Identification of significant genes in genomics using Bayesian variable selection methods

Lin, Eugene; Huang, Lung-Cheng

doi:10.2147/aabc.s3624

Cited by 7 publications

(10 citation statements)

References 24 publications

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“…Mice from two N ‐ethyl‐ N ‐nitrosourea (ENU) lines show abnormal, low amplitude voluntary wheel‐running activity patterns. Circadian activity patterns are double plotted for ease of visualization, with each horizontal line representing 48 hours and successive days plotted along the Y‐axis. Wheel revolutions are depicted as black marks on the horizontal lines.…”

Section: Resultsmentioning

confidence: 99%

“…There is ample evidence of a genetic basis for the development and manifestation of CFS . Polymorphisms in several genes (eg, the adrenergic receptor a1 ( ADRA1A ), the serotonin transporter ( SLC6A4 ) or receptor ( HTR2A ), tyrosine hydroxylase ( TH ), corticosteroid‐binding globulin ( CBG ), corticotropin releasing hormone receptor 1 ( CRHR1 ), the cytokine IL‐1B, neuronal PAS domain protein 2 ( NPAS2 ), the nuclear receptor subfamily 3; group C, member 1 glucocorticoid receptor ( NR3C1 ) and the glutamate receptor‐ionotropic kinase 2 ( GRIK2 ) have all been linked to either the occurrence or the severity of fatigue symptomology in humans . Furthermore, a recent study comparing CFS patients to healthy controls identified 442 additional candidate genes associated with CFS .…”

Section: Introductionmentioning

confidence: 99%

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A novel mutation in Slc2a4 as a mouse model of fatigue

Groot

Castorena

Cox

et al. 2019

Genes Brain and Behavior

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Chronic fatigue is a debilitating disorder with widespread consequences, but effective treatment strategies are lacking. Novel genetic mouse models of fatigue may prove invaluable for studying its underlying physiological mechanisms and for testing treatments and interventions. In a screen of voluntary wheel‐running behavior in N‐ethyl‐N‐nitrosourea mutagenized C57BL/6J mice, we discovered two lines with low body weights and aberrant wheel‐running patterns suggestive of a fatigue phenotype. Affected progeny from these lines had lower daily activity levels and exhibited low amplitude circadian rhythm alterations. Their aberrant behavior was characterized by frequent interruptions and periods of inactivity throughout the dark phase of the light‐dark cycle and increased levels of activity during the rest or light phase. Expression of the behavioral phenotypes in offspring of strategic crosses was consistent with a recessive inheritance pattern. Mapping of phenotypic abnormalities showed linkage with a single locus on chromosome 1, and whole exome sequencing identified a single point mutation in the Slc2a4 gene encoding the GLUT4 insulin‐responsive glucose transporter. The single nucleotide change (A‐T, which we named “twiggy”) was in the distal end of exon 10 and resulted in a premature stop (Y440*). Additional metabolic phenotyping confirmed that these mice recapitulate phenotypes found in GLUT4 knockout mice. However, to the best of our knowledge, this is the first time a mutation in this gene has been shown to result in extensive changes in general behavioral patterns. These findings suggest that GLUT4 may be involved in circadian behavioral abnormalities and could provide insights into fatigue in humans.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel mutation in Slc2a4 as a mouse model of fatigue

Groot

Castorena

Cox

et al. 2019

Genes Brain and Behavior

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“…Accounting for models is not a trivial task because even a relatively small set of factors results in the large number of possible models [61]. For example, if we study 10 factors, then these 10 factors yield 2 10 possible models.…”

Section: Data Reduction and Feature Selection Approachmentioning

confidence: 99%

Machine learning and systems genomics approaches for multi-omics data

2017

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In light of recent advances in biomedical computing, big data science, and precision medicine, there is a mammoth demand for establishing algorithms in machine learning and systems genomics (MLSG), together with multi-omics data, to weigh probable phenotype-genotype relationships. Software frameworks in MLSG are extensively employed to analyze hundreds of thousands of multi-omics data by high-throughput technologies. In this study, we reviewed the MLSG software frameworks and future directions with respect to multi-omics data analysis and integration. Our review was targeted at researching recent approaches and technical solutions for the MLSG software frameworks using multi-omics platforms.

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“…The SSVS method has been extended to linear and generalized linear mixed models (Chen and Dunson, 2003; Kinney and Dunson, 2007), and to survival models (Lee and Mallick, 2004). Because of its ability to select among a larger number of potential predictors, it has been applied to high dimensional data including genomics and other complex disease risk factor studies (Beattie et al , 2002; Lee et al , 2003; Swartz et al , 2008; Lin and Huang, 2008). …”

Section: Introductionmentioning

confidence: 99%

Bayesian variable selection under the proportional hazards mixed-effects model

Lee

Kim

2014

Computational Statistics & Data Analysis

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Over the past decade much statistical research has been carried out to develop models for correlated survival data; however, methods for model selection are still very limited. A stochastic search variable selection (SSVS) approach under the proportional hazards mixed-effects model (PHMM) is developed. The SSVS method has previously been applied to linear and generalized linear mixed models, and to the proportional hazards model with high dimensional data. Because the method has mainly been developed for hierarchical normal mixture distributions, it operates on the linear predictor under the Cox type models. The PHMM naturally incorporates the normal distribution via the random effects, which enables SSVS to efficiently search through the candidate variable space. The approach was evaluated through simulation, and applied to a multi-center lung cancer clinical trial data set, for which the variable selection problem was previously debated upon in the literature.

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Identification of significant genes in genomics using Bayesian variable selection methods

Cited by 7 publications

References 24 publications

A novel mutation in Slc2a4 as a mouse model of fatigue

A novel mutation in Slc2a4 as a mouse model of fatigue

Machine learning and systems genomics approaches for multi-omics data

Bayesian variable selection under the proportional hazards mixed-effects model

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