scholarly journals Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood

2018 ◽  
Author(s):  
Ting Qi ◽  
Yang Wu ◽  
Jian Zeng ◽  
Futao Zhang ◽  
Angli Xue ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Genetic Effects ◽  
Sample Sizes ◽  
Specific Effects ◽  
The Difference ◽  
Highly Correlated ◽  
Using Data ◽  
Larger Sample

AbstractUnderstanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes associated with brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top associated cis-expression (cis-eQTLs or cis-mQTLs) between brain and blood for genes expressed (or CpG sites methylated) in both tissues, while accounting for errors in their estimated effects (rb). Using publicly available data (n = 72 to l,366), we find that the genetic effects of cis-eQTLs (PeQTL < 5×10−8) or mQTLs (PmQTL < 1×10−10) are highly correlated between independent brain and blood samples ( with SE = 0.015 for cis-eQTL and with SE = 0.006 for cis-mQTLs). Using meta-analyzed brain eQTL/mQTL data (n = 526 to 1,194), we identify 61 genes and 167 DNA methylation (DNAm) sites associated with 4 brain-related traits and disorders. Most of these associations are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = l,980 to 14,115). We further find that cis-eQTLs with tissue-specific effects are approximately uniformly distributed across all the functional annotation categories, and that mean difference in gene expression level between brain and blood is almost independent of the difference in the corresponding cis-eQTL effect. Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL or cis-mQTL data with large sample sizes.

scholarly journals Tissue specific regulation of transcription in endometrium and association with disease

2020 ◽  
Vol 35 (2) ◽  
pp. 377-393 ◽  
Author(s):  
Sally Mortlock ◽  
Raden I Kendarsari ◽  
Jenny N Fung ◽  
Greg Gibson ◽  
Fei Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Study ◽  
Genetic Variants ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Enrichment Analysis ◽  
Reproductive Traits ◽  
Genetic Effects ◽  
Genotype Data ◽  
Tissue Specific

Abstract STUDY QUESTION Are genetic effects on endometrial gene expression tissue specific and/or associated with reproductive traits and diseases? SUMMARY ANSWER Analyses of RNA-sequence data and individual genotype data from the endometrium identified novel and disease associated, genetic mechanisms regulating gene expression in the endometrium and showed evidence that these mechanisms are shared across biologically similar tissues. WHAT IS KNOWN ALREADY The endometrium is a complex tissue vital for female reproduction and is a hypothesized source of cells initiating endometriosis. Understanding genetic regulation specific to, and shared between, tissue types can aid the identification of genes involved in complex genetic diseases. STUDY DESIGN, SIZE, DURATION RNA-sequence and genotype data from 206 individuals was analysed and results were compared with large publicly available datasets. PARTICIPANTS/MATERIALS, SETTING, METHODS RNA-sequencing and genotype data from 206 endometrial samples was used to identify the influence of genetic variants on gene expression, via expression quantitative trait loci (eQTL) analysis and to compare these endometrial eQTLs with those in other tissues. To investigate the association between endometrial gene expression regulation and reproductive traits and diseases, we conducted a tissue enrichment analysis, transcriptome-wide association study (TWAS) and summary data-based Mendelian randomisation (SMR) analyses. Transcriptomic data was used to test differential gene expression between women with and without endometriosis. MAIN RESULTS AND THE ROLE OF CHANCE A tissue enrichment analysis with endometriosis genome-wide association study summary statistics showed that genes surrounding endometriosis risk loci were significantly enriched in reproductive tissues. A total of 444 sentinel cis-eQTLs (P &lt; 2.57 × 10−9) and 30 trans-eQTLs (P &lt; 4.65 × 10−13) were detected, including 327 novel cis-eQTLs in endometrium. A large proportion (85%) of endometrial eQTLs are present in other tissues. Genetic effects on endometrial gene expression were highly correlated with the genetic effects on reproductive (e.g. uterus, ovary) and digestive tissues (e.g. salivary gland, stomach), supporting a shared genetic regulation of gene expression in biologically similar tissues. The TWAS analysis indicated that gene expression at 39 loci is associated with endometriosis, including five known endometriosis risk loci. SMR analyses identified potential target genes pleiotropically or causally associated with reproductive traits and diseases including endometriosis. However, without taking account of genetic variants, a direct comparison between women with and without endometriosis showed no significant difference in endometrial gene expression. LARGE SCALE DATA The eQTL dataset generated in this study is available at http://reproductivegenomics.com.au/shiny/endo_eqtl_rna/. Additional datasets supporting the conclusions of this article are included within the article and the supplementary information files, or are available on reasonable request. LIMITATIONS, REASONS FOR CAUTION Data are derived from fresh tissue samples and expression levels are an average of expression from different cell types within the endometrium. Subtle cell-specifc expression changes may not be detected and differences in cell composition between samples and across the menstrual cycle will contribute to sample variability. Power to detect tissue specific eQTLs and differences between women with and without endometriosis was limited by the sample size in this study. The statistical approaches used in this study identify the likely gene targets for specific genetic risk factors, but not the functional mechanism by which changes in gene expression may influence disease risk. WIDER IMPLICATIONS OF THE FINDINGS Our results identify novel genetic variants that regulate gene expression in endometrium and the majority of these are shared across tissues. This allows analysis with large publicly available datasets to identify targets for female reproductive traits and diseases. Much larger studies will be required to identify genetic regulation of gene expression that will be specific to endometrium. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the National Health and Medical Research Council (NHMRC) under project grants GNT1026033, GNT1049472, GNT1046880, GNT1050208, GNT1105321, GNT1083405 and GNT1107258. G.W.M is supported by a NHMRC Fellowship (GNT1078399). J.Y is supported by an ARC Fellowship (FT180100186). There are no competing interests.

