Molecular Marker-assisted

Breeding in Rice

Jian-Long Xu

Institute of Crop Sciences, CAAS

Email: xujlcaas@yahoo.com.cn

Expertise & experiencesMolecular rice breeding (including allele mining& marker-assisted breeding)

August 2003 ~ presentMolecular Rice Breeder in the Institute of Crop Sciences, CAAS

2008 ~ 2012One month per year for Consultant in PBGB Division, IRRI

2005 ~ 2007Three months per year for Collaboration Research in PBGB Division, IRRI

January 2002 ~ October 2003Postdoctoral Fellow in PBGB Division, IRRIPostdoctoral Fellow in PBGB Division, IRRI

March 1999 ~ October 2000PhD thesis research in PBGB Division, IRRI

August 1990 – July 2003Senior Rice Breeder in Zhejiang Academy of Agricultural Sciences

PhD 2001 Zhejiang University,

China

Genetics (minor in

Statistics)

MSc 1990 Zhejiang Agricultural

University, China

Plant Breeding and

Genetics

BSc 1977 Zhejiang Agricultural

University, China

Plant Breeding and

Genetics

Successful breeding depends on:

(1)Variation: Sufficient (novel) genetic variation for

target traits in breeding populations

(2) Selection efficiency: Effective selection approach

to identify desirable alleles or allelic combinations for

the target traits in breeding populations

Traditional breeding depends on phenotypic selections.

Efficiency of selection is largely influenced by environment,

gene interaction, and gene by environment interaction.

Genetic markers can improve efficiency of selection. Genetic

markers include morphological marker (plant height, leaf

color), cytological marker (chr structure and no mutant),

biochemical marker (isozyme), and molecular marker (SSR).

DNA

RNA

Direct

selection:

Indirect

selection

Phenotypic indirect selection

(based on correlation between

traits)

Based on phenotypic value

Protein

Phenotype

selectiontraits)

Genotypic indirect selection

(based on markers associated

with a gene or QTL)

Marker-assisted selection (MAS) is a method whereby

a phenotype is selected on the genotype of the linked marker.

Note: marker isn’t the target gene itself, there is just an

association between them.

Linkage of the target gene with the marker

Genotypes of the parents

Resistant donor Recipient

Genotypes of the parents

Genotypes of the F1

Three genotypes of the F2 population

Selection with 95% confidence based on

marker genotypes when recombination

rate (r) of 5%

The advantages of MAS:

(1) Time saving from the substitution of complex field trials (that need

to be conducted at particular times of year or at specific locations,

or are technically complicated) with molecular tests;

(2) Elimination of unreliable phenotypic evaluation associated with

field trials due to environmental effects;

(3) Selection of genotypes at seedling stage;

(4) Gene ‘pyramiding’ or combining multiple genes simultaneously;(4) Gene ‘pyramiding’ or combining multiple genes simultaneously;

(5) Avoid the transfer of undesirable or deleterious genes (‘linkage

drag’; this is of particular relevance when the introgression of

genes from wild species is involved);

(6) Selecting for traits with low heritability;

(7) Testing for specific traits where phenotypic evaluation is not

feasible (e.g. quarantine restrictions may prevent exotic pathogens

to be used for screening).

Population development

Gene or QTL mapping

Linkage map construction/ phenotypic

evaluation for traits/ QTL analysis

QTL validation

Procedure of MAS

Considering mapping and

breeding purposes

QTL validation

Confirmation of position and effect of QTL/

verification of QTL in different populations and

genetic backgrounds / fine-mapping

Marker validation

Testing of marker in important

breeding parents

Marker-assisted selection

Requirements for large-scale application of MAS

◆◆◆◆ Validation of QTL in breeding materials

Multiple markers in vicinity of QTL desirable.

◆◆◆◆ Simple, quick, inexpensive protocols for tissue sampling,

DNA extraction, genotyping and data collectionDNA extraction, genotyping and data collection

◆◆◆◆ Efficient data tracking, management and intergration

with phenotypic data

◆◆◆◆ Decision support tools for breeders

optimal design of selection strategies

accurate selection of genotypes

Strategies of MAS

1 Foreground selection

Selection against the target gene.

◆◆◆◆ Single marker selection

Reliability: depends on linkage between the marker and the

target gene. For example, marker locus (M/m) links with the target gene. For example, marker locus (M/m) links with the

target gene locus (S/s), if the recombination rate between the

two loci is r, the probability of selection of genotype S/S based

on marker genotype of M/M is

P=(1-r)2

So, reliability of MAS will sharply decrease with the increase of

recombination rate. To ensure reliability of MAS more than

90%, the r should be lower than 5%.

If the probability to select 1 target plant is P, the minimum

number of plants with marker genotype M/M will be

calculated as:

N=log(1-P)2/log(1-r)2

So, when the recombination rate (r) is high as 30%, So, when the recombination rate (r) is high as 30%,

selection of 7 plants with M/M genotype will ensure to

obtain 1 target plant with probability of 99%, whereas we

must select 16 plants if MAS isn’t applied (namely, there is

no linkage between the marker and the target gene).

MAS scheme for early generation selection in a typical breeding program for disease resistance. A susceptible (S) parent is crossed with a resistant (R) parent and the F1

plant is self-pollinated to produce a F2 population. In this diagram, a robust marker has been developed for a major QTL controlling disease resistance (indicated by the arrow). By using a marker to assist selection, plant breeders may substitute large field trials and eliminate many unwanted genotypes (indicated by crosses) and retain only those plants possessing the desirable genotypes (indicated by arrows). Note that 75% of plants may be eliminated after one cycle of MAS.

◆◆◆◆ Bilateral marker selection

Bilateral marker selection will greatly improve reliability of

MAS.

If marker loci M1 and M2 locate each side of the target gene

locus S, and the recombination is r1 and r2 respectively,

thus F genotype is M SM /m sm , F -derived Fthus F1 genotype is M1SM2/m1sm2, F1-derived F2

population has two genotypes, M1SM2 (harbor the target

gene) and M1sM2 (without the target allele). In view of

probability of double crossing over is very low, so selecting

genotypes at M1 and M2 loci to track the garget gene S is

high reliable.

