Ten years ago it was widely expected that the genetic basis of common disease would be resolved by genome-wide association studies (GWAS), large-scale studies in which the entire genome is covered by ...genetic markers. However, the bulk of heritable variance remains unexplained. The authors consider several alternative research strategies. For instance, whereas it has been hypothesized that a common disease is associated primarily with common genetic variants, it is now plausible that multiple rare variants each have a potent effect on disease risk and that they could accumulate to become a substantial component of common disease risk. This idea has become more appealing since the discovery that copy number variants (CNVs) are a substantial source of human mutations and are associated with multiple common diseases. CNVs are structural genomic variants consisting of microinsertions, microdeletions, and transpositions in the human genome. It has been argued that numerous rare CNVs are plausible causes of a substantial proportion of common disease, and rare CNVs have been found to be potent risk factors in schizophrenia and autism. Another approach is to "parse the genome," i.e., reanalyze subsets of current GWAS data, since the noise inherent in genome-wide approaches may be hiding valid associations. Lastly, technological advances and declining costs may allow large-scale genome-wide sequencing that would comprehensively identify all genetic variants. Study groups even larger than the 10,000 subjects in current meta-analyses would be required, but the outcomes may lead to resolution of our current dilemma in common diseases: Where is the missing heritability?
Recent genetic findings of high-impact genetic variants in bipolar disorder, schizophrenia, and autism spectrum disorder (ASD) must lead to profound changes in genetic and family counseling. The ...authors present risk calculations, discuss the ethical implications of these findings, and outline the changes now required in the risk counseling process.
The authors use data from recent mega-analyses and reviews of common and rare risk variants in bipolar disorder, schizophrenia, and ASD to calculate risks of illness based on genetic marker tests. They then consider new ethical issues in mental disorders presented by these risks, including within-family conflicts over genetic testing; effects of genetic discoveries on stigma, abortion, preimplantation procedures, and population screening for susceptibility; and genetic tests as a factor in marital choice.
New structural mutations (de novo copy number variants CNVs, which are chromosomal microdeletions and micro-duplications) are present in 4%27% of patients with bipolar disorder, schizophrenia, or ASD and can occur almost anywhere in the genome. For a person with a de novo CNV, the absolute risk of bipolar disorder, schizophrenia, or ASD is 14%, much higher than the population risk. Rare CNVs have also been identified that are generally not new mutations but constitute very high-effect risk factors, ranging up to 82%.
A substantial minority of patients with bipolar disorder, schizophrenia, and ASD have high-impact detectable genetic events. This greatly changes psychiatric genetic counseling for these patients and families. A psychotherapeutic approach may be needed as a routine part of risk counseling, particularly for resolution of ethical issues and for within-family stigma and conflicts over genetic test results.
Elevations in peripheral inflammatory markers have been reported in patients with psychosis. Whether this represents an inflammatory process defined by individual or subgroups of markers is unclear. ...Further, relationships between peripheral inflammatory marker elevations and brain structure, cognition, and clinical features of psychosis remain unclear. We hypothesized that a pattern of plasma inflammatory markers, and an inflammatory subtype established from this pattern, would be elevated across the psychosis spectrum and associated with cognition and brain structural alterations. Clinically stable psychosis probands (Schizophrenia spectrum, n = 79; Psychotic Bipolar disorder, n = 61) and matched healthy controls (HC, n = 60) were assessed for 15 peripheral inflammatory markers, cortical thickness, subcortical volume, cognition, and symptoms. A combination of unsupervised exploratory factor analysis and hierarchical clustering was used to identify inflammation subtypes. Levels of IL6, TNFα, VEGF, and CRP were significantly higher in psychosis probands compared to HCs, and there were marker-specific differences when comparing diagnostic groups. Individual and/or inflammatory marker patterns were associated with neuroimaging, cognition, and symptom measures. A higher inflammation subgroup was defined by elevations in a group of 7 markers in 36% of Probands and 20% of HCs. Probands in the elevated inflammatory marker group performed significantly worse on cognitive measures of visuo-spatial working memory and response inhibition, displayed elevated hippocampal, amygdala, putamen and thalamus volumes, and evidence of gray matter thickening compared to the proband group with low inflammatory marker levels. These findings specify the nature of peripheral inflammatory marker alterations in psychotic disorders and establish clinical, neurocognitive and neuroanatomic associations with increased inflammatory activation in psychosis. The identification of a specific subgroup of patients with inflammatory alteration provides a potential means for targeting treatment with anti-inflammatory medications.
