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Fusari CM, Di Rienzo JA, Troglia C, Nishinakamasu V, Moreno MV, Maringolo C, Quiroz F, Álvarez D, Escande A, Hopp E, Heinz R, Lia VV, Paniego NB. Association mapping in sunflower for Sclerotinia Head Rot resistance. BMC PLANT BIOLOGY 2012; 12:93. [PMID: 22708963 PMCID: PMC3778846 DOI: 10.1186/1471-2229-12-93] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2011] [Accepted: 05/21/2012] [Indexed: 05/04/2023]
Abstract
BACKGROUND Sclerotinia Head Rot (SHR) is one of the most damaging diseases of sunflower in Europe, Argentina, and USA, causing average yield reductions of 10 to 20 %, but leading to total production loss under favorable environmental conditions for the pathogen. Association Mapping (AM) is a promising choice for Quantitative Trait Locus (QTL) mapping, as it detects relationships between phenotypic variation and gene polymorphisms in existing germplasm without development of mapping populations. This article reports the identification of QTL for resistance to SHR based on candidate gene AM. RESULTS A collection of 94 sunflower inbred lines were tested for SHR under field conditions using assisted inoculation with the fungal pathogen Sclerotinia sclerotiorum. Given that no biological mechanisms or biochemical pathways have been clearly identified for SHR, 43 candidate genes were selected based on previous transcript profiling studies in sunflower and Brassica napus infected with S. sclerotiorum. Associations among SHR incidence and haplotype polymorphisms in 16 candidate genes were tested using Mixed Linear Models (MLM) that account for population structure and kinship relationships. This approach allowed detection of a significant association between the candidate gene HaRIC_B and SHR incidence (P < 0.01), accounting for a SHR incidence reduction of about 20 %. CONCLUSIONS These results suggest that AM will be useful in dissecting other complex traits in sunflower, thus providing a valuable tool to assist in crop breeding.
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Affiliation(s)
- Corina M Fusari
- Instituto de Biotecnología, Centro Investigación en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA), 1686, Hurlingham, Buenos Aires, Argentina
| | - Julio A Di Rienzo
- Cátedra de Estadística y Biometría, Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba, 5000, Córdoba, Argentina
| | - Carolina Troglia
- Estación Experimental Agropecuaria Balcarce, INTA, 7620, Balcarce, Buenos Aires, Argentina
| | - Verónica Nishinakamasu
- Instituto de Biotecnología, Centro Investigación en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA), 1686, Hurlingham, Buenos Aires, Argentina
| | - María Valeria Moreno
- Estación Experimental Agropecuaria Manfredi, INTA, 5988, Manfredi, Córdoba, Argentina
| | - Carla Maringolo
- Estación Experimental Agropecuaria Balcarce, INTA, 7620, Balcarce, Buenos Aires, Argentina
| | - Facundo Quiroz
- Estación Experimental Agropecuaria Balcarce, INTA, 7620, Balcarce, Buenos Aires, Argentina
| | - Daniel Álvarez
- Estación Experimental Agropecuaria Manfredi, INTA, 5988, Manfredi, Córdoba, Argentina
| | - Alberto Escande
- Estación Experimental Agropecuaria Balcarce, INTA, 7620, Balcarce, Buenos Aires, Argentina
| | - Esteban Hopp
- Instituto de Biotecnología, Centro Investigación en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA), 1686, Hurlingham, Buenos Aires, Argentina
- Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Ruth Heinz
- Instituto de Biotecnología, Centro Investigación en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA), 1686, Hurlingham, Buenos Aires, Argentina
- Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Verónica V Lia
- Instituto de Biotecnología, Centro Investigación en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA), 1686, Hurlingham, Buenos Aires, Argentina
- Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Norma B Paniego
- Instituto de Biotecnología, Centro Investigación en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA), 1686, Hurlingham, Buenos Aires, Argentina
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155
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Bouchet S, Pot D, Deu M, Rami JF, Billot C, Perrier X, Rivallan R, Gardes L, Xia L, Wenzl P, Kilian A, Glaszmann JC. Genetic structure, linkage disequilibrium and signature of selection in Sorghum: lessons from physically anchored DArT markers. PLoS One 2012; 7:e33470. [PMID: 22428056 PMCID: PMC3302775 DOI: 10.1371/journal.pone.0033470] [Citation(s) in RCA: 71] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2011] [Accepted: 02/09/2012] [Indexed: 11/19/2022] Open
Abstract
