a gene regulatory network (GRN) that complements the genome-wide association study (GWAS) locus and the crosstalk underlying multiple agronomical traits remains a major challenge. In this study, we generate 558 transcriptional profiles of lint-bearing ovules at one day post-anthesis from a selective core cotton germplasm, from which 12,207 expression quantitative trait loci (eQTLs) are identified. Sixty-six known phenotypic GWAS loci are colocalized with 1,090 eQTLs, forming 38 functional GRNs associated predominantly with seed yield. Of the eGenes, 34 exhibit pleiotropic effects. Combining the eQTLs within the seed yield GRNs significantly increases the portion of narrow-sense heritability. The extreme gradient boosting (XGBoost) machine learning approach is applied to predict seed cotton yield phenotypes on the basis of gene expression. Top-ranking eGenes (NF-YB3, FLA2, and GRDP1) derived with pleiotropic effects on yield traits are validated, along with their potential roles by correlation analysis, domestication selection analysis, and transgenic plants.
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Affiliation(s)
- Ting Zhao
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Hongyu Wu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Xutong Wang
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Yongyan Zhao
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Luyao Wang
- Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Jiaying Pan
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Huan Mei
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Jin Han
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Siyuan Wang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Kening Lu
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Menglin Li
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Mengtao Gao
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Zeyi Cao
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Hailin Zhang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Ke Wan
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Jie Li
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Lei Fang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Tianzhen Zhang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Xueying Guan
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China.
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Chen J, Wang Z, Tan K, Huang W, Shi J, Li T, Hu J, Wang K, Wang C, Xin B, Zhao H, Song W, Hufford MB, Schnable JC, Jin W, Lai J. A complete telomere-to-telomere assembly of the maize genome. Nat Genet 2023:10.1038/s41588-023-01419-6. [PMID: 37322109 DOI: 10.1038/s41588-023-01419-6] [Citation(s) in RCA: 60] [Impact Index Per Article: 60.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 05/05/2023] [Indexed: 06/17/2023]
Abstract
A complete telomere-to-telomere (T2T) finished genome has been the long pursuit of genomic research. Through generating deep coverage ultralong Oxford Nanopore Technology (ONT) and PacBio HiFi reads, we report here a complete genome assembly of maize with each chromosome entirely traversed in a single contig. The 2,178.6 Mb T2T Mo17 genome with a base accuracy of over 99.99% unveiled the structural features of all repetitive regions of the genome. There were several super-long simple-sequence-repeat arrays having consecutive thymine-adenine-guanine (TAG) tri-nucleotide repeats up to 235 kb. The assembly of the entire nucleolar organizer region of the 26.8 Mb array with 2,974 45S rDNA copies revealed the enormously complex patterns of rDNA duplications and transposon insertions. Additionally, complete assemblies of all ten centromeres enabled us to precisely dissect the repeat compositions of both CentC-rich and CentC-poor centromeres. The complete Mo17 genome represents a major step forward in understanding the complexity of the highly recalcitrant repetitive regions of higher plant genomes.
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Affiliation(s)
- Jian Chen
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, P. R. China
| | - Zijian Wang
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, P. R. China
| | - Kaiwen Tan
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, P. R. China
| | - Wei Huang
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, P. R. China
| | - Junpeng Shi
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, P. R. China
| | - Tong Li
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, P. R. China
| | - Jiang Hu
- Grandomics Biosciences, Wuhan, P. R. China
| | - Kai Wang
- Grandomics Biosciences, Wuhan, P. R. China
| | - Chao Wang
- Grandomics Biosciences, Wuhan, P. R. China
| | - Beibei Xin
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, P. R. China
| | - Haiming Zhao
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, P. R. China
| | - Weibin Song
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, P. R. China
| | - Matthew B Hufford
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Weiwei Jin
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, P. R. China
| | - Jinsheng Lai
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, P. R. China.
- Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, Beijing, P. R. China.
- Sanya Institute of China Agricultural University, Sanya, P. R. China.
- Hainan Yazhou Bay Seed Laboratory, Sanya, P. R. China.
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Li D, Zhang Z, Gao X, Zhang H, Bai D, Wang Q, Zheng T, Li YH, Qiu LJ. The elite variations in germplasms for soybean breeding. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:37. [PMID: 37312749 PMCID: PMC10248635 DOI: 10.1007/s11032-023-01378-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/03/2023] [Indexed: 06/15/2023]
Abstract
The genetic base of soybean cultivars (Glycine max (L.) Merr.) has been narrowed through selective domestication and specific breeding improvement, similar to other crops. This presents challenges in breeding new cultivars with improved yield and quality, reduced adaptability to climate change, and increased susceptibility to diseases. On the other hand, the vast collection of soybean germplasms offers a potential source of genetic variations to address those challenges, but it has yet to be fully leveraged. In recent decades, rapidly improved high-throughput genotyping technologies have accelerated the harness of elite variations in soybean germplasm and provided the important information for solving the problem of a narrowed genetic base in breeding. In this review, we will overview the situation of maintenance and utilization of soybean germplasms, various solutions provided for different needs in terms of the number of molecular markers, and the omics-based high-throughput strategies that have been used or can be used to identify elite alleles. We will also provide an overall genetic information generated from soybean germplasms in yield, quality traits, and pest resistance for molecular breeding.
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Affiliation(s)
- Delin Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Zhengwei Zhang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Xinyue Gao
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Hao Zhang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Dong Bai
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Qi Wang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
- College of Agriculture, Northeast Agricultural University, Harbin, 150030 China
| | - Tianqing Zheng
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Ying-Hui Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Li-Juan Qiu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
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Zheng Z, Guo B, Dutta S, Roy V, Liu H, Schnable PS. The 2020 derecho revealed limited overlap between maize genes associated with root lodging and root system architecture. PLANT PHYSIOLOGY 2023:kiad194. [PMID: 36974884 DOI: 10.1093/plphys/kiad194] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 03/03/2023] [Accepted: 03/24/2023] [Indexed: 06/18/2023]
Abstract
Roots anchor plants in soil, and the failure of anchorage (i.e., root lodging) is a major cause of crop yield loss. Anchorage is often assumed to be driven by root system architecture. We made use of a natural experiment to measure the overlap between the genetic regulation of root system architecture and anchorage. After one of the most devastating derechos ever recorded in August 2020, we phenotyped root lodging in a maize (Zea mays) diversity panel consisting of 369 genotypes grown in six environments affected by the derecho. Genome-wide association studies and transcriptome-wide association studies identified 118 candidate genes associated with root lodging. Thirty-four percent (40/118) of these were homologs of genes from Arabidopsis (Arabidopsis thaliana) that affect traits such as root morphology and lignin content, expected to affect root lodging. Finally, Gene Ontology enrichment analysis of the candidate genes and their predicted interaction partners at the transcriptional and translational levels revealed the complex regulatory networks of physiological and biochemical pathways underlying root lodging in maize. Limited overlap between genes associated with lodging resistance and root system architecture in this diversity panel suggests that anchorage depends in part on factors other than gross characteristics of root system architecture.
