scholarly journals Model of gene expression in extreme cold - reference transcriptome for the high-Antarctic cryopelagic notothenioid fish Pagothenia borchgrevinki

BMC Genomics ◽  
2013 ◽  
Vol 14 (1) ◽  
pp. 634 ◽  
Author(s):  
Kevin T Bilyk ◽  
C-H Cheng
Keyword(s):  
Gene Expression ◽  
Reference Transcriptome ◽  
Notothenioid Fish ◽  
Pagothenia Borchgrevinki ◽  
Extreme Cold

scholarly journals Is there a role for sarcolipin in avian facultative thermogenesis in extreme cold?

2020 ◽  
Vol 16 (6) ◽  
pp. 20200078
Author(s):  
Maria Stager ◽  
Zachary A. Cheviron
Keyword(s):  
Gene Expression ◽  
Endoplasmic Reticulum ◽  
Oxygen Consumption ◽  
Heat Generation ◽  
Atp Hydrolysis ◽  
Transcript Abundance ◽  
Peak Oxygen Consumption ◽  
Gene Coding ◽  
Extreme Cold ◽  
Shivering Thermogenesis

Endotherms defend their body temperature in the cold by employing shivering (ST) and/or non-shivering thermogenesis (NST). Although NST is well documented in mammals, its importance to avian heat generation is unclear. Recent work points to a prominent role for the sarco/endoplasmic reticulum Ca 2+ ATPase (SERCA) in muscular NST. SERCA's involvement in both ST and NST, however, posits a tradeoff between these two heat-generating mechanisms. To explore this tradeoff, we assayed pectoralis gene expression of adult songbirds exposed to chronic temperature acclimations. Counter to mammal models, we found that cold-acclimated birds downregulated the expression of sarcolipin ( SLN ), a gene coding for a peptide that promotes heat generation by uncoupling SERCA Ca 2+ transport from ATP hydrolysis, indicating a reduced potential for muscular NST. We also found differential expression of many genes involved in Ca 2+ cycling and muscle contraction and propose that decreased SLN could promote increased pectoralis contractility for ST. Moreover, SLN transcript abundance negatively correlated with peak oxygen consumption under cold exposure (a proxy for ST) across individuals, and higher SLN transcript abundance escalated an individual's risk of hypothermia in acute cold. Our results therefore suggest that SLN-mediated NST may not be an important mechanism of—and could be a hindrance to—avian thermoregulation in extreme cold.

scholarly journals Powerful Variance-Component TWAS method identifies novel and known risk genes for clinical and pathologic Alzheimer’s dementia phenotypes

2020 ◽  
Author(s):  
Shizhen Tang ◽  
Aron S. Buchman ◽  
Philip L. De Jager ◽  
David A. Bennett ◽  
Michael P. Epstein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Variance Component ◽  
Association Studies ◽  
Alzheimer’S Dementia ◽  
Effect Sizes ◽  
Reference Dataset ◽  
Alzheimer's Dementia ◽  
Risk Genes ◽  
Test Dataset ◽  
Reference Transcriptome

AbstractTranscriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, existing TWAS methods impute gene expression by creating a weighted sum that aggregates SNPs with their corresponding cis-eQTL effects on transcriptome estimated from reference datasets. Existing TWAS methods then apply a linear regression model to assess the association between imputed gene expression and test phenotype, thereby assuming the effect of a cis-eQTL SNP on test phenotype is a linear function of the eQTL’s estimated effect on reference transcriptome. Thus, existing TWAS methods make a strong assumption that cis-eQTL effect sizes on reference transcriptome are reflective of their corresponding SNP effect sizes on test phenotype. To increase TWAS robustness to this assumption, we propose a Variance-Component TWAS procedure (VC-TWAS) that assumes the effects of cis-eQTL SNPs on phenotype are random (with variance proportional to corresponding cis-eQTL effects in reference dataset) rather than fixed. By doing so, we show VC-TWAS is more powerful than traditional TWAS when cis-eQTL SNP effects on test phenotype truly differ from their eQTL effects within reference dataset. We further applied VC-TWAS using cis-eQTL effect sizes estimated by a nonparametric Bayesian method to study Alzheimer’s dementia (AD) related phenotypes and detected 13 genes significantly associated with AD, including 6 known GWAS risk loci. All significant loci are proximal to the major known risk loci APOE for AD. Further, we add this VC-TWAS function into our previously developed tool TIGAR for public use.

