Molecular Mapping of Seed Tocopherols in Soybean HEINRICH S. WOHLESER 1, YUKIO KAKUDA 2, and ISTVAN RAJCAN 3 1 University of Guelph, Department of Plant.

Slides:



Advertisements
Similar presentations
Linkage and Genetic Mapping
Advertisements

The genetic dissection of complex traits
Planning breeding programs for impact
Potato Mapping / QTLs Amir Moarefi VCR
Frary et al. Advanced Backcross QTL analysis of a Lycopersicon esculentum x L. pennellii cross and identification of possible orthologs in the Solanaceae.
Bulk method Bulk is an extension of the pedigree method. In contrast to pedigree, early generations are grown as bulk populations w/o selection. The last.
Resistance to powdery mildew in wheat germplasm with different resistance sources L. M. Miranda, J. P. Murphy, D. S. Marshall and S.Leath NC STATE UNIVERSITY.
Cameron Peace, Washington State University
Functional Variation for DIMBOA Content in Maize Butrón A 1, Chen Y-C 2, Rottinghaus GE 2, Guill K 3, McMullen MD 3, 1 Misión Biológica de Galicia (CSIC),
Association Mapping as a Breeding Strategy
Genetic Architecture of Kernel Composition in the Nested Association Mapping (NAM) Population Sherry Flint-Garcia USDA-ARS Columbia, MO.
Qualitative and Quantitative traits
ASSOCIATION MAPPING WITH TASSEL Presenter: VG SHOBHANA PhD Student CPMB.
ILVO - Plant (Applied Genetics and Breeding) Development of EST markers and evaluation of their use in evergreen.
Mapping Autotetraploid Alfalfa Joseph G. Robins and E. Charles Brummer.
Complex Microsynteny among Wheat, Rice and Barley at the Qfhs.ndsu-3BS Region S. Liu and J. A. Anderson Department of Agronomy and Plant Genetics, University.
Backcross Breeding.
Chapter 9: Genetic linkage and maps in breeding applications
QTL Mapping R. M. Sundaram.
Plant of the day! Pebble plants, Lithops, dwarf xerophytes Aizoaceae
Chapter 7: Molecular markers in breeding
Computer Simulation in Plant Breeding Introduction Outline Application I: Breeding Method Application II: Gene Mapping Application III: Genetic Modeling.
Piyaporn Phansak 1, Watcharin Soonsuwan 1, James E. Specht 1, George L. Graef 1, Perry B. Cregan 2, and David L. Hyten 2 1 Department of Agronomy and Horticulture,
Mapping Basics MUPGRET Workshop June 18, Randomly Intermated P1 x P2  F1  SELF F …… One seed from each used for next generation.
PLANT BIOTECHNOLOGY & GENETIC ENGINEERING (3 CREDIT HOURS)
BIO341 Meiotic mapping of whole genomes (methods for simultaneously evaluating linkage relationships among large numbers of loci)
Genetic Diversity and Association Analysis of Protein and Oil Content in Food-type Soybean Ainong Shi, Pengyin Chen, Bo Zhang, and Anfu Hou University.
Identification of Elemental Processes Controlling Genetic Variation in Soybean Seed Composition José L. Rotundo, Silvia Cianzio & Mark Westgate Iowa State.
GENOMIC MAPPING FOR DROUGHT TOLERANCE IN SORGHUM Introduction Drought is a major abiotic factor limiting crop production. Sorghum is one of the most drought.
Module 7: Estimating Genetic Variances – Why estimate genetic variances? – Single factor mating designs PBG 650 Advanced Plant Breeding.
Artificial Selection and the Genome: “ Deep Pedigree ” Analysis in an Elite Soybean Cultivar Chris M Grainger, Elizabeth A Lee and Istvan Rajcan Department.
Natural Variation in Arabidopsis ecotypes. Using natural variation to understand diversity Correlation of phenotype with environment (selective pressure?)
Genetic Mapping Oregon Wolfe Barley Map (Szucs et al., The Plant Genome 2, )
Non-Mendelian Genetics
Introduction of Plant Biotechnology
Genotyping and association analysis of Gossypium hirsutum lines for resistance in Reniform Nematodes Megha V. Sharma*, Stella Kantartzi, David Weaver,
Characterisation of post harvest shelf life in broccoli. Emma Skipper, Vicky Buchanan-Wollaston and David Pink. Warwick HRI, University of Warwick, Wellesbourne,
Fig. S1 The non-metric multi-dimensional scaling of 24 double haploid (DH) lines (colored in grey) in the background of 225 DH lines (colored in blue)
Experimental Design and Data Structure Supplement to Lecture 8 Fall
Quantitative Genetics. Continuous phenotypic variation within populations- not discrete characters Phenotypic variation due to both genetic and environmental.
