scholarly journals Quantifying mediation between omics layers and complex traits

2021 ◽  
Author(s):  
Marie C Sadler ◽  
Chiara Marie Paula Auwerx ◽  
Eleonora Porcu ◽  
Zoltan Kutalik
Keyword(s):  
Complex Traits ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Irritable Bowel Disease ◽  
Protein Levels ◽  
Integrative Framework ◽  
Multiple Transcripts ◽  
Irritable Bowel ◽  
In Cis

Background: High-dimensional omics datasets provide valuable resources to determine the causal role of molecular traits in mediating the path from genotype to phenotype. Making use of quantitative trait loci (QTL) and genome-wide association studies (GWASs) summary statistics, we developed a multivariable Mendelian randomization (MVMR) framework to quantify the connectivity between three omics layers (DNA methylome (DNAm), transcriptome and proteome) and their cascading causal impact on complex traits and diseases. Results: Evaluating 50 complex traits, we found that on average 37.8% (95% CI: [36.0%-39.5%]) of DNAm-to-trait effects were mediated through transcripts in the cis-region, while only 15.8% (95% CI: [11.9%-19.6%]) are mediated through proteins in cis. DNAm sites typically regulate multiple transcripts, and while found to predominantly decrease gene expression, this was only the case for 53.4% across ~47,000 significant DNAm-transcript pairs. The average mediation proportion for transcript-to-trait effects through proteins (encoded for by the assessed transcript or located in trans) was estimated to be 5.27% (95%CI: [4.11%-6.43%]). Notable differences in the transcript and protein QTL architectures were detected with only 22% of protein levels being causally driven by their corresponding transcript levels. Several regulatory mechanisms were hypothesized including an example where cg10385390 (chr1:8,022,505) increases the risk of irritable bowel disease by reducing PARK7 transcript and protein expression. Conclusions: The proposed integrative framework identified putative causal chains through omics layers providing a powerful tool to map GWAS signals. Quantification of causal effects between successive layers indicated that molecular mechanisms can be more complex than what the central dogma of biology would suggest.

scholarly journals Investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

2019 ◽  
Author(s):  
Yuhua Zhang ◽  
Corbin Quick ◽  
Ketian Yu ◽  
Alvaro Barbeira ◽  
Francesca Luca ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Complex Trait ◽  
Causal Effects ◽  
Biological Mechanisms ◽  
Integrative Framework ◽  
Eqtl Data

AbstractTranscriptome-wide association studies (TWAS), an integrative framework using expression quantitative trait loci (eQTLs) to construct proxies for gene expression, have emerged as a promising method to investigate the biological mechanisms underlying associations between genotypes and complex traits. However, challenges remain in interpreting TWAS results, especially regarding their causality implications. In this paper, we describe a new computational framework, probabilistic TWAS (PTWAS), to detect associations and investigate causal relationships between gene expression and complex traits. We use established concepts and principles from instrumental variables (IV) analysis to delineate and address the unique challenges that arise in TWAS. PTWAS utilizes probabilistic eQTL annotations derived from multi-variant Bayesian fine-mapping analysis conferring higher power to detect TWAS associations than existing methods. Additionally, PTWAS provides novel functionalities to evaluate the causal assumptions and estimate tissue- or cell-type specific causal effects of gene expression on complex traits. These features make PTWAS uniquely suited for in-depth investigations of the biological mechanisms that contribute to complex trait variation. Using eQTL data across 49 tissues from GTEx v8, we apply PTWAS to analyze 114 complex traits using GWAS summary statistics from several large-scale projects, including the UK Biobank. Our analysis reveals an abundance of genes with strong evidence of eQTL-mediated causal effects on complex traits and highlights the heterogeneity and tissue-relevance of these effects across complex traits. We distribute software and eQTL annotations to enable users performing rigorous TWAS analysis by leveraging the full potentials of the latest GTEx multi-tissue eQTL data.

Brain imaging genomics: influences of genomic variability on the structure and function of the human brain

2020 ◽  
Vol 32 (1) ◽  
pp. 47-56
Author(s):  
Thomas W. Mühleisen ◽  
Andreas J. Forstner ◽  
Per Hoffmann ◽  
Sven Cichon
Keyword(s):  
Brain Imaging ◽  
Complex Traits ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Phenotypic Variability ◽  
Effect Sizes ◽  
Future Research ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Imaging Genomics

