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Niu Y, Yung WS, Sze CC, Wong FL, Li MW, Chung G, Lam HM. Developing an SNP dataset for efficiently evaluating soybean germplasm resources using the genome sequencing data of 3,661 soybean accessions. BMC Genomics 2024; 25:475. [PMID: 38745120 PMCID: PMC11092025 DOI: 10.1186/s12864-024-10382-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 05/06/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Single nucleotide polymorphism (SNP) markers play significant roles in accelerating breeding and basic crop research. Several soybean SNP panels have been developed. However, there is still a lack of SNP panels for differentiating between wild and cultivated populations, as well as for detecting polymorphisms within both wild and cultivated populations. RESULTS This study utilized publicly available resequencing data from over 3,000 soybean accessions to identify differentiating and highly conserved SNP and insertion/deletion (InDel) markers between wild and cultivated soybean populations. Additionally, a naturally occurring mutant gene library was constructed by analyzing large-effect SNPs and InDels in the population. CONCLUSION The markers obtained in this study are associated with numerous genes governing agronomic traits, thus facilitating the evaluation of soybean germplasms and the efficient differentiation between wild and cultivated soybeans. The natural mutant gene library permits the quick identification of individuals with natural mutations in functional genes, providing convenience for accelerating soybean breeding using reverse genetics.
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Affiliation(s)
- Yongchao Niu
- Centre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China
- School of Life Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Wai-Shing Yung
- Centre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China
- School of Life Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Ching-Ching Sze
- Centre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China
- School of Life Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Fuk-Ling Wong
- Centre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China
- School of Life Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Man-Wah Li
- Centre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China
- School of Life Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Gyuhwa Chung
- Department of Biotechnology, Chonnam National University, Yeosu-Si, Republic of Korea
| | - Hon-Ming Lam
- Centre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China.
- School of Life Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, 518000, China.
- Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong SAR, China.
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Hardigan MA, Feldmann MJ, Carling J, Zhu A, Kilian A, Famula RA, Cole GS, Knapp SJ. A medium-density genotyping platform for cultivated strawberry using DArTag technology. THE PLANT GENOME 2023; 16:e20399. [PMID: 37940627 DOI: 10.1002/tpg2.20399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/22/2023] [Indexed: 11/10/2023]
Abstract
Genomic prediction in breeding populations containing hundreds to thousands of parents and seedlings is prohibitively expensive with current high-density genetic marker platforms designed for strawberry. We developed mid-density panels of molecular inversion probes (MIPs) to be deployed with the "DArTag" marker platform to provide a low-cost, high-throughput genotyping solution for strawberry genomic prediction. In total, 7742 target single nucleotide polymorphism (SNP) regions were used to generate MIP assays that were tested with a screening panel of 376 octoploid Fragaria accessions. We evaluated the performance of DArTag assays based on genotype segregation, amplicon coverage, and their ability to produce subgenome-specific amplicon alignments to the FaRR1 assembly and subsequent alignment-based variant calls with strong concordance to DArT's alignment-free, count-based genotype reports. We used a combination of marker performance metrics and physical distribution in the FaRR1 assembly to select 3K and 5K production panels for genotyping of large strawberry populations. We show that the 3K and 5K DArTag panels are able to target and amplify homologous alleles within subgenomic sequences with low-amplification bias between reference and alternate alleles, supporting accurate genotype calling while producing marker genotypes that can be treated as functionally diploid for quantitative genetic analysis. The 3K and 5K target SNPs show high levels of polymorphism in diverse F. × ananassa germplasm and UC Davis cultivars, with mean pairwise diversity (π) estimates of 0.40 and 0.32 and mean heterozygous genotype frequencies of 0.35 and 0.33, respectively.
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Affiliation(s)
- Michael A Hardigan
- USDA-ARS, Horticultural Crops Production and Genetic Improvement Research Unit, Corvallis, Oregon, USA
- Department of Plant Sciences, University of California Davis, Davis, California, USA
| | - Mitchell J Feldmann
- Department of Plant Sciences, University of California Davis, Davis, California, USA
| | - Jason Carling
- Diversity Arrays Technology, University of Canberra, Bruce, Australian Capital Territory, Australia
| | - Anyu Zhu
- Diversity Arrays Technology, University of Canberra, Bruce, Australian Capital Territory, Australia
| | - Andrzej Kilian
- Diversity Arrays Technology, University of Canberra, Bruce, Australian Capital Territory, Australia
| | - Randi A Famula
- Department of Plant Sciences, University of California Davis, Davis, California, USA
| | - Glenn S Cole
- Department of Plant Sciences, University of California Davis, Davis, California, USA
| | - Steven J Knapp
- Department of Plant Sciences, University of California Davis, Davis, California, USA
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Arifuzzaman M, Mamidi S, Sanz-Saez A, Zakeri H, Scaboo A, Fritschi FB. Identification of loci associated with water use efficiency and symbiotic nitrogen fixation in soybean. FRONTIERS IN PLANT SCIENCE 2023; 14:1271849. [PMID: 38034552 PMCID: PMC10687445 DOI: 10.3389/fpls.2023.1271849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/20/2023] [Indexed: 12/02/2023]
Abstract
Soybean (Glycine max) production is greatly affected by persistent and/or intermittent droughts in rainfed soybean-growing regions worldwide. Symbiotic N2 fixation (SNF) in soybean can also be significantly hampered even under moderate drought stress. The objective of this study was to identify genomic regions associated with shoot carbon isotope ratio (δ13C) as a surrogate measure for water use efficiency (WUE), nitrogen isotope ratio (δ15N) to assess relative SNF, N concentration ([N]), and carbon/nitrogen ratio (C/N). Genome-wide association mapping was performed with 105 genotypes and approximately 4 million single-nucleotide polymorphism markers derived from whole-genome resequencing information. A total of 11, 21, 22, and 22 genomic loci associated with δ13C, δ15N, [N], and C/N, respectively, were identified in two environments. Nine of these 76 loci were stable across environments, as they were detected in both environments. In addition to the 62 novel loci identified, 14 loci aligned with previously reported quantitative trait loci for different C and N traits related to drought, WUE, and N2 fixation in soybean. A total of 58 Glyma gene models encoding for different genes related to the four traits were identified in the vicinity of the genomic loci.
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Affiliation(s)
- Muhammad Arifuzzaman
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, United States
| | - Sujan Mamidi
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, United States
| | - Alvaro Sanz-Saez
- Department of Crop, Soil and Environmental Sciences, Auburn University, Auburn, AL, United States
| | - Hossein Zakeri
- College of Agriculture, California State University-Chico, Chico, CA, United States
| | - Andrew Scaboo
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, United States
| | - Felix B. Fritschi
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, United States
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Deng S, Qiu Y, Zhuang Z, Wu J, Li X, Ruan D, Xu C, Zheng E, Yang M, Cai G, Yang J, Wu Z, Huang S. Genome-Wide Association Study of Body Conformation Traits in a Three-Way Crossbred Commercial Pig Population. Animals (Basel) 2023; 13:2414. [PMID: 37570223 PMCID: PMC10417164 DOI: 10.3390/ani13152414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/28/2023] [Accepted: 07/05/2023] [Indexed: 08/13/2023] Open
Abstract
Body conformation is the most direct production index, which can fully reflect pig growth status and is closely related to critical economic traits. In this study, we conducted a genome-wide association study (GWAS) on body conformation traits in a population of 1518 Duroc × (Landrace × Yorkshire) commercial pigs. These traits included body length (BL), body height (BH), chest circumference (CC), abdominal circumference (AC), and waist circumference (WC). Both the mixed linear model (MLM) and fixed and random model circulating probability unification (FarmCPU) approaches were employed for the analysis. Our findings revealed 60 significant single nucleotide polymorphisms (SNPs) associated with these body conformation traits in the crossbred pig population. Specifically, sixteen SNPs were significantly associated with BL, three SNPs with BH, thirteen SNPs with CC, twelve SNPs with AC, and sixteen SNPs with WC. Moreover, we identified several promising candidate genes located within the genomic regions associated with body conformation traits. These candidate genes include INTS10, KIRREL3, SOX21, BMP2, MAP4K3, SOD3, FAM160B1, ATL2, SPRED2, SEC16B, and RASAL2. Furthermore, our analysis revealed a novel significant quantitative trait locus (QTL) on SSC7 specifically associated with waist circumference, spanning an 84 kb interval. Overall, the identification of these significant SNPs and potential candidate genes in crossbred commercial pigs enhances our understanding of the genetic basis underlying body conformation traits. Additionally, these findings provide valuable genetic resources for pig breeding programs.
