An Integrated Approach for the Assessment of Chromosomal Abnormalities

Transcription

1 An Integrated Approach for the Assessment of Chromosomal Abnormalities Department of Biostatistics Johns Hopkins Bloomberg School of Public Health June 26, 2007

2 Karyotypes

3 Karyotypes General Cytogenetics Information

4 FISH Courtesy of the Pevsner Laboratory

5 SNP chip data

6 Estimation 1 By SNP: Estimate genotype and copy number for each SNP. 2 Within a sample: Borrow strength between SNPs to infer regions of LOH and copy number changes. 3 Between samples: Comparison between normal and disease populations to find chromosomal alterations associated with disease.

7 Deletion

8 Amplification

9 Uniparental Isodisomy

10 Cancer samples

11 Mosaicism

12 SNPchip S4 classes and methods

13 The structure of the data we observe At each SNP, we observe a noisy measure of the true copy number and genotype (and possibly also measures of confidence in those estimates).

14 Another HMM...? Novel (and we believe, important) HMM features: 1 Model the observation sequence of genotype calls and copy number jointly (Vanilla) 2 Integrate confidence estimates of the genotype calls and copy number estimates (ICE)

15 The Vanilla HMM components Observations ĈN and ĜT Hidden states Initial state probability distribution Transition probabilities Emission probabilities

16 Hidden states

17 Transition probabilities Following suggestions in the literature, we model the transition probabilities as a function of the distance d between SNPs. Specifically, let θ(d) 1 e 2d denote the probability that SNP i is not informative (I c ) for SNP at i + 1. For example: τ =P { (d) = P { = P { P i+1 i, d } i+1, I i, d } + P i+1, I c i, d i+1 I, i, d } P { I i, d } + i+1 I c, i, d P I c i, d = P { } θ(d).

18 Emission probabilities We assume conditional independence between copy number estimates and the genotype calls. For example: f( c CN, c GT ) = f( c CN ) f( c GT ) n o n o = f ccn f cgt = β n ccn o β n cgt o.

19 Vanilla HMM 5.0 A D B C E Amp LOH vanilla Norm Del

20 More information The confidence in genotype calls can differ substantially between SNPs! 4 2 sense antisense

21 Integrating confidence estimates for genotype calls Let S cgt be the confidence score for the genotype estimate. We can estimate from Hapmap the following densities: n o n o n f SĤOM ĤOM, HOM, f SĤOM ĤOM, HET, f S HET d HET, d o n HOM, f S HET d HET, d o HET. Note: n o f SĤOM ĤOM, n f S HET d HET, d o n o f SĤOM ĤOM, HOM n f S HET d HET, d o HOM.

22 Emission Probabilities - Loss Recall that f( c CN, c GT ) = f( c CN ) f( c GT ) n o n o = f ccn f cgt = β n ccn o β n cgt o. If the state for a particular SNP is Loss, we have β n cgt, S cgt o = f n o n cgt f S cgt GT, c o.

23 Emission Probabilities - Retention For retention, the true genotype can be HET or HOM: β n cgt, Sd GT o n o n = f cgt f S GT d GT, c o n o n = f cgt f S GT d, HOM GT, c o n + f S d GT, HET GT, c o n o n = f cgt f S GT d HOM, GT, c o n f HOM GT, c o n + f S GT d HET, GT, c o n f HET GT, c o n o n = f cgt f S GT d HOM, GT c o n f HOM GT, c o n + f S GT d HET, GT c o n f HET GT, c o

24 Vanilla ICE comparison 5.0 A D B C E Amp LOH vanilla Norm Del Amp LOH ICE Norm Del Bioconductor package: ICE Amp LOH A D vanilla B vanilla E Norm Del Amp LOH A vanilla D vanilla ICE B ICE E Norm Del ICE ICE

25 A HapMap sample LOH 2 normal AA/BB AB Mb

26 Many HapMap samples

27 SNP Trio

28 SNP Trio

29 SNP Trio

30 HMM for SNP Trio chromosome 10 chromosome 22 BPI BPI UPI F UPI F UPI M UPI M MI D MI D MI S non BPI MI S non BPI BPI BPI position (Mb) position (Mb)

31 Acknowledgments Rob Scharpf Giovanni Parmigiani Rafael Irizarry Benilton Carvalho, Wenyi Wang Jonathan Pevsner Nate Miller, Eli Roberson, Jason Ting

32 References Carvalho B, Bengtsson H, Speed TP, Irizarry RA (2007) Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data. Biostatistics, 8(2): Scharpf RB, Ting JC, Pevsner J, Ruczinski I (2007). SNPchip: R classes and methods for SNP array data. Bioinformatics, 23(5): Scharpf RB, Parmigiani G, Ruczinski I (2007). A hidden markov model for joint estimation of genotype and copy number in high-throughput SNP chips. JHU Biostatistics Working papers, #136. Ting JC, Ye Y, Thomas GH, Ruczinski I, Pevsner J (2006). Analysis and visualization of chromosomal abnormalities in SNP data with SNPscan. BMC Bioinformatics, 7(1):25. Ting JC, Roberson ED, Miller N, et al, Ruczinski I, Thomas GH, Pevsner J (2007). Visualization of uniparental inheritance, Mendelian inconsistencies, deletions and parent of origin effects in single nucleotide polymorphism trio data with SNPtrio. Human Mutation, (in press). Wang W, Caravalho B, Miller N, Pevsner J, Chakravarti A, Irizarry RA (2006) Estimating genome-wide copy number using allele specific mixture models. JHU Biostatistics Working papers, #122.