ESTIMATING GENOME-WIDE COPY NUMBER USING ALLELE SPECIFIC MIXTURE MODELS

Genomic changes such as copy number alterations are thought to be one of the major underlying causes of human phenotypic variation among normal and disease subjects [23,11,25,26,5,4,7,18]. These include chromosomal regions with so-called copy number alterations: instead of the expected two copies, a section of the chromosome for a particular individual may have zero copies (homozygous deletion), one copy (hemizygous deletions), or more than two copies (amplifications). The canonical example is Down syndrome which is caused by an extra copy of chromosome 21. Identification of such abnormalities in smaller regions has been of great interest, because it is believed to be an underlying cause of cancer. More than one decade ago comparative genomic hybridization (CGH)technology was developed to detect copy number changes in a high-throughput fashion. However, this technology only provides a 10 MB resolution which limits the ability to detect copy number alterations spanning small regions. It is widely believed that a copy number alteration as small as one base can have significant downstream effects, thus microarray manufacturers have developed technologies that provide much higher resolution. Unfortunately, strong probe effects and variation introduced by sample preparation procedures have made single-point copy number estimates too imprecise to be useful. CGH arrays use a two-color hybridization, usually comparing a sample of interest to a reference sample, which to some degree removes the probe effect. However, the resolution is not nearly high enough to provide single-point copy number estimates. Various groups have proposed statistical procedures that pool data from neighboring locations to successfully improve precision. However, these procedure need to average across relatively large regions to work effectively thus greatly reducing the resolution. Recently, regression-type models that account for probe-effect have been proposed and appear to improve accuracy as well as precision. In this paper, we propose a mixture model solution specifically designed for single-point estimation, that provides various advantages over the existing methodology. We use a 314 sample database, constructed with public datasets, to motivate and fit models for the conditional distribution of the observed intensities given allele specific copy numbers. With the estimated models in place we can compute posterior probabilities that provide a useful prediction rule as well as a confidence measure for each call. Software to implement this procedure will be available in the Bioconductor oligo packagehttp://www.bioconductor.org)

Using the R Package crlmm for Genotyping and Copy Number Estimation

Genotyping platforms such as Affymetrix can be used to assess genotype-phenotype as well as copy number-phenotype associations at millions of markers. While genotyping algorithms are largely concordant when assessed on HapMap samples, tools to assess copy number changes are more variable and often discordant. One explanation for the discordance is that copy number estimates are susceptible to systematic differences between groups of samples that were processed at different times or by different labs. Analysis algorithms that do not adjust for batch effects are prone to spurious measures of association. The R package crlmm implements a multilevel model that adjusts for batch effects and provides allele-specific estimates of copy number. This paper illustrates a workflow for the estimation of allele-specific copy number and integration of the marker-level estimates with complimentary Bioconductor software for inferring regions of copy number gain or loss. All analyses are performed in the statistical environment R

TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays

<p>Abstract</p> <p>Background</p> <p>High-throughput genotyping microarrays assess both total DNA copy number and allelic composition, which makes them a tool of choice for copy number studies in cancer, including total copy number and loss of heterozygosity (LOH) analyses. Even after state of the art preprocessing methods, allelic signal estimates from genotyping arrays still suffer from systematic effects that make them difficult to use effectively for such downstream analyses.</p> <p>Results</p> <p>We propose a method, TumorBoost, for normalizing allelic estimates of one tumor sample based on estimates from a single matched normal. The method applies to any paired tumor-normal estimates from any microarray-based technology, combined with any preprocessing method. We demonstrate that it increases the signal-to-noise ratio of allelic signals, making it significantly easier to detect allelic imbalances.</p> <p>Conclusions</p> <p>TumorBoost increases the power to detect somatic copy-number events (including copy-neutral LOH) in the tumor from allelic signals of Affymetrix or Illumina origin. We also conclude that high-precision allelic estimates can be obtained from a single pair of tumor-normal hybridizations, if TumorBoost is combined with single-array preprocessing methods such as (allele-specific) CRMA v2 for Affymetrix or BeadStudio's (proprietary) XY-normalization method for Illumina. A bounded-memory implementation is available in the open-source and cross-platform R package <it>aroma.cn</it>, which is part of the Aroma Project (<url>http://www.aroma-project.org/</url>).</p

Estimating Genome-Wide Copy Number Using Allele-Specific Mixture Models

Genomic changes such as copy number alterations are one of the major underlying causes of human phenotypic variation among normal and disease subjects. Array comparative genomic hybridization (CGH) technology was developed to detect copy number changes in a high-throughput fashion. However, this technology provides only a >30-kb resolution, which limits the ability to detect copy number alterations spanning small regions. Higher resolution technologies such as single nucleotide polymorphism (SNP) microarrays allow detection of copy number alterations at least as small as several thousand base pairs. Unfortunately, strong probe effects and variation introduced by sample preparation procedures have made single-point copy number estimates too imprecise to be useful. Various groups have proposed statistical procedures that pool data from neighboring locations to successfully improve precision. However, these procedure need to average across relatively large regions to work effectively, thus greatly reducing resolution. Recently, regression-type models that account for probe effects have been proposed and appear to improve accuracy as well as precision. In this paper, we propose a mixture model solution, specifically designed for single-point estimation, that provides various advantages over the existing methodology. We use a 314-sample database, to motivate and fit models for the conditional distribution of the observed intensities given allele-specific copy number. We can then compute posterior probabilities that provide a useful prediction rule as well as a confidence measure for each call. Software to implement this procedure will be available in the Bioconductor oligo package (www.bioconductor.org)