Genetic Association Studies: An Information Content Perspective

Wu, Cen; Li, Shaoyu; Cui, Yuehua

doi:10.2174/138920212803251382

Cited by 22 publications

(24 citation statements)

References 74 publications

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“…The allele and genotype frequencies of two IFN‐γ variants, +874 T>A and UTR5644 A>T, in patients with active brucellosis and controls are listed in Table . It has been established that OR < 1 along with P < 0.05 is associated with lower risk of disease (protective factor), whereas OR > 1 along with P < 0.05 is associated with higher risk of disease (risk factor) . A significant difference was found between the two groups regarding allelic and genotyping distribution of the position at +874 T>A.…”

Section: Resultsmentioning

confidence: 99%

Relationship between γ‐interferon gene polymorphisms and susceptibility to brucellosis infection

Eskandari-Nasab

Moghadampour

Hasani³

et al. 2013

Microbiology and Immunology

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Interferon-gamma (IFN-g) is a pro-inflammatory cytokine that plays a pivotal role in the defense mechanism against Brucella infection. It was hypothesized that the IFN-g in (þ874 A/T in intron 1) TT and þ5644 T/A, TT genotypes, which are reportedly associated with high IFN production, are associated with susceptibility to brucellosis in Iranian subjects. Genotyping of these IFN-g variants by an allele-specific polymerase chain reaction method was performed in 281 subjects, comprising 153 patients with active brucellosis and 128 healthy controls. It was found that the þ874 minor allele (A) and homozygote genotype (AA) were significantly more frequently present in brucellosis patients than in controls (OR ¼ 2.588; 95% CI, 1.313-5.104; P ¼ 0.006 for the AA genotype; OR ¼ 1.575; 95% CI, 1.124-2.216; P ¼ 0.010 for the A allele). However, the allelic and genotypic distribution of the IFN-g polymorphism at position UTR5644 A>T did not differ significantly between patients and controls (P > 0.05). The distribution of haplotypes in this study suggests that the T/A haplotype (þ874/ UTR5644), which was present more frequently in controls than in patients, may protect subjects against Brucella infection. It is suggested that IFN-g þ874 AA genotype and A allele are risk factors for developing brucellosis infection in Iranian subjects.

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Section: Resultsmentioning

confidence: 99%

Relationship between γ‐interferon gene polymorphisms and susceptibility to brucellosis infection

Eskandari-Nasab

Moghadampour

Hasani³

et al. 2013

Microbiology and Immunology

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“…In this paper, we are interested in the identification of lipid-treatment (or environment) interactions through penalization. The success of set based analysis, including those for the gene set [44] and SNP set [45,46], has tremendously motivated the development of statistical methods for G × E interactions from marginal analyses ( [47,48]) to penalization methods [17,18,49]. Our model can be potentially extended in the following aspects.…”

Section: Discussionmentioning

confidence: 99%

Penalized Variable Selection for Lipid–Environment Interactions in a Longitudinal Lipidomics Study

Zhou

Ren

et al. 2019

Genes

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Lipid species are critical components of eukaryotic membranes. They play key roles in many biological processes such as signal transduction, cell homeostasis, and energy storage. Investigations of lipid–environment interactions, in addition to the lipid and environment main effects, have important implications in understanding the lipid metabolism and related changes in phenotype. In this study, we developed a novel penalized variable selection method to identify important lipid–environment interactions in a longitudinal lipidomics study. An efficient Newton–Raphson based algorithm was proposed within the generalized estimating equation (GEE) framework. We conducted extensive simulation studies to demonstrate the superior performance of our method over alternatives, in terms of both identification accuracy and prediction performance. As weight control via dietary calorie restriction and exercise has been demonstrated to prevent cancer in a variety of studies, analysis of the high-dimensional lipid datasets collected using 60 mice from the skin cancer prevention study identified meaningful markers that provide fresh insight into the underlying mechanism of cancer preventive effects.

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“…In marginal analysis, statistical testing of G×E interactions prevails, which spans from the classical linear model with interactions in a wide range of studies, such as case-control study, case only study and the two-stage screening study, to more sophisticated models, such as empirical Bayesian models and nonparametric and semiparametric models. 44 On the other hand, the joint methods, especially the penalized variable selection methods, for G×E interactions, have been motivated by the success of gene set-based association analysis over marginal analysis, as demonstrated by Wu and Cui, 45 Wu et al, 46 and Schaid et al 47 Recently, multiple penalization methods have been proposed to identify important G×E interactions under parametric, semiparametric, and nonparametric models recently. 7,8,11,12 Within the Bayesian framework, nonlinear interaction has not been sufficiently considered for G×E interactions.…”

Section: Discussionmentioning

confidence: 99%

Semiparametric Bayesian variable selection for gene‐environment interactions

Ren

Zhou

et al. 2019

Statistics in Medicine

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Many complex diseases are known to be affected by the interactions between genetic variants and environmental exposures beyond the main genetic and environmental effects. Study of gene-environment (G×E) interactions is important for elucidating the disease etiology. Existing Bayesian methods for G×E interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. Many studies have shown the advantages of penalization methods in detecting G×E interactions in "large p, small n" settings. However, Bayesian variable selection, which can provide fresh insight into G×E study, has not been widely examined. We propose a novel and powerful semiparametric Bayesian variable selection model that can investigate linear and nonlinear G×E interactions simultaneously. Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main-effects-only case within the Bayesian framework.Spike-and-slab priors are incorporated on both individual and group levels to identify the sparse main and interaction effects. The proposed method conducts Bayesian variable selection more efficiently than existing methods. Simulation shows that the proposed model outperforms competing alternatives in terms of both identification and prediction. The proposed Bayesian method leads to the identification of main and interaction effects with important implications in a high-throughput profiling study with high-dimensional SNP data.

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Genetic Association Studies: An Information Content Perspective

Cited by 22 publications

References 74 publications

Relationship between γ‐interferon gene polymorphisms and susceptibility to brucellosis infection

Relationship between γ‐interferon gene polymorphisms and susceptibility to brucellosis infection

Penalized Variable Selection for Lipid–Environment Interactions in a Longitudinal Lipidomics Study

Semiparametric Bayesian variable selection for gene‐environment interactions

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