scholarly journals Redefining tissue specificity of genetic regulation of gene expression in the presence of allelic heterogeneity

2021 ◽  
Author(s):  
Marios Arvanitis ◽  
Karl Tayeb ◽  
Benjamin J Strober ◽  
Alexis Battle
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Tissue Specificity ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Genetic Effects ◽  
Allelic Heterogeneity ◽  
Common Genetic Variants ◽  
Causal Variants ◽  
Disease Loci

Understanding the mechanisms that underlie genetic regulation of gene expression is crucial to explaining the diversity that governs complex traits. Large scale expression quantitative trait locus (eQTL) studies have been instrumental in identifying genetic variants that influence the expression of target genes. However, a large fraction of disease-associated genetic variants have not been clearly explained by current eQTL data, frustrating attempts to use these data to comprehensively characterize disease loci. One notable observation from recent studies is that cis-eQTL effects are often shared across different cell types and tissues. This would suggest that common genetic variants impacting steady-state, adult gene expression are largely tolerated, shared across tissues, and less relevant to disease. However, allelic heterogeneity and complex patterns of linkage disequilibrium (LD) within each locus may skew the quantification of sharing of genetic effects between tissues, impede our ability to identify causal variants, and hinder the identification of regulatory effects for disease-associated genetic variants. Indeed, recent research suggests that multiple causal variants are often present in many eQTL and complex trait associated loci. Here, we re-analyze tissue-specificity of genetic effects in the presence of LD and allelic heterogeneity, proposing a novel method, CAFEH, that improves the identification of causal regulatory variants across tissues and their relationship to disease loci.

scholarly journals Alternative polyadenylation mediates genetic regulation of gene expression

2019 ◽  
Author(s):  
Briana Mittleman ◽  
Sebastian Pott ◽  
Shane Warland ◽  
Tony Zeng ◽  
Mayher Kaur ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Protein Expression ◽  
Mrna Expression ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Alternative Polyadenylation ◽  
Genetic Effects ◽  
Nuclear Fraction ◽  
Expression Levels