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Affiliation(s)
- Zihao Zheng
- Department of Agronomy, Iowa State University, Ames, IA 50011-1051, USA
- Interdepartmental Genetics and Genomics Graduate Program, Iowa State University, Ames, IA 50011-3650, USA
| | - Bufei Guo
- Department of Statistics, Iowa State University, Ames, IA, 50011-1090, USA
| | - Somak Dutta
- Department of Statistics, Iowa State University, Ames, IA, 50011-1090, USA
| | - Vivekananda Roy
- Department of Statistics, Iowa State University, Ames, IA, 50011-1090, USA
| | - Huyu Liu
- Department of Agronomy, Iowa State University, Ames, IA 50011-1051, USA
- Interdepartmental Genetics and Genomics Graduate Program, Iowa State University, Ames, IA 50011-3650, USA
| | - Patrick S Schnable
- Department of Agronomy, Iowa State University, Ames, IA 50011-1051, USA
- Interdepartmental Genetics and Genomics Graduate Program, Iowa State University, Ames, IA 50011-3650, USA
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Gavgani HN, Grotewold E, Gray J. Methodology for Constructing a Knowledgebase for Plant Gene Regulation Information. Methods Mol Biol 2023; 2698:277-300. [PMID: 37682481 DOI: 10.1007/978-1-0716-3354-0_17] [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] [Indexed: 09/09/2023]
Abstract
The amount of biological data is growing at a rapid pace as many high-throughput omics technologies and data pipelines are developed. This is resulting in the growth of databases for DNA and protein sequences, gene expression, protein accumulation, structural, and localization information. The diversity and multi-omics nature of such bioinformatic data requires well-designed databases for flexible organization and presentation. Besides general-purpose online bioinformatic databases, users need narrowly focused online databases to quickly access a meaningful collection of related data for their research. Here, we describe the methodology used to implement a plant gene regulatory knowledgebase, with data, query, and tool features, as well as the ability to expand to accommodate future datasets. We exemplify this methodology for the GRASSIUS knowledgebase, but it is applicable to developing and updating similar plant gene regulatory knowledgebases. GRASSIUS organizes and presents gene regulatory data from grass species with a central focus on maize (Zea mays). The main class of data presented include not only the families of transcription factors (TFs) and co-regulators (CRs) but also protein-DNA interaction data, where available.
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Affiliation(s)
- Hadi Nayebi Gavgani
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
- Dandelions Therapeutics Inc., San Francisco, CA, USA
| | - Erich Grotewold
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | - John Gray
- Department of Biological Sciences, University of Toledo, Toledo, OH, USA.
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9
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Mural RV, Sun G, Grzybowski M, Tross MC, Jin H, Smith C, Newton L, Andorf CM, Woodhouse MR, Thompson AM, Sigmon B, Schnable JC. Association mapping across a multitude of traits collected in diverse environments in maize. Gigascience 2022; 11:giac080. [PMID: 35997208 PMCID: PMC9396454 DOI: 10.1093/gigascience/giac080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/25/2022] [Indexed: 11/14/2022] Open
Abstract
Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data-18M markers-from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction.
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Affiliation(s)
- Ravi V Mural
- Center for Plant Science Innovation, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
| | - Guangchao Sun
- Center for Plant Science Innovation, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
| | - Marcin Grzybowski
- Center for Plant Science Innovation, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
| | - Michael C Tross
- Center for Plant Science Innovation, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
| | - Hongyu Jin
- Center for Plant Science Innovation, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
| | - Christine Smith
- Center for Plant Science Innovation, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
| | - Linsey Newton
- Department of Plant Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Carson M Andorf
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50010, USA
- Department of Computer Science, Iowa State University, Ames, IA 50011, USA
| | | | - Addie M Thompson
- Department of Plant Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Brandi Sigmon
- Department of Plant Pathology, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
| | - James C Schnable
- Center for Plant Science Innovation, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
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10
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Wu D, Li X, Tanaka R, Wood JC, Tibbs-Cortes LE, Magallanes-Lundback M, Bornowski N, Hamilton JP, Vaillancourt B, Diepenbrock CH, Li X, Deason NT, Schoenbaum GR, Yu J, Buell CR, DellaPenna D, Gore MA. Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain. Genetics 2022; 221:6603118. [PMID: 35666198 PMCID: PMC9339294 DOI: 10.1093/genetics/iyac091] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 06/01/2022] [Indexed: 11/20/2022] Open
Abstract
Tocochromanols (tocopherols and tocotrienols, collectively vitamin E) are lipid-soluble antioxidants important for both plant fitness and human health. The main dietary sources of vitamin E are seed oils that often accumulate high levels of tocopherol isoforms with lower vitamin E activity. The tocochromanol biosynthetic pathway is conserved across plant species but an integrated view of the genes and mechanisms underlying natural variation of tocochromanol levels in seed of most cereal crops remains limited. To address this issue, we utilized the high mapping resolution of the maize Ames panel of ∼1,500 inbred lines scored with 12.2 million single-nucleotide polymorphisms to generate metabolomic (mature grain tocochromanols) and transcriptomic (developing grain) data sets for genetic mapping. By combining results from genome- and transcriptome-wide association studies, we identified a total of 13 candidate causal gene loci, including 5 that had not been previously associated with maize grain tocochromanols: 4 biosynthetic genes (arodeH2 paralog, dxs1, vte5, and vte7) and a plastid S-adenosyl methionine transporter (samt1). Expression quantitative trait locus (eQTL) mapping of these 13 gene loci revealed that they are predominantly regulated by cis-eQTL. Through a joint statistical analysis, we implicated cis-acting variants as responsible for colocalized eQTL and GWAS association signals. Our multiomics approach provided increased statistical power and mapping resolution to enable a detailed characterization of the genetic and regulatory architecture underlying tocochromanol accumulation in maize grain and provided insights for ongoing biofortification efforts to breed and/or engineer vitamin E and antioxidant levels in maize and other cereals.
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Affiliation(s)
| | | | | | - Joshua C Wood
- Department of Crop & Soil Sciences, Institute of Plant Breeding, Genetics, & Genomics, University of Georgia, Athens, GA 30602, USA
| | | | - Maria Magallanes-Lundback
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Nolan Bornowski
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - John P Hamilton
- Department of Crop & Soil Sciences, Institute of Plant Breeding, Genetics, & Genomics, University of Georgia, Athens, GA 30602, USA
| | - Brieanne Vaillancourt
- Department of Crop & Soil Sciences, Institute of Plant Breeding, Genetics, & Genomics, University of Georgia, Athens, GA 30602, USA
| | | | - Xianran Li
- United States Department of Agriculture, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA
| | - Nicholas T Deason
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | | | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - C Robin Buell
- Department of Crop & Soil Sciences, Institute of Plant Breeding, Genetics, & Genomics, University of Georgia, Athens, GA 30602, USA
| | - Dean DellaPenna
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Michael A Gore
- Corresponding author: Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.
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11
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Yang Z, Xu G, Zhang Q, Obata T, Yang J. Genome-wide mediation analysis: an empirical study to connect phenotype with genotype via intermediate transcriptomic data in maize. Genetics 2022; 221:6572813. [PMID: 35460234 PMCID: PMC9157066 DOI: 10.1093/genetics/iyac057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
Mapping genotype to phenotype is an essential topic in genetics and genomics research. As the Omics data become increasingly available, 2-variable methods have been widely applied to associate genotype with the phenotype (genome-wide association study), gene expression with the phenotype (transcriptome-wide association study), and genotype with gene expression. However, signals detected by these 2-variable association methods suffer from low mapping resolution or inexplicit causality between genotype and phenotype, making it challenging to interpret and validate the molecular mechanisms of the underlying genomic variations and the candidate genes. Under the context of genetics research, we hypothesized a causal chain from genotype to phenotype partially mediated by intermediate molecular processes, i.e. gene expression. To test this hypothesis, we applied the high-dimensional mediation analysis, a class of causal inference method with an assumed causal chain from the exposure to the mediator to the outcome, and implemented it with a maize association panel (N = 280 lines). Using 40 publicly available agronomy traits, 66 newly generated metabolite traits, and published RNA-seq data from 7 different tissues, our empirical study detected 736 unique mediating genes. Noticeably, 83/736 (11%) genes were identified in mediating more than 1 trait, suggesting the prevalence of pleiotropic mediating effects. We demonstrated that several identified mediating genes are consistent with their known functions. In addition, our results provided explicit hypotheses for functional validation and suggested that the mediation analysis is a powerful tool to integrate Omics data to connect genotype to phenotype.
