scholarly journals Cross-species alcohol dependence-associated gene networks: Co-analysis of mouse brain gene expression and human genome-wide association data

2018 ◽  
Author(s):  
Kristin M. Mignogna ◽  
Silviu A. Bacanu ◽  
Brien P. Riley ◽  
Aaron R. Wolen ◽  
Michael F. Miles
Keyword(s):  
Gene Expression ◽  
Alcohol Dependence ◽  
Mouse Brain ◽  
Gene Networks ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractGenome-wide association studies on alcohol dependence, by themselves, have yet to account for the estimated heritability of the disorder and provide incomplete mechanistic understanding of this complex trait. Integrating brain ethanol-responsive gene expression networks from model organisms with human genetic data on alcohol dependence could aid in identifying dependence-associated genes and functional networks in which they are involved. This study used a modification of the Edge-Weighted Dense Module Searching for genome-wide association studies (EW-dmGWAS) approach to co-analyze whole-genome gene expression data from ethanol-exposed mouse brain tissue, human protein-protein interaction databases and alcohol dependence-related genome-wide association studies. Results revealed novel ethanol-regulated and alcohol dependence-associated gene networks in prefrontal cortex, nucleus accumbens, and ventral tegmental area. Three of these networks were overrepresented with genome-wide association signals from an independent dataset. These networks were significantly overrepresented for gene ontology categories involving several mechanisms, including actin filament-based activity, transcript regulation, Wnt and Syndecan-mediated signaling, and ubiquitination. Together, these studies provide novel insight for brain mechanisms contributing to alcohol dependence.

scholarly journals Cross-species alcohol dependence-associated gene networks: Co-analysis of mouse brain gene expression and human genome-wide association data

PLoS ONE ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. e0202063 ◽  
Author(s):  
Kristin M. Mignogna ◽  
Silviu A. Bacanu ◽  
Brien P. Riley ◽  
Aaron R. Wolen ◽  
Michael F. Miles
Keyword(s):  
Gene Expression ◽  
Alcohol Dependence ◽  
Human Genome ◽  
Mouse Brain ◽  
Gene Networks ◽  
Genome Wide Association ◽  
Brain Gene Expression ◽  
Genome Wide ◽  
Association Data

scholarly journals CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies

2019 ◽  
Author(s):  
Jianhua Wang ◽  
Dandan Huang ◽  
Yao Zhou ◽  
Hongcheng Yao ◽  
Huanhuan Liu ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Genetic Variants ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genome Wide ◽  
Credible Sets ◽  
Causal Variants

Abstract Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

scholarly journals Gene expression levels as endophenotypes in genome-wide association studies of Alzheimer disease

Neurology ◽  
2010 ◽  
Vol 74 (6) ◽  
pp. 480-486 ◽  
Author(s):  
F. Zou ◽  
M. M. Carrasquillo ◽  
V. S. Pankratz ◽  
O. Belbin ◽  
K. Morgan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer Disease ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Expression Levels ◽  
Genome Wide ◽  
Gene Expression Levels

scholarly journals Transcriptome Analysis in Domesticated Species: Challenges and Strategies

2015 ◽  
Vol 9S4 ◽  
pp. BBI.S29334 ◽  
Author(s):  
Jessica P. Hekman ◽  
Jennifer L Johnson ◽  
Anna V. Kukekova
Keyword(s):  
Complex Traits ◽  
Gene Networks ◽  
Association Studies ◽  
Cultural Value ◽  
Genomic Research ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Rna Seq ◽  
Genome Wide ◽  
Genome Assemblies

Domesticated species occupy a special place in the human world due to their economic and cultural value. In the era of genomic research, domesticated species provide unique advantages for investigation of diseases and complex phenotypes. RNA sequencing, or RNA-seq, has recently emerged as a new approach for studying transcriptional activity of the whole genome, changing the focus from individual genes to gene networks. RNA-seq analysis in domesticated species may complement genome-wide association studies of complex traits with economic importance or direct relevance to biomedical research. However, RNA-seq studies are more challenging in domesticated species than in model organisms. These challenges are at least in part associated with the lack of quality genome assemblies for some domesticated species and the absence of genome assemblies for others. In this review, we discuss strategies for analyzing RNA-seq data, focusing particularly on questions and examples relevant to domesticated species.

scholarly journals Genome-wide association studies on endometriosis and endometriosis-related infertility

2018 ◽  
Author(s):  
Geneviève Galarneau ◽  
Pierre Fontanillas ◽  
Caterina Clementi ◽  
Tina Hu-Seliger ◽  
David-Emlyn Parfitt ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Association Studies ◽  
Reproductive Age ◽  
Genome Wide Association ◽  
European Ancestry ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Gwas Study ◽  
Genome Wide ◽  
Genome Wide Significance

AbstractEndometriosis affects ∼10% of women of reproductive age. It is characterized by the growth of endometrial-like tissue outside the uterus and is frequently associated with severe pain and infertility. We performed the largest endometriosis genome-wide association study (GWAS) to date, with 37,183 cases and 251,258 controls. All women were of European ancestry. We also performed the first GWAS of endometriosis-related infertility, including 2,969 cases and 3,770 controls. Our endometriosis GWAS study replicated, at genome-wide significance, seven loci identified in previous endometriosis GWASs (CELA3A-CDC42, SYNE1, KDR, FSHB-ARL14EP, GREB1, ID4, and CEP112) and identified seven new candidate loci with genome-wide significance (NGF, ATP1B1-F5, CD109, HEY2, OSR2-VPS13B, WT1, and TEX11-SLC7A3). No loci demonstrated genome-wide significance for endometriosis-related infertility, however, the three most strongly associated loci (MCTP1, EPS8L3-CSF1, and LPIN1) were in or near genes associated with female fertility or embryonic lethality in model organisms. These results reveal new candidate genes with potential involvement in the pathophysiology of endometriosis and endometriosis-related infertility.

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