Molecular Markers and QTL Mapping; An Introduction,...

Molecular Markers and QTL Mapping; An Introduction, Review and

Discussion

Seth C. MurrayAssistant Professor

Quantitative Genetics and Maize Breeding

09/10/10 – TAMU Plant Breeding Roundtable

Crop Improvement and Genetic Diversity

Review of Genetic Variation - Focus on Gene (Point) Mutations

What are Morphological Markers?

What are Molecular Markers?- Restriction Fragment Length Polymorphisms- Polymerase Chain Reaction

- SSRs- SNPs - Sequence Based

What is a Quantitative Trait Locus QTL?

How do you perform QTL mapping?

What is the difference between QTL and a gene?

DISCUSSION: Using QTL for Crop Improvement

- Crop Improvement via Linked Loci

- Crop Improvement via specific genes

- Transgenics

BIG PICTURE –Why Understand Genetics for Crop Improvement

FOCUS – What is a (Molecular) Markerand How Does it Help Characterize Diversity?

BIG PICTURE –How Do Molecular Markers Help Us in Crop Improvement?

Overview

FOCUS – What is a QTLand How Does it Help us to Characterize and Use Diversity?

Where Does Genetic Variation Come From?

1. Polyploidy (changes in number of chromosomes)

2. Gene or point mutations3. Recombination4. Changes in chromosome

structure5. Transposition: mobile

genetic elements

Using Gene or Point Mutations in Crop Improvement

Mutation at a single gene is usually deleterious

Naturally occurring mutations are rare and spontaneous

-1 × 10−8 /bp/generation (0.00000001)

ACTGCATG ACCGCATG (Transitions)

ACTGCATG AC_GCATG (Deletions)

ACTGCATG ACCCGCATG (Insertions)

Human Induced Mutations

-Gamma radiation

-Chemical - Ethyl methyl sulfonate (EMS)

-Popular in 1940’s and 1950’s for breeding

-Now used primarily for genetic studies

insertion deletioninsertion C G

Transversion

C A Transversion

Wild Species

Crop Landraces

A Real Diversity Example From Sorghum

What is a Marker?

-Websters Dictionary defines as:“…something that serves to identify, predict, or characterize […the

GENETIC VARIATION present]”

Morphological (phenotypic) markers- A trait you can observe and/or measure as different between two

individuals (must be heritable, genetic). (Example ~ corn mutants)

Genetic (molecular, DNA) markers- A measurable DNA mutation which may or may not have an effect

on the phenotype (also must be heritable, genetic).

Molecular markers are much more common than phenotypic markers

Most gene (point) mutations do not result in phenotypic changes.

www.cals.cornell.edu/.../images/mutant-corn.jpg

How are Genetic Linkage Maps Made?- In progeny from a segregating two parent cross:

- Markers on different chromosomes are inherited independently- Markers on the same chromosome will have the more similar inheritance in the progeny the closer they are located because recombination is less likely to separate them.

- Most linkage maps have many loci so computer software is needed

http://www.animalgenome.org/edu/QTL/Julius_notes/05_linkagemap.PDF

Morphological (Phenotypic) Markers

-Developing the first morphological (phenotypic) markers and linkage maps

- Corn mutants

- Chromosome 4 mutant linkage map

www.cals.cornell.edu/.../images/mutant-corn.jpg

Corn Mutant Linkage Mapping

Cornell University

Burnham

Beadle (Nobel in 1958)

Rhodes

Emerson

McClintock (Nobel Prize in 1983)

How do we Make More Mutations Measurable?

Molecular markers!

- Isozymes- RFLPs (Restriction fragment length polymorphisms):

-The first genetic markers

- Require a lot of DNA, blotting and radiation

-”Rock Solid” markers for amplifying across species

- PCR (Polymerase Chain Reaction)

-Very little DNA needed

-AFLPs

-SSRs

-Sequencing and SNPs

From Morphological Maps to Molecular Maps Example

Tomato was one of the first to use Molecular Markers (1985)

