scholarly journals Genetic Basis of Dual Diagnosis: A Review of Genome-Wide Association Studies (GWAS) Focusing on Patients with Mood or Anxiety Disorders and Co-Occurring Alcohol-Use Disorder

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1055
Author(s):  
Kaloyan Stoychev ◽  
Dancho Dilkov ◽  
Elahe Naghavi ◽  
Zornitsa Kamburova
Keyword(s):  
Alcohol Use ◽  
Anxiety Disorders ◽  
Dual Diagnosis ◽  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Mood And Anxiety Disorders ◽  
Genome Wide

(1) Background: Comorbidity between Alcohol Use Disorders (AUD), mood, and anxiety disorders represents a significant health burden, yet its neurobiological underpinnings are still elusive. The current paper reviews all genome-wide association studies conducted in the past ten years, sampling those on AUD and mood or anxiety disorders. (2) Methods: In keeping with PRISMA guidelines, we searched EMBASE, Medline/PUBMED, and PsycINFO databases (January 2010 to December 2020), including references of enrolled studies. Study selection was based on predefined criteria and data underwent a multistep revision process. (3) Results: 15 studies were included. Some of them explored dual diagnoses phenotypes directly while others employed correlational analysis based on polygenic risk score approach. Their results support the significant overlap of genetic factors involved in AUDs and mood and anxiety disorders. Comorbidity risk seems to be conveyed by genes engaged in neuronal development, connectivity, and signaling although the precise neuronal pathways and mechanisms remain unclear. (4) Conclusion: given that genes associated with complex traits including comorbid clinical presentations are of small effect, and individually responsible for a very low proportion of total variance, larger samples consisting of multiple refined comorbid combinations confirmed by re-sequencing approaches will be necessary to disentangle the genetic architecture of dual diagnosis.

Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies

2021 ◽  
Vol 42 (1) ◽  
Author(s):  
Dinesh K. Saini ◽  
Yuvraj Chopra ◽  
Jagmohan Singh ◽  
Karansher S. Sandhu ◽  
Anand Kumar ◽  
...  
Keyword(s):  
Complex Traits ◽  
Comprehensive Evaluation ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Mapping Complex Traits

scholarly journals GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

2020 ◽  
Author(s):  
Nasa Sinnott-Armstrong ◽  
Sahin Naqvi ◽  
Manuel Rivas ◽  
Jonathan K Pritchard
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Uk Biobank ◽  
The Core ◽  
Genome Wide ◽  
Core Genes

SummaryGenome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. However, for most traits it remains difficult to interpret what genes and biological processes are impacted by the top hits. Here, as a contrast, we describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—that are biologically simpler than most diseases, and for which we know a great deal in advance about the core genes and pathways. Unlike most GWAS of complex traits, for all three traits we find that most top hits are readily interpretable. We observe huge enrichment of significant signals near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of variation in each trait, including insights into differences in testosterone regulation between females and males. Meanwhile, in other respects the results are reminiscent of GWAS for more-complex traits. In particular, even these molecular traits are highly polygenic, with most of the variance coming not from core genes, but from thousands to tens of thousands of variants spread across most of the genome. Given that diseases are often impacted by many distinct biological processes, including these three, our results help to illustrate why so many variants can affect risk for any given disease.

scholarly journals GWAS-Flow: A GPU accelerated framework for efficient permutation based genome-wide association studies

2019 ◽  
Author(s):  
Jan A. Freudenthal ◽  
Markus J. Ankenbrand ◽  
Dominik G. Grimm ◽  
Arthur Korte
Keyword(s):  
Complex Traits ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Large Datasets ◽  
Genome Wide Association ◽  
Small Data ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Non Gaussian

AbstractMotivationGenome-wide association studies (GWAS) are one of the most commonly used methods to detect associations between complex traits and genomic polymorphisms. As both genotyping and phenotyping of large populations has become easier, typical modern GWAS have to cope with massive amounts of data. Thus, the computational demand for these analyses grew remarkably during the last decades. This is especially true, if one wants to implement permutation-based significance thresholds, instead of using the naïve Bonferroni threshold. Permutation-based methods have the advantage to provide an adjusted multiple hypothesis correction threshold that takes the underlying phenotypic distribution into account and will thus remove the need to find the correct transformation for non Gaussian phenotypes. To enable efficient analyses of large datasets and the possibility to compute permutation-based significance thresholds, we used the machine learning framework TensorFlow to develop a linear mixed model (GWAS-Flow) that can make use of the available CPU or GPU infrastructure to decrease the time of the analyses especially for large datasets.ResultsWe were able to show that our application GWAS-Flow outperforms custom GWAS scripts in terms of speed without loosing accuracy. Apart from p-values, GWAS-Flow also computes summary statistics, such as the effect size and its standard error for each individual marker. The CPU-based version is the default choice for small data, while the GPU-based version of GWAS-Flow is especially suited for the analyses of big data.AvailabilityGWAS-Flow is freely available on GitHub (https://github.com/Joyvalley/GWAS_Flow) and is released under the terms of the MIT-License.

Genome-wide association and genomic selection in animal breedingThis article is one of a selection of papers from the conference “Exploiting Genome-wide Association in Oilseed Brassicas: a model for genetic improvement of major OECD crops for sustainable farming”.

Genome ◽  
2010 ◽  
Vol 53 (11) ◽  
pp. 876-883 ◽  
Author(s):  
Ben Hayes ◽  
Mike Goddard
Keyword(s):  
Genomic Selection ◽  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association ◽  
Relationship Matrix ◽  
Genome Wide Association Studies ◽  
Simple Method ◽  
Breeding Values ◽  
Genome Wide ◽  
A Genome

Results from genome-wide association studies in livestock, and humans, has lead to the conclusion that the effect of individual quantitative trait loci (QTL) on complex traits, such as yield, are likely to be small; therefore, a large number of QTL are necessary to explain genetic variation in these traits. Given this genetic architecture, gains from marker-assisted selection (MAS) programs using only a small number of DNA markers to trace a limited number of QTL is likely to be small. This has lead to the development of alternative technology for using the available dense single nucleotide polymorphism (SNP) information, called genomic selection. Genomic selection uses a genome-wide panel of dense markers so that all QTL are likely to be in linkage disequilibrium with at least one SNP. The genomic breeding values are predicted to be the sum of the effect of these SNPs across the entire genome. In dairy cattle breeding, the accuracy of genomic estimated breeding values (GEBV) that can be achieved and the fact that these are available early in life have lead to rapid adoption of the technology. Here, we discuss the design of experiments necessary to achieve accurate prediction of GEBV in future generations in terms of the number of markers necessary and the size of the reference population where marker effects are estimated. We also present a simple method for implementing genomic selection using a genomic relationship matrix. Future challenges discussed include using whole genome sequence data to improve the accuracy of genomic selection and management of inbreeding through genomic relationships.

Genome-wide association studies for complex traits: consensus, uncertainty and challenges

2008 ◽  
Vol 9 (5) ◽  
pp. 356-369 ◽  
Author(s):  
Mark I. McCarthy ◽  
Gonçalo R. Abecasis ◽  
Lon R. Cardon ◽  
David B. Goldstein ◽  
Julian Little ◽  
...  
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide
Sign in / Sign up

Export Citation Format

Share Document