Development of marker-assisted selection (MAS) technology in crop improvement: an experience                 with forage c...
What is MAS?                  Introduction use of DNA markers to select plants/animals with desirable traitsWhy do we nee...
Introduction ContdMAS requires   a ‘library’ of DNA markers and a precise genetic linkage map    indicating their positio...
A case studyPhenotypic assessment and QTL analysis of  herbage and seed production traits in  perennial ryegrass (Lolium p...
Supervisors   Associate Prof. Cory Matthew,     Institute of Natural Resources, Massey University, Private      Bag 1122...
General background ofPerennial ryegrass          Perennial ryegrass (Lolium perenne L.) is           diploid (2n = 14), a...
ObjectivesTo identify in perennial ryegrass;    morphogenetic and structural traits that are     associated with increase...
MethodsPlant material and experimental design   Full-sib F1 mapping population (n = 200), ‘I×S’ constructed from a pair  ...
Methods cont.
Methods cont.
Methods cont.Phenotype data collectedHerbage production   herbage dry weight (DW)   leaf appearance interval (ALf)   li...
Methods cont.Seed production   seed yield per plant (SdYP)   seed yield per head (SdYH)   floret per spikelet (FS)   f...
Methods cont.Markers analysis and linkage map construction   863 EST-SSR primer pairs selected based on array length   s...
Methods cont.QTL analysis   Simple interval and multiple QTL model mapping (MapQTL® 4.0 software)   using phenotypic tra...
Methods cont.QTL marker validation    Half-sib F1 population of perennial ryegrass    n=100 families with two plants per...
Results and Discussion   Season         Temperature (oC)    Solar   radiation Daylength                Range          Mean...
Results and Discussion cont Trait                               Autumn (2003)           Mean          Range       Skewness...
Results and Discussion cont.Trait                               Spring (2004)          Mean          Range       Skewness ...
Results and Discussion cont.               DW      ALf     ALg     LED     LER      LL     TN      TW      Autumn   0.15AL...
Results and Discussion cont.                   Autumn 2003                     Spring 2004           PC1        PC2       ...
Results and Discussion cont.                               HbTrait                 Autumn        SpringDW                 ...
Results and Discussion cont.Major traits for herbage productiona. Phenotype analysis:   TN, TW, LER and LL   LED and ALf...
Results and Discussion cont.Trait      Mean          Range         S       I     LSD0.05   Hb     Skewness   KurtosisSdYP ...
Results and Discussion cont.       SdYP    SdYH     FS     FH      SH       RT     TMH      SL     DH      TSW     PGH    ...
Results and Discussion cont.Traits      PC1 (25%)   PC2 (19%)   PC3 (16%)SDYP         -0.501      -0.187       0.047SDYH  ...
Results and Discussion cont.Major seed yield traits   reproductive tillers (RT), especially    those with matured heads (...
Results and Discussion cont.          lg1                    lg2                lg3                    lg4                ...
lg1                                                                                            lg2                        ...
lg4                                                                                lg5                          lg6       ...
Results and Discussion cont.b. QTL analysis   Multiple QTL (between 1 -7 significant QTL) identified across Lg for    all...
Results and Discussion cont.   4 QTL identified for DW                                                                   ...
LG1                                                                     LG2                                               ...
LG5                                     LG6                                                                               ...
Results and Discussion cont.                                                           LG2   QTL for SdYP identified on L...
Results and Discussion cont.   No significant QTL for RT (r=0.62) and TMH (r=0.66) (critical traits    in seed production...
Results and Discussion cont.favourable QTL alleles can be derived from parent that showed poorphenotypic performance for ...
Results and Discussion cont.      Trait      QTL (LOD, %PVE)                              Number     of    Allele sizes   ...
Conclusions   Yield is determined by complex interaction of multiple traits   QTL and SSR markers for herbage and seed y...
Conf. Proceedings and Journal Publications   C. Matthew, A.M. Sartie, and H.S. Easton (2008). Tiller weight versus    til...
Conf. Proceedings and Journal Publicationscont.     A. M. Sartie, H. S. Easton and C. Matthew (In prep). Range of plant  ...
Acknowledgements   New Zealand Foundation for Research, Science and    Technology, by a Bright Futures Fellowship   Agri...
