use of DNA markers to select plants/animals with desirable traits,Phenotypic assessment and QTL analysis of herbage and seed production traits in perennial ryegrass

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

2. What is MAS?
Introduction
 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

3. 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

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

5. 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

6. 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

7. 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

8. 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.

9. Methods cont.

10. Methods cont.

11. 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)

12. 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)

13. 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)

14. 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’

15. 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

16. Results and Discussion
Season Temperature (oC) Solar radiation Daylength
Range Mean (MJ/m2/day) (hours)
Autumn (2003) 17-28 21 2.6 10.2
Spring (2004) 16-28 20 8.5 13.1

17. 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

18. 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

19. Results and Discussion cont.
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

20. Results and Discussion cont.
Autumn 2003 Spring 2004
PC1 PC2 PC3 PC1 PC2 PC3
Traits (36%) (23%) (17%) (32%) (24%) (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

21. Results and Discussion cont.
Hb
Trait 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

22. 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

23. Results and Discussion cont.
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)

24. 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

25. 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

26. 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

27. Results and Discussion cont.
lg1 lg2 lg3 lg4 lg5 lg6 lg7
pps0098b
pps0490b pps0132a pps0766b
pps0154z pps0457a ppt007a
pps0290b pps0265a* pps0197b pps0049a***
0 pps0038a pps0252b pps0766c pps0761a pps0509b pps0013d nfa024b
5 pps0136b pps1071b pps0463z pps0494b***
pps0270a* pps0410a pps0577b pps0111b* pps0432a pps0466b***
10 pps0094y pps0223b pps0198a nfa015b pps0065b
15 pps0586b** pps0113y pps0504b pps1004a pps0060a**
20 pps0255a pps0122a pps0133b****** pps0388b pps0210a pps0376a***
pps0319y pps0755b pps1146a pps0192b pps0462y**
25 Pps0502a*
pps0963b* pps0663b pps0385y pps0052a pps0593b
30 pps0030y* pps0037a pps0558c pps1116b pps0374a pps0521c
pps0488c pps0312c**
35 pps0066b pps0153a pps0869a* pps0189d ppt003b
pps0711c pps0710a pps0326a
40 pps0732b pps0052e pps0031a pps0736a
pps0698a****** pps0642b pps0048d
pps0328z pps0273y pps0892a pps0447b
45 pps0381c pps0469a pps0040y pps0032a pps0777a
pps0080x pps0617b
50 pps0251b pps0419y pps0202a pps0404b pps0624a
pps0497a pps0310b
pps0295b pps0433b pps0073a pps0099a
55 pps0810a
pps0068y pps0753b pps0074y pps0342x
pps1091y pps0022x
60 pps0051a pps0356b pps0359b
pps0400b pps0450a
65 pps0339y pps0130y pps0036b
pps0395y pps0523a
pps0373x pps0018a** pps0377a
70 pps0660b
pps0698b pps0439c pps0718b
pps0551b
75 pps0061a* pps0345a** pps0397c
pps0188b pps0495b
80 pps0234b pps0213b pps0601a
pps0146b pps0817y
pps0347a pps0163z
85 pps0423y pps0502b
pps0172z pps0560b
90 pps0687b pps0261x pps0149a
nfa023b pps0317a pps0002x
95 pps0420a** pps0164a
nfa109a pps0150b
100 pps0123a nfa071a
pps0759a
105 pps0724z pps0983a
110 pps0322b pps1099b
pps0483x pps0106b
pps0284z
pps0714b
pps1135b
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?

