Dataset Information

Bayesian model to detect phenotype-specific genes for copy number data.

ABSTRACT:

Background

An important question in genetic studies is to determine those genetic variants, in particular CNVs, that are specific to different groups of individuals. This could help in elucidating differences in disease predisposition and response to pharmaceutical treatments. We propose a Bayesian model designed to analyze thousands of copy number variants (CNVs) where only few of them are expected to be associated with a specific phenotype.

Results

The model is illustrated by analyzing three major human groups belonging to HapMap data. We also show how the model can be used to determine specific CNVs related to response to treatment in patients diagnosed with ovarian cancer. The model is also extended to address the problem of how to adjust for confounding covariates (e.g., population stratification). Through a simulation study, we show that the proposed model outperforms other approaches that are typically used to analyze this data when analyzing common copy-number polymorphisms (CNPs) or complex CNVs. We have developed an R package, called bayesGen, that implements the model and estimating algorithms.

Conclusions

Our proposed model is useful to discover specific genetic variants when different subgroups of individuals are analyzed. The model can address studies with or without control group. By integrating all data in a unique model we can obtain a list of genes that are associated with a given phenotype as well as a different list of genes that are shared among the different subtypes of cases.

SUBMITTER: Gonzalez JR

PROVIDER: S-EPMC3576305 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Publications

Bayesian model to detect phenotype-specific genes for copy number data.

González Juan R JR Abellán Carlos C Abellán Juan J JJ

BMC bioinformatics 20120613

<h4>Background</h4>An important question in genetic studies is to determine those genetic variants, in particular CNVs, that are specific to different groups of individuals. This could help in elucidating differences in disease predisposition and response to pharmaceutical treatments. We propose a Bayesian model designed to analyze thousands of copy number variants (CNVs) where only few of them are expected to be associated with a specific phenotype.<h4>Results</h4>The model is illustrated by an ...[more]

PMID: 22694346

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Project description:BACKGROUND: Cancer and other disorders are due to genomic lesions. SNP-microarrays are able to measure simultaneously both genotype and copy number (CN) at several Single Nucleotide Polymorphisms (SNPs) along the genome. CN is defined as the number of DNA copies, and the normal is two, since we have two copies of each chromosome. The genotype of a SNP is the status given by the nucleotides (alleles) which are present on the two copies of DNA. It is defined homozygous or heterozygous if the two alleles are the same or if they differ, respectively. Loss of heterozygosity (LOH) is the loss of the heterozygous status due to genomic events. Combining CN and LOH data, it is possible to better identify different types of genomic aberrations. For example, a long sequence of homozygous SNPs might be caused by either the physical loss of one copy or a uniparental disomy event (UPD), i.e. each SNP has two identical nucleotides both derived from only one parent. In this situation, the knowledge of the CN can help in distinguishing between these two events. RESULTS: To better identify genomic aberrations, we propose a method (called gBPCR) which infers the type of aberration occurred, taking into account all the possible influence in the microarray detection of the homozygosity status of the SNPs, resulting from an altered CN level. Namely, we model the distributions of the detected genotype, given a specific genomic alteration and we estimate the parameters involved on public reference datasets. The estimation is performed similarly to the modified Bayesian Piecewise Constant Regression, but with improved estimators for the detection of the breakpoints.Using artificial and real data, we evaluate the quality of the estimation of gBPCR and we also show that it outperforms other well-known methods for LOH estimation. CONCLUSIONS: We propose a method (gBPCR) for the estimation of both LOH and CN aberrations, improving their estimation by integrating both types of data and accounting for their relationships. Moreover, gBPCR performed very well in comparison with other methods for LOH estimation and the estimated CN lesions on real data have been validated with another technique.

Project description:BACKGROUND: Some diseases, like tumors, can be related to chromosomal aberrations, leading to changes of DNA copy number. The copy number of an aberrant genome can be represented as a piecewise constant function, since it can exhibit regions of deletions or gains. Instead, in a healthy cell the copy number is two because we inherit one copy of each chromosome from each our parents. Bayesian Piecewise Constant Regression (BPCR) is a Bayesian regression method for data that are noisy observations of a piecewise constant function. The method estimates the unknown segment number, the endpoints of the segments and the value of the segment levels of the underlying piecewise constant function. The Bayesian Regression Curve (BRC) estimates the same data with a smoothing curve. However, in the original formulation, some estimators failed to properly determine the corresponding parameters. For example, the boundary estimator did not take into account the dependency among the boundaries and succeeded in estimating more than one breakpoint at the same position, losing segments. RESULTS: We derived an improved version of the BPCR (called mBPCR) and BRC, changing the segment number estimator and the boundary estimator to enhance the fitting procedure. We also proposed an alternative estimator of the variance of the segment levels, which is useful in case of data with high noise. Using artificial data, we compared the original and the modified version of BPCR and BRC with other regression methods, showing that our improved version of BPCR generally outperformed all the others. Similar results were also observed on real data. CONCLUSION: We propose an improved method for DNA copy number estimation, mBPCR, which performed very well compared to previously published algorithms. In particular, mBPCR was more powerful in the detection of the true position of the breakpoints and of small aberrations in very noisy data. Hence, from a biological point of view, our method can be very useful, for example, to find targets of genomic aberrations in clinical cancer samples.

Dataset Information

Bayesian model to detect phenotype-specific genes for copy number data.

Background

Results

Conclusions

Publications

Bayesian model to detect phenotype-specific genes for copy number data.

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