antipsychotics; genome-wide association; neurotransmitters; pathways; prostaglandin; schizophrenia
schizophrenia; sequencing; SNV; genetic; association; mutation; DISC1
Chronic fatigue syndrome (CFS) has been found to be comorbid with various medical conditions in clinical samples, but little research has investigated CFS comorbidity in population-based samples.
This study investigated conditions concurrent with a CFS-like illness among twins in the population-based Mid-Atlantic Twin Registry (MATR), including chronic widespread pain (CWP), irritable bowel syndrome (IBS), and major depression (MD).
A survey was mailed to participants in the MATR in 1999. Generalized estimating equations were used to estimate odds ratios to assess associations between CFS-like illness and each comorbid condition.
A total of 4,590 completed surveys were collected. Most participants were female (86.3%); mean age was 44.7 years. Among participants with a CFS-like illness, lifetime prevalence of CWP was 41%, IBS was 16%, and MD was 57%. Participants reporting at least one of the three comorbid conditions were about 14 times more likely to have CFS-like illness than those without CWP, IBS, or MD (95% confidence interval 8.1–21.3%). Only MD showed a temporal pattern of presentation during the same year as diagnosis of CFS-like illness. Age, gender, body mass index, age at illness onset, exercise level, self-reported health status, fatigue symptoms, and personality measures did not differ between those reporting CFS-like illness with and without comorbidity.
These results support findings in clinically based samples that CFS-like illness is frequently cormorbid with CWP, IBS, and/or MD. We found no evidence that CFS-like illnesses with comorbidities are clinically distinct from those without comorbidities.
Research into the etiology of schizophrenia, particularly the possible genetic basis, has never been as interesting and as provocative as in the past three years. Sullivan looks critically at the key research.
Structural variation is an important class of genetic variation in mammals. High-throughput sequencing (HTS) technologies promise to revolutionize copy-number variation (CNV) detection but present substantial analytic challenges. Converging evidence suggests that multiple types of CNV-informative data (e.g. read-depth, read-pair, split-read) need be considered, and that sophisticated methods are needed for more accurate CNV detection. We observed that various sources of experimental biases in HTS confound read-depth estimation, and note that bias correction has not been adequately addressed by existing methods. We present a novel read-depth–based method, GENSENG, which uses a hidden Markov model and negative binomial regression framework to identify regions of discrete copy-number changes while simultaneously accounting for the effects of multiple confounders. Based on extensive calibration using multiple HTS data sets, we conclude that our method outperforms existing read-depth–based CNV detection algorithms. The concept of simultaneous bias correction and CNV detection can serve as a basis for combining read-depth with other types of information such as read-pair or split-read in a single analysis. A user-friendly and computationally efficient implementation of our method is freely available.
Genome wide association studies (GWAS) have proven a powerful method to identify common genetic variants contributing to susceptibility to common diseases. Here we show that extremely low-coverage sequencing (0.1–0.5x) captures almost as much of the common (>5%) and low-frequency (1–5%) variation across the genome as SNP arrays. As an empirical demonstration, we show that genome-wide SNP genotypes can be inferred at a mean r2 of 0.71 using off-target data (0.24x average coverage) in a whole-exome study of 909 samples. Using both simulated and real exome sequencing datasets we show that association statistics obtained using ultra low-coverage sequencing data attain similar P-values at known associated variants as genotyping arrays, without an excess of false positives. Within the context of reductions in sample preparation and sequencing costs, funds invested in ultra low-coverage sequencing can yield several times the effective sample size of SNP-array GWAS, and a commensurate increase in statistical power.
QT prolongation is associated with increased risk of cardiac arrhythmias. Identifying the genetic variants that mediate antipsychotic induced prolongation may help to minimize this risk, which might prevent the removal of efficacious drugs from the market. We performed candidate gene analysis and five drug specific genome-wide association studies (GWAS) with 492K SNPs to search for genetic variation mediating antipsychotic induced QT prolongation in 738 schizophrenia patients from the Clinical Antipsychotic Trial of Intervention Effectiveness (CATIE) study.
