Category: Genetics

An integrated approach to infer causal associations among gene expression, genotype variation, and disease

Abstract:

Gene expression data and genotype variation data are now capable of providing genome-wide patterns across many different clinical conditions. However, the separate analysis of these data has limitations in elucidating the complex network of gene interactions underlying complex traits, such as common human diseases. More information about the identity of key driver genes of common diseases comes from integrating these two heterogeneous types of data. We developed a two-step procedure to characterize complex diseases by integrating genotype variation data and gene expression data.

The first step elucidates the causal relationship among genetic variation, gene expression level, and disease. Based on the causal relationship determined at the first step, the second step identifies significant gene expression traits whose effects on disease status or whose responses to disease status are modified by the specific genotype variation. For the selected significant genes, a pathway enrichment analysis can be performed to identify the genetic mechanism of a complex disease. The proposed two-step procedure was shown to be an effective method for integrating three different levels of data, i.e., genotype variation, gene expression and disease status.

By applying the proposed procedure to a chronic fatigue syndrome (CFS) dataset, we identified a list of potential causal genes for CFS, and found an evidence for difference in genetic mechanisms of the etiology between CFS without ‘a major depressive disorder with melancholic features’ (CFS) and CFS with ‘a major depressive disorder with melancholic features’ (CFS-MDD/m). Especially, the SNPs within NR3C1 gene were shown to differently influence the susceptibility of developing CFS and CFS-MDD/m through integrative action with gene expression levels.

 

Source: Lee E, Cho S, Kim K, Park T. An integrated approach to infer causal associations among gene expression, genotype variation, and disease. Genomics. 2009 Oct;94(4):269-77. doi: 10.1016/j.ygeno.2009.06.002. Epub 2009 Jun 18. http://www.sciencedirect.com/science/article/pii/S0888754309001347 (Full article)

 

Gene expression in peripheral blood leukocytes in monozygotic twins discordant for chronic fatigue: no evidence of a biomarker

Abstract:

BACKGROUND: Chronic fatiguing illness remains a poorly understood syndrome of unknown pathogenesis. We attempted to identify biomarkers for chronic fatiguing illness using microarrays to query the transcriptome in peripheral blood leukocytes.

METHODS: Cases were 44 individuals who were clinically evaluated and found to meet standard international criteria for chronic fatigue syndrome or idiopathic chronic fatigue, and controls were their monozygotic co-twins who were clinically evaluated and never had even one month of impairing fatigue. Biological sampling conditions were standardized and RNA stabilizing media were used. These methodological features provide rigorous control for bias resulting from case-control mismatched ancestry and experimental error. Individual gene expression profiles were assessed using Affymetrix Human Genome U133 Plus 2.0 arrays.

FINDINGS: There were no significant differences in gene expression for any transcript.

CONCLUSIONS: Contrary to our expectations, we were unable to identify a biomarker for chronic fatiguing illness in the transcriptome of peripheral blood leukocytes suggesting that positive findings in prior studies may have resulted from experimental bias.

 

Source: Byrnes A, Jacks A, Dahlman-Wright K, Evengard B, Wright FA, Pedersen NL, Sullivan PF. Gene expression in peripheral blood leukocytes in monozygotic twins discordant for chronic fatigue: no evidence of a biomarker. PLoS One. 2009 Jun 5;4(6):e5805. doi: 10.1371/journal.pone.0005805. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2688030/ (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

 

Gene profiling of patients with chronic fatigue syndrome/myalgic encephalomyelitis

Abstract:

Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is a multisystem disease, the pathogenesis of which remains undetermined. Following two microarray studies, we reported the differential expression of 88 human genes in patients with CFS; 85 of these genes were upregulated and 3 were downregulated.

The top functional categories of these 88 genes were hematologic disease and function, immunologic disease and function, cancer, cell death, immune response, and infection. Clustering of quantitative polymerase chain reaction data from CFS/ME patients revealed seven subtypes with distinct differences in Short Form (SF)-36 scores, clinical phenotypes, and severity. Gene signatures in each subtype implicate five human genes as possible targets for specific therapy.

