Rare Genetic Variants Underlie Outlying levels of DNA Methylation and Gene-Expression

AbstractTesting the effect of rare variants on phenotypic variation is difficult due to the need for extremely large cohorts to identify associated variants given expected effect sizes. An alternative approach is to investigate the effect of rare genetic variants on low-level genomic traits, such as gene expression or DNA methylation (DNAm), as effect sizes are expected to be larger for low-level compared to higher-order complex traits. Here, we investigate DNAm in healthy ageing populations - the Lothian Birth cohorts of 1921 and 1936 and identify both transient and stable outlying DNAm levels across the genome. We find an enrichment of rare genetic variants within 1kb of DNAm sites in individuals with stable outlying DNAm, implying genetic control of this extreme variation. Using a family-based cohort, the Brisbane Systems Genetics Study, we observed increased sharing of DNAm outliers among more closely related individuals, consistent with these outliers being driven by rare genetic variation. We demonstrated that outlying DNAm levels have a functional consequence on gene expression levels, with extreme levels of DNAm being associated with gene expression levels towards the tails of the population distribution. Overall, this study demonstrates the role of rare variants in the phenotypic variation of low-level genomic traits, and the effect of extreme levels of DNAm on gene expression.

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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 ◽

10.3390/genes12060854 ◽

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.

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Occupational exposure to gases/fumes and mineral dust affect DNA methylation levels of genes regulating expression

Human Molecular Genetics ◽

10.1093/hmg/ddz067 ◽

2019 ◽

Vol 28 (15) ◽

pp. 2477-2485 ◽

Cited By ~ 1

Author(s):

Diana A van der Plaat ◽

Judith M Vonk ◽

Natalie Terzikhan ◽

Kim de Jong ◽

Maaike de Vries ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Occupational Exposure ◽

Health Effects ◽

Mineral Dust ◽

Occupational Exposures ◽

Expression Levels ◽

Adverse Health Effects ◽

Never Smokers ◽

Gene Expression Levels

Abstract Many workers are daily exposed to occupational agents like gases/fumes, mineral dust or biological dust, which could induce adverse health effects. Epigenetic mechanisms, such as DNA methylation, have been suggested to play a role. We therefore aimed to identify differentially methylated regions (DMRs) upon occupational exposures in never-smokers and investigated if these DMRs associated with gene expression levels. To determine the effects of occupational exposures independent of smoking, 903 never-smokers of the LifeLines cohort study were included. We performed three genome-wide methylation analyses (Illumina 450 K), one per occupational exposure being gases/fumes, mineral dust and biological dust, using robust linear regression adjusted for appropriate confounders. DMRs were identified using comb-p in Python. Results were validated in the Rotterdam Study (233 never-smokers) and methylation-expression associations were assessed using Biobank-based Integrative Omics Study data (n = 2802). Of the total 21 significant DMRs, 14 DMRs were associated with gases/fumes and 7 with mineral dust. Three of these DMRs were associated with both exposures (RPLP1 and LINC02169 (2×)) and 11 DMRs were located within transcript start sites of gene expression regulating genes. We replicated two DMRs with gases/fumes (VTRNA2-1 and GNAS) and one with mineral dust (CCDC144NL). In addition, nine gases/fumes DMRs and six mineral dust DMRs significantly associated with gene expression levels. Our data suggest that occupational exposures may induce differential methylation of gene expression regulating genes and thereby may induce adverse health effects. Given the millions of workers that are exposed daily to occupational exposures, further studies on this epigenetic mechanism and health outcomes are warranted.

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A Genome-Wide Study of DNA Methylation Patterns and Gene Expression Levels in Multiple Human and Chimpanzee Tissues

PLoS Genetics ◽

10.1371/journal.pgen.1001316 ◽

2011 ◽

Vol 7 (2) ◽

pp. e1001316 ◽

Cited By ~ 142

Author(s):

Athma A. Pai ◽

Jordana T. Bell ◽

John C. Marioni ◽

Jonathan K. Pritchard ◽

Yoav Gilad

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Expression Levels ◽

Genome Wide ◽

A Genome ◽

Methylation Patterns ◽

Genome Wide Study ◽

Gene Expression Levels

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Genetic Variants Associated mRNA Stability in Lung

10.21203/rs.3.rs-770241/v1 ◽

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.