Without interrupt, the probability to obtain genotype S/S

by selection of bilateral marker genotypes M1M2/M1M2 is:

P=(1-r1)2 (1-r2)

2/[(1-r1)2 (1-r2)

2 + r1r2]

◆◆◆◆ When r1=r2 (the target gene is located in the middle of

the two marker loci), P will be minimum.the two marker loci), P will be minimum.

◆◆◆◆ In fact, two single crossing over generally interrupt

each other, thus resulting in even small probability of

double crossing over, so reliability of bilateral marker

selection is higher than expected.

Comparison of target control between single

marker and bilateral marker

It is clearly indicated that control of the

target gene by a single marker isn’t so

satisfactory in most cases. The marker

must be as close as 1 cM to the target to

keep the risk of ‘losing’ the target below

5% after five BC generations. Even with

a single marker at 1 cM, the risk of losing

the target is close to 10% in BC10. For

greater distance of a single marker, the

risk becomes rapidly too high.

For the case of bilateral markers, even if

the two marker loci are far apart, for

example 10 cM, efficiency of keeping the

risk of losing the target is almost same as

that in the case of 1 cM under single

marker. Obviously, breaking linkage

between marker locus and the target

gene in bilateral markers more difficult

than in single marker.

2 Background selection

Besides selection of the target gene (foreground selection), background

selection will be implemented if to keep original characters of a variety.

◆◆◆◆ MAS method: use a set of markers, which are evenly selected from

the whole genome to identify the genotype of the recurrent parent.

Normally screening background will be focused on those plants with

target gene.

◆◆◆◆ Consecutive backcrossing: backcrossing progeny will soon recover its

recurrent parental genome after several rounds of backcrossing.

Breeding method BC1F1 BC2F1 BC3F1 BC6F1

Traditional backcrossing 75 87.7 93.3 99

MAS-based backcrossing 85.5 98 100

% of the recurrent parental genome

Young & Tanksley 1989

Traditional

BC breeding

Year

Comparison of MAS and traditional BC breeding for

recovery of genetic background of the recurrent parent

MAS BC

breeding

YearBlack bar represents donor

genome

Only two BC generations, the target segment can be narrowed

down into 2 cM by MAS and completely diminish linkage drag

from donor parent.

MAS application in qualitative traits

In most cases, it is unnecessary to apply MAS for

qualitative traits. However, MAS does improve efficiency

of selection of qualitative traits in following cases:

◆◆◆◆ Pyramiding different resistance genes;◆◆◆◆ Pyramiding different resistance genes;

◆◆◆◆Difficulty in or high cost of phenotyping;

◆◆◆◆ Hope to select in early growing stage but the traits

normally express in late developing stages

◆◆◆◆ Screening genetic background besides the target

traits

1 Pyramiding of multiple genes

Pyramid different genes dispersed in various varieties into

one variety by MAS.

Different genes for the same target trait: to improve Different genes for the same target trait: to improve

trait value.

Multiple genes underlying different traits into the

same variety: ensure new variety having more

favorable traits

Example of genes for pyramiding in cereals

Chr6 Chr11 Chr12

Three bBlast resistance genes used for pyramiding

Zheng et al. 1995

C101LAC x C101A51

Pi-1 Pi-2

C101LAC x C101PKT

Pi-1 Pi-4

F1 F1

F2 150 plants F2 150 plants

Scheme of thre blast resistance genes pyramiding

10 plants homozygous

at Pi-1 & Pi-2

10 plants homozygous

at Pi-1 & Pi-4

Bilateral marker selection

X

F1

X

F2 150 plants

MAS

Plants with 3 resistance genes

Resistance

geneChr.

Marker

name

Linkage

distance

(cM)

Primer sequenceAnnealing

temperature

Size of

amplified

fragment (bp)

Pi-GD-1(t) 8

RM6208 3.4TCGAGCAGTACGTGGATCTG

55 90CACACGTACATCTGCAAGGG

To pyramid different blast resistant genes in Zanhuangzhan2 (3

major genes and 1 QTL) and one brown planthopper resistant gene

(Bph18(t)) in IR65482 into 3 dominant restorer lines (Chen et al. 2012)

Information of resistant genes and their linked markers

Pi-GD-1(t)

-G18

R8M10 3.4ACCAAACAAGCCCTAGAATT

56 235TGAGAAAGATGGCAGGACGC

Pi-GD-2(t)

–G29 RM3855 3.2

AATTTCTTGGGGAGGAGAGG55 424

AGTATCCGGTGATCTTCCCC

Pi-GD-3(t)

–G312 RM179 4.8

CCCCATTAGTCCACTCCACCAC

C 61 190

CCAATCAGCCTCATGCCTCCCC

GLP8-6(t)

–G88 G8-6ID-1 2.8

ATCCGGCACTACCTTTCCC55 235

CTGCTCCCACCGCATCTGT

Bph18(t) 12 7312.T4A 1.3AACAGCAGAGGGTTTGGCTA

50 1078CAGACTTTTCTTGGGGGTCA

Minghui86, Shuhui527 and

Zhehui7954 (Recurrent parent, RP)

Sanhuangzhan 2、、、、IR65482

(Donor parent, DP)x

F1

RP

BC1F1Pyramiding F1

Pyramiding

BC2F1

BC3F1

F2

F3

MAS

MAS

MAS

MAS

RP

RP

MAS MAS

BC3F2

BC3F3

F4

F5

MAS

MAS

MAS

Evaluation on resistance and agronomic traits for restorer

lines and their derived hybrids

Test-crosses with II-32A and Huhan11A

Scheme of molecular improvement of blast and brown

planthopper resistance for restorer lines

Restorer lines

Strain ReactionResistance

frequency

(%)S

1

S

2

S

3

S

4

S

5

S

6

S

7

S

8

S

9

S

10

S

11

S

12

S

13

S

14

S

15

S

16

S

17

S

18

S

19

S

20S R

CO39 S S S S S S S S S S S S S R R S S S S S 18 2 10

Sanhuangzhan2 R S R R R R R R R S R R R R R R R R R R 2 18 90

Minghui86 R R R R R R R R R S R R R R R R S R R R 2 18 90

Shuhui527 R S R R R S R R R R R S R R R R R R R R 3 17 85

Zhehui7954 R S S S S S S S S S R S S R R R S S R R 13 7 35

Evaluation of resistance of newly bred restorer lines to Pyricularia grisea Sacc.