Our candidate gene study suggests the involvement of NOS1AP and NUBPL (p-values =1.45×10−05 and 2.66×10−13, respectively). Furthermore, our top GWAS hit achieving genome-wide significance, defined as a q-value <0.10, (p-value =1.54×10−7, q-value =0.07), located in SLC22A23, mediated the effects of quetiapine on prolongation. SLC22A23 belongs to a family of organic ion transporters that shuttle a variety of compounds including drugs, environmental toxins, and endogenous metabolites across the cell membrane. This gene is expressed in the heart and is integral in mouse heart development. The genes mediating antipsychotic induced QT prolongation partially overlap with the genes affecting normal QT interval variation. However, some genes may also be unique for drug induced prolongation. This study demonstrates the potential of GWAS to discover genes and pathways that mediate antipsychotic induced QT prolongation.
candidate gene analysis; genome-wide association study; schizophrenia; adverse effects; CATIE
To investigate the prevalence and patterns of transitions between cigarette and snus use.
Cross-sectional study within the population-based Swedish Twin Registry.
Setting and participants
A total of 31 213 male and female twins 42–64 years old.
Age-adjusted prevalence odds ratios (POR) and 95% confidence intervals (CIs) described the association between gender and tobacco use, while Kaplan–Meier survival methods produced cumulative incidence curves of age at onset of tobacco use. Life-time tobacco use histories were constructed using ages at onset of tobacco use and current tobacco use status.
Although more males reported ever smoking (64.4%) than females (61.7%), more males were former smokers (POR: 1.33, 95% CI: 1.27–1.39). Males were far more likely to use snus than females (POR: 18.0, 95% CI: 16.17–20.04). Age at onset of cigarette smoking occurred almost entirely before age 25, while the age at onset of snus use among males occurred over a longer time period. Most men began using cigarettes first, nearly one-third of whom switched to using cigarettes and snus in combination. While 30.6% of these combined users quit tobacco completely, only 7.4% quit snus and currently use cigarettes, while 47.7% quit cigarettes and currently use snus.
Current cigarette smoking is more prevalent among Swedish women than men, while snus use is more prevalent among men. Among men who reported using both cigarettes and snus during their life-time, it was more common to quit cigarettes and currently use snus than to quit snus and currently use cigarettes. Once snus use was initiated, more men continued using snus rather than quit tobacco completely.
Cigarettes; prevalence; snus; Sweden; tobacco
Schizophrenia is a complex disorder caused by both genetic and environmental factors. Using 9,087 cases, 12,171 controls and 915,354 imputed SNPs from the Psychiatric GWA Consortium for schizophrenia (PGC-SCZ) we estimate that 23% (s.e. 1%) of variation in liability to schizophrenia is captured by SNPs. We show that an important proportion of this variation must be due to common causal variants, that the variance explained by each chromosome is linearly related to its length (r = 0.89, p = 2.6 × 10−8), that the genetic basis of schizophrenia is the same in males and females, and that a disproportionate proportion of variation is attributable to a set of 2725 genes expressed in the central nervous system (CNS) (p = 7.6 ×10−8). These results are consistent with a polygenic genetic architecture and imply more individual SNP associations will be detected for this disease as sample size increases.
heritability; missing heritability; genomic variance; SNPs; GWAS
Understanding individual differences in the development of extra-pyramidal side effects (EPS) as a response to antipsychotic therapy is essential to individualize treatment.
We performed genome-wide association studies to search for genetic susceptibility to EPS. Our sample consists of 738 schizophrenia patients, genotyped for 492K SNPs. We studied three quantitative measures of antipsychotic adverse drug reactions, the Simpson-Angus scale (SAS) for parkinsonism, the Barnes akathisia rating scale, and the abnormal involuntary movement scale (AIMS) as well as a clinical diagnosis of probable tardive dyskinesia.
Two SNPs for SAS, rs17022444 and rs2126709 with p=1.2×10-10 and p=3.8×10-7, respectively, and one for AIMS, rs7669317 with p=7.7×10-8, reached genome-wide significance (q-value <0.1). Rs17022444 and rs7669317 were located in intergenic regions and rs2126709 was located in ZNF202 on 11q24. Fourteen additional signals were potentially interesting (q-value <0.5). The ZNF202 is a transcriptional repressor controlling, among other genes, PLP1 which is the major protein in myelin. Mutations in PLP1 cause Pelizaeus-Merzbacher disease, which has parkinsonism as an occurring symptom. Altered mRNA expression of PLP1 is associated with schizophrenia.