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Affiliation(s)
- Zhikai Yang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA,Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Gen Xu
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA,Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Qi Zhang
- Department of Mathematics and Statistics, University of New Hampshire, Durham, NH 03824, USA
| | - Toshihiro Obata
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA,Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Jinliang Yang
- Corresponding author: Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
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12
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Sun G, Mural RV, Turkus JD, Schnable JC. Quantitative Resistance Loci to Southern Rust Mapped in a Temperate Maize Diversity Panel. PHYTOPATHOLOGY 2022; 112:579-587. [PMID: 34282952 DOI: 10.1094/phyto-04-21-0160-r] [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] [Indexed: 06/13/2023]
Abstract
Southern rust is a severe foliar disease of maize (Zea mays) resulting from infection with the obligate biotrophic fungus Puccinia polysora. This disease reduces photosynthetic productivity, which in turn reduces yields, with the greatest yield losses (up to 50%) associated with earlier onset infections. P. polysora urediniospores overwinter only in tropical and subtropical regions but cause outbreaks when environmental conditions favor initial infection. Increased temperatures and humidity during the growing season combined with an increased frequency of moderate winters are likely to increase the frequency of severe southern rust outbreaks in the U.S. Corn Belt. In summer 2020, a severe outbreak of southern rust was observed in eastern Nebraska, United States. We scored a replicated maize association panel planted in Lincoln, NE for disease severity and found that disease incidence and severity showed significant variation among maize genotypes. Genome-wide association studies identified four loci associated with significant quantitative variation in disease severity. These loci were associated with candidate genes with plausible links to quantitative disease resistance. A transcriptome-wide association study identified additional genes associated with disease severity. Together, these results indicate that substantial diversity in resistance to southern rust exists among current temperate-adapted maize germplasm, including several candidate loci that may explain the observed variation in resistance to southern rust.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.
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Affiliation(s)
- Guangchao Sun
- Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, NE 68588
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588
| | - Ravi V Mural
- Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, NE 68588
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588
| | - Jonathan D Turkus
- Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, NE 68588
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588
| | - James C Schnable
- Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, NE 68588
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588
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13
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Guan H, Chen X, Wang K, Liu X, Zhang D, Li Y, Song Y, Shi Y, Wang T, Li C, Li Y. Genetic Variation in ZmPAT7 Contributes to Tassel Branch Number in Maize. Int J Mol Sci 2022; 23:2586. [PMID: 35269730 PMCID: PMC8910302 DOI: 10.3390/ijms23052586] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 02/22/2022] [Accepted: 02/24/2022] [Indexed: 02/04/2023] Open
Abstract
Tassel branch number (TBN) is one of the important agronomic traits that contribute to the efficiency of seed production and has been selected strongly during the modern maize breeding process. However, the genetic mechanisms of TBN in maize are not entirely clear. In this study, we used a B73 × CML247 recombination inbred lines (RILs) population to detect quantitative trait loci (QTLs) for TBN. A total of four QTLs (qTBN2a, qTBN2b, qTBN4, and qTBN6) and six candidate genes were identified through expression analysis. Further, one of the candidates (GRMZM2G010011, ZmPAT7) encoding an S-acyltransferase was selected to validate its function by CRISPR-Cas9 technology, and its loss-of-function lines showed a significant increase in TBN. A key SNP(-101) variation in the promoter of ZmPAT7 was significantly associated with TBN. A total of 17 distant eQTLs associated with the expression of ZmPAT7 were identified in expression quantitative trait loci (eQTL) analysis, and ZmNAC3 may be a major factor involved in regulating ZmPAT7. These findings of the present study promote our understanding of the genetic basis of tassel architecture and provide new gene resources for maize breeding improvement.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Chunhui Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (H.G.); (X.C.); (K.W.); (X.L.); (D.Z.); (Y.L.); (Y.S.); (Y.S.); (T.W.)
| | - Yu Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (H.G.); (X.C.); (K.W.); (X.L.); (D.Z.); (Y.L.); (Y.S.); (Y.S.); (T.W.)
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14
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Albert E, Sauvage C. Identification and Validation of Candidate Genes from Genome-Wide Association Studies. Methods Mol Biol 2022; 2481:249-272. [PMID: 35641769 DOI: 10.1007/978-1-0716-2237-7_15] [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] [Indexed: 06/15/2023]
Abstract
Exploiting the statistical associations coming out from a GWAS experiment to identify and validate candidate genes may be potentially difficult and time consuming. To fill the gap between the identification of candidate genes toward their functional validation onto the trait performance, the prioritization of variants underlying the GWAS-associated regions is necessary. In parallel, recent developments in genomics and statistical methods have been achieved notably in human genetic and they are accordingly being adopted in plant breeding toward the study of the genetic architecture of traits to sustain genetic gains. In this chapter, we aim at providing both theoretical and practical aspects underlying three main options including (1) the MetaGWAS analysis, (2) the statistical fine mapping and (3) the integration of functional data toward the identification and validation of candidate genes from a GWAS experiment.
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15
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Varshney RK, Bohra A, Roorkiwal M, Barmukh R, Cowling WA, Chitikineni A, Lam HM, Hickey LT, Croser JS, Bayer PE, Edwards D, Crossa J, Weckwerth W, Millar H, Kumar A, Bevan MW, Siddique KHM. Fast-forward breeding for a food-secure world. Trends Genet 2021; 37:1124-1136. [PMID: 34531040 DOI: 10.1016/j.tig.2021.08.002] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 10/20/2022]
Abstract
Crop production systems need to expand their outputs sustainably to feed a burgeoning human population. Advances in genome sequencing technologies combined with efficient trait mapping procedures accelerate the availability of beneficial alleles for breeding and research. Enhanced interoperability between different omics and phenotyping platforms, leveraged by evolving machine learning tools, will help provide mechanistic explanations for complex plant traits. Targeted and rapid assembly of beneficial alleles using optimized breeding strategies and precise genome editing techniques could deliver ideal crops for the future. Realizing desired productivity gains in the field is imperative for securing an adequate future food supply for 10 billion people.
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Affiliation(s)
- Rajeev K Varshney
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India; State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch WA 6150, Western Australia, Australia; The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia.
| | - Abhishek Bohra
- ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, India
| | - Manish Roorkiwal
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India; The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Rutwik Barmukh
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India
| | - Wallace A Cowling
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Annapurna Chitikineni
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India
| | - Hon-Ming Lam
- School of Life Sciences and Center for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, QLD, Australia
| | - Janine S Croser
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Philipp E Bayer
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia; School of Biological Sciences, The University of Western Australia, Crawley, WA, Australia
| | - David Edwards
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia; School of Biological Sciences, The University of Western Australia, Crawley, WA, Australia
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, Vienna Metabolomics Center (VIME), University of Vienna, Vienna, Austria
| | - Harvey Millar
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia; ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, WA, Australia
| | - Arvind Kumar
- Deputy Director General's Office, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India
| | | | - Kadambot H M Siddique
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
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16
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Li D, Liu Q, Schnable PS. TWAS results are complementary to and less affected by linkage disequilibrium than GWAS. PLANT PHYSIOLOGY 2021; 186:1800-1811. [PMID: 33823025 PMCID: PMC8331151 DOI: 10.1093/plphys/kiab161] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 03/12/2021] [Indexed: 06/12/2023]
Abstract
A genome-wide association study (GWAS) is used to identify genetic markers associated with phenotypic variation. In contrast, a transcriptome-wide association study (TWAS) detects associations between gene expression levels and phenotypic variation. It has previously been shown that in the cross-pollinated species, maize (Zea mays), GWAS, and TWAS identify complementary sets of trait-associated genes, many of which exhibit characteristics of true positives. Here, we extend this conclusion to the self-pollinated species, Arabidopsis thaliana and soybean (Glycine max). Linkage disequilibrium (LD) can result in the identification, via GWAS, of false-positive associations. In all three analyzed plant species, most trait-associated genes identified via TWAS are well separated physically from other candidate genes. Hence, TWAS is less affected by LD than is GWAS, demonstrating that TWAS is particularly well suited for association studies in genomes with slow rates of LD decay, such as soybean. TWAS is reasonably robust to the plant organs/tissues used to determine expression levels. In summary, this study confirms that TWAS is a promising approach for accurate gene-level association mapping in plants that is complementary to GWAS, and established that TWAS can exhibit substantial advantages relative to GWAS in species with slow rates of LD decay.
