Finding disease candidate genes by liquid association

Li, Ker-Chau; Palotie, Aarno; Yuan, Shinsheng; Bronnikov, Denis; Chen, Daniel; Wei, Xuelian; Choi, Oi-Wa; Saarela, Janna; Peltonen, Leena

doi:10.1186/gb-2007-8-10-r205

Cited by 31 publications

(21 citation statements)

References 45 publications

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“…After transformation, we use the formulae, LA ( X , Y | Z ) = (X 1 Y 1 Z 1 +...+X 88 Y 88 Z 88 )/88, to compute the LA score [22,28]. The LAP3 website [52] was developed to enhance the online computation of LA.…”

Section: Methodsmentioning

confidence: 99%

Genome-wide expression links the electron transfer pathway of Shewanella oneidensis to chemotaxis

Tai

Yuan

et al. 2010

BMC Genomics

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BackgroundBy coupling the oxidation of organic substrates to a broad range of terminal electron acceptors (such as nitrate, metals and radionuclides), Shewanella oneidensis MR-1 has the ability to produce current in microbial fuel cells (MFCs). omcA, mtrA, omcB (also known as mtrC), mtrB, and gspF are some known genes of S. oneidensis MR-1 that participate in the process of electron transfer. How does the cell coordinate the expression of these genes? To shed light on this problem, we obtain the gene expression datasets of MR-1 that are recently public-accessible in Gene Expression Omnibus. We utilize the novel statistical method, liquid association (LA), to investigate the complex pattern of gene regulation.ResultsThrough a web of information obtained by our data analysis, a network of transcriptional regulatory relationship between chemotaxis and electron transfer pathways is revealed, highlighting the important roles of the chemotaxis gene cheA-1, the magnesium transporter gene mgtE-1, and a triheme c-type cytochrome gene SO4572.ConclusionWe found previously unknown relationship between chemotaxis and electron transfer using LA system. The study has the potential of helping researchers to overcome the intrinsic metabolic limitation of the microorganisms for improving power density output of an MFC.

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Section: Methodsmentioning

confidence: 99%

Genome-wide expression links the electron transfer pathway of Shewanella oneidensis to chemotaxis

Tai

Yuan

et al. 2010

BMC Genomics

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“…The entries of Δ 0 can also be interpreted as the differences in the partial covariances of each pair of genes between the two groups. This type of model for a differential network has been adopted by others as well, for example in Li et al (2007), Danaher et al (2013), and an unpublished technical report by Städler and Mukherjee (arxiv:1308.2771).…”

Section: Introductionmentioning

confidence: 99%

Direct estimation of differential networks

2014

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It is often of interest to understand how the structure of a genetic network differs between two conditions. In this paper, each condition-specific network is modeled using the precision matrix of a multivariate normal random vector, and a method is proposed to directly estimate the difference of the precision matrices. In contrast to other approaches, such as separate or joint estimation of the individual matrices, direct estimation does not require those matrices to be sparse, and thus can allow the individual networks to contain hub nodes. Under the assumption that the true differential network is sparse, the direct estimator is shown to be consistent in support recovery and estimation. It is also shown to outperform existing methods in simulations, and its properties are illustrated on gene expression data from late-stage ovarian cancer patients.

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“…The difference between the two precision matrices, denoted by Δ = ( δ i,j ) = Ω 1 – Ω 2 , is called the differential network. This type of model for a differential network has been used in Li et al (2007) and Danaher et al (2014). We thus propose to test for gene-by-gene interactions both by testing the global hypotheses

H_{0} : normalΔ = 0 versus H_{1} : normalΔ \neq 0,

and by simultaneously testing the hypotheses

H_{0, i, j} : δ_{i, j} = 0 versus H_{1, i, j} : δ_{i, j} \neq 0, 1 \leq i < j \leq p,

while controlling for the overall false discovery rate at a pre-specified level.…”

Section: Introductionmentioning

confidence: 99%

Testing differential networks with applications to the detection of gene-gene interactions

2015

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Summary Model organisms and human studies have led to increasing empirical evidence that interactions among genes contribute broadly to genetic variation of complex traits. In the presence of gene-by-gene interactions, the dimensionality of the feature space becomes extremely high relative to the sample size. This imposes a significant methodological challenge in identifying gene-by-gene interactions. In the present paper, through a Gaussian graphical model framework, we translate the problem of identifying gene-by-gene interactions associated with a binary trait D into an inference problem on the difference of two high-dimensional precision matrices, which summarize the conditional dependence network structures of the genes. We propose a procedure for testing the differential network globally that is particularly powerful against sparse alternatives. In addition, a multiple testing procedure with false discovery rate control is developed to infer the specific structure of the differential network. Theoretical justification is provided to ensure the validity of the proposed tests and optimality results are derived under sparsity assumptions. A simulation study demonstrates that the proposed tests maintain the desired error rates under the null and have good power under the alternative. The methods are applied to a breast cancer gene expression study.

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Finding disease candidate genes by liquid association

Abstract: A novel approach to finding candidate genes by using gene-expression data has been developed and used to identify a multiple sclerosis susceptibility candidate genes.

Cited by 31 publications

References 45 publications

Genome-wide expression links the electron transfer pathway of Shewanella oneidensis to chemotaxis

Genome-wide expression links the electron transfer pathway of Shewanella oneidensis to chemotaxis

Direct estimation of differential networks

Testing differential networks with applications to the detection of gene-gene interactions

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