28. lg1 lg2 lg3
pps0490b
pps0154z
pps0251b
qALg-03-1 qDW-03-1.1 qDW-03-1.2
0 pps0381c pps0265a pps0766c
qLL-03-3.1
pps0252b
qALg-04-1
pps0698a
qTN-03-2
5 pps1071b pps0577b
pps0711c
10 pps0410a pps0198a
pps0066b
qLL-03-1
qLER-03-1
pps0223b pps0133b
15 pps0030y
pps0113y pps0502a
qALf-04-1 qTN-03-1
20 pps0963b
pps0122a pps0558c
pps0319y
qTW-03-3.1
pps0755b pps0488c
qALf-04-3
25 pps0255a
qLED-04-3
pps0586b pps0663b pps0710a
30 pps0642b
pps0094y pps0037a
qLL-03-2
qLED-04-2.1
35 pps0153a pps0469a
pps0270a
pps0419y
qALg-04-3
40 pps0136b pps0732b
pps0328z pps0295b
45 pps0038a
pps0080x pps0068y
qDW-04-2.2
50 pps0497a pps0051a
qLED-04-2.2
pps0810a pps0339y
qPI-04-2
55
pps1091y pps0373x
qLER-03-3.1
60 pps0400b pps0698b
65 pps0395y pps0061a
70 pps0660b pps0213b
pps0551b pps0163z
75 Key: pps0188b pps0560b
qLER-03-3.2 qLL-03-3.3
80 pps0234b pps0687b
pps0347a pps0164a
85 DW pps0172z nfa109a
90 ALf nfa023b pps0759a
qTW-03-3.2
95 pps0420a pps0724z
ALg pps0123a
qPI-04-3
100
LED
105
LER pps0322b
110 pps0483x
LL
TN
TW
PI

29. lg4 lg5 lg6 lg7
pps0098b
pps0132a pps0766b
pps0457a ppt007a
pps0197b pps0049a
0 pps0761a pps0509b nfa024b
qLED-04-6
pps0013d
qALf-03-5
5 pps0463z pps0494b
qALg-03-6 qALf-03-6
pps0111b pps0466b
qALg-04-6
10 pps0432a
nfa015b pps0065b
qLER-03-7.1
15 pps0504b pps0060a
pps1004a
20 pps0388b pps0210a pps0376a
25 pps1146a pps0192b pps0462y
pps0385y pps0052a pps0593b
qLL-03-7
30
qLER-03-7.2
pps1116b pps0374a pps0521c
pps0312c
qLED-03-7
35 pps0869a pps0189d ppt003b
qTN-03-6
qLER-04-6
pps0326a
qPI-03-6
qDW-03-6
pps0052e pps0031a pps0736a
40 pps0048d
pps0273y pps0892a pps0447b
45 pps0777a
qTW-03-4.1
pps0040y pps0032a pps0617b
50 pps0202a pps0404b pps0310b pps0624a
qALg-04-4
pps0433b pps0099a
qLL-03-4.1
qALf-04-4.1
55 pps0073a pps0022x
pps0753b pps0074y pps0450a pps0342x
60 pps0356b pps0359b pps0523a
65 pps0130y pps0036b
qLED-04-4
pps0018a pps0377a
qLL-03-4.2
70
qALg-03-4
qALf-03-4
pps0439c pps0718b
qLED-03-4.1
75 pps0345a pps0397c
80 pps0495b pps0601a Key: pps0817y
pps0146b
85 pps0423y pps0502b
90 pps0261x pps0149a DW
95 pps0317a ALf pps0002x
pps0150b
100 nfa071a ALg
105 pps0983a
pps1099b
LED
110
pps0106b LER
pps0284z
pps0714b LL
pps1135b TN
TW
PI

30. 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)