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Estimating the proportion of disease heritability mediated by gene expression levels

10.1101/118018 ◽

2017 ◽

Cited By ~ 10

Author(s):

Luke J. O’Connor ◽

Alexander Gusev ◽

Xuanyao Liu ◽

Po-Ru Loh ◽

Hilary K. Finucane ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Disease Risk ◽

Genetic Component ◽

Effect Sizes ◽

Expression Levels ◽

Risk Variants ◽

Regulatory Effects ◽

Disease Effect ◽

Gene Expression Levels

AbstractDisease risk variants identified by GWAS are predominantly noncoding, suggesting that gene regulation plays an important role. eQTL studies in unaffected individuals are often used to link disease-associated variants with the genes they regulate, relying on the hypothesis that noncoding regulatory effects are mediated by steady-state expression levels. To test this hypothesis, we developed a method to estimate the proportion of disease heritability mediated by the cis-genetic component of assayed gene expression levels. The method, gene expression co-score regression (GECS regression), relies on the idea that, for a gene whose expression level affects a phenotype, SNPs with similar effects on the expression of that gene will have similar phenotypic effects. In order to distinguish directional effects mediated by gene expression from non-directional pleiotropic or tagging effects, GECS regression operates on pairs of cis SNPs in linkage equilibrium, regressing pairwise products of disease effect sizes on products of cis-eQTL effect sizes. We verified that GECS regression produces robust estimates of mediated effects in simulations. We applied the method to eQTL data in 44 tissues from the GTEx consortium (average NeQTL = 158 samples) in conjunction with GWAS summary statistics for 30 diseases and complex traits (average NGWAS = 88K) with low pairwise genetic correlation, estimating the proportion of SNP-heritability mediated by the cis-genetic component of assayed gene expression in the union of the 44 tissues. The mean estimate was 0.21 (s.e. = 0.01) across 30 traits, with a significantly positive estimate (p < 0.001) for every trait. Thus, assayed gene expression in bulk tissues mediates a statistically significant but modest proportion of disease heritability, motivating the development of additional assays to capture regulatory effects and the use of our method to estimate how much disease heritability they mediate.

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Gene expression levels of DNA methyltransferase enzymes in Shank3-deficient mouse model of autism during early development

Endocrine Regulations ◽

10.2478/enr-2021-0025 ◽

2021 ◽

Vol 55 (4) ◽

pp. 234-237

Author(s):

Annamaria Srancikova ◽

Alexandra Reichova ◽

Zuzana Bacova ◽

Jan Bakos

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Brain Development ◽

Early Development ◽

Molecular Mechanisms ◽

Altered Expression ◽

Expression Levels ◽

The Brain ◽

Deficient Mice ◽

Gene Expression Levels

Abstract Objectives. The balance between DNA methylation and demethylation is crucial for the brain development. Therefore, alterations in the expression of enzymes controlling DNA methylation patterns may contribute to the etiology of neurodevelopmental disorders, including autism. SH3 and multiple ankyrin repeat domains 3 (Shank3)-deficient mice are commonly used as a well-characterized transgenic model to investigate the molecular mechanisms of autistic symptoms. DNA methyltransferases (DNMTs), which modulate several cellular processes in neurodevelopment, are implicated in the pathophysiology of autism. In this study, we aimed to describe the gene expression changes of major Dnmts in the brain of Shank3-deficient mice during early development. Methods and Results. The Dnmts gene expression was analyzed by qPCR in 5-day-old homo-zygous Shank3-deficient mice. We found significantly lower Dnmt1 and Dnmt3b gene expression levels in the frontal cortex. However, no such changes were observed in the hippocampus. However, significant increase was observed in the expression of Dnmt3a and Dnmt3b genes in the hypothalamus of Shank3-deficient mice. Conclusions. The present data indicate that abnormalities in the Shank3 gene are accompanied by an altered expression of DNA methylation enzymes in the early brain development stages, therefore, specific epigenetic control mechanisms in autism-relevant models should be more extensively investigated.