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Affiliation(s)
- Delin Li
- Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, China
- Data Biotech (Beijing) Co. Ltd., Beijing, 100085, China
- National Key Facility for Gene Resources and Genetic Improvement, Key Lab of Crop Germplasm Utilization, Ministry of Agriculture, Institute of Crop Sciences, Chinese Academy of Agricultural Science, Beijing, 100081, China
| | - Qiang Liu
- Department of Agronomy, Iowa State University, Ames, Iowa 50011-3650, USA
| | - Patrick S Schnable
- Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, China
- Department of Agronomy, Iowa State University, Ames, Iowa 50011-3650, USA
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17
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Wu X, Feng H, Wu D, Yan S, Zhang P, Wang W, Zhang J, Ye J, Dai G, Fan Y, Li W, Song B, Geng Z, Yang W, Chen G, Qin F, Terzaghi W, Stitzer M, Li L, Xiong L, Yan J, Buckler E, Yang W, Dai M. Using high-throughput multiple optical phenotyping to decipher the genetic architecture of maize drought tolerance. Genome Biol 2021; 22:185. [PMID: 34162419 PMCID: PMC8223302 DOI: 10.1186/s13059-021-02377-0] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 05/10/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Drought threatens the food supply of the world population. Dissecting the dynamic responses of plants to drought will be beneficial for breeding drought-tolerant crops, as the genetic controls of these responses remain largely unknown. RESULTS Here we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days, and collected multiple optical images, including color camera scanning, hyperspectral imaging, and X-ray computed tomography images. We develop high-throughput analysis pipelines to extract image-based traits (i-traits). Of these i-traits, 10,080 were effective and heritable indicators of maize external and internal drought responses. An i-trait-based genome-wide association study reveals 4322 significant locus-trait associations, representing 1529 quantitative trait loci (QTLs) and 2318 candidate genes, many that co-localize with previously reported maize drought responsive QTLs. Expression QTL (eQTL) analysis uncovers many local and distant regulatory variants that control the expression of the candidate genes. We use genetic mutation analysis to validate two new genes, ZmcPGM2 and ZmFAB1A, which regulate i-traits and drought tolerance. Moreover, the value of the candidate genes as drought-tolerant genetic markers is revealed by genome selection analysis, and 15 i-traits are identified as potential markers for maize drought tolerance breeding. CONCLUSION Our study demonstrates that combining high-throughput multiple optical phenotyping and GWAS is a novel and effective approach to dissect the genetic architecture of complex traits and clone drought-tolerance associated genes.
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Affiliation(s)
- Xi Wu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan laboratory, Wuhan, 430070, China
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Di Wu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Shijuan Yan
- Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Pei Zhang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wenbin Wang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jun Zhang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Junli Ye
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Guoxin Dai
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yuan Fan
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Weikun Li
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Baoxing Song
- School of Integrative Plant Sciences, Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, 14850, USA
| | - Zedong Geng
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wanli Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Guoxin Chen
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Feng Qin
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - William Terzaghi
- Department of Biology, Wilkes University, Wilkes-Barre, PA, 18766, USA
| | - Michelle Stitzer
- School of Integrative Plant Sciences, Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, 14850, USA
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Lizhong Xiong
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan laboratory, Wuhan, 430070, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan laboratory, Wuhan, 430070, China
| | - Edward Buckler
- School of Integrative Plant Sciences, Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, 14850, USA
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14850, USA
- Agricultural Research Service, United States Department of Agriculture, Ithaca, NY, 14850, USA
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China.
| | - Mingqiu Dai
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, and Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China.
- Hubei Hongshan laboratory, Wuhan, 430070, China.
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18
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Li X, Guo T, Wang J, Bekele WA, Sukumaran S, Vanous AE, McNellie JP, Tibbs-Cortes LE, Lopes MS, Lamkey KR, Westgate ME, McKay JK, Archontoulis SV, Reynolds MP, Tinker NA, Schnable PS, Yu J. An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops. MOLECULAR PLANT 2021; 14:874-887. [PMID: 33713844 DOI: 10.1016/j.molp.2021.03.010] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/03/2021] [Accepted: 03/09/2021] [Indexed: 05/08/2023]
Abstract
Identifying mechanisms and pathways involved in gene-environment interplay and phenotypic plasticity is a long-standing challenge. It is highly desirable to establish an integrated framework with an environmental dimension for complex trait dissection and prediction. A critical step is to identify an environmental index that is both biologically relevant and estimable for new environments. With extensive field-observed complex traits, environmental profiles, and genome-wide single nucleotide polymorphisms for three major crops (maize, wheat, and oat), we demonstrated that identifying such an environmental index (i.e., a combination of environmental parameter and growth window) enables genome-wide association studies and genomic selection of complex traits to be conducted with an explicit environmental dimension. Interestingly, genes identified for two reaction-norm parameters (i.e., intercept and slope) derived from flowering time values along the environmental index were less colocalized for a diverse maize panel than for wheat and oat breeding panels, agreeing with the different diversity levels and genetic constitutions of the panels. In addition, we showcased the usefulness of this framework for systematically forecasting the performance of diverse germplasm panels in new environments. This general framework and the companion CERIS-JGRA analytical package should facilitate biologically informed dissection of complex traits, enhanced performance prediction in breeding for future climates, and coordinated efforts to enrich our understanding of mechanisms underlying phenotypic variation.
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Affiliation(s)
- Xianran Li
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Tingting Guo
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Jinyu Wang
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Wubishet A Bekele
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Sivakumar Sukumaran
- International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
| | - Adam E Vanous
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - James P McNellie
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | | | - Marta S Lopes
- International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
| | - Kendall R Lamkey
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Mark E Westgate
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - John K McKay
- Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO 80523, USA
| | | | - Matthew P Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
| | - Nicholas A Tinker
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | | | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA.
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19
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Sadhukhan A, Agrahari RK, Wu L, Watanabe T, Nakano Y, Panda SK, Koyama H, Kobayashi Y. Expression genome-wide association study identifies that phosphatidylinositol-derived signalling regulates ALUMINIUM SENSITIVE3 expression under aluminium stress in the shoots of Arabidopsis thaliana. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2021; 302:110711. [PMID: 33288018 DOI: 10.1016/j.plantsci.2020.110711] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 10/02/2020] [Accepted: 10/04/2020] [Indexed: 06/12/2023]
Abstract
To identify unknown regulatory mechanisms leading to aluminium (Al)-induction of the Al tolerance gene ALS3, we conducted an expression genome-wide association study (eGWAS) for ALS3 in the shoots of 95 Arabidopsis thaliana accessions in the presence of Al. The eGWAS was conducted using a mixed linear model with 145,940 genome-wide single nucleotide polymorphisms (SNPs) and the association results were validated using reverse genetics. We found that many SNPs from the eGWAS were associated with genes related to phosphatidylinositol metabolism as well as stress signal transduction, including Ca2+signals, inter-connected in a co-expression network. Of these, PLC9, CDPK32, ANAC071, DIR1, and a hypothetical protein (AT4G10470) possessed amino acid sequence/ gene expression level polymorphisms that were significantly associated with ALS3 expression level variation. Furthermore, T-DNA insertion mutants of PLC9, CDPK32, and ANAC071 suppressed shoot ALS3 expression in the presence of Al. This study clarified the regulatory mechanisms of ALS3 expression in the shoot and provided genetic evidence of the involvement of phosphatidylinositol-derived signal transduction under Al stress.
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Affiliation(s)
- Ayan Sadhukhan
- Applied Biological Sciences, Gifu University, Yanagido 1-1, Gifu, Gifu 501-1193, Japan
| | - Raj Kishan Agrahari
- Applied Biological Sciences, Gifu University, Yanagido 1-1, Gifu, Gifu 501-1193, Japan
| | - Liujie Wu
- Applied Biological Sciences, Gifu University, Yanagido 1-1, Gifu, Gifu 501-1193, Japan
| | - Toshihiro Watanabe
- Research Faculty of Agriculture, Hokkaido University, Kita-9, Nishi-9, Kitaku, Sapporo, 060-8589, Japan
| | - Yuki Nakano
- Applied Biological Sciences, Gifu University, Yanagido 1-1, Gifu, Gifu 501-1193, Japan
| | - Sanjib Kumar Panda
- Department of Biochemistry, Central University of Rajasthan, Rajasthan 305817, India
| | - Hiroyuki Koyama
- Applied Biological Sciences, Gifu University, Yanagido 1-1, Gifu, Gifu 501-1193, Japan
| | - Yuriko Kobayashi
- Applied Biological Sciences, Gifu University, Yanagido 1-1, Gifu, Gifu 501-1193, Japan.