-These were integrated with known morphological markers

Morphological Markers are in RED

Molecular Markers are in BLUE

Restriction Digests for RFLP’s

DNA Strand

Restriction Enzyme – Cuts Specific DNA Patterns

100kbp

50kbp

20kbp10kbp

80kbp - kilobase pairs 50kbp 10kbpG/AATTC G/AATTC

Digesting the DNA

Run Gel Electrophoresis

DNA standardDigested DNA

Restriction Fragment Probes

100kbp

50kbp

20kbp10kbp

80kbp 50kbp 10kbpG/AATTC G/AATTC

Radioactive probe that binds to specific DNA sequence

GGCCTTAATTCCGG

GGCCTTAATTCCGG

Run Gel Electrophoresis

Hybridize Radioactive Probe

GCATTC

RFLP’s -Restriction Fragment Length Polymorphisms

100kbp

50kbp

20kbp10kbp

130kbp 10kbpG/AATTC

GGCCTTAATTCCGG

80kbp 50kbp 10kbpG/AATTC G/AATTC

GGCCTTAATTCCGG

150kbpDifferent Sizes = Polymorphism!

MeasurableMutations!

Digestion Can NOT Cut Due toAC Transversion

Polymerase Chain Reaction - PCR

Allows the selective replication and amplification of specific(targeted) DNA sequences.

PCR basics1. Know some sequence of the piece of DNA to be targeted

2. Make primers - sequences of DNA that are complementary to the DNA sequence of interest

3. Add a cocktail of -DNA template-Primers-A,C,T,G’s – The four nucleotide building blocks -Taq1 - DNA polymerase

Steps in DNA amplification via PCR

Polymerase Chain Reaction (PCR)

Denaturation

Each DNA primer anneals, bindingto its complementary sequenceon the template DNA

DNA template is denatured with high heat to separate strands.

Annealing

Extension DNA polymerase creates a new strand of DNA complementaryto the template DNA starting from the primer.

Multiple rounds of denaturation-annealing-extension areperformed to create many copies of the template DNA between the two primer sequences.

Primers must match sequences close enough to drive amplification

Depending on conditions and primers used, DNA amplified is 1 to ~6000 bp

Steps in DNA amplification via PCR

Amplification potential: How many copies after 35 cycles of amplification?

Single / Simple Sequence Repeat (SSR marker)

Repeated simple sequence that causes polymerase slippage

CATGTTACGCATCATCATCATGTAGGGTCA

CATGTTACGCATCATCAT- - - GTAGGGTCA

CATGTTACGCATCAT- - - - - - GTAGGGTCA

* Highest mutation rate in genome

* Many alleles at a locus

seq.mc.vanderbilt.edu/DNA/images/mma.jpgwww.epibio.com/f6_1/Fig2trace.gif

NICE

NICE

Stutter

Stutter

Agro 643 – Molecular Markers

PCR Based Molecular Markers Continued

Sequencing

-Get the actual DNA sequence or code between two primers

SNPs (Single Nucleotide Polymorphisms)

-Newest, most popular marker

-Detects a single base pair (bp) mutation only

-Must find the polymorphism first by sequencing

Chromatagram/ Trace File for Sequence Data

Notice it is not always clear which base is being

observed.

genecodes.com/.../Var_detail_report.gifbioinformatics.utmem.edu


File for SNP Polymorphism on Illumina Beadstation, Similar to K-biosciences

www.biotech.uiuc.edu

aaAAAa

aA


Agro 643 – MAS and Genomic Selection – Genotyping Platforms

Kbiosciences systems

http://www.kbioscience.co.uk/

pipeline


Illumina Makes Sense for Mapping But NOT for MAS

http://www.genomecenter.ucdavis.edu/dna_technologies/prices.html

Illumina Golden Gate Genotyping UC Recharge RateNon-Profit Recharge

RateIndustry Recharge Rate

Bead Array 96 SNPs (per sample) 42 63 75




BeadXpress 96 SNPs (per sample) 17 25 30

BeadXpress 384 SNPs (per sample) 37 55 66

1536 SNP bead chip, 16 samples 1810 2751 3285

1536 SNP bead chip, 32 samples 3170 4818 5753


Whole Genome Sequencing

Coming soon from DOE!- soybean- cotton- re-sequencing sorghum


Whole Genome RE-Sequencing is Here!

http://www.hpcgg.org/Genotyping/index.jsp

http://www.sequenom.com/

http://www.illumina.com/

http://www.sequenom.com/

Dr. Patricia Klein will be speaking on her work in this area here on Oct. 1st!

What are Molecular Markers Good ForGenetic Diversity Measurements

- Selecting what genotypes to use in breeding

- Narrowing germplasm searches (only if less costly then phenotyping!)