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Affiliation(s)
- Shaoxiong Deng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Yibin Qiu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Jie Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Xuehua Li
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Donglin Ruan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Cineng Xu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Enqing Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Ming Yang
- College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China;
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu 527400, China
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu 527400, China
| | - Sixiu Huang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
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Jin H, Yang X, Zhao H, Song X, Tsvetkov YD, Wu Y, Gao Q, Zhang R, Zhang J. Genetic analysis of protein content and oil content in soybean by genome-wide association study. FRONTIERS IN PLANT SCIENCE 2023; 14:1182771. [PMID: 37346139 PMCID: PMC10281628 DOI: 10.3389/fpls.2023.1182771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/09/2023] [Indexed: 06/23/2023]
Abstract
Soybean seed protein content (PC) and oil content (OC) have important economic value. Detecting the loci/gene related to PC and OC is important for the marker-assisted selection (MAS) breeding of soybean. To detect the stable and new loci for PC and OC, a total of 320 soybean accessions collected from the major soybean-growing countries were used to conduct a genome-wide association study (GWAS) by resequencing. The PC ranged from 37.8% to 46.5% with an average of 41.1% and the OC ranged from 16.7% to 22.6% with an average of 21.0%. In total, 23 and 29 loci were identified, explaining 3.4%-15.4% and 5.1%-16.3% of the phenotypic variations for PC and OC, respectively. Of these, eight and five loci for PC and OC, respectively, overlapped previously reported loci and the other 15 and 24 loci were newly identified. In addition, nine candidate genes were identified, which are known to be involved in protein and oil biosynthesis/metabolism, including lipid transport and metabolism, signal transduction, and plant development pathway. These results uncover the genetic basis of soybean protein and oil biosynthesis and could be used to accelerate the progress in enhancing soybean PC and OC.
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Affiliation(s)
- Hui Jin
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Xue Yang
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Haibin Zhao
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Xizhang Song
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Yordan Dimitrov Tsvetkov
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - YuE Wu
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Qiang Gao
- Horticultural Branch of Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Rui Zhang
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Jumei Zhang
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
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Xia Z, Zhai H, Zhang Y, Wang Y, Wang L, Xu K, Wu H, Zhu J, Jiao S, Wan Z, Zhu X, Gao Y, Liu Y, Fan R, Wu S, Chen X, Liu J, Yang J, Song Q, Tian Z. QNE1 is a key flowering regulator determining the length of the vegetative period in soybean cultivars. SCIENCE CHINA. LIFE SCIENCES 2022; 65:2472-2490. [PMID: 35802303 DOI: 10.1007/s11427-022-2117-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/22/2022] [Indexed: 06/15/2023]
Abstract
The soybean E1 gene is a major regulator that plays an important role in flowering time and maturity. However, it remains unclear how cultivars carrying the dominant E1 allele adapt to the higher latitudinal areas of northern China. We mapped the novel quantitative trait locus QNE1 (QTL near E1) for flowering time to the region proximal to E1 on chromosome 6 in two mapping populations. Positional cloning revealed Glyma.06G204300, encoding a TCP-type transcription factor, as a strong candidate gene for QNE1. Association analysis further confirmed that functional single nucleotide polymorphisms (SNPs) at nucleotides 686 and 1,063 in the coding region of Glyma.06G204300 were significantly associated with flowering time. The protein encoded by the candidate gene is localized primarily to the nucleus. Furthermore, soybean and Brassica napus plants overexpressing Glyma.06G204300 exhibited early flowering. We conclude that despite their similar effects on flowering time, QNE1 and E4 may control flowering time through different regulatory mechanisms, based on expression studies and weighted gene co-expression network analysis of flowering time-related genes. Deciphering the molecular basis of QNE1 control of flowering time enriches our knowledge of flowering gene networks in soybean and will facilitate breeding soybean cultivars with broader latitudinal adaptation.
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Affiliation(s)
- Zhengjun Xia
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Harbin, 150081, China.
| | - Hong Zhai
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Harbin, 150081, China
| | - Yanfeng Zhang
- Hybrid Rapeseed Research Center of Shaanxi Province, Yangling, 712100, China
| | - Yaying Wang
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Harbin, 150081, China
| | - Lu Wang
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Harbin, 150081, China
| | - Kun Xu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Harbin, 150081, China
| | - Hongyan Wu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Harbin, 150081, China
| | - Jinglong Zhu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Harbin, 150081, China
| | - Shuang Jiao
- College of Life Science, Northeast Agricultural University, Harbin, 150030, China
| | - Zhao Wan
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Harbin, 150081, China
| | - Xiaobin Zhu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Harbin, 150081, China
| | - Yi Gao
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Harbin, 150081, China
| | - Yingxiang Liu
- College of Life Science, Northeast Agricultural University, Harbin, 150030, China
| | - Rong Fan
- College of Life Science, Northeast Agricultural University, Harbin, 150030, China
| | - Shihao Wu
- Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China
| | - Xin Chen
- Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China
| | - Jinyu Liu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Harbin, 150081, China
| | - Jiayin Yang
- Huaiyin Institute of Agricultural Science of Xuhuai Region, Jiangsu Academy of Agricultural Sciences, Huai'an, 223001, China
| | - Qijian Song
- USDA ARS, Soybean Genome & Improvement Lab, Beltsville, 20705, USA
| | - Zhixi Tian
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China.
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Zhao YX, Gao GX, Zhou Y, Guo CX, Li B, El-Ashram S, Li ZL. Genome-wide association studies uncover genes associated with litter traits in the pig. Animal 2022; 16:100672. [PMID: 36410176 DOI: 10.1016/j.animal.2022.100672] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 10/17/2022] [Accepted: 10/18/2022] [Indexed: 12/24/2022] Open
Abstract
Litter traits are critical economic variables in the pig industry as they represent a production indicator that can serve to determine sow fertility. In this study, a genome-wide association study on litter traits, including total number born (TNB), number born alive (NBA), litter birth weight (LBW), average birth weight (ABW), and piglet uniformity (PU), was carried out on two pig breeds (Yorkshire and Landrace). A total of 3 637 pigs of both breeds were genotyped using the GeneSeek GGP Porcine 50K SNP BeadChip. A mixed linear model (MLM) and fixed and random model circulating probability unification (FarmCPU) were employed in the genome-wide association studies for litter traits using combined data from the two pig breeds and data from each breed separately. Additionally, the heritability of traits was estimated using three methods-pedigree-based best linear unbiased prediction (PBLUP), genomic best linear unbiased prediction (GBLUP), and single-step best linear unbiased prediction (ssGBLUP)-and was found to lie between 0.065 and 0.1289, 0.0478 and 0.0938, 0.0793 and 0.0935, 0.1862 and 0.2163, and 0.0327 and 0.0419 for TNB, NBA, LBW, ABW, and PU, respectively. We also compared the genomic prediction accuracies and unbiasedness for litter traits of the three BLUP models. Our results indicated that the ssGBLUP method provided higher predictive accuracies and more rational unbiasedness compared with the PBLUP and GBLUP methodologies. Furthermore, based on their possible roles, eight candidate genes (INHBA, LEPR, HDHD2, CTNND2, RNF216, HMX1, PAPPA2, and NTN1) were identified as being linked with litter traits. In the middle of the test, these genes were found to be connected with pig metabolism and ovulation rate. Our results provide the insights into the genetic architecture of litter traits in pigs, and the potential single nucleotide polymorphisms (SNPs) and candidate genes identified may benefit economic profits in pig-breeding industry and contribute to improve litter traits.
