expression variance
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2021 ◽  
Author(s):  
Ryo Yamamoto ◽  
Ryan Chung ◽  
Juan Manuel Vazquez ◽  
Huanjie Sheng ◽  
Philippa Steinberg ◽  
...  

Age is the primary risk factor for many common human diseases including heart disease, Alzheimer's dementias, cancers, and diabetes. Determining how and why tissues age differently is key to understanding the onset and progression of such pathologies. Here, we set out to quantify the relative contributions of genetics and aging to gene expression patterns from data collected across 27 tissues from 948 humans. We show that gene expression patterns become more erratic with age in several different tissues reducing the predictive power of expression quantitative trait loci. Jointly modelling the contributions of age and genetics to transcript level variation we find that the heritability (h2) of gene expression is largely consistent among tissues. In contrast, the average contribution of aging to gene expression variance varied by more than 20-fold among tissues with R2age > h2 in 5 tissues. We find that the coordinated decline of mitochondrial and translation factors is a widespread signature of aging across tissues. Finally, we show that while in general the force of purifying selection is stronger on genes expressed early in life compared to late in life as predicted by Medawar's hypothesis, a handful of highly proliferative tissues exhibit the opposite pattern. In contrast, gene expression variation that is under genetic control is strongly enriched for genes under relaxed constraint. Together we present a novel framework for predicting gene expression phenotypes from genetics and age and provide insights into the tissue-specific relative contributions of genes and the environment to phenotypes of aging.


Animals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 2667
Author(s):  
Irina Chadaeva ◽  
Petr Ponomarenko ◽  
Rimma Kozhemyakina ◽  
Valentin Suslov ◽  
Anton Bogomolov ◽  
...  

Belyaev’s concept of destabilizing selection during domestication was a major achievement in the XX century. Its practical value has been realized in commercial colors of the domesticated fox that never occur in the wild and has been confirmed in a wide variety of pet breeds. Many human disease models involving animals allow to test drugs before human testing. Perhaps this is why investigators doing transcriptomic profiling of domestic versus wild animals have searched for breed-specific patterns. Here we sequenced hypothalamic transcriptomes of tame and aggressive rats, identified their differentially expressed genes (DEGs), and, for the first time, applied principal component analysis to compare them with all the known DEGs of domestic versus wild animals that we could find. Two principal components, PC1 and PC2, respectively explained 67% and 33% of differential-gene-expression variance (hereinafter: log2 value) between domestic and wild animals. PC1 corresponded to multiple orthologous DEGs supported by homologs; these DEGs kept the log2 value sign from species to species and from tissue to tissue (i.e., a common domestication pattern). PC2 represented stand-alone homologous DEG pairs reversing the log2 value sign from one species to another and from tissue to tissue (i.e., representing intraspecific and interspecific variation).


Author(s):  
Wei-Yun Lai ◽  
Christian Schlötterer

AbstractShifts in trait means are widely considered as evidence for adaptive responses, but the impact on phenotypic variance remains largely unexplored. Here, we studied gene expression variance of Drosophila simulans males before and after 100 generations of adaptation to a novel hot laboratory environment. In each of the two independently evolved replicate populations the variance of about 150 genes changed significantly (mostly reduction). Although different genes were affected in both replicates, these genes are related to digestion in the gut. This non-parallel selection response on the gene level in combination with a convergent response at a higher phenotypic level reflects genetic redundancy, a characteristic hallmark of polygenic adaptation. We propose that the constant and simple food source in the laboratory resulted in selection for reduced variance in digestive genes. In natural populations adaptation to diverse types of food may be beneficial, resulting in higher phenotypic variance. This empirical evidence of phenotypic variance being the direct target of selection during adaptation has important implications for strategies to identify selection signatures.


BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Ning Li ◽  
Ben A. Flanagan ◽  
MacKenzie Partridge ◽  
Elaine J. Huang ◽  
Suzanne Edmands

