A Bayesian Method for Disentangling Dependent Structure of Epistatic Interaction

Zhang, Jing; Qu, Zhang; Lewis, Darrin P.; Zhang, Michael Q.

doi:10.3844/amjbsp.2011.1.10

Cited by 4 publications

(6 citation statements)

References 12 publications

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“…This is an example of the statistical study in which disease underlying biological processes can be extracted from determined statistical associations. (Zhang and Liu, 2007) Epistasis detection AMD GWA data set a More powerful than previous approaches BEAM2 (Zhang et al, 2011a) Epistasis/LD-block detection WTCCC T1D b Many previous loci+new two-way associations RBP (Zhang et al, 2011b) Detailed independence dbMHC c T1D data set Confirmed previously known saturated structure of epistasis interactions BEAM3 (Zhang, 2012) Bayesian graph model for WTCCC IBD d data set All previous IBD loci+2 new+2 interchr. f epistasis/LD interactions Bayesian Classification Tree Classification tree model/ Crohn's disease data Possible epistasis identified (Chen et al, 2011a) recursive partitioning Haplotype Block Differences Separate LD-block determination WTCCC T1D and RA e Detected differences around previously (Kozyryev and Zhang, 2012) for cases and controls chr6 data sets known loci + near new positions BEAM+BEAM2…”

Section: Resultsmentioning

confidence: 99%

“…However, it is possible that with the improved statistical methods outlined above the situation will soon change after the detailed understanding of the interactions involved emerges. Additionally, the main criticism of the GWAS based on the SNPs analysis, is that it is hard to understand the causal biology taking place in the disease formation (Hall, 2010); however, with the development of the recent Bayesian models that provide the detailed structure of the multilocus interactions (Zhang et al, 2011b; detailed etiopathogenesis of many diseases may soon be elucidated.…”

Section: Discussionmentioning

confidence: 99%

“…interaction partition structure determination: While successful in inferring epistatic interactions in GWAS, both BEAM and BEAM2 had a disadvantage of using saturated models which limited the ability of the algorithms to accurately determine the structure of the epistatic interactions among different disease related markers. However, recent studies showed that such interaction details arising due to encoding of the complicated regulatory mechanisms might play an important role in the disease formation (Zhang et al, 2011b;Yang et al, 2009;WTCCC, 2007). In order to be able to carefully explore the etiopathogenesis and genetic mechanisms of diseases, Zhang et al (2011b) proposed the Recursive Bayesian Partition (RBP) algorithm.…”

Section: Detailedmentioning

confidence: 99%

“…However, recent studies showed that such interaction details arising due to encoding of the complicated regulatory mechanisms might play an important role in the disease formation (Zhang et al, 2011b;Yang et al, 2009;WTCCC, 2007). In order to be able to carefully explore the etiopathogenesis and genetic mechanisms of diseases, Zhang et al (2011b) proposed the Recursive Bayesian Partition (RBP) algorithm. The RBP approach attempts to search for conditional independence and independence groups among interacting markers.…”

Section: Detailedmentioning

confidence: 99%

“…It can be shown (Zhang, 2012) that the joint probability of all SNPs X, parameters, including disease graph and association status (G, I) and disease status indicator (Y) is given by: Fig. 2: A diagram of the procedure for the inference of a detailed dependence structure among disease related variants or mutations when using recursive Bayesian partition (RBP) as done in Zhang et al (2011b). In this simple example, five SNPs (numbered SNP1 though SNP5) were assumed to be associated with the phenotype in question.…”

Section: Detailedmentioning

confidence: 99%

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Bayesian Exploration of Multilocus Interactions on the Genome-Wide Scale

Mohammad¹

2012

American Journal of Bioinformatics

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Problem statement: Recent technological and scientific advances propelled the field of Genome-Wide Association Study (GWAS), which promises to be instrumental in linking many common complex diseases to their genetic origin. While so far such large-scale surveys have been moderately successful in identifying disease related genetic variants, much of disease heritability is still not accounted for by the discovered loci. There is an urgent need for advanced statistical methods for efficient automatic detection of complicated multilocus interactions on significant scales. Approach: Novel statistical methods based on Bayesian data analysis ideas, specifically Bayesian modeling, Bayesian variable partitioning, graphical and network models are promising to aid in search for missing disease heritability and shed light on complex biological processes involved in disease development. First crucial difference setting these methods apart from all the mainstream previous approaches (hypothesis testing methods) is their joint disease mapping capability via the simultaneous fitting of a statistical model for the whole case-control data set. Additionally, such Bayesian methods allow for the construction of complicated data models and quantitative incorporation of diverse prior information into the final statistical model. Results: The use of Bayesian techniques has already yielded new insights into the details of epistatic interactions across the genome associated with various important diseases. Conclusion/Recommendations: Bayesian approaches provide a way to detect and understand complicated multilocus interactions that already started to elucidate important disease pathways. As the field of GWAS matures, Bayesian strategies can surely aid in converting such multiple surveys into useful biomedical information.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Detailedmentioning

confidence: 99%

Section: Detailedmentioning

confidence: 99%

Section: Detailedmentioning

confidence: 99%

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Bayesian Exploration of Multilocus Interactions on the Genome-Wide Scale

