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The use of complex populations in breeding with markers. SBC “Breeding with molecular markers” David Francis Contact: francis.77@osu.edu. breeding programs tend to have complex population structures consisting of many independent crosses.

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The use of complex populations in breeding with markers
The use of complex populations in breeding with markers

SBC “Breeding with molecular markers”

David Francis

Contact: francis.77@osu.edu


The use of complex populations in breeding with markers

breeding programs tend to have complex population structures consisting of many independent crosses

Genetic studies tend to focus on bi-parental crosses with defined structure.


The use of complex populations in breeding with markers

Jargon: consisting of many independent crosses

QTL

LD

SNP Structure

Mixed Model Analysis of Variance

Identity by descent

Please stop me and ask when a definition will help clarify


The use of complex populations in breeding with markers

Objectives consisting of many independent crosses

Understand the diversity of populations that are being used to test marker-trait associations (linkage).

Understand the difference between the discovery of linkage and use of markers for selection.

Use this information to facilitate interaction with colleagues from other disciplines (field, marker support, analysis, etc…).

Use information to design and implement discovery and selection projects.


The use of complex populations in breeding with markers

Background consisting of many independent crosses

Introduction to Populations

Case study

Discovery Populations

Selection Populations

Association Mapping

Single Marker analysis of variance

Changes to the model used for analysis:

Account for population structure

Haplotypes to gain information


The use of complex populations in breeding with markers

Standard populations for inbred species (line crosses) consisting of many independent crosses

F2

RIL (recombinant inbred lines)

BC (back cross)

AB (Advanced Back Cross) *

IBC (Inbred Back cross) *

Emerging populations for association mapping

Natural populations

Unstructured populations

Family-based *

Nested Association Mapping (NAM; a variation of RIL)


The use of complex populations in breeding with markers

Standard populations for inbred species (line crosses) consisting of many independent crosses

F2Few meiosis, population not fixed

RIL Few meiosis, population fixed (can replicate)

BC Few meiosis, population not fixed

AB Few meiosis, population not fixed

IBC Few meiosis, population fixed

Emerging populations for association mapping

Nat. pop. Samples all meiosis in history of species, pop.

often fixed.

Unst. pop. Samples all meiosis since pop. established

Family-based Samples all meiosis in pedigree

NAM See RIL. Meiosis increased due to size of pop/

and multiple crosses.


Populations
Populations consisting of many independent crosses

  • Early generation (F2, BC1)

    • Strong theoretical basis

    • Balanced designs

    • Tools for interval mapping (point of analysis)

    • Most breeding programs do not collect extensive data on early generation populations

    • Retain too much “donor” Parent

  • AB and IBC populations

    • Reduce donor parent, isolate genetic factors, allow detection

    • Unbalanced design may limit power

  • Unstructured (natural populations)

    • More like populations that breeders use


Frequency of heterozygotes cc and homozygotes cc cc in each generation of selfing a hybrid f1

Review: affect of inbreeding consisting of many independent crosses

Frequency of heterozygotes (Cc) and homozygotes (CC+cc) in each generation of selfing a hybrid (F1).

Freq CC = p2 + pqF

Freq Cc = 2pq (1-F)

Freq cc = q2 + pqF


The use of complex populations in breeding with markers

Advanced Backcross and Inbred Backcross Populations consisting of many independent crosses

Parent 1 x Parent 2 (Donor)

F1 x ‘Parent 1

BC1 (n lines)

BC1-1 x Parent 1BC2-1S0⊗ . . . BC2-1S5

BC1-2 x Parent 1BC2-2S0⊗ . . . BC2-2S5

.

.

BC1-n x Parent 1 BC2-nS0⊗ . . . BC2-nS5

AB IBC


Statistical considerations with ab ibc and association populations
Statistical considerations with AB, IBC, and association populations

Unequal sample size/unbalanced data

Donor class is under represented

Need to adjust Df for F-test

proper F-test {Mj/Gk(Mj)}

These considerations affect power and whether significance level is accurately estimated


The use of complex populations in breeding with markers

  • Take home messages: populations

  • Genotyping throughput and reagent packaging favors working with very large populations (~480)

  • Measuring traits (Phenotyping) is the limiting factor

  • C) For elite polpulations, marker number and the ability to distinguish descent (IBD) from state (IBS) are limitations (this is a function of linkage phase and LD)

  • D) Incorporating pedigree data or population structure data into analysis improves detection of trait associations (QTL) and the efficiency of MAS (defined as relative efficiency of selection).

  • E) We can detect some known QTL, but not all known QTL in complex populations. Power goes up with population size and marker number.

  • F) Phenotypic selection is effective.


Case study mapping and selection of bacterial spot resistance in tomato populations

Case study: mapping and selection of bacterial spot resistance in tomato populations.

David Francis, Sung-Chur Sim, Hui Wang, Matt Robbins, Wencai Yang.


The use of complex populations in breeding with markers

Bacterial Spot is a disease complex caused by ~4 species of resistance in tomato populations.Xanthomonas bacteria. There are physiological races.

