1,721,442 research outputs found
Genetic and Environmental Contribution to Major Depressive Disorder and Self-declared Depression
Full text linkThe Role of Genetics in Bipolar Disorder
No full textBipolar disorder (BP) is a highly heritable disease, with heritability estimated between 60 and 85% by twin studies. The underlying genetic architecture was poorly understood for years since the available technology was limited to the candidate gene approach that did not allow to explore the contribution of multiple loci throughout the genome. BP is a complex disorder, which pathogenesis is influenced by a number of genetic variants, each with small effect size, and environmental exposures. Genome-wide association studies (GWAS) provided meaningful insights into the genetics of BP, including replicated genetic variants, and allowed the development of novel multi-marker methods for gene/pathway analysis and for estimating the genetic overlap between BP and other traits. However, the existing GWAS had also relevant limitations. Notably insufficient statistical power and lack of consideration of rare variants, which may be responsible for the relatively low heritability explained (~20% in the largest GWAS) compared to twin studies. The availability of data from large biobanks and automated phenotyping from electronic health records or digital phenotyping represent key steps for providing samples with adequate power for genetic analysis. Next-generation sequencing is becoming more and more feasible in terms of costs, leading to the rapid growth in the number of samples with whole-genome or whole-exome sequence data. These recent and unprecedented resources are of key importance for a more comprehensive understanding of the specific genetic factors involved in BP and their mechanistic action in determining disease onset and prognosis
The search for personalized antidepressant treatments: What have we learned and where are we going
No full textOver 20 years after the initial report of gene variants within the central nervous system modulating antidepressant response, we are now facing for the first time routine clinical pharmacogenetic applications. The scientific community is divided between enthusiasm and skepticism. It seems clear that the benefit of existing tools is not huge, at least for the central nervous system gene variants, while it is generally accepted for the metabolic gene variants. Findings from large international consortia suggest for the first time in psychiatric genetic research history that cumulative scores comprising many variants across the whole genome may reliably constitute liability factors for psychiatric disorders, this approach will most likely improve also present pharmacogenetic tools. A composite genetic score complemented with clinical risk factors for each patient is the most promising approach for a more effective method of targeted treatment for patients with depression
How to Utilize Clinical and Genetic Information for Personalized Treatment of Major Depressive Disorder: Step by Step Strategic Approach
Full text linkDepression is the single largest contributor to non-fatal health loss and affects 322 million people globally. The clinical heterogeneity of this disorder shows biological correlates and it makes the personalization of antidepressant prescription an important pillar of treatment. There is increasing evidence of genetic overlap between depression, other psychiatric and non-psychiatric disorders, which varies across depression subtypes. Therefore, the first step of clinical evaluation should include a careful assessment of psychopathology and physical health, not limited to previously diagnosed disorders. In part of the patients indeed the pathogenesis of depression may be strictly linked to inflammatory and metabolic abnormalities, and the treatment should target these as much as the depressive symptoms themselves. When the evaluation of the symptom and drug tolerability profile, the concomitant biochemical abnormalities and physical conditions is not enough and at least one pharmacotherapy failed, the genotyping of variants in CYP2D6/CYP2C19 (cytochromes responsible for antidepressant metabolism) should be considered. Individuals with altered metabolism through one of these enzymes may benefit from some antidepressants rather than others or need dose adjustments. Finally, if available, the polygenic predisposition towards cardio-metabolic disorders can be integrated with non-genetic risk factors to tune the identification of patients who should avoid medications associated with this type of side effects. A sufficient knowledge of the polygenic risk of complex medical and psychiatric conditions is becoming relevant as this information can be obtained through direct-to-consumer genetic tests and in the future it may provided by national health care systems
Genetics of treatment outcomes in major depressive disorder: Present and future
Full text linkPharmacogenetic testing is a useful and increasingly widespread tool to assist in antidepressant prescription. More than ten antidepressants (including tricyclics, selective serotonin reuptake inhibitors and venlafaxine) have already genetic biomarkers of response/side effects in clinical guidelines and drug labels. These are represented by functional genetic variants in genes coding for cytochrome enzymes (CYP2D6 and CYP2C19). Depending on the predicted metabolic activity, guidelines provide recommendations on drug choice and dosing. Despite not conclusive, the current evidence suggests that testing can be useful in patients who did not respond or tolerate at least one previous pharmacotherapy. However, the current recommendations are based on pharmacokinetic genes only (CYP450 enzymes), while pharmacodynamic genes (modulating antidepressant mechanisms of action in the brain) are still being studied because of their greater complexity. This may be captured by polygenic risk scores, which reflect the cumulative contribution of many genetic variants to a trait, and they may provide future clinical applications of pharmacogenetics. A more extensive use of genotyping in clinical practice may lead to improvement in treatment outcomes thanks to personalized treatments, but possible ethical issues and disparities should be taken into account and prevented
Shared genetics among major psychiatric disorders.
No full textSince 2005, the National Human Genome Research Institute1 database of published genome-wide association studies (GWAS) has accumulated more than 5110 entries for over 500 traits. The rapid growth of data repositories has enabled researchers to undertake large studies and meta-analyses, and has increased the power for detection of trait-associated variants. In The Lancet, the Psychiatric Genomics Consortium (PGC) describes its analysis of genome-wide single nucleotide polymorphism (SNP) data for 33 332 cases and 27 888 controls distributed among the five major psychiatric disorders in the PGC (major depressive disorder, bipolar disorder, schizophrenia, autism spectrum disorders, and attention deficit hyperactivity disorder).2 The study has combined some of the leading methodological approaches in genetics to examine the possibility of shared genetic make-up for these diseases. The main innovative contribution of the present study is the combination of qualitative and quantitative analyses of the shared genetic features associated with vulnerability to these five disorders.
Reliability of the results was strengthened by an accurate methodological design. The investigators addressed some typical limitations of GWAS (cryptic population stratification and unknown biological relevance of the detected variants) by inclusion of several case-control samples, all of European ancestry, and by corroboration of results by pathway and expression quantitative trait loci (eQTL) analysis. Pathway analysis might balance genetic heterogeneity bias3 (ie, the analysis of a whole molecular pathway avoids spurious associations due to simple interpopulation and intrapopulation individual allele stratification) and eQTL corroborated genetic findings at the functional level; both techniques are crucial to confirmation of the hypothesis-free results of GWAS. Because the unit of analysis is set to functionally interacting molecules, pathway analysis also reduces the risk of type 2 error. Indeed, although individual SNP markers did not reach significance in many GWAS, ranking of SNPs associated previously with different psychiatric disorders identified convergence of pathways in synaptogenesis, axonal guidance, and synaptic plasticity,4 and now calcium signalling,2 which is pivotal in the mechanisms of all these biological processes. Nevertheless, genetic effects with odds ratios around 1 are difficult to disentangle from cryptic population stratification, thus deep sequencing of the top regions in homogeneous populations would be appropriate for confirmation of these findings. The design of the present study ensures the collection of a large sample with some degree of diagnostic reliability, but data for patients were obtained only with use of general disease categories; substantial clinical heterogeneity is expected, which could lead to a high risk of missing markers showing genuine associations.
In addition to methodological issues which are pertinent to researchers, genetic studies should provide translational value for clinicians. With this perspective, the present study might contribute to future nosographic systems, which could be based not only on statistically determined clinical categories, but also on biological pathogenic factors that are pivotal to the identification of suitable treatments. Consistent with the present results,2 voltage-dependent calcium channel antagonists produce antidepressant-like effects in mice,5 and the inositol-1,4,5-triphosphate receptor is a fundamental regulator of calcium release from intracellular stores6 and a target of lithium.
The overlap of genetic factors in major psychiatric disorders confirms previously reported evidence of abundant pleiotropy in human complex disorders (pleiotropy might involve roughly 17% of genes that are associated with diseases or disease traits7). Thus, the same variant might contribute to the risk of different diseases, possibly through specific endophenotypes (heritable traits that segregate with one or more diseases), modulated in both prenatal and postnatal environments through epigenetic changes that associate with chromatin-modifying complexes8 and involve neuroplastic adaptations (figure).9 With a simplified perspective, the high frequency of pleiotropic effects suggests that a perturbation (the introduction of a genetic variant in this case) in a biological system determines alterations in a number of downstream molecular processes, which is due to the redundancy of biological systems. Calcium signalling is a crucial regulator of neuronal growth and development, thus abundant pleiotropy in variants affecting this pathway was expected and has now been confirmed.2 However, family studies suggest some degree of genetic overlap, but also consistent diversity among disorders. This finding is exemplified by the increased risk of major depressive disorder in relatives of patients affected by bipolar disorder, but not the converse. Therefore we agree about the presence of some transdiagnostic risk factors, but many genes and polymorphisms are expected to confer a liability to individual psychiatric diseases.
Full-size image (101 K)
Figure. Pathogenesis of major psychiatric disorders according to present knowledgeDisorders can share a substantial proportion of their genetic susceptibility. Circles at the top of the figure represent six possible genetic profiles. Each profile contains genetic variants that are specific (different colour) or common with other profiles (same colour). Pleiotropic effects and disease-specific genetic variants are not reported to reduce complexity. Environmental exposures (the main known risk factors were reported) also modulate the development of specific diseases or health maintenance. BPD=bipolar disorder. MDD=major depressive disorder. ASD=autism spectrum disorders. ADHD=attention deficit hyperactivity disorder.
Figure options
Although some methodological limitations remain, much progress has been made. New generation exome and full genome sequencing and genome-wide pathway analysis are among the most appealing methodologies. We therefore believe that genetics, possibly thanks to more comprehensive phenotype and endophenotype assessments, can contribute to prediction and prevention of psychiatric diseases, along with the identification of molecular targets for new generations of psychotropic drugs
Clinical application of antidepressant pharmacogenetics: Considerations for the design of future studies
No full textA frustrating inertia has affected the development of clinical applications of antidepressant pharmacogenetics and personalized treatments of depression are still lacking 20 years after the first findings. Candidate gene studies provided replicated findings for some polymorphisms, but each of them shows at best a small effect on antidepressant efficacy and the cumulative effect of different polymorphisms is unclear. Further, no candidate was immune by at least some negative studies. These considerations give rise to some concerns about the clinical benefits of currently available pharmacogenetic tests since they are based on the results of candidate gene studies. Clinical guidelines in fact suggest that only polymorphisms that alter cytochrome 2D6 or 2C19 enzymatic activity probably provide useful clinical indications, while variants in genes involved in antidepressant pharmacodynamics have no recommended clinical applications. The present review discusses possible strategies to facilitate the identification of genetic biomarkers with clinical usefulness in guiding antidepressant treatments. These include analysis methods for the study of the polygenic/omnigenic nature of antidepressant response, the prioritization of polymorphisms on the basis of functional considerations, the incorporation of clinical-demographic predictors in pharmacogenetic studies (e.g. mixed polygenic and clinical risk scores), the application of methodological improvements to the design of future studies in order to maximize the comparability of results and improve power
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