Minghui86-G2 R R R R R R R R R S R R R R R R R R R R 1 19 95

Minghui86-G1-G2 R R R R R R R R R S R R R R R R S R R R 2 18 90

Shuhui527-G2 R R R R R S R R R R R R R R R R R R R R 1 19 95

Shuhui527-G1-G2 R R R R R S R R R R R R R R R R R R R R 1 19 95

Zhehui7954-G1-G2 R S R R R R R R R S R R R R R S R R R R 3 17 85

Zhehui7954-G1-G2-G8 S R R R R R R R R S R R R R R R R R R R 2 18 90

Zhehui7954-G1 -G8-

Bph18(t)R S S S R S R S S R R R S R R R S S R R 9 11 55

Zheshu-G2-G8 R S R R R R R R R S R R R R R R R R R R 2 18 90

Mingzhe-G2-G8 R S S R R R R R R S R R R R R R R R R S 4 16 80

Mingzhe-G1-G2-G8 R R R R R R R R R S R R R R R R R R R R 1 19 95

Mingzhe-G1-G2-Bph18(t) S R R R R R S S R R R S R S S R R R R R 6 14 70

NameResistant

gene

Seedlings

inoculated

No. of

survival

Resistant

score

Minghui86 - 19 0 9

Shuhui527 - 19 0 9

Zhehui7954 - 20 0 9

TN1(CK) - 20 0 9

Performance of resistance-improved restorer lines to brown planthopper

TN1(CK) - 20 0 9

IR65482 Bph18(t) 20 20 1~3

Shuhui527-Bph18(t) Bph18(t) 20 15 3

Zhehui7954-G1-G8-Bph18(t) Bph18(t) 18 12 5

Mingzhe-G1-G2-Bph18(t) Bph18(t) 20 16 3

Restorer line or combination PL SF SNP TGW PH HD GY

(cm) (%) (g) (cm) (d) (g/plant)

Minghui86 24.9 83.5 180.1 29.9 103.5 106.0 17.6

Minghui86-G2 26.0 86.8 174.7 26.6 99.7 110.0 18.3

Minghui86-G1-G2 22.9 92.8 169.8 27.5 98.2 107.0 17.9

II-32A/ II-32A/Minghui86 25.0 96.7 198.0 28.0 99.0 98.0 23.8

II-32A/ II-32A/Minghui86-G1-G2 24.0 94.9 188.2 25.9 95.3 97.0 24.9

LSD0.05 0.8 5.4 24.5 1.8 9.2 1.2 3.1

LSD0.01 1.1 7.7 34.9 2.5 13.1 1.7 4.4

Shuhui527 26.4 92.0 182.8 33.0 101.4 111.0 17.5

Shuhui527-G2 25.9 88.5 153.7 31.3 85.8 112.0 16.9

Shuhui527-G1-G2 24.5 85.7 179 27.1 97.5 107.0 21.0

Shuhui527-Bph18(t) 25.9 87.2 178.8 32.1 95.1 111.5 24.4

Agronomic performance of newly bred restorer lines and their hybrids during

2011 winter season in Hainan

Shuhui527-Bph18(t) 25.9 87.2 178.8 32.1 95.1 111.5 24.4

II-32A/ II-32A/Shuhui527 23.4 79.3 190.8 26.1 89.2 97.0 18.9

II-32A/ II-32A/Shuhui527-G2 24.4 92.8 203.4 26.9 93.6 101.0 21.5

II-32A/ Shuhui 527-G1-G2 22.7 92.4 170.9 26.6 89.5 100.0 17.8

II-32A/Shuhui527-Bph18(t) 23.8 92.1 174.8 29 95.7 102.0 21.2

LSD0.05 1 4.5 23.8 0.8 4.1 1.9 3.5

LSD0.01 1.3 6.1 32.3 1.1 5.6 2.5 4.7

Zhehui7954 19.6 83.5 203.3 26.8 89.2 104.0 19.0

Zhehui7954-G1-G2 19.7 90.6 178.7 25.7 91.2 100.0 23

Zhehui7954-G1-G2-G8 21.9 84.0 207.5 27.5 92.1 106.5 24.9

Zhehui7954-G1-G8-Bph18(t) 24.1 94.1 156.8 27.8 93.5 107.0 25.7

II-32A/Zhehui7954 22.2 89.3 211.1 26.1 90.7 97.5 23.8

II-32A/Zhehui7954-G1-G2-G8 22.0 93.1 176.6 27.4 96.7 100.0 23.5

II-32A/Zhehui7954-G1-G8-Bph18(t) 23.7 94.3 195.3 26.7 96.9 98.5 28.7

LSD0.05 0.9 3.4 24.5 1.3 4.6 1.1 4.1

Some important issues about MAS improvement of

resistance for restorer lines

(1) Firstly, the resistance improvement of parental lines of hybrids is

much different from that of conventional varieties. In the backcross

progenies of restorer parental lines, selections were performed not

only for similarity to the recurrent parents (RP), but also for their

fertility restoring gene(s) and specific combining ability to the CMS

lines.

◆◆◆◆ background recovery of the RP◆◆◆◆ background recovery of the RP

◆◆◆◆ the qualitatively inherited fertility restoring gene(s) of the RP

◆◆◆◆ the quantitatively inherited specific combining ability. It is gradually

recovered through backcrossing in different individuals to a varying

extent.