Abstract Brain imaging genomics is an emerging discipline in which genomic and brain imaging data are integrated in order to elucidate the molecular mechanisms that underly brain phenotypes and diseases, including neuropsychiatric disorders. As with all genetic analyses of complex traits and diseases, brain imaging genomics has evolved from small, individual candidate gene investigations towards large, collaborative genome-wide association studies. Recent investigations, mostly population-based, have studied well-powered cohorts comprising tens of thousands of individuals and identified multiple robust associations of single-nucleotide polymorphisms and copy number variants with structural and functional brain phenotypes. Such systematic genomic screens of millions of genetic variants have generated initial insights into the genetic architecture of brain phenotypes and demonstrated that their etiology is polygenic in nature, involving multiple common variants with small effect sizes and rare variants with larger effect sizes. Ongoing international collaborative initiatives are now working to obtain a more complete picture of the underlying biology. As in other complex phenotypes, novel approaches – such as gene–gene interaction, gene–environment interaction, and epigenetic analyses – are being implemented in order to investigate their contribution to the observed phenotypic variability. An important consideration for future research will be the translation of brain imaging genomics findings into clinical practice.

scholarly journals Sexual differences in genetic architecture in UK Biobank

2020 ◽  
Author(s):  
Elena Bernabeu ◽  
Oriol Canela-Xandri ◽  
Konrad Rawlik ◽  
Andrea Talenti ◽  
James Prendergast ◽  
...  
Keyword(s):  
Sexual Dimorphism ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Mammalian Species ◽  
Genetic Correlations ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Uk Biobank

ABSTRACTSex is arguably the most important differentiating characteristic in most mammalian species, separating populations into different groups, with varying behaviors, morphologies, and physiologies based on their complement of sex chromosomes. In humans, despite males and females sharing nearly identical genomes, there are differences between the sexes in complex traits and in the risk of a wide array of diseases. Gene by sex interactions (GxS) are thought to account for some of this sexual dimorphism. However, the extent and basis of these interactions are poorly understood.Here we provide insights into both the scope and mechanism of GxS across the genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK Biobank. We found small yet widespread differences in genetic architecture across traits through the calculation of sex-specific heritability, genetic correlations, and sex-stratified genome-wide association studies (GWAS). We also found that, in some cases, sex-agnostic GWAS efforts might be missing loci of interest, and looked into possible improvements in the prediction of high-level phenotypes. Finally, we studied the potential functional role of the dimorphism observed through sex-biased eQTL and gene-level analyses.This study marks a broad examination of the genetics of sexual dimorphism. Our findings parallel previous reports, suggesting the presence of sexual genetic heterogeneity across complex traits of generally modest magnitude. Our results suggest the need to consider sex-stratified analyses for future studies in order to shed light into possible sex-specific molecular mechanisms.

scholarly journals Tissue-specific multiOMICs analysis of atrial fibrillation

2020 ◽  
Author(s):  
Ines Assum ◽  
Julia Krause ◽  
Markus O. Scheinhardt ◽  
Christian Müller ◽  
Elke Hammer ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Molecular Networks ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Protein Levels ◽  
Atrial Tissue ◽  
Regulatory Variants

AbstractGenome-wide association studies (GWAS) for atrial fibrillation (AF) have uncovered numerous disease-associated variants. Their underlying molecular mechanisms, especially consequences for mRNA and protein expression remain largely elusive. Thus, novel multiOMICs approaches are needed for deciphering the underlying molecular networks. Here, we integrated genomics, transcriptomics, and proteomics of human atrial tissue which allowed for identifying widespread effects of genetic variants on both transcript (cis eQTL) and protein (cis pQTL) abundance. We further established a novel targeted trans QTL approach based on polygenic risk scores to identify candidates for AF core genes. Using this approach, we identified two trans eQTLs and four trans pQTLs for AF GWAS hits, and elucidated the role of the transcription factor NKX2-5 as a link between the GWAS SNP rs9481842 and AF. Altogether, we present an integrative multiOMICs method to uncover trans-acting networks in small datasets and provide a rich resource of atrial tissue-specific regulatory variants for transcript and protein levels for cardiovascular disease gene prioritization.

scholarly journals An integrative analysis of GWAS and intermediate molecular trait data reveals common molecular mechanisms supporting genetic similarity between seemingly unrelated complex traits

2019 ◽  
Author(s):  
Jialiang Gu ◽  
Chris Fuller ◽  
Jiashun Zheng ◽  
Hao Li
Keyword(s):  
Breast Cancer ◽  
Complex Traits ◽  
Genetic Similarity ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Integrative Analysis ◽  
Ros Generation ◽  
Genome Wide Association Studies ◽  
Statistical Framework ◽  
Significant Gene

