scholarly journals Variability in Gene Expression is Associated with Incomplete Penetrance in Inherited Eye Disorders

Genes ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 179 ◽  
Author(s):  
David J. Green ◽  
Shalaw R. Sallah ◽  
Jamie M. Ellingford ◽  
Simon C. Lovell ◽  
Panagiotis I. Sergouniotis
Keyword(s):  
Gene Expression ◽  
Human Gene ◽  
Tissue Expression ◽  
Incomplete Penetrance ◽  
Expression Levels ◽  
Mendelian Disorders ◽  
Eye Disorders ◽  
Mutation Database ◽  
Gene Expression Levels ◽  
Variable Penetrance

Inherited eye disorders (IED) are a heterogeneous group of Mendelian conditions that are associated with visual impairment. Although these disorders often exhibit incomplete penetrance and variable expressivity, the scale and mechanisms of these phenomena remain largely unknown. Here, we utilize publicly-available genomic and transcriptomic datasets to gain insights into variable penetrance in IED. Variants in a curated set of 340 IED-implicated genes were extracted from the Human Gene Mutation Database (HGMD) 2019.1 and cross-checked with the Genome Aggregation Database (gnomAD) 2.1 control-only dataset. Genes for which >1 variants were encountered in both HGMD and gnomAD were considered to be associated with variable penetrance (n = 56). Variability in gene expression levels was then estimated for the subset of these genes that was found to be adequately expressed in two relevant resources: the Genotype-Tissue Expression (GTEx) and Eye Genotype Expression (EyeGEx) datasets. We found that genes suspected to be associated with variable penetrance tended to have significantly more variability in gene expression levels in the general population (p = 0.0000015); this finding was consistent across tissue types. The results of this study point to the possible influence of cis and/or trans-acting elements on the expressivity of variants causing Mendelian disorders. They also highlight the potential utility of quantifying gene expression as part of the investigation of families showing evidence of variable penetrance.

scholarly journals Variability in gene expression is associated with incomplete penetrance in inherited eye disorders

2020 ◽  
Author(s):  
David J. Green ◽  
Shalaw R. Sallah ◽  
Jamie M. Ellingford ◽  
Simon C. Lovell ◽  
Panagiotis I. Sergouniotis
Keyword(s):  
Gene Expression ◽  
General Population ◽  
Visual Impairment ◽  
Incomplete Penetrance ◽  
Potential Utility ◽  
Expression Levels ◽  
Mendelian Disorders ◽  
Eye Disorders ◽  
Gene Expression Levels ◽  
Variable Penetrance

AbstractInherited eye disorders (IED) are a heterogeneous group of Mendelian conditions that are associated with visual impairment. Although these disorders often exhibit incomplete penetrance and variable expressivity, the scale and mechanisms of these phenomena remain largely unknown. Here, we utilize publicly-available genomic and transcriptomic datasets to gain insights into variable penetrance in IED. Variants in a curated set of 340 IED-implicated genes were extracted from HGMD 2019.1 and cross-checked with the gnomAD 2.1 control-only dataset. Genes for which >1 variant was encountered in both HGMD and gnomAD were considered to be associated with variable penetrance (n=56). Variability in gene expression levels was then estimated for the subset of these genes that was found to be adequately expressed in two relevant resources, GTEx and EyeGEx. We found that genes suspected to be associated with variable penetrance tended to have significantly more variability in gene expression levels in the general population (p=0.0000015); this finding was consistent across tissue types. The results of this study point to a possible influence of cis and/or trans-acting elements on the expressivity of variants causing Mendelian disorders. They also highlight the potential utility of quantifying gene expression as part of the investigation of families showing evidence of variable penetrance.

scholarly journals Genetic Variants Associated mRNA Stability in Lung

2021 ◽  
Author(s):  
Jian-Rong Li ◽  
Mabel Tang ◽  
Yafang Li ◽  
Christopher I Amos ◽  
Chao Cheng
Keyword(s):  
Gene Expression ◽  
Mrna Stability ◽  
Genetic Variants ◽  
Molecular Mechanisms ◽  
Rna Binding ◽  
Tissue Expression ◽  
Rna Seq ◽  
Genetic Traits ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background: Expression quantitative trait loci (eQTLs) analyses have been widely used to identify genetic variants associated with gene expression levels to understand what molecular mechanisms underlie genetic traits. The resultant eQTLs might affect the expression of associated genes through transcriptional or post-transcriptional regulation. In this study, we attempt to distinguish these two types of regulation by identifying genetic variants associated with mRNA stability of genes (stQTLs).Results: Here, we presented a computational framework that take the advantage of recently developed methods to infer the mRNA stability of genes based on RNA-seq data and performed association analysis to identify stQTLs. Using the Genotype-Tissue Expression (GTEx) lung RNA-Seq data, we identified a total of 142,801 stQTLs for 3,942 genes and 186,132 eQTLs for 4,751 genes from 15,122,700 genetic variants for 13,476 genes, respectively. Interesting, our results indicated that stQTLs were enriched in the CDS and 3’UTR regions, while eQTLs are enriched in the CDS, 3’UTR, 5’UTR, and upstream regions. We also found that stQTLs are more likely than eQTLs to overlap with RNA binding protein (RBP) and microRNA (miRNA) binding sites. Our analyses demonstrate that simultaneous identification of stQTLs and eQTLs can provide more mechanistic insight on the association between genetic variants and gene expression levels.

scholarly journals Genetic effects on gene expression across human tissues

Nature ◽  
2017 ◽  
Vol 550 (7675) ◽  
pp. 204-213 ◽  
Author(s):  
Keyword(s):  
Gene Expression ◽  
Genetic Basis ◽  
Tissue Expression ◽  
Genetic Effects ◽  
Human Tissues ◽  
Cellular Mechanisms ◽  
Genetic Traits ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

scholarly journals Genome-Wide Expression and Alternative Splicing in Domesticated Sunflowers (Helianthus annuus L.) under Flooding Stress

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 92
Author(s):  
Joon Seon Lee ◽  
Lexuan Gao ◽  
Laura Melissa Guzman ◽  
Loren H. Rieseberg
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Mixed Model ◽  
Agricultural Land ◽  
Alcohol Fermentation ◽  
Expression Levels ◽  
Flooding Stress ◽  
Genome Wide ◽  
And Control ◽  
Gene Expression Levels

Approximately 10% of agricultural land is subject to periodic flooding, which reduces the growth, survivorship, and yield of most crops, reinforcing the need to understand and enhance flooding resistance in our crops. Here, we generated RNA-Seq data from leaf and root tissue of domesticated sunflower to explore differences in gene expression and alternative splicing (AS) between a resistant and susceptible cultivar under both flooding and control conditions and at three time points. Using a combination of mixed model and gene co-expression analyses, we were able to separate general responses of sunflower to flooding stress from those that contribute to the greater tolerance of the resistant line. Both cultivars responded to flooding stress by upregulating expression levels of known submergence responsive genes, such as alcohol dehydrogenases, and slowing metabolism-related activities. Differential AS reinforced expression differences, with reduced AS frequencies typically observed for genes with upregulated expression. Significant differences were found between the genotypes, including earlier and stronger upregulation of the alcohol fermentation pathway and a more rapid return to pre-flooding gene expression levels in the resistant genotype. Our results show how changes in the timing of gene expression following both the induction of flooding and release from flooding stress contribute to increased flooding tolerance.

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