Complex Traits Most neurobehavioral traits are complex Multifactorial
Quantitative Genetics
QTL Mapping in Heterogeneous Stocks Talbot et al, Nature Genetics (1999) 21: Mott et at, PNAS (2000) 97:
INTRODUCTION TO ASSOCIATION MAPPING
QTL Associated with Maize Kernel Traits among Illinois High Oil × B73 Backcross-Derived Lines By J.J. Wassom, J.C. Wong, and T.R. Rocheford University.
Mapping and cloning Human Genes. Finding a gene based on phenotype ’s of DNA markers mapped onto each chromosome – high density linkage map. 2.
PT Sampoerna Agro Tbk Sampoerna Strategic Square North Tower, 28th Floor Jl. Jend. Sudirman Kav. 45 Jakarta, Indonesia,12930 Development of Marker Assisted.
MOLECULAR MAPPING OF LEAF CUTICULAR WAXES IN WHEAT S. MONDAL, R.E. MASON, F. BEECHER AND D.B.HAYS TEXAS A& M UNIVERSITY, DEPT. OF SOIL & CROP SCIENCES,
Fast test for multiple locus mapping By Yi Wen Nisha Rajagopal.
Chapter 22 - Quantitative genetics: Traits with a continuous distribution of phenotypes are called continuous traits (e.g., height, weight, growth rate,
A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb Final Remarks Genetical.
Using Merlin in Rheumatoid Arthritis Analyses Wei V. Chen 05/05/2004.
Types of genome maps Physical – based on bp Genetic/ linkage – based on recombination from Thomas Hunt Morgan's 1916 ''A Critique of the Theory of Evolution'',
Genetic mapping and QTL analysis - JoinMap and QTLNetwork -
Comparative mapping of Brassica oleracea using sequence-based markers derived from other Brassica relatives and transcriptome sequences generated from.
GENOME ORGANIZATION AS REVEALED BY GENOME MAPPING WHY MAP GENOMES? HOW TO MAP GENOMES?
Jinkwan Jo1, Jelli Venkatesh1, Koeun Han1 and Byoung-Cheorl Kang1*
Fall HORT6033 Molecular Plant Breeding
Genotypic and Phenotypic Variance in Soybean Oil
upstream vs. ORF binding and gene expression?
Enhancing soybean for resistance to Sclerotinia stem rot
J. J. Maxwell1, G. Brown-Guedira2, C. Cowger2, D. Marshall2, and J. P
Backcross Breeding.
Mapping Quantitative Trait Loci
Genome-wide Association Studies
Analysis of Quantitative Trait Loci for Seed Coat Cracking from Two Soybean Populations. Sung-Taeg Kang*1, Hyeun-Kyeung Kim2, Min-Jung Seo1, Jung-Kyung.
In these studies, expression levels are viewed as quantitative traits, and gene expression phenotypes are mapped to particular genomic loci by combining.
Linkage analysis and genetic mapping
Barley (Hordeum vulgare subsp. vulgare)
M. D. Jasani, J. H. Kamdar, A. K. Maurya and S. K. Bera
Presentation transcript:

Molecular Mapping of Seed Tocopherols in Soybean HEINRICH S. WOHLESER 1, YUKIO KAKUDA 2, and ISTVAN RAJCAN 3 1 University of Guelph, Department of Plant Agriculture, Guelph ON, N1G 2W1, Canada, 1 University of Guelph, Department of Plant Agriculture, Guelph ON, N1G 2W1, Canada, 2 University of Guelph, Department of Food Science, Guelph ON, N1G 2W1, Canada, 3 University of Guelph, Department of Plant Agriculture, Guelph ON, N1G 2W1, Canada, INTRODUCTION INTRODUCTION Soybeans contain among many beneficial components considerable amounts of tocopherol (  -,  -,  -,  -tocopherol). Tocopherols are vitamin E active components with major antioxidant properties. Many studies have shown that natural tocopherols play an important role in preventing chronic diseases. Beside its health benefits tocopherols have a high market value and are utilized by the food and pharmaceutical industry for diverse applications. The genetic control of these constituents is not well understood. Therefore an identification of markers closely associated with tocopherol accumulation is the first step to generate an understanding of the genetic control. Furthermore these markers are potentially useful for plant breeders to develop high tocopherol soybean lines through marker assisted selection (MAS). We used simple sequence repeat (SSR) markers to construct a genetic linkage map based on an recombinant inbreed line (RIL) population derived from crossing two high-yielding commercial soybean cultivars with different tocopherol profiles. Therefore, the main objective of this study was to identify major genes or quantitative trait loci (QTLs) controlling tocopherol accumulation. MATERIALS AND METHODS MATERIALS AND METHODS RESULTS RESULTS CONCLUSIONSCONCLUSIONS ACKNOWLEDGMENTSACKNOWLEDGMENTS 1.This is the first report of finding markers and QTL for tocopherol accumulation in soybean. Markers associated with alpha and delta tocopherol have been identified on LG C2 and F, therefore suggesting the location of genes involved in this trait. 