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Genetic variants in R-Spondin/RNF43 complex and gene expression levels to predict efficacy of cetuximab (cet) in patients (pts) with metastatic colorectal cancer (mCRC): Data from the FIRE-3 phase III trial.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.4_suppl.190 ◽

2020 ◽

Vol 38 (4_suppl) ◽

pp. 190-190

Author(s):

Francesca Battaglin ◽

Yi Xiao ◽

Joshua Millstein ◽

Andreas Seeber ◽

Hiroyuki Arai ◽

...

Keyword(s):

Gene Expression ◽

Genetic Variants ◽

Multivariable Analysis ◽

Colorectal Carcinogenesis ◽

Phase Iii ◽

First Line ◽

Tissue Samples ◽

Expression Levels ◽

The Impact ◽

Gene Expression Levels

190 Background: Wnt signaling deregulation is a primary driver of colorectal carcinogenesis. RNF43 is a key suppressor of Wnt activation while R-Spodin inhibits RNF43 activity. RNF43 mutations are associated with the serrated neoplasia pathway, BRAF mutation and MSI. We hypothesized that genetic variants in the R-Spodin/RNF43 complex and corresponding genes expression levels may predict cetuximab efficacy in mCRC pts. Methods: Genomic DNA from blood samples of pts enrolled in the randomized FIRE-3 trial was genotyped through the OncoArray, a custom array manufactured by Illumina. The impact on outcome of 17 functional SNPs within RNF43/ ZNRF3, LGR4/5 and RSPO1/2/3 was analyzed in 129 pts treated with first-line FOLFIRI/cet and 107 pts treated with FOLFIRI/bevacizumab (bev). Gene expression levels were measured from tumor tissue samples from 102 pts in the cet arm by HTG EdgeSeq Oncology Biomarker Panel. False discovery rate (FDR) for gene expression analysis was computed using the Benjamini-Hochberg approach (significant Q < 0.1). Results: In the cet cohort, pts with the C/C genotype of ZNRF3 rs132531 had significantly shorter overall survival compared to any T allele carriers (mOS: 20.3 vs 52 mo) in both univariable (HR 3.61, 95% CI 1.65-7.88, P < .001) and multivariable analysis (adjusted P = .01). Conversely, RSPO1 rs4652964 any G allele carriers showed increased tumor response (TR) rates compared to the A/A genotype (83 vs 66 %, P = .04). These associations were not observed in bev arm. Lower gene expression levels of RNF43 were associated with shorter PFS in pts with right-sided tumors receiving FOLFIRI/cet ( P = .006, Q < 0.1). RSPO1 expression levels were also associated with TR in the same subgroup (70 vs 10% in high vs low; P = .001, Q < .05). RNF43 expression was associated with TR in pts with left-sided tumors (82% in high vs 58% in low, P = .014, Q = 0.1). Conclusions: Our results provide the first evidence that germline polymorphisms and tumor gene expression levels of RNF43/ ZNRF3 and RSPO1 may have a predictive value in mCRC pts receiving first-line cetuximab-based treatment and contribute to modulate anti-EGFRs activity.

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Sodium valproate and 5-aza-2′-deoxycytidine differentially modulate DNA demethylation in G1 phase-arrested and proliferative HeLa cells

Scientific Reports ◽

10.1038/s41598-019-54848-x ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Marina Amorim Rocha ◽

Giovana Maria Breda Veronezi ◽

Marina Barreto Felisbino ◽

Maria Silvia Viccari Gatti ◽

Wirla M. S. C. Tamashiro ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Sodium Valproate ◽