Familial neuropsychological deficits are well established in schizophrenia but remain less well characterized in other psychotic disorders. This study from the Bipolar-Schizophrenia Network on ...Intermediate Phenotypes (B-SNIP) consortium 1) compares cognitive impairment in schizophrenia and bipolar disorder with psychosis, 2) tests a continuum model of cognitive dysfunction in psychotic disorders, 3) reports familiality of cognitive impairments across psychotic disorders, and 4) evaluates cognitive impairment among nonpsychotic relatives with and without cluster A personality traits.
Participants included probands with schizophrenia (N=293), psychotic bipolar disorder (N=227), schizoaffective disorder (manic, N=110; depressed, N=55), their first-degree relatives (N=316, N=259, N=133, and N=64, respectively), and healthy comparison subjects (N=295). All participants completed the Brief Assessment of Cognition in Schizophrenia (BACS) neuropsychological battery.
Cognitive impairments among psychotic probands, compared to healthy comparison subjects, were progressively greater from bipolar disorder (z=-0.77) to schizoaffective disorder (manic z=-1.08; depressed z=-1.25) to schizophrenia (z=-1.42). Profiles across subtests of the BACS were similar across disorders. Familiality of deficits was significant and comparable in schizophrenia and bipolar disorder. Of particular interest were similar levels of neuropsychological deficits in relatives with elevated cluster A personality traits across proband diagnoses. Nonpsychotic relatives of schizophrenia probands without these personality traits exhibited significant cognitive impairments, while relatives of bipolar probands did not.
Robust cognitive deficits are present and familial in schizophrenia and psychotic bipolar disorder. Severity of cognitive impairments across psychotic disorders was consistent with a continuum model, in which more prominent affective features and less enduring psychosis were associated with less cognitive impairment. Cognitive dysfunction in first-degree relatives is more closely related to psychosis-spectrum personality disorder traits in psychotic bipolar disorder than in schizophrenia.
We have observed extensive interindividual differences in DNA methylation of 8590 CpG sites of 6229 genes in 153 human adult cerebellum samples, enriched in CpG island “shores” and at further ...distances from CpG islands. To search for genetic factors that regulate this variation, we performed a genome-wide association study (GWAS) mapping of methylation quantitative trait loci (mQTLs) for the 8590 testable CpG sites.
cis association refers to correlation of methylation with SNPs within 1 Mb of a CpG site. 736 CpG sites showed phenotype-wide significant
cis association with 2878 SNPs (after permutation correction for all tested markers and methylation phenotypes). In
trans analysis of methylation, which tests for distant regulation effects, associations of 12 CpG sites and 38 SNPs remained significant after phenotype-wide correction. To examine the functional effects of mQTLs, we analyzed 85 genes that were with genetically regulated methylation we observed and for which we had quality gene expression data. Ten genes showed SNP-methylation-expression three-way associations—the same SNP simultaneously showed significant association with both DNA methylation and gene expression, while DNA methylation was significantly correlated with gene expression. Thus, we demonstrated that DNA methylation is frequently a heritable continuous quantitatively variable trait in human brain. Unlike allele-specific methylation, genetic polymorphisms mark both
cis- and
trans-regulatory genetic sites at measurable distances from their CpG sites. Some of the genetically regulated DNA methylation is directly connected with genetically regulated gene expression variation.
The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray studies published annually are ...confounded by "batch effects," the systematic error introduced when samples are processed in multiple batches. Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch. A number of programs are now available to adjust microarray data for batch effects prior to analysis. We systematically evaluated six of these programs using multiple measures of precision, accuracy and overall performance. ComBat, an Empirical Bayes method, outperformed the other five programs by most metrics. We also showed that it is essential to standardize expression data at the probe level when testing for correlation of expression profiles, due to a sizeable probe effect in microarray data that can inflate the correlation among replicates and unrelated samples.
Recently, a biologically-driven psychosis classification (B-SNIP Biotypes) was derived using brain-based cognitive and electrophysiological markers. Here, we characterized a local ...functional-connectivity measure, regional homogeneity (ReHo), as a biomarker across Biotypes and conventional DSM diagnoses.
Whole-brain ReHo measures of resting-state functional MRI were examined in psychosis patients and healthy controls organized by Biotype and by DSM-IV-TR diagnosis (n = 737). Group-level ANOVA and individual-level prediction models using support vector machines (SVM) were employed to evaluate the discriminative characteristics in comparisons of 1) DSM diagnostic groups, 2) Biotypes, to controls, and 3) within-proband subgroups with each other.