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Affiliation(s)
- Y X Zhao
- School of Life Science and Engineering, Foshan University, Foshan, Guangdong 528000, China; Guangxi Yangxiang Agricultural and Animal Husbandry Co, Ltd, Guigang, Guangxi 537100, China
| | - G X Gao
- School of Life Science and Engineering, Foshan University, Foshan, Guangdong 528000, China
| | - Y Zhou
- Guangxi Yangxiang Agricultural and Animal Husbandry Co, Ltd, Guigang, Guangxi 537100, China
| | - C X Guo
- Guangxi Yangxiang Agricultural and Animal Husbandry Co, Ltd, Guigang, Guangxi 537100, China
| | - B Li
- Guangxi Yangxiang Agricultural and Animal Husbandry Co, Ltd, Guigang, Guangxi 537100, China
| | - S El-Ashram
- Faculty of Science, Kafrelsheikh University, Kafr El-Sheikh 33516, Egypt
| | - Z L Li
- School of Life Science and Engineering, Foshan University, Foshan, Guangdong 528000, China.
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Sun R, Sun B, Tian Y, Su S, Zhang Y, Zhang W, Wang J, Yu P, Guo B, Li H, Li Y, Gao H, Gu Y, Yu L, Ma Y, Su E, Li Q, Hu X, Zhang Q, Guo R, Chai S, Feng L, Wang J, Hong H, Xu J, Yao X, Wen J, Liu J, Li Y, Qiu L. Dissection of the practical soybean breeding pipeline by developing ZDX1, a high-throughput functional array. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1413-1427. [PMID: 35187586 PMCID: PMC9033737 DOI: 10.1007/s00122-022-04043-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 01/22/2022] [Indexed: 05/13/2023]
Abstract
KEY MESSAGE We developed the ZDX1 high-throughput functional soybean array for high accuracy evaluation and selection of both parents and progeny, which can greatly accelerate soybean breeding. Microarray technology facilitates rapid, accurate, and economical genotyping. Here, using resequencing data from 2214 representative soybean accessions, we developed the high-throughput functional array ZDX1, containing 158,959 SNPs, covering 90.92% of soybean genes and sites related to important traits. By application of the array, a total of 817 accessions were genotyped, including three subpopulations of candidate parental lines, parental lines and their progeny from practical breeding. The fixed SNPs were identified in progeny, indicating artificial selection during the breeding process. By identifying functional sites of target traits, novel soybean cyst nematode-resistant progeny and maturity-related novel sources were identified by allele combinations, demonstrating that functional sites provide an efficient method for the rapid screening of desirable traits or gene sources. Notably, we found that the breeding index (BI) was a good indicator for progeny selection. Superior progeny were derived from the combination of distantly related parents, with at least one parent having a higher BI. Furthermore, new combinations based on good performance were proposed for further breeding after excluding redundant and closely related parents. Genomic best linear unbiased prediction (GBLUP) analysis was the best analysis method and achieved the highest accuracy in predicting four traits when comparing SNPs in genic regions rather than whole genomic or intergenic SNPs. The prediction accuracy was improved by 32.1% by using progeny to expand the training population. Collectively, a versatile assay demonstrated that the functional ZDX1 array provided efficient information for the design and optimization of a breeding pipeline for accelerated soybean breeding.
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Affiliation(s)
- Rujian Sun
- College of Agriculture, Northeast Agricultural University, Harbin, 150030, People's Republic of China
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Bincheng Sun
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Yu Tian
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Shanshan Su
- Beijing Compass Biotechnology Co, Ltd, Beijing, 102200, People's Republic of China
| | - Yong Zhang
- Keshan Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihar, 161600, People's Republic of China
| | - Wanhai Zhang
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Jingshun Wang
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Ping Yu
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Bingfu Guo
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Huihui Li
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Yanfei Li
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Huawei Gao
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Yongzhe Gu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Lili Yu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Yansong Ma
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Erhu Su
- Inner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Hohhot, 010000, People's Republic of China
| | - Qiang Li
- Inner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Hohhot, 010000, People's Republic of China
| | - Xingguo Hu
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Qi Zhang
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Rongqi Guo
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Shen Chai
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Lei Feng
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Jun Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Huilong Hong
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Jiangyuan Xu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Xindong Yao
- Department of Crop Sciences, University of Natural Resources and Life Sciences Vienna (BOKU), 3430, Tulln, Austria
| | - Jing Wen
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Jiqiang Liu
- Beijing Compass Biotechnology Co, Ltd, Beijing, 102200, People's Republic of China
| | - Yinghui Li
- College of Agriculture, Northeast Agricultural University, Harbin, 150030, People's Republic of China.
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China.
| | - Lijuan Qiu
- College of Agriculture, Northeast Agricultural University, Harbin, 150030, People's Republic of China.
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China.
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Mendonça HC, Pereira LFP, Maldonado dos Santos JV, Meda AR, Sant’ Ana GC. Genetic Diversity and Selection Footprints in the Genome of Brazilian Soybean Cultivars. FRONTIERS IN PLANT SCIENCE 2022; 13:842571. [PMID: 35432410 PMCID: PMC9006619 DOI: 10.3389/fpls.2022.842571] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 02/14/2022] [Indexed: 06/14/2023]
Abstract
Although Brazil is currently the largest soybean producer in the world, only a small number of studies have analyzed the genetic diversity of Brazilian soybean. These studies have shown the existence of a narrow genetic base. The objectives of this work were to analyze the population structure and genetic diversity, and to identify selection signatures in the genome of soybean germplasms from different companies in Brazil. A panel consisting of 343 soybean lines from Brazil, North America, and Asia was genotyped using genotyping by sequencing (GBS). Population structure was assessed by Bayesian and multivariate approaches. Genetic diversity was analyzed using metrics such as the fixation index, nucleotide diversity, genetic dissimilarity, and linkage disequilibrium. The software BayeScan was used to detect selection signatures between Brazilian and Asian accessions as well as among Brazilian germplasms. Region of origin, company of origin, and relative maturity group (RMG) all had a significant influence on population structure. Varieties belonging to the same company and especially to the same RMG exhibited a high level of genetic similarity. This result was exacerbated among early maturing accessions. Brazilian soybean showed significantly lower genetic diversity when compared to Asian accessions. This was expected, because the crop's region of origin is its main genetic diversity reserve. We identified 7 genomic regions under selection between the Brazilian and Asian accessions, and 27 among Brazilian varieties developed by different companies. Associated with these genomic regions, we found 96 quantitative trait loci (QTLs) for important soybean breeding traits such as flowering, maturity, plant architecture, productivity components, pathogen resistance, and seed composition. Some of the QTLs associated with the markers under selection have genes of great importance to soybean's regional adaptation. The results reported herein allowed to expand the knowledge about the organization of the genetic variability of the Brazilian soybean germplasm. Furthermore, it was possible to identify genomic regions under selection possibly associated with the adaptation of soybean to Brazilian environments.