scholarly journals PrediXcan: Trait Mapping Using Human Transcriptome Regulation

2015 ◽  
Author(s):  
Eric R Gamazon ◽  
Heather E Wheeler ◽  
Kaanan Shah ◽  
Sahar V Mozaffari ◽  
Keston Aquino-Michaels ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Prediction Models ◽  
Association Studies ◽  
Regulatory Function ◽  
Genetic Profile ◽  
Genome Wide Association Studies ◽  
Reverse Causality ◽  
Reference Transcriptome ◽  
Regulated Gene Expression

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates the “imputed” gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. The genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome datasets. PrediXcan enjoys the benefits of gene- based approaches such as reduced multiple testing burden, more comprehensive annotation of gene function compared to that derived from single variants, and a principled approach to the design of follow-up experiments while also integrating knowledge of regulatory function. Since no actual expression data are used in the analysis of GWAS data - only in silico expression - reverse causality problems are largely avoided. PrediXcan harnesses reference transcriptome data for disease mapping studies. Our results demonstrate that PrediXcan can detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations.

Characterization of gene expression of QM from Caragana jubata, a plant species that grows under extreme cold

2009 ◽  
Vol 37 (2) ◽  
pp. 1003-1010 ◽  
Author(s):  
Pardeep Kumar Bhardwaj ◽  
Paramvir Singh Ahuja ◽  
Sanjay Kumar
Keyword(s):  
Gene Expression ◽  
Plant Species ◽  
Extreme Cold

scholarly journals Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer’s dementia

PLoS Genetics ◽  
2021 ◽  
Vol 17 (4) ◽  
pp. e1009482
Author(s):  
Shizhen Tang ◽  
Aron S. Buchman ◽  
Philip L. De Jager ◽  
David A. Bennett ◽  
Michael P. Epstein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Linear Function ◽  
Variance Component ◽  
Significant Risk ◽  
Gwas Data ◽  
Risk Genes ◽  
Reference Transcriptome ◽  
Individual Level ◽  
Level Data ◽  
The Individual

Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, traditional two-stage TWAS methods first impute gene expression by creating a weighted sum that aggregates SNPs with their corresponding cis-eQTL effects on reference transcriptome. Traditional TWAS methods then employ a linear regression model to assess the association between imputed gene expression and test phenotype, thereby assuming the effect of a cis-eQTL SNP on test phenotype is a linear function of the eQTL’s estimated effect on reference transcriptome. To increase TWAS robustness to this assumption, we propose a novel Variance-Component TWAS procedure (VC-TWAS) that assumes the effects of cis-eQTL SNPs on phenotype are random (with variance proportional to corresponding reference cis-eQTL effects) rather than fixed. VC-TWAS is applicable to both continuous and dichotomous phenotypes, as well as individual-level and summary-level GWAS data. Using simulated data, we show VC-TWAS is more powerful than traditional TWAS methods based on a two-stage Burden test, especially when eQTL genetic effects on test phenotype are no longer a linear function of their eQTL genetic effects on reference transcriptome. We further applied VC-TWAS to both individual-level (N = ~3.4K) and summary-level (N = ~54K) GWAS data to study Alzheimer’s dementia (AD). With the individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by traditional TWAS methods. With the summary-level data, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs, which also validated our findings with the individual-level GWAS data. Our VC-TWAS method is implemented in the TIGAR tool for public use.

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