2.No QTLs were found for beta and gamma tocopherol, although ANOVA showed evidence towards potential QTLs. Fine mapping would eventually reveal additional QTLs for these constituents. 3.Most QTL were consistent among growing environments confirming the genomic regions where these gene(s) involved in this biosynthetic pathway reside. 4.Enough genetic variation is present in elite germplasm to allow for a rapid development of high tocopherol soybean lines. 5. Beta and gamma tocopherol seems to be more influenced by growing conditions than alpha and delta. AOCS. (1997). Determination of tocopherols in vegetable oils and fats by high performance liquid chromatography Ce D. Firestone ed. In Official methods and recommended practices of the American Oil Chemist’s Society. 5 th. Ed. Champaign, Illinois. Cregan et al., An Integrated Genetic Linkage Map of The Soybean Genome. Crop Science, 39: 1464 – SAS Institute. (1998). SAS/STAT User’s Guide, Version 8, 2th Edition, Vol. 1. SAS Institute, Inc. Carry, N.C. REFERENCESREFERENCES The authors wish to thank Wade Montminy and the field crew for their great help during the planting and harvesting period. In addition we would like to thank Luidy Rodriguez, Chris Grainger, and Patricia Egea for their skilled help. Finally we want to acknowledge the Ontario Ministry of Agriculture for their generous financial contribution to this research project. Combined ANOVA  Genotypes (G) and locations (L) considered fixed effects; alpha, beta, gamma, and delta tocopherol showed significant G, L, and GxL interactions at  <0.05.  QTL analysis conducted on location basis rather than combining locations. Marker verification  26 linkage groups were determined including 14 unlinked markers.  Chi-square test detected 49 markers with segregation distortion at  <0.05. Marker Trait Association  A total of 11markers were found at St. Pauls, 12 at Woodstock and 11 markers at Elora as significantly associated with alpha tocopherol accumulation (Table 1).  Over all 3 markers were found at St. Pauls, 3 at Woodstock and 4 markers at Elora as significantly associated with delta tocopherol accumulation (Table 1).  Fewer markers were closely associated with beta and gamma tocopherol, only Satt335 was significantly associated with gamma tocopherol across locations (Table 1). Interval Mapping (MAPMAKER/QTL)  St. Pauls: 2 QTLs for alpha tocopherol both on LG C2 (combined R 2 = 22.8%); one QTL for delta tocopherol located on LG F (Figure 1).  Elora: 2 QTLs for alpha tocopherol both on LG C2 (combined R 2 = 34%); one QTL for delta tocopherol located on LG F (Figure 1).  Woodstock: 3 QTLs for alpha tocopherol, two on LG C2 and one on LG F (combined R 2 = 35.7%) (Figure 1); no QTLs were found for delta tocopherol but data suggests (LOD 1.9) a possibility of a QTL at the end of LG F near Satt522.  No QTLs associated with beta and gamma tocopherol were found. Figure 1: Partial linkage map of a cross between OAC Shire and OAC Bayfield showing QTLs associated with tocopherol accumulation in soybean seeds, including LOD-scores and R 2 values. Linkage group (LG) designations are used according to a public consensus map (Cregan et. al. 1999). Numbers on the left of the LGs indicate distances in centimorgans (cm) between marker pairs. Table 1: Marker trait associations based on single factor analysis of variance (ANOVA), including P-values (<=0.01) and R 2, in context with alpha-, beta-, gamma-, and delta-tocopherol accumulation. Bottom shows range of tocopherols in RIL population at 3 locations. Tocopherol Analyses: According to AOCS method Ce 8-89 (AOCS, 1997); Saponification of soybean meal and extraction of tocopherols using hexane; Extracts were washed with ddH 2 O to increase purity; Tocopherol quantification using HPLC (high-pressure-liquid-chromatography) expressing amounts in ug/g meal. Statistical Data Evaluation: SAS: SAS for Windows, version 8.2 (SAS Institute, Cary NC) with Type-1-error rate (  ) of 0.05; Computation of LSMEANS; ANOVA (Proc GLM) to determined genotype (G), location (L), and GxL effects. Mapmaker/EXP: Linkage map was constructed using a likelihood of odds (LOD) score of 3.0, and a maximum linkage distance of 40 centimorgans (cm). Mapmaker/QTL: Interval mapping to determine QTLs at a threshold of LOD 2. Marker verification: Leave tissue collection and DNA extraction of 93 RILs; Parental screen using 450 SSRs; RILs screened with 120 polymorphic markers; Linkage map construction based on marker data (MAPMAKER/EXP); QTL detection by combining marker and phenotypic data using interval mapping approach (MAPMAKER/QTL).