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Affiliation(s)
| | - Luiz Filipe Protasio Pereira
- Centro de Ciências Biológicas, State University of Londrina, Londrina, Brazil
- Laboratório de Biotecnologia, Instituto de Desenvolvimento Rural do Paraná, Embrapa Café, Londrina, Brazil
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Hu B, Li Y, Wu H, Zhai H, Xu K, Gao Y, Zhu J, Li Y, Xia Z. Identification of quantitative trait loci underlying five major agronomic traits of soybean in three biparental populations by specific length amplified fragment sequencing (SLAF-seq). PeerJ 2021; 9:e12416. [PMID: 34993010 PMCID: PMC8679901 DOI: 10.7717/peerj.12416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 10/10/2021] [Indexed: 11/20/2022] Open
Abstract
Flowering time, plant height, branch number, node numbers of main stem and pods per plant are important agronomic traits related to photoperiodic sensitivity, plant type and yield of soybean, which are controlled by multiple genes or quantitative trait loci (QTL). The main purpose of this study is to identify new QTL for five major agronomic traits, especially for flowering time. Three biparental populations were developed by crossing cultivars from northern and central China. Specific loci amplified fragment sequencing (SLAF-seq) was used to construct linkage map and QTL mapping was carried out. A total of 10 QTL for flowering time were identified in three populations, some of which were related to E1 and E2 genes or the other reported QTL listed in Soybase. In the Y159 population (Xudou No.9 × Kenfeng No.16), QTL for flowering time on chromosome 4, qFT4_1 and qFT4_2 were new. Compared with the QTL reported in Soybase, 1 QTL for plant height (PH), 3 QTL for branch number (BR), 5 QTL for node numbers of main stem, and 3 QTL for pods per plant were new QTL. Major E genes were frequently detected in different populations indicating that major the E loci had a great effect on flowering time and adaptation of soybean. Therefore, in order to further clone minor genes or QTL, it may be of great significance to carefully select the genotypes of known loci. These results may lay a foundation for fine mapping and clone of QTL/genes related to plant-type, provided a basis for high yield breeding of soybean.
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Affiliation(s)
- Bo Hu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Harbin, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yuqiu Li
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Harbin, China
| | - Hongyan Wu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Harbin, China
| | - Hong Zhai
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Harbin, China
| | - Kun Xu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Harbin, China
| | - Yi Gao
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Harbin, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jinlong Zhu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Harbin, China
| | - Yuzhuo Li
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Harbin, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhengjun Xia
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Harbin, China
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Yan W, Karikari B, Chang F, Zhao F, Zhang Y, Li D, Zhao T, Jiang H. Genome-Wide Association Study to Map Genomic Regions Related to the Initiation Time of Four Growth Stage Traits in Soybean. Front Genet 2021; 12:715529. [PMID: 34594361 PMCID: PMC8476948 DOI: 10.3389/fgene.2021.715529] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/09/2021] [Indexed: 12/13/2022] Open
Abstract
The time to flowering (DF), pod beginning (DPB), seed formation (DSF), and maturity initiation (DMI) in soybean (Glycine max [L.] Merr) are important characteristics of growth stage traits (GSTs) in Chinese summer-sowing soybean, and are influenced by genetic as well as environmental factors. To better understand the molecular mechanism underlying the initiation times of GSTs, we investigated four GSTs of 309 diverse soybean accessions in six different environments and Best Linear Unbiased Prediction values. Furthermore, the genome-wide association study was conducted by a Fixed and random model Circulating Probability Unification method using over 60,000 single nucleotide polymorphism (SNP) markers to identify the significant quantitative trait nucleotide (QTN) regions with phenotypic data. As a result, 212 SNPs within 102 QTN regions were associated with four GSTs. Of which, eight stable regions were repeatedly detected in least three datasets for one GST. Interestingly, half of the QTN regions overlapped with previously reported quantitative trait loci or well-known soybean growth period genes. The hotspots associated with all GSTs were concentrated on chromosome 10. E2 (Glyma10g36600), a gene with a known function in regulating flowering and maturity in soybean, is also found on this chromosome. Thus, this genomic region may account for the strong correlation among the four GSTs. All the significant SNPs in the remaining 7 QTN regions could cause the significant phenotypic variation with both the major and minor alleles. Two hundred and seventy-five genes in soybean and their homologs in Arabidopsis were screened within ± 500 kb of 7 peak SNPs in the corresponding QTN regions. Most of the genes are involved in flowering, response to auxin stimulus, or regulation of seed germination, among others. The findings reported here provide an insight for genetic improvement which will aid in breeding of soybean cultivars that can be adapted to the various summer sowing areas in China and beyond.
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Affiliation(s)
- Wenliang Yan
- Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing, China.,College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
| | - Benjamin Karikari
- Department of Crop Science, Faculty of Agriculture, Food and Consumer Sciences, University for Development Studies, Tamale, Ghana
| | - Fangguo Chang
- Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing, China
| | - Fangzhou Zhao
- Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing, China
| | - Yinghu Zhang
- Institute of Agricultural Sciences in Jiangsu Coastal Region, Yancheng, China
| | - Dongmei Li
- Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing, China.,College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
| | - Tuanjie Zhao
- Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing, China
| | - Haiyan Jiang
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
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12
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Malovichko YV, Shikov AE, Nizhnikov AA, Antonets KS. Temporal Control of Seed Development in Dicots: Molecular Bases, Ecological Impact and Possible Evolutionary Ramifications. Int J Mol Sci 2021; 22:ijms22179252. [PMID: 34502157 PMCID: PMC8430901 DOI: 10.3390/ijms22179252] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 08/20/2021] [Accepted: 08/23/2021] [Indexed: 12/21/2022] Open
Abstract
In flowering plants, seeds serve as organs of both propagation and dispersal. The developing seed passes through several consecutive stages, following a conserved general outline. The overall time needed for a seed to develop, however, may vary both within and between plant species, and these temporal developmental properties remain poorly understood. In the present paper, we summarize the existing data for seed development alterations in dicot plants. For genetic mutations, the reported cases were grouped in respect of the key processes distorted in the mutant specimens. Similar phenotypes arising from the environmental influence, either biotic or abiotic, were also considered. Based on these data, we suggest several general trends of timing alterations and how respective mechanisms might add to the ecological plasticity of the families considered. We also propose that the developmental timing alterations may be perceived as an evolutionary substrate for heterochronic events. Given the current lack of plausible models describing timing control in plant seeds, the presented suggestions might provide certain insights for future studies in this field.
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Affiliation(s)
- Yury V. Malovichko
- Laboratory for Proteomics of Supra-Organismal Systems, All-Russia Research Institute for Agricultural Microbiology (ARRIAM), 196608 St. Petersburg, Russia; (Y.V.M.); (A.E.S.); (A.A.N.)
- Faculty of Biology, St. Petersburg State University, 199034 St. Petersburg, Russia
| | - Anton E. Shikov
- Laboratory for Proteomics of Supra-Organismal Systems, All-Russia Research Institute for Agricultural Microbiology (ARRIAM), 196608 St. Petersburg, Russia; (Y.V.M.); (A.E.S.); (A.A.N.)
- Faculty of Biology, St. Petersburg State University, 199034 St. Petersburg, Russia
| | - Anton A. Nizhnikov
- Laboratory for Proteomics of Supra-Organismal Systems, All-Russia Research Institute for Agricultural Microbiology (ARRIAM), 196608 St. Petersburg, Russia; (Y.V.M.); (A.E.S.); (A.A.N.)
- Faculty of Biology, St. Petersburg State University, 199034 St. Petersburg, Russia
| | - Kirill S. Antonets
- Laboratory for Proteomics of Supra-Organismal Systems, All-Russia Research Institute for Agricultural Microbiology (ARRIAM), 196608 St. Petersburg, Russia; (Y.V.M.); (A.E.S.); (A.A.N.)