AbstractWith the exception of mRNA splicing, little is known about co-transcriptional or post-transcriptional regulatory mechanisms that link noncoding variation to variation in organismal traits. To begin addressing this gap, we used 3’ Seq to characterize alternative polyadenylation (APA) in the nuclear and total RNA fractions of 52 HapMap Yoruba lymphoblastoid cell lines, which we have studied extensively in the past. We identified thousands of polyadenylation sites that are differentially detected in nuclear mRNA and whole cell mRNA, and found that APA is an important mediator of genetic effects on gene regulation and complex traits. Specifically, we mapped 602 apaQTLs at 10% FDR, of which 152 were found only in the nuclear fraction. Nuclear-specific apaQTLs are highly enriched in introns and are also often associated with changes in steady-state expression levels, suggesting a widespread mechanism whereby genetic variants decrease mRNA expression levels by increasing usage of intronic PAS. We identified 24 apaQTLs associated with protein expression levels, but not mRNA expression, and found that eQTLs that are not associated with chromatin QTLs are enriched in apaQTLs. These findings support multiple independent pathways through which genetic effects on APA can impact gene regulation. Finally, we found that 19% of apaQTLs were also previously associated with disease. Thus, our work demonstrates that APA links genetic variation to variation in gene expression levels, protein expression levels, and disease risk, and reveals uncharted modes of genetic regulation.

scholarly journals sn-spMF: matrix factorization informs tissue-specific genetic regulation of gene expression

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuan He ◽  
◽  
Surya B. Chhetri ◽  
Marios Arvanitis ◽  
Kaushik Srinivasan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Matrix Factorization ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Tissue Expression ◽  
Disease Etiology ◽  
Tissue Specific ◽  
Specific Effects ◽  
Factorization Model ◽  
Biological Similarity

Abstract Genetic regulation of gene expression, revealed by expression quantitative trait loci (eQTLs), exhibits complex patterns of tissue-specific effects. Characterization of these patterns may allow us to better understand mechanisms of gene regulation and disease etiology. We develop a constrained matrix factorization model, sn-spMF, to learn patterns of tissue-sharing and apply it to 49 human tissues from the Genotype-Tissue Expression (GTEx) project. The learned factors reflect tissues with known biological similarity and identify transcription factors that may mediate tissue-specific effects. sn-spMF, available at https://github.com/heyuan7676/ts_eQTLs, can be applied to learn biologically interpretable patterns of eQTL tissue-specificity and generate testable mechanistic hypotheses.

scholarly journals Genetic Regulation of Transcription in the Endometrium in Health and Disease

2022 ◽  
Vol 3 ◽  
Author(s):  
Sally Mortlock ◽  
Brett McKinnon ◽  
Grant W. Montgomery
Keyword(s):  
Gene Expression ◽  
Risk Factors ◽  
Genetic Variation ◽  
Menstrual Cycle ◽  
Gene Mapping ◽  
Genetic Risk ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Genetic Risk Factors ◽  
Genetic Effects

The endometrium is a complex and dynamic tissue essential for fertility and implicated in many reproductive disorders. The tissue consists of glandular epithelium and vascularised stroma and is unique because it is constantly shed and regrown with each menstrual cycle, generating up to 10 mm of new mucosa. Consequently, there are marked changes in cell composition and gene expression across the menstrual cycle. Recent evidence shows expression of many genes is influenced by genetic variation between individuals. We and others have reported evidence for genetic effects on hundreds of genes in endometrium. The genetic factors influencing endometrial gene expression are highly correlated with the genetic effects on expression in other reproductive (e.g., in uterus and ovary) and digestive tissues (e.g., salivary gland and stomach), supporting a shared genetic regulation of gene expression in biologically similar tissues. There is also increasing evidence for cell specific genetic effects for some genes. Sample size for studies in endometrium are modest and results from the larger studies of gene expression in blood report genetic effects for a much higher proportion of genes than currently reported for endometrium. There is also emerging evidence for the importance of genetic variation on RNA splicing. Gene mapping studies for common disease, including diseases associated with endometrium, show most variation maps to intergenic regulatory regions. It is likely that genetic risk factors for disease function through modifying the program of cell specific gene expression. The emerging evidence from our gene mapping studies coupled with tissue specific studies, and the GTEx, eQTLGen and EpiMap projects, show we need to expand our understanding of the complex regulation of gene expression. These data also help to link disease genetic risk factors to specific target genes. Combining our data on genetic regulation of gene expression in endometrium, and cell types within the endometrium with gene mapping data for endometriosis and related diseases is beginning to uncover the specific genes and pathways responsible for increased risk of these diseases.

