35 research outputs found
Evidence of causal effect of major depression on alcohol dependence: findings from the psychiatric genomics consortium
BACKGROUND
Despite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood. We leveraged genome-wide data from the Psychiatric Genomics Consortium (PGC) and UK Biobank to test for the presence of shared genetic mechanisms and causal relationships among MD, AD, and AC.
METHODS
Linkage disequilibrium score regression and Mendelian randomization (MR) were performed using genome-wide data from the PGC (MD: 135 458 cases and 344 901 controls; AD: 10 206 cases and 28 480 controls) and UK Biobank (AC-frequency: 438 308 individuals; AC-quantity: 307 098 individuals).
RESULTS
Positive genetic correlation was observed between MD and AD (rgMD−AD = + 0.47, P = 6.6 × 10−10). AC-quantity showed positive genetic correlation with both AD (rgAD−AC quantity = + 0.75, P = 1.8 × 10−14) and MD (rgMD−AC quantity = + 0.14, P = 2.9 × 10−7), while there was negative correlation of AC-frequency with MD (rgMD−AC frequency = −0.17, P = 1.5 × 10−10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these four traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e. causal relationship) with an effect of MD on AD (beta = 0.28, P = 1.29 × 10−6). There was no evidence for reverse causation.
CONCLUSION
This study supports a causal role for genetic liability of MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity addresses important public health concerns and has the potential to facilitate prevention and intervention efforts
The genetics of addiction—a translational perspective
Addictions are serious and common psychiatric disorders, and are among the leading contributors to preventable death. This selective review outlines and highlights the need for a multi-method translational approach to genetic studies of these important conditions, including both licit (alcohol, nicotine) and illicit (cannabis, cocaine, opiates) drug addictions and the behavioral addiction of disordered gambling. First, we review existing knowledge from twin studies that indicates both the substantial heritability of substance-specific addictions and the genetic overlap across addiction to different substances. Next, we discuss the limited number of candidate genes which have shown consistent replication, and the implications of emerging genomewide association findings for the genetic architecture of addictions. Finally, we review the utility of extensions to existing methods such as novel phenotyping, including the use of endophenotypes, biomarkers and neuroimaging outcomes; emerging methods for identifying alternative sources of genetic variation and accompanying statistical methodologies to interpret them; the role of gene-environment interplay; and importantly, the potential role of genetic variation in suggesting new alternatives for treatment of addictions
Evidence for Increased Genetic Risk Load for Major Depression in Patients Assigned to Electroconvulsive Therapy
Electroconvulsive therapy (ECT) is the treatment of choice for severe and treatment-resistant
depression; disorder severity and unfavorable treatment outcomes are shown to be influenced
by an increased genetic burden for major depression (MD). Here, we tested whether ECT assignment
and response/nonresponse are associated with an increased genetic burden for major
depression (MD) using polygenic risk score (PRS), which summarize the contribution of diseaserelated
common risk variants. Fifty-one psychiatric inpatients suffering from a major depressive
episode underwent ECT. MD-PRS were calculated for these inpatients and a separate
population-based sample (n = 3,547 healthy; n = 426 self-reported depression) based on summary
statistics from the Psychiatric Genomics Consortium MDD-working group (Cases:
n = 59,851; Controls: n = 113,154). MD-PRS explained a significant proportion of disease status
between ECT patients and healthy controls (p = .022, R2 = 1.173%); patients showed higher
MD-PRS. MD-PRS in population-based depression self-reporters were intermediate between
ECT patients and controls (n.s.). Significant associations between MD-PRS and ECT response
(50% reduction in Hamilton depression rating scale scores) were not observed. Our findings indicate
that ECT cohorts show an increased genetic burden for MD and are consistent with the
hypothesis that treatment-resistant MD patients represent a subgroup with an increased genetic
risk for MD. Larger samples are needed to better substantiate these findings
Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes
publisher: Elsevier articletitle: Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes journaltitle: Cell articlelink: https://doi.org/10.1016/j.cell.2018.05.046 content_type: article copyright: © 2018 Elsevier Inc
Single cell and population activities in cortical-like systems
Dynamics of single cells and large cell populations are the subject of investigation by using differently detailed models. Multicompartmental modeling techniques are used to systematically investigate the location-dependent effects of GABA-ergic inhibition on the firing patterns of hippocampal pyramidal cells. Appearance of stochastic resonance in a model of mitral and granule cells of the olfactory bulb is demonstrated by using a single-compartmental model approach. Spatial propagation of synchronized activities in hippocampal slices are studied by a model of large neural populations
Nanopore long-read RNAseq reveals widespread transcriptional variation among the surface receptors of individual B cells
Understanding gene regulation and function requires a genome-wide method capable of capturing both gene expression levels and isoform diversity at the single-cell level. Short-read RNAseq is limited in its ability to resolve complex isoforms because it fails to sequence full-length cDNA copies of RNA molecules. Here, we investigate whether RNAseq using the long-read single-molecule Oxford Nanopore MinION sequencer is able to identify and quantify complex isoforms without sacrificing accurate gene expression quantification. After benchmarking our approach, we analyse individual murine B1a cells using a custom multiplexing strategy. We identify thousands of unannotated transcription start and end sites, as well as hundreds of alternative splicing events in these B1a cells. We also identify hundreds of genes expressed across B1a cells that display multiple complex isoforms, including several B cell-specific surface receptors. Our results show that we can identify and quantify complex isoforms at the single cell level
Multimodal profiling of single-cell morphology, electrophysiology, and gene expression using Patch-seq
Neurons exhibit a rich diversity of morphological phenotypes, electrophysiological properties, and gene-expression patterns. Understanding how these different characteristics are interrelated at the single-cell level has been difficult because of the lack of techniques for multimodal profiling of individual cells. We recently developed Patch-seq, a technique that combines whole-cell patch-clamp recording, immunohistochemistry, and single-cell RNA-sequencing (scRNA-seq) to comprehensively profile single neurons from mouse brain slices. Here, we present a detailed step-by-step protocol, including modifications to the patching mechanics and recording procedure, reagents and recipes, procedures for immunohistochemistry, and other tips to assist researchers in obtaining high-quality morphological, electrophysiological, and transcriptomic data from single neurons. Successful implementation of Patch-seq allows researchers to explore the multidimensional phenotypic variability among neurons and to correlate gene expression with phenotype at the level of single cells. The entire procedure can be completed in ∼2 weeks through the combined efforts of a skilled electrophysiologist, molecular biologist, and biostatistician