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20
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Němcová L, Marková S, Kotlík P. Gene Expression Variation of Candidate Endogenous Control Genes Across Latitudinal Populations of the Bank Vole (Clethrionomys glareolus). Front Ecol Evol 2020. [DOI: 10.3389/fevo.2020.562065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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21
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Gui Y, Thomas MH, Garcia P, Karout M, Halder R, Michelucci A, Kollmus H, Zhou C, Melmed S, Schughart K, Balling R, Mittelbronn M, Nadeau JH, Williams RW, Sauter T, Buttini M, Sinkkonen L. Pituitary Tumor Transforming Gene 1 Orchestrates Gene Regulatory Variation in Mouse Ventral Midbrain During Aging. Front Genet 2020; 11:566734. [PMID: 33173537 PMCID: PMC7538689 DOI: 10.3389/fgene.2020.566734] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 08/27/2020] [Indexed: 01/07/2023] Open
Abstract
Dopaminergic neurons in the midbrain are of particular interest due to their role in diseases such as Parkinson’s disease and schizophrenia. Genetic variation between individuals can affect the integrity and function of dopaminergic neurons but the DNA variants and molecular cascades modulating dopaminergic neurons and other cells types of ventral midbrain remain poorly defined. Three genetically diverse inbred mouse strains – C57BL/6J, A/J, and DBA/2J – differ significantly in their genomes (∼7 million variants), motor and cognitive behavior, and susceptibility to neurotoxins. To further dissect the underlying molecular networks responsible for these variable phenotypes, we generated RNA-seq and ChIP-seq data from ventral midbrains of the 3 mouse strains. We defined 1000–1200 transcripts that are differentially expressed among them. These widespread differences may be due to altered activity or expression of upstream transcription factors. Interestingly, transcription factors were significantly underrepresented among the differentially expressed genes, and only one transcription factor, Pttg1, showed significant differences between all three strains. The changes in Pttg1 expression were accompanied by consistent alterations in histone H3 lysine 4 trimethylation at Pttg1 transcription start site. The ventral midbrain transcriptome of 3-month-old C57BL/6J congenic Pttg1–/– mutants was only modestly altered, but shifted toward that of A/J and DBA/2J in 9-month-old mice. Principle component analysis (PCA) identified the genes underlying the transcriptome shift and deconvolution of these bulk RNA-seq changes using midbrain single cell RNA-seq data suggested that the changes were occurring in several different cell types, including neurons, oligodendrocytes, and astrocytes. Taken together, our results show that Pttg1 contributes to gene regulatory variation between mouse strains and influences mouse midbrain transcriptome during aging.
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Affiliation(s)
- Yujuan Gui
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Mélanie H Thomas
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Pierre Garcia
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.,National Center of Pathology, Laboratoire National de Santé, Dudelange, Luxembourg.,Luxembourg Centre of Neuropathology, Dudelange, Luxembourg
| | - Mona Karout
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Rashi Halder
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Alessandro Michelucci
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.,Department of Oncology, Luxembourg Institute of Health, Luxembourg City, Luxembourg
| | - Heike Kollmus
- Department of Infection Genetics, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Cuiqi Zhou
- Cedars Sinai Medical Centre, Los Angeles, CA, United States
| | - Shlomo Melmed
- Cedars Sinai Medical Centre, Los Angeles, CA, United States
| | - Klaus Schughart
- Department of Infection Genetics, Helmholtz Centre for Infection Research, Braunschweig, Germany.,Department of Infection Genetics, University of Veterinary Medicine Hannover, Hanover, Germany.,Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Michel Mittelbronn
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.,National Center of Pathology, Laboratoire National de Santé, Dudelange, Luxembourg.,Luxembourg Centre of Neuropathology, Dudelange, Luxembourg.,Department of Oncology, Luxembourg Institute of Health, Luxembourg City, Luxembourg
| | - Joseph H Nadeau
- Pacific Northwest Research Institute, Seattle, WA, United States.,Maine Medical Center Research Institute, Scarborough, ME, United States
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Manuel Buttini
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Lasse Sinkkonen
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
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22
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Awany D, Chimusa ER. Heritability jointly explained by host genotype and microbiome: will improve traits prediction? Brief Bioinform 2020; 22:5893981. [PMID: 32810866 DOI: 10.1093/bib/bbaa175] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 07/09/2020] [Accepted: 07/10/2020] [Indexed: 11/14/2022] Open
Abstract
As we observe the $70$th anniversary of the publication by Robertson that formalized the notion of 'heritability', geneticists remain puzzled by the problem of missing/hidden heritability, where heritability estimates from genome-wide association studies (GWASs) fall short of that from twin-based studies. Many possible explanations have been offered for this discrepancy, including existence of genetic variants poorly captured by existing arrays, dominance, epistasis and unaccounted-for environmental factors; albeit these remain controversial. We believe a substantial part of this problem could be solved or better understood by incorporating the host's microbiota information in the GWAS model for heritability estimation and may also increase human traits prediction for clinical utility. This is because, despite empirical observations such as (i) the intimate role of the microbiome in many complex human phenotypes, (ii) the overlap between genetic variants associated with both microbiome attributes and complex diseases and (iii) the existence of heritable bacterial taxa, current GWAS models for heritability estimate do not take into account the contributory role of the microbiome. Furthermore, heritability estimate from twin-based studies does not discern microbiome component of the observed total phenotypic variance. Here, we summarize the concept of heritability in GWAS and microbiome-wide association studies, focusing on its estimation, from a statistical genetics perspective. We then discuss a possible statistical method to incorporate the microbiome in the estimation of heritability in host GWAS.
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Affiliation(s)
- Denis Awany
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Emile R Chimusa
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
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23
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Maize adaptation across temperate climates was obtained via expression of two florigen genes. PLoS Genet 2020; 16:e1008882. [PMID: 32673315 DOI: 10.1371/journal.pgen.1008882] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 07/28/2020] [Accepted: 05/22/2020] [Indexed: 11/19/2022] Open
Abstract
Expansion of the maize growing area was central for food security in temperate regions. In addition to the suppression of the short-day requirement for floral induction, it required breeding for a large range of flowering time that compensates the effect of South-North gradients of temperatures. Here we show the role of a novel florigen gene, ZCN12, in the latter adaptation in cooperation with ZCN8. Strong eQTLs of ZCN8 and ZCN12, measured in 327 maize lines, accounted for most of the genetic variance of flowering time in platform and field experiments. ZCN12 had a strong effect on flowering time of transgenic Arabidopsis thaliana plants; a path analysis showed that it directly affected maize flowering time together with ZCN8. The allelic composition at ZCN QTLs showed clear signs of selection by breeders. This suggests that florigens played a central role in ensuring a large range of flowering time, necessary for adaptation to temperate areas.
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24
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Liang Z, Qiu Y, Schnable JC. Genome-Phenome Wide Association in Maize and Arabidopsis Identifies a Common Molecular and Evolutionary Signature. MOLECULAR PLANT 2020; 13:907-922. [PMID: 32171733 DOI: 10.1016/j.molp.2020.03.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 01/20/2020] [Accepted: 03/08/2020] [Indexed: 06/10/2023]
Abstract
Linking natural genetic variation to trait variation can help determine the functional roles ofdifferent genes. Variations of one or several traits are often assessed separately. High-throughput phenotyping and data mining can capture dozens or hundreds of traits from the same individuals. Here, we test the association between markers within a gene and many traits simultaneously. This genome-phenome wide association study (GPWAS) is both a multi-marker and multi-trait test. Genes identified using GPWAS with 260 phenotypic traits in maize were enriched for genes independently linked to phenotypic variation. Traits associated with classical mutants were consistent with reported phenotypes for mutant alleles. Genes linked to phenomic variation in maize using GPWAS shared molecular, population genetic, and evolutionary features with classical mutants in maize. Genes linked to phenomic variation in Arabidopsis using GPWAS are significantly enriched in genes with known loss-of-function phenotypes. GPWAS may be an effective strategy to identify genes in which loss-of-function alleles produce mutant phenotypes. The shared signatures present in classical mutants and genes identified using GPWAS may be markers for genes with a role in specifying plant phenotypes generally or pleiotropy specifically.