Hela Cells ◽

Dna Methyltransferase ◽

Dna Demethylation ◽

G1 Phase ◽

Expression Levels ◽

Demethylation Pathway ◽

Gene Expression Levels

AbstractSodium valproate/valproic acid (VPA), a histone deacetylase inhibitor, and 5-aza-2-deoxycytidine (5-aza-CdR), a DNA methyltransferase 1 (DNMT1) inhibitor, induce DNA demethylation in several cell types. In HeLa cells, although VPA leads to decreased DNA 5-methylcytosine (5mC) levels, the demethylation pathway involved in this effect is not fully understood. We investigated this process using flow cytometry, ELISA, immunocytochemistry, Western blotting and RT-qPCR in G1 phase-arrested and proliferative HeLa cells compared to the presumably passive demethylation promoted by 5-aza-CdR. The results revealed that VPA acts predominantly on active DNA demethylation because it induced TET2 gene and protein overexpression, decreased 5mC abundance, and increased 5-hydroxy-methylcytosine (5hmC) abundance, in both G1-arrested and proliferative cells. However, because VPA caused decreased DNMT1 gene expression levels, it may also act on the passive demethylation pathway. 5-aza-CdR attenuated DNMT1 gene expression levels but increased TET2 and 5hmC abundance in replicating cells, although it did not affect the gene expression of TETs at any stage of the cell cycle. Therefore, 5-aza-CdR may also function in the active pathway. Because VPA reduces DNA methylation levels in non-replicating HeLa cells, it could be tested as a candidate for the therapeutic reversal of DNA methylation in cells in which cell division is arrested.

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Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels

Genome Medicine ◽

10.1186/s13073-015-0152-4 ◽

2015 ◽

Vol 7 (1) ◽

Cited By ~ 39

Author(s):

Patrick Deelen ◽

Daria V Zhernakova ◽

Mark de Haan ◽

Marijke van der Sijde ◽

Marc Jan Bonder ◽

...

Keyword(s):

Gene Expression ◽

Rna Sequencing ◽

Genetic Variants ◽

Sequencing Data ◽

Expression Levels ◽

Affect Gene Expression ◽

Gene Expression Levels

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Probability of the Alzheimer’s disease based on common and rare genetic variants

10.21203/rs.3.rs-448021/v1 ◽

2021 ◽

Author(s):

Valentina Escott-Price ◽

Karl Michael Schmidt

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

High Risk ◽

Genetic Variants ◽

Rare Variants ◽

Disease Risk ◽

Age Groups ◽

Disease Prevalence ◽

Effect Sizes ◽

Rare Genetic Variants

Abstract Background: Alzheimer’s disease, among other neurodegenerative disorders, spans decades in individuals’ life and exhibits complex progression, symptoms and pathophysiology. Early diagnosis is essential for disease prevention and therapeutic intervention. Genetics may help identify individuals at high risk. As thousands of genetic variants may contribute to the genetic risk of Alzheimer’s disease, the polygenic risk score (PRS) approach has been shown to be useful for disease risk prediction. The APOE- ε4 allele is a known common variant associated with high risk to AD, but also associated with earlier onset. Rare variants usually have higher effect sizes than common ones; their impact may not be well captured by the PRS. Instead of standardised PRS, we propose to calculate the disease probability as a measure of disease risk that allows comparison between individuals. Methods: We estimate AD risk as a probability based on PRS and separately accounting for APOE, AD rare variants and the disease prevalence in age groups. The mathematical framework makes use of genetic variants effect sizes from summary statistics and AD disease prevalence in age groups. Results: The AD probability varies with respect to age, APOE status and presence of rare variants. In age group 65+ the probability of AD grows from 0.03 to 0.18 (without APOE), and 0.07 to 0.7 (APOE e4e4 carriers) as PRS increases. In 85+, these values are 0.08-0.6 and 0.3-0.85. Presence of rare mutations, e.g. in TREM2, may increase the probability (in 65+) from 0.02 at the negative tail of the PRS to 0.3.Conclusions: Our approach accounts for the varying disease prevalence in different genotype and age groups when modelling the APOE and rare genetic variants risk in addition to PRS. This approach can be directly implemented in a clinical setting and easily updated for novel rare variants and for other populations when appropriate ethnic GWASes appear.

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