scholarly journals Combining Genome Wide Association Studies and Differential Gene Expression Data Analyses Identifies Candidate Genes Affecting Mastitis Caused by Two Different Pathogens in the Dairy Cow

2015 ◽  
Vol 05 (04) ◽  
pp. 358-393 ◽  
Author(s):  
Xing Chen ◽  
Zhangrui Cheng ◽  
Shujun Zhang ◽  
Dirk Werling ◽  
D. Claire Wathes
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Dairy Cow ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Expression Data ◽  
Data Analyses ◽  
Genome Wide ◽  
Differential Gene

scholarly journals Integrative Genomic Analysis for the Discovery of Biomarkers in Prostate Cancer

2014 ◽  
Vol 9 ◽  
pp. BMI.S13729 ◽  
Author(s):  
Chindo Hicks ◽  
Tejaswi Koganti ◽  
Shankar Giri ◽  
Memory Tekere ◽  
Ritika Ramani ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Gene Expression Data ◽  
Genetic Variants ◽  
Association Studies ◽  
Biological Pathways ◽  
Great Success ◽  
Genome Wide Association Studies ◽  
Expression Data ◽  
Increased Risk

Genome-wide association studies (GWAS) have achieved great success in identifying single nucleotide polymorphisms (SNPs, herein called genetic variants) and genes associated with risk of developing prostate cancer. However, GWAS do not typically link the genetic variants to the disease state or inform the broader context in which the genetic variants operate. Here, we present a novel integrative genomics approach that combines GWAS information with gene expression data to infer the causal association between gene expression and the disease and to identify the network states and biological pathways enriched for genetic variants. We identified gene regulatory networks and biological pathways enriched for genetic variants, including the prostate cancer, IGF-1, JAK2, androgen, and prolactin signaling pathways. The integration of GWAS information with gene expression data provides insights about the broader context in which genetic variants associated with an increased risk of developing prostate cancer operate.

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 MixMir: microRNA motif discovery from gene expression data using mixed linear models

2014 ◽  
Author(s):  
LIYANG Diao ◽  
Antoine Marcais ◽  
Scott Norton ◽  
Kevin C. Chen
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Motif Discovery ◽  
Linear Models ◽  
Developmental Stages ◽  
Sequence Similarity ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Expression Data ◽  
Mixed Linear Models

MicroRNAs (miRNAs) are a class of ~22nt non-coding RNAs that potentially regulate over 60% of human protein-coding genes. MiRNA activity is highly specific, differing between cell types, developmental stages and environmental conditions, so the identification of active miRNAs in a given sample is of great interest. Here we present a novel computational approach for analyzing both mRNA sequence and gene expression data, called MixMir. Our method corrects for 3' UTR background sequence similarity between transcripts, which is known to correlate with mRNA transcript abundance. We demonstrate that after accounting for kmer sequence similarities in 3' UTRs, a statistical linear model based on motif presence/absence can effectively discover active miRNAs in a sample. MixMir utilizes fast software implementations for solving mixed linear models which are widely-used in genome-wide association studies (GWAS). Essentially we use 3' UTR sequence similarity in place of population cryptic relatedness in the GWAS problem. Compared to similar methods such as miREDUCE, Sylamer and cWords, we found that MixMir performed better at discovering true miRNA motifs in Dicer knockout CD4+ T-cells, as well as protein and mRNA expression data obtained from miRNA transfection experiments in human cell lines. MixMir can be freely downloaded from https://github.com/ldiao/MixMir.

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.

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