scholarly journals Dynamic genetic regulation of gene expression during cellular differentiation

Science ◽  
2019 ◽  
Vol 364 (6447) ◽  
pp. 1287-1290 ◽  
Author(s):  
B. J. Strober ◽  
R. Elorbany ◽  
K. Rhodes ◽  
N. Krishnan ◽  
K. Tayeb ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Specific Gene ◽  
Genetic Associations ◽  
Sequencing Data ◽  
Using Data ◽  
Induced Pluripotent

Genetic regulation of gene expression is dynamic, as transcription can change during cell differentiation and across cell types. We mapped expression quantitative trait loci (eQTLs) throughout differentiation to elucidate the dynamics of genetic effects on cell type–specific gene expression. We generated time-series RNA sequencing data, capturing 16 time points during the differentiation of induced pluripotent stem cells to cardiomyocytes, in 19 human cell lines. We identified hundreds of dynamic eQTLs that change over time, with enrichment in enhancers of relevant cell types. We also found nonlinear dynamic eQTLs, which affect only intermediate stages of differentiation and cannot be found by using data from mature tissues. These fleeting genetic associations with gene regulation may explain some of the components of complex traits and disease. We highlight one example of a nonlinear eQTL that is associated with body mass index.

scholarly journals On Using Local Ancestry to Characterize the Genetic Architecture of Human Phenotypes: Genetic Regulation of Gene Expression in Multiethnic or Admixed Populations as a Model

2018 ◽  
Author(s):  
Yizhen Zhong ◽  
Minoli Perera ◽  
Eric R. Gamazon
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Type I ◽  
Eqtl Mapping ◽  
Entire Genome ◽  
Local Ancestry ◽  
Heritability Estimation

AbstractBackgroundUnderstanding the nature of the genetic regulation of gene expression promises to advance our understanding of the genetic basis of disease. However, the methodological impact of use of local ancestry on high-dimensional omics analyses, including most prominently expression quantitative trait loci (eQTL) mapping and trait heritability estimation, in admixed populations remains critically underexplored.ResultsHere we develop a statistical framework that characterizes the relationships among the determinants of the genetic architecture of an important class of molecular traits. We estimate the trait variance explained by ancestry using local admixture relatedness between individuals. Using National Institute of General Medical Sciences (NIGMS) and Genotype-Tissue Expression (GTEx) datasets, we show that use of local ancestry can substantially improve eQTL mapping and heritability estimation and characterize the sparse versus polygenic component of gene expression in admixed and multiethnic populations respectively. Using simulations of diverse genetic architectures to estimate trait heritability and the level of confounding, we show improved accuracy given individual-level data and evaluate a summary statistics based approach. Furthermore, we provide a computationally efficient approach to local ancestry analysis in eQTL mapping while increasing control of type I and type II error over traditional approaches.ConclusionOur study has important methodological implications on genetic analysis of omics traits across a range of genomic contexts, from a single variant to a prioritized region to the entire genome. Our findings highlight the importance of using local ancestry to better characterize the heritability of complex traits and to more accurately map genetic associations.

scholarly journals How (Epi)Genetic Regulation of the LIM-Domain Protein FHL2 Impacts Multifactorial Disease

