The 9th Workshop on QTL Mapping and Breeding Simulation

Through lectures, practices and discussions, you will learn:

l Plant breeding methodology

l Applied quantitative genetics

l Estimation of recombination between two linked loci

l Construction of genetic linkage maps

l Principles of QTL mapping and statistical comparison of different mapping methods

l Identification of quantitative trait genes

l QTL by environment analysis

l Modeling of plant breeding

l Comparison and optimization of plant breeding strategies

l Integration of known gene information into conventional plant breeding

 

Dr. Richard Trethowan, The University of Sydney

Dr. Jiankang Wang, CIMMYT China and Chinese Academy of Agricultural Sciences

Dr. Luyan Zhang, Chinese Academy of Agricultural Sciences

 

Graduate students, plant geneticists and plant breeders in Australia who are interested in applied quantitative genetics, linkage analysis, linkage map construction, QTL mapping, simulation and optimization of breeding strategies will benefit from this workshop. Participants should be familiar with basic methods in plant genetics, plant breeding and statistics.

Costs: There will not be any charge for the workshop and lunches will be provided by The University of Sydney. Each participant will have to cover his/her own travelling and accommodation expenses.

 

Computers: Each participant must bring a laptop computer that can run Microsoft Windows applications. A USB memory stick will be distributed to all participants at the beginning of the workshop. This contains the lecture presentations, QTL IciMapping integrated software V3.2, QU-GENE simulation tools, exercises and answers, etc.

 

Location: Plant Breeding Institute, 107 Cobbitty Rd., Cobbitty NSW 2570

 

Accommodations: On your own.

 

Contact details: Please address any enquires and the registration to Prof. Richard Trethowan, Email: richard.trethowan@sydney.edu.au; Phone: +61 2 9351 8860; Fax: +61 2 9351 8875; Mobile: +61 400 320 558

 

Registration form (send to contact person by 25 February 2012)

Introduction of plant breeding and quantitative genetics, genetic linkage analysis, linkage map construction, principle of QTL mapping, and Inclusive Composite Interval Mapping (ICIM) of QTLs

 

9:00 – 12:30

 

Opening lecture: Overview of plant breeding (Richard Trethowan)

Lecture 1: Introduction of quantitative genetics (Jiankang Wang)

Lecture 2: Linkage analysis and linkage map construction (Jiankang Wang)

Practice 1: Install the QTL IciMapping software, and Get familiar with the QTL IciMapping software (Luyan Zhang)

Practice 2: Linkage map construction (MAP functionality in QTL IciMapping) (Luyan Zhang)

 

12:30 – 13:30 Working lunch break

 

13:30 – 17:00

 

Lecture 3: Principle of QTL mapping and Inclusive Composite Interval Mapping (ICIM) (Jiankang Wang)

Lecture 4: QTL mapping in F2 populations (Luyan Zhang)

Practice 3: QTL mapping in biparental populations (BIP mapping functionality in QTL IciMapping) (Luyan Zhang)

QTL by environment analysis, other QTL mapping methods, and frequently asked questions in QTL mapping studies

 

9:00 – 12:30

 

Lecture 5: QTL by environment interaction and segregation distortion locus mapping (Luyan Zhang)

Lecture 6: QTL mapping with chromosome segment substitution (CSS) lines and other QTL mapping methods (Jiankang Wang)

Practice 4: Comparison of different mapping methods through simulation (BIP simulation functionality in QTL IciMapping) (Luyan Zhang)

Practice 5: QTL mapping with chromosome segment substitution (CSS) lines (CSL functionality in QTL IciMapping) (Jiankang Wang)

Practice 6: Identification of segregation distortion loci (SDL functionality in QTL IciMapping) (Luyan Zhang)

 

12:30 – 13:30 Working lunch break

 

13:30 – 17:00

 

Lecture 7: Joint ICIM with the nested association mapping (NAM) design (Jiankang Wang)

Lecture 8: Frequently asked questions and answers in QTL mapping (Jiankang Wang)

