An integrative approach to detect epigenetic mechanisms that putatively mediate the influence of lifestyle exposures on disease susceptibility

Abstract Background There is mounting evidence that our environment and lifestyle has an impact on epigenetic regulatory mechanisms, such as DNA methylation. It has been suggested that these molecular processes may mediate the effect of risk factors on disease susceptibility, although evidence in this regard has been challenging to uncover. Using genetic variants as surrogate variables, we have used two-sample Mendelian randomization (2SMR) to investigate the potential implications of putative changes to DNA methylation levels on disease susceptibility. Methods To illustrate our approach, we identified 412 CpG sites where DNA methylation was associated with prenatal smoking. We then applied 2SMR to investigate potential downstream effects of these putative changes on 643 complex traits using findings from large-scale genome-wide association studies. To strengthen evidence of mediatory mechanisms, we used multiple-trait colocalization to assess whether DNA methylation, nearby gene expression and complex trait variation were all influenced by the same causal genetic variant. Results We identified 22 associations that survived multiple testing (P < 1.89 × 10–7). In-depth follow-up analyses of particular note suggested that the associations between DNA methylation at the ASPSCR1 and REST/POL2RB gene regions, both linked with reduced lung function, may be mediated by changes in gene expression. We validated associations between DNA methylation and traits using independent samples from different stages across the life course. Conclusion Our approach should prove valuable in prioritizing CpG sites that may mediate the effect of causal risk factors on disease. In-depth evaluations of findings are necessary to robustly disentangle causality from alternative explanations such as horizontal pleiotropy.

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A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

10.1101/563379 ◽

2019 ◽

Cited By ~ 2

Author(s):

Tom G Richardson ◽

Gibran Hemani ◽

Tom R Gaunt ◽

Caroline L Relton ◽

George Davey Smith

Keyword(s):

Gene Expression ◽

Genetic Variants ◽

Complex Traits ◽

Mendelian Randomization ◽

Drug Repositioning ◽

Association Studies ◽

Thyroid Tissue ◽

Genome Wide Association Studies ◽

Tissue Specific ◽

Genome Wide

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

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CONQUER: an interactive toolbox to understand functional consequences of GWAS hits

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqaa085 ◽

2020 ◽

Vol 2 (4) ◽

Author(s):

Gerard A Bouland ◽

Joline W J Beulens ◽

Joey Nap ◽

Arno R van der Slik ◽

Arnaud Zaldumbide ◽

...

Keyword(s):

Risk Factors ◽

Association Studies ◽

R Package ◽

Integrative Approach ◽

Individual Risk ◽

Genome Wide Association Studies ◽

Genome Database ◽

Intergenic Regions ◽

Functional Consequences ◽

Individual Risk Factors

Abstract Numerous large genome-wide association studies have been performed to understand the influence of genetics on traits. Many identified risk loci are in non-coding and intergenic regions, which complicates understanding how genes and their downstream pathways are influenced. An integrative data approach is required to understand the mechanism and consequences of identified risk loci. Here, we developed the R-package CONQUER. Data for SNPs of interest are acquired from static- and dynamic repositories (build GRCh38/hg38), including GTExPortal, Epigenomics Project, 4D genome database and genome browsers. All visualizations are fully interactive so that the user can immediately access the underlying data. CONQUER is a user-friendly tool to perform an integrative approach on multiple SNPs where risk loci are not seen as individual risk factors but rather as a network of risk factors.

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Causal Inference for Heritable Phenotypic Risk Factors Using Heterogeneous Genetic Instruments

10.1101/2020.05.06.077982 ◽

2020 ◽

Author(s):

Jingshu Wang ◽

Qingyuan Zhao ◽

Jack Bowden ◽

Gilbran Hemani ◽

George Davey Smith ◽

...

Keyword(s):

Risk Factors ◽

Complex Traits ◽

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Genome Wide ◽

Genetic Instruments

Over a decade of genome-wide association studies have led to the finding that significant genetic associations tend to spread across the genome for complex traits. The extreme polygenicity where "all genes affect every complex trait" complicates Mendelian Randomization studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing Mendelian Randomization methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE (Genome-wide mR Analysis under Pervasive PLEiotropy) to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using summary statistics from genome-wide association studies, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, adjust for confounding risk factors, and determine the causal direction. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and the potential pleiotropic pathways.

