Faculty Opinions recommendation of Genomewide association studies: history, rationale, and prospects for psychiatric disorders.

2009 ◽  
Author(s):  
Giampaolo Perna ◽  
Massimiliano Grassi
Keyword(s):  
Psychiatric Disorders ◽  
Association Studies ◽  
Genomewide Association

scholarly journals Structural brain imaging studies offer clues about the effects of the shared genetic etiology among neuropsychiatric disorders

2021 ◽  
Author(s):  
Nevena V. Radonjić ◽  
Jonathan L. Hess ◽  
Paula Rovira ◽  
Ole Andreassen ◽  
Jan K. Buitelaar ◽  
...  
Keyword(s):  
Association Studies ◽  
Genetic Correlations ◽  
Neuropsychiatric Disorders ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Genetic Etiology ◽  
Compulsive Disorder ◽  
Pairwise Correlations ◽  
Genomewide Association ◽  
Structural Brain

AbstractGenomewide association studies have found significant genetic correlations among many neuropsychiatric disorders. In contrast, we know much less about the degree to which structural brain alterations are similar among disorders and, if so, the degree to which such similarities have a genetic etiology. From the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium, we acquired standardized mean differences (SMDs) in regional brain volume and cortical thickness between cases and controls. We had data on 41 brain regions for: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), epilepsy, major depressive disorder (MDD), obsessive compulsive disorder (OCD), and schizophrenia (SCZ). These data had been derived from 24,360 patients and 37,425 controls. The SMDs were significantly correlated between SCZ and BD, OCD, MDD, and ASD. MDD was positively correlated with BD and OCD. BD was positively correlated with OCD and negatively correlated with ADHD. These pairwise correlations among disorders were correlated with the corresponding pairwise correlations among disorders derived from genomewide association studies (r = 0.494). Our results show substantial similarities in sMRI phenotypes among neuropsychiatric disorders and suggest that these similarities are accounted for, in part, by corresponding similarities in common genetic variant architectures.

scholarly journals Prediction of response to drug therapy in psychiatric disorders

Open Biology ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 180031 ◽  
Author(s):  
Shani Stern ◽  
Sara Linker ◽  
Krishna C. Vadodaria ◽  
Maria C. Marchetto ◽  
Fred H. Gage
Keyword(s):  
Autism Spectrum Disorder ◽  
Bipolar Disorder ◽  
Major Depression ◽  
Personalized Medicine ◽  
Psychiatric Disorders ◽  
Association Studies ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Genome Wide Association Studies ◽  
Patient Reported

Personalized medicine has become increasingly relevant to many medical fields, promising more efficient drug therapies and earlier intervention. The development of personalized medicine is coupled with the identification of biomarkers and classification algorithms that help predict the responses of different patients to different drugs. In the last 10 years, the Food and Drug Administration (FDA) has approved several genetically pre-screened drugs labelled as pharmacogenomics in the fields of oncology, pulmonary medicine, gastroenterology, haematology, neurology, rheumatology and even psychiatry. Clinicians have long cautioned that what may appear to be similar patient-reported symptoms may actually arise from different biological causes. With growing populations being diagnosed with different psychiatric conditions, it is critical for scientists and clinicians to develop precision medication tailored to individual conditions. Genome-wide association studies have highlighted the complicated nature of psychiatric disorders such as schizophrenia, bipolar disorder, major depression and autism spectrum disorder. Following these studies, association studies are needed to look for genomic markers of responsiveness to available drugs of individual patients within the population of a specific disorder. In addition to GWAS, the advent of new technologies such as brain imaging, cell reprogramming, sequencing and gene editing has given us the opportunity to look for more biomarkers that characterize a therapeutic response to a drug and to use all these biomarkers for determining treatment options. In this review, we discuss studies that were performed to find biomarkers of responsiveness to different available drugs for four brain disorders: bipolar disorder, schizophrenia, major depression and autism spectrum disorder. We provide recommendations for using an integrated method that will use available techniques for a better prediction of the most suitable drug.