Although our findings require replication and validation, this study demonstrates the potential of GWAS to discover genes and pathways that mediate adverse effects of antipsychotics.
genome-wide association; antipsychotic; pharmacogenetics; personalized medicine; single nucleotide polymorphism; copy number variation; schizophrenia
Tardive dyskinesia (TD) is a debilitating, unpredictable and often irreversible side effect resulting from chronic treatment with typical antipsychotic agents such as haloperidol. TD is characterized by repetitive, involuntary, purposeless movements primarily of the orofacial region. In order to investigate genetic susceptibility to TD, we used a validated mouse model for a systems genetics analysis geared toward detecting genetic predictors of TD in human patients. Phenotypic data from 27 inbred strains chronically treated with haloperidol and phenotyped for vacuous chewing movements were subject to a comprehensive genomic analysis involving 426,493 SNPs, 4,047 CNVs, brain gene expression, along with gene network and bioinformatic analysis. Our results identified ~50 genes that we expect to have high prior probabilities for association with haloperidol-induced TD, most of which have never been tested for association with human TD. Among our top candidates were genes regulating the development of brain motor control regions (Zic4, Nkx6-1), glutamate receptors (Grin1, Grin2a), and an indirect target of haloperidol (Drd1a) that has not been as well studied as the direct target, Drd2.
pharmacogenetic; adverse drug reaction; QTL; haloperidol; mouse
The purpose of this invited review is to summarize the state of genetic research into the etiology of schizophrenia (SCZ) and to consider options for progress. The fundamental uncertainty in SCZ genetics has always been the nature of the beast, the underlying genetic architecture. If this were known, studies using the appropriate technologies and sample sizes could be designed with an excellent chance of producing high-confidence results. Until recently, few pertinent data were available, and the field necessarily relied on speculation. However, for the first time in the complex and frustrating history of inquiry into the genetics of SCZ, we now have empirical data about the genetic basis of SCZ that implicate specific loci and that can be used to plan the next steps forward.
schizophrenia; genetics; review; genome-wide association; genome-wide linkage; next-generation sequencing
Neurocognitive deficits are a core feature of schizophrenia and, therefore, represent potentially critical outcome variables for assessing antipsychotic treatment response. We performed genome-wide association studies (GWAS) with 492K single nucleotide polymorphisms (SNPs) in a sample of 738 patients with schizophrenia from the Clinical Antipsychotic Trials of Intervention Effectiveness study. Outcome variables consisted of a neurocognitive battery administered at multiple time points over an 18-month period, measuring processing speed, verbal memory, vigilance, reasoning, and working memory domains. Genetic mediation of improvements in each of these five domains plus a composite neurocognitive measure was assessed for each of five antipsychotics (olanzapine, perphenazine, quetiapine, risperidone, and ziprasidone). Six SNPs achieved genome-wide significance using a pre-specified threshold that ensures, on average, only 1 in 10 findings is a false discovery. These six SNPs were located within, or in close proximity to, genes EHF, SLC26A9, DRD2, GPR137B, CHST8, and IL1A. The more robust findings, that is those significant across multiple neurocognitive domains and having adjacent SNPs showing evidence for association, were rs286913 at the EHF gene (p-value 6.99 × 10−8, q-value 0.034, mediating the effects of ziprasidone on vigilance), rs11240594 at SLC26A9 (p-value 1.4 × 10−7, q-value 0.068, mediating the effects of olanzapine on processing speed), and rs11677416 at IL1A (p-value 6.67 × 10−7, q-value 0.081, mediating the effects of olanzapine on working memory). This study has generated several novel candidate genes for antipsychotic response. However, our findings will require replication and functional validation. To facilitate replication efforts, we provide all GWAS p-values for download.
