genomic selection to breed for resistance to bacterial ...tgc.ifas.ufl.edu/2016/3. genomic selection...

Genomic

Selection to breed

for resistance to

bacterial spot of

tomato

TBRT 2016

Deb Liabeuf

The Ohio State University

Introduction

Bacterial spot

Xanthomonas

X. euvesicatoria

X. vesicatoria

X. perforans

X. gardneri

Introduction Tomato Species

S. lycopersicum

S. pimpinellifolium

S. lycopersicum var

cerasiforme

Line

Ha7998

PI 128216

PI 114490

Resistance to

The 4 species

X. perforans

The 4 species

Gene/QTL

Rx3 on ch.5

QTL on ch.11

Rx4 on ch.11

QTLs on ch.

2, 3, 10, and 11

Combine resistance

Elite cultivar with all genes/QTLs of resistance

Marker assisted selection?

Only regions with major effects taken into

account

Introduction

Genomic selection (GS)

Introduction

(Heffner, Sorrells et al. 2009)

Predicts the performance of an individual based on genetic

information across the whole genome

GEBV = Genomic Estimated Breeding Values

Train the model

y = Xβ + ε

Use the model

y = Xβ + ε

Introduction

Vector of

phenotypic

values

[n,1] Marker

matrix

[n,m]

Vector of

Marker effects

[m,1] Give a value to

each marker

representing its

effect on the trait

To empirically compare phenotypic and genomic selection for bacterial spot resistance

Evaluate the effect on prediction accuracy of Modeling methods

Marker density and selection

Objectives

A B C D E F

A

B X

C X X

D X X

E X X X

F X X X

Introgress resistance in cultivated background

Crosses between resistant lines

F1 crossed together Segregating population

Population

51 inbred progenies

Self pollination

7 lines and hybrids

cross pollination

Workflow

1,110 individuals

109 individuals

109 families

phenotypic selection

Self pollination

Training population

Testing populations

Complex population

Phenotyping

Develop GS models

Marker effects and GEBVs

Genotyping

Cross validation

phenotypic evaluation

Compare phenotypic values and GEBVs

Empirical validation

population # of locs # of blocks per loc

Training pop. 1 2

Inbred progeny 2 4

Lines and hybrids 1 2

Phenotypic value =

Best Linear Unbiased Predictors

Corrected mean from a

random model

BLUPs

𝑌𝑖𝑗 = 𝜇 +𝑔𝑖 𝑏𝑗 + 𝜀𝑖𝑗

Yij = Phenotypic value

gi Genotype effect

bj = Block effect

𝜀𝑖𝑗 = error

+

𝑌𝑖𝑗 = 𝜇 +𝑔𝑖 + 𝜀𝑖𝑗 + 𝑏𝑗 𝑙𝑘 𝑙𝑘 +

lk = Location effect

+ 𝑙𝑘: 𝑔𝑖

Random models

For each location:

Across locations:

Experimental design = RCBD

𝜇 = grand mean

Phenotyping

Field inoculated with X. euvesicatoria

Plots rated with quantitative scale 0 to12

(Horsfall and Barratt 1945)

Genotyping

SolCAP infinium Array 397 SNP markers

Series based on prior knowledge and coverage

(Sim et al. 2012)

x cM

x cM

x cM

x cM

ch01 ch02 ch03 ch04 ch05 ch06

ch07 ch08 ch09 ch10 ch11 ch12

x cM

x cM

x cM

x cM

SolCAP infinium Array 397 SNP markers

Series based on prior knowledge and coverage

Genotyping

1

2

3

4

5

6 7

8

9

10

11

12

Manhattan plot -10log(p-value)

(Sim et al. 2015)

Sim et al, 2012

ch01 ch02 ch03 ch04 ch05 ch06

ch07 ch08 ch09 ch10 ch11 ch12

Ridge Regression Random model all markers as random effect

Ridge Regression Fixed model markers associated with QTLs as fixed effect

other markers as random effects

Bayesian LASSO (Least Absolute Shrinkage and Selection Operator)

Modeling

(Endelman 2013)

rrBLUP package on R

(Pérez et al. 2010)

BLR package on R

Training population

Train the model

on 108 families

Obtain GEBV with

only genotypic data

for 1 family

Repeat 109 times

Modeling

Marker effect: average

across repetitions

• Leave-one-out cross validation

Evaluation of the model accuracy:

Prediction accuracy

Validation

correlation coefficient between

phenotypic values and GEBVs

Phenotypic values

GEB

Vs

Phenotypic values

GEB

Vs

Model with high prediction accuracy Model with low prediction accuracy

Prediction accuracy of GS models from the leave-

one-out cross validation

Ridge regression – all markers as random effect

Cross validation

0 100 200 300 400

0.4

0.2

0.0

-0.2

Pre

dic

tio

n a

cc

ura

cy

Number of markers

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1P

red

ictio

n a

cc

ura

cy

cross-validation

progeny

hybrid

Validations

Selection made with Ridge Regression models with

fixed effect compared to phenotypic selection

Inbred progeny

phenotypic

selection Genomic

selection training pop.