- Faculty of Biology, St. Petersburg State University, 199034 St. Petersburg, Russia
- Correspondence:
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13
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Ravelombola W, Qin J, Shi A, Song Q, Yuan J, Wang F, Chen P, Yan L, Feng Y, Zhao T, Meng Y, Guan K, Yang C, Zhang M. Genome-wide association study and genomic selection for yield and related traits in soybean. PLoS One 2021; 16:e0255761. [PMID: 34388193 PMCID: PMC8362977 DOI: 10.1371/journal.pone.0255761] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/22/2021] [Indexed: 12/23/2022] Open
Abstract
Soybean [Glycine max (L.) Merr.] is a crop of great interest worldwide. Exploring molecular approaches to increase yield genetic gain has been one of the main challenges for soybean breeders and geneticists. Agronomic traits such as maturity, plant height, and seed weight have been found to contribute to yield. In this study, a total of 250 soybean accessions were genotyped with 10,259 high-quality SNPs postulated from genotyping by sequencing (GBS) and evaluated for grain yield, maturity, plant height, and seed weight over three years. A genome-wide association study (GWAS) was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model. Genomic selection (GS) was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that 20, 31, 37, and 23 SNPs were significantly associated with maturity, plant height, seed weight, and yield, respectively; Many SNPs were mapped to previously described maturity and plant height loci (E2, E4, and Dt1) and a new plant height locus was mapped to chromosome 20. Candidate genes were found in the vicinity of the two SNPs with the highest significant levels associated with yield, maturity, plant height, seed weight, respectively. A 11.5-Mb region of chromosome 10 was associated with both yield and seed weight. Overall, the accuracy of GS was dependent on the trait, year, and population structure, and high accuracy indicates that these agronomic traits can be selected in molecular breeding through GS. The SNP markers identified in this study can be used to improve yield and agronomic traits through the marker-assisted selection and GS in breeding programs.
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Affiliation(s)
- Waltram Ravelombola
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States of America
| | - Jun Qin
- The Key Laboratory of Crop Genetics and Breeding of Hebei Province, National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Laboratory of Crop Genetics and Breeding of Hebei Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, Hebei, China
| | - Ainong Shi
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States of America
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, Beltsville Agricultural Research Center, USDA-ARS, Beltsville, MD, United States of America
| | - Jin Yuan
- The Key Laboratory of Crop Genetics and Breeding of Hebei Province, National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Laboratory of Crop Genetics and Breeding of Hebei Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, Hebei, China
| | - Fengmin Wang
- The Key Laboratory of Crop Genetics and Breeding of Hebei Province, National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Laboratory of Crop Genetics and Breeding of Hebei Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, Hebei, China
| | - Pengyin Chen
- Fisher Delta Research Center, University of Missouri, MO, United States of America
| | - Long Yan
- The Key Laboratory of Crop Genetics and Breeding of Hebei Province, National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Laboratory of Crop Genetics and Breeding of Hebei Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, Hebei, China
| | - Yan Feng
- The Key Laboratory of Crop Genetics and Breeding of Hebei Province, National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Laboratory of Crop Genetics and Breeding of Hebei Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, Hebei, China
| | - Tiantian Zhao
- The Key Laboratory of Crop Genetics and Breeding of Hebei Province, National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Laboratory of Crop Genetics and Breeding of Hebei Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, Hebei, China
| | - Yaning Meng
- The Key Laboratory of Crop Genetics and Breeding of Hebei Province, National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Laboratory of Crop Genetics and Breeding of Hebei Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, Hebei, China
| | - Kexin Guan
- The Key Laboratory of Crop Genetics and Breeding of Hebei Province, National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Laboratory of Crop Genetics and Breeding of Hebei Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, Hebei, China
| | - Chunyan Yang
- The Key Laboratory of Crop Genetics and Breeding of Hebei Province, National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Laboratory of Crop Genetics and Breeding of Hebei Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, Hebei, China
| | - Mengchen Zhang
- The Key Laboratory of Crop Genetics and Breeding of Hebei Province, National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Laboratory of Crop Genetics and Breeding of Hebei Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, Hebei, China
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14
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Gao G, Gao N, Li S, Kuang W, Zhu L, Jiang W, Yu W, Guo J, Li Z, Yang C, Zhao Y. Genome-Wide Association Study of Meat Quality Traits in a Three-Way Crossbred Commercial Pig Population. Front Genet 2021; 12:614087. [PMID: 33815461 PMCID: PMC8010252 DOI: 10.3389/fgene.2021.614087] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 02/12/2021] [Indexed: 01/12/2023] Open
Abstract
Meat quality is an important trait for pig-breeding programs aiming to meet consumers' demands. Geneticists must improve meat quality based on their understanding of the underlying genetic mechanisms. Previous studies showed that most meat-quality indicators were low-to-moderate heritability traits; therefore, improving meat quality using conventional techniques remains a challenge. Here, we performed a genome-wide association study of meat-quality traits using the GeneSeek Porcine SNP50K BeadChip in 582 crossbred Duroc × (Landrace × Yorkshire) commercial pigs (249 males and 333 females). Meat conductivity, marbling score, moisture, meat color, pH, and intramuscular fat (IMF) content were investigated. The genome-wide association study was performed using both fixed and random model Circulating Probability Unification (FarmCPU) and a mixed linear model (MLM) with the rMVP software. The genomic heritability of the studied traits ranged from 0.13 ± 0.07 to 0.55 ± 0.08 for conductivity and meat color, respectively. Thirty-two single-nucleotide polymorphisms (SNPs) were identified for meat quality in the crossbred pigs using both FarmCPU and MLM. Among the detected SNPs, five, nine, seven, four, six, and five were significantly associated with conductivity, IMF, marbling score, meat color, moisture, and pH, respectively. Several candidate genes for meat quality were identified in the detected genomic regions. These findings will contribute to the ongoing improvement of meat quality, meeting consumer demands and improving the economic outlook for the swine industry.
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Affiliation(s)
- Guangxiong Gao
- School of Life Sciences and Engineering, Foshan University, Foshan, China
| | - Ning Gao
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
- Guangxi Yangxiang Co., Ltd., Guigang, China
| | - Sicheng Li
- Guangxi Yangxiang Co., Ltd., Guigang, China
| | - Weijian Kuang
- School of Life Sciences and Engineering, Foshan University, Foshan, China
| | - Lin Zhu
- Guangxi Yangxiang Co., Ltd., Guigang, China
| | - Wei Jiang
- Guangxi Yangxiang Co., Ltd., Guigang, China
| | - Weiwei Yu
- School of Life Sciences and Engineering, Foshan University, Foshan, China
| | - Jinbiao Guo
- School of Life Sciences and Engineering, Foshan University, Foshan, China
| | - Zhili Li
- School of Life Sciences and Engineering, Foshan University, Foshan, China
| | - Chengzhong Yang
- School of Life Sciences and Engineering, Foshan University, Foshan, China
| | - Yunxiang Zhao
- School of Life Sciences and Engineering, Foshan University, Foshan, China
- Guangxi Yangxiang Co., Ltd., Guigang, China
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15
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Thudi M, Palakurthi R, Schnable JC, Chitikineni A, Dreisigacker S, Mace E, Srivastava RK, Satyavathi CT, Odeny D, Tiwari VK, Lam HM, Hong YB, Singh VK, Li G, Xu Y, Chen X, Kaila S, Nguyen H, Sivasankar S, Jackson SA, Close TJ, Shubo W, Varshney RK. Genomic resources in plant breeding for sustainable agriculture. JOURNAL OF PLANT PHYSIOLOGY 2021; 257:153351. [PMID: 33412425 PMCID: PMC7903322 DOI: 10.1016/j.jplph.2020.153351] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 12/14/2020] [Accepted: 12/14/2020] [Indexed: 05/19/2023]
Abstract
Climate change during the last 40 years has had a serious impact on agriculture and threatens global food and nutritional security. From over half a million plant species, cereals and legumes are the most important for food and nutritional security. Although systematic plant breeding has a relatively short history, conventional breeding coupled with advances in technology and crop management strategies has increased crop yields by 56 % globally between 1965-85, referred to as the Green Revolution. Nevertheless, increased demand for food, feed, fiber, and fuel necessitates the need to break existing yield barriers in many crop plants. In the first decade of the 21st century we witnessed rapid discovery, transformative technological development and declining costs of genomics technologies. In the second decade, the field turned towards making sense of the vast amount of genomic information and subsequently moved towards accurately predicting gene-to-phenotype associations and tailoring plants for climate resilience and global food security. In this review we focus on genomic resources, genome and germplasm sequencing, sequencing-based trait mapping, and genomics-assisted breeding approaches aimed at developing biotic stress resistant, abiotic stress tolerant and high nutrition varieties in six major cereals (rice, maize, wheat, barley, sorghum and pearl millet), and six major legumes (soybean, groundnut, cowpea, common bean, chickpea and pigeonpea). We further provide a perspective and way forward to use genomic breeding approaches including marker-assisted selection, marker-assisted backcrossing, haplotype based breeding and genomic prediction approaches coupled with machine learning and artificial intelligence, to speed breeding approaches. The overall goal is to accelerate genetic gains and deliver climate resilient and high nutrition crop varieties for sustainable agriculture.
