scholarly journals Genome-Wide Association Studies, Field Synopses, and the Development of the Knowledge Base on Genetic Variation and Human Diseases

2009 ◽  
Vol 170 (3) ◽  
pp. 269-279 ◽  
Author(s):  
M. J. Khoury ◽  
L. Bertram ◽  
P. Boffetta ◽  
A. S. Butterworth ◽  
S. J. Chanock ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Knowledge Base ◽  
Association Studies ◽  
Human Diseases ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Hypothesis-driven candidate genes for schizophrenia compared to genome-wide association results

2011 ◽  
Vol 42 (3) ◽  
pp. 607-616 ◽  
Author(s):  
A. L. Collins ◽  
Y. Kim ◽  
P. Sklar ◽  
M. C. O'Donovan ◽  
P. F. Sullivan ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Candidate Gene ◽  
Candidate Genes ◽  
Association Studies ◽  
Human Diseases ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Common Genetic Variation ◽  
Genome Wide ◽  
Candidate Gene Studies

BackgroundCandidate gene studies have been a key approach to the genetics of schizophrenia (SCZ). However, the results of these studies are confusing and no genes have been unequivocally implicated. The hypothesis-driven candidate gene literature can be appraised by comparison with the results of genome-wide association studies (GWAS).MethodWe describe the characteristics of hypothesis-driven candidate gene studies from the SZGene database, and use pathway analysis to compare hypothesis-driven candidate genes with GWAS results from the International Schizophrenia Consortium (ISC).ResultsSZGene contained 732 autosomal genes evaluated in 1374 studies. These genes had poor statistical power to detect genetic effects typical for human diseases, assessed only 3.7% of genes in the genome, and had low marker densities per gene. Most genes were assessed once or twice (76.9%), providing minimal ability to evaluate consensus across studies. The ISC studies had 89% power to detect a genetic effect typical for common human diseases and assessed 79% of known autosomal common genetic variation. Pathway analyses did not reveal enrichment of smaller ISCpvalues in hypothesis-driven candidate genes, nor did a comprehensive evaluation of meta-hypotheses driving candidate gene selection (SCZ as a disease of the synapse or neurodevelopment). The most studied hypothesis-driven candidate genes (COMT,DRD3,DRD2,HTR2A,NRG1,BDNF,DTNBP1andSLC6A4) had no notable ISC results.ConclusionsWe did not find support for the idea that the hypothesis-driven candidate genes studied in the literature are enriched for the common genetic variation involved in the etiology of SCZ. Larger samples are required to evaluate this conclusion definitively.

scholarly journals Bayesian multivariate reanalysis of large genetic studies identifies many new associations

2019 ◽  
Author(s):  
Michael C. Turchin ◽  
Matthew Stephens
Keyword(s):  
Genetic Variation ◽  
Multivariate Analyses ◽  
Association Studies ◽  
Plasma Lipid ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Multiple Traits ◽  
Association Analyses ◽  
New Associations ◽  
Genome Wide

AbstractGenome-wide association studies (GWAS) have now been conducted for hundreds of phenotypes of relevance to human health. Many such GWAS involve multiple closely-related phenotypes collected on the same samples. However, the vast majority of these GWAS have been analyzed using simple univariate analyses, which consider one phenotype at a time. This is de-spite the fact that, at least in simulation experiments, multivariate analyses have been shown to be more powerful at detecting associations. Here, we conduct multivariate association analyses on 13 different publicly-available GWAS datasets that involve multiple closely-related phenotypes. These data include large studies of anthropometric traits (GIANT), plasma lipid traits (GlobalLipids), and red blood cell traits (HaemgenRBC). Our analyses identify many new associations (433 in total across the 13 studies), many of which replicate when follow-up samples are available. Overall, our results demonstrate that multivariate analyses can help make more effective use of data from both existing and future GWAS.1Author SummaryGenome-wide association studies (GWAS) have become a common and powerful tool for identifying significant correlations between markers of genetic variation and physical traits of interest. Often these studies are conducted by comparing genetic variation against single traits one at a time (‘univariate’); however, it has previously been shown that it is possible to increase your power to detect significant associations by comparing genetic variation against multiple traits simultaneously (‘multivariate’). Despite this apparent increase in power though, researchers still rarely conduct multivariate GWAS, even when studies have multiple traits readily available. Here, we reanalyze 13 previously published GWAS using a multivariate method and find >400 additional associations. Our method makes use of univariate GWAS summary statistics and is available as a software package, thus making it accessible to other researchers interested in conducting the same analyses. We also show, using studies that have multiple releases, that our new associations have high rates of replication. Overall, we argue multivariate approaches in GWAS should no longer be overlooked and how, often, there is low-hanging fruit in the form of new associations by running these methods on data already collected.