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Affiliation(s)
- Zhikai Liang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA; Plant Science Innovation Center, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Yumou Qiu
- Department of Statistics, Iowa State University, Ames, IA, USA
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA; Plant Science Innovation Center, University of Nebraska-Lincoln, Lincoln, NE, USA.
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25
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Zhou P, Li Z, Magnusson E, Gomez Cano F, Crisp PA, Noshay JM, Grotewold E, Hirsch CN, Briggs SP, Springer NM. Meta Gene Regulatory Networks in Maize Highlight Functionally Relevant Regulatory Interactions. THE PLANT CELL 2020; 32:1377-1396. [PMID: 32184350 PMCID: PMC7203921 DOI: 10.1105/tpc.20.00080] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 03/06/2020] [Accepted: 03/16/2020] [Indexed: 05/22/2023]
Abstract
The regulation of gene expression is central to many biological processes. Gene regulatory networks (GRNs) link transcription factors (TFs) to their target genes and represent maps of potential transcriptional regulation. Here, we analyzed a large number of publically available maize (Zea mays) transcriptome data sets including >6000 RNA sequencing samples to generate 45 coexpression-based GRNs that represent potential regulatory relationships between TFs and other genes in different populations of samples (cross-tissue, cross-genotype, and tissue-and-genotype samples). While these networks are all enriched for biologically relevant interactions, different networks capture distinct TF-target associations and biological processes. By examining the power of our coexpression-based GRNs to accurately predict covarying TF-target relationships in natural variation data sets, we found that presence/absence changes rather than quantitative changes in TF gene expression are more likely associated with changes in target gene expression. Integrating information from our TF-target predictions and previous expression quantitative trait loci (eQTL) mapping results provided support for 68 TFs underlying 74 previously identified trans-eQTL hotspots spanning a variety of metabolic pathways. This study highlights the utility of developing multiple GRNs within a species to detect putative regulators of important plant pathways and provides potential targets for breeding or biotechnological applications.
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Affiliation(s)
- Peng Zhou
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
| | - Zhi Li
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Erika Magnusson
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
| | - Fabio Gomez Cano
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824
| | - Peter A Crisp
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
| | - Jaclyn M Noshay
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
| | - Erich Grotewold
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Steven P Briggs
- Division of Biological Sciences, University of California, San Diego, La Jolla, California 92093
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
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26
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Zheng Z, Hey S, Jubery T, Liu H, Yang Y, Coffey L, Miao C, Sigmon B, Schnable JC, Hochholdinger F, Ganapathysubramanian B, Schnable PS. Shared Genetic Control of Root System Architecture between Zea mays and Sorghum bicolor. PLANT PHYSIOLOGY 2020; 182:977-991. [PMID: 31740504 PMCID: PMC6997706 DOI: 10.1104/pp.19.00752] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 11/03/2019] [Indexed: 05/08/2023]
Abstract
Determining the genetic control of root system architecture (RSA) in plants via large-scale genome-wide association study (GWAS) requires high-throughput pipelines for root phenotyping. We developed Core Root Excavation using Compressed-air (CREAMD), a high-throughput pipeline for the cleaning of field-grown roots, and Core Root Feature Extraction (COFE), a semiautomated pipeline for the extraction of RSA traits from images. CREAMD-COFE was applied to diversity panels of maize (Zea mays) and sorghum (Sorghum bicolor), which consisted of 369 and 294 genotypes, respectively. Six RSA-traits were extracted from images collected from >3,300 maize roots and >1,470 sorghum roots. Single nucleotide polymorphism (SNP)-based GWAS identified 87 TAS (trait-associated SNPs) in maize, representing 77 genes and 115 TAS in sorghum. An additional 62 RSA-associated maize genes were identified via expression read depth GWAS. Among the 139 maize RSA-associated genes (or their homologs), 22 (16%) are known to affect RSA in maize or other species. In addition, 26 RSA-associated genes are coregulated with genes previously shown to affect RSA and 51 (37% of RSA-associated genes) are themselves transe-quantitative trait locus for another RSA-associated gene. Finally, the finding that RSA-associated genes from maize and sorghum included seven pairs of syntenic genes demonstrates the conservation of regulation of morphology across taxa.
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Affiliation(s)
- Zihao Zheng
- Department of Agronomy, Iowa State University, Ames, Iowa 50011
- Interdepartmental Genetics and Genomics Graduate Program, Iowa State University, Ames, Iowa 50011
| | - Stefan Hey
- Department of Agronomy, Iowa State University, Ames, Iowa 50011
- INRES, Institute of Crop Science and Resource Conservation, Crop Functional Genomics, University of Bonn, Bonn 53113, Germany
| | - Talukder Jubery
- Department of Mechanical Engineering, Iowa State University, Ames, Iowa 50011
| | - Huyu Liu
- Department of Agronomy, Iowa State University, Ames, Iowa 50011
- Interdepartmental Genetics and Genomics Graduate Program, Iowa State University, Ames, Iowa 50011
- Department of Plant Genetics & Breeding, China Agricultural University, Beijing 100193, China
| | - Yu Yang
- Department of Agronomy, Iowa State University, Ames, Iowa 50011
- Department of Plant Genetics & Breeding, China Agricultural University, Beijing 100193, China
| | - Lisa Coffey
- Department of Agronomy, Iowa State University, Ames, Iowa 50011
| | - Chenyong Miao
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska 68583
| | - Brandi Sigmon
- Department of Plant Pathology, University of Nebraska-Lincoln, Lincoln, Nebraska 68583
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska 68583
| | - Frank Hochholdinger
- INRES, Institute of Crop Science and Resource Conservation, Crop Functional Genomics, University of Bonn, Bonn 53113, Germany
| | | | - Patrick S Schnable
- Department of Agronomy, Iowa State University, Ames, Iowa 50011
- Interdepartmental Genetics and Genomics Graduate Program, Iowa State University, Ames, Iowa 50011
- Department of Plant Genetics & Breeding, China Agricultural University, Beijing 100193, China
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27
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Anderson SN, Stitzer MC, Zhou P, Ross-Ibarra J, Hirsch CD, Springer NM. Dynamic Patterns of Transcript Abundance of Transposable Element Families in Maize. G3 (BETHESDA, MD.) 2019; 9:3673-3682. [PMID: 31506319 PMCID: PMC6829137 DOI: 10.1534/g3.119.400431] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 09/08/2019] [Indexed: 12/21/2022]
Abstract
Transposable Elements (TEs) are mobile elements that contribute the majority of DNA sequences in the maize genome. Due to their repetitive nature, genomic studies of TEs are complicated by the difficulty of properly attributing multi-mapped short reads to specific genomic loci. Here, we utilize a method to attribute RNA-seq reads to TE families rather than particular loci in order to characterize transcript abundance for TE families in the maize genome. We applied this method to assess per-family expression of transposable elements in >800 published RNA-seq libraries representing a range of maize development, genotypes, and hybrids. While a relatively small proportion of TE families are transcribed, expression is highly dynamic with most families exhibiting tissue-specific expression. A large number of TE families were specifically detected in pollen and endosperm, consistent with reproductive dynamics that maintain silencing of TEs in the germ line. We find that B73 transcript abundance is a poor predictor of TE expression in other genotypes and that transcript levels can differ even for shared TEs. Finally, by assessing recombinant inbred line and hybrid transcriptomes, complex patterns of TE transcript abundance across genotypes emerged. Taken together, this study reveals a dynamic contribution of TEs to maize transcriptomes.
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Affiliation(s)
| | - Michelle C Stitzer
- Department of Evolution and Ecology and Center for Population Biology and
| | - Peng Zhou
- Department of Plant and Microbial Biology and
| | - Jeffrey Ross-Ibarra
- Department of Evolution and Ecology and Center for Population Biology and
- Genome Center, University of California, Davis, California 95616
| | - Cory D Hirsch
- Department of Plant Pathology, University of Minnesota, St. Paul, Minnesota 55108, and
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28
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Transcriptome-Wide Association Supplements Genome-Wide Association in Zea mays. G3-GENES GENOMES GENETICS 2019; 9:3023-3033. [PMID: 31337639 PMCID: PMC6723120 DOI: 10.1534/g3.119.400549] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Modern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The usefulness of endophenotypes for delineating the regulatory landscape of the genome and genetic dissection of complex trait variation remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299-genotype and seven-tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation acts through altered transcript abundance for maize kernel traits, including 30 grain carotenoid abundance traits, 20 grain tocochromanol abundance traits, and 22 field-measured agronomic traits. Comparing the efficacy of TWAS with genome-wide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the largely independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits than the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. This not only improves the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants.
