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Genomic Data Analysis for Rare Recessive Diseases and Pedigree Modeling

This presentation delves into utilizing genomic data for pedigree analysis to identify disease gene locations for rare recessive diseases. Topics covered include computing IBD from genomic data, posterior IBD probabilities, gene mapping using FLOD scores, Taybi-Linder Syndrome case study, LOD and FLOD results, haplotype analysis, and road maps for graphical models. The discussion also encompasses foundational concepts, inference methods, approximate inference approaches, learning structures and parameters, scoring methods, search strategies, and various applications in fields such as medical diagnostics, error correction codes, image processing, and bioinformatics network learning.

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Genomic Data Analysis for Rare Recessive Diseases and Pedigree Modeling

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  1. Applications and Summary .

  2. Presented By Dan Geiger Journal Club of the Pharmacogenetics Group Meeting Technion .

  3. Rare Recessive Diseases Pedigree 1C A Given such pedigree our program Superlink produces a LOD score determining if this is a coincidence or suggestive of disease gene location. How probable is it to be IBD (denoted f)? .

  4. Modeling The IBD Process L X1 X2 XL-1 XL Xi No change of coancestry Assumptions: No interferance, No errors in genetic maps. ={ a , f } are parameters that can be estimated (e.g. by ML), if IBD data is available. .

  5. Adding genomic data X1 X2 XL-1 XL Xk Y1 Y2 YL-1 YL Yk .

  6. X1 X2 XL-1 XL Xi Y1 Y2 YL-1 YL Yi Computing IBD from genomic data P(y1,…,yL, x1,…,xL) Forward-Backward formula: P(y1,…,yL,xi) = P(y1,…,yi,xi) P(yi+1,…,yL | xi)  f(xi) b(xi) Likelihood of Evidence: P(y1,…,yL) = xiP(y1,…,yL,xi). Posterior IBD Probabilities: P(xi | y1,…,yL) = P(y1,…,yL,xi)/ xiP(y1,…,yL,xi).

  7. Simulation Results For First Degree Cousins (1C) .

  8. Gene mapping: The FLOD score P(Homozigosity for allele of frequency q at location Xi) = q P(Xk=1 | Y) + q2P(Xk = 0 | Y) P(Homozigosity for allele of frequency q by random) = qf + q2(1-f) Total FLOD score is the sum of the FLOD for all individuals. .

  9. The Taybi-Linder Syndrome .

  10. Data and Inbreeding Coeffcients .

  11. LOD and FLOD results genomewise .

  12. LOD and FLOD results for Chromosome 2 FLOD FLODe4 LOD .

  13. LOD and FLOD results for Chromosome 7 FLODe4 LOD FLOD .

  14. Haplotype Analysis .

  15. Road Map For Graphical Models • Foundations • Probability theory –subjective versus objective • Other formalisms for uncertainty (Fuzzy, Possibilistic, belief functions) • Type of graphical models: Directed, Undirected, Chain Graphs, Dynamic networks, factored HMM, etc • Discrete versus continuous distributions • Causality versus correlation • Inference • Exact Inference • Variable elimination, clique trees, message passing • Using internal structure like determinism or zeroes • Queries: MLE, MAP, Belief update, sensitivityApproximate Inference • Sampling methods • Loopy propagation (minimizing some energy function) • Variational method

  16. Road Map For Graphical Models • Learning • Complete data versus incomplete data • Observed variables versus hidden variables • Learning parameters versus learning structure • Scoring methods versus conditional independence tests methods • Exact scores versus asymptotic scores • Search strategies vs. Optimal learning of trees/polytrees/TANs • Applications • Diagnostic tools: printer problems to airplanes failures • Medical diagnostic • Error correcting codes: Turbo codes • Image processing • Applications in Bioinformatics: gene mapping, regulatory, metabolic, and other network learning

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