Probands grouped by Biotype versus controls showed a unique abnormality pattern: Biotype-1 displayed bidirectional ReHo differences in more widespread areas, with higher ReHo in para-hippocampus, fusiform, inferior temporal, cerebellum, thalamus and caudate, plus lower ReHo in the postcentral gyrus, middle temporal, cuneus, and middle occipital cortex; Biotype-2 and Biotype-3 showed lesser and unidirectional ReHo changes. Among diagnostic groups, only schizophrenia showed higher ReHo versus control values in the inferior/middle temporal area and fusiform gyrus. For within-patient comparisons, Biotype-1 showed characteristic ReHo when compared to Biotype-2 and Biotype-3. SVM results more accurately identified Biotypes than DSM diagnoses.
We characterized patterns of ReHo abnormalities across both Biotypes and DSM sub-groups. Both group-level statistical and machine-learning methods were more sensitive in capturing ReHo deficits in Biotypes than DSM. Overall ReHo is a robust psychosis biomarker.
While it is known that rare copy-number variants (CNVs) contribute to risk for some neuropsychiatric disorders, the role of CNVs in bipolar disorder is unclear. Here, we reasoned that a contribution ...of CNVs to mood disorders might be most evident for de novo mutations. We performed a genome-wide analysis of de novo CNVs in a cohort of 788 trios. Diagnoses of offspring included bipolar disorder (n = 185), schizophrenia (n = 177), and healthy controls (n = 426). Frequencies of de novo CNVs were significantly higher in bipolar disorder as compared with controls (OR = 4.8 1.4,16.0, p = 0.009). De novo CNVs were particularly enriched among cases with an age at onset younger than 18 (OR = 6.3 1.7,22.6, p = 0.006). We also confirmed a significant enrichment of de novo CNVs in schizophrenia (OR = 5.0 1.5,16.8, p = 0.007). Our results suggest that rare spontaneous mutations are an important contributor to risk for bipolar disorder and other major neuropsychiatric diseases.
► Strong association of rare de novo CNVs with bipolar disorder ► Rare de novo CNVs influence age at disease onset in bipolar disorder ► Replication of earlier findings of a high frequency of de novo CNVs in schizophrenia
“Resting‐state” functional magnetic resonance imaging (rs‐fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are ...computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task‐fMRI—regression dynamic causal modeling (rDCM)—extends to rs‐fMRI and offers both directional estimates and scalability to whole‐brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal‐to‐noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs‐fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole‐brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.
“Resting‐state” functional magnetic resonance imaging (rs‐fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task‐fMRI—regression dynamic causal modeling (rDCM)—extends to rs‐fMRI and offers both directional estimates and scalability to whole‐brain networks.
Abstract Genetic association studies on schizophrenia (SZ) have been repeatedly performed over the last two decades, resulting in a consensus that results are generally inconsistent. This consensus ...has begun to change as a result of meta-analyses ( e.g. , Glatt, S.J. and Jonsson, E.G., 2006 . The Cys allele of the DRD2 Ser311Cys polymorphism has a dominant effect on risk for schizophrenia: evidence from fixed- and random-effects meta-analyses. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 141, 149–154.). The SchizophreniaGene database ( http://www.schizophreniaforum.org/res/sczgene/default.asp ) has been a leader in meta-analyses of SZ association data, by dynamically and comprehensively cataloging all public genetic association studies, and preparing meta-analyses of case–control data. There are 19 “top” candidate genes from these analyses (access on December 20, 2007), showing the highest effect sizes and nominally significant associations of at least one variant in the meta-analyses of all ethnic samples or of samples of Caucasian ancestry. We selected 40 polymorphisms in 12 selected “top” genes for additional meta-analyses, which had at least one familial association data. We found gene-wide (correction for the number of meta-analyses for each gene) significant allelic association evidence for seven genes in the combined samples. The odds ratios (ORs) of the associated minor risk alleles range from 1.072 to 1.121, for DRD4 , MTHFR , PPP3CC and TP53 . For protective allele associations, the ORs are between 0.842 and 0.886, for DAO , IL1B , and SLC6A4 . In population-based sub-analyses, we found significant results in four genes in Asians (ORs between 1.084 and 1.309 for DRD4 , GABRB2 , PPP3CC , and TP53 ), and one gene in European (OR of 0.888 for SLC6A4 ). The association of rs1816072 of GABRB2 with SZ in Asians was significant (adjusted P = 0.048 after correction for 80 tests). No significant heterogeneity between case–control and family-based study designs was detected in 35 out of 40 polymorphisms. Our results further support eight potential SZ candidate genes and suggest that family data can reasonably be included in the meta-analysis of genetic associations.
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