A HIERARCHICAL BAYESIAN MODEL FOR INFERENCE OF COPY NUMBER VARIANTS AND THEIR ASSOCIATION TO GENE EXPRESSION.

@article{Cassese2014AHB,
  title={A HIERARCHICAL BAYESIAN MODEL FOR INFERENCE OF COPY NUMBER VARIANTS AND THEIR ASSOCIATION TO GENE EXPRESSION.},
  author={Alberto Cassese and M. Guindani and Mahlet G. Tadesse and Francesco Falciani and Marina Vannucci},
  journal={The annals of applied statistics},
  year={2014},
  volume={8 1},
  pages={
          148-175
        }
}
A number of statistical models have been successfully developed for the analysis of high-throughput data from a single source, but few methods are available for integrating data from different sources. Here we focus on integrating gene expression levels with comparative genomic hybridization (CGH) array measurements collected on the same subjects. We specify a measurement error model that relates the gene expression levels to latent copy number states which, in turn, are related to the observed… 
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References

SHOWING 1-10 OF 69 REFERENCES
An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping
TLDR
This paper presents an integrated hierarchical Bayesian model that jointly models all genes and SNPs to detect eQTLs and shows that it outperforms other popular methods for eZTL detection, including QTLBIM, R-QTL, remMap and M-SPLS.
A Double-Layered Mixture Model for the Joint Analysis of DNA Copy Number and Gene Expression Data
TLDR
This work describes a novel Bayesian inferential approach for a double-layered mixture model (DLMM) which directly models the stochastic nature of copy number data and identifies abnormally expressed genes due to aberrant copy number.
Bayesian Hidden Markov Modeling of Array CGH Data
TLDR
A Bayesian approach is adopted, relying on the hidden Markov model to account for the inherent dependence in the intensity ratios of test and reference DNA samples, and a Metropolis-within-Gibbs algorithm is implemented for efficient simulation-based inference.
Bayesian hierarchical mixture modeling to assign copy number from a targeted CNV array
TLDR
A copy number assignment algorithm, CNVCALL, for a targeted CNV array, such as that used by the Wellcome Trust Case Control Consortium's recent CNV association study, is developed that robustly identifies both the number of different copy number classes at a specific locus as well as relative copy number for each individual in the sample.
A BAYESIAN GRAPHICAL MODELING APPROACH TO MICRORNA REGULATORY NETWORK INFERENCE.
TLDR
A Bayesian graphical modeling approach is proposed that infers the miRNA regulatory network by integrating expression levels of miRNAs with their potential mRNA targets and, via the prior probability model, with their sequence/structure information.
Modeling genetic inheritance of copy number variations
TLDR
This work developed a statistical framework for intensity-based CNV detection platforms using family data, and produces accurate CNV calls, gives probabilistic estimates of CNV transmission and builds a solid foundation for the development of linkage and association tests utilizing CNVs.
Detecting differential gene expression with a semiparametric hierarchical mixture method.
TLDR
This work proposes a hierarchical mixture model to provide methodology that is both sensitive in detecting differential expression and sufficiently flexible to account for the complex variability of normalized microarray data.
QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data
TLDR
QuantiSNP is a novel algorithm for high-resolution CNV/aneuploidy detection with application to clinical genetics, cancer and disease association studies and it is believed that the OB-HMM framework has widespread applicability in genomic research.
PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data.
TLDR
PennCNV, a hidden Markov model (HMM) based approach, is presented for kilobase-resolution detection of CNVs from Illumina high-density SNP genotyping data, demonstrating the feasibility of whole-genome fine-mapping ofCNVs via high- density SNP genotypesing.
Genetic Association Studies of Copy-Number Variation: Should Assignment of Copy Number States Precede Testing?
TLDR
The results indicate that pooled marker-level testing is capable of offering a dramatic increase in power over CNV- level testing, particularly for small CNVs, but when CNVs are large and rare; understanding these tradeoffs is an important consideration in conducting association studies of structural variation.
...
...