Population structure, extent of linkage disequilibrium (LD) as well as signatures of selection were investigated in sorghum using a core sample representative of worldwide diversity. A total of 177 accessions were genotyped with 1122 informative physically anchored DArT markers. The properties of DArTs to describe sorghum genetic structure were compared to those of SSRs and of previously published RFLP markers. Model-based (STRUCTURE software) and Neighbor-Joining diversity analyses led to the identification of 6 groups and confirmed previous evolutionary hypotheses. Results were globally consistent between the different marker systems. However, DArTs appeared more robust in terms of data resolution and bayesian group assignment. Whole genome linkage disequilibrium as measured by mean r(2) decreased from 0.18 (between 0 to 10 kb) to 0.03 (between 100 kb to 1 Mb), stabilizing at 0.03 after 1 Mb. Effects on LD estimations of sample size and genetic structure were tested using i. random sampling, ii. the Maximum Length SubTree algorithm (MLST), and iii. structure groups. Optimizing population composition by the MLST reduced the biases in small samples and seemed to be an efficient way of selecting samples to make the best use of LD as a genome mapping approach in structured populations. These results also suggested that more than 100,000 markers may be required to perform genome-wide association studies in collections covering worldwide sorghum diversity. Analysis of DArT markers differentiation between the identified genetic groups pointed out outlier loci potentially linked to genes controlling traits of interest, including disease resistance genes for which evidence of selection had already been reported. In addition, evidence of selection near a homologous locus of FAR1 concurred with sorghum phenotypic diversity for sensitivity to photoperiod.
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Affiliation(s)
| | - David Pot
- UMR AGAP, CIRAD, Montpellier, France
| | | | | | | | | | | | | | - Ling Xia
- Diversity Arrays Technology Pty Ltd., Yarralumla, Australia
| | - Peter Wenzl
- Diversity Arrays Technology Pty Ltd., Yarralumla, Australia
| | - Andrzej Kilian
- Diversity Arrays Technology Pty Ltd., Yarralumla, Australia
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158
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Valdés-López O, Thibivilliers S, Qiu J, Xu WW, Nguyen TH, Libault M, Le BH, Goldberg RB, Hill CB, Hartman GL, Diers B, Stacey G. Identification of quantitative trait loci controlling gene expression during the innate immunity response of soybean. PLANT PHYSIOLOGY 2011; 157:1975-86. [PMID: 21963820 PMCID: PMC3327182 DOI: 10.1104/pp.111.183327] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2011] [Accepted: 09/29/2011] [Indexed: 05/21/2023]
Abstract
Microbe-associated molecular pattern-triggered immunity (MTI) is an important component of the plant innate immunity response to invading pathogens. However, most of our knowledge of MTI comes from studies of model systems with relatively little work done with crop plants. In this work, we report on variation in both the microbe-associated molecular pattern-triggered oxidative burst and gene expression across four soybean (Glycine max) genotypes. Variation in MTI correlated with the level of pathogen resistance for each genotype. A quantitative trait locus analysis on these traits identified four loci that appeared to regulate gene expression during MTI in soybean. Likewise, we observed that both MTI variation and pathogen resistance were quantitatively inherited. The approach utilized in this study may have utility for identifying key resistance loci useful for developing improved soybean cultivars.
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Affiliation(s)
- Oswaldo Valdés-López
- Department of Statistics (J.Q.) and Divisions of Biochemistry and Plant Sciences, National Center for Soybean Biotechnology, C.S. Bond Life Sciences Center (O.V.-L., S.T., T.H.N.N., M.L., G.S.), University of Missouri, Columbia, Missouri 65211; Minnesota Supercomputing Institute for Advanced Computational Research, University of Minnesota, Minneapolis, Minnesota 55455 (W.W.X.); Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, California 90095 (B.H.L., R.B.G.); United States Department of Agriculture-Agricultural Research Service (G.L.H.) and Department of Crop Sciences (C.B.H., G.L.H., B.D.), University of Illinois, Urbana, Illinois 61801
| | - Sandra Thibivilliers