Development of a diagnostic test for subtype status is now a priority. The possibility that these subtypes represent individual host responses to particular microbial infections is being investigated and may provide another route to specific therapies for CFS patients.

 

Source: Kerr JR. Gene profiling of patients with chronic fatigue syndrome/myalgic encephalomyelitis. Curr Rheumatol Rep. 2008 Dec;10(6):482-91. https://www.ncbi.nlm.nih.gov/pubmed/19007540

 

Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome

Abstract:

BACKGROUND: Systems biologic approaches such as Weighted Gene Co-expression Network Analysis (WGCNA) can effectively integrate gene expression and trait data to identify pathways and candidate biomarkers. Here we show that the additional inclusion of genetic marker data allows one to characterize network relationships as causal or reactive in a chronic fatigue syndrome (CFS) data set.

RESULTS: We combine WGCNA with genetic marker data to identify a disease-related pathway and its causal drivers, an analysis which we refer to as “Integrated WGCNA” or IWGCNA. Specifically, we present the following IWGCNA approach: 1) construct a co-expression network, 2) identify trait-related modules within the network, 3) use a trait-related genetic marker to prioritize genes within the module, 4) apply an integrated gene screening strategy to identify candidate genes and 5) carry out causality testing to verify and/or prioritize results. By applying this strategy to a CFS data set consisting of microarray, SNP and clinical trait data, we identify a module of 299 highly correlated genes that is associated with CFS severity. Our integrated gene screening strategy results in 20 candidate genes. We show that our approach yields biologically interesting genes that function in the same pathway and are causal drivers for their parent module. We use a separate data set to replicate findings and use Ingenuity Pathways Analysis software to functionally annotate the candidate gene pathways.

CONCLUSION: We show how WGCNA can be combined with genetic marker data to identify disease-related pathways and the causal drivers within them. The systems genetics approach described here can easily be used to generate testable genetic hypotheses in other complex disease studies.

 

Source: Presson AP, Sobel EM, Papp JC, Suarez CJ, Whistler T, Rajeevan MS, Vernon SD, Horvath S. Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome.BMC Syst Biol. 2008 Nov 6;2:95. doi: 10.1186/1752-0509-2-95. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2625353/ (Full article)

 

Identification of marker genes for differential diagnosis of chronic fatigue syndrome

Abstract:

Chronic fatigue syndrome (CFS) is a clinically defined condition characterized by long-lasting disabling fatigue. Because of the unknown mechanism underlying this syndrome, there still is no specific biomarker for objective assessment of the pathological fatigue. We have compared gene expression profiles in peripheral blood between 11 drug-free patients with CFS and age- and sex-matched healthy subjects using a custom microarray carrying complementary DNA probes for 1,467 stress-responsive genes.

We identified 12 genes whose mRNA levels were changed significantly in CFS patients. Of these 12 genes, quantitative real-time PCR validated the changes in 9 genes encoding granzyme in activated T or natural killer cells (GZMA), energy regulators (ATP5J2, COX5B, and DBI), proteasome subunits (PSMA3 and PSMA4), putative protein kinase c inhibitor (HINT ), GTPase (ARHC), and signal transducers and activators of transcription 5A (STAT5A).

Next, we performed the same microarray analysis on 3 additional CFS patients and 20 other patients with the chief complaint of long-lasting fatigue related to other disorders (non-CFS patients) and found that the relative mRNA expression of 9 genes classified 79% (11/14) of CFS and 85% (17/20) of the non-CFS patients.

Finally, real-time PCR measurements of the levels of the 9 involved mRNAs were done in another group of 18 CFS and 12 non-CFS patients. The expression pattern correctly classified 94% (17/18) of CFS and 92% (11/12) of non-CFS patients. Our results suggest that the defined gene cluster (9 genes) may be useful for detecting pathological responses in CFS patients and for differential diagnosis of this syndrome.