Sources of resistance are mostly close relatives of cultivated tomato Solanum lycopersicum or Solanum pimpinellifolium.

Hawaii 7998 (T1)

Hawaii 7981 (T3)

PI128216 (T3)

PI 114490 (T1, T2, T3, T4)


The use of complex populations in breeding with markers

Field rating based on Horsfall-Barratt scale quantitative scale (1-12)

en.wikipedia/org/wiki/Horsfall-Barratt_scale

Distribution approaches normal (ANOVA, regression, mixed models)

GH rating based on HR

Scored 0 or 1 (non-parametric)

Fig.3

Fig.3

A


Bacterial spot qtl discovery in ibc populations
Bacterial spot QTL discovery in IBC Populations scale (1-12)

Ohio, T2 & T1

(2000-2004)

FL, T3 and T4

(2002-2004)

Brasil

T3 2002-2004


The use of complex populations in breeding with markers

Results of discovery studies: scale (1-12)

Three IBC populations

[[OH88119 x Ha7998]x(OH88119)] x(OH88119)

[[OH88119 x PI128216]x(OH88119)] x(OH88119)

[FL7600 x PI114490]x(OH9242)]x(OH9242)

Multiple F2 populations

IBC x elite parent

OH7870 x Ha7981

Results:

Hawaii 7998 (T1) Rx-1, Rx-2, Rx-3, Chr11 QTL

Hawaii 7981 (T3) R-Xv3

PI128216 (T3) Rx-4, Chr11

PI 114490 (T1, T2, T3, T4) QTL Chr 11, Chr3, Chr4

X

X


The use of complex populations in breeding with markers

We have IBC lines and IBC x elite derived lines that “look good” and we want to integrate them with the elite breeding program. Strategy:

1) Develop populations to combine loci for resistance to multiple races

2) Validate Marker-QTL associations in order to assess feasibility of MAS

3) Conduct simultaneous

phenotypic and MAS.


The use of complex populations in breeding with markers

Genes good” and we want to integrate them with the elite breeding program. Strategy:

Parents

Rx-3 (5) Rx-4(11) QTL11 QTL11 ? ?

OH75 FL82 K64 OH86 OH74 MR13

“Population” consisting of 11 independent crosses, progeny segregate


The use of complex populations in breeding with markers
First segregating generation: grow ~100 plants in the field (total populations size 1,100) and select plants from each extreme (n = 110)


The use of complex populations in breeding with markers

Following year: Evaluate plots (total populations size 1,100) and select plants from each extreme (n = 110)

RCB, two replicates, rating based on a plot (not single plant), scale 1-12.


The use of complex populations in breeding with markers

Phenotypic evaluation (Focus on T1). (total populations size 1,100) and select plants from each extreme (n = 110)

Selection conducted in 2007 was predictive of plot performance in 2008 based on both nonparametric analysis and analysis of variance (p < 0.0001).

Heritability estimated from the parent-offspring regression suggests a narrow sense heritability of 0.32.

Plants rated as resistant in 2007 produced plots with an average disease rating of 4.02 in 2008; plants rated as susceptible produced plots with an average disease rating of 5.16 in 2008 (LSD 0.39).

Realized gain under selection ~13% decrease in disease

OH75 rated 3.5; OH88119 rated 9.0


The use of complex populations in breeding with markers

Marker analysis using The Unified Mixed Model (total populations size 1,100) and select plants from each extreme (n = 110)

Buckler Lab, TASSEL

Y = μ REPy + Qw + Markerα + Zv + Error

Sequence variation linked to traits


The use of complex populations in breeding with markers

%macro (total populations size 1,100) and select plants from each extreme (n = 110) Mol(mark);

proc mixed data = three;

class &mark gen rep;

model T1 = &mark / solution;

random gen rep;

%mend;

%Mol(TOM144);

%Mol(CT10737I);

%Mol(CT20244I);

%Mol(pto);

%Mol(SL10526);

%Mol(rx3);

Markerα


The use of complex populations in breeding with markers

Rx-3 (total populations size 1,100) and select plants from each extreme (n = 110)

single-point analysis


The use of complex populations in breeding with markers

Adding matrix of population structure can correct for background effects and can add insight to which crosses, pedigrees, subpopulations have highest breeding value

Y = μ REPy + Qw + Markerα + Zv + Error


The use of complex populations in breeding with markers

Qw background effects and can add insight to which crosses, pedigrees, subpopulations have highest breeding value

Pedigree information

Proportion of genome from a parent (pedigree)

Designation of cross (0/1)

Q – Matrix from Structure


The use of complex populations in breeding with markers

%macro background effects and can add insight to which crosses, pedigrees, subpopulations have highest breeding value Mol(mark);

proc mixed data = three;

class &mark gen rep;

model T1 = OH75 FL82 K64 OH86 OH74 &mark / solution;

random gen rep;

%mend;

%Mol(TOM144);

%Mol(CT10737I);

%Mol(CT20244I);

%Mol(pto);