- Department of Statistics (J.Q.) and Divisions of Biochemistry and Plant Sciences, National Center for Soybean Biotechnology, C.S. Bond Life Sciences Center (O.V.-L., S.T., T.H.N.N., M.L., G.S.), University of Missouri, Columbia, Missouri 65211; Minnesota Supercomputing Institute for Advanced Computational Research, University of Minnesota, Minneapolis, Minnesota 55455 (W.W.X.); Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, California 90095 (B.H.L., R.B.G.); United States Department of Agriculture-Agricultural Research Service (G.L.H.) and Department of Crop Sciences (C.B.H., G.L.H., B.D.), University of Illinois, Urbana, Illinois 61801
| | - Jing Qiu
- Department of Statistics (J.Q.) and Divisions of Biochemistry and Plant Sciences, National Center for Soybean Biotechnology, C.S. Bond Life Sciences Center (O.V.-L., S.T., T.H.N.N., M.L., G.S.), University of Missouri, Columbia, Missouri 65211; Minnesota Supercomputing Institute for Advanced Computational Research, University of Minnesota, Minneapolis, Minnesota 55455 (W.W.X.); Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, California 90095 (B.H.L., R.B.G.); United States Department of Agriculture-Agricultural Research Service (G.L.H.) and Department of Crop Sciences (C.B.H., G.L.H., B.D.), University of Illinois, Urbana, Illinois 61801
| | - Wayne Wenzhong Xu
- Department of Statistics (J.Q.) and Divisions of Biochemistry and Plant Sciences, National Center for Soybean Biotechnology, C.S. Bond Life Sciences Center (O.V.-L., S.T., T.H.N.N., M.L., G.S.), University of Missouri, Columbia, Missouri 65211; Minnesota Supercomputing Institute for Advanced Computational Research, University of Minnesota, Minneapolis, Minnesota 55455 (W.W.X.); Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, California 90095 (B.H.L., R.B.G.); United States Department of Agriculture-Agricultural Research Service (G.L.H.) and Department of Crop Sciences (C.B.H., G.L.H., B.D.), University of Illinois, Urbana, Illinois 61801
| | - Tran H.N. Nguyen
- Department of Statistics (J.Q.) and Divisions of Biochemistry and Plant Sciences, National Center for Soybean Biotechnology, C.S. Bond Life Sciences Center (O.V.-L., S.T., T.H.N.N., M.L., G.S.), University of Missouri, Columbia, Missouri 65211; Minnesota Supercomputing Institute for Advanced Computational Research, University of Minnesota, Minneapolis, Minnesota 55455 (W.W.X.); Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, California 90095 (B.H.L., R.B.G.); United States Department of Agriculture-Agricultural Research Service (G.L.H.) and Department of Crop Sciences (C.B.H., G.L.H., B.D.), University of Illinois, Urbana, Illinois 61801
| | | | - Brandon H. Le
- Department of Statistics (J.Q.) and Divisions of Biochemistry and Plant Sciences, National Center for Soybean Biotechnology, C.S. Bond Life Sciences Center (O.V.-L., S.T., T.H.N.N., M.L., G.S.), University of Missouri, Columbia, Missouri 65211; Minnesota Supercomputing Institute for Advanced Computational Research, University of Minnesota, Minneapolis, Minnesota 55455 (W.W.X.); Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, California 90095 (B.H.L., R.B.G.); United States Department of Agriculture-Agricultural Research Service (G.L.H.) and Department of Crop Sciences (C.B.H., G.L.H., B.D.), University of Illinois, Urbana, Illinois 61801
| | - Robert B. Goldberg
- Department of Statistics (J.Q.) and Divisions of Biochemistry and Plant Sciences, National Center for Soybean Biotechnology, C.S. Bond Life Sciences Center (O.V.-L., S.T., T.H.N.N., M.L., G.S.), University of Missouri, Columbia, Missouri 65211; Minnesota Supercomputing Institute for Advanced Computational Research, University of Minnesota, Minneapolis, Minnesota 55455 (W.W.X.); Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, California 90095 (B.H.L., R.B.G.); United States Department of Agriculture-Agricultural Research Service (G.L.H.) and Department of Crop Sciences (C.B.H., G.L.H., B.D.), University of Illinois, Urbana, Illinois 61801
| | - Curtis B. Hill
- Department of Statistics (J.Q.) and Divisions of Biochemistry and Plant Sciences, National Center for Soybean Biotechnology, C.S. Bond Life Sciences Center (O.V.-L., S.T., T.H.N.N., M.L., G.S.), University of Missouri, Columbia, Missouri 65211; Minnesota Supercomputing Institute for Advanced Computational Research, University of Minnesota, Minneapolis, Minnesota 55455 (W.W.X.); Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, California 90095 (B.H.L., R.B.G.); United States Department of Agriculture-Agricultural Research Service (G.L.H.) and Department of Crop Sciences (C.B.H., G.L.H., B.D.), University of Illinois, Urbana, Illinois 61801
| | - Glen L. Hartman