Practice 7: QTL and environment analysis (MET functionality in QTL IciMapping) (Luyan Zhang)

Practice 8: QTL mapping in NAM populations (NAM functionality in QTL IciMapping) (Luyan Zhang)

Practice 9: Use of your own genetic populations in QTL IciMapping (All Participants)

 

Principles of breeding simulation, defining genetic models in QU-GENE, defining breeding methods in QuLine, comparing breeding methods through simulation, and use of know genes in plant breeding

 

9:00 – 12:30

 

Lecture 9: Principle of modeling and breeding simulation (Jiankang Wang)

Lecture 10: Strategic applications of breeding simulation (Jiankang Wang)

Practice 10: Define a genetic model for the QU-GENE engine (Luyan Zhang)

 

12:30 – 13:30 Working lunch break

 

13:30 – 17:00

 

Lecture 11: Tactical applications of breeding simulation: use of known gene information in breeding (Jiankang Wang)

Lecture 12: Design approaches of plant breeding (Jiankang Wang)

Practice 11: Define a breeding strategy for the simulation tool QuLine (Luyan Zhang)

Practice 12: Run a QU-GENE simulation experiment (Luyan Zhang)

 

Exercise 1. The following table gives the frequency distributions on flower length (mm) of tobacco in two fixed parental lines P₁ and P₂, and their F₁ and F₂ generations (East 1913). Assuming P₁ has all the alleles increasing flower length, P₂ has all the alleles reducing flower length.

lEstimate the effective number of genes on flower length.

lEstimate the gene effect under the polygene hypothesis.

 

Exercise 2. Assuming the red flower colour is a dominant trait versus white flower colour, and the two alleles affecting flower color are A and a. Red individuals in an F₂ population can have genotype AA or Aa. F₃ families are needed to determine the genotype of red-flower individuals. When no segregation for flower color is observed in an F₃ family, the F3 family is said to be derived from the homozygous genotype AA. In contrast, when segregation is observed, the F3 family is said from the heterozygous genotype Aa.

lIf one F₂ individual has the genotype Aa, and 5 individuals from the selfed seed are grown in the following F₃ family, what is the probability that the F₂ individual will be AA?

lIf we wish the error probability to be below 0.05, how many F₃ individuals should be grown?

lIf we wish the error probability to be below 0.01, how many F₃ individuals should be grown?

 

Exercise 3. Use the barley DH population (…\Examples\MAP\BarleyDH.map, BarleyDH.xls or BarleyDH.xlsx) to construct the genetic linkage maps.

lConstruct the seven linkage maps of barley

lOutput the seven barley linkage maps

lSplit one chromosome into two at the largest marker interval

lIdentify the segregation distortion loci in this population

 

Exercise 4. Use the rice F2 population (…\Examples\MAP\RiceF2.map, RiceF2.xls or RiceF2.xlsx) to construct the genetic linkage map.

lConstruct the 12 rice linkage maps

lOutput the 12 rice linkage maps

lSplit one chromosome into two at the largest marker interval

lIdentify the segregation distortion loci in this population

 

Exercise5. Use the barley DH population (…\Examples\BIP\BarleyDH.bip, BarleyDH.xls or BarleyDH.xlsx) to conduct QTL mapping.

lFind additive QTLs controlling kernel weight by using Interval Mapping and ICIM, and determine the source of the QTL allele that increases kernel weight.

lCompare the mapping results from different mapping parameters.

lFind the largest interaction from ICIM epistatic mapping

 

Exercise6. Use the rice F2 population (…\Examples\BIP\ RiceF2.bip, RiceF2.xls or RiceF2.xlsx) to conduct QTL mapping.

lFind additive and dominance QTL controlling the resistance by using Interval Mapping and ICIM. For the identified QTLs, determine the source of the allele that reduces the resistance.

lCompare the mapping results from different mapping parameters.

 

Exercise7. Compare two mapping methods by simulation using RIL populations with a size of 200: Assume there are 5 chromosomes, each of 150 cM, and evenly distributed with 16 markers. Two traits of interest are plant height and grain yield.