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Affiliation(s)
- Mahendar Thudi
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India; University of Southern Queensland, Toowoomba, Australia
| | - Ramesh Palakurthi
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | | | - Annapurna Chitikineni
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | | | - Emma Mace
- Agri-Science Queensland, Department of Agriculture & Fisheries (DAF), Warwick, Australia
| | - Rakesh K Srivastava
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - C Tara Satyavathi
- Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute (IARI), New Delhi, India
| | - Damaris Odeny
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Nairobi, Kenya
| | | | - Hon-Ming Lam
- Center for Soybean Research of the State Key Laboratory of Agrobiotechnology and School of Life Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region
| | - Yan Bin Hong
- Crops Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Vikas K Singh
- South Asia Hub, International Rice Research Institute (IRRI), Hyderabad, India
| | - Guowei Li
- Shandong Academy of Agricultural Sciences, Jinan, China
| | - Yunbi Xu
- International Maize and Wheat Improvement Center (CYMMIT), Mexico DF, Mexico; Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaoping Chen
- Crops Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Sanjay Kaila
- Department of Biotechnology, Ministry of Science and Technology, Government of India, India
| | - Henry Nguyen
- National Centre for Soybean Research, University of Missouri, Columbia, USA
| | - Sobhana Sivasankar
- Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture, Vienna, Austria
| | | | | | - Wan Shubo
- Shandong Academy of Agricultural Sciences, Jinan, China
| | - Rajeev K Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
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16
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Kim JH, Park JS, Lee CY, Jeong MG, Xu JL, Choi Y, Jung HW, Choi HK. Dissecting seed pigmentation-associated genomic loci and genes by employing dual approaches of reference-based and k-mer-based GWAS with 438 Glycine accessions. PLoS One 2020; 15:e0243085. [PMID: 33259564 PMCID: PMC7707508 DOI: 10.1371/journal.pone.0243085] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 11/15/2020] [Indexed: 11/19/2022] Open
Abstract
The soybean is agro-economically the most important among all cultivated legume crops, and its seed color is considered one of the most attractive factors in the selection-by-breeders. Thus, genome-wide identification of genes and loci associated with seed colors is critical for the precision breeding of crop soybeans. To dissect seed pigmentation-associated genomic loci and genes, we employed dual approaches by combining reference-based genome-wide association study (rbGWAS) and k-mer-based reference-free GWAS (rfGWAS) with 438 Glycine accessions. The dual analytical strategy allowed us to identify four major genomic loci (designated as SP1-SP4 in this study) associated with the seed colors of soybeans. The k-mer analysis enabled us to find an important recombination event that occurred between subtilisin and I-cluster B in the soybean genome, which could describe a special structural feature of ii allele within the I locus (SP3). Importantly, mapping analyses of both mRNAs and small RNAs allowed us to reveal that the subtilisin-CHS1/CHS3 chimeric transcripts generate and act as an initiator towards 'mirtron (i.e., intron-harboring miRNA precursor)'-triggered silencing of chalcone synthase (CHS) genes. Consequently, the results led us to propose a working model of 'mirtron-triggered gene silencing (MTGS)' to elucidate a long-standing puzzle in the genome-wide CHS gene silencing mechanism. In summary, our study reports four major genomic loci, lists of key genes and genome-wide variations that are associated with seed pigmentation in soybeans. In addition, we propose that the MTGS mechanism plays a crucial role in the genome-wide silencing of CHS genes, thereby suggesting a clue to currently predominant soybean cultivars with the yellow seed coat. Finally, this study will provide a broad insight into the interactions and correlations among seed color-associated genes and loci within the context of anthocyanin biosynthetic pathways.
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Affiliation(s)
- Jin-Hyun Kim
- Department of Medical Bioscience, Dong-A University, Busan, Republic of Korea
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju-si, Jeollabuk-do, Republic of Korea
| | - Joo-Seok Park
- Department of Applied Bioscience, Dong-A University, Busan, Republic of Korea
| | - Chae-Young Lee
- Department of Medical Bioscience, Dong-A University, Busan, Republic of Korea
| | - Min-Gyun Jeong
- Department of Applied Bioscience, Dong-A University, Busan, Republic of Korea
| | - Jiu Liang Xu
- Systems Biotechnology Research Center, Korea Institute of Science and Technology (KIST), Gangneung, Republic of Korea
| | - Yongsoo Choi
- Systems Biotechnology Research Center, Korea Institute of Science and Technology (KIST), Gangneung, Republic of Korea
| | - Ho-Won Jung
- Department of Molecular Genetics, Dong-A University, Busan, Republic of Korea
| | - Hong-Kyu Choi
- Department of Molecular Genetics, Dong-A University, Busan, Republic of Korea
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17
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Talini RF, Brandolini A, Miculan M, Brunazzi A, Vaccino P, Pè ME, Dell'Acqua M. Genome-wide association study of agronomic and quality traits in a world collection of the wild wheat relative Triticum urartu. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 102:555-568. [PMID: 31826330 DOI: 10.1111/tpj.14650] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 10/17/2019] [Accepted: 12/05/2019] [Indexed: 06/10/2023]
Abstract
Triticum urartu (2n = 2x = 14, subgenome Au Au ), a wild diploid wheat progenitor, features broad allelic diversity for a number of traits of agronomic relevance. A thorough characterization of the diversity of T. urartu natural accessions may provide wheat breeders with new alleles potentially contributing to wheat improvement. In this study, we performed an extensive genotypic and phenotypic characterization of a world collection of 299 T. urartu ex situ accessions, developing 441 327 single nucleotide polymorphisms and recording trait values for agronomic and quality traits. The collection was highly diverse, with broad variation in phenology and plant architecture traits. Seed features were also varied, and analyses of flour quality reported 18 distinct patterns of glutenins, and carotenoid concentrations and sedimentation volumes in some cases surpassing those of cultivated materials. The genome-wide molecular markers developed on the collection were used to conduct a genome-wide association study reporting 25 highly significant quantitative trait nucleotides for the traits under examination, only partially overlapping loci already reported in wheat. Our data show that T. urartu may be considered a valuable allele pool to support the improvement of wheat agronomy and quality.
