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Population Approaches to Detecting and Genotyping Copy Number Variation

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Population Approaches to Detecting and Genotyping Copy Number Variation

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    1. Population Approaches to Detecting and Genotyping Copy Number Variation Lachlan Coin July 2010

    2. Outline Population-haplotype approach to CNV detecting and genotyping Application to SNP and CGH data Application to NGS sequence data

    3. cnvHap approach to CNV discovery and genotyping

    4. Example of trained model

    5. cnvHap models haploid CN transitions Specify an per-base global transition rate matrix

    6. cnvHap joint model of CNV + SNP haplotypes

    7. Cluster positions modelled using a linear model

    8. Using Illumina SNP arrays

    9. Combined Illumina and Agilent arrays

    10. Some CNVs exhibit shared structure

    11. Improved CNV genotyping accuracy

    12. A deletion at 16p11.2 in a patient with extreme obesity

    13. 16p11.2 deletions in obesity and population cohorts

    14. Coverage affected by GC content

    15. Regression model fit to correct for GC bias

    16. Loess curves fit to remove residual spatial variation of coverage

    17. Detecting CNVS with NGS data

    18. NGS versus CGH data

    19. NGS vs CGH data

    20. Haplotype structure of deletion

    21. NGS amplification

    22. With consistent break-points in population

    23. Polyploid phasing and imputation

    24. Conclusions Population-haplotype model enables joint CNV discovery and genotyping using array data Preliminary results indicate this will also help using NGS data Combining information from multiple platforms improves sensitivity Imputation still works for ploidy > 2, phasing becomes more difficult

    25. Acknowledgements

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