genome-wide association; schizophrenia; pharmacogenomics; personalized medicine; single nucleotide polymorphisms; biological psychiatry; cognition; schizophrenia; antipsychotics; pharmacogenetics; pharmacogenomics; genome-wide association; single nucleotide polymorphism
Mouse models play a crucial role in the study of human behavioral traits and diseases. Variation of gene expression in brain may play a critical role in behavioral phenotypes, and thus it is of great importance to understand regulation of transcription in mouse brain. In this study, we analyzed the role of two important factors influencing steady-state transcriptional variation in mouse brain. First we considered the effect of assessing whole brain vs. discrete regions of the brain. Second, we investigated the genetic basis of strain effects on gene expression. We examined the transcriptome of three brain regions using Affymetrix expression arrays: whole brain, forebrain, and hindbrain in adult mice from two common inbred strains (C57BL/6J vs. NOD/ShiLtJ) with eight replicates for each brain region and strain combination. We observed significant differences between the transcriptomes of forebrain and hindbrain. In contrast, the transcriptomes of whole brain and forebrain were very similar. Using 4.3 million single-nucleotide polymorphisms identified through whole-genome sequencing of C57BL/6J and NOD/ShiLtJ strains, we investigated the relationship between strain effect in gene expression and DNA sequence similarity. We found that cis-regulatory effects play an important role in gene expression differences between strains and that the cis-regulatory elements are more often located in 5′ and/or 3′ transcript boundaries, with no apparent preference on either 5′ or 3′ ends.
Mouse Genetic Resource; Mouse Collaborative Cross; mouse; gene expression; whole brain; forebrain; hindbrain; sequence variation
Summary: seeQTL is a comprehensive and versatile eQTL database, including various eQTL studies and a meta-analysis of HapMap eQTL information. The database presents eQTL association results in a convenient browser, using both segmented local-association plots and genome-wide Manhattan plots.
Availability and implementation: seeQTL is freely available for non-commercial use at http://www.bios.unc.edu/research/genomic_software/seeQTL/.
Contact: email@example.com; firstname.lastname@example.org
Supplementary information: Supplementary data are available at Bioinformatics online.
The Psychiatric GWAS consortium was founded with the aim of conducting statistically rigorous and comprehensive GWAS meta-analyses for five major psychiatric disorders, ADHD, autism, bipolar disorder, major depressive disorder and schizophrenia. In the era of GWAS and high throughput genomics, a major trend has been the emergence of collaborative, consortia approaches. Taking advantage of the scale that collaborative, consortia approaches can bring to a problem, the PGC has been a major driver in psychiatric genetics and provides a model for how similar approaches may be applied to other disease communities.
Variation in personality traits is 30% to 60% attributed to genetic influences. Attempts to unravel these genetic influences at the molecular level have, so far, been inconclusive. We performed the first genome-wide association study of Cloninger’s temperament scales in a sample of 5117 individuals, in order to identify common genetic variants underlying variation in personality. Participants’ scores on Harm Avoidance, Novelty Seeking, Reward Dependence, and Persistence were tested for association with 1,252,387 genetic markers. We also performed gene-based association tests and biological pathway analyses. No genetic variants that significantly contribute to personality variation were identified, while our sample provides over 90% power to detect variants that explain only 1% of the trait variance. This indicates that individual common genetic variants of this size or greater do not contribute to personality trait variation, which has important implications regarding the genetic architecture of personality and the evolutionary mechanisms by which heritable variation is maintained.
genome-wide association; genes; personality; temperament; mutation; selection; maintenance of genetic variation; evolution
schizophrenia; 5-HTTLPR; rs25531; neurocognition; association study
Purpose of review
To describe the rationale for searching for genes for schizophrenia, prior efforts via candidate gene association and genomewide linkage studies, and to set the stage for the numerous genomewide association studies that will emerge by the end of 2008.
Genomewide association studies have identified dozens of new and previously unsuspected candidate genes for many biomedical disorders. At least seven new studies of approximately 20,000 cases plus controls are expected to be completed by the end of 2008.
Current results have few implications for clinical practice or research, and it is possible that this recommendation could be dramatically different in a year.
schizophrenia; genetics; genomewide association; genomewide linkage
Understanding individual differences in the susceptibility to metabolic side effects as a response to antipsychotic therapy is essential to optimize the treatment of schizophrenia. Here we perform genomewide association studies (GWAS) to search for genetic variation affecting the susceptibility to metabolic side effects. The analysis sample consisted of 738 schizophrenia patients, successfully genotyped for 492K SNPs, from the genomic subsample of the Clinical Antipsychotic Trial of Intervention Effectiveness (CATIE) study. Outcomes included twelve indicators of metabolic side effects, quantifying antipsychotic-induced change in weight, blood lipids, glucose and hemoglobin A1c, blood pressure and heart rate. Our criterion for genomewide significance was a pre-specified threshold that ensures, on average, only 10% of the significant findings are false discoveries. Twenty-one SNPs satisfied this criterion. The top finding indicated a SNP in MEIS2 mediated the effects of risperidone on hip circumference (q =.004). The same SNP was also found to mediate risperidone's effect on waist circumference (q =.055). Genomewide significant finding were also found for SNPs in PRKAR2B, GPR98, FHOD3, RNF144A, ASTN2, SOX5 and ATF7IP2, as well as several intergenic markers. PRKAR2B and MEIS2 both have previous research indicating metabolic involvement and PRKAR2B has previously been shown to mediate antipsychotic response. Although our findings require replication and functional validation, this study demonstrates the potential of GWAS to discover genes and pathways that potentially mediate adverse effects of antipsychotic medication.