scholarly journals No genetic evidence for involvement of alcohol dehydrogenase genes in risk for Parkinson’s disease

2019 ◽  
Author(s):  
Jonggeol Jeffrey Kim ◽  
Sara Bandres-Ciga ◽  
Cornelis Blauwendraat ◽  
Ziv Gan-Or ◽  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Genetic Variation ◽  
Alcohol Dehydrogenase ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Causal Gene ◽  
Risk Variants ◽  
Genome Wide

AbstractMultiple genes have been implicated in Parkinson’s disease (PD), including causal gene variants and risk variants typically identified using genome-wide association studies (GWAS). Variants in the alcohol dehydrogenase genes ADH1C and ADH1B are among the genes that have been associated with PD, suggesting that this family of genes may be important in PD. As part of the International Parkinson’s Disease Genomics Consortium’s (IPDGC) efforts to scrutinize previously reported risk factors for PD, we explored genetic variation in the alcohol dehydrogenase genes ADH1A, ADH1B, ADH1C, ADH4, ADH5, ADH6, and ADH7 using imputed GWAS data from 15,097 cases and 17,337 healthy controls. Rare-variant association tests and single-variant score tests did not show any statistically significant association of alcohol dehydrogenase genetic variation with the risk for PD.

scholarly journals Genome-Wide Association Studies: Progress in Identifying Genetic Biomarkers in Common, Complex Diseases

2007 ◽  
Vol 2 ◽  
pp. 117727190700200 ◽  
Author(s):  
Stephen F. Kingsmore ◽  
Ingrid E. Lindquist ◽  
Joann Mudge ◽  
William D. Beavis
Keyword(s):  
Genetic Heterogeneity ◽  
Complex Traits ◽  
Biomarker Discovery ◽  
Association Studies ◽  
Complex Diseases ◽  
Human Diseases ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Biomarkers ◽  
Genome Wide

Novel, comprehensive approaches for biomarker discovery and validation are urgently needed. One particular area of methodologic need is for discovery of novel genetic biomarkers in complex diseases and traits. Here, we review recent successes in the use of genome wide association (GWA) approaches to identify genetic biomarkers in common human diseases and traits. Such studies are yielding initial insights into the allelic architecture of complex traits. In general, it appears that complex diseases are associated with many common polymorphisms, implying profound genetic heterogeneity between affected individuals.

scholarly journals Dissecting the phenotype in genome-wide association studies of psychiatric illness

2009 ◽  
Vol 195 (2) ◽  
pp. 97-99 ◽  
Keyword(s):  
Psychiatric Disorders ◽  
Psychiatric Illness ◽  
Genetic Architecture ◽  
Association Studies ◽  
Human Diseases ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
The Past ◽  
Genome Wide

SummaryOver the past 2 years genome-wide association studies have made major contributions to understanding the genetic architecture of many common human diseases. This editorial outlines the development of such studies in psychiatry and highlights the opportunities for advancing understanding of the biological underpinnings and nosological structure of psychiatric disorders.

Sign in / Sign up

Export Citation Format

Share Document