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Development of marker-assisted selection (MAS) technology in crop improvement: an experience with forage crop

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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

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

  1. 1. Development of marker-assisted selection (MAS) technology in crop improvement: an experience with forage crop
  2. 2. What is MAS? Introduction use of DNA markers to select plants/animals with desirable traitsWhy 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. 3. Introduction ContdMAS 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. 4. A case studyPhenotypic assessment and QTL analysis of herbage and seed production traits in perennial ryegrass (Lolium perenne L.)
  5. 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. 6. General background ofPerennial 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. 7. ObjectivesTo 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. 8. MethodsPlant 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. 9. Methods cont.
  10. 10. Methods cont.
  11. 11. Methods cont.Phenotype data collectedHerbage 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. 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. 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. 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. 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. 16. Results and Discussion Season Temperature (oC) Solar radiation Daylength Range Mean (MJ/m2/day) (hours)Autumn (2003) 17-28 21 2.6 10.2Spring (2004) 16-28 20 8.5 13.1
  17. 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. 18. Results and Discussion cont.Trait Spring (2004) Mean Range Skewness Kurtosis S I LSD0.05DW 1.9 ± 0.05 0.9 - 2.8 -0.16 -0.129 1.4 1.4 0.7ALf 11.2 ± 0.71 8.5 - 14.5 0.46 0.017 8.8 9.7 2.0ALg 11.3 ± 1.05 8.2 - 15.2 0.332 -0.235 7.8 10.7 2.9LED 13.3 ± 0.96 9.3 - 18.3 0.369 0.521 10.7 11.3 2.7LER 2.0 ± 0.23 1.2 - 3.1 0.345 0.409 2.3 2.9 0.7LL 26.5 ± 2.47 18.0 – 37.0 0.02 0.484 24.0 32.6 7.0TN 31.3 ± 1.18 14.3 – 63.7 0.706 0.399 14.0 32.0 5.0TW 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. 19. Results and Discussion cont. DW ALf ALg LED LER LL TN TW Autumn 0.15ALf Spring 0.04 Autumn 0.06 0.86ALg Spring 0.07 0.68 Autumn 0.18 0.77 0.67LED Spring 0.08 0.82 0.57 Autumn 0.08 -0.44 -0.33 -0.41LER Spring -0.01 -0.49 -0.27 -0.59 Autumn 0.22 0.28 0.30 0.22 0.62LL Spring 0.09 0.11 0.15 0.16 0.68 Autumn 0.44 0.23 0.21 0.26 -0.24 -0.18TN 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 -0.73TW 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 -0.65PI Spring 0.92 0.09 0.14 0.14 -0.08 0.05 0.18 0.05
  20. 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. 21. Results and Discussion cont. HbTrait Autumn SpringDW 0.62 0.52ALf 0.74 0.63ALg 0.67 0.45LED 0.51 0.57LER 0.44 0.46LL 0.61 0.43TN 0.74 0.63TW 0.75 0.62PI 0.63 0.56
