Tag: genomics

Prediction of complex human diseases from pathway-focused candidate markers by joint estimation of marker effects: case of chronic fatigue syndrome

Abstract:

BACKGROUND: The current practice of using only a few strongly associated genetic markers in regression models results in generally low power in prediction or accounting for heritability of complex human traits.

PURPOSE: We illustrate here a Bayesian joint estimation of single nucleotide polymorphism (SNP) effects principle to improve prediction of phenotype status from pathway-focused sets of SNPs. Chronic fatigue syndrome (CFS), a complex disease of unknown etiology with no laboratory methods for diagnosis, was chosen to demonstrate the power of this Bayesian method. For CFS, such a genetic predictive model in combination with clinical evidence might lead to an earlier diagnosis than one based solely on clinical findings.

METHODS: One of our goals is to model disease status using Bayesian statistics which perform variable selection and parameter estimation simultaneously and which can induce the sparseness and smoothness of the SNP effects. Smoothness of the SNP effects is obtained by explicit modeling of the covariance structure of the SNP effects.

RESULTS: The Bayesian model achieved perfect goodness of fit when tested within the sampled data. Tenfold cross-validation resulted in 80% accuracy, one of the best so far for CFS in comparison to previous prediction models. Model reduction aspects were investigated in a computationally feasible manner. Additionally, genetic variation estimates provided by the model identified specific genetic markers for their biological role in the disease pathophysiology.

CONCLUSIONS: This proof-of-principle study provides a powerful approach combining Bayesian methods, SNPs representing multiple pathways and rigorous case ascertainment for accurate genetic risk prediction modeling of complex diseases like CFS and other chronic diseases.

 

Source: Bhattacharjee M, Rajeevan MS, Sillanpää MJ. Prediction of complex human diseases from pathway-focused candidate markers by joint estimation of marker effects: case of chronic fatigue syndrome. Hum Genomics. 2015 Jun 11;9:8. doi: 10.1186/s40246-015-0030-6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4479222/ (Full article)

 

Excess of activating killer cell immunoglobulin‑like receptors and lack of HLA-Bw4 ligands: a two‑edged weapon in chronic fatigue syndrome

Abstract:

Chronic fatigue syndrome (CFS) is an inflammatory disease of unknown aetiology. Researchers have proposed infectious, neurological and immunological causes of this syndrome. Recently, the xenotropic murine leukemia virus-related virus was detected in 67% of patients with CFS in a US study. This observation is in agreement with one ascertained aspect of the disease: a decreased efficiency in NK cell lytic activity in CFS patients. Here, we analyzed the genomic polymorphism of killer cell immunoglobulin-like receptors (KIRs) and their HLA class I cognate ligands in patients with certified CFS. An excess of KIR3DS1 was found in CFS patients with respect to controls, as well as an increased frequency of the genotype missing KIR2DS5. Forty-four CFS patients and 50 controls also underwent genomic typing for the HLA-ligands. In the patients, a great proportion of KIR3DL1 and KIR3DS1 receptors were found to be missing their HLA-Bw4Ile80 binding motif. We hypothesize that an excess of KIR3DS1, combined with an excess of ligand-free KIR3DL1 and KIR3DS1 receptors, may hamper the clearance of a pathogen via NK cells, thus favouring the chronicity of the infection.

Source: Pasi A, Bozzini S, Carlo-Stella N, Martinetti M, Bombardieri S, De Silvestri A, Salvaneschi L, Cuccia M. Excess of activating killer cell immunoglobulin‑like receptors and lack of HLA-Bw4 ligands: a two‑edged weapon in chronic fatigue syndrome. Mol Med Rep. 2011 May-Jun;4(3):535-40. doi: 10.3892/mmr.2011.447. Epub 2011 Mar 4. https://www.ncbi.nlm.nih.gov/pubmed/21468604

Functional genomics of serotonin receptor 2A (HTR2A): interaction of polymorphism, methylation, expression and disease association

Abstract:

Serotonergic neurotransmission plays a key role in the pathophysiology of neuropsychiatric illnesses. The functional significance of a promoter polymorphism, -1438G/A (rs6311), in one of the major genes of this system (serotonin receptor 2A, HTR2A) remains poorly understood in the context of epigenetic factors, transcription factors and endocrine influences. We used functional and structural equation modeling (SEM) approaches to assess the contributions of the polymorphism (rs6311), DNA methylation and clinical variables to HTR2A expression in chronic fatigue syndrome (CFS) subjects from a population-based study. HTR2A was up-regulated in CFS through allele-specific expression modulated by transcription factors at critical sites in its promoter: an E47 binding site at position -1,438, (created by the A-allele of rs6311 polymorphism), a glucocorticoid receptor (GR) binding site encompassing a CpG at position -1,420, and Sp1 binding at CpG methylation site -1,224. Methylation at -1,420 was strongly correlated with methylation at -1,439, a CpG site that is dependent upon the G-allele of rs6311 at position -1,438. SEM revealed a strong negative interaction between E47 and GR binding (in conjunction with cortisol level) on HTR2A expression. This study suggests that the promoter polymorphism (rs6311) can affect both transcription factor binding and promoter methylation, and this along with an individual’s stress response can impact the rate of HTR2A transcription in a genotype and methylation-dependent manner. This study can serve as an example for deciphering the molecular determinants of transcriptional regulation of major genes of medical importance by integrating functional genomics and SEM approaches. Confirmation in an independent study population is required.

 

Source: Falkenberg VR, Gurbaxani BM, Unger ER, Rajeevan MS. Functional genomics of serotonin receptor 2A (HTR2A): interaction of polymorphism, methylation, expression and disease association. Neuromolecular Med. 2011 Mar;13(1):66-76. doi: 10.1007/s12017-010-8138-2. Epub 2010 Oct 13. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044825/ (Full article)

 

A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data

Abstract:

BACKGROUND: In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for the association studies of disease susceptibility. In this work, our goal was to compare computational tools with and without feature selection for predicting chronic fatigue syndrome (CFS) using genetic factors such as single nucleotide polymorphisms (SNPs).

METHODS: We employed the dataset that was original to the previous study by the CDC Chronic Fatigue Syndrome Research Group. To uncover relationships between CFS and SNPs, we applied three classification algorithms including naive Bayes, the support vector machine algorithm, and the C4.5 decision tree algorithm. Furthermore, we utilized feature selection methods to identify a subset of influential SNPs. One was the hybrid feature selection approach combining the chi-squared and information-gain methods. The other was the wrapper-based feature selection method.

RESULTS: The naive Bayes model with the wrapper-based approach performed maximally among predictive models to infer the disease susceptibility dealing with the complex relationship between CFS and SNPs.

CONCLUSION: We demonstrated that our approach is a promising method to assess the associations between CFS and SNPs.

 

Source: Huang LC, Hsu SY, Lin E. A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data. J Transl Med. 2009 Sep 22;7:81. doi: 10.1186/1479-5876-7-81. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2765429/ (Full article)

 

A gene signature for post-infectious chronic fatigue syndrome

Abstract:

BACKGROUND: At present, there are no clinically reliable disease markers for chronic fatigue syndrome. DNA chip microarray technology provides a method for examining the differential expression of mRNA from a large number of genes. Our hypothesis was that a gene expression signature, generated by microarray assays, could help identify genes which are dysregulated in patients with post-infectious CFS and so help identify biomarkers for the condition.

METHODS: Human genome-wide Affymetrix GeneChip arrays (39,000 transcripts derived from 33,000 gene sequences) were used to compare the levels of gene expression in the peripheral blood mononuclear cells of male patients with post-infectious chronic fatigue (n = 8) and male healthy control subjects (n = 7).

RESULTS: Patients and healthy subjects differed significantly in the level of expression of 366 genes. Analysis of the differentially expressed genes indicated functional implications in immune modulation, oxidative stress and apoptosis. Prototype biomarkers were identified on the basis of differential levels of gene expression and possible biological significance.

CONCLUSION: Differential expression of key genes identified in this study offer an insight into the possible mechanism of chronic fatigue following infection. The representative biomarkers identified in this research appear promising as potential biomarkers for diagnosis and treatment.