 

Source: Saiki T, Kawai T, Morita K, Ohta M, Saito T, Rokutan K, Ban N. Identification of marker genes for differential diagnosis of chronic fatigue syndrome. Mol Med. 2008 Sep-Oct;14(9-10):599-607. doi: 10.2119/2007-00059.Saiki. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2442021/ (Full article)

 

Gene expression subtypes in patients with chronic fatigue syndrome/myalgic encephalomyelitis

Abstract:

Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is a multisystem disease, the pathogenesis of which remains undetermined. We set out to determine the precise abnormalities of gene expression in the blood of patients with CFS/ME. We analyzed gene expression in peripheral blood from 25 patients with CFS/ME diagnosed according to the Centers for Disease Control and Prevention diagnostic criteria and 50 healthy blood donors, using a microarray with a cutoff fold difference of expression of >or=2.5. Genes showing differential expression were further analyzed in 55 patients with CFS/ME and 75 healthy blood donors, using quantitative polymerase chain reaction.

Differential expression was confirmed for 88 genes; 85 were upregulated, and 3 were downregulated. Highly represented functions were hematological disease and function, immunological disease and function, cancer, cell death, immune response, and infection. Clustering of quantitative polymerase chain reaction data from patients with CFS/ME revealed 7 subtypes with distinct differences in Medical Outcomes Survey Short Form-36 scores, clinical phenotypes, and severity.

 

Source: Kerr JR, Petty R, Burke B, Gough J, Fear D, Sinclair LI, Mattey DL, Richards SC, Montgomery J, Baldwin DA, Kellam P, Harrison TJ, Griffin GE, Main J,Enlander D, Nutt DJ, Holgate ST. Gene expression subtypes in patients with chronic fatigue syndrome/myalgic encephalomyelitis. J Infect Dis. 2008 Apr 15;197(8):1171-84. doi: 10.1086/533453.  http://jid.oxfordjournals.org/content/197/8/1171.long (Full article)

 

Genetic evaluation of the serotonergic system in chronic fatigue syndrome

Abstract:

Chronic fatigue syndrome (CFS) is a debilitating disorder of unknown etiology with no known lesions, diagnostic markers or therapeutic intervention. The pathophysiology of CFS remains elusive, although abnormalities in the central nervous system (CNS) have been implicated, particularly hyperactivity of the serotonergic (5-hydroxytryptamine; 5-HT) system and hypoactivity of the hypothalamic-pituitary-adrenal (HPA) axis. Since alterations in 5-HT signaling can lead to physiologic and behavioral changes, a genetic evaluation of the 5-HT system was undertaken to identify serotonergic markers associated with CFS and potential mechanisms for CNS abnormality.

A total of 77 polymorphisms in genes related to serotonin synthesis (TPH2), signaling (HTR1A, HTR1E, HTR2A, HTR2B, HTR2C, HTR3A, HTR3B, HTR4, HTR5A, HTR6, and HTR7), transport (SLC6A4), and catabolism (MAOA) were examined in 137 clinically evaluated subjects (40 CFS, 55 with insufficient fatigue, and 42 non-fatigued, NF, controls) derived from a population-based CFS surveillance study in Wichita, Kansas.

Of the polymorphisms examined, three markers (-1438G/A, C102T, and rs1923884) all located in the 5-HT receptor subtype HTR2A were associated with CFS when compared to NF controls. Additionally, consistent associations were observed between HTR2A variants and quantitative measures of disability and fatigue in all subjects. The most compelling of these associations was with the A allele of -1438G/A (rs6311) which is suggested to have increased promoter activity in functional studies. Further, in silico analysis revealed that the -1438 A allele creates a consensus binding site for Th1/E47, a transcription factor implicated in the development of the nervous system. Electrophoretic mobility shift assay supports allele-specific binding of E47 to the A allele but not the G allele at this locus.

These data indicate that sequence variation in HTR2A, potentially resulting in its enhanced activity, may be involved in the pathophysiology of CFS.

