Fine recombination mapping in S. cerevisiae using tiling microarrays

1. Fine recombination mapping in S. cerevisiae using tiling microarrays Richard Bourgon 20 September 2007 bourgon@ebi.ac.uk

2. Meiotic recombination Meiosis… • Two divisions, yielding 4 haploid daughter cells. • Double-stranded breaks (DSBs) initiate recombination. • Both crossover and non-crossover resolutions are possible.

3. Meiotic recombination Meiosis… • Two divisions, yielding 4 haploid daughter cells. • Double-stranded breaks (DSBs) initiate recombination. • Both crossover and non-crossover resolutions are possible. Based on B de Massy, TRENDS in Genetics 19(9), 2003

4. Meiotic recombination Meiosis… • Two divisions, yielding 4 haploid daughter cells. • Double-stranded breaks (DSBs) initiate recombination. • Both crossover and non-crossover resolutions are possible. Based on B de Massy, TRENDS in Genetics 19(9), 2003

5. Meiotic recombination Meiosis… • Two divisions, yielding 4 haploid daughter cells. • Double-stranded breaks (DSBs) initiate recombination. • Both crossover and non-crossover resolutions are possible. Based on B de Massy, TRENDS in Genetics 19(9), 2003

6. Research questions • In S. cerevisiae, where do hotspots occur and what are the local recombination rates? • Do hotspots (binary) or recombination rates (quantitative) correlate with features of DNA sequence or chromatin structure? • How large are conversion regions? Can we identify the various resolution patterns? • What is the relative frequency of the various DSB-repair pathways? • Does the observed pattern among recombination events concur with current models for “interference”? Do mutations impact interference?

7. Saccharomyces cerevisiae microarray data • Two strains • S96, isogenic to the common laboratory strain S288c. • YJM789, isogenic to the clinical isolate YJM145. • In alignable regions ≈ 56,000 SNPs, 30,000 insertions and deletions. • Tiling microarrays • ≈ 6.5 M 5µ features, tiling non-repetitive S96 every 4 bases. • ≈ 4% of probes are specific to YJM789 sequence. • Data • 25 parental genomic DNA hybridizations. • 208 wildtype offspring hybridizations. • 20 msh4 mutant offspring hybridizations.

8. “Single feature polymorphisms” • Hybridization efficiency depends on the number and position of mismatches. • Differential hybridization provides a means of detecting polymorphisms, even when only the reference genome sequence is known: SFPs. • Winzeler et al., Science 281(5380), 1998. • Brem et al., Science 296(5568), 2002. • Steinmetz et al., Nature 416(6878), 2002. • Borevitz et al., Genome Research 13(3), 2003. • Given parental behavior, genotype segregants via supervised classification.

9. Single-probe methods • Polymorphism detection • Winzeler et al. (and others): ANOVA testing 1 = 1. • Borevitz et al.: moderated t-test using the SAM adjustment. • Brem et al.: moderated t-test. Then cluster all data (parental and segregant) and discard SFPs for which clusters don’t separate the parental data. • Segregant genotyping • ANOVA and t-test methods use the estimated posterior probability of class membership, with a uniform prior on the classes: • Brem et al. augment this: are estimated from clustered data.

10. S96: CCTCCTGACCGGGATTGAAGTGATAAACATGTCTAGCGTTA YJM789: CCTCCTGACCGGGATTGAACTGATAAACATGTCTAGCGTTA Probe sets: SNP interrogation 6: CTTCACTATTTGTACAGATCGCAAT Probe sets: groups of probes which each exactly map to a unique location, and which interrogate a common polymorphism. 5: CTAACTTCACTATTTGTACAGATCG 4: GGCCCTAACTTCACTATTTGTACAG 2: GACTGGCCCTAACTTCACTATTTGT 1: GGAGGACTGGCCCTAACTTCACTAT 3: GACTGGCCCTAACTTGACTATTTGT

11. Marginal probe behavior

12. A multi-probe method: SNPScanner Gresham et al., Science 311(5769), 2006: • Model the decrease in a given probe’s intensity in the presence of a single SNP, as a function of • Position within the probe, • Probe response to reference sequence, • Probe GC content, and • Nucleotides surround the SNP position. • Fit model parameters using two sequenced strains with known SNPs. • To genotype a segregant or new strain at a given base, assume probes in a probe set are independent and compute a likelihood ratio: vs. with assumed to be common for both genotypes.

