Development of marker-assisted selection (MAS) technology in crop improvement: an experience with forage crop

Development of marker-assisted selection (MAS) technology in crop improvement: an experience

with forage crop

IntroductionWhat is MAS? use of DNA markers to select plants/animals with desirable traits

Why do we need MAS? Increase selection efficiency in breeding programmes

Conventional breeding has enabled a range of improvements in crop performance, but it is

laborious, time-consuming and sometimes imprecise because

Base on visual assessment of phenotype, and

Phenotype expression affected by gene(s) and growth environment

MAS in combination with conventional field and glasshouse evaluation can increase breeding efficiency by

maximising genetic gain from selection

improving traits that are not amenable to improvement by conventional breeding alone

MAS expedites availability of novel cultivars in the market as superior plants will be selected before field tested

Introduction Contd

MAS requires

a ‘library’ of DNA markers and a precise genetic linkage map indicating their positions in the genome

quantitative trait locus (QTL) analysis to assess correlation between traits of interest and particular marker, and

validation of the marker-QTL/trait linkage in another population and environment

A case study

Phenotypic assessment and QTL analysis of herbage and seed production traits in perennial ryegrass (Lolium perenne L.)

Supervisors

Associate Prof. Cory Matthew, Institute of Natural Resources, Massey University, Private

Bag 11222, Palmerston North, New Zealand

Dr. H. S. Easton, Plant Breeder and Head, Forage Improvement,

AgResearch Ltd., Grasslands Research Centre, Private Bag 11008, Palmerston North, New Zealand

Dr. M.J. Faville, Forage Genomes Mapping, AgResearch Ltd., Grasslands

Research Centre, Private Bag 11008, Palmerston North, New Zealand

General background of Perennial ryegrass

Perennial ryegrass (Lolium perenne L.) is diploid (2n = 14), and belongs to the Poaceae family

an out-breeder (cross-pollinated)

native to Europe, temperate Asia, and North Africa but now cultivated in many other parts of the world, including North and South America, New Zealand, and Australia

used as a forage crop and as an amenity grass or turf

main source of energy and protein for grazing livestock in New Zealand

Objectives

To identify in perennial ryegrass;

morphogenetic and structural traits that are associated with increased herbage and seed production

QTL for the traits, and DNA markers associated with QTL for use in MAS

Methods

Plant material and experimental design Full-sib F1 mapping population (n = 200), ‘I×S’ constructed from a pair

cross between one plant of cv ‘Grasslands Impact’ (I) (♀) and one plant of cv ‘Grasslands Samson’ (S) (♂)

Population and parents evaluated

glasshouse for herbage production traits in autumn (April to July

2003) and in spring (Sept to Oct 2004)

Temperature, solar radiation and daylength recorded

field as spaced plants for seed production (2004/2005)

RCB design with 3 (glasshouse) and 4 (field) replicates, one copy of each plant per replicate.

Methods cont.

Methods cont.

Methods cont.Phenotype data collected

Herbage production

herbage dry weight (DW)

leaf appearance interval (ALf)

ligule appearance interval (ALg)

leaf elongation duration (LED)

leaf elongation rate (LER)

leaf lamina length (LL)

tiller number (TN)

tiller weight (TW)

plant productivity index (PI)

PI = Log(TW) + 1.5 x Log(TN/A)

Methods cont.Seed production seed yield per plant (SdYP) seed yield per head (SdYH) floret per spikelet (FS) floret per head (FH) spikelets per head (SH) reproductive tiller number (RT) % reproductive tillers with matured heads (TMH) at harvest spike length (SL) days to heading from transplanting (DH) spread of heading (SOH) seed weight (TSW) plant growth habit (PGH) floret site utilization (FSU)

Methods cont.

Markers analysis and linkage map construction

863 EST-SSR primer pairs selected based on array length

screened for amplification efficiency and polymorphism in mapping population parents

Genotypic data generated for mapping population using polymorphic primer pairs

Linkage analysis and map construction performed (JoinMap® 3.0 software)

Markers grouped at LOD 6.0 and 7.0 for parental and consensus maps respectively

Markers ordered at LOD 2.0, recombination = 0.40.

Map distances calculated in Kosambi centimorgans (cM)

Methods cont.QTL analysis Simple interval and multiple QTL model mapping (MapQTL® 4.0 software)

using phenotypic trait mean value for each genotype

Direction of allelic effect estimated following model of Knott et al (1997), used by Sewell et al (2000, 2002) as;

Maternal effect (I) = (ac + ad) – (bc + bd)Paternal effect (S) = (ac + bc) – (ad + bd)Interaction effect (INT) = (ac + bd) – (ad + bc)

ab = genotype of the maternal parent ‘I’ cd = genotype of the paternal parent ’S’

Methods cont.