%Mol(SL10526);

%Mol(rx3);

Qw

Markerα


The use of complex populations in breeding with markers

Rx-3 background effects and can add insight to which crosses, pedigrees, subpopulations have highest breeding value

single-point analysis

single-point analysis corrected for population structure


The use of complex populations in breeding with markers

M1 background effects and can add insight to which crosses, pedigrees, subpopulations have highest breeding value

M2

M1

M1

M2

M2

M1 M2

OH75: 1, R, 1

OH86: 0, S, 1

FL82 1, S, 0

Rx-3

rx-3

rx-3

OH75 x OH86, M1 can be used for selection, M2 cannot

OH75 x FL82, M2 can be used for selection, M2 cannot

What happens when the breeding material is a combination of progeny from both crosses?


The use of complex populations in breeding with markers

M1 background effects and can add insight to which crosses, pedigrees, subpopulations have highest breeding value

M2

M1

M1

M2

M2

M1 M2

OH75: 1, R, 1

OH86: 0, S, 1

FL82 1, S, 0

Rx-3

rx-3

rx-3

Reality check: Markers are identical by state but not by descent (presumably because of LD decay). Potential solution is to use haplotypes.


The use of complex populations in breeding with markers

proc mixed background effects and can add insight to which crosses, pedigrees, subpopulations have highest breeding value data = three;

class mark1 mark2 gen rep;

model T1 = mark1*mark2 OH75 FL82 K64 OH86 OH74 / solution;

random gen rep;

M1 M2 M3 M4 M5 M6

M1*M2, M2*M3, M3*M4, M5*M6

Interactions term defines haplotypes


The use of complex populations in breeding with markers

Rx-3 background effects and can add insight to which crosses, pedigrees, subpopulations have highest breeding value

single-point analysis

single-point analysis corrected for population structure

indicates haplotype analysis

haplotype analysis corrected for population structure.


The use of complex populations in breeding with markers

Genome-Wide Scan background effects and can add insight to which crosses, pedigrees, subpopulations have highest breeding value


The use of complex populations in breeding with markers

We can detect resistance conferred by the Rx-3 locus on chromosome 5

We can detect resistance conferred by Rx-4 on chromosome 11

We cannot detect QTL on chromosome 11

We can detect a strong interaction between loci on 11 and 5 (data not shown)

What needs to happen to improve prospects for “whole genome” discovery and/or selection?

More markers

Larger populations

F = Gen/Error (non-replicated)

F = Gen/Gen(Marker) (replicated)

Best

Worst (breeding pop)

Worst (genetic pop)


Population sizes
Population sizes chromosome 5

  • F-test

    • Marker/Gen(Marker)

    • Larger F from greater marker effect (strength of locus or closely linked to the causal gene)

    • Larger F by decreasing error

    • For maker studies it will nearly always be more powerful to increase the number of genotypes rather than increasing replicates of genotypes


Sample size power estimates
Sample size power estimates chromosome 5

False +

False -

Proportion σ2P


The use of complex populations in breeding with markers

Discovery populations: chromosome 5

Magnitude of difference between R and S is large

Gen(Marker) variation moderate

Breeding populations

Difference between R and S is moderate

Gen(Marker) variation is moderate

Detecting significant marker trait associations is more difficult when magnitude of difference between genotypic classes is reduced


The use of complex populations in breeding with markers

Population sizes can be increased by decreasing plot replication.

“Augmented designs” with a few checks highly replicated

Checks provide “error” to assess significance of differences between un-replicated genotypes

Checks can be used to normalize data (nearest check, flanking checks, etc…)


The use of complex populations in breeding with markers

  • Take home messages: replication.

  • Genotyping throughput and reagent packaging favors working with very large populations (~480) (effective MAS implementation will require larger populations)

  • Measuring traits (Phenotyping) is the limiting factor. (scoring larger populations will minimize Gen(Marker) error)

  • C) For elite polpulations, marker number and the ability to distinguish descent (IBD) from state (IBS) are limitations (this is a function of linkage phase and LD) (haplotypes)

  • D) Incorporating pedigree data or population structure data into analysis improves detection of trait associations (QTL) and the efficiency of MAS (defined as relative efficiency of selection). (corrects for structure; avoids false positives)

  • E) We can detect some known QTL, but not all known QTL in complex populations. Power goes up with population size and marker number. (Marker analysis is still more descriptive than predictive)

  • F) Phenotypic selection is effective.


Acknowledgments
Acknowledgments replication.

Francis Group

Matt Robbins

Sung-Chur Sim

Troy Aldrich

Collaborators, OSU

Esther van der Knaap

Bert Bishop

Tea Meulia

Sally Miller

Melanie Lewis Ivey

Collaborators, CAU

Hui Wang

Wencai Yang

Collaborators, UFL

Jay Scott

Sam Hutton

Collaborators, UCD

Allen Van Deynze

Kevin Stoffel

Alex Kozic

Funding

USDA/AFRI

OARDC RECGP matching funds grant; MAFPA