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Population Approaches to Detecting and Genotyping Copy Number Variation

Population Approaches to Detecting and Genotyping Copy Number Variation. Lachlan Coin July 2010. Outline. Population-haplotype approach to CNV detecting and genotyping Application to SNP and CGH data Application to NGS sequence data. cnvHap approach to CNV discovery and genotyping.

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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 Coin et al, 2010, Nature Methods 7, 541 - 546 (2010) 

  4. Example of trained model

  5. cnvHap models haploid CN transitions copy number to • Specify an per-base global transition rate matrix 0 1 2 3 4 q00 q10 …. 0 1 2 3 4 … copy number from • Rate matrix multiplied by position specific scalar rate • Values trained using EM, following the approach of Klosterman et al, used in Xrate for finding substitution rates

  6. cnvHap joint model of CNV + SNP haplotypes

  7. Cluster positions modelled using a linear model Model fitted using Ridge regression carried at each iteration of E-M algorithm

  8. Using Illumina SNP arrays

  9. Combined Illumina and Agilent arrays Illumina Agilent Illumina Agilent Illumina Agilent

  10. Some CNVs exhibit shared structure

  11. Improved CNV genotyping accuracy Cumulative Frequency of Squared Pearson Correlation

  12. MLPA probes Segmental duplication +1 0 log2 ratio - 1 - 2 - 3 28.9 Mb 29.2 Mb 29.5 Mb 29.8 Mb 30.1 Mb 30.4 Mb 30.7 Mb q21 q12.2 q23.1 p12.3 p12.1 q22.2 p13.2 p11.2 q23.3 q24.2 p13.12 chromosome 16 A deletion at 16p11.2 in a patient with ‘extreme obesity’ • estimated by aCGH to be 546kb-700kb • flanked by segmental duplication (>99% sequence identity)‏ • probably arises by NAHR, implying deletion is 739kb • BMI = 29.2 kg.m-2 at age 7½ • learning difficulties, delayed speech RG Walters et al.Nature463, 671-675 (2010) doi:10.1038/nature08727

  13. Cohort Obese Lean/Normal Weight French child obesity case:control 4/643 0/530 British extreme early-onset obesity (SCOOP)‏ 3/931 - French adult obesity case:control 4/705 0/669 French bariatric surgery patients 2/141 - Swedish discordant siblings 2/159 0/140 Population cohorts(NFBC1966, CoLaus, EGPUT)‏ 3/1592 1/6235 16p11.2 deletions in obesity and population cohorts Obesity: P = 5.8x10-7 OR = 29.8 [3.9–225] Morbid obesity: P = 6.4x10-8 OR = 43.0 [5.6–329]

  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 Depth/haploid coverage B-allele frequency

  18. NGS versus CGH data NGS data chrom1:350mb-351mb CGH data chrom1:350mb-351mb

  19. NGS vs CGH data

  20. Haplotype structure of deletion

  21. NGS amplification Depth/coverage

  22. With consistent break-points in population

  23. Polyploid phasing and imputation Switch error rate Imputation error rate

  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 Evangelos Bellos Shu-Yi Su Robin Walters David Balding (UCL) Rob Sladek (McGill) Julian Asher Alex Blakemore Adam de Smith Phillipe Froguel Julia El-Sayed Moustafa

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