Mohammad¹

2012

American Journal of Bioinformatics

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Bayesian Analysis of Complex Interacting Mutations in HIV Drug Resistance and Cross-Resistance

Kozyryev

Zhang

2014

Advances in Experimental Medicine and Biology

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A successful treatment of AIDS world-wide is severely hindered by the HIV virus' drug resistance capability resulting from complicated mutation patterns of viral proteins. Such a system of mutations enables the virus to survive and reproduce despite the presence of various antiretroviral drugs by disrupting their binding capability. Although these interacting mutation patterns are extremely difficult to efficiently uncover and interpret, they contribute valuable information to personalized therapeutic regimen design. The use of Bayesian statistical modeling provides an unprecedented opportunity in the field of anti-HIV therapy to understand detailed interaction structures of drug resistant mutations. Multiple Bayesian models equipped with Markov Chain Monte Carlo (MCMC) methods have been recently proposed in this field (Zhang et al. in PNAS 107:1321, 2010 [1]; Zhang et al. in J Proteome Sci Comput Biol 1:2, 2012 [2]; Svicher et al. in Antiviral Res 93(1):86-93, 2012 [3]; Svicher et al. in Antiviral Therapy 16(7):1035-1045, 2011 [4]; Svicher et al. in Antiviral Ther 16(4):A14-A14, 2011 [5]; Svicher et al. in Antiviral Ther 16(4):A85-A85, 2011 [6]; Alteri et al. in Signature mutations in V3 and bridging sheet domain of HIV-1 gp120 HIV-1 are specifically associated with dual tropism and modulate the interaction with CCR5 N-Terminus, 2011 [7]). Probabilistically modeling mutations in the HIV-1 protease or reverse transcriptase (RT) isolated from drug-treated patients provides a powerful statistical procedure that first detects mutation combinations associated with single or multiple-drug resistance, and then infers detailed dependence structures among the interacting mutations in viral proteins (Zhang et al. in PNAS 107:1321, 2010 [1]; Zhang et al. in J Proteome Sci Comput Biol 1:2, 2012 [2]). Combined with molecular dynamics simulations and free energy calculations, Bayesian analysis predictions help to uncover genetic and structural mechanisms in the HIV treatment resistance. Results obtained with such stochastic methods pave the way not only for optimization of the use for existing HIV drugs, but also for the development of the new more efficient antiretroviral medicines. In this chapter we survey current challenges in the bioinformatics of anti-HIV therapy, and outline how recently emerged Bayesian methods can help with the clinical management of HIV-1 infection. We will provide a rigorous review of the Bayesian variable partition model and the recursive model selection procedure based on probability theory and mathematical data analysis techniques while highlighting real applications in HIV and HBV studies including HIV drug resistance (Zhang et al. in PNAS 107:1321, 2010 [1]), cross-resistance (Zhang et al. in J Proteome Sci Comput Biol 1:2, 2012 [2]), HIV coreceptor usage (Svicher et al. in Antiviral Therapy 16(7):1035-1045, 2011 [4]; Svicher et al. in Antiviral Ther 16(4):A14-A14, 2011 [5]; Alteri et al. in Signature mutations in V3 and bridging sheet domain of HIV-1 gp120 HIV-1 are specifically associated wi...

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Bayesian Determination of Disease Associated Differences in Haplotype Blocks

Siti¹

2012

American Journal of Bioinformatics

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Problem statement: While experimental ascertainment of haplotype blocks in the genomescale case-control studies is expensive, accurate computational phasing is still a daunting task for bioinformatics approaches. We used a statistical method to determine differences, potentially associated with a certain disease, in linkage disequilibrium block boundaries in whole-genome Single Nucleotide Polymorphisms (SNPs) data. Approach: We utilized a Bayesian model for calculating the posterior probabilities of the block boundaries in the SNPs data and used Metropolis-Hastings algorithm to sample from that posterior distribution. Our method was applied to search for haplotypeblock boundary differences associated with two autoimmune diseases: Type I Diabetes (T1D) and Rheumatoid Arthritis (RA). Results: We located the regions on chromosome 6 with significant control-case difference in haplotype blocks around the SNPs and genes that were previously known to be associated with T1D and RA (in the HLA complex), as well as around genes whose association with the autoimmune diseases should be further explored in future studies. Conclusion/Recommendations: The statistical approach explored in this study provides an efficient and accurate way to study connection of haplotype-block differences to multiple important diseases.

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A Bayesian Method for Disentangling Dependent Structure of Epistatic Interaction

Cited by 4 publications

References 12 publications

Bayesian Exploration of Multilocus Interactions on the Genome-Wide Scale

Bayesian Exploration of Multilocus Interactions on the Genome-Wide Scale

Bayesian Analysis of Complex Interacting Mutations in HIV Drug Resistance and Cross-Resistance

Bayesian Determination of Disease Associated Differences in Haplotype Blocks

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