  22. 22. Results and Discussion cont.Major traits for herbage productiona. 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. 23. Results and Discussion cont.Trait Mean Range S I LSD0.05 Hb Skewness KurtosisSdYP 35.3 (±5.1) 11.9 - 64.5 27.7 11.9 14.5 0.75 0.16 -0.32SdYH 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 -0.04FH 226 (±22) 162 – 298 224 162 61 0.33 0.20 -0.10SH 27 (±1) 21 – 31 29 23 4 0.27 -0.38 0.78RT 392 (±51) 184 – 592 272 184 145 0.59 0.00 -0.20TMH 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 0.15DH 92 (±1) 79 – 102 84 102 3 0.94 -0.34 0.23TSW 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 0.64SOH 6.6 (±1.1) 2.0 - 13.0 13 7 3.1 0.58 0.38 0.49FSU 0.22 (±0.0) 0.09 – 0.42 0.26 0.31 0.0 0.94 0.43 0.38)
  24. 24. Results and Discussion cont. SdYP SdYH FS FH SH RT TMH SL DH TSW PGH SOHSdYH 0.74FS 0.23 0.14FH 0.24 0.22 0.87SH 0.14 0.23 0.27 0.70RT 0.62 -0.05 0.23 0.15 -0.04TMH 0.66 0.04 0.20 0.12 -0.04 0.96SL 0.15 0.23 0.11 0.15 0.15 -0.02 -0.02DH -0.07 -0.01 -0.47 -0.43 -0.16 -0.12 -0.09 -0.12TSW 0.19 0.23 -0.08 -0.11 -0.10 0.00 0.01 0.07 0.08PGH 0.15 0.11 0.07 0.05 -0.01 0.06 0.06 -0.04 -0.04 -0.19SOH -0.44 -0.25 0.09 0.05 -0.03 -0.35 -0.40 -0.02 -0.41 0.03 -0.21FSU 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. 25. Results and Discussion cont.Traits PC1 (25%) PC2 (19%) PC3 (16%)SDYP -0.501 -0.187 0.047SDYH -0.331 -0.179 0.483FS -0.279 0.41 0.017FH -0.295 0.477 0.145SH -0.183 0.313 0.266RT -0.379 -0.046 -0.484TMH -0.400 -0.092 -0.441SL -0.116 0.045 0.241DH 0.115 -0.41 0.013TSW -0.024 -0.101 0.034PGH -0.109 -0.047 0.042SOH 0.266 0.337 0.078FSU -0.165 -0.365 0.419
  26. 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. 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. 28. lg1 lg2 lg3 pps0490b pps0154z pps0251b qALg-03-1 qDW-03-1.1 qDW-03-1.20 pps0381c pps0265a pps0766c qLL-03-3.1 pps0252b qALg-04-1 pps0698a qTN-03-25 pps1071b pps0577b pps0711c10 pps0410a pps0198a pps0066b qLL-03-1 qLER-03-1 pps0223b pps0133b15 pps0030y pps0113y pps0502a qALf-04-1 qTN-03-120 pps0963b pps0122a pps0558c pps0319y qTW-03-3.1 pps0755b pps0488c qALf-04-325 pps0255a qLED-04-3 pps0586b pps0663b pps0710a30 pps0642b pps0094y pps0037a qLL-03-2 qLED-04-2.135 pps0153a pps0469a pps0270a pps0419y qALg-04-340 pps0136b pps0732b pps0328z pps0295b45 pps0038a pps0080x pps0068y qDW-04-2.250 pps0497a pps0051a qLED-04-2.2 pps0810a pps0339y qPI-04-255 pps1091y pps0373x qLER-03-3.160 pps0400b pps0698b65 pps0395y pps0061a70 pps0660b pps0213b pps0551b pps0163z75 Key: pps0188b pps0560b qLER-03-3.2 qLL-03-3.380 pps0234b pps0687b pps0347a pps0164a85 DW pps0172z nfa109a90 ALf nfa023b pps0759a qTW-03-3.295 pps0420a pps0724z ALg pps0123a qPI-04-3100 LED105 LER pps0322b110 pps0483x LL TN TW PI
  29. 29. lg4 lg5 lg6 lg7 pps0098b pps0132a pps0766b pps0457a ppt007a pps0197b pps0049a0 pps0761a pps0509b nfa024b qLED-04-6 pps0013d qALf-03-55 pps0463z pps0494b qALg-03-6 qALf-03-6 pps0111b pps0466b qALg-04-610 pps0432a nfa015b pps0065b qLER-03-7.115 pps0504b pps0060a pps1004a20 pps0388b pps0210a pps0376a25 pps1146a pps0192b pps0462y pps0385y pps0052a pps0593b qLL-03-730 qLER-03-7.2 pps1116b pps0374a pps0521c pps0312c qLED-03-735 pps0869a pps0189d ppt003b qTN-03-6 qLER-04-6 pps0326a qPI-03-6 qDW-03-6 pps0052e pps0031a pps0736a40 pps0048d pps0273y pps0892a pps0447b45 pps0777a qTW-03-4.1 pps0040y pps0032a pps0617b50 pps0202a pps0404b pps0310b pps0624a qALg-04-4 pps0433b pps0099a qLL-03-4.1 qALf-04-4.155 pps0073a pps0022x pps0753b pps0074y pps0450a pps0342x60 pps0356b pps0359b pps0523a65 pps0130y pps0036b qLED-04-4 pps0018a pps0377a qLL-03-4.270 qALg-03-4 qALf-03-4 pps0439c pps0718b qLED-03-4.175 pps0345a pps0397c80 pps0495b pps0601a Key: pps0817y pps0146b85 pps0423y pps0502b90 pps0261x pps0149a DW95 pps0317a ALf pps0002x pps0150b100 nfa071a ALg105 pps0983a pps1099b LED110 pps0106b LER pps0284z pps0714b LL pps1135b TN TW PI