- Managing germplasm collections

Intellectual Property Protection

- Preventing others from using your proprietary technology

Food Safety

- Detecting transgenes

- Detecting pathogens

QTL Mapping

- We will discuss today

Marker-Assisted Selection

- Backcrossing in a transgene

- Maintaining or crossing in a QTL

Genomic Selection (too complex to discuss here)

Gene Frequencies Mirror Geography Within European Humans

Novembre et al. 2008. Genes mirror geography within Europe.Nature. 456(7218):98-101.

Variance Explained = 0.36

Vari

ance

Exp

lain

ed =

0.2

1

Modern sugar and energy, MN -landraces

Historical and modern syrup

Amber

.

Markers for Predicting Diversity

Labate, J., K.R. Lamkey, M. Lee, and W.L. Woodman. 1999. Population genetics of increased hybrid performance between two maize populations under reciprocal recurrent selection. p. 127–137. In J. Coors and S. Pandey(ed.) Genetics and Exploitation of Heterosis in Crops, CIMMYT, Mexico City. 17–22 Aug. 1997. ASA, Madison, WI.

Agro 643 - Relationships and Genetic Diversity – Measurements and Visualizations of Genetic Diversity

What is a Quantitative Trait Locus (QTL)A statistically significant locus (not necessarily a gene) that

quantitatively affects a phenotype of interest with physical boundaries defined by linked molecular markers.

aaAa

AA

Single Marker Analysis

QTLQTL

Composite Interval Mapping

Agro 643 – QTL Mapping - Introduction

Genetic Markers

Why do We Conduct QTL Mapping?Mapped QTL ($$$$+)

- Identify genetic control of a trait (inheritance, position, co-localization) - Identify molecular markers for Marker Assisted Selection (MAS) - Identify homology with other genes/ other species (comparative mapping) - Get hints on genome organization- Use to select ‘elite individuals’ by predicting breeding value.- Clone a QTL – can do many more things once cloned but a lot of work!

Forward Genetics:Phenotypic Variation QTL Gene Functional Polymorphism Reverse Genetics:Gene Functional Polymorphism Phenotypic Variation

Agro 643 – QTL Mapping – General

What is the plural of QTL?:Quantitative Trait Loci , but can still be called QTLs to draw attention to the fact that there is more than one.

QTL and QTL mappingWhat do we need to map QTL?- A controlled segregating population

-*Heritable variation in the population is necessary, phenotypic variation in the parents is NOT (think of transgressive segregation; parents with different genes for height can phenotypically look the same.)

- Phenotypic data- A molecular marker based linkage map- Recombination and linkage disequilibrium

What is the mapping strategy (simple overview)-Test phenotypic value difference in progeny separated by marker state for significant difference (t-test, ANOVA, regression)- A significant difference is indicative of a marker linked to a QTL- Difference between mean value of separated progeny classes is an estimate of the QTL effect. - Replicate and test across environments to:

- Minimize error variance- Identify QTL that are consistently expressed - QTL only expressed in one (rare) environment are of little use – except if preparing for a stress expected to become more common

Agro 643 – QTL Mapping - Introduction

Single marker QTL analysis (F2) – Simplest Case of a “Perfect” Marker

Basic Regression- Code genotypic data (Parent 1 alleles = 0, Parent 2 alleles =1)

- Missing genotypes get treated as the mean probability of both parents (0.5 for F2 or RIL’s, 0.75 for backcross 1)

- Create genetic map (not necessary for most basic test)- Prepare phenotypic data (BLUP’s, location means, transform to normality)- Regress genotypes onto phenotypes (same result as t-test, ANOVA)- Significant genotypic marker means the marker is likely linked to a QTL- Estimation of the regression slope = estimate of QTL effect

Data simulated in R (additive)AA<-rnorm(10,110,3)Aa<-rnorm(20,105,3)aa<-rnorm(10,100,3)

Regression found difference in height to be 5.213cm compared to 5cm that we specified

aaAa

AA

y = 5.213x + 94.904R² = 0.6085

85

90

95

100

105

110

115

120

HEI

GH

T (C

M)

R: #Single Marker QTL analysis

Agro 643 – QTL Mapping – Single Marker Analysis

QTL and QTL mapping

Agro 643 - Heritability - Genetic and Environmental Variances

Five primary types of QTL mapping with increasing complexity and (theoretically) power- Single marker analysis- Interval mapping (IM)- Composite interval mapping (CIM)- Multiple interval mapping (MIM)- Bayesian ( Hidden Markov Model)- Others that are more rare.Variety of programs for QTL mapping (only free software)- QTL Cartographer