QTL marker validation Half-sib F1 population of perennial ryegrass

n=100 families with two plants per family

QTL flanking markers associated with ALf and LL in autumn assayed for association with these traits in the validation population

Tests for association undertaken using binomial logistic regression analyses implemented in GenStat, significance declared at p<0.01

Results and Discussion

Season Temperature (oC) Solar radiation

(MJ/m2/day)

Daylength

(hours)Range Mean

Autumn (2003) 17-28 21 2.6 10.2

Spring (2004) 16-28 20 8.5 13.1

Results and Discussion cont

Trait Autumn (2003)

Mean Range Skewness Kurtosis S I LSD0.05

DW 3.2 ± 0.26 2.2 - 4.9 0.074 0.874 3.2 2.7 0.7

ALf 13.3 ± 0.77 9.0 - 17.0 0.336 -0.344 9.0 13.5 2.2

ALg 12.5 ± 0.89 7.8 - 17.2 0.36 0.201 9.2 13.2 2.5

LED 15.2 ± 1.09 10.0 - 19.0 -0.14 0.267 11.4 14.6 3.1

LER 1.2 ± 0.13 0.6 - 1.9 0.659 1.982 1.6 1.0 0.4

LL 17.6 ± 1.52 11.6 - 25.8 0.419 0.558 17.8 14.8 3.8

TN 34.2 ± 1.09 21.0 – 61.0 0.328 0.597 27.0 49.0 1.3

TW 0.09 ± 0.01 0.05 - 0.14 0.049 0.089 0.12 0.06 0.02

PI 5.3± 0.09 5.5 – 6.6 0.276 0.624 5.8 6.3 0.1

Results and Discussion cont.

Trait Spring (2004)

Mean Range Skewness Kurtosis S I LSD0.05

DW 1.9 ± 0.05 0.9 - 2.8 -0.16 -0.129 1.4 1.4 0.7

ALf 11.2 ± 0.71 8.5 - 14.5 0.46 0.017 8.8 9.7 2.0

ALg 11.3 ± 1.05 8.2 - 15.2 0.332 -0.235 7.8 10.7 2.9

LED 13.3 ± 0.96 9.3 - 18.3 0.369 0.521 10.7 11.3 2.7

LER 2.0 ± 0.23 1.2 - 3.1 0.345 0.409 2.3 2.9 0.7

LL 26.5 ± 2.47 18.0 – 37.0 0.02 0.484 24.0 32.6 7.0

TN 31.3 ± 1.18 14.3 – 63.7 0.706 0.399 14.0 32.0 5.0

TW 0.05 ± 0.00 0.03 - 0.1 0.226 0.277 0.08 0.04 0.02

PI 3.6± 0.02 3.1 – 3.8 -0.857 1.332 3.3 3.5 0.2


DW ALf ALg LED LER LL TN TW

Autumn 0.15 ALf

Spring 0.04

Autumn 0.06 0.86 ALg

Spring 0.07 0.68

Autumn 0.18 0.77 0.67 LED

Spring 0.08 0.82 0.57

Autumn 0.08 -0.44 -0.33 -0.41 LER

Spring -0.01 -0.49 -0.27 -0.59

Autumn 0.22 0.28 0.30 0.22 0.62 LL

Spring 0.09 0.11 0.15 0.16 0.68

Autumn 0.44 0.23 0.21 0.26 -0.24 -0.18 TN

Spring 0.17 -0.25 -0.04 -0.19 0.40 0.30

Autumn 0.28 -0.14 -0.20 -0.15 0.32 0.36 -0.73 TW

Spring 0.38 -0.11 -0.19 -0.10 0.19 0.18 -0.07

Autumn 0.53 0.23 0.20 0.26 -0.22 -0.15 0.99 -0.65 PI

Spring 0.92 0.09 0.14 0.14 -0.08 0.05 0.18 0.05


Autumn 2003 Spring 2004

Traits

PC1

(36%)

PC2

(23%)

PC3

(17%)

PC1

(32%)

PC2

(24%)

PC3

(18%)

