scholarly journals Large Scale Gene Expression Meta-Analysis Reveals Tissue-Specific, Sex-Biased Gene Expression in Humans

2016 ◽  
Vol 7 ◽  
Author(s):  
Benjamin T. Mayne ◽  
Tina Bianco-Miotto ◽  
Sam Buckberry ◽  
James Breen ◽  
Vicki Clifton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Meta Analysis ◽  
Tissue Specific

T247. Oxytocin Gene Networks in the Human Brain: A Gene Expression and Large-Scale fMRI Meta-Analysis Study

2018 ◽  
Vol 83 (9) ◽  
pp. S225
Author(s):  
Daniel Quintana ◽  
Jaroslav Rokicki ◽  
Dennis van der Meer ◽  
Dag Alnæs ◽  
Tobias Kaufmann ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Brain ◽  
Gene Networks ◽  
Large Scale ◽  
Meta Analysis ◽  
Analysis Study

scholarly journals Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets

Cell Reports ◽  
2017 ◽  
Vol 21 (9) ◽  
pp. 2597-2613 ◽  
Author(s):  
Max Lam ◽  
Joey W. Trampush ◽  
Jin Yu ◽  
Emma Knowles ◽  
Gail Davies ◽  
...  
Keyword(s):  
Drug Targets ◽  
Large Scale ◽  
Meta Analysis ◽  
Tissue Specific ◽  
Nootropic Drug

scholarly journals A Further Comment on ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ by Lam et al.

2018 ◽  
Vol 21 (6) ◽  
pp. 538-545 ◽  
Author(s):  
W. D. Hill
Keyword(s):  
Cognitive Ability ◽  
Drug Targets ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Tissue Specific ◽  
Nootropic Drug ◽  
Genome Wide ◽  
Quality Control Metrics

Lam et al. (2018) respond to a commentary of their paper entitled ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ Lam et al. (2017). While Lam et al. (2018) have now provided the recommended quality control metrics for their paper, problems remain. Specifically, Lam et al. (2018) do not dispute that the results of their multi-trait analysis of genome-wide association study (MTAG) analysis has produced a phenotype with a genetic correlation of one with three measures of education, but do claim the associations found are specific to the trait of cognitive ability. In this brief paper, it is empirically demonstrated that the phenotype derived by Lam et al. (2017) is more genetically similar to education than cognitive ability. In addition, it is shown that of the genome-wide significant loci identified by Lam et al. (2017) are loci that are associated with education rather than with cognitive ability.

scholarly journals Intrinsic Subtypes and Androgen Receptor Gene Expression in Primary Breast Cancer. A Meta-Analysis

Biology ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 834
Author(s):  
Paola Cruz-Tapias ◽  
Wilson Rubiano ◽  
Milena Rondón-Lagos ◽  
Victoria-E. Villegas ◽  
Nelson Rangel
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Androgen Receptor ◽  
Large Scale ◽  
Histological Grade ◽  
Meta Analysis ◽  
Receptor Gene ◽  
Mrna Level ◽  
Mrna Levels ◽  
Luminal A

The androgen receptor (AR) is frequently expressed in breast cancer (BC), but its association with clinical and biological parameters of BC patients remains unclear. Here, we investigated the association of AR gene expression according to intrinsic BC subtypes by meta-analysis of large-scale microarray transcriptomic datasets. Sixty-two datasets including 10315 BC patients were used in the meta-analyses. Interestingly, AR mRNA level is significantly increased in patients categorized with less aggressive intrinsic molecular subtypes including, Luminal A compared to Basal-like (standardized mean difference, SMD: 2.12; 95% confidence interval, CI: 1.88 to 2.35; p < 0.001) or when comparing Luminal B to Basal-like (SMD: 1.53; CI: 1.33 to 1.72; p < 0.001). The same trend was observed when analyses were performed using immunohistochemistry-based surrogate subtypes. Consistently, the AR mRNA expression was higher in patients with low histological grade (p < 0.001). Furthermore, our data revealed higher levels of AR mRNA in BC patients expressing either estrogen or progesterone receptors (p < 0.001). Together, our findings indicate that high mRNA levels of AR are associated with BC subgroups with the less aggressive clinical features.

scholarly journals A sequence-based global map of regulatory activity for deciphering human genetics