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Trans effects on gene expression can drive omnigenic inheritance

10.1101/425108 ◽

2018 ◽

Cited By ~ 7

Author(s):

Xuanyao Liu ◽

Yang I Li ◽

Jonathan K Pritchard

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Formal Model ◽

Association Studies ◽

Direct Consequence ◽

Large Contribution ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Genetic Contributions ◽

Core Genes

Early genome-wide association studies (GWAS) led to the surprising discovery that, for typical complex traits, the most significant genetic variants contribute only a small fraction of the estimated heritability. Instead, it has become clear that a huge number of common variants, each with tiny effects, explain most of the heritability. Previously, we argued that these patterns conﬂict with standard conceptual models, and that new models are needed. Here we provide a formal model in which genetic contributions to complex traits can be partitioned into direct effects from core genes, and indirect effects from peripheral genes acting as trans-regulators. We argue that the central importance of peripheral genes is a direct consequence of the large contribution of trans-acting variation to gene expression variation. In particular, we propose that if the core genes for a trait are co-regulated – as seems likely – then the effects of peripheral variation can be amplified by these co-regulated networks such that nearly all of the genetic variance is driven by peripheral genes. Thus our model proposes a framework for understanding key features of the architecture of complex traits.

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Leveraging brain cortex-derived molecular data to elucidate epigenetic and transcriptomic drivers of neurological function and disease

10.1101/429134 ◽

2018 ◽

Author(s):

Charlie Hatcher ◽

Caroline L. Relton ◽

Tom R. Gaunt ◽

Tom G. Richardson

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Histone Acetylation ◽

Genetic Variants ◽

Large Scale ◽

Genetic Variant ◽

Association Studies ◽

Genome Wide Association Studies ◽

Epigenetic Mechanisms ◽

Trait Variation

AbstractIntegrative approaches which harness large-scale molecular datasets can help develop mechanistic insight into findings from genome-wide association studies (GWAS). We have performed extensive analyses to uncover transcriptional and epigenetic processes which may play a role in neurological trait variation.This was undertaken by applying Bayesian multiple-trait colocalization systematically across the genome to identify genetic variants responsible for influencing intermediate molecular phenotypes as well as neurological traits. In this analysis we leveraged high dimensional quantitative trait loci data derived from prefrontal cortex tissue (concerning gene expression, DNA methylation and histone acetylation) and GWAS findings for 5 neurological traits (Neuroticism, Schizophrenia, Educational Attainment, Insomnia and Alzheimer’s disease).There was evidence of colocalization for 118 associations suggesting that the same underlying genetic variant influenced both nearby gene expression as well as neurological trait variation. Of these, 73 associations provided evidence that the genetic variant also influenced proximal DNA methylation and/or histone acetylation. These findings support previous evidence at loci where epigenetic mechanisms may putatively mediate effects of genetic variants on traits, such as KLC1 and schizophrenia. We also uncovered evidence implicating novel loci in neurological disease susceptibility, including genes expressed predominantly in brain tissue such as MDGA1, KIRREL3 and SLC12A5.An inverse relationship between DNA methylation and gene expression was observed more than can be accounted for by chance, supporting previous findings implicating DNA methylation as a transcriptional repressor. Our study should prove valuable in helping future studies prioritise candidate genes and epigenetic mechanisms for in-depth functional follow-up analyses.

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Large-scale integration of DNA methylation and gene expression array platforms

10.1101/2021.08.12.455267 ◽

2021 ◽

Author(s):

Eva E Lancaster ◽

Vladimir I Vladimirov ◽

Brien P Riley ◽

Joseph W Landry ◽

Roxann Roberson-Nay ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Large Scale ◽