DW 0.175 0.052 -0.578 0.069 -0.658 -0.211

ALf 0.393 0.382 0.134 0.494 0.032 0.126

ALg 0.363 0.369 0.133 0.397 -0.013 0.179

LED 0.382 0.329 0.100 0.469 0.004 0.061

LER -0.300 0.111 -0.493 -0.330 -0.181 0.549

LL -0.038 0.488 -0.397 0.007 -0.239 0.722

TN 0.412 -0.327 -0.283 0.495 0.024 0.125

TW -0.309 0.387 -0.143 -0.082 -0.364 0.053

PI 0.405 -0.305 -0.339 0.115 -0.586 -0.251


Trait

Hb

Autumn Spring

DW 0.62 0.52

ALf 0.74 0.63

ALg 0.67 0.45

LED 0.51 0.57

LER 0.44 0.46

LL 0.61 0.43

TN 0.74 0.63

TW 0.75 0.62

PI 0.63 0.56

Results and Discussion cont.Major traits for herbage production

a. Phenotype analysis: TN, TW, LER and LL

LED and ALf

Independent confirmation (phenotype analysis only) Chapman and Lemaire 1993; Hernandez Garay et al. 1999; Bahmani et al. 2000;

Yamada et al. 2004

GxE effect on trait expression

TN and LL important for autumn

LER and TW important in autumn and in spring

Difference in trait value between parent differed seasonally Variation in more traits in autumn than in spring


Trait Mean Range S I LSD0.05 Hb Skewness Kurtosis

SdYP 35.3 (±5.1) 11.9 - 64.5 27.7 11.9 14.5 0.75 0.16 -0.32

SdYH 91.9 (±10.9) 46.7 - 169.6 103.6 65.1 30.6 0.75 0.90 2.60

FS 8.4 (±0.6) 7.0 - 11.0 8 7 1.7 0.33 0.12 -0.04

FH 226 (±22) 162 – 298 224 162 61 0.33 0.20 -0.10

SH 27 (±1) 21 – 31 29 23 4 0.27 -0.38 0.78

RT 392 (±51) 184 – 592 272 184 145 0.59 0.00 -0.20

TMH 83 (±4) 65 – 96 81 90 11 0.62 0.10 -0.20

SL 19.6 (±0.9) 16.1 - 23.6 19 19.8 2.7 0.39 0.08 0.15

DH 92 (±1) 79 – 102 84 102 3 0.94 -0.34 0.23

TSW 1.9 (±0.1) 1.4 – 2.5 2.1 1.4 0.3 0.76 0.18 0.13)

PGH 3 (±1) 1 -7 1 7 1 0.77 0.48 0.64

SOH 6.6 (±1.1) 2.0 - 13.0 13 7 3.1 0.58 0.38 0.49

FSU 0.22 (±0.0) 0.09 – 0.42 0.26 0.31 0.0 0.94 0.43 0.38)

Results and Discussion cont.SdYP SdYH FS FH SH RT TMH SL DH TSW PGH SOH

SdYH 0.74

FS 0.23 0.14

FH 0.24 0.22 0.87

SH 0.14 0.23 0.27 0.70

RT 0.62 -0.05 0.23 0.15 -0.04

TMH 0.66 0.04 0.20 0.12 -0.04 0.96

SL 0.15 0.23 0.11 0.15 0.15 -0.02 -0.02

DH -0.07 -0.01 -0.47 -0.43 -0.16 -0.12 -0.09 -0.12

TSW 0.19 0.23 -0.08 -0.11 -0.10 0.00 0.01 0.07 0.08

PGH 0.15 0.11 0.07 0.05 -0.01 0.06 0.06 -0.04 -0.04 -0.19

SOH -0.44 -0.25 0.09 0.05 -0.03 -0.35 -0.40 -0.02 -0.41 0.03 -0.21

FSU 0.48 0.75 -0.20 -0.17 -0.05 -0.14 -0.03 0.12 0.21 -0.13 0.13 -0.28

Results and Discussion cont.Traits PC1 (25%) PC2 (19%) PC3 (16%)

SDYP -0.501 -0.187 0.047

SDYH -0.331 -0.179 0.483

FS -0.279 0.41 0.017

FH -0.295 0.477 0.145

SH -0.183 0.313 0.266

RT -0.379 -0.046 -0.484

TMH -0.400 -0.092 -0.441

SL -0.116 0.045 0.241

DH 0.115 -0.41 0.013

TSW -0.024 -0.101 0.034

PGH -0.109 -0.047 0.042

SOH 0.266 0.337 0.078

FSU -0.165 -0.365 0.419

Results and Discussion cont.Major seed yield traits reproductive tillers (RT), especially

those with matured heads (TMH)

seed yield per head (SdYH)

florets per head (FH)

florets per spikelet (FS)

spikelet per head (SH)

floret site utilization (FSU)

1000 seed weight (TSW)

Spread of heading (SOH) Negative effect


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Map contains 163 loci

and spans 582.2 cM

with a mean locus density of 3.6 cM

Segregation distortion at 21 loci with Lg 7 being particularly affected

Gene influencing embryo viability?