2021 ◽  
Author(s):  
Kathleen M Chen ◽  
Aaron K Wong ◽  
Olga G Troyanskaya ◽  
Jian Zhou
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Human Genetics ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Sequence Information ◽  
Regulatory Activity ◽  
Tissue Specific ◽  
Regulatory Architecture ◽  
Sequence Class

Sequence is at the basis of how the genome shapes chromatin organization, regulates gene expression, and impacts traits and diseases. Epigenomic profiling efforts have enabled large-scale identification of regulatory elements, yet we still lack a sequence-based map to systematically identify regulatory activities from any sequence, which is necessary for predicting the effects of any variant on these activities. We address this challenge with Sei, a new framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Our framework systematically learns a vocabulary for the regulatory activities of sequences, which we call sequence classes, using a new deep learning model that predicts a compendium of 21,907 chromatin profiles across >1,300 cell lines and tissues, the most comprehensive to-date. Sequence classes allow for a global view of sequence and variant effects by quantifying diverse regulatory activities, such as loss or gain of cell-type-specific enhancer function. We show that sequence class predictions are supported by experimental data, including tissue-specific gene expression, expression QTLs, and evolutionary constraints based on population allele frequencies. Finally, we applied our framework to human genetics data. Sequence classes uniquely provide a non-overlapping partitioning of GWAS heritability by tissue-specific regulatory activity categories, which we use to characterize the regulatory architecture of 47 traits and diseases from UK Biobank. Furthermore, the predicted loss or gain of sequence class activities suggest specific mechanistic hypotheses for individual regulatory pathogenic mutations. We provide this framework as a resource to further elucidate the sequence basis of human health and disease.

scholarly journals COMBINING MACHINE LEARNING WITH AUTOMATED NEMATODE LIFESPAN ANALYSIS TO IDENTIFY MODIFIERS OF ALZHEIMER’S DISEASE

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S96-S96
Author(s):  
Joshua Russell ◽  
Matt Kaeberlein
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Meta Analysis ◽  
Brain Regions ◽  
Data Sets ◽  
Data Types ◽  
C Elegans

Abstract Here we present new computational and experimental methods to leverage the gene expression and neuropathology data collected from several large-scale studies of Alzheimer’s disease . These data sets include diverse data types, including transcriptomics, neuropathology phenotypes such as quantification of amyloid beta plaques and tau tangles in different brain regions, as well as assessments of dementia prior to death. This meta-analysis is a complex undertaking because the available data are from different studies and/or brain regions involving study-specific confounders and/or region-specific biological processes. We have therefore taken neural network and probabilistic computational approaches that reduce the data dimensionality, allowing statistical comparison across all brain samples. These approaches identify gene expression changes that are significantly associated with clinical and neuropathological assessment of Alzheimer’s disease. We then conduct in vivo validation of the genes through genetic screening of C. elegans models of Alzheimer's disease utilizing our automated robotic lifespan analysis platform. This approach allows for the greater leverage of existing Alzheimer’s disease biobank data to identify deep genetic signatures that could help identify new clinical gene-expression markers and pharmacological targets for Alzheimer’s disease.

scholarly journals A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes

2008 ◽  
Vol 105 (52) ◽  
pp. 20870-20875 ◽  
Author(s):  
K. Lage ◽  
N. T. Hansen ◽  
E. O. Karlberg ◽  
A. C. Eklund ◽  
F. S. Roque ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Disease ◽  
Large Scale ◽  
Disease Genes ◽  
Scale Analysis ◽  
Tissue Specific ◽  
Large Scale Analysis ◽  
Human Disease Genes

scholarly journals A novel tissue-specific meta-analysis approach for gene expression predictions, initiated with a mammalian gene expression testis database

BMC Genomics ◽  
2010 ◽  
Vol 11 (1) ◽  
pp. 467 ◽  
Author(s):  
Kshitish K Acharya ◽  
Darshan S Chandrashekar ◽  
Neelima Chitturi ◽  
Hardik Shah ◽  
Varun Malhotra ◽  
...  
Keyword(s):  
Gene Expression ◽  
Meta Analysis ◽  
Analysis Approach ◽  
Tissue Specific ◽  
Mammalian Gene ◽  
Mammalian Gene Expression
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