Ph

en

oty

pic

BLU

Ps

Inbred

progeny Parents

Conclusion

Even with small training population and low marker

density GS allowed to accurately predict resistance in

progeny

In our population, prediction for resistance to bacterial

spot of tomato, are better when taking into account markers associated with QTLs

Benefit of doing an Association

analysis on the training population

before developing GS models!

Endelman, J. B. (2011). "Ridge regression and other kernels for genomic selection with R package rrBLUP." The Plant Genome 4(3): 250-255.

Heffner, E. L., et al. (2009). "Genomic Selection for Crop Improvement."

Crop Sci. 49(1): 1-12.

Horsfall, J. G. and R. W. Barratt (1945). "An improved grading system for

measuring plant diseases." Phytopathology 35: 656.

Pérez, P., et al. (2010). "Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R." The Plant Genome 3(2): 106.

Sim, S.-C., et al. (2012). "Development of a Large SNP Genotyping Array

and Generation of High-Density Genetic Maps in Tomato." PLoS ONE 7(7): e40563.

Sim, S.-C., et al. (2015). "Association Analysis for Bacterial Spot Resistance

in a Directionally Selected Complex Breeding Population of Tomato." Phytopathology 105(11): 1437-1445.

References

The Francis lab Dr David Francis

Bernard Eriku

Nicolas Lara

Eduardo Bernal

Eka Sari

Regis Carvalho

Troy Aldrich

JiHeun Cho

Thanks to…

the Ohio Department of Agriculture,

specialty crop research program

The Ohio State University Research

Enhancement Competitive Grant Program

Mid-America Food Processors Association

Funding

under award number 2014-67013-22410

Others Dr Antonio Cabrera

Ashley Markazi

The Francis Lab

Inoculation and evaluation

Phenotyping

Spray

inoculation Disease score:

Horsfall-Barratt scale

0 3 12

R S

X. euvesicatoria

80% of fruit maturity Ready for harvest

Maximal disease pressure

Phenotypic value =

Random model used:

Best Linear Unbiased Predictors

Corrected mean from a

random model

BLUPs

𝑌𝑖𝑗 = 𝜇 +𝑔𝑖 𝑏𝑗 + 𝜀𝑖𝑗

Phenotypic value

Genotype effect Block effect

error +

Phenotyping training pop.

Comparison between p-value from AA and

breeding value for each marker

Genomic model

x

Testing population

Prediction accuracy of GS models from test on the

hybrids and lines derived from the training

population

Number of markers 0 100 200 300 400

0.50

0.25

0.0

-0.25

Co

r. c

oe

ff

FG10_504

FG10_507

FG10_529

FG10_530 Fla8233

OH7663 OH8245

OH88119

y = 0.4553x - 0.0737

R² = 0.1601

-0.30

-0.20

-0.10

0.00

0.10

0.20

-0.20 -0.10 0.00 0.10 0.20 0.30 0.40

GEB

Vs

phenotypic BLUPs

397 markers

FG10_504

FG14_507

FG10_529

FG10_530 Fla.8233

OH7663 OH8245

OH88119

y = 3.3907x - 0.6337

R² = 0.4442

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

-0.20 -0.10 0.00 0.10 0.20 0.30 0.40

GEB

Vs

phentoypic BLUPs

markers associated with resistance

Testing population

Prediction accuracy of Ridge Regression with

markers associated with QTLs as fixed effect

Inbred progeny

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

loc1 loc2 across loc

pre

dic

tio

n a

cc

ura

cy

phenotypic

genotypic

Selection made with Ridge Regression models with

fixed effect compared to phenotypic selection

Inbred progeny

phenotypic selection

Across_locs Loc1 Loc2 Across_locs Loc1 Loc2

Genomic selection training pop.

Ph

en

oty

pic

BLU

Ps

rrBLUP random effect

Phenotypic prediction accuracy

Adj. R2 between F3 and F4 phenotypic values (rp)

Genomic prediction accuray

Adj. R2 between GEBVs and F4 phenotypic values (rg)

Inbred progeny

Loc1 Loc2 Across

Loc

rp 0.54*** 0.36** 0.51***

rg full set 0.44*** 0.13NS 0.30*

rg QTL 0.62*** 0.41*** 0.58***

rg/rp Prediction ability of genomic selection

compared to phenotypic selection

Inbred progeny

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Loc1 Loc2 Across Loc

GS a

cc

ura

cy

re

lative

to

Ph

en

oty

pic

ac

cu

rac

y

1st set = associated to QTL

1st set = random

24 markers

119 markers

217 makers

397 markers (full set)

Directional selection

Disease level

# of

individuals

S individuals R individuals

µ σ σ

12 resistant lines

15 susceptible lines