 

Source: Smith AK, Dimulescu I, Falkenberg VR, Narasimhan S, Heim C, Vernon SD, Rajeevan MS. Genetic evaluation of the serotonergic system in chronic fatigue syndrome. Psychoneuroendocrinology. 2008 Feb;33(2):188-97. https://www.ncbi.nlm.nih.gov/pubmed/18079067

 

Seven genomic subtypes of chronic fatigue syndrome/myalgic encephalomyelitis: a detailed analysis of gene networks and clinical phenotypes

Abstract:

AIM: Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is a multisystem disease, the pathogenesis of which remains undetermined. The authors have recently reported a study of gene expression that identified differential expression of 88 human genes in patients with CFS/ME. Clustering of quantitative PCR (qPCR) data from patients with CFS/ME revealed seven distinct subtypes with distinct differences in Medical Outcomes Survey Short Form-36 scores, clinical phenotypes and severity.

METHODS: In this study, for each CFS/ME subtype, those genes whose expression differed significantly from that of normal blood donors were identified, and then gene interactions, disease associations and molecular and cellular functions of those gene sets were determined. Genomic analysis was then related to clinical data for each CFS/ME subtype.

RESULTS: Genomic analysis revealed some common (neurological, haematological, cancer) and some distinct (metabolic, endocrine, cardiovascular, immunological, inflammatory) disease associations among the subtypes. Subtypes 1, 2 and 7 were the most severe, and subtype 3 was the mildest. Clinical features of each subtype were as follows: subtype 1 (cognitive, musculoskeletal, sleep, anxiety/depression); subtype 2 (musculoskeletal, pain, anxiety/depression); subtype 3 (mild); subtype 4 (cognitive); subtype 5 (musculoskeletal, gastrointestinal); subtype 6 (postexertional); subtype 7 (pain, infectious, musculoskeletal, sleep, neurological, gastrointestinal, neurocognitive, anxiety/depression).

CONCLUSION: It was particularly interesting that in the seven genomically derived subtypes there were distinct clinical syndromes, and that those which were most severe were also those with anxiety/depression, as would be expected in a disease with a biological basis.

 

Source: Kerr JR, Burke B, Petty R, Gough J, Fear D, Mattey DL, Axford JS, Dalgleish AG, Nutt DJ. Seven genomic subtypes of chronic fatigue syndrome/myalgic encephalomyelitis: a detailed analysis of gene networks and clinical phenotypes. J Clin Pathol. 2008 Jun;61(6):730-9. Epub 2007 Dec 5. https://www.ncbi.nlm.nih.gov/pubmed/18057078

 

Twin analyses of fatigue

Abstract:

Prolonged fatigue equal to or greater than 1 month duration and chronic fatigue equal to or greater than 6 months duration are both commonly seen in clinical practice, yet little is known about the etiology or epidemiology of either symptom. Chronic fatigue syndrome (CFS), while rarer, presents similar challenges in determining cause and epidemiology. Twin studies can be useful in elucidating genetic and environmental influences on fatigue and CFS. The goal of this article was to use biometrical structural equation twin modeling to examine genetic and environmental influences on fatigue, and to investigate whether these influences varied by gender. A total of 1042 monozygotic (MZ) twin pairs and 828 dizygotic (DZ) twin pairs who had completed the University of Washington Twin Registry survey were assessed for three fatigue-related variables: prolonged fatigue, chronic fatigue, and CFS. Structural equation twin modeling was used to determine the relative contributions of additive genetic effects, shared environmental effects, and individual-specific environmental effects to the 3 fatigue conditions. In women, tetrachoric correlations were similar for MZ and DZ pairs for prolonged and chronic fatigue, but not for CFS. In men, however, the correlations for prolonged and chronic fatigue were higher in MZ pairs than in DZ pairs. About half the variance for both prolonged and chronic fatigue in males was due to genetic effects, and half due to individual-specific environmental effects. For females, most variance was due to individual environmental effects.

 

Source: Schur E, Afari N, Goldberg J, Buchwald D, Sullivan PF. Twin analyses of fatigue. Twin Res Hum Genet. 2007 Oct;10(5):729-33. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2953372/ (Full article)