1. Genomics of Water Use Efficiency Advisory Committee Meeting Nov 2003 Comparative mapping –FISH software and related computational methods –Application to tomato fine-mapping QTL mapping – experimental design and analysis methodology QTL data management –web application

2. Comparative mapping: computational aspects Software needed for two tasks –Identification of homologous chromosomal segments given two marker maps and information about homology among markers (FISH) –Prediction of gene content within homologous segments (ongoing work)

3. Homology matrix for Arabidopsis

4. We need to allow for non-colinearity in marker order the presence of ‘singleton’ markers

5. Going beyond eyeballing LineUp – Hampson et al (2003) –Designed for genetic maps with error ADHoRe – Van der Poele (2002) –Designed for unambiguous marker order data Both perform automatic detection of blocks For statistics, both employ permutation tests –Computationally intensive –p-values are approximate –What is the null model?

6. Two contributions Local genome alignment –Dynamic programming approach Fast Guarantee of optimality –Can be generalized to multiple alignments Statistics –An explicit null model for marker homology –Analytic p-values (i.e. no permutation testing) Contributors –Sugata Chakravarty (Masters, UNC Operations Research) –Peter Calabrese (collaborator, USC)

7. From homology matrix to graph nodes (  ) –represent dots in the homology matrix

8. From homology matrix to graph nodes (  ) –represent dots in the homology matrix edges (  ) –connect nodes with nearest neighbors –are unidirectional –have an associated distance –must be shorter than some threshold

9. From homology matrix to graph nodes (  ) –represent dots in the homology matrix edges (  ) –connect nodes with nearest neighbors –are unidirectional –have an associated distance –must be shorter than some threshold paths (  ) –traverse shortest available edges –can be efficiently computed –can be considered candidate blocks

10. Block statistics An explicit null model –Within a genome: homologies are due to the duplication of a feature followed by its insertion into a random position –Between genomes: homologies are due to the above process plus the transposition of features between randomly chosen positions. Number of blocks of a given size is approximately Poisson We can calculate –The expected number of blocks of a given size –A conservative matrix-wide p-value

11. kobsstderrupboundlowbound 245.80.0647.640.1 32.280.022.391.78 40.1130.0030.1200.079 50.0060.0010.0060.004 60.00030.00020.00030.0002 How often are blocks of size k observed under the null model compared with expectation (in simulated data)?

12. FISH v.1.0 Released in July 2003: –http://www.bio.unc.edu/faculty/vision/lab/FISH –source code –compiled executables –documentation –sample data Publication Calabrese PP, Chakravarty S, Vision TJ (2003) Fast identification and statistical evaluation of segmental homologies in comparative maps. Bioinformatics 19, i74-i80

13. Bancroft (2001) TIG 17, 89 after Ku et al (2000) PNAS 97, 9121

14. Prediction of gene content Explicitly model gene loss among homologous segments Perform multiple rather than pairwise alignment To provide –Markers for fine-mapping –Candidate genes

15. Phytome (http://www.phytome.org) Funded independently through PGRP A web interface to a relational database for plant comparative genomics –Integrating organismal phylogenies, genetic maps and gene phylogenies –Inclusive of major model plant species Functionality –Explore relationships among genes/proteins and chromosome segments within and between species –Predict gene content in uncharacterized chromosomal regions. Current status –One can search for, retrieve, visualize and manipulate protein sequences, gene families, multiple alignments and phylogenetic trees for nine species –Will be made live during 2004 Ongoing work to integrate “phylocartographic” data and tools –Curation –Analysis –Visualization

16. Homolog identification (BLAST) Multiple sequence alignment (CLUSTALW) Protein sequence prediction (ESTWise) Protein family clustering (TRIBE-MCL) Phylogenetic inference (PHYLIP) Unigene collections GenBank IDs Descriptions GO terms Protein sequences Protein families Phylogenetic trees Phytome Multiple alignments

18. Comparative mapping in aid of marker development: application Complementary to marker development strategy at OK State Proposed work (within coming year) –Combine computational predictions and experimental validation to design PCR-based markers in tomato based on known genes in homologous segments of Arabidopsis –To be used for fine mapping of QTLs in pennellii (and possible hirsutum).