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Sparse model identification and learning for ultra-high-dimensional additive partially linear models. J MULTIVARIATE ANAL 2019. [DOI: 10.1016/j.jmva.2019.02.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Kimotho RN, Baillo EH, Zhang Z. Transcription factors involved in abiotic stress responses in Maize ( Zea mays L.) and their roles in enhanced productivity in the post genomics era. PeerJ 2019; 7:e7211. [PMID: 31328030 PMCID: PMC6622165 DOI: 10.7717/peerj.7211] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 05/26/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Maize (Zea mays L.) is a principal cereal crop cultivated worldwide for human food, animal feed, and more recently as a source of biofuel. However, as a direct consequence of water insufficiency and climate change, frequent occurrences of both biotic and abiotic stresses have been reported in various regions around the world, and recently, this has become a constant threat in increasing global maize yields. Plants respond to abiotic stresses by utilizing the activities of transcription factors (TFs), which are families of genes coding for specific TF proteins. TF target genes form a regulon that is involved in the repression/activation of genes associated with abiotic stress responses. Therefore, it is of utmost importance to have a systematic study on each TF family, the downstream target genes they regulate, and the specific TF genes involved in multiple abiotic stress responses in maize and other staple crops. METHOD In this review, the main TF families, the specific TF genes and their regulons that are involved in abiotic stress regulation will be briefly discussed. Great emphasis will be given on maize abiotic stress improvement throughout this review, although other examples from different plants like rice, Arabidopsis, wheat, and barley will be used. RESULTS We have described in detail the main TF families in maize that take part in abiotic stress responses together with their regulons. Furthermore, we have also briefly described the utilization of high-efficiency technologies in the study and characterization of TFs involved in the abiotic stress regulatory networks in plants with an emphasis on increasing maize production. Examples of these technologies include next-generation sequencing, microarray analysis, machine learning, and RNA-Seq. CONCLUSION In conclusion, it is expected that all the information provided in this review will in time contribute to the use of TF genes in the research, breeding, and development of new abiotic stress tolerant maize cultivars.
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Affiliation(s)
- Roy Njoroge Kimotho
- Key Laboratory of Agricultural Water Resources, Hebei Laboratory of Agricultural Water Saving, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang, Hebei, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Elamin Hafiz Baillo
- Key Laboratory of Agricultural Water Resources, Hebei Laboratory of Agricultural Water Saving, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang, Hebei, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhengbin Zhang
- Key Laboratory of Agricultural Water Resources, Hebei Laboratory of Agricultural Water Saving, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang, Hebei, China
- University of Chinese Academy of Sciences, Beijing, China
- Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, China
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31
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Li X, Wang L, Nettleton D. Additive partially linear models for ultra‐high‐dimensional regression. Stat (Int Stat Inst) 2019. [DOI: 10.1002/sta4.223] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Xinyi Li
- SAMSI/Department of Statistics and Operations Research University of North Carolina at Chapel Hill Chapel Hill North Carolina
| | - Li Wang
- Department of Statistics Iowa State University Ames Iowa
| | - Dan Nettleton
- Department of Statistics Iowa State University Ames Iowa
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Liang Y, Liu Q, Wang X, Huang C, Xu G, Hey S, Lin HY, Li C, Xu D, Wu L, Wang C, Wu W, Xia J, Han X, Lu S, Lai J, Song W, Schnable PS, Tian F. ZmMADS69 functions as a flowering activator through the ZmRap2.7-ZCN8 regulatory module and contributes to maize flowering time adaptation. THE NEW PHYTOLOGIST 2019; 221:2335-2347. [PMID: 30288760 DOI: 10.1111/nph.15512] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 09/14/2018] [Indexed: 05/26/2023]
Abstract
Flowering time is a major determinant of the local adaptation of plants. Although numerous loci affecting flowering time have been mapped in maize, their underlying molecular mechanisms and roles in adaptation remain largely unknown. Here, we report the identification and characterization of MADS-box transcription factor ZmMADS69 that functions as a flowering activator through the ZmRap2.7-ZCN8 regulatory module and contributes to adaptation. We show that ZmMADS69 underlies a quantitative trait locus controlling the difference in flowering time between maize and its wild ancestor, teosinte. Maize ZmMADS69 allele is expressed at a higher level at floral transition and confers earlier flowering than the teosinte allele under long days and short days. Overexpression of ZmMADS69 causes early flowering, while a transposon insertion mutant of ZmMADS69 exhibits delayed flowering. ZmMADS69 shows pleiotropic effects for multiple traits of agronomic importance. ZmMADS69 functions upstream of the flowering repressor ZmRap2.7 to downregulate its expression, thereby relieving the repression of the florigen gene ZCN8 and causing early flowering. Population genetic analyses showed that ZmMADS69 was a target of selection and may have played an important role as maize spread from the tropics to temperate zones. Our findings provide important insights into the regulation and adaptation of flowering time.
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Affiliation(s)
- Yameng Liang
- National Maize Improvement Center of China, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Qiang Liu
- Department of Agronomy, Iowa State University, Ames, IA, 50010-3650, USA
| | - Xufeng Wang
- National Maize Improvement Center of China, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
- Guangdong Provincial Key Laboratory for Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Cheng Huang
- National Maize Improvement Center of China, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
- College of Agronomy, Hunan Agricultural University, Changsha, 410128, China
| | - Guanghui Xu
- National Maize Improvement Center of China, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Stefan Hey
- Department of Agronomy, Iowa State University, Ames, IA, 50010-3650, USA
| | - Hung-Ying Lin
- Department of Agronomy, Iowa State University, Ames, IA, 50010-3650, USA
| | - Cong Li
- National Maize Improvement Center of China, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Dingyi Xu
- National Maize Improvement Center of China, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Lishuan Wu
- National Maize Improvement Center of China, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Chenglong Wang
- National Maize Improvement Center of China, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Weihao Wu
- National Maize Improvement Center of China, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Jinliang Xia
- National Maize Improvement Center of China, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Xu Han
- National Maize Improvement Center of China, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Sijia Lu
- School of Life Sciences, Guangzhou University, Guangzhou, 510006, China
| | - Jinsheng Lai
- National Maize Improvement Center of China, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Weibin Song
- National Maize Improvement Center of China, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
| | - Patrick S Schnable
- Department of Agronomy, Iowa State University, Ames, IA, 50010-3650, USA
- Department of Plant Genetics & Breeding, China Agricultural University, Beijing, 100193, China
| | - Feng Tian
- National Maize Improvement Center of China, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China
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Zaidem ML, Groen SC, Purugganan MD. Evolutionary and ecological functional genomics, from lab to the wild. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:40-55. [PMID: 30444573 DOI: 10.1111/tpj.14167] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 11/10/2018] [Accepted: 11/13/2018] [Indexed: 05/12/2023]
Abstract
Plant phenotypes are the result of both genetic and environmental forces that act to modulate trait expression. Over the last few years, numerous approaches in functional genomics and systems biology have led to a greater understanding of plant phenotypic variation and plant responses to the environment. These approaches, and the questions that they can address, have been loosely termed evolutionary and ecological functional genomics (EEFG), and have been providing key insights on how plants adapt and evolve. In particular, by bringing these studies from the laboratory to the field, EEFG studies allow us to gain greater knowledge of how plants function in their natural contexts.