Molecule-Based Genetic Association Studies On Psychiatric Disorders

2017 ◽  
Vol 27 ◽  
pp. S462
Author(s):  
Wen Li ◽  
Yunpeng Wang ◽  
Chun Chieh Fan ◽  
Tuomo Maki-Marttunen ◽  
Wesley Thompson ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies

Inflammation and Pruning May Inform Risk to Psychiatric Disorders. Lessons From Large Genetic Data

2017 ◽  
Vol 41 (S1) ◽  
pp. S56-S56
Author(s):  
C. Crisafulli
Keyword(s):  
Psychiatric Disorders ◽  
Molecular Mechanics ◽  
Association Studies ◽  
Molecular Pathway ◽  
Starting Point ◽  
Single Effect ◽  
Competing Interest ◽  
The Brain ◽  
New Hypothesis

BackgroundIt's known that psychiatric disorders are caused to either environmental and genetics factors. Through the years several hypotheses were tested and many genes were screened for association, resulting in a huge amount of data available for the scientific community. Despite that, the molecular mechanics behind psychiatric disorders remains largely unknown. Traditional association studies may be not enough to pinpoint the molecular underpinnings of psychiatric disorder. We tried to applying a methodology that investigates molecular-pathway-analysis that takes into account several genes per time, clustered in consistent molecular groups and may successfully capture the signal of a number of genetic variations with a small single effect on the disease. This approach might reveal more of the molecular basis of psychiatric disorders.Methodsi)We collected data on studies available in literature for the studied disorder (e.g. Schizophrenia, Bipolar Disorder);ii)We extracted a pool of genes that are likely involved with the disease;iii)We used these genes as starting point to map molecular cascades function-linked. The molecular cascades are then analyzed and pathways and sub-pathways, possibly involved with them, are identified and tested for association.Results/discussionWe obtained interesting results. In particular, signals of enrichment (association) were obtained multiple times on the molecular pathway associated with the pruning activity and inflammation. Molecular mechanics related to neuronal pruning were focused as a major and new hypothesis for the pathophysiology of psychiatric disorders and the role of inflammatory events has been extensively investigated in psychiatry. intersting, inflammatory mechanics in the brain may also play a role in neuronal pruning during the early development of CNS.Disclosure of interestThe author has not supplied his declaration of competing interest.

A systematic analysis of genetically regulated differences in gene expression and the role of co-expression networks across 16 psychiatric disorders and substance use phenotypes

2021 ◽  
Author(s):  
Zachary F Gerring ◽  
Jackson G Thorp ◽  
Eric R Gamazon ◽  
Eske M Derks
Keyword(s):  
Gene Expression ◽  
Mental Health ◽  
Substance Use ◽  
Prefrontal Cortex ◽  
Psychiatric Disorders ◽  
Developmental Disorders ◽  
Association Studies ◽  
Genetic Correlations ◽  
Autism Spectrum ◽  
Genome Wide Association Studies

ABSTRACTGenome-wide association studies (GWASs) have identified thousands of risk loci for many psychiatric and substance use phenotypes, however the biological consequences of these loci remain largely unknown. We performed a transcriptome-wide association study of 10 psychiatric disorders and 6 substance use phenotypes (collectively termed “mental health phenotypes”) using expression quantitative trait loci data from 532 prefrontal cortex samples. We estimated the correlation due to predicted genetically regulated expression between pairs of mental health phenotypes, and compared the results with the genetic correlations. We identified 1,645 genes with at least one significant trait association, comprising 2,176 significant associations across the 16 mental health phenotypes of which 572 (26%) are novel. Overall, the transcriptomic correlations for phenotype pairs were significantly higher than the respective genetic correlations. For example, attention deficit hyperactivity disorder and autism spectrum disorder, both childhood developmental disorders, showed a much higher transcriptomic correlation (r=0.84) than genetic correlation (r=0.35). Finally, we tested the enrichment of phenotype-associated genes in gene co-expression networks built from prefrontal cortex. Phenotype-associated genes were enriched in multiple gene co-expression modules and the implicated modules contained genes involved in mRNA splicing and glutamatergic receptors, among others. Together, our results highlight the utility of gene expression data in the understanding of functional gene mechanisms underlying psychiatric disorders and substance use phenotypes.

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