13. An alternative multi-probe method • Residual correlation remains after centering log intensities for each probe within inferred genotype class: a multivariate approach is justified.

14. An alternative multi-probe method • Residual correlation remains after centering log intensities for each probe within inferred genotype class: a multivariate approach is justified. • Parental arrays are informative, but do not always provide an ideal model. Supervised classification of offspring (i) wastes information, and (ii) may be misleading.

15. An alternative multi-probe method • Residual correlation remains after centering log intensities for each probe within inferred genotype class: a multivariate approach is justified. • Parental arrays are informative, but do not always provide an ideal model. Supervised classification of offspring (i) wastes information, and (ii) may be misleading. • Clear division into two distributions is necessary but not sufficient. Quantitative aspects of the inferred clusters are useful.

16. Semi-supervised, model-based clustering (SSC) Semi-supervised clustering via EM algorithm: • Assume a two-component mixture, with 1 = 2 = 1/2. • (Xi,Yi) with latent class variable Y. Y is known for parental arrays. • Assume X|Y multivariate normal. • Begin with E-step: initialize the unknown Y with any simple clustering scheme: k-means, hierarchical agglomeration, etc. • Iteratively estimate parameters, E(Yi|Xi), parameters, etc. • Classify segregant i based on final estimated E(Yi|Xi). For diagnostic purposes only: • Multivariate Gaussian fit to dimension-reduced parental data. • Unsupervised clustering of offspring data, by EM algorithm, with k{2,3}.

17. Examples of SSC probe set results

18. Chromosome-level SSC results

19. Filtering • Array level • Excess “genotype switching”. • Large RMS residual (Mahalanobis). • Probe set level • High estimated misclassification rate. • Aberrant cluster behavior. • Very unusual genotype ratio. • Call level • Intermediate posterior probability of class membership.

20. Aberrant probe sets: non-response

21. Aberrant probe sets: possible cross-hybridization?

22. Filtering

23. Chromosome-level SSC results (unfiltered)

24. Chromosome-level SSC results (filtered)

25. Chromosome-level SSC results (filtered)

26. Genotyping accuracy • 82 usable forward sequencing runs. (Reverse similar.) • 16 spores sequenced. • Sequenced regions include 322 array-interrogated SNPs. • Sequenced samples had a range of array qualities. • Sequenced regions focused on single-marker conversions with a range of probe set quality scores.

27. SNPScanner: approximately correct distributions

28. SNPScanner: wrong distributions, right calls

29. SNPScanner: inaccurate covariance estimation

30. Genotype call comparison: SNPScanner vs. SSC • Filter for both methods: • Remove probe bad arrays and aberrant probe sets. • Remove probe sets with poorly separated clusters. • Drop calls falling between two observed clusters. • Only consider polymorphisms with at least one S288c-specific probe. • Compute concordance rate between the two methods.

31. SSC calls

32. SNPScanner calls

33. Disagreement

34. PC plots for probe sets with strong disagreement

35. PC plots for probe sets with strong disagreement

36. PC plots for probe sets with strong disagreement

37. Usable probes per polymorphisms

38. Tetrad-level results

39. High resolution in crossover regions

40. Crossovers accompanied by events on other strands

41. Double crossovers

42. Msh4 mutant

43. Recombination rates

44. Recombination rates

45. Summary and future work • Summary • Semi-supervised clustering out-performs supervised classification: • Parental data are often not a faithful indicator of offspring behavior. • Offspring clusters contain a lot of information. • Filtering is important for small event detection: • Aberrant or error-prone probe sets create spurious small “events” • Correct distribution estimates are required to detect the latter. • Future work • Exploration and recovery of aberrant probe sets. • Unanticipated polymorphism detection. • Application to a single sequenced genome. • Rate/count adjustments given varying marker spacing. • Hotspots, conversion/crossover ratio, sizes, spacing and interference. • New mms4 mutant…

46. Acknowledgements • EMBL Heidelberg • Eugenio Mancera Ramos • Lars Steinmetz • Julien Gagneur • Zhenyu Xu • EBI • Wolfgang Huber • EBI, and Istituto Europeo di Oncologia, Milan • Alessandro Brozzi • EBI, and Higgins Lab, University College, Dublin • Paul McGettigan