It was indicated that a minimum of three backcrosses in conjunction

with stringent phenotypic selection for the RP in each BC progenies

and combining ability testing on a relatively large scale, guarantees

the recovery of recurrent parental characteristics even without MAS

against the background of the RP

(2) Secondly, the level of hybrid rice resistance is determined by the

restorer line when CMS is susceptible, whereas the resistance level of

F1 is controlled by the interaction between CMS and restorer line

when CMS is resistant. Expression of many resistance genes such as

Xa21, etc., are affected by genetic background. So resistance of

hybrids derived from the resistant restorer lines probably compromise

and show resistance inferior to our expected. So we should choose

highly resistance genes for resistance improvement of hybrid rice.

(3) Backcrossing is a very efficient strategy to improve single trait. (3) Backcrossing is a very efficient strategy to improve single trait.

However, the newly released lines are phenotypically identical to the

RP, i.e. there is no break through in traits of the new variety. So

composite intercrossing is recommended to pyramid multiple

resistance genes as well as to create new variety. In MAS breeding

programs, polymorphic markers are the key problem when multiple

parents are involved. So it is better to develop linked markers showing

polymorphism among all parents, otherwise efficiency of MAS will be

degraded.

MAS for quantitative genes

Most important agronomic traits are genetically quantitative

and controlled by polygenes. In the past decades, some major

QTLs have been implemented by MAS.

Procedures MAS for quantitative traits:

◆◆◆◆ QTL initial mapping◆◆◆◆ QTL initial mapping

◆◆◆◆ Fine-mapping of major QTL

◆◆◆◆ Verification of gene effect using NILs

◆◆◆◆ Validation of molecular markers

◆◆◆◆ MAS application

RM283

R844

S2139

RM23

0.0

27.4

28.4

40.0

RM283

R844

S2139

RM23

0.0

27.4

28.4

40.0

RM283

R844

S2139

RM23

0.0

27.4

28.4

40.0

AP3206

RM3412CP03970

RM8094

0.0

1.0

1.3

Short arm of chromosome 1

Progress of Saltot locus

• Saturated map of the

Chromosome 1

(Saltol segment) is

developed

60.6 RM23

RM140

RM113

S1715S13994RM9

RM5

C1456RM237RM246

40.0

64.9

66.2

71.2

75.3

77.2

91.998.299.1103.1

119.5123.5

129.9

A

C52903S

C1733S

R2374B

C52903S

C1733S

R2374B

RM23

RM140

RM113

S1715S13994RM9

RM5

C1456RM237RM246

40.0

75.3

77.2

91.998.299.1103.1

119.5123.5

129.9

RM23

RM140

RM113

S1715S13994RM9

RM5

C1456RM237RM246

40.0

75.3

77.2

91.998.299.1103.1

119.5123.5

129.9

RM8094RM493CP6224

RM140

1.8

1.9

1.2

1.3

• Closely linked

markers linked to

the saltol locus

identified

• MAS is being

validated in 3

breeding populations

60.6

(Source: Glenn B. Gregorio)

RM3412

CP010136

AP3206

Chromosome location of associated QTL of Salinity tolerance trait

LOD threshold

RM140

CP6224

RM493

RM8094

CP03970

0.0

b

a

2.5

12.11Mb 12.27Mb

12.0Mb 12.27 Mb

preprotein translocase, SecA subunit

Sec23/Sec24 trunk

WD40

Ser Thr Kc

Receptor like kinase

SAM synthetase

cold shockprotein

chloroplast membrane protein

secretory peroxidase

CBL-interacting protein kinase 19

Peroxidase, putative

S_Tkc;WD40

0.27 Mb

SALtol Region ( Major QTL K+/Na+)

(~40 genes)

11.9 Mb 12.13 Mb

12.25Mb 12.40Mb

11.10Mb 12.7Mb

60.6 60.9 62.5 64.9 65.4 66.2 67.6 67.9

cM

65.8

Chromosome 1 of Rice

B1135C02

OSJNBa0011P19

P0426D06

B1153f04

Salt tolerant rice varieties developed by IRRI and released

in Philippines

IRRI 112 - PSBRc48 (Hagonoy) IRRI 113 - PSBRc50 (Bicol) IRRI 124 - PSBRc84 (Sipocot) IRRI 125 - PSBRc86 (Matnog) IRRI 126 - PSBRc88 (Naga) IRRI 128 - NSICRc106 IRRI 128 - NSICRc106

Other salt-tolerant rice varieties

CSR10, CSR13, CSR23, CSR27, CSR30, CSR36 and Lunishree, Vytilla 1, Vytilla 2, Vytilla 3, Vytilla 4, Panvel 1, Panvel 2, Sumati, Usar dhan 1, 2 & 3 (India); BRRI dhan 40, BRRI dhan 41 (Bangladesh); OM2717, OM2517, OM3242 (Vietnam)

Progress of Sub1A locus

A major QTL on chrom. 9 for

submergence tolerance – Sub1 QTL

10

15

20

IR40931-26 PI543851

0 10 20 30 40

LOD score

50cM

OPN4

OPAB16

C1232

RZ698

OPS14

RG553

R1016RZ206

RZ422

Sub-1(t)

1200

850

900

OPH7950

OPQ1600

1 2 3 4 5 6 7 8 9

0

5

Submergence tolerance score

Segregation in an F3 population

100cM

150cM

C985

RG570

RG451

RZ404

Xu and Mackill (1996) Mol Breed 2: 219

Sub1 locus, there are three structurally related genes Sub1A,

Sub1B, and Sub1C present in the same QTL region, encoding

ethylene-responsive factor (ERF) genes.

Fukao, et al., Annals of Botany, 2009,103: 143–150

Development of the submergence-tolerant Swarna-Sub1 with details of markers

used for foreground, recombinant, and background selection.

Field plot test of submergence tolerance of Sub1 and non-Sub1 varieties. The SUB1 locus from

FR13A was introduced into the rice varieties IR64 and Samba Mahsuri by marker-assisted

backcrossing and into IR49830-7-1-2-2 through conventional breeding. A field trial performed

at IRRI in 2007 included Sub1 lines, the progenitors, and IR49830-7-1-2-2 (tolerant, used as

SUB1 donor) and IR42 (sensitive) as checks. Fourteen-day-old seedlings were transplanted

into a field with high levees, grown for 14 days and then completely submerged with about 1.25

m of water for 17 days. The field was drained, and the plants were allowed to recover under

non-stress conditions. The photograph shows the performance of the lines about 60 days after

de-submergence.