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Affiliation(s)
- Rebecca F Talini
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Andrea Brandolini
- Consiglio per la Ricerca in agricoltura e l'analisi dell'economia agraria - Unità di Ricerca per la Zootecnia e l'Acquacoltura (CREA-ZA), Sant'Angelo Lodigiano (LO), Italy
| | - Mara Miculan
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Alice Brunazzi
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Patrizia Vaccino
- Consiglio per la Ricerca in agricoltura e l'analisi dell'economia agraria - Research Centre for Cereal and Industrial Crops, Vercelli, Italy
| | - Mario Enrico Pè
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy
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18
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Knoch D, Abbadi A, Grandke F, Meyer RC, Samans B, Werner CR, Snowdon RJ, Altmann T. Strong temporal dynamics of QTL action on plant growth progression revealed through high-throughput phenotyping in canola. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:68-82. [PMID: 31125482 PMCID: PMC6920335 DOI: 10.1111/pbi.13171] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 05/13/2019] [Accepted: 05/15/2019] [Indexed: 05/08/2023]
Abstract
A major challenge of plant biology is to unravel the genetic basis of complex traits. We took advantage of recent technical advances in high-throughput phenotyping in conjunction with genome-wide association studies to elucidate genotype-phenotype relationships at high temporal resolution. A diverse Brassica napus population from a commercial breeding programme was analysed by automated non-invasive phenotyping. Time-resolved data for early growth-related traits, including estimated biovolume, projected leaf area, early plant height and colour uniformity, were established and complemented by fresh and dry weight biomass. Genome-wide SNP array data provided the framework for genome-wide association analyses. Using time point data and relative growth rates, multiple robust main effect marker-trait associations for biomass and related traits were detected. Candidate genes involved in meristem development, cell wall modification and transcriptional regulation were detected. Our results demonstrate that early plant growth is a highly complex trait governed by several medium and many small effect loci, most of which act only during short phases. These observations highlight the importance of taking the temporal patterns of QTL/allele actions into account and emphasize the need for detailed time-resolved analyses to effectively unravel the complex and stage-specific contributions of genes affecting growth processes that operate at different developmental phases.
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Affiliation(s)
- Dominic Knoch
- Molecular Genetics/HeterosisLeibniz Institute of Plant Genetics and Crop Plant Research (IPK)SeelandGermany
| | - Amine Abbadi
- Norddeutsche Pflanzenzucht Innovation GmbH (NPZi)HoltseeGermany
| | - Fabian Grandke
- Department of Plant BreedingResearch Centre for BiosystemsLand Use and Nutrition (iFZ)Justus‐Liebig‐University GiessenGiessenGermany
| | - Rhonda C. Meyer
- Molecular Genetics/HeterosisLeibniz Institute of Plant Genetics and Crop Plant Research (IPK)SeelandGermany
| | - Birgit Samans
- Department of Plant BreedingResearch Centre for BiosystemsLand Use and Nutrition (iFZ)Justus‐Liebig‐University GiessenGiessenGermany
- Present address:
Technische Hochschule Mittelhessen (THM), University of Applied SciencesFachbereich Gesundheit35390GiessenGermany
| | - Christian R. Werner
- Department of Plant BreedingResearch Centre for BiosystemsLand Use and Nutrition (iFZ)Justus‐Liebig‐University GiessenGiessenGermany
- Present address:
The Roslin InstituteUniversity of EdinburghEaster Bush CampusMidlothianEH25 9RGUK
| | - Rod J. Snowdon
- Department of Plant BreedingResearch Centre for BiosystemsLand Use and Nutrition (iFZ)Justus‐Liebig‐University GiessenGiessenGermany
| | - Thomas Altmann
- Molecular Genetics/HeterosisLeibniz Institute of Plant Genetics and Crop Plant Research (IPK)SeelandGermany
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19
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Qin J, Shi A, Song Q, Li S, Wang F, Cao Y, Ravelombola W, Song Q, Yang C, Zhang M. Genome Wide Association Study and Genomic Selection of Amino Acid Concentrations in Soybean Seeds. FRONTIERS IN PLANT SCIENCE 2019; 10:1445. [PMID: 31803203 PMCID: PMC6873630 DOI: 10.3389/fpls.2019.01445] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 10/17/2019] [Indexed: 05/15/2023]
Abstract
Soybean is a major source of protein for human consumption and animal feed. Releasing new cultivars with high nutritional value is one of the major goals in soybean breeding. To achieve this goal, genome-wide association studies of seed amino acid contents were conducted based on 249 soybean accessions from China, US, Japan, and South Korea. The accessions were evaluated for 15 amino acids and genotyped by sequencing. Significant genetic variation was observed for amino acids among the accessions. Among the 231 single nucleotide polymorphisms (SNPs) significantly associated with variations in amino acid contents, fifteen SNPs localized near 14 candidate genes involving in amino acid metabolism. The amino acids were classified into two groups with five in one group and seven amino acids in the other. Correlation coefficients among the amino acids within each group were high and positive, but the correlation coefficients of amino acids between the two groups were negative. Twenty-five SNP markers associated with multiple amino acids can be used to simultaneously improve multi-amino acid concentration in soybean. Genomic selection analysis of amino acid concentration showed that selection efficiency of amino acids based on the markers significantly associated with all 15 amino acids was higher than that based on random markers or markers only associated with individual amino acid. The identified markers could facilitate selection of soybean varieties with improved seed quality.
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Affiliation(s)
- Jun Qin
- National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Laboratory of Crop Genetics and Breeding of Hebei, Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, China
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Ainong Shi
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Qijian Song
- Soybean Genomics and Improvement Lab, USDA-ARS, Beltsville, MD, United States
| | - Song Li
- Crop and Soil Environmental Science, Virginia Tech, Blacksburg, VA, United States
| | - Fengmin Wang
- National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Laboratory of Crop Genetics and Breeding of Hebei, Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, China
| | - Yinghao Cao
- Bioinformatics Center, Allife Medical Science and Technology Co., Ltd, Beijing, China
| | - Waltram Ravelombola
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Qi Song
- Crop and Soil Environmental Science, Virginia Tech, Blacksburg, VA, United States
| | - Chunyan Yang
- National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Laboratory of Crop Genetics and Breeding of Hebei, Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, China
| | - Mengchen Zhang
- National Soybean Improvement Center Shijiazhuang Sub-Center, North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Laboratory of Crop Genetics and Breeding of Hebei, Cereal & Oil Crop Institute, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, China
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20
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Song C, Li W, Pei X, Liu Y, Ren Z, He K, Zhang F, Sun K, Zhou X, Ma X, Yang D. Dissection of the genetic variation and candidate genes of lint percentage by a genome-wide association study in upland cotton. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:1991-2002. [PMID: 30982110 DOI: 10.1007/s00122-019-03333-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Accepted: 03/20/2019] [Indexed: 05/10/2023]
Abstract
A genome-wide associated study identified six novel QTLs for lint percentage. Two candidate genes underlying this trait were also detected. Increasing lint percentage (LP) is a core goal of cotton breeding. To better understand the genetic basis of LP, a genome-wide association study (GWAS) was conducted using 276 upland cotton accessions planted in multiple environments and genotyped with a CottonSNP63K array. After filtering, 10,660 high-quality single-nucleotide polymorphisms (SNPs) were retained. Population structure, principal component and neighbor-joining phylogenetic tree analyses divided the accessions into two subpopulations. These results along with linkage disequilibrium decay indicated accessions were not highly structured and exhibited weak relatedness. GWAS uncovered 23 polymorphic SNPs and 15 QTLs significantly associated with LP, with six new QTLs identified. Two candidate genes, Gh_D05G0313 and Gh_D05G1124, both contained one significant SNP, highly expressed during ovule and fiber development stages, implying that the two genes may act as the most promising regulators of LP. Furthermore, the phenotypic value of LP was found to be positively correlated with the number of favorable SNP alleles. These favorable alleles for LP identified in the study may be useful for improving lint yield.