  30. 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. 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. 32. LG1 LG2 LG3 LG4 pps0490b pps0154z pps0251b0 pps0381c pps0265a pps0766c pps0761a qSOH-03-2.1 pps0698a pps0252b qDH-04-25 qSOH-03-1 qPC2-03-2-2 pps1071b pps0577b qPC2-03-2-1 qDH-03-2 pps0711c10 pps0410a pps0198a qFSU-03-1 qPC2-03-1 pps0066b pps0223b pps0133b15 pps0030y qSL-03-1 pps0963b pps0113y pps0502a20 pps0122a pps0558c pps0319y25 pps0755b pps0488c pps0255a qFH-03-1 qTSW-03-3 qSH-03-1 pps0586b pps0663b pps0710a30 qSdYP-03-2 pps0642b qPGH-03-2 pps0037a qFS-03-2 qTSW-03-2 qPC1-03-2 pps0094y pps0312c qFH-03-235 pps0153a pps0469a pps0326a pps0270a pps0732b pps0419y qSH-03-240 pps0136b pps0048d pps0328z pps0295b45 pps0038a pps0040y pps0080x pps0068y pps0202a qSL-03-350 pps0497a pps0051a qPGH-03-4 pps0433b qPC2-03-4 qDH-04-4 pps0810a pps0339y qFS-03-455 pps0753b pps1091y qSOH-03-2.2 pps0373x60 qDH-03-4 pps0400b pps0698b pps0356b65 pps0395y pps0061a pps0130y pps0660b pps0213b pps0018a70 pps0439c pps0551b pps0163z75 pps0188b pps0560b pps0345a80 pps0234b pps0687b pps0495b pps0347a pps0164a pps0146b85 pps0423y pps0172z nfa109a qSH-03-490 nfa023b pps0759a pps0261x95 pps0420a pps0724z pps0317a pps0123a pps0150b100 nfa071a105 pps0983a pps0322b pps1099b110 pps0483x pps0106b pps0284z pps0714b pps1135b
  33. 33. LG5 LG6 LG7 pps0098b pps0132a pps0766b pps0457a ppt007a qSOH-03-6 qPC1-03-6 pps0049a qPGH-03-7 pps0197b qDH-04-70 pps0509b nfa024b pps0013d qFSU-03-6 qSdYH-03-6 qSdYP-03-65 pps0463z pps0494b qTSW-03-6 qSdYH-03-7-110 pps0111b pps0432a pps0466b nfa015b pps0065b15 pps0504b pps0060a pps1004a qPC3-03-620 pps0388b pps0210a pps0376a qSL-03-525 pps1146a pps0192b pps0462y pps0385y pps0593b qSH-03-6 pps0052a30 pps1116b pps0521c pps0374a qDH-03-7 ppt003b qFH-03-6 qSdYH-03-7-235 pps0869a pps0189d pps0052e pps0031a pps0736a40 qFSU-03-5.1 pps0273y pps0892a pps0447b45 pps0032a pps0777a qSdYH-03-5 pps0617b50 pps0404b pps0310b pps0624a pps0073a pps0022x pps0099a55 pps0342x pps0074y pps0450a60 pps0359b pps0523a65 pps0036b pps0377a70 pps0718b75 pps0397c80 pps0601a pps0817y85 pps0502b90 pps0149a95 pps0002x100105110
  34. 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. 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. 36. Results and Discussion cont.favourable QTL alleles can be derived from parent that showed poorphenotypic 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. 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 21Allele frequency Marker Trait Percent Change Performance Mean Value Error Probability1 0 missing Marker Absent Marker Present66 80 17 pps0698 LL 5.9 % 27.91 29.56 0.003673 99 18 pps0495 ALf 4.6 % 10.07 9.63 0.0095
  38. 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. 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. 40. Conf. Proceedings and Journal Publicationscont.  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. 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. 42. Thank you for listening!!

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