Swarna with Sub1

MAS of Minor-effect QTLs

At present, using limited number of markers and small

mapping populations, only few QTLs with relatively large

phenotypic-effect have been identified, which account for a

small portion of QTLs affecting the target traits. Moreover,

QTL epistasis has great effect on selection. So, it is difficult QTL epistasis has great effect on selection. So, it is difficult

to implement MAS for minor-QTLs.

Genome selection (GS) will provide a new strategy for

mionr-QTLs (introduced later).

Genome-wide selection

Training population: used for genotyping with high throughput

SNP marker and phenotyping in the target environment, setting

up genetic predict model to estimate all possible QTL effects

affecting a trait

Breeding population: used for genotyping and predicting breeding

values for selection

In a training population (both genotypic and phenotypic data available),

fit a large number of markers as random effects in a linear model to

estimate all genetic effects simultaneously for a quantitative trait. The

aim is to capture all of the additive genetic variance due to alleles with

both large and small effects on the trait.

In a breeding population (only genotypic data available), use estimates

of marker effects to predict breeding values and select individuals with

the best GEBVs (genomic estimated breeding values).

GS consists of three steps:

(1) Prediction model training and validation

A training population (TP) consisting of germplasm having both

phenotypic and genome-wide marker data is used to estimate

marker effects.

(2) Breeding value prediction of single-crosses

The combination of all marker effect estimates and the marker data of

the single crosses is used to calculate genomic estimated breeding the single crosses is used to calculate genomic estimated breeding

values (GEBVs).

(3) selection based on these predictions

Selection is then imposed on the single crosses using GEBVs as

selection criterion. Thus, GS attempts to capture the total additive

genetic variance with genome-wide marker coverage and effect

estimates, contrasting with MARS strategies that utilize a small

number of significant markers for prediction and selection.

Advantages of GS:

◆◆◆◆ It is especially important for quantitative traits conferred by a

large number of genes each with a small effect.

◆◆◆◆ GS includes all markers in the model so that effect estimates are

unbiased and small effect QTL can be accounted for.

◆◆◆◆ Reduce the frequency of phenotyping because selection is based on

genotypic data rather than phenotypic data.

◆◆◆◆ Reduce cycle time, thereby increasing annual gains from selection.

Disadvantages of GS:

◆◆◆◆ Traits with lower heritability require larger TPs to maintain high

accuracies.

◆◆◆◆ When single crosses are unrelated to the training population (TP),

even if sufficient markers and training records are available, marker

effects could be inconsistent because of the presence of different

alleles, allele frequencies, and genetic background effects, i.e.

epistasis. So genetic model isn’t universal in different populations.

Most agronomic important traits are quantitatively inherited. A wide

range of segregating populations derived from bi-parental crosses,

including RILs, DHs, F2 and its derived populations, and BC or testcross

populations, have been used for QTL mapping. And many major

important QTLs have been cloned in rice. Oppositely, slow progresses

have been made so far in MAS-based breeding for complex traits, mainly

due to the following two aspects.

Summary of MAS for quantitative traits

due to the following two aspects.

(1) Segregation populations derived from bi-parents can’t identify

favorable alleles for the target traits. So we don’t have information about

favorable alleles for the target trait which will be best used in molecular

breeding.

(2) QTL mapping is separate from breeding program. Owing to QTL

mapping results are seriously dependent on genetic background. So QTL

information from mapping populations can’t be directly applied in MAS-

breeding.

So, integration of QTL mapping with MAS-based

breeding in the same genetic background has been

strongly recommended for complex quantitative traits by

Tanksley and Nelson (1996). So far, AB-QTL method has

been widely used in QTL identification from germplasm.

However, there are still some defects:

(1) Relative high expenses resulting from phenotyping and

genotyping for a large mapping population.

(2) Favorable alleles can not be mined using populations

derived from bi-parents.

With the development of sequencing technologies and the sharp

decreased sequencing cost, genome wide association (GWS) has

been recently used for QTL mapping and allele mining from

germplasm resources and made good progresses. However, there

are still some problems with this method.

(1) Wide variations in plant height and heading date of a natural

population seriously affect growth and development for some population seriously affect growth and development for some

early and dwarf entries, thus resulting in inaccurate phenotyping

for those parts of entries.

(2) There is population structure effect on QTL association

mapping.

(3) GWS and MAS-based breeding is still separate.

Germplasm holds a large of genetic variation for improving agricultural

crops. However, in the past favorable genes from germplasm have not

been efficiently used in plant breeding due to linkage drag. Although

backcross is effective to simple qualitative traits, it has not been

successful to improve quantitative traits by backcross breeding

procedure.

Here we demonstrate a new breeding strategy of backcross combined

molecular marker technology to efficiently identify QTL and improve

multiple complex traits based on designed QTL pyramiding (DQP).multiple complex traits based on designed QTL pyramiding (DQP).

RP x donors (many) F1s x RP BC1F1s x RP

~25 BC2F1s/donor x RP

x

BC2F3-5 bulk populations

BC3F1s x RP

1, 2, 3, 4, 5, 6, ……

BC3F2-3 bulk populations

Self and bulk

harvest

Selection for target traits

and backcrossing

BC4F1s

BC4F2s

x

x

Self and bulk

harvest

1, 2, 3, 4, 5, 6, ……

Screening for target traits such as tolerances to drought, salinity,

high temperature, anaerobic germ., P & Zn def., BPH, etc.

Strategy of integration of QTL mining with QTL-designed pyramiding using backcross introgression lines in elite background

Confirmation of the selected traits by replicated phenotyping

then genotyping of trait-specific lines (ILs)

Crosses made between sister ILs

having unlinked desirable

QTLs for target ecosystem

DQP & MAS for pyramiding desirable

QTLs and against undesirable donor

segments for target ecosystem

Develop multiple stress tolerant lines for different ecosystems and release

NILs for individual genes/QTLs for functional genomic studies

high temperature, anaerobic germ., P & Zn def., BPH, etc.