genomewide association; antipsychotics; pharmacogenomics; personalized medicine; metabolic side effects
Neurocognitive deficits are a core feature of schizophrenia and therefore represent potentially critical outcome variables for assessing antipsychotic treatment response. We performed genome-wide association studies (GWAS) with 492K single nucleotide polymorphisms (SNPs) in a sample of 738 patients with schizophrenia from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. Outcome variables consisted of a neurocognitive battery administered at multiple time points over an 18 month period, measuring Processing Speed, Verbal Memory, Vigilance, Reasoning and Working Memory domains. Genetic mediation of improvements in each of these five domains plus a composite neurocognitive measure was assessed for each of five antipsychotics (olanzapine, perphenazine, quetiapine, risperidone and ziprasidone). Six SNPs achieved genome-wide significance using a pre-specified threshold that ensures, on average, only one in ten findings is a false discovery. These six SNPs were located within, or in close proximity to, genes EHF, SLC26A9, DRD2, GPR137B, CHST8 and IL1A. The more robust findings, i.e. those significant across multiple neurocognitive domains and having adjacent SNPs showing evidence for association, were rs286913 at the EHF gene (p-value 6.99×10−8, q-value 0.034, mediating the effects of ziprasidone on Vigilance), rs11240594 at SLC26A9 (p-value 1.4×10−7, q-value 0.068, mediating the effects of olanzapine on Processing Speed) and rs11677416 at IL1A (p-value 6.67×10−7, q-value 0.081, mediating the effects of olanzapine on Working Memory). This study has generated several novel candidate genes for antipsychotic response. However, our findings will require replication and functional validation. To facilitate replication efforts, we provide all GWAS p-values for download.
genome-wide association; schizophrenia; pharmacogenomics; personalized medicine; single nucleotide polymorphisms
The etiology of complex psychiatric disorders results from both genetics and the environment. No definitive environmental factor has been implicated, but studies suggest that deficits in maternal care and bonding may be an important contributing factor in the development of anxiety and depression. Perinatal mood disorders such as postpartum depression occur in approximately 10% of pregnant women and can result in detriments in infant care and bonding. The consequences of impaired maternal–infant attachment during critical early brain development may lead to adverse effects on socioemotional and neurocognitive development in infants resulting in long-term behavioral and emotional problems, including increased vulnerability for mental illness. The exact mechanisms by which environmental stressors such as poor maternal care increase the risk for psychiatric disorders are not known and studies in humans have proven challenging. Two inbred mouse strains may prove useful for studying the interaction between maternal care and mood disorders. BALB/c (BALB) mice are considered an anxious strain in comparison to C57BL/6 (B6) mice in behavioral models of anxiety. These strain differences are most often attributed to genetics but may also be due to environment and gene by environment interactions. For example, BALB mice are described as poor mothers and B6 mice as good mothers and mothering behavior in rodents has been reported to affect both anxiety and stress behaviors in offspring. Changes in gene methylation patterns in response to maternal care have also been reported, providing evidence for epigenetic mechanisms. Characterization of these two mouse inbred strains over the course of pregnancy and in the postpartum period for behavioral and neuroendocrine changes may provide useful information by which to inform human studies, leading to advances in our understanding of the etiology of anxiety and depression and the role of genetics and the environment.
perinatal; anxiety; depression; mothering; bonding; genetic; epigenetic; mice
Motivation: The quality control (QC) filtering of single nucleotide polymorphisms (SNPs) is an important step in genome-wide association studies to minimize potential false findings. SNP QC commonly uses expert-guided filters based on QC variables [e.g. Hardy–Weinberg equilibrium, missing proportion (MSP) and minor allele frequency (MAF)] to remove SNPs with insufficient genotyping quality. The rationale of the expert filters is sensible and concrete, but its implementation requires arbitrary thresholds and does not jointly consider all QC features.
