SGC Meeting, JIC, 6-7 th April 2006

1. "Implications of partitioned genetic diversity for linkage disequilibrium mapping in elite UK cereal germplasm". Donal O’Sullivan SGC Meeting, JIC, 6-7th April 2006

2. Purpose To explore the prudent use of ‘populations’ of elite cereal varieties as LD mapping panels Why use ‘elite’ varieties? • Most familiar and obvious material • Reasonable levels of diversity present • Relevant to current markets • Obtainable in quantity • Extensive ‘historic series’ of robust field data for most relevant phenotypes

3. Partitioning in 500+ Gediflux Barley varieties

4. Association mapping in wheat:proof of principle • Gediflux data set: 499 genotyped varieties. • 73 SSAPs, 42 SSRs, 72 NBS • 1B1R, pinb haplotypes • Historic trial data: 193 varieties with 18 phenotypes (incomplete ) • yield +/- treated, hardness (113 lines) • Lodging, disease, etc. • Use pinb as a candidate with known phenotypic effect • Use SSRs for structured association • Analyse using “Structure” and “Strat” • Use SSAPs for genomic control • Analyse trait by trait by logistic regression

5. Mining historic endosperm texture data Historic NL trial data <2001

6. Structured association Structure: burn-in 1 million iterations 1 million No. populations (K) 8 No. replicate runs 2

7. Gediflux 500+ winter wheat K = 8 cluster 1 vs cluster 5 Proportion of each individual in cluster 5. Proportion of each individual in cluster 1.

8. Cluster 5 Cluster 1 Pedigree of lines with highest ancestry in clusters 1 and 5.

9. Descendants of a cluster 5 founder

10. Parentage of lines for clusters 1 and 5. parents cluster 1 cluster 5 progeny cluster 1 13 1 cluster 5 3 19 odds ratio 82 p-value 0.00001 Cluster membership is genetic!

11. Association test using STRAT / structure Pinb and hardness test chi sq p-value Assuming no structure in population 44.44 0 Corrected (run a) 21.89 0 Corrected (run b) 21.42 0

12. STRAT and structure - QC Hardness and 55 SSAP markers, p-values <0.05 Test No. <0.05 Assuming no pop. structure 14 Adjusted, run a 6 Adjusted, run b 6 Expected 3 May be under correcting.

13. 5 5 5 5 5 5 5 3 3 Pedigree relationships between SBCMV resistant varieties Red = Tested R, Blue = Tested S, Grey = Untested

14. Genomic Control • Method: use multilocus genotype data to detect and correct for stratification • Premise: admixture operates over the whole genome but LD operates locally at short scales • 18 traits • 58 SSAP • 1044 logistic regression analyses

15. Genomic control: p-values, pinb original GC Dry matter content treated 1.000 1.000 Hagberg number treated 0.005 0.317 Percent leaning treated 0.733 0.842 Percent lodging treated 0.008 0.053 Protein content treated 0.045 0.298 Specific weight treated 0.132 0.526 Straw length treated 0.053 0.403 Yield treated 0.723 0.865 Brown rust not treated 0.0080.012 Hagberg number not treated 0.583 0.828 Percent leaning not treated 0.463 0.691 Percent lodging not treated 0.264 0.276 Mildew not treated 0.796 0.888 Protein content not treated 0.001 0.184 Septoria tritici not treated 0.091 0.426 Specific weight not treated 0.080 0.288 Straw length not treated 0.583 0.808 Hardness 0.0000.000

16. Genomic control: QC Test markers across all traits No. of tests 1044 P-value <0.05 OBS original 313 OBS GC 34 EXP 52 May be overcorrecting

17. Conclusions • Population structure may be evident e.g. spring-winter/row number divide or less so • Carry out LD mapping within major sub-groups • UK winter wheat shows cryptic population structure which groups varieties consistent with known pedigree • Genomic control and/or structured association both effective in detecting known associations and reducing false +ves to realistic levels • Roll on new phenotype and genotype data!

18. NIAB Fiona Leigh John Law Ian Mackay Wayne Powell Gediflux Partners Barley Marion Roeder (IPK) Wheat Rob Koebner, Simon Orford (JIC) Martin Ganal (Trait Genetics) Acknowledgements