Association Studies ◽

Adolescent And Young Adult ◽

Expression Array ◽

Cpg Sites ◽

Disease States ◽

Large Scale Integration ◽

Scale Integration

Epigenome-wide association studies (EWAS) aim to provide evidence that marks of DNA methylation (DNAm) have downstream consequences that can result in the development of human diseases. Although these methods have been successful in identifying DNAm patterns associated with disease states, any further characterization of etiologic mechanisms remains elusive. This knowledge gap does not originate from a lack of DNAm-trait associations, but rather stems from study design issues that affect the interpretability of EWAS results. Despite known limitations in predicting the function of a particular CpG site, most EWAS maintain the broad assumption that altered DNAm results in a concomitant change of transcription at the most proximal gene. This study integrated DNAm and gene expression (GE) measurements in two cohorts, the Adolescent and Young Adult Twin Study (AYATS) and the Pregnancy, Race, Environment, Genes (PREG) study, to improve the understanding of epigenomic regulatory mechanisms. CpG sites associated with GE in cis were enriched in areas of transcription factor binding and areas of intermediate-to-low CpG density. CpG sites associated with trans GE were also enriched in areas of known regulatory significance, including enhancer regions. These results highlight issues with restricting DNAm-transcript annotations to small genomic intervals and question the validity of assuming a canonical cis DNAm-GE pathway. Based on these findings, the interpretation of EWAS results is limited in studies without multi-omic support and further research should identify genomic regions in which GE-associated DNAm is overrepresented.

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Abstract P455: Epistatic Regulation of Hematological-related Traits and Gene Expression in Mice Reveals Additional Heritability Missed by Standard GWAS-type Analyses

Circulation ◽

10.1161/circ.141.suppl_1.p455 ◽

2020 ◽

Vol 141 (Suppl_1) ◽

Author(s):

Anna Miller ◽

Anlu Chen ◽

David Buchner ◽

Scott Williams

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Association Studies ◽

Additive Models ◽

Blood Analysis ◽

Gene Interactions ◽

Genome Wide Association Studies ◽

Epistatic Qtls ◽

Distribution Width ◽

Additive Qtls

The genetic contribution of additive versus non-additive (epistasis) effects in the regulation of hematologic and other complex traits is unclear. Although many variants have been associated with a range of complex traits via genome wide association studies (GWAS), these loci combined in additive models do not account for most of the trait heritability. GWAS-type analyses typically ignore gene-gene interactions, in part because of the difficulty in detecting them in complex multicellular organisms, especially humans. We have previously shown that mouse chromosome substitution strains (CSSs) are a powerful model for detecting epistasis, and that for certain complex traits the relative contribution of epistasis to heritability is as important as additivity. We have now applied the use of these CSSs to identify and map additive and epistatic loci that regulate a range of hematological-related traits and hepatic gene expression levels. A modified backcross was performed with CSS strains carrying the A/J-derived substituted chromosomes 4 and 6 on an otherwise C57BL/6J genetic background. By analyzing the transcriptomes of offspring from this cross, we identified and mapped additive quantitative trait loci (QTLs) that regulated the expression of 770 genes, and epistatic QTLs for 802 genes. Similarly we performed a complete blood analysis of offspring from the cross and identified additive QTLs for platelets and percentage of granulocyte in the blood as well as epistatic QTLs controlling the percentage of lymphocytes in the blood (rs13477644, rs13478739; LOD = 3.4) and red cell distribution width (rs13477864, rs13478802; LOD = 3.7). The variance attributable to the epistatic QTLs was approximately equal to that of the additive QTLs, highlighting the importance of identifying genetic interactions. Of note, even the SNPs associated with the most significant epistatic interactions were undetected in our single loci GWAS-like association analyses, demonstrating the need to specifically test for gene-gene interactions in studies of complex traits. In summary, our studies identified epistatic loci in mice that are important regulators of hematological-related traits and gene expression. Additionally, our studies call attention to the importance of extending single loci GWAS-type analyses to include analyses of gene-gene interactions to improve our ability to identify genetic variants that regulate complex traits.

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Epigenome-wide association study of serum urate reveals insights into urate co-regulation and the SLC2A9 locus

Nature Communications ◽

10.1038/s41467-021-27198-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Adrienne Tin ◽

Pascal Schlosser ◽

Pamela R. Matias-Garcia ◽

Chris H. L. Thio ◽

Roby Joehanes ◽

...