Results: We propose an algorithm that is based on principal component analysis and clustering analysis to identify low-quality SNPs. The method minimizes the use of arbitrary cutoff values, allows a collective consideration of the QC features and provides conditional thresholds contingent on other QC variables (e.g. different MSP thresholds for different MAFs). We apply our method to the seven studies from the Wellcome Trust Case Control Consortium and the major depressive disorder study from the Genetic Association Information Network. We measured the performance of our method compared to the expert filters based on the following criteria: (i) percentage of SNPs excluded due to low quality; (ii) inflation factor of the test statistics (λ); (iii) number of false associations found in the filtered dataset; and (iv) number of true associations missed in the filtered dataset. The results suggest that with the same or fewer SNPs excluded, the proposed algorithm tends to give a similar or lower value of λ, a reduced number of false associations, and retains all true associations.
Availability: The algorithm is available at http://www4.stat.ncsu.edu/˜jytzeng/software.php
Supplementary information: Supplementary data are available at Bioinformatics online.
Schizophrenia is an often devastating neuropsychiatric illness. Understanding the genetic variation affecting response to antipsychotics is important to develop novel diagnostic tests to match individual schizophrenic patients to the most effective and safe medication. Here we use a genomewide approach to detect genetic variation underlying individual differences in response to treatment with the antipsychotics olanzapine, quetiapine, risperidone, ziprasidone and perphenazine. Our sample consisted of 738 subjects with DSM-IV schizophrenia who took part in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE). Subjects were genotyped using the Affymetrix 500K genotyping platform plus a custom 164K chip to improve genomewide coverage. Treatment outcome was measured using the Positive and Negative Syndrome Scale (PANSS). Our criterion for genomewide significance was a pre-specified threshold that ensures, on average, only 10% of the significant findings are false discoveries. The top statistical result reached significance at our pre-specified threshold and involved a SNP in an intergenic region on chromosome 4p15. In addition, SNPs in ANKS1B and CNTNAP5 that mediated the effects of olanzapine and risperidone on Negative symptoms were very close to our threshold for declaring significance. The most significant SNP in CNTNAP5 is nonsynonymous, giving rise to an amino acid substitution. In addition to highlighting our top results, we provide all p-values for download as a resource for investigators with the requisite samples to carry out replication. This study demonstrates the potential of GWAS to discover novel genes that mediate effects of antipsychotics, which eventually could help to tailor drug treatment to schizophrenic patients.
genomewide association; antipsychotic; pharmacogenetics; schizophrenia; personalized medicine; single nucleotide polymorphism
This study aimed to investigate empirically how and in what way individuals with symptoms of functional somatic syndromes should be classified. We also aimed to look into genetic and environmental influences on the classification.
A total of 28531 twins aged 41–64 underwent screening interviews via a computer-assisted data collection system from 1998 to 2002. Nine functional somatic symptoms (abnormal tiredness, general muscular pain, recurrent abdominal discomfort, back pain, gastroesophageal reflux, recurrent headache, recurrent urinary problem, dizziness, breathlessness at rest) were assessed using structured questions in a blinded manner. Latent class analysis was applied to the data. Structural equation modeling was further performed in order to estimate the relative importance of genetic and environmental influences on class probability.
Latent class analysis resulted in a 5-class solution. Individuals in the first class did not show any health problems. Those assigned to the second, third, and fourth classes tended to have abnormal tiredness, gastrointestinal problems, and pain-related symptoms, respectively. Individuals in the fifth class had multiple symptoms to a greater extent than the other classes. All the five classes showed modest genetic influences (7 – 29% of the total variation) with gender differences except Class 3; however, the majority of influences on the class membership derived from unique environmental effects.
The findings suggested the necessity of re-defining the existing classification criteria for functional somatic syndromes in terms of single (uncomplicated) or multiple (complicated) syndromes. Environmental influences are important for the aetiology of functional somatic syndromes.
functional somatic syndromes; chronic fatigue syndrome; chronic widespread pain; irritable bowel syndrome; comorbidity; latent class analysis