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Results and Discussion cont.b. QTL analysis Multiple QTL (between 1 -7 significant QTL) identified across Lg for

all traits Confirm polygenic basis for traits

G x E effect on QTL for most traits useful in MAS to select genotypes for specific environment

ALg (Lg1 and Lg6) stable across environments

useful in MAS breeding for diverse environments

QTL for DW co-located with QTL for other traits TN, LL and LER (Lg1)

PI, LED (Lg2)

TN, PI (Lg6)

Results and Discussion cont. 4 QTL identified for DW

2 on Lg 1 and 1 on Lg6 in autumn

1 on Lg 2 (PVE 9.2%) spring

Lg 6 QTL (largest PVE 13.4%) may be useful across environments co-located with QTL for LER (spring),

PI (autumn) and TN (autumn).

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Lg 6 QTL markers (pps0022 and pps0450) may be good candidates for MAS breeding across seasons after validation in other populations

and environments

DW QTL on Lgs 1 and 2 are environmentally sensitive



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3-6

qF

H-0

3-6

qS

H-0

3-6

qF

SU

-03-6

qT

SW

-03-6

qS

OH

-03-6

qP

C1

-03-6

qP

C3

-03-6

pps0766bppt007a

pps0049anfa024b

pps0494bpps0466bpps0065bpps0060apps0376apps0462ypps0593bpps0521c

ppt003bpps0736apps0447bpps0777apps0624apps0099apps0342x

pps0817y

pps0502b

pps0002x

LG7

qS

dY

H-0

3-7

-1qS

dY

H-0

3-7

-2

qP

GH

-03-7

qD

H-0

3-7

qD

H-0

4-7


QTL for SdYP identified on Lg 2 and Lg 6

QTL for SdYP co-located with related traits SdYH, FH, FS, SH, FSU, TSW, SOH

Lg2 QTL (PVE 7.4%) co-located with FH, SH, FS and PGH

Lg6 QTL (PVE 14%) co-located with SdYH, TSW and FSU may be useful for increased

production of quality seed selection for increased SdYH increases

seed production in ryegrass (Bugge 1987; Marshall and Wilkins 2003)

Lgs2 and 6 QTL markers (pps0113 and pps0432 respectively) represent robust candidates for MAS for improvement in seed production

pps0490b

pps0154zpps0265apps0252bpps1071bpps0410apps0223bpps0113ypps0122apps0755bpps0663bpps0037apps0153apps0732bpps0328zpps0080xpps0497apps0810apps1091ypps0400bpps0395ypps0660bpps0551bpps0188bpps0234bpps0347apps0172z


LG2

qS

dY

P-0

3-2

qF

H-0

3-2

qS

H-0

3-2

qF

S-0

3-2

qS

OH

-03-2

.1qS

OH

-03-2

.2

qD

H-0

3-2

qD

H-0

4-2

qT

SW

-03-2

qP

GH

-03-2

qP

C1-0

3-2

qP

C2

-03-2

-1

qP

C2-0

3-2

-2



LG6

qS

dY

P-0

3-6

qS

dY

H-0

3-6

qF

H-0

3-6

qS

H-0

3-6

qF

SU

-03-6

qT

SW

-03

-6

qS

OH

-03-6

qP

C1-0

3-6

qP

C3-0

3-6

Results and Discussion cont. No significant QTL for RT (r=0.62) and TMH (r=0.66) (critical traits

in seed production)

Reasons

QTL governing traits occur in a region not covered by the

genetic linkage map

complex traits (integrating tiller number, proportion of tillers

developing spikes and timing of this process)

many loci likely to be involved in their genetic control,

and in this data set no one locus assumed statistical significance.

epistasis may be a factor, but not assessed


I S ac ad bc bd I S INT

qTN-03-1 1 1.56 1.57 1.62 1.57 -0.06 0.03 -0.06

qTN-03-6 6 1.56 1.52 1.55 1.50 0.03 0.09 -0.01

qSdYP-03-2 2 34.49 36.69 30.50 29.67 11.02 -1.36 -3.03

qSdYP-03-6 6 34.64 38.74 28.27 31.12 14.00 -6.95 -1.25

Seed yield per head 65.1 103.6 qSdYH-03-6 6 97.31 105.42 78.70 90.24 33.79 -19.65 3.43