31. Results and Discussion cont.
 4 QTL identified for DW lg1
 2 on Lg 1 and 1 on Lg6 in autumn
 1 on Lg 2 (PVE 9.2%) spring lg2 pps0251b
qALg-03-1 qDW-03-1.1 qDW-03-1.2
pps0381c
qALg-04-1
pps0698a
pps0490b pps0711c
pps0066b
qLL-03-1
pps0154z
Lg 6 QTL (largest PVE 13.4%) may
qLER-03-1
 pps0265a pps0030y
qALf-04-1 qTN-03-1
pps0963b
be useful across environments
pps0252b
qTN-03-2
pps1071b pps0319y
pps0410a pps0255a
 co-located with QTL for LER (spring), pps0223b
pps0113y
pps0586b
pps0094y
PI (autumn) and TN (autumn). pps0122a pps0270a
pps0136b
pps0755b
 verified in multi-location field pps0663b pps0038a
experiments (Faville et al, submitted)
pps0037a
qLL-03-2
qLED-04-2.1
pps0153a
pps0732b lg6
pps0328z
pps0080x
Lg 6 QTL markers (pps0022 and
pps0098b
qDW-04-2.2
pps0497a
 pps0132a
qLED-04-2.2
pps0810a
qPI-04-2
pps0450) may be good candidates
pps0457a
pps1091y pps0197b
pps0400b
qLED-04-6
for MAS breeding across seasons
pps0013d
pps0395y
pps0463z
qALg-03-6 qALf-03-6
pps0660b
qALg-04-6
pps0432a
 after validation in other populations pps0551b
pps0188b
nfa015b
and environments
pps1004a
pps0234b pps0210a
pps0347a
pps0192b
pps0172z pps0052a
nfa023b
pps0374a
DW QTL on Lgs 1 and 2 are
pps0420a pps0189d

qTN-03-6
qLER-04-6
qPI-03-6
qDW-03-6
pps0123a pps0031a
environmentally sensitive pps0892a
pps0617b
pps0310b
pps0022x
pps0450a
pps0523a

32. LG1 LG2 LG3 LG4
pps0490b
pps0154z
pps0251b
0 pps0381c pps0265a pps0766c pps0761a
qSOH-03-2.1
pps0698a pps0252b
qDH-04-2
5
qSOH-03-1
qPC2-03-2-2
pps1071b pps0577b
qPC2-03-2-1
qDH-03-2
pps0711c
10 pps0410a pps0198a
qFSU-03-1 qPC2-03-1
pps0066b
pps0223b pps0133b
15 pps0030y
qSL-03-1
pps0963b pps0113y pps0502a
20 pps0122a pps0558c
pps0319y
25 pps0755b pps0488c
pps0255a
qFH-03-1
qTSW-03-3
qSH-03-1
pps0586b pps0663b pps0710a
30
qSdYP-03-2
pps0642b
qPGH-03-2
pps0037a
qFS-03-2 qTSW-03-2
qPC1-03-2
pps0094y pps0312c
qFH-03-2
35 pps0153a pps0469a pps0326a
pps0270a
pps0732b pps0419y
qSH-03-2
40 pps0136b pps0048d
pps0328z pps0295b
45 pps0038a pps0040y
pps0080x pps0068y
pps0202a
qSL-03-3
50 pps0497a pps0051a
qPGH-03-4
pps0433b
qPC2-03-4
qDH-04-4
pps0810a pps0339y
qFS-03-4
55 pps0753b
pps1091y qSOH-03-2.2 pps0373x
60
qDH-03-4
pps0400b pps0698b pps0356b
65 pps0395y pps0061a pps0130y
pps0660b pps0213b pps0018a
70 pps0439c
pps0551b pps0163z
75 pps0188b pps0560b pps0345a
80 pps0234b pps0687b pps0495b
pps0347a pps0164a pps0146b
85 pps0423y
pps0172z nfa109a
qSH-03-4
90 nfa023b pps0759a pps0261x
95 pps0420a pps0724z pps0317a
pps0123a pps0150b
100 nfa071a
105 pps0983a
pps0322b pps1099b
110 pps0483x pps0106b
pps0284z
pps0714b
pps1135b