QTL identification and allele mining

ST-ILs selected from four

introgression populations in

Minghui86 background at the

overall growth stage

Minghui86/Shennong265 (40)

Minghui86/Zaoxian14 (33)

Minghui86/Y134 (40)

Minghui86/Gayabyeo (37)

Salt tolerant introgression lines (ILs) and QTL mapping

Principle of using selected ILs and molecular

markers to identify QTLs

QTL detection

Taken allele frequency of the random population as an expected value, a

significant deviation (excess or deficiency) of donor allele frequency at

single loci in the selected IL population from the expected level implies a

positive selection favoring the donor allele (in excess), or negative positive selection favoring the donor allele (in excess), or negative

selection against the donor allele (in deficiency). Significant deviation

loci are considered as QTLs affecting the selected traits.

Gene action at putative QTLs

●●●● Excess of the donor homozygote additive gene action

●●●● Excess of the heterozygote overdominance gene action

●●●● Excess of both the donor homozygote and heterozygote partial

or complete dominance gene action

0.33 0.31 0.64 <.000123.4 2.44Bin3,13RM231

–0.290.34 0.05 0.0001 17.9 31.06Bin2,62RM240

0.57 0.22 0.78 0.0000 68.5 17.80 Bin2,32LT62

0.25 0.03 0.28 <.0001102.60.56 0.25 0.81 <.000168.4 10.07Bin2,22RM29

0.38 0.63 1.00 <.000124.043.24 Bin1,81LT44

0.46 0.08 0.54 0.0000 104.8 34.64 Bin1,61LT35

Diff.Random

pop.ST-ILs

P X2

Frequency of

introgressionP X2

Minghui8686/Shennong265 (15)Minghui86/Gayabyeo (13)

Physical Physical Physical Physical position position position position

/Mb

BinChr.Chr.Chr.Chr.MarkerMarkerMarkerMarker

0.65 0.00 0.65 0.0002 16.90.49 0.20 0.69 0.0000 56.6 2.57 Bin1,11LT3

QTLs for ST detected in Minghui86/Gayabyeo and Minghui86/Shennong265 ILs

Frequency of

introgression

ST-ILs Random

pop.Diff.

0.39 0.13 0.51 <.000151.69.93 Bin12,212LT365

0.50 0.09 0.59 <.0001100.8 17.31Bin11,311RM209

0.30 0.16 0.46 <.000128.40.75 Bin11,111LT326

0.33 0.14 0.46 <.000138.817.68 Bin10,310LT319

0.54 0.24 0.78 0.0000 58.7 3.53 Bin10,110LT305

0.29 0.25 0.54 0.0006 15.0 5.46Bin9,19RM444

0.34 0.27 0.61 0.0001 19.2 18.40 Bin8,38LT268 0.77 0.00 0.77 <.000124.0

0.48 0.33 0.80 <.000141.14.49 Bin8,18LT253

–0.340.42 0.08 0.0001 18.6 20.50 Bin6,46LT207

0.53 0.31 0.84 <.000154.1 0.20 0.15 0.35 0.0006 15.0 0.63 Bin6,16LT186

0.52 0.28 0.80 <.000161.7 26.37Bin5,65RM26

0.35 0.29 0.64 <.0001187.5 6.99Bin5,25RM169

0.31 0.23 0.54 <.000121.5–0.380.49 0.11 0.0000 21.2 31.49 Bin4,64LT150

0.45 0.50 0.95 <.000132.421.14 Bin4,54LT140

0.28 0.20 0.49 0.0000 21.2 16.70 Bin3,33LT97

–0.310.42 0.11 0.0001 19.6 9.81Bin3,23RM7

0.33 0.31 0.64 <.000123.4 2.44Bin3,13RM231

0.31 0.10 0.41 <.000147.1 36.06Bin3,53

–0.310.34 0.03 0.0002 16.8 34.94Bin2,62

0.39 0.07 0.46 <.0001146.6 10.07Bin2,22

0.29 0.23 0.53 0.0001 17.7 27.11Bin1,51

1.31 0.15 1.47 <.000164.7 17.89 Bin1,41

2.74 Bin1,11

RM85

RM266

RM29

RM246

Mo18

Mo3

0.02 0.51 0.53 <.000145.50

0.16 0.03 0.19 <.000121.7

QTLs for ST detected in Minghui86/Zaoxian14 and Minghui86/Y134 ILs

Diff.Random

pop.ST-ILs

P X2

Frequency of

introgressionP X2

Minghui8686/Y134 (10)Minghui86/Zaoxian14 (9)

Physical Physical Physical Physical position position position position

/Mb

BinChr.Chr.Chr.Chr.MarkerMarkerMarkerMarkerFrequency of

introgression

ST-ILs Random

pop.Diff.

19.71Bin12,412

20.52Bin10,310

18.63Bin9,39

0.59Bin9,19

29.26Bin7,77

12.32Bin7,37

–0.801.08 0.28 0.0006 14.76 3.40 Bin6,16

0.51 0.03 0.54 <.0001110.28 26.91 Bin5,65

1.31 0.61 1.92 0.0017 12.70 15.55 Bin5,45

6.99Bin5,25

0.29 0.28 0.56 <.000120.5 2.02Bin4,14

0.31 0.10 0.41 <.000147.1 36.06Bin3,53

RM519

RM147

RM189

RM296

RM248

Mo233

Mo192

Mo185

Mo173

RM169

RM518

RM85

–0.460.67 0.21 <.000136.42

–0.020.36 0.34 <.000127.06

0.15 0.03 0.18 <.000128.90

0.22 0.11 0.33 0.0004 15.47

–0.360.51 0.15 <.000120.19

0.150.030.18<.000121.67

0.20 0.03 0.22 <.000133.3

0.18 0.04 0.21 <.000124.18

ST-QTLs detected in at least the two different

ST-IL populations

Gayabyeo Shennong265 Zaoxian14 Y134

Bin2.2 √ √ √ √

Bin1.1 √ √ √

Bin6.1 √ √ √

Bin2.6 √ √

Bin4.6 √ √

Bin5.2 √ √Bin5.2 √ √

Bin5.4 √ √

Bin5.6 √ √

Bin8.3 √ √

Bin9.1 √ √

Bin10.3 √ √

Based on phenotypic value and QTL allele distribution, we can easily

select ideal ILs to pyramid different alleles from different donors to

improve the target traits.

