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Anitha Kannan and John Winn

Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations. Anitha Kannan and John Winn. Jim Huang *.

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Anitha Kannan and John Winn

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  1. Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations Anitha Kannan and John Winn Jim Huang* Probabilistic and Statistical Inference Group, Edward S. Rogers Department of Electrical and Computer Engineering University of Toronto Toronto, ON, Canada Microsoft Research Cambridge Machine Learning and Perception Group Cambridge, UK ISMB/ECCB 2007 ISMB/ECCB 2007 24/07/2007

  2. Outline • Main contributions: • Joint Bayesian modelling of genetic variation data and quantitative trait measurements • Rich probabilistic model for genotype data • State-of-the-art results on predicting missing genotypes ISMB/ECCB 2007 ISMB/ECCB 2007 24/07/2007

  3. Outline Genotype: Unordered pair of SNPs along both chromosomes Presence of recombination hotspots partitions haplotypes into blocks [Daly, 2001] Haplotype: Ordered set of SNPs along a chromosome ISMB/ECCB 2007

  4. Part I: Learning haplotype block structure • Our model for genotype data should: • Account for phase & parent-child information • Account for uncertainty in ancestral haplotypes • Account for uncertainty in block structure • Account for population-specific haplotype block statistics • Allow for prior knowledge of haplotype block structure ISMB/ECCB 2007

  5. Previous models for genotype data • Previous methods learn a low-dimensional representation of the genotype data: • HAPLOBLOCK (Greenspan, G. and Geiger, D. RECOMB 2003) • Hard partitioning of data into set of haplotype blocks using low-dimensional “ancestral” haplotypes • fastPHASE (Scheet P. and Stephens, M. Am J Hum Genet 2006) • Learn ancestral haplotypes from high-dimensional genotype data while accounting for uncertainty in haplotype blocks • Jojic, N., Jojic, V. and Heckerman, D. UAI 2004. ISMB/ECCB 2007 ISMB/ECCB 2007 24/07/2007

  6. Probabilistic generative model for genotype data Unsupervised learning via maximum likelihood Low-dimensional latent representation High-dimensional data ISMB/ECCB 2007

  7. Predicting missing genotype data • Have we learned a good density model for genotype data? • Gains from • Accounting for uncertainty in haplotype block structure • Accounting for uncertainty in ancestral haplotypes • Accounting for parental relationships • Assess model using cross-validation/test prediction error ISMB/ECCB 2007

  8. Predicting missing genotype data • Crohn’s/5q31 data set (Daly et al., 2001) • Crohn’s disease data from Chromosome 5q31 containing genotypes for 129 children + 258 parents across 103 loci (phases given for children) • For each test set, make ρ fraction of data missing • Retain model parameters from model learned from training data, then draw 1000 samples over missing data • Compute fill-in error rate over 1000 samples, for all missing data ISMB/ECCB 2007

  9. Prediction error for Crohn’s/5q31 data ISMB/ECCB 2007

  10. Comparative performance for Crohn’s/5q31 data ISMB/ECCB 2007

  11. Establishing haplotype block boundaries • Define the recombination priorγ on transition probabilities • Different γ correspond to different “blockiness” of data • For each locus k, can compute the probability of transition pk • Can establish a threshold t and establish block boundaries • Once blocks are defined, can assign block labelslb= (m,n) ISMB/ECCB 2007

  12. Haplotype block structure in the ENm006 region • 573 SNP markers for 270 individuals from 3 sub-populations: • 90 Yoruba individuals (30 parent-parent-offspring trios) from Ibadan, Nigeria (YRI); • 90 individuals (30 trios) of European descent from Utah (CEU) • 45 Han Chinese individuals from Beijing (CHB+JPT)/45 Japanese individuals from Tokyo (JPT) ISMB/ECCB 2007

  13. Part II: Linking haplotype block structure and gene expression data ISMB/ECCB 2007

  14. A model for linking haplotype structure to quantitative trait measurements Label 3 Label 4 Label 1 Label 2 Individual 1 Observed quantitative trait profile Individual 2 Haplotype block 1 Individual 3 Individual 4 Individual 5 Individual 1 Individual 2 Haplotype block 2 Individual 3 Individual 4 Individual 5 Relevance variable Latent block profile x 1.0 x + = x x 0.0 ISMB/ECCB 2007

  15. A Bayesian model for linking haplotype structure to quantitative measurements blocks b = 1,…,B Tbj quantitative traits g = 1,…,G individuals j = 1,…,J wbg π0 Block label Relevance variable Latent block profile Sbj μbg τ0,μ0 ρg zgj α0,β0 Noise precision Observed trait ISMB/ECCB 2007

  16. Linking haplotype blocks to phenotype Test cases (sorted) Test data splits • 387 individuals with Crohn’s (+1) or non-Crohn’s (-1) phenotype; • Link 10 haplotype blocks from 5q31 to phenotype • Average cross-validation error: 23.1% + 3.45% Haplotype blocks 2 and 10 most relevant to Crohn’s phenotype (p < 4.76 x 10-5) ISMB/ECCB 2007

  17. Linking haplotype blocks to gene expression • ENm006 data set: • 19 haplotype blocks (573 SNPs) • 28 gene expression profiles in ENm006 region (Stranger et al., 2007) ISMB/ECCB 2007

  18. Addressing population stratification The population variable affects phenotype/gene expression… …whereas variation between individuals is the effect we’re interested in ISMB/ECCB 2007

  19. Associations between haplotype blocks and gene expression p < 3.33 x 10-4 p < 2.5 x 10-4 GDI1 - HapBlock2 (YRI) GDI1 - HapBlock5 (CHB+JPT) ISMB/ECCB 2007

  20. Summary • Enhanced version of Jojic et al. (UAI 2004) model for haplotype inference/ discovering block structure • Novel Bayesian model for associating haplotype blocks to gene expression • We re-discover population-specific block structures across populations in the HapMap data • Predictions for Crohn’s disease from Chromosome 5q31 data • Cis- associations between blocks and gene expression in ENm006 in presence of non-genetic factors • Cis- association between HapBlocks 2 and 5 and GDI1 ISMB/ECCB 2007

  21. The road ahead… • Applying to larger portions of the HapMap data • Finding trans- associations • Non-linear models for associating block structure to quantitative traits • Joint learning of haplotype block structure and associations • Accounting for patterns of gene co-expression/similar phenotypes ISMB/ECCB 2007

  22. Acknowledgements • Manolis Dermitzakis and Richard Durbin, Wellcome Trust Sanger Institute • Nebojsa Jojic, Microsoft Research Redmond • Paul Scheet, University of Michigan - Ann Arbor • US National Science Foundation (NSF) ISMB/ECCB 2007

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