 

Source: Gow JW, Hagan S, Herzyk P, Cannon C, Behan PO, Chaudhuri A. A gene signature for post-infectious chronic fatigue syndrome. BMC Med Genomics. 2009 Jun 25;2:38. doi: 10.1186/1755-8794-2-38. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2716361/ (Full article)

 

A Bayesian approach to gene-gene and gene-environment interactions in chronic fatigue syndrome

Abstract:

INTRODUCTION: In the study of genomics, it is essential to address gene-gene and gene-environment interactions for describing the complex traits that involves disease-related mechanisms. In this work, our goal is to detect gene-gene and gene-environment interactions resulting from the analysis of chronic fatigue syndrome patients’ genetic and demographic factors including SNPs, age, gender and BMI.

MATERIALS & METHODS: We employed the dataset that was original to the previous study by the Centers for Disease Control and Prevention Chronic Fatigue Syndrome Research Group. To investigate gene-gene and gene-environment interactions, we implemented a Bayesian based method for identifying significant interactions between factors. Here, we employed a two-stage Bayesian variable selection methodology based on Markov Chain Monte Carlo approaches.

RESULTS: By applying our Bayesian based approach, NR3C1 was found in the significant two-locus gene-gene effect model, as well as in the significant two-factor gene-environment effect model. Furthermore, a significant gene-environment interaction was identified between NR3C1 and gender. These results support the hypothesis that NR3C1 and gender may play a role in biological mechanisms associated with chronic fatigue syndrome.

CONCLUSION: We demonstrated that our Bayesian based approach is a promising method to assess the gene-gene and gene-environment interactions in chronic fatigue syndrome patients by using genetic factors, such as SNPs, and demographic factors such as age, gender and BMI.

 

Source: Lin E, Hsu SY. A Bayesian approach to gene-gene and gene-environment interactions in chronic fatigue syndrome. Pharmacogenomics. 2009 Jan;10(1):35-42. Doi: 10.2217/14622416.10.1.35. https://www.ncbi.nlm.nih.gov/pubmed/19102713

 

Evidence of inflammatory immune signaling in chronic fatigue syndrome: A pilot study of gene expression in peripheral blood

Abstract:

BACKGROUND: Genomic profiling of peripheral blood reveals altered immunity in chronic fatigue syndrome (CFS) however interpretation remains challenging without immune demographic context. The object of this work is to identify modulation of specific immune functional components and restructuring of co-expression networks characteristic of CFS using the quantitative genomics of peripheral blood.

METHODS: Gene sets were constructed a priori for CD4+ T cells, CD8+ T cells, CD19+ B cells, CD14+ monocytes and CD16+ neutrophils from published data. A group of 111 women were classified using empiric case definition (U.S. Centers for Disease Control and Prevention) and unsupervised latent cluster analysis (LCA). Microarray profiles of peripheral blood were analyzed for expression of leukocyte-specific gene sets and characteristic changes in co-expression identified from topological evaluation of linear correlation networks.

RESULTS: Median expression for a set of 6 genes preferentially up-regulated in CD19+ B cells was significantly lower in CFS (p = 0.01) due mainly to PTPRK and TSPAN3 expression. Although no other gene set was differentially expressed at p < 0.05, patterns of co-expression in each group differed markedly. Significant co-expression of CD14+ monocyte with CD16+ neutrophil (p = 0.01) and CD19+ B cell sets (p = 0.00) characterized CFS and fatigue phenotype groups. Also in CFS was a significant negative correlation between CD8+ and both CD19+ up-regulated (p = 0.02) and NK gene sets (p = 0.08). These patterns were absent in controls.

CONCLUSION: Dissection of blood microarray profiles points to B cell dysfunction with coordinated immune activation supporting persistent inflammation and antibody-mediated NK cell modulation of T cell activity. This has clinical implications as the CD19+ genes identified could provide robust and biologically meaningful basis for the early detection and unambiguous phenotyping of CFS.

 

Source: Aspler AL, Bolshin C, Vernon SD, Broderick G. Evidence of inflammatory immune signaling in chronic fatigue syndrome: A pilot study of gene expression in peripheral blood. Behav Brain Funct. 2008 Sep 26;4:44. doi: 10.1186/1744-9081-4-44. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2569951/ (Full article)