A case study of high yield (HY), drought

and salinity tolerance (DT, ST)

MAS-based pyramiding of QTLs

and salinity tolerance (DT, ST)

using the selected ILs

Development of HY-, DT- and

ST-ILs for QTL mapping

SN89366 Bg94-1 GH122 YJ7 JXSM

Feng-Ai-Zhan 1 (FAZ1) Backcross & selfing

with HY selection

Pyramiding of QTLs

for HY, DT and ST

IL1 IL2×××× IL3 IL4×××× IL5 IL6×××× IL7 IL8××××

For DT For ST

F1 F1 F1 F1

F2 populations

Pop. 1 Pop. 2 Pop. 3 Pop. 4 Pop. 5BC3F5

HY & DT ILs HY & ST ILs

DT screening ST screening

HY &

DT ILs

FAZ1/SN89366 (IL1)

FAZ1/Bg94-1 (IL2)

FAZ1/GH122 (IL3)

FAZ1/YJ7 (IL4)

FAZ1/SN89366 (IL5)

FAZ1/Bg94-1 (IL6)

FAZ1/JXSM (IL7)

FAZ1/BG94-1 (IL8)

HY &

ST ILs

60 random

plants

~30 HY

plants

~30 DT

plants

~30 ST

plants

Confirmed or cross-testing of

selected ILs for QTL mapping

New breeding lines with HY, DT and/or ST

Promising lines for RYT

QTL mapping QTL mapping

QTLs affecting high yield (HY), drought tolerance (DT) and salinity tolerance (ST)

detected in two pyramiding populations by frequency distortion of genotypes

Pop. Locus Ch. Posi. HY DT ST

X2

P Gene

action

X2

P Gene

action

X2

P Gene

action

IL3/IL4

(DTP2)

F2

RM486 1 153.5 18.75 0 OD 27.34 0 OD 25.87 0 OD

OSR14 2 6.9 7.76 0.0206 PD

RM471 4 53.8 13.46 0.0011 OD

RM584 6 26.2 7.74 0.0208 OD

RM3 6 74.3 7.67 0.0216 AD 13.66 0.001 OD

RM2 7 8.08 0.0175 OD

RM547 8 58.1 19.97 0 OD 27.89 0 OD 30.97 0 ODRM547 8 58.1 19.97 0 OD 27.89 0 OD 30.97 0 OD

RM21 11 85.7 10.78 0.0045 AD

RM4A 12 5.2 11.93 0.0025 OD

IL5/IL6

(STP1)

F2

RM297 1 155.9 10.45 0.0053 AD 6.49 0.0389 AD 9.93 0.0069 AD

RM324 2 66 6.31 0.0426 PD

RM55 3 168.2 6.51 0.0385 PD

RM3 6 74.3 13.44 0.0012 AD 9.48 0.0087 AD 7.7 0.0212 AD

RM444 9 3.3 56.43 0 PD

RM434 9 57.7 30.82 0 AD

RM4A 12 5.2 6.29 0.043 OD

RM519 12 62.6 8.19 0.0166 OD

RM235 12 91.3 12.67 0.0017 PD

RM582 RM57266.4RM31271.6RM2478.4

RM594.9RM488101.4

RM246115.2

RM302147.8RM212148.7RM486153.5RM297155.9

Chr1

RM764.0

RM25179.1

RM411127.9

RM55 RM186168.2

RM227182.1

Chr3

RM33521.5

RM47153.8

Chr4

RM1220.0

RM31118.8

RM87129.2

Chr5

1

1 11

4

2 2 2

3 3 3

2 1 1

1

1 1 3

1 1 1

4

2RM6

OSR14 RM1106.9

RM52158.4RM324 RM42466.0RM29068.0RM26270.2RM34182.7

RM47592.5

RM6154.7

Chr2

1 1

4

3

21 3 4

1 2 3 4

QTLs for HY identified in pyramiding populations

QTLs for DT identified in pyramiding populations

RM213186.4

RM4692.2RM190 RM5887.4RM58710.7RM51020.8RM225 RM584RM225

26.2

RM27640.3

RM374.3

Chr6RM236.0RM43243.5

RM1890.4

RM248116.6

Chr7RM408RM5060.0RM4075.7

RM54758.1

RM22380.5

RM21090.3

RM80103.7

RM447124.6

Chr8RM2960.0RM4443.3

RM56647.7

RM43457.7RM25766.1RM10873.3RM55376.7

Chr9RM2860.0

RM2185.7

RM206102.9

Chr11RM4A5.2

RM51962.6RM31365.5

RM23591.3

RM12 RM17109.1

Chr12

2

1

2 2 3 3 3

4 4

2

1 2 2 2

1

3 4

3

14

1

2

2 3

34

3

1 2 3 4

1 2 3 4

QTLs for DT identified in pyramiding populations

QTLs for ST identified in pyramiding populations

Distributions of QTLs

affecting HY, DT and ST

Selected pop. Intercross or

repeated screening

trait

No. of selected

lines

Line # Yield of introgression line (g) Salt tolerance of introgression line at the seedling stage

Trait value

Check of

higher value parent

±±±±% comp. with check

No. of survival days Score of salt toxicity of leaves

Trait value

Check of higher parent

±±±±% comp check

Trait value

Check of higher parent

±±±±% comp check

DT selected (30)

HY 1 QP49 43.5 30.1 44.8 10 8.8 13.6 4.5 5.5 18.2

ST 10

QP47 31.8 30.1 5.5 11 8.8 20.6 4.5 5.5 18.2

QP48 29.8 30.1 -0.9 11 8.8 22.9 4.5 5.5 18.2

QP63 24.3 30.1 -19.3 12 8.8 36.4 4.5 5.5 18.2

QP60 26.3 30.1 -12.6 12 8.8 31.8 4 5.5 27.3

QP61 28.8 30.1 -4.3 11 8.8 30.3 4 5.5 27.3

Promising pyramiding lines selected from intercross or repeated

screening for HY and ST from IL1x IL2 population

QP36 28 30.1 -7 11 8.8 29.5 4 5.5 27.3

QP37 28.2 30.1 -6.3 11 8.8 29.7 5 5.5 9.1

HY selected (30)