AbstractThe rapid accumulation of Genome Wide Association Studies (GWAS) and association studies of intermediate molecular traits provides new opportunities for comparative analysis of the genetic basis of complex human phenotypes. Using a newly developed statistical framework called Sherlock-II that integrates GWAS with eQTL (expression Quantitative Trait Loci) and metabolite-QTL data, we systematically analyzed 445 GWAS datasets, and identified 1371 significant gene-phenotype associations and 308 metabolites-phenotype associations (passing a Q value cutoff of 1/3). This integrative analysis allows us to translate SNP-phenotype associations into functionally informative gene-phenotype association profiles. Genetic similarity analyses based on these profiles clustered phenotypes into sub-trees that reveal both expected and unexpected relationships. We employed a statistical approach to delineate sets of functionally related genes that contribute to the similarity between their association profiles. This approach suggested common molecular mechanisms that connect the phenotypes in a subtree. For example, we found that fasting insulin, fasting glucose, breast cancer, prostate cancer, and lung cancer clustered into a subtree, and identified cyclic AMP/GMP signaling that connects breast cancer and insulin, NAPDH oxidase/ROS generation that connects the three cancers, and apoptosis that connects all five phenotypes. Our approach can be used to assess genetic similarity and suggest mechanistic connections between phenotypes. It has the potential to improve the diagnosis and treatment of a disease by mapping mechanistic insights from one phenotype onto others based on common molecular underpinnings.

scholarly journals Fine-mapping cis-regulatory variants in diverse human populations

2018 ◽  
Author(s):  
Ashley K. Tehranchi ◽  
Brian Hie ◽  
Michael Dacre ◽  
Irene M. Kaplow ◽  
Kade P Pettie ◽  
...  
Keyword(s):  
Long Range ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Chromatin Accessibility ◽  
Human Populations ◽  
Genome Wide Association Studies ◽  
Diverse Populations ◽  
Regulatory Variants ◽  
Causal Variants ◽  
In Cis

AbstractGenome-wide association studies (GWAS) are a powerful approach for connecting genotype to phenotype. Most GWAS hits are located in cis-regulatory regions, but the underlying causal variants and their molecular mechanisms remain unknown. To better understand human cis-regulatory variation, we mapped quantitative trait loci for chromatin accessibility (caQTLs)—a key step in cis-regulation—in 1000 individuals from 10 diverse populations. Most caQTLs were shared across populations, allowing us to leverage the genetic diversity to fine-map candidate causal regulatory variants, several thousand of which have been previously implicated in GWAS. In addition, many caQTLs that affect the expression of distal genes also alter the landscape of long-range chromosomal interactions, suggesting a mechanism for long-range expression QTLs. In sum, our results show that molecular QTL mapping integrated across diverse populations provides a high-resolution view of how worldwide human genetic variation affects chromatin accessibility, gene expression, and phenotype.

scholarly journals Testing and controlling for horizontal pleiotropy with the probabilistic Mendelian randomization in transcriptome-wide association studies

2019 ◽  
Author(s):  
Zhongshang Yuan ◽  
Huanhuan Zhu ◽  
Ping Zeng ◽  
Sheng Yang ◽  
Shiquan Sun ◽  
...  
Keyword(s):  
Complex Traits ◽  
Molecular Mechanisms ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Pleiotropic Effects ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Wide Spread ◽  
Disease Etiology

AbstractIntegrating association results from both genome-wide association studies (GWASs) and expression quantitative trait locus (eQTL) mapping studies has the potential to shed light on the molecular mechanisms underlying disease etiology. Several statistical methods have been recently developed to integrate GWASs with eQTL studies in the form of transcriptome-wide association studies (TWASs). These existing methods can all be viewed as a form of two sample Mendelian randomization (MR) analysis, which has been widely applied in various GWASs for inferring the causal relationship among complex traits. Unfortunately, most existing TWAS and MR methods make an unrealistic modeling assumption and assume that instrumental variables do not exhibit horizontal pleiotropic effects. However, horizontal pleiotropic effects have been recently discovered to be wide spread across complex traits, and, as we will show here, are also wide spread across gene expression traits. Therefore, not allowing for horizontal pleiotropic effects can be overly restrictive, and, as we will be show here, can lead to a substantial inflation of test statistics and subsequently false discoveries in TWAS applications. Here, we present a probabilistic MR method, which we refer to as PMR-Egger, for testing and controlling for horizontal pleiotropic effects in TWAS applications. PMR-Egger relies on an MR likelihood framework that unifies many existing TWAS and MR methods, accommodates multiple correlated instruments, tests the causal effect of gene on trait in the presence of horizontal pleiotropy, and, with a newly developed parameter expansion version of the expectation maximization algorithm, is scalable to hundreds of thousands of individuals. With extensive simulations, we show that PMR-Egger provides calibrated type I error control for causal effect testing in the presence of horizontal pleiotropic effects, is reasonably robust for various types of horizontal pleiotropic effect mis-specifications, is more powerful than existing MR approaches, and, as a by-product, can directly test for horizontal pleiotropy. We illustrate the benefits of PMR-Egger in applications to 39 diseases and complex traits obtained from three GWASs including the UK Biobank. In these applications, we show how PMR-Egger can lead to new biological discoveries through integrative analysis.