Abstract Background Patterns of gene expression can be dramatically different between males and females of the same species, in part due to genes on sex chromosomes. Here we test for sex differences in early transcriptomic response to oxidative stress in a species which lacks heteromorphic sex chromosomes, the copepod Tigriopus californicus. Results Male and female individuals were separately exposed to control conditions and pro-oxidant conditions (hydrogen peroxide and paraquat) for periods of 3 hours and 6 hours. Variance partitioning showed the greatest expression variance among individuals, highlighting the important information that can be obscured by the common practice of pooling individuals. Gene expression variance between sexes was greater than that among treatments, showing the profound effect of sex even when males and females share the same genome. Males exhibited a larger response to both pro-oxidants, differentially expressing more than four times as many genes, including up-regulation of more antioxidant genes, heat shock proteins and protease genes. While females differentially expressed fewer genes, the magnitudes of fold change were generally greater, indicating a more targeted response. Although females shared a smaller fraction of differentially expressed genes between stressors and time points, expression patterns of antioxidant and protease genes were more similar between stressors and more GO terms were shared between time points. Conclusions Early transcriptomic responses to the pro-oxidants H2O2 and paraquat in copepods revealed substantial variation among individuals and between sexes. The finding of such profound sex differences in oxidative stress response, even in the absence of sex chromosomes, highlights the importance of studying both sexes and the potential for developing sex-specific strategies to promote optimal health and aging in humans.


PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9554
Author(s):  
Patrick Evans ◽  
Nancy J. Cox ◽  
Eric R. Gamazon

The development of explanatory models of protein sequence evolution has broad implications for our understanding of cellular biology, population history, and disease etiology. Here we analyze the GTEx transcriptome resource to quantify the effect of the transcriptome on protein sequence evolution in a multi-tissue framework. We find substantial variation among the central nervous system tissues in the effect of expression variance on evolutionary rate, with highly variable genes in the cortex showing significantly greater purifying selection than highly variable genes in subcortical regions (Mann–Whitney U p = 1.4 × 10−4). The remaining tissues cluster in observed expression correlation with evolutionary rate, enabling evolutionary analysis of genes in diverse physiological systems, including digestive, reproductive, and immune systems. Importantly, the tissue in which a gene attains its maximum expression variance significantly varies (p = 5.55 × 10−284) with evolutionary rate, suggesting a tissue-anchored model of protein sequence evolution. Using a large-scale reference resource, we show that the tissue-anchored model provides a transcriptome-based approach to predicting the primary affected tissue of developmental disorders. Using gradient boosted regression trees to model evolutionary rate under a range of model parameters, selected features explain up to 62% of the variation in evolutionary rate and provide additional support for the tissue model. Finally, we investigate several methodological implications, including the importance of evolutionary-rate-aware gene expression imputation models using genetic data for improved search for disease-associated genes in transcriptome-wide association studies. Collectively, this study presents a comprehensive transcriptome-based analysis of a range of factors that may constrain molecular evolution and proposes a novel framework for the study of gene function and disease mechanism.


2019 ◽  
Author(s):  
David Lamparter ◽  
Rajat Bhatnagar ◽  
Katja Hebestreit ◽  
T. Grant Belgard ◽  
Victor Hanson-Smith

1AbstractA longstanding goal of regulatory genetics is to understand how variants in genome sequences lead to changes in gene expression. Here we present a method named Bayesian Annotation Guided eQTL Analysis (BAGEA), a variational Bayes framework to model cis-eQTLs using directed and undirected genomic annotations. In a use case, we integrated directed genomic annotations with eQTL summary statistics from tissues of various origins. This analysis revealed epigenetic marks that are relevant for gene expression in different tissues and cell types. We estimated the predictive power of the models that were fitted based on directed genomic annotations. This analysis showed that, depending on the underlying eQTL data used, the directed genomic annotations could predict up to 1.5% of the variance observed in the expression of genes with top nominal eQTL association p-values < 10−7. For genes with estimated effect sizes in the top 25% quantile, up to 5% of the expression variance could be predicted. Based on our results, we recommend the use of BAGEA for the analysis of cis-eQTL data to reveal annotations relevant to expression biology.


2019 ◽  
Author(s):  
Thiago S. Guzella ◽  
Vasco M. Barreto ◽  
Jorge Carneiro

AbstractPhenotypic variation in the copy number of gene products expressed by cells or tissues has been the focus of intense investigation. To what extent the observed differences in cellular expression levels are persistent or transient is an intriguing question. Here, we develop a quantitative framework that resolves the expression variation into stable and unstable components. The difference between the expression means in two cohorts isolated from any cell population is shown to converge to an asymptotic value, with a characteristic time, τT, that measures the timescale of the unstable dynamics. The asymptotic difference in the means, relative to the initial value, measures the stable proportion of the original population variance . Empowered by this insight, we analysed the T-cell receptor (TCR) expression variation in CD4 T cells. About 70% of TCR expression variance is stable in a diverse polyclonal population, while over 80% of the variance in an isogenic TCR transgenic population is volatile. In both populations the TCR levels fluctuate with a characteristic time of 32 hours. This systematic characterisation of the expression variation dynamics, relying on time series of cohorts’ means, can be combined with technologies that measure gene or protein expression in single cells or in bulk.


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