HY 2QP163 38.6 30.1 28.4 9.6 8.8 9.1 5 5.5 9.1

QP167 36.6 30.1 21.8 11.4 8.8 29.5 4 5.5 27.3

ST 7

QP171 35.8 30.1 18.9 10 8.8 17.1 4.5 5.5 18.2

QP169 32.1 30.1 6.7 12 8.8 33 4.5 5.5 18.2

QP168 25.4 30.1 -15.6 13 8.8 51.1 4 5.5 27.3

QP166 28.3 30.1 -6 11 8.8 29.1 4 5.5 27.3

QP164 23 30.1 -23.4 11 8.8 25.7 4 5.5 27.3

QP170 17.4 30.1 -42.2 11 8.8 25.1 4.5 5.5 18.2

QP165 24.5 30.1 -18.7 11 8.8 20.6 4 5.5 27.3

ST selected (33) HY 2QP327 36.6 30.1 21.6 NA NA NA NA NA NA

QP337 34.9 30.1 15.9 NA NA NA NA NA NA

Based on phenotypic and QTL information of trait-specific ILs, a new line with

HY, DT and ST was developed by pyramiding of different target QTLs

Zhong-Guang-Lv 1（（（（HY, DT & ST））））

RYT in Yunnan province in 2011

Zhong-Guang-You 2

RYT in Guangxi province in 2010-11

Molecular recurrent selection systems for improving

multiple complex traits based on trait-specific

ILs and dominant male sterile (DMS) line

Developments of MAS-based improvement strategies required for

multiple traits should include understanding the correlation between

different traits

◆◆◆◆ Interaction between components of a very complex trait such as

drought tolerance

◆◆◆◆ Genetic dissection of the developmental correlation

◆◆◆◆ Understanding of genetic networks

◆◆◆◆ Construction of selection indices across multiple traits.

Selection for multiple traits

◆◆◆◆ Construction of selection indices across multiple traits.

The methods for pyramiding genes affecting a specific trait can be used

to accumulate QTL alleles controlling different traits. A distinct

difference in concept is that alleles at different trait loci to be

accumulated may have different favorable directions, i.e. negative alleles

are favorable for some traits but positive alleles are favorable for others.

Therefore, we may need to combine the positive QTL alleles of some

traits with the negative alleles of others to meet breeding objectives.

Jiafuzhan (Rr, sterile)

Jiafuzhan (rr, fertile)

Development of a DMS line in HHZ background

Spontaneous mutation

x Jiafuzhan (rr, fertile)

Jiafuzhan (1Rr sterile : 1rr fertile)

x HHZ (rr)

F1 (1Rr sterile : 1rr fertile)

x HHZ (rr), backcross 4-5 times

HHZ (1Rr sterile : 1rr fertile)Anthers with different fertility

A: full sterile anther

B: full fertile anther

C,D: partial fertile anther

Composition of the molecular RS (MRS) populations:

30-50 ILs/PLs carrying favorable QTL alleles from different

donors plus the DMS line in the same genetic backgrounds (HHZ)

Ovals or boxes of different colors represent different ILs carrying genes/QTLs

MRS population in HHZ GB

Bulk harvest

seeds from

fertile plants

to be screened carrying genes/QTLs for different target traits

Development of RS population is still under the way

to be screened

for target traits

Bulk harvest

seeds from

sterile plants

for next round

of RS

HHZ MS

line

Each fertile individual has even chance to pollinate with DMS plants,

ensuring all possible recombination produced inside the RS population

50% fertile plants

RS populations based on trait-specific ILs and a DMS line in the same GB

Irrigated(YP)

Abiotic stresses

Biotic stresses

Trait-improved

New ILs/PLs

Continued introgression breeding/DQP

50% DMS plants

Combine DMS line-based RS system with whole genome selection

RILs

GS model

Trait screening

New MRS

population for

next round

New lines with multiple traits by pyramiding

Trait-improved lines

RYT and NCT under different

target Es

Farmers in dif. target Es

Continuation

of MRS

GS model

GS

GS

Precise and high-throughput phenotyping

High-throughput and precision phenotyping is critical for genetic

analysis of traits using molecular markers, and for time- and cost-

effective implementation of MAS in breeding. To match up with

the capacity and costefficiency of currently available genotyping

systems, a precision phenotyping system needs high-throughput

data generation, collection, processing, analysis, and delivery.

High Resolution Plant Phenomics The Plant Accelerator

The High Resolution Plant Phenomics Centre (HRPPC)

Phenomics technology in the field

Phenomobile

Designed: to straddle a plot and collect measurements of canopy temperature, crop stress indices, crop chemometrics, canopy volume, biomass and crop ground cover

Phenotower

From 16 meters above the crop canopy. Phenotower collects infra-red thermography and colour imagery of field plots.This data is used for spatial comparison of canopy temperature, leaf greenness and groundcover between genotypes at a single point in time.

Measurements include:

◆◆◆◆ Leaf size

◆◆◆◆ Number of leaves

◆◆◆◆ Shape

◆◆◆◆ Topology (study of constant properties)

◆◆◆◆ Surface orientation

◆◆◆◆ Leaf color

◆◆◆◆ Plant area and volume

Plant scan

Tethered blimp

The blimp will carry both infrared

and digital color cameras operating

in a height range of 10 m to 80 m

above the field.

It will identify the relative

differences in canopy temperature

indicating plant water use.

Remote Sensing techniques

A flowchart for whole-genome strategies in marker-assisted plant breeding. The system starts with natural and artificial crop populations to develop novel germplasm through four key platforms, genotyping, phenotyping, e-typing (environmental assay), and breeding informatics, which need decision support system in various steps towards product development.

Discussion

Thank You for

Your Attention!