scholarly journals Pig genome functional annotation enhances the biological interpretation of complex traits and human disease

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhangyuan Pan ◽  
Yuelin Yao ◽  
Hongwei Yin ◽  
Zexi Cai ◽  
Ying Wang ◽  
...  
Keyword(s):  
Adaptive Evolution ◽  
Complex Traits ◽  
Functional Annotation ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Regulatory Elements ◽  
Neutral Evolution ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Slow Evolution

AbstractThe functional annotation of livestock genomes is crucial for understanding the molecular mechanisms that underpin complex traits of economic importance, adaptive evolution and comparative genomics. Here, we provide the most comprehensive catalogue to date of regulatory elements in the pig (Sus scrofa) by integrating 223 epigenomic and transcriptomic data sets, representing 14 biologically important tissues. We systematically describe the dynamic epigenetic landscape across tissues by functionally annotating 15 different chromatin states and defining their tissue-specific regulatory activities. We demonstrate that genomic variants associated with complex traits and adaptive evolution in pig are significantly enriched in active promoters and enhancers. Furthermore, we reveal distinct tissue-specific regulatory selection between Asian and European pig domestication processes. Compared with human and mouse epigenomes, we show that porcine regulatory elements are more conserved in DNA sequence, under both rapid and slow evolution, than those under neutral evolution across pig, mouse, and human. Finally, we provide biological insights on tissue-specific regulatory conservation, and by integrating 47 human genome-wide association studies, we demonstrate that, depending on the traits, mouse or pig might be more appropriate biomedical models for different complex traits and diseases.

scholarly journals Pig genome functional annotation enhances biological interpretations of complex traits and comparative epigenomics

2021 ◽  
Author(s):  
Huaijun Zhou ◽  
Zhangyuan Pan ◽  
Yuelin Yao ◽  
Hongwei Ying ◽  
Zexi Cai ◽  
...  
Keyword(s):  
Adaptive Evolution ◽  
Complex Traits ◽  
Functional Annotation ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Regulatory Elements ◽  
Neutral Evolution ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Slow Evolution

Abstract The functional annotation of livestock genomes is crucial for understanding the molecular mechanisms that underpin complex traits of economic importance, adaptive evolution and comparative genomics. Here, we provide the most comprehensive catalogue to date of regulatory elements in the pig (Sus scrofa) by integrating 223 epigenomic and transcriptomic data sets, representing 14 biologically important tissues. We systematically describe the dynamic epigenetic landscape across tissues by functionally annotating 15 different chromatin states and defining their tissue-specific regulatory activities. We demonstrate that genomic variants associated with complex traits and adaptive evolution in pig are significantly enriched in active promoters and enhancers. Furthermore, we reveal distinct tissue-specific regulatory selection between Asian and European pig domestication processes. Compared with human and mouse epigenomes, we show that porcine regulatory elements are more conserved in DNA sequence, under both rapid and slow evolution, than those under neutral evolution across pig, mouse, and human. Finally, we provide novel biological insights on tissue-specific regulatory conservation and demonstrate that, depending on the traits, mouse or pig might be more appropriate biomedical models for different complex traits and diseases in humans through integrating comparative epigenomes with 47 human genome-wide association studies.

scholarly journals Fine-mapping cis-regulatory variants in diverse human populations

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Ashley Tehranchi ◽  
Brian Hie ◽  
Michael Dacre ◽  
Irene Kaplow ◽  
Kade Pettie ◽  
...  
Keyword(s):  
Long Range ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Chromatin Accessibility ◽  
Editorial Note ◽  
Human Populations ◽  
Genome Wide Association Studies ◽  
Diverse Populations ◽  
Regulatory Variants ◽  
In Cis

Genome-wide association studies (GWAS) are a powerful approach for connecting genotype to phenotype. Most GWAS hits are located in cis-regulatory regions, but the underlying causal variants and their molecular mechanisms remain unknown. To better understand human cis-regulatory variation, we mapped quantitative trait loci for chromatin accessibility (caQTLs)—a key step in cis-regulation—in 1000 individuals from 10 diverse populations. Most caQTLs were shared across populations, allowing us to leverage the genetic diversity to fine-map candidate causal regulatory variants, several thousand of which have been previously implicated in GWAS. In addition, many caQTLs that affect the expression of distal genes also alter the landscape of long-range chromosomal interactions, suggesting a mechanism for long-range expression QTLs. In sum, our results show that molecular QTL mapping integrated across diverse populations provides a high-resolution view of how worldwide human genetic variation affects chromatin accessibility, gene expression, and phenotype.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that minor issues remain unresolved (<xref ref-type="decision-letter" rid="SA1">see decision letter</xref>).

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