19. Comparative map of IL5-4 TG23TG351TG60CHS3T1584TG69CT130CT145T0633TG238 TG597 At1g45160 At1g45474 At1g48490 At1g48520 At2g37840 At2g38050 Atg308720 At3g08940 At4g23650 At4g23710 3 2 20 18 5

20. Strategy select Arabidopsis genes in putative regions of synteny BLAST Arabidopsis genes against tomato EST database map best match tomato EST in a subset of the IL population design primers to amplify tomato locus from both parents no match maps elsewhere primers fail sequence products from both parents to detect polymorphisms no polymorphism convert to CAPS or dCAPS markers

21. QTL Data Converter Tool A utility that converts QTL data files to and from the various software formats Currently, the utility can do the following: –Convert comma-delimited (CSV) genotype, phenotype and map data files to the following formats: QTL Cartographer cross.inp and map.inp input files Qgene filename.cro and filename.map input files. –Error-check the input data files. –Transpose data file rows and columns, if desired. –Tag special data with prefixes, for use in Qgene. –Summarize data file characteristics.

23. Future plans Optimize XML code Add additional software formats –MapMaker –MapPop –others as needed (JoinMap, MultiQTL, etc.) Release in mid-2004 Advertise availability –Published note –Mailing list announcements

24. QTL mapping methodology Problem –QTL analysis in mapping populations where individuals have been selected to optimize marker map resolution. Work to date –Effect of selective sampling on crossover distributions –Effect of selective sampling on bias, power, and resolution in QTL mapping Change of plans from proposal –QTL mapping software tailored to selected samples is not necessary Manuscript in preparation for Genetical Research

26. Bins and map resolution full population optimized sample X random sample

27. Selective mapping Genotype framework markers (1/20cM) base population Use MapPop to select optimized sample selected sample Genotype additional markers (>1/cM) Use MapPop to locate markers with bin mapping

28. Experimental design parameters Population type (F2, RI, DH, etc.) Base population size Selected sample size –Sample fraction (f) Framework marker density

29. Maize RI population (184 markers, 4140 cM) Bin LengthWhole N=976 Optimized N=90 Random N=90 Maximum1.87.512.7 Expected0.31.72.6

30. Advantage of optimizing expected (versus maximum) bin size

31. Recombination enrichment and pseudo-interference random selected

32. Recombination enrichment Fixed map length=1000 cM Fixed marker spacing = 10 cM RE= # of crossovers in selected sample / # of crossovers in random sample marker spacing map lgth

33. Predicting recombination enrichment Empirical formula: L = map length in cM f = sample fraction popA R 2 RI 500 0.965 BRI 750 0.976 DH 12000.983

34. Pseudo-interference and map functions Translates between the map distance (in cM) and the expected frequency of crossovers between two points Haldane map function: no interference Karlin map function: allows variable interference When N>5, r K ~ r H

35. Pseudointerference is very minor L=1000 cM L=100 cML=500 cM L=2500 cM

36. Significance of findings Since –We can predict RE very accurately and easily from the experimental design –Pseudointerference is minimal for realistic values of RE We can use standard QTL mapping methods for selected samples once we have multiplied map distances by the RE factor. No need for specialized software

37. Do selected samples have better QTL resolution? A simulation study Variables –Population type (RI, BRI, DH) –Map length –Marker spacing (always even) –Sample fraction (optimized for expected bin lgth) –Genetic effects Additive ~ Gamma(1,2) Dominance ~ Beta(1,1) Pairwise epistasis (when >1 QTL) QTL analysis –Marker regression (QTL Cartographer)

38. QTL detection power Reduced in a selected sample in proportion to distance between marker and QTL Experimental design –5 QTL –Map length 1000 cM –Base population 500 –Sample fraction 0.2

39. QTL resolution Resolution increases with recombination enrichment Resolution here measured as width of 95% confidence interval (cM) Experimental design –1 QTL –Map length 100 cM –Base population 500 –Sample fraction 0.1

40. Relationship between power and resolution marker 1 2 3 4

41. QTLs for cell wall composition in the maize IBM population data from Hazen SP, Hawley RM et al. (2003) Plant Physiology fRE 0.5 1.24 0.6 1.19 0.7 1.14 0.8 1.09 0.9 1.05

42. Summary of findings: QTL mapping methodology Selection can result in substantial RE with only minor pseudointerference Corrected map distances can be obtained using a simple formula for RE (which will depend on the experimental design) Currently available QTL mapping methods are appropriate for analysis of selected samples. Selected samples –Have increased QTL mapping resolution (relative to random ones) –Do not bias estimates of QTL position or effect size