3 rd UK Cereal Genetics and Genomics Workshop. Next generation Genomics challenges

1. 3rd UK Cereal Genetics and Genomics Workshop. Next generation Genomics challenges The challenge of connecting traits to genes through genomics Daryl J. Somers and Mark Jordan Agriculture and Agri-Food Canada – Cereal Research Centre Winnipeg, MB, Canada John Innes Centre, Norwich, 6–7 April 2006

2. Cereal improvement through breeding, and molecular genetics has always benefited by knowing the precise location and function of genes. “next generation challenges” 25 years ago? understand structure of the cereal genomes (JIC groups!) Sequences of genes with key biological relevance. Today – 25 years later: Good understanding of the wheat/barley genomes. 750K – 1M gene sequences, most of unknown biological relevance.

3. “Next generation genomics challenges” ….still, knowing the precise location, sequence, function of genes toward applied cereal improvement. • Applied genomics: 3 simple elements… • We need to identify TARGETS for producers, processors, consumers. • Design/invent/improve GENOMIC TECHNOLOGY to characterize the targets. • Perform the research with APPLICATION and VALIDATION of the result/discovery.

4. Target – improved cereal quality for bread making and nutritional value. largely a consumer target, but also processor benefits. Genomic technology – Fusion of genetic mapping, association genetics and microarray-based gene expression analysis. Application/validation – Genetic experiments and seed quality analysis to validate the discoveries. All elements require a multidisciplinary team: genomics, breeding, chemistry

5. Independent approaches to identifying targets for research. 1. Expression level polymorphisms (eQTL) (M. Jordan, T. Banks) RL4452 x AC Domain (HRS, 40 DH lines) segregates for dough, milling, bread quality popln is mapped and QTL analysis (49 traits) 2. Association genetics (T. Banks, AAFC, U of SK) Analysis of 192 HRS 370 loci 96 durum 245 loci Examine popln structure, LD analysis, association analysis.

6. Phenotypic data is the expression level of a single gene which is a “quantitative assessment of gene activity” (Doerge 2002). The change in activity of a single gene is an expression level polymorphism (ELP) (St. Clair, Michelmore, Doerge).

7. BB0AAABABBBAABBBBBAAABBBAAAAAAABBBABBAAABBBBAAAAAA BBABBBABABBABABBAB QTL analysis Gene 1,2,3,4 Gene 5,6,7,8 Identification of Regulatory Regions Adapted from: http://www.stat.purdue.edu/research/coalesce/bioinformatics/Center_for_Bioinformatics/combining_qtl_analysis_with_microarray_data.html

8. 2004/2005 Gene Expression Experiment RL4452 x AC Domain 3 Locations, 3 reps/location Total of 43 entries (including parents) Grand total of 387 rows RNA samples at 5 dpa for all lines, 3 and 10 dpa for some

9. 2004/2005 collection of developing seeds, 5 dpa.

10. Affymetrix wheat gene chip data collection 5 genotypes, 1 location, 3 reps Rep effect was non-significant ELP Mapping Data: 39 genotypes plus parents 1 location - 2 replicate RNA samples

11. Procedure for analysis of Affymetrix data RMA pre-processing of all chips, normalize to median 1 site 2 reps Determine the genes significantly different among genotypes by ANOVA (Benjamini Hochberg False Discovery Rate, 0.001 error level) using CGEM (GeneSpring) as only 2 reps. Significant genes (1,327) ranked by kurtosis to identify bi-modal (qualitative) data, quantitative data and data skewed by off samples. 558 negative kurtosis (lowest -1.9) 577 greater than 1 (max 32).

12. Domain RL4452 RL4452 Domain Domain Domain RL4452 RL4452 Domain RL4452 Domain RL4452 Highly Negative Kurtosis Qualitative ELP Quantitative ELP (eQTL) Transcriptional Frequency Classes (Gibson and Weir 2005)

13. ELP Mapping Summary Top 800 genes ranked by kurtosis were considered 101 were binarized based on qualitative distribution. 77 were mapped (40 individuals using JoinMap V3.0) 24 unassigned 699 were subjected to CIM (QTL Cartographer). 402 were assigned to an interval (1 major LOD peak). 297 had more than 1 peak and were not assigned.

14. 3B Ta.27101.7.S1_at LOD 2.5 3B 21 chromosome QTL scan QTL Cartograher CIM analysis

15. The ELP genomics challenge… We have the very low hanging fruit. How do you get at the rest? If back off stringency introduce more errors (more reps). Lack of annotations- gene expression to phenotype Cis eQTLs have larger effects on transcription.As stringency increases proportion of cis eQTLs increases. We have ~60% cis when the reported average is closer to 33% (Gibson and Weir 2005). Still, even at this level there is a high trans effect. Need to develop an automated statistically based method coupled with assignment to “transcriptional frequency classes”. Identification of cis vs trans requires more physical transcript mapping (SNPs?) – and an easier way to move between Affy gene chip annotation and the bin-mapping results.

16. Marker order is based on consensus map (Somers et al. 2004) B genome -T. aestivum B genome - T. durum Linkage Disequilibrium and Association Genetics Wheat microsatellite allele database: 192 HRS x 370 loci 96 durum x 245 loci Whole genome LD calculations Syntenic pairs of loci across genome LD within 8 subpopulations

17. Population Structure NTSYS – UPGMA Structure (Pritchard et al.) 192 bread wheat – 42 SSRs

18. Common Haplotypes barc98 wmc457 exp obs 177 178 6.6 0 174 178 6.6 11 177 176 7.1 14 174 176 7.1 0 Disequlibrium! Total Popln Alleles and Haplotypes in LD barc98 wmc457 177 15 180 1 174 15 178 14 171 2 176 15 fail 2 32 32 1 2 3 4 5 6 7 8

19. LD analysis on chromosome 4D Total Popln 1 2 3 4 5 6 Northern US introductions 7 8

20. wmc418-barc164 Common haplotypes: 289-151 289-null Transfer haplotype specific segments. Examine ELP and seed quality. TARGETS and GENOMICS TECHNOLOGY are described….. APPLICATION and VALIDATION: An example: Interval on 3B could be examined through genetics.

21. Acknowledgements: Western Grains Research Foundation AAF-Matching Investment Initiative AAFC-Canadian Crop Genomics Initiative Brenda Terwisscha – Affy hybs Kerry Ward- Affy data analysis Travis Banks- assorted bioinformatics tasks Zlatko Popovic – SSR allele database Monika Eng – SSR allele database Daryl’s and Mark’s labs plus many summer students- days spent in the field, rain or shine. Breeders: J. Clarke, C. Pozniak, S. Fox, R. Depauw, G. Humphreys