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Affiliation(s)
- Maricris L Zaidem
- Department of Biology, Center for Genomics and Systems Biology, New York University, 12 Waverly Place, New York, NY, 10003, USA
| | - Simon C Groen
- Department of Biology, Center for Genomics and Systems Biology, New York University, 12 Waverly Place, New York, NY, 10003, USA
| | - Michael D Purugganan
- Department of Biology, Center for Genomics and Systems Biology, New York University, 12 Waverly Place, New York, NY, 10003, USA
- Center for Genomics and Systems Biology, NYU Abu Dhabi Research Institute, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi, United Arab Emirates
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Guo Z, Qin J, Zhou X, Zhang Y. Insect Transcription Factors: A Landscape of Their Structures and Biological Functions in Drosophila and beyond. Int J Mol Sci 2018; 19:ijms19113691. [PMID: 30469390 PMCID: PMC6274879 DOI: 10.3390/ijms19113691] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 11/16/2018] [Accepted: 11/16/2018] [Indexed: 12/17/2022] Open
Abstract
Transcription factors (TFs) play essential roles in the transcriptional regulation of functional genes, and are involved in diverse physiological processes in living organisms. The fruit fly Drosophila melanogaster, a simple and easily manipulated organismal model, has been extensively applied to study the biological functions of TFs and their related transcriptional regulation mechanisms. It is noteworthy that with the development of genetic tools such as CRISPR/Cas9 and the next-generation genome sequencing techniques in recent years, identification and dissection the complex genetic regulatory networks of TFs have also made great progress in other insects beyond Drosophila. However, unfortunately, there is no comprehensive review that systematically summarizes the structures and biological functions of TFs in both model and non-model insects. Here, we spend extensive effort in collecting vast related studies, and attempt to provide an impartial overview of the progress of the structure and biological functions of current documented TFs in insects, as well as the classical and emerging research methods for studying their regulatory functions. Consequently, considering the importance of versatile TFs in orchestrating diverse insect physiological processes, this review will assist a growing number of entomologists to interrogate this understudied field, and to propel the progress of their contributions to pest control and even human health.
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Affiliation(s)
- Zhaojiang Guo
- Department of Plant Protection, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
| | - Jianying Qin
- Department of Plant Protection, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
- Longping Branch, Graduate School of Hunan University, Changsha 410125, China.
| | - Xiaomao Zhou
- Longping Branch, Graduate School of Hunan University, Changsha 410125, China.
| | - Youjun Zhang
- Department of Plant Protection, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
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Empirical Comparisons of Different Statistical Models To Identify and Validate Kernel Row Number-Associated Variants from Structured Multi-parent Mapping Populations of Maize. G3-GENES GENOMES GENETICS 2018; 8:3567-3575. [PMID: 30213868 PMCID: PMC6222574 DOI: 10.1534/g3.118.200636] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Advances in next generation sequencing technologies and statistical approaches enable genome-wide dissection of phenotypic traits via genome-wide association studies (GWAS). Although multiple statistical approaches for conducting GWAS are available, the power and cross-validation rates of many approaches have been mostly tested using simulated data. Empirical comparisons of single variant (SV) and multi-variant (MV) GWAS approaches have not been conducted to test if a single approach or a combination of SV and MV is effective, through identification and cross-validation of trait-associated loci. In this study, kernel row number (KRN) data were collected from a set of 6,230 entries derived from the Nested Association Mapping (NAM) population and related populations. Three different types of GWAS analyses were performed: 1) single-variant (SV), 2) stepwise regression (STR) and 3) a Bayesian-based multi-variant (BMV) model. Using SV, STR, and BMV models, 257, 300, and 442 KRN-associated variants (KAVs) were identified in the initial GWAS analyses. Of these, 231 KAVs were subjected to genetic validation using three unrelated populations that were not included in the initial GWAS. Genetic validation results suggest that the three GWAS approaches are complementary. Interestingly, KAVs in low recombination regions were more likely to exhibit associations in independent populations than KAVs in recombinationally active regions, probably as a consequence of linkage disequilibrium. The KAVs identified in this study have the potential to enhance our understanding of the genetic basis of ear development.
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Liu S, Schnable JC, Ott A, Yeh CTE, Springer NM, Yu J, Muehlbauer G, Timmermans MCP, Scanlon MJ, Schnable PS. Intragenic Meiotic Crossovers Generate Novel Alleles with Transgressive Expression Levels. Mol Biol Evol 2018; 35:2762-2772. [PMID: 30184112 PMCID: PMC6231493 DOI: 10.1093/molbev/msy174] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Meiotic recombination is an evolutionary force that generates new genetic diversity upon which selection can act. Whereas multiple studies have assessed genome-wide patterns of recombination and specific cases of intragenic recombination, few studies have assessed intragenic recombination genome-wide in higher eukaryotes. We identified recombination events within or near genes in a population of maize recombinant inbred lines (RILs) using RNA-sequencing data. Our results are consistent with case studies that have shown that intragenic crossovers cluster at the 5′ ends of some genes. Further, we identified cases of intragenic crossovers that generate transgressive transcript accumulation patterns, that is, recombinant alleles displayed higher or lower levels of expression than did nonrecombinant alleles in any of ∼100 RILs, implicating intragenic recombination in the generation of new variants upon which selection can act. Thousands of apparent gene conversion events were identified, allowing us to estimate the genome-wide rate of gene conversion at SNP sites (4.9 × 10−5). The density of syntenic genes (i.e., those conserved at the same genomic locations since the divergence of maize and sorghum) exhibits a substantial correlation with crossover frequency, whereas the density of nonsyntenic genes (i.e., those which have transposed or been lost subsequent to the divergence of maize and sorghum) shows little correlation, suggesting that crossovers occur at higher rates in syntenic genes than in nonsyntenic genes. Increased rates of crossovers in syntenic genes could be either a consequence of the evolutionary conservation of synteny or a biological process that helps to maintain synteny.
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Affiliation(s)
- Sanzhen Liu
- Department of Plant Pathology, Kansas State University, Manhattan, KS.,Department of Agronomy, Iowa State University, Ames, IA
| | - James C Schnable
- Department of Agriculture and Horticulture, University of Nebraska-Lincoln, Lincoln, NE
| | - Alina Ott
- Department of Agronomy, Iowa State University, Ames, IA.,Roche Sequencing Solutions, 500 S Rosa Road, Madison, WI
| | | | - Nathan M Springer
- Department of Plant and Microbial Biology, Microbial and Plant Genomics Institute, University of Minnesota, Saint Paul, MN
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA
| | - Gary Muehlbauer
- Department of Agronomy and Plant Genetics, Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN
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Li Y, Zhang X, Liu D, Gong J, Wang DD, Li S, Peng Z, Li Y, Wang X, Lin PP, Li M, Shen L. Evolutionary Expression of HER2 Conferred by Chromosome Aneuploidy on Circulating Gastric Cancer Cells Contributes to Developing Targeted and Chemotherapeutic Resistance. Clin Cancer Res 2018; 24:5261-5271. [PMID: 30012565 DOI: 10.1158/1078-0432.ccr-18-1205] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 06/07/2018] [Accepted: 07/10/2018] [Indexed: 11/16/2022]
MESH Headings
- Aneuploidy
- Antineoplastic Combined Chemotherapy Protocols/therapeutic use
- Biomarkers, Tumor
- Cell Line, Tumor
- Chromosomes, Human, Pair 8
- Drug Resistance, Neoplasm/genetics
- Flow Cytometry
- Fluorescent Antibody Technique
- Gene Expression Regulation, Neoplastic
- Humans
- Immunohistochemistry
- In Situ Hybridization, Fluorescence
- Kaplan-Meier Estimate
- Neoplastic Cells, Circulating/metabolism
- Neoplastic Cells, Circulating/pathology
- Prognosis
- Receptor, ErbB-2/genetics
- Stomach Neoplasms/diagnosis
- Stomach Neoplasms/drug therapy
- Stomach Neoplasms/genetics
- Stomach Neoplasms/mortality
- Tomography, X-Ray Computed
- Trastuzumab/pharmacology
- Trastuzumab/therapeutic use
- Treatment Outcome
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Affiliation(s)
- Yilin Li
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Xiaotian Zhang
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Dan Liu
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Jifang Gong
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | | | - Shan Li
- Department of Medicine, The University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Zhi Peng
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yanyan Li
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Xiaojuan Wang
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | | | - Min Li
- Department of Medicine, The University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Lin Shen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China.
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