scholarly journals Dissecting the many genetic faces of schizophrenia

2009 ◽  
Vol 18 (2) ◽  
pp. 91-95 ◽  
Author(s):  
Dan Rujescu ◽  
David A. Collier
Keyword(s):  
Association Studies ◽  
Copy Number Variants ◽  
Genome Wide Association ◽  
Diagnostic Tools ◽  
Genome Wide Association Studies ◽  
Severe Mental Disorder ◽  
Genome Wide ◽  
The Many ◽  
Genomic Disorders ◽  
Gwa Studies

AbstractRecent genome-wide association studies in schizophrenia have provided strongest evidence for association and this strengthened when the affected phenotype included bipolar disorder suggesting that genes may not always associate with operationalised diagnostic entities. Several further large Genome Wide Association (GWA) studies on schizophrenia are under way and identified and replicated further loci in well-powered cohorts. The last 2 years have also witnessed an explosion of interest in human Copy Number Variants (CNVs). Deletions recently identified in schizophrenia (1q21.1; 2p16.3; 15q11.2; 15q13.3) have also been most recently found in further neurodevelopmental diseases. Thus, a significant fraction of individuals with neurodevelopmental diseases including schizophrenia carry CNVs and many will be defined as “genomic disorders” in the coming years. These findings could represent a decisive step towards understanding the causes of this severe mental disorder as well as developing new potential treatments. There is new hope that these new avenues will help understanding the neurobiology of schizophrenia in more depth leading to the development of new innovative diagnostic tools and therapies as was the case after the discovery of rare APP and presenilin 1 and 2 mutations in Alzheimer's disease.

scholarly journals Genome-wide association studies: a powerful tool for neurogenomics

2010 ◽  
Vol 28 (1) ◽  
pp. E2 ◽  
Author(s):  
Matthew C. Cowperthwaite ◽  
Deepankar Mohanty ◽  
Mark G. Burnett
Keyword(s):  
Neurological Diseases ◽  
Association Studies ◽  
Genome Wide Association ◽  
Great Promise ◽  
Genome Wide Association Studies ◽  
Entire Genome ◽  
Genome Wide ◽  
New Generation ◽  
Theoretical Issues ◽  
Gwa Studies

As their power and utility increase, genome-wide association (GWA) studies are poised to become an important element of the neurosurgeon's toolkit for diagnosing and treating disease. In this paper, the authors review recent findings and discuss issues associated with gathering and analyzing GWA data for the study of neurological diseases and disorders, including those of neurosurgical importance. Their goal is to provide neurosurgeons and other clinicians with a better understanding of the practical and theoretical issues associated with this line of research. A modern GWA study involves testing hundreds of thousands of genetic markers across an entire genome, often in thousands of individuals, for any significant association with a particular disease. The number of markers assayed in a study presents several practical and theoretical issues that must be considered when planning the study. Genome-wide association studies show great promise in our understanding of the genes underlying common neurological diseases and disorders, as well as in leading to a new generation of genetic tests for clinicians.

scholarly journals Assessing the performance of genome-wide association studies for predicting disease risk

2019 ◽  
Author(s):  
Jonas Patron ◽  
Arnau Serra-Cayuela ◽  
Beomsoo Han ◽  
Carin Li ◽  
David Scott Wishart
Keyword(s):  
Disease Risk ◽  
Association Studies ◽  
Roc Curves ◽  
Gwas Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Link Type ◽  
Genome Wide ◽  
Risk Predictors ◽  
Gwa Studies

AbstractTo date more than 3700 genome-wide association studies (GWAS) have been published that look at the genetic contributions of single nucleotide polymorphisms (SNPs) to human conditions or human phenotypes. Through these studies many highly significant SNPs have been identified for hundreds of diseases or medical conditions. However, the extent to which GWAS-identified SNPs or combinations of SNP biomarkers can predict disease risk is not well known. One of the most commonly used approaches to assess the performance of predictive biomarkers is to determine the area under the receiver-operator characteristic curve (AUROC). We have developed an R package called G-WIZ to generate ROC curves and calculate the AUROC using summary-level GWAS data. We first tested the performance of G-WIZ by using AUROC values derived from patient-level SNP data, as well as literature-reported AUROC values. We found that G-WIZ predicts the AUROC with <3% error. Next, we used the summary level GWAS data from GWAS Central to determine the ROC curves and AUROC values for 569 different GWA studies spanning 219 different conditions. Using these data we found a small number of GWA studies with SNP-derived risk predictors that have very high AUROCs (>0.75). On the other hand, the average GWA study produces a multi-SNP risk predictor with an AUROC of 0.55. Detailed AUROC comparisons indicate that most SNP-derived risk predictions are not as good as clinically based disease risk predictors. All our calculations (ROC curves, AUROCs, explained heritability) are in a publicly accessible database called GWAS-ROCS (http://gwasrocs.ca). The G-WIZ code is freely available for download at https://github.com/jonaspatronjp/GWIZ-Rscript/.

scholarly journals New Genes and Emerging Mechanisms of Type 1 Diabetes

2021 ◽  
Vol 2 (2) ◽  
pp. 47-51
Author(s):  
Aysha Karim Kiani ◽  
Asima Zia ◽  
Parveen Akhtar ◽  
Sadaf Moeez ◽  
Attya Bhatti ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Diabetes Susceptibility ◽  
Multiple Loci ◽  
New Genes ◽  
Genome Wide ◽  
Gwa Studies

Type 1 Diabetes susceptibility depends upon the complex interaction between numerous genetic as well as environmental factors. 50% of the familial clustering of T1D is explained by HLA locus alleles. Other multiple loci contribute the rest of the susceptibility, in which very little were known since last few years. Four novel loci were found from the results of stage-I, genome wide association (GWA) studies which were carried out with high-density genotyping arrays. As the stage-II of the Genome Wide Association studies completed, hopefully, most of the genetic reasons of Type 1 Diabetes will be identified. 

scholarly journals Genome‑Wide Association Studies in Schizophrenia, and Potential Etiological and Functional Implications of Their Results

2012 ◽  
Vol 55 (1) ◽  
pp. 3-11 ◽  
Author(s):  
Ladislav Hosák ◽  
Petr Šilhan ◽  
Jiřina Hosáková
Keyword(s):  
Association Studies ◽  
Copy Number Variants ◽  
Genome Wide Association ◽  
Substantial Part ◽  
Genome Wide Association Studies ◽  
Genetic Studies ◽  
Genome Wide ◽  
National Human ◽  
Functional Implications ◽  
Scientific Endeavour

Background: Despite the fact that the genetic basis of schizophrenia has been intensively studied for more than two decades, our contemporary knowledge in this field is rather fractional, and a substantial part of it is still missing. The aim of this review article is to sum up the data coming from genome‑wide association genetic studies in schizophrenia, and indicate prospective directions of further scientific endeavour. Methods: We searched the National Human Genome Research Institute’s Catalog of genome‑wide association studies for schizophrenia to identify all papers related to this topic. In consequence, we looked up the possible relevancy of these findings for etiology and pathogenesis of schizophrenia using the computer gene and PubMed databases. Results: Eighteen genome‑wide association studies in schizophrenia have been published till now, referring to fifty‑seven genes supposedly involved into schizophrenia’s etiopathogenesis. Most of these genes are related to neurodevelopment, neuroendocrinology, and immunology. Conclusions: It is reasonable to predict that complex studies of sufficiently large samples, involving detection of copy number variants and assessment of endophenotypes, will produce definitive discoveries of genetic risk factors for schizophrenia in the future.

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