Cells ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2611
Author(s):  
Jayron J. Habibe ◽  
Maria P. Clemente-Olivo ◽  
Carlie J. de Vries
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Current Knowledge ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Lim Domain ◽  
Multifactorial Diseases ◽  
Lim Domains ◽  
Age Related

Susceptibility to complex pathological conditions such as obesity, type 2 diabetes and cardiovascular disease is highly variable among individuals and arises from specific changes in gene expression in combination with external factors. The regulation of gene expression is determined by genetic variation (SNPs) and epigenetic marks that are influenced by environmental factors. Aging is a major risk factor for many multifactorial diseases and is increasingly associated with changes in DNA methylation, leading to differences in gene expression. Four and a half LIM domains 2 (FHL2) is a key regulator of intracellular signal transduction pathways and the FHL2 gene is consistently found as one of the top hyper-methylated genes upon aging. Remarkably, FHL2 expression increases with methylation. This was demonstrated in relevant metabolic tissues: white adipose tissue, pancreatic β-cells, and skeletal muscle. In this review, we provide an overview of the current knowledge on regulation of FHL2 by genetic variation and epigenetic DNA modification, and the potential consequences for age-related complex multifactorial diseases.

Regulation of gene expression by DNA methylation with cytotoxic T lymphocytes evaluation in consensus molecular subtypes of colorectal cancer.

2020 ◽  
Vol 38 (5_suppl) ◽  
pp. 25-25
Author(s):  
Yuanyuan Shen ◽  
Justin Hummel ◽  
Isabel Cristina Trindade ◽  
Christos Papageorgiou ◽  
Chi-Ren Shyu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Epigenetic Modification ◽  
Pearson Correlation ◽  
Critical Role ◽  
Regulation Of Gene Expression ◽  
The Cancer Genome Atlas ◽  
Highly Correlated

25 Background: Low cytotoxic T lymphocyte (CTLs) infiltration in colorectal cancer (CRC) tumors is a challenge to treatment with immune checkpoint inhibitors. Consensus molecular subtypes (CMS) classify patients based on tumor attributes, and CMS1 patients include the majority of patients with high CTL infiltration and “inflamed” tumors. Epigenetic modification plays a critical role in gene expression and therapy resistance. Therefore, in this study we compared DNA methylation, gene expression, and CTL infiltration of CMS1 patients to other CMS groups to determine targets for improving immunotherapy in CRC. Methods: RNA-seq (n = 511) and DNA methylation (n = 316) from The Cancer Genome Atlas databases were used to determine gene expression and methylation profiles based on CMSs. CMS1 was used as a reference and compared to other subtypes (CMS2-4). Microenvironment Cell Populations- counter (MCPcounter) was used to determine tumor CTL infiltration. Genes with significantly different expression (p < 0.01, LogFC≥|1.5|) and difference of mean methylation β value ≥|0.25| were integrated for Pearson correlation coefficient analysis with MCPcounter score (r > |0.7|). Results: Comparing CMS1 and CMS2, ARHGAP9, TBX21, and LAG3 were differentially methylated and correlated with CTL scores. ARHGAP9 and TBX21 were decreased and hypomethylated in CMS2. Comparing CMS1 and CMS3, ARHGAP9, TBX21, FMNL1, HLA-DPB1, and STX11 were downregulated in CMS3 and highly correlated with CTL scores. ARHGAP9, FMNL1, HLA-DPB1, and STX11 were hypomethylated in CMS3 and TBX21 was methylated in both, but had a higher methylation ratio in CMS1. Comparing CMS1 and CMS4, TBX21 was the only gene downregulated, hypomethylated, and highly correlated with CTL scores in CMS4 patients. Conclusions: We found six genes differentially expressed, differentially methylated, and highly correlated with CTL infiltration when comparing CMS1 to other CMS groups. Specifically, TBX21 was the only gene highly correlated with CTL scores with differential gene expression and methylation in CMS2-4 when compared to CMS1. Thus, T-bet may be a critical regulator of T cell responses in CRC.

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