- Command Line- WinQTL Cartographer

- Nicest GUI- Less up to date then QTL Cartographer

- MapQTL5- Nice GUI

- PLABQTL- Command Line

-R/QTL- Command line / Most flexible- Offers Bayesian (most technically complex ) R/QTL

- for more Brian Yandell keeps a great reference at:http://www.stat.wisc.edu/~yandell/statgen/reference/software.html

Types of Populations – Inbred Derived


F2/ F3Good

- Quick to create- Can estimate both additive and dominance effects

Bad- Lower power (more unknowns – especially with dominant markers)- Not “immortalized” – genetic map is only good for that generation- Limited to no ability to replicate (environments, replicates)- Limited recombination

Recombinant Inbred Lines (RILs)Good

- Lots of recombination- Immortalized and easily replicated and shared

Bad- Takes years to create (not even possible for some species/ crosses)- Only look at additive effects (no heterozygotes)

Types of Populations – Inbred Derived


Doubled HaploidGood

- Quick to create- Immortalized and easily replicated and shared

Bad- Limited recombination- Can be difficult and expensive - Can only look at additive effects (no heterozygotes)

Backcross Good

- Can be combined with trait introgression breeding - Moderate recombination

Bad- Difficult to replicate unless further inbred- Can not evaluate additive effects (no donor parent recessive homozygotes)

Types of Populations - Goals

Want to find QTL that will improve

trait of interest for breeding

Agro 643 – QTL Mapping – Types of Populations

Population derived from an Elite x Elite cross (Only progeny

must segregate)- Primary improvement may only be on transgressive segregation

Want to find underlying genetic

causes of trait variation

Population derived from an extreme low parent x extreme high

parent cross (Note parents and progeny

segregate)

Crude_protein_leafCrude_protein_leafCrude_protein_leaf

Lignin_leafLignin_leafLignin_leafLignin_leafLignin_leafLignin_leafLignin_leafLignin_leaf

Hemicellulose_leafHemicellulose_leafHemicellulose_leaf

Cellulose_leafCellulose_leaf

Crude_protein_stem(%solids)Crude_protein_stem(%solids)Crude_protein_stem(%solids)Crude_protein_stem(%solids)

Lignin_stem(%solids)Lignin_stem(%solids)Lignin_stem(%solids)Lignin_stem(%solids)Lignin_stem(%solids)

Hemi-cellulose_stem(%solids)Hemi-cellulose_stem(%solids)

Cellulose_stem_(%solids)

Thousand_Seed_WeightThousand_Seed_WeightThousand_Seed_WeightThousand_Seed_WeightThousand_Seed_WeightThousand_Seed_WeightThousand_Seed_WeightThousand_Seed_WeightThousand_Seed_WeightThousand_Seed_WeightThousand_Seed_WeightThousand_Seed_Weight

ADF_grainADF_grainADF_grainADF_grainADF_grain

Crude_protein_grainCrude_protein_grainCrude_protein_grainCrude_protein_grainCrude_protein_grainCrude_protein_grainCrude_protein_grainCrude_protein_grainCrude_protein_grain

Fat_grainFat_grainFat_grainFat_grainFat_grain

Starch_grainStarch_grainStarch_grainStarch_grainStarch_grainStarch_grainStarch_grain

BrixBrixBrixBrixBrixBrix

Sugar_yieldSugar_yieldSugar_yieldSugar_yieldSugar_yieldSugar_yield

Leaf_dry_matter_yieldLeaf_dry_matter_yieldLeaf_dry_matter_yieldLeaf_dry_matter_yieldLeaf_dry_matter_yieldLeaf_dry_matter_yield

Stem_structural_dry_yieldStem_structural_dry_yieldStem_structural_dry_yieldStem_structural_dry_yieldStem_structural_dry_yieldStem_structural_dry_yieldStem_structural_dry_yieldStem_structural_dry_yield

Grain_dry_matter_yieldGrain_dry_matter_yieldGrain_dry_matter_yieldGrain_dry_matter_yieldGrain_dry_matter_yieldGrain_dry_matter_yieldGrain_dry_matter_yield

Total_Biomass_dry_yieldTotal_Biomass_dry_yieldTotal_Biomass_dry_yieldTotal_Biomass_dry_yieldTotal_Biomass_dry_yieldTotal_Biomass_dry_yieldTotal_Biomass_dry_yield

Mean_stem_thickness

stand_densitystand_densitystand_densitystand_densitystand_densitystand_densitystand_densitystand_densitystand_densitystand_density

Flowering_timeFlowering_timeFlowering_timeFlowering_time

Hgt_(cm)Hgt_(cm)Hgt_(cm)Hgt_(cm)Hgt_(cm)Hgt_(cm)

QTL Mapping For Biomass in College Station, TX 2005

Height

Flower

Tiller

S diameter

Biomass

Grain

Stem

Leaf

Sugar

Brix

G starch

G fat

G protein

G fiber

G 1K Wt.