 

Plant height has a heritability of 0.7, and is controlled by 3 independent QTLs, located at 18 cM, 55 cM, and 101 cM on chromosomes 1, 2, and 3, respectively. Additive effects of the three QTL are 10 cm, 4 cm, and -6 cm, and the population mean is 100 cm. Dominance and gene interaction are not considered.

 

Grain yield has a heritability of 0.5, and is controlled by 7 QTLs. One QTL is located at 25 cM on chromosome 1; two are located at 35 cM and 73 cM on chromosome 2; two are located at 18 cM, and 55 cM on chromosome 3; and two are located at 39 cM and 131 cM on chromosome 4. Additive effects of the 7 QTL are 1 t/ha, -1 t/ha, 1 t/ha, 1 t/ha, 1 t/ha, -1 t/ha, and 1 t/ha, respectively, and the population mean is 3 t/ha. Dominance and gene interaction are not considered.

 

Assume the support interval is 10 cM, i.e., in a simulated population one predefined QTL is declared to be correctly identified if there is a significant peak in a chromosomal interval of 10 cM. The true QTL location is at the center of the support interval. One hundred populations are simulated.

 

lDraw the average LOD profile of IM and ICIM for plant height and grain yield

lFind out the detection power of IM and ICIM for each plant height and grain yield QTL

lFind out the false discovery rate of IM and ICIM for plant height and grain yield

lWhat else can you find from the power simulation?

 

Exercise8. In Exercise 7, assume each chromosome is evenly distributed with 31 markers, i.e., the marker density is 5 cM. How will the denser markers change the QTL detection?

 

Exercise 9. Use the rice CSSL population (…\Examples\CSL\CslMapping.csl, CslMapping.xls, or CslMapping.xlsx） to conduct QTL mapping.

lCalculate the broad sense heritability for grain length

lFind out the donor chromosomal segments which affect the grain length; Explain whether these segments increase or reduce the grain length

lHow stable is the expression of these donor chromosomal segments?

 

Exercise 10. Do you have any comments and suggestions on the QTL IciMapping software? Do you have any questions concerning the use of your own mapping populations in QTL IciMapping?

 

Exercise 11. Build a QU-GENE input file (QUG), and then run the QU-GENE engine to generate one GES file and one POP file. The POP file consists of 50 homozygous parental lines. Other requirements are:

lOne environment type named Obregon in the TPE (target population of environment

lTwo traits Maturity and Yield have the broad sense heritabilities of 0.4 and 0.2 at the individual plant level, respectively. The among-plot error variance is assumed to be equal to the within-plot error variance for both traits.

lThere are two alleles at each locus.

lSix genes control Maturity, where m=140 day, a=3 day, and d=0 day. The shortest genetic Maturity will be 122 days; and the longest genetic Maturity will be 158 days.

lTwenty one per se genes control Yield, where m=5000 kg/ha, a=100 kg/ha, and d=0 kg/ha. The last two maturity genes have a pleiotropic effect of a=100 kg/ha on yield as well. The longer the maturity, the higher the yield. The lowest genetic Yield will be 2700 kg/ha; and the highest genetic Yield will be 7300 kg/ha.

lThe six maturity genes are linked with six yield genes on six respective chromosomes, i.e. 1A, 1B, 1D, 2A, 2B, and 2D, with recombination frequencies 0.05, 0.12, 0.15, 0.23, 0.30, and 0.17. The other 15 yield genes are located on the other 15 chromosomes.

lAll alleles have a frequency of 0.5 in the initial parental population, consisting of 50 homozygous pure lines.

 

Exercise 12. Build a QuLine input file (QMP) based on the following breeding procedure.

 

Exercise 13. Run QuLine using the GES and POP files from Ex. 11 and the QMP file from Ex. 12.

lFind out the genetic gains on yield and maturity after one breeding cycle.

lFind out when the genetic gain on yield reaches a selection plateau, assuming one generation can be grown in one year.