33. LG5 LG6 LG7
pps0098b
pps0132a pps0766b
pps0457a ppt007a
qSOH-03-6 qPC1-03-6
pps0049a
qPGH-03-7
pps0197b
qDH-04-7
0 pps0509b nfa024b
pps0013d
qFSU-03-6
qSdYH-03-6
qSdYP-03-6
5 pps0463z pps0494b
qTSW-03-6
qSdYH-03-7-1
10 pps0111b pps0432a pps0466b
nfa015b pps0065b
15 pps0504b pps0060a
pps1004a
qPC3-03-6
20 pps0388b pps0210a pps0376a
qSL-03-5
25 pps1146a pps0192b pps0462y
pps0385y pps0593b
qSH-03-6
pps0052a
30 pps1116b pps0521c
pps0374a
qDH-03-7
ppt003b
qFH-03-6
qSdYH-03-7-2
35 pps0869a pps0189d
pps0052e pps0031a pps0736a
40
qFSU-03-5.1
pps0273y pps0892a pps0447b
45 pps0032a pps0777a
qSdYH-03-5
pps0617b
50 pps0404b pps0310b pps0624a
pps0073a pps0022x pps0099a
55 pps0342x
pps0074y pps0450a
60 pps0359b pps0523a
65 pps0036b
pps0377a
70
pps0718b
75 pps0397c
80 pps0601a
pps0817y
85 pps0502b
90 pps0149a
95 pps0002x
100
105
110

34. Results and Discussion cont.
LG2
 QTL for SdYP identified on Lg 2 and pps0490b
Lg 6 pps0154z
pps0265a
pps0252b
qSOH-03-2.1
pps1071b
qDH-04-2
QTL for SdYP co-located with related
qPC2-03-2-2
qPC2-03-2-1
qDH-03-2
 pps0410a
pps0223b
traits pps0113y
pps0122a
 SdYH, FH, FS, SH, FSU, TSW, SOH pps0755b
pps0663b
pps0037a
qSdYP-03-2
qPGH-03-2
Lg2 QTL (PVE 7.4%) co-located with
qFS-03-2 qTSW-03-2
qPC1-03-2
pps0153a
qFH-03-2
 pps0732b
FH, SH, FS and PGH
qSH-03-2
pps0328z
pps0080x
pps0497a
pps0810a LG6
 Lg6 QTL (PVE 14%) co-located with pps1091y
qSOH-03-2.2
pps0400b
SdYH, TSW and FSU pps0395y
pps0660b
pps0098b
pps0132a
 may be useful for increased pps0551b pps0457a
qSOH-03-6 qPC1-03-6
pps0197b
production of quality seed
pps0188b
pps0013d
pps0234b
qFSU-03-6
qSdYH-03-6
qSdYP-03-6
pps0463z
 selection for increased SdYH increases pps0347a
qTSW-03-6
pps0432a
pps0172z
seed production in ryegrass (Bugge nfa023b nfa015b
pps1004a
qPC3-03-6
1987; Marshall and Wilkins 2003)
pps0420a
pps0210a
pps0123a
pps0192b
qSH-03-6
pps0052a
Lgs2 and 6 QTL markers (pps0113
pps0374a

qFH-03-6
pps0189d
and pps0432 respectively) represent pps0031a
pps0892a
robust candidates for MAS for pps0617b
pps0310b
improvement in seed production pps0022x
pps0450a
pps0523a

35. 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

36. Results and Discussion cont.
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 Trait mean Genotype class means Allele direction
I S QTL LG ac ad bc bd I S INT
Tiller number qTN-03-1 1 1.56 1.57 1.62 1.57 -0.06 0.03 -0.06
49.0 27.0
qTN-03-6 6 1.56 1.52 1.55 1.50 0.03 0.09 -0.01
Seed yield per plant qSdYP-03-2 2 34.49 36.69 30.50 29.67 11.02 -1.36 -3.03
11.9 27.7
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

37. Results and Discussion cont.
Trait QTL (LOD, %PVE) Number of Allele sizes
EST-SSRs tested alleles
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

38. 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

39. 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

40. 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

41. 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

42. Thank you for listening!!