S cellulose

S h-cellu

S lignin

S protein

L Cellulose

L h-cellul

L lignin

L protein

Chr. 1 Chr. 3Chr. 2 Chr. 4 Chr. 5 Chr. 6 Chr. 7 Chr. 8 Chr. 9 Chr. 10

RioBTx623

Agro 643 – QTL Mapping – QTL Verification – Multiple Traits




















Mean_stem_thickness




Height

Flower

Tiller

S diameter

Biomass

Grain

Stem

Leaf

Sugar

Brix

G starch

G fat

G protein

G fiber

G 1K Wt.

S cellulose

S h-cellu

S lignin

S protein

L Cellulose

L h-cellul

L lignin

L protein


RioBTx623


QTL Mapping For Biomass in Stem and Leaf Tissue in College Station, TX 2005




















Mean_stem_thickness




Height

Flower

Tiller

S diameter

Biomass

Grain

Stem

Leaf

Sugar

Brix

G starch

G fat

G protein

G fiber

G 1K Wt.

S cellulose

S h-cellu

S lignin

S protein

L Cellulose

L h-cellul

L lignin

L protein


RioBTx623


QTL Mapping For Multiple Traits in College Station, TX 2005




















Mean_stem_thickness




QTL College Station, TX 2005 – QTL Co-localization – Linkage vs. Plieotropy

Height

Flower

Tiller

S diameter

Biomass

Grain

Stem

Leaf

Sugar

Brix

G starch

G fat

G protein

G fiber

G 1K Wt.

S cellulose

S h-cellu

S lignin

S protein

L Cellulose

L h-cellul

L lignin

L protein


RioBTx623

QTL Meta-analysisUsing 50 separate disease resistance QTL studies in maize to understand broad spectrum quantitative disease resistance

Wisser RJ, Balint-Kurti PJ, Nelson RJ (2006) The genetic architecture of disease resistance in maize: a synthesis of published studies. Phytopathology96:120–129

Agro 643 – Epistasis – HIFS

QTL Meta-analysis and Candidate GenesLeverage 16 separate published QTL studies along with a sequenced genome helps to further gain detection power.

Wisser, R.J., Q. Sun, S.H. Hulbert, S. Kresovich, and R.J. Nelson. 2005. Identification and characterization of regions of the rice genome associated with broad-spectrum, quantitative disease resistance. Genetics 169:2277–2293.


Power of QTL detection is directly related to heritability

Bernardo, 2004

Power (%)

0

10

20

30

40

50

60

70

80

90

100

Heritability

0.4 0.5 0.6 0.7 0.8 0.9 1.0

N = 600

N = 300

N = 100

Utz and Melchinger, 1994

Agro 643 – QTL Mapping – Sample Size and Power

XCross parents different at trait(s) of interest

Self F1

F2’s

F1

Self to homozygosity

RIL’s

Collect DNA (molecular) markers data on all progeny

Marker Phenotype SignificanceRFLP 12 Height 0.0001***AFLP 57 Grain Weight 0.051SSR 26 Disease Resistant 0.0023**

OR

Perform statistical test for significance (Genotype vs. Phenotype) based on a null model

Is this marker not important? OrDid we not have enough data to reject the null hypothesis at (p< 0.05)?

Bi-Parental Linkage QTL Mapping

In Real Life If we only had five markers across a chromosome, we would not capture a lot of what is going on which can lead to reduced power and/ or increased error!

M N PChromosome ‘X’

Real Life Challenges?

