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Genomics of Water Use Efficiency Advisory Committee Meeting Nov 2003

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.

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Genomics of Water Use Efficiency Advisory Committee Meeting Nov 2003

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  1. Genomics of Water Use Efficiency Advisory Committee MeetingNov 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. How often are blocks of size k observed under the null model compared with expectation (in simulated data)? • kobsstderrupboundlowbound • 45.8 0.06 47.6 40.1 • 2.28 0.02 2.39 1.78 • 0.113 0.003 0.120 0.079 • 0.006 0.001 0.006 0.004 • 0.0003 0.0002 0.0003 0.0002

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

  17. 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).

  18. Comparative map of IL5-4 TG23 TG351 TG60 CHS3 T1584 TG69 CT130 CT145 T0633 TG238 TG597 3 At1g45160 At1g45474 2 At1g48490 At1g48520 20 At2g38050 At2g37840 18 Atg308720 At3g08940 5 At4g23650 At4g23710

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

  20. 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.

  21. 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

  22. 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

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

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

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

  26. Maize RI population(184 markers, 4140 cM)

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

  28. Recombination enrichment and pseudo-interference random selected

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

  30. Predicting recombination enrichment Empirical formula: L = map length in cM f = sample fraction pop A R2 RI 500 0.965 BRI 750 0.976 DH 1200 0.983

  31. 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, rK ~ rH

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

  33. 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

  34. 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)

  35. 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

  36. 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

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

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

  39. 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

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