Keyword(s):

Dna Methylation ◽

Complex Traits ◽

Cardiometabolic Risk ◽

Association Studies ◽

Meta Analysis ◽

Elevated Serum ◽

Complex Trait ◽

Serum Urate ◽

Genome Wide Association Studies ◽

Genome Wide

AbstractElevated serum urate levels, a complex trait and major risk factor for incident gout, are correlated with cardiometabolic traits via incompletely understood mechanisms. DNA methylation in whole blood captures genetic and environmental influences and is assessed in transethnic meta-analysis of epigenome-wide association studies (EWAS) of serum urate (discovery, n = 12,474, replication, n = 5522). The 100 replicated, epigenome-wide significant (p < 1.1E–7) CpGs explain 11.6% of the serum urate variance. At SLC2A9, the serum urate locus with the largest effect in genome-wide association studies (GWAS), five CpGs are associated with SLC2A9 gene expression. Four CpGs at SLC2A9 have significant causal effects on serum urate levels and/or gout, and two of these partly mediate the effects of urate-associated GWAS variants. In other genes, including SLC7A11 and PHGDH, 17 urate-associated CpGs are associated with conditions defining metabolic syndrome, suggesting that these CpGs may represent a blood DNA methylation signature of cardiometabolic risk factors. This study demonstrates that EWAS can provide new insights into GWAS loci and the correlation of serum urate with other complex traits.

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Effect sizes of causal variants for gene expression and complex traits differ between populations

10.1101/2021.12.06.471235 ◽

2021 ◽

Author(s):

Roshni A. Patel ◽

Shaila A. Musharoff ◽

Jeffrey P. Spence ◽

Harold Pimentel ◽

Catherine Tcheandjieu ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Association Studies ◽

Causal Variant ◽

Effect Sizes ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Polygenic Scores ◽

Causal Variants ◽

Variant Effect

Despite the growing number of genome-wide association studies (GWAS) for complex traits, it remains unclear whether effect sizes of causal genetic variants differ between populations. In principle, effect sizes of causal variants could differ between populations due to gene-by-gene or gene-by-environment interactions. However, comparing causal variant effect sizes is challenging: it is difficult to know which variants are causal, and comparisons of variant effect sizes are confounded by differences in linkage disequilibrium (LD) structure between ancestries. Here, we develop a method to assess causal variant effect size differences that overcomes these limitations. Specifically, we leverage the fact that segments of European ancestry shared between European-American and admixed African-American individuals have similar LD structure, allowing for unbiased comparisons of variant effect sizes in European ancestry segments. We apply our method to two types of traits: gene expression and low-density lipoprotein cholesterol (LDL-C). We find that causal variant effect sizes for gene expression are significantly different between European-Americans and African-Americans; for LDL-C, we observe a similar point estimate although this is not significant, likely due to lower statistical power. Cross-population differences in variant effect sizes highlight the role of genetic interactions in trait architecture and will contribute to the poor portability of polygenic scores across populations, reinforcing the importance of conducting GWAS on individuals of diverse ancestries and environments.

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Scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction

10.1101/2020.04.22.050666 ◽

2020 ◽

Author(s):

Yanyu Liang ◽

François Aguet ◽

Alvaro Barbeira ◽

Kristin Ardlie ◽

Hae Kyung Im

Keyword(s):

Gene Expression ◽

Fine Mapping ◽

Complex Traits ◽

Association Studies ◽

Average Power ◽

Specific Gene ◽

Genome Wide Association Studies ◽

Unified Framework ◽

Causal Genes ◽

Allele Specific

AbstractGenome-wide association studies (GWAS) have been highly successful in identifying genomic loci associated with complex traits. However, identification of the causal genes that mediate these associations remains challenging, and many approaches integrating transcriptomic data with GWAS have been proposed. However, there currently exist no computationally scalable methods that integrate total and allele-specific gene expression to maximize power to detect genetic effects on gene expression. Here, we describe a unified framework that is scalable to studies with thousands of samples. Using simulations and data from GTEx, we demonstrate an average power gain equivalent to a 29% increase in sample size for genes with sufficient allele-specific read coverage. We provide a suite of freely available tools, mixQTL, mixFine, and mixPred, that apply this framework for mapping of quantitative trait loci, fine-mapping, and prediction.

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