- Department of Statistics (J.Q.) and Divisions of Biochemistry and Plant Sciences, National Center for Soybean Biotechnology, C.S. Bond Life Sciences Center (O.V.-L., S.T., T.H.N.N., M.L., G.S.), University of Missouri, Columbia, Missouri 65211; Minnesota Supercomputing Institute for Advanced Computational Research, University of Minnesota, Minneapolis, Minnesota 55455 (W.W.X.); Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, California 90095 (B.H.L., R.B.G.); United States Department of Agriculture-Agricultural Research Service (G.L.H.) and Department of Crop Sciences (C.B.H., G.L.H., B.D.), University of Illinois, Urbana, Illinois 61801
| | - Brian Diers
- Department of Statistics (J.Q.) and Divisions of Biochemistry and Plant Sciences, National Center for Soybean Biotechnology, C.S. Bond Life Sciences Center (O.V.-L., S.T., T.H.N.N., M.L., G.S.), University of Missouri, Columbia, Missouri 65211; Minnesota Supercomputing Institute for Advanced Computational Research, University of Minnesota, Minneapolis, Minnesota 55455 (W.W.X.); Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, California 90095 (B.H.L., R.B.G.); United States Department of Agriculture-Agricultural Research Service (G.L.H.) and Department of Crop Sciences (C.B.H., G.L.H., B.D.), University of Illinois, Urbana, Illinois 61801
| | - Gary Stacey
- Department of Statistics (J.Q.) and Divisions of Biochemistry and Plant Sciences, National Center for Soybean Biotechnology, C.S. Bond Life Sciences Center (O.V.-L., S.T., T.H.N.N., M.L., G.S.), University of Missouri, Columbia, Missouri 65211; Minnesota Supercomputing Institute for Advanced Computational Research, University of Minnesota, Minneapolis, Minnesota 55455 (W.W.X.); Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, California 90095 (B.H.L., R.B.G.); United States Department of Agriculture-Agricultural Research Service (G.L.H.) and Department of Crop Sciences (C.B.H., G.L.H., B.D.), University of Illinois, Urbana, Illinois 61801
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159
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Li Y, Böck A, Haseneyer G, Korzun V, Wilde P, Schön CC, Ankerst DP, Bauer E. Association analysis of frost tolerance in rye using candidate genes and phenotypic data from controlled, semi-controlled, and field phenotyping platforms. BMC PLANT BIOLOGY 2011; 11:146. [PMID: 22032693 PMCID: PMC3228716 DOI: 10.1186/1471-2229-11-146] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2011] [Accepted: 10/27/2011] [Indexed: 05/02/2023]
Abstract
BACKGROUND Frost is an important abiotic stress that limits cereal production in the temperate zone. As the most frost tolerant small grain cereal, rye (Secale cereale L.) is an ideal cereal model for investigating the genetic basis of frost tolerance (FT), a complex trait with polygenic inheritance. Using 201 genotypes from five Eastern and Middle European winter rye populations, this study reports a multi-platform candidate gene-based association analysis in rye using 161 single nucleotide polymorphisms (SNPs) and nine insertion-deletion (Indel) polymorphisms previously identified from twelve candidate genes with a putative role in the frost responsive network. RESULTS Phenotypic data analyses of FT in three different phenotyping platforms, controlled, semi-controlled and field, revealed significant genetic variations in the plant material under study. Statistically significant (P < 0.05) associations between FT and SNPs/haplotypes of candidate genes were identified. Two SNPs in ScCbf15 and one in ScCbf12, all leading to amino acid exchanges, were significantly associated with FT over all three phenotyping platforms. Distribution of SNP effect sizes expressed as percentage of the genetic variance explained by individual SNPs was highly skewed towards zero with a few SNPs obtaining large effects. Two-way epistasis was found between 14 pairs of candidate genes. Relatively low to medium empirical correlations of SNP-FT associations were observed across the three platforms underlining the need for multi-level experimentation for dissecting complex associations between genotypes and FT in rye. CONCLUSIONS Candidate gene based-association studies are a powerful tool for investigating the genetic basis of FT in rye. Results of this study support the findings of bi-parental linkage mapping and expression studies that the Cbf gene family plays an essential role in FT.
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Affiliation(s)
- Yongle Li
- Plant Breeding, Technische Universität München, Freising, Germany
| | - Andreas Böck
- Biostatistics Unit, Technische Universität München, Freising, Germany
| | - Grit Haseneyer
- Plant Breeding, Technische Universität München, Freising, Germany
| | | | | | | | - Donna P Ankerst
- Department of Mathematics, Technische Universität München, Garching, Germany
| | - Eva Bauer
- Plant Breeding, Technische Universität München, Freising, Germany
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