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Affiliation(s)
- Chengxiang Song
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- College of Agriculture, Yangtze University, Jingzhou, 434025, China
| | - Wei Li
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Xiaoyu Pei
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Yangai Liu
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Zhongying Ren
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Kunlun He
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Fei Zhang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Kuan Sun
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Xiaojian Zhou
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Xiongfeng Ma
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China.
| | - Daigang Yang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China.
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21
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Tang Z, Xu J, Yin L, Yin D, Zhu M, Yu M, Li X, Zhao S, Liu X. Genome-Wide Association Study Reveals Candidate Genes for Growth Relevant Traits in Pigs. Front Genet 2019; 10:302. [PMID: 31024621 PMCID: PMC6459934 DOI: 10.3389/fgene.2019.00302] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Accepted: 03/19/2019] [Indexed: 12/02/2022] Open
Abstract
Improvement of the growth rate is a challenge in the pig industry, the Average Daily Gain (ADG) and Days (AGE) to 100 kg are directly related to growth performance. We performed genome-wide association study (GWAS) and genetic parameters estimation for ADG and AGE using the genomic and phonemic from four breed (Duroc, Yorkshire, Landrace, and Pietrain) populations. All analyses were performed by a multi-loci GWAS model, FarmCPU. The GWAS results of all four breeds indicate that five genome-wide significant SNPs were associated with ADG, and the nearby genomic regions explained 4.08% of the genetic variance and 1.90% of the phenotypic variance, respectively. For AGE, six genome-wide significant SNPs were detected, and the nearby genomic regions explained 8.09% of the genetic variance and 3.52% of phenotypic variance, respectively. In total, nine candidate genes were identified to be associated with growth and metabolism. Among them, TRIB3 was reported to associate with pig growth, GRP, TTR, CNR1, GLP1R, BRD2, HCRTR2, SEC11C, and ssc-mir-122 were reported to associate with growth traits in human and mouse. The newly detected candidate genes will advance the understanding of growth related traits and the identification of the novel variants will suggest a potential use in pig genomic breeding programs.
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Affiliation(s)
- Zhenshuang Tang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Jingya Xu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Lilin Yin
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Dong Yin
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Mengjin Zhu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Mei Yu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Xinyun Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Xiaolei Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
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22
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Yao F, Zhang X, Ye X, Li J, Long L, Yu C, Li J, Wang Y, Wu Y, Wang J, Jiang Q, Li W, Ma J, Wei Y, Zheng Y, Chen G. Characterization of molecular diversity and genome-wide association study of stripe rust resistance at the adult plant stage in Northern Chinese wheat landraces. BMC Genet 2019; 20:38. [PMID: 30914040 PMCID: PMC6434810 DOI: 10.1186/s12863-019-0736-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 03/03/2019] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Stripe rust is a serious fungal disease of wheat (Triticum aestivum L.) caused by Puccinia striiformis f. sp. tritici (Pst), which results in yield reduction and decreased grain quality. Breeding for genetic resistance to stripe rust is the most cost-effective method to control the disease. In the present study, a genome-wide association study (GWAS) was conducted to identify markers linked to stripe rust resistance genes (or loci) in 93 Northern Chinese wheat landraces, using Diversity Arrays Technology (DArT) and simple sequence repeat (SSR) molecular marker technology based on phenotypic data from two field locations over two growing seasons in China. RESULTS Seventeen accessions were verified to display stable and high levels of adult plant resistance (APR) to stripe rust via multi-environment field assessments. Significant correlations among environments and high heritability were observed for stripe rust infection type (IT) and disease severity (DS). Using mixed linear models (MLM) for the GWAS, a total of 32 significantly associated loci (P < 0.001) were detected. In combination with the linkage disequilibrium (LD) decay distance (6.4 cM), 25 quantitative trait loci (QTL) were identified. Based on the integrated map of previously reported genes and QTL, six QTL located on chromosomes 4A, 6A and 7D were mapped far from resistance regions identified previously, and represent potentially novel stripe rust resistance loci at the adult plant stage. CONCLUSIONS The present findings demonstrated that identification of genes or loci linked to significant markers in wheat by GWAS is feasible. Seventeen elite accessions conferred with stable and high resistance to stripe rust, and six putative newly detected APR loci were identified among the 93 Northern Chinese wheat landraces. The results illustrate the potential for acceleration of molecular breeding of wheat, and also provide novel sources of stripe rust resistance with potential utility in the breeding of improved wheat cultivars.
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Affiliation(s)
- Fangjie Yao
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
| | - Xuemei Zhang
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
| | - Xueling Ye
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
| | - Jian Li
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
| | - Li Long
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
| | - Can Yu
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
| | - Jing Li
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
| | - Yuqi Wang
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
| | - Yu Wu
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
| | - Jirui Wang
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
- State Key Laboratory of Crop Genetics of Disease Resistance and Disease Control, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
| | - Qiantao Jiang
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
| | - Wei Li
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
| | - Jian Ma
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
| | - Yuming Wei
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
- State Key Laboratory of Crop Genetics of Disease Resistance and Disease Control, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
| | - Youliang Zheng
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
- State Key Laboratory of Crop Genetics of Disease Resistance and Disease Control, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
| | - Guoyue Chen
- Triticeae Research Institute, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
- State Key Laboratory of Crop Genetics of Disease Resistance and Disease Control, Sichuan Agricultural University, Wenjiang, Chengdu, Sichuan 611130 People’s Republic of China
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23
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Zhang K, Liu S, Li W, Liu S, Li X, Fang Y, Zhang J, Wang Y, Xu S, Zhang J, Song J, Qi Z, Tian X, Tian Z, Li WX, Ning H. Identification of QTNs Controlling Seed Protein Content in Soybean Using Multi-Locus Genome-Wide Association Studies. FRONTIERS IN PLANT SCIENCE 2018; 9:1690. [PMID: 30519252 PMCID: PMC6258895 DOI: 10.3389/fpls.2018.01690] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 10/31/2018] [Indexed: 05/19/2023]
Abstract
Protein content (PC), an important trait in soybean (Glycine max) breeding, is controlled by multiple genes with relatively small effects. To identify the quantitative trait nucleotides (QTNs) controlling PC, we conducted a multi-locus genome-wide association study (GWAS) for PC in 144 four-way recombinant inbred lines (FW-RILs). All the FW-RILs were phenotyped for PC in 20 environments, including four locations over 4 years with different experimental treatments. Meanwhile, all the FW-RILs were genotyped using SoySNP660k BeadChip, producing genotype data for 109,676 non-redundant single-nucleotide polymorphisms. A total of 129 significant QTNs were identified by five multi-locus GWAS methods. Based on the 22 common QTNs detected by multiple GWAS methods or in multiple environments, pathway analysis identified 8 potential candidate genes that are likely to be involved in protein synthesis and metabolism in soybean seeds. Using superior allele information for 22 common QTNs in 22 elite and 7 inferior lines, we found higher superior allele percentages in the elite lines and lower percentages in the inferior lines. These findings will contribute to the discovery of the polygenic networks controlling PC in soybean, increase our understanding of the genetic foundation and regulation of PC, and be useful for molecular breeding of high-protein soybean varieties.
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Affiliation(s)
- Kaixin Zhang
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Shulin Liu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Wenbin Li
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Shiping Liu
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Xiyu Li
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Yanlong Fang
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Jun Zhang
- College of Agronomy, Jilin Agricultural University, Changchun, China
| | - Yue Wang
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Shichao Xu
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Jianan Zhang
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Jie Song
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Zhongying Qi
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Xiaocui Tian
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Zhixi Tian
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Wen-Xia Li
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
- *Correspondence: Wen-Xia Li, Hailong Ning,
| | - Hailong Ning
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
- *Correspondence: Wen-Xia Li, Hailong Ning,
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