scholarly journals PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuhua Zhang ◽  
◽  
Corbin Quick ◽  
Ketian Yu ◽  
Alvaro Barbeira ◽  
...  

Abstract We propose a new computational framework, probabilistic transcriptome-wide association study (PTWAS), to investigate causal relationships between gene expressions and complex traits. PTWAS applies the established principles from instrumental variables analysis and takes advantage of probabilistic eQTL annotations to delineate and tackle the unique challenges arising in TWAS. PTWAS not only confers higher power than the existing methods but also provides novel functionalities to evaluate the causal assumptions and estimate tissue- or cell-type-specific gene-to-trait effects. We illustrate the power of PTWAS by analyzing the eQTL data across 49 tissues from GTEx (v8) and GWAS summary statistics from 114 complex traits.

2020 ◽  
Author(s):  
Diptavo Dutta ◽  
Yuan He ◽  
Ashis Saha ◽  
Marios Arvanitis ◽  
Alexis Battle ◽  
...  

AbstractLarge scale genetic association studies have identified many trait-associated variants and understanding the role of these variants in downstream regulation of gene-expressions can uncover important mediating biological mechanisms. In this study, we propose Aggregative tRans assoCiation to detect pHenotype specIfic gEne-sets (ARCHIE), as a method to establish links between sets of known genetic variants associated with a trait and sets of co-regulated gene-expressions through trans associations. ARCHIE employs sparse canonical correlation analysis based on summary statistics from trans-eQTL mapping and genotype and expression correlation matrices constructed from external data sources. We propose a resampling based