Seed yield per plant11.9 27.7

Tiller number49.0 27.0

Genotype class means Allele directionTrait Trait mean

QTL LG

favourable QTL alleles can be derived from parent that showed poor

phenotypic performance for the trait

e.g. SdYP, SdYH and TN (alleles increasing traits come from poor performing

parent

epistatic effect?

indicates difficulty in conventional breeding

necessitates molecular technique in breeding programmes as it provides better

information on the genetics of a trait.


Trait QTL (LOD, %PVE)

EST-SSRs tested

Number of

alleles

Allele sizes

ALf qALf-03-4 (10.4, 24.0) pps0146 2 228, 231

pps0423 4 247, 248, 256, 260

pps0495 3 167, 168, 190

LL qLL-03-1 (8.2, 22.0) pps0066 2 136, 140

pps0030 4 147, 150, 153, 156

pps0698 4 131, 133, 138, 145

pps0711 2 157, 172

Total 7 21

Allele frequency Marker Trait Percent Change Performance Mean Value Error Probability

1 0 missing Marker Absent Marker Present

66 80 17 pps0698 LL 5.9 % 27.91 29.56 0.0036

73 99 18 pps0495 ALf 4.6 % 10.07 9.63 0.0095

Conclusions Yield is determined by complex interaction of multiple traits

QTL and SSR markers for herbage and seed yield, and component traits identified for perennial ryegrass improvement

Markers may be useful in MAS, after validation across populations and environments

Markers for ALf and LL validated in another population

G x E effect associated with QTL discovery, and plant growth performances were different between autumn and spring.

alleles increasing traits sometimes come from poor performing parent

QTL discovery difficult for some complex traits

Conf. Proceedings and Journal Publications C. Matthew, A.M. Sartie, and H.S. Easton (2008). Tiller weight versus

tiller number in a perennial ryegrass population: a productivity index. XXI International Grassland Congress, July 2008, Beijing, China.

A.M. Sartie, H.S.Easton, C. Matthew and M.J. Faville (2006). A quantitative trait locus analysis of seed production traits in perennial ryegrass (Lolium perenne L.). Grassland Research and Practice Series 12, 71-75.

Alieu Sartie (2006). Ryegrass’ gene secrets revealed. New Zealand Dairy Exporter, June 2006, Vol.8 Issue 11, p87

A.M. Sartie, H.S. Easton, M.J. Faville and C. Matthew (2005). Quantitative trait loci for vegetative traits in perennial ryegrass (Lolium perenne L). In ‘Molecular breeding for the genetic improvement of forage crop and turf. Proceedings of the 4th international symposium on the molecular breeding of forage and turf, a satellite workshop of the XXth international Grassland Congress, July 2005, Aberystwyth, Wales (Ed. M.O.Humphreys) pp. 156

Conf. Proceedings and Journal Publications cont.

A. M. Sartie, H. S. Easton and C. Matthew (In prep). Range of plant morphology differences in two perennial ryegrass cultivars used to generate a mapping population for marker assisted selection

A. M. Sartie, C. Matthew, H. S. Easton and M. J. Faville (In prep). QTL analysis of herbage production component traits in perennial ryegrass (Lolium perenne L.)

A. M. Sartie, H. S. Easton, C. Matthew , P. Rolston and M. J. Faville (In prep). QTL for seed production in perennial ryegrass (Lolium perrene L.)

A. M. Sartie, M. J. Faville, C. Matthew, H. S. Easton and B. Barrett (In prep). Validation of the association of SSR markers to leaf appearance interval and leaf lamina length in perennial ryegrass

Acknowledgements

New Zealand Foundation for Research, Science and Technology, by a Bright Futures Fellowship

Agricom New Zealand Ltd (now part of PGG Wrightson Seeds)

AgResearch Ltd

Tom Lyons (transplanting and harvesting), Mike Hickey (transplanting), Sarah Matthew (harvesting and seed processing), Robert Southward, Mark Osborne and Tom Dodd (seed counting).

My wife and children for coping with my long hours of absence from home

Thank you for listening!!

Development of marker-assisted selection (MAS) technology in crop improvement: an experience with forage crop

Technology

seed production qtl

markers analysis

seed production seed

herbage production traits

new zealand

methods cont

software markers

plant sdyp seed yield