Agro 643 – QTL Mapping – Composite Interval Mapping

L OINDIVIDUAL 1INDIVIDUAL 2INDIVIDUAL 3INDIVIDUAL 4INDIVIDUAL 5INDIVIDUAL 6INDIVIDUAL 7INDIVIDUAL 8INDIVIDUAL 9

XCross parents different at trait(s) of interest

No recombination

F2’s

F1

Self to homozygosity

RIL’s

Bi-Parental Linkage QTL Mapping Resolution : Limited by Recombination Events

36 detectable recombinationevents

27 detectable recombinationevents

Simulated: 100 loci , 1 chromosome, 15 individuals

Only here do we get close to “gene” resolution

Raven, 1999.

Sample Size and Power

Agro 643 – QTL Mapping – Sample Size and Power

Before asking the questions of what sample size we should use and how much detection power we expect to have, we should note the factors that influence this.1) What is the experimental goal?

2) What is the heritability of a trait?

3) How many QTL are involved?The more QTL to detect, the more individuals and markers you will need

4) How large of a QTL effect do you want to be able to find?To detect smaller and smaller QTL effects we need an exponentially larger

population because of the associated error

5) What are the effects of the trait?Dominant, additive, over-dominant, this will effect the population you use and

hence the sample size.

6) Is there any reason to believe there is epistasis?Yes! Do you want to detect it – probably do not have the resources too.

7) Is there any reason for using a smaller than optimum sample size?Yes! Time to create population, money to genotype and phenotype population

Many QTL Can Be / Are False!

Agro 643 – QTL Mapping – General

Bernardo, R. 2004. What proportion of declared QTL in plants are false? Theor. Appl. Genet. 109:419–424.

Null hypothesis

is True

Null hypothesis

is FalseReject the

Null Hypothesis

Type 1 Error!α

Fail to Reject the

Null Hypothesis

Type 2 Error!β

Note that this was a simulation of an F2 population (1 environment) with 150 individuals, 100 markers, multiple regression for detection, no permutation test and α=0.05.

When the author changed any of these things the results were not so dire.

Type III error: provides the right answer to the wrong question (discrepancy between the research focus and the research question )

Stability in QTLMost journals would not accept a QTL study with any less than three environments. A major reason for this has to do with stability. If a QTL is only detected in one environment, it suggests it may only be useful in that one environment.

A good example is photoperiod response. If two flowering time QTLs are identified, one expressed only in northern latitudes (photoperiod sensitivity) and one expressed in all environments (true flowering time). Introgression of the photoperiod sensitivity QTL is likely to decrease the yield stability where as introgressing a true flowering time QTL is likely to make the plant behave predictably.

Agro 643 - Genetic and Environmental Variances – Yield stability

Context Dependency in QTLThe same allele in different backgrounds will have different effects

QTL Verification

QTL VerificationLocus effect quantification – How large is the difference between alleles?Plieotropy – Would unmeasured traits be affected? Are there negative effects?QTL x Environment Interaction – Is there a year or environment effect? How large?QTL x QTL interaction – Is there epistasis that may make some QTL more or less valuableUnderlying gene(s) – Can we, do we want to identify these?

Approaches for VerificationCompare multiple traitsCompare in multiple environmentsDevelop and use independent populationsFine Mapping (discussed later)Create Near Isogenic Lines (discussed later)Association mapping verification (discussed later)Cloning & Transformation (discussed later)

Agro 643 – QTL Mapping – QTL Verification

QTL Cloning Using Fine MappingGo from a statistically identifiable region to a functional polymorphism that can be tested directly.

Identified QTLMARKER A

MARKER B

Backcross NIL’sHeterogeneous Inbred Families (HIFs)

NIL looks just like recurrent parent except with substitution at gene

Why do We Want to Clone QTL(s)?Mapped QTL ($$$$+)

- Identify genetic control of a trait (inheritance, position, co-localization) - Identify molecular markers for Marker Assisted Selection (MAS) - Identify homology with other genes/ other species (comparative mapping)

Cloned QTL ($$$,$$$+)

- ‘Perfect’ marker for gene to use in MAS - Transform into another organism (G.M.O.) - Knock out, turn off, over-express, etc. - Identify the genetic pathway (may suggest other genes of interest)

What is the pathway for stem sugar accumulation?- Identify homology with other genes/ other species

What do these genes do in maize and sugarcane?- Look for natural variation in other alleles at that gene

Are there other alleles that would accumulate even more sugar?

Forward Genetics:Phenotypic Variation QTL Gene Functional Polymorphism Reverse Genetics:Gene Functional Polymorphism Phenotypic Variation

CloningCrop Improvement Genes

Cloning the gene is when we know the DNA sequence of the gene CAUSING the morphological (phenotypic) difference.

We do this by finding and mapping molecular markers closer and closer to our morphological marker.

This lets us do many neat things for both crop improvement and evolution studies but is A LOT of work!

Example:Cloning the First Domestication Gene

- Tomato fw2.2

Doebley JF, Gaut BS, Smith BD (2006) The molecular genetics of crop domestication. Cell. 29;127(7): 1309-21

Markers for QTL Cloning Need a very high density of markers around the gene of interest


QTL Cloning Using Fine Mapping

Li, J., M. Thomson, and S.R. McCouch. 2004. Fine mapping of a grain-weight quantitative trait locus in the pericentromeric region of rice chromosome 3. Genetics 168:2187–2195.

Gene Cloning In the F2 is Possible When There is A Large Effect

150 plants

1000 plants

9000 plants!

Orsi CH, Tanksley SD. 2009. Natural variation in an ABC transporter gene associated with seed size evolution in tomato species. PLoS Genet. 5(1):e1000347.

Dissecting a QTL Yielded Two Genes With Opposite Effects

Thomson, M. J., J. D. Edwards, E. M. Septiningsih, S. E. Harrington and S. R. McCouch, 2006 Substitution mapping of dth1.1, a flowering-time quantitative trait locus (QTL) associated with transgressivevariation in rice, reveals multiple Sub-QTL. Genetics 172: 2501–2514.

Dissecting A Quantitative Trait:Time Versus Resolution

Resolution in bp

1x1071

Rese

arch

Tim

e in

Yea

rs

5

1Associations

1x104

F2 QTL Mapping

NILs Positional Cloning

RI QTL Mapping

Stolen from Dr. Edward Buckler – USDA-ARS

Resolution Versus Allelic Range

Resolution in bp

1x1071

Alle

les

Eval

uate

d

>40

1

Associations In Diverse Germplasm

1x104

NIL

Pedigree

F2 or RIL Mapping

Positional Cloning

Associations In Narrow Germplasm

Stolen from Dr. Edward Buckler – USDA-ARS

Improving A Quantitative Trait:Cost vs. Usefullness

Usefulness to Crop Improvementmoreless

Cost

s Fo

r a

Use

ful S

tudy

more

less

Associations

NILs

RIL QTL Mapping

Not Stolen

F2 QTL Mapping Genomic Selection

Selection Mapping

HIFs

Copyright © 2008 by the Genetics Society of AmericaYu, J. et al. Genetics 2008;178:539-551

FIGURE 1. Diagram of genome reshuffling between 25 diverse founders and the common parent and the resulting 5000 immortal genotypes

Nested Association Mapping (NAM)Combines association mapping with Bi-parental linkage mapping

An aside into segregation distortion con’t

Agro 643 - Relationships and Genetic Diversity – Inbreeding Coefficient


Technology Needed for MAS (and Genetic Fingerprinting)

MARKERS x GENOTYPES = DATA POINTSMost of the applications we have discussed so far (gene / polymorphism discovery) involve the identification of many markers on a few number of genotypes to cover the genome. QTL mapping:100 – 1,000 markers X 100-500 individuals = 10,000 to 500,000 data points

Association mapping:100 – 1,000,000 markers X 100-7000 individuals = 10,000 to 7,000,000,000 data points

Once the subset of useful/ important markers has been established, we now want to evaluate these over many individuals. This requires different technology to be cost efficient.

MAS:1 – 100 markers X 100 – 10,000 individuals = 10,000 to 1,000,000 data points

In general this is a need only for plant and animal breeders, biotechnologists and some people who do gene diversity studies – therefore the technology market is smaller than for what human geneticists and evolutionary biologists may use.

Transition To Use (Linked) Markers to Select for Crop Improvement Traits

Once we find a marker linked to our trait of interest (exp. disease resistance) we can use this marker to make selections rather then screen all of the plants for disease resistance.

This is called Marker Assisted Selection

!!! NOTE: This marker is unlikely to be the point mutation or the gene that gives the disease resistance. It is only LINKED to the disease resistance gene of interest.

Thus: WE DO NOT KNOW WHICH GENE CAUSES THE DISEASE RESISTANCE WITH THE MARKER, BUT WE CAN MAKE SELECTIONS FOR DISEASE RESISTANT PLANTS BASED ON THE MARKER.

Molecular Markers and QTL Mapping; An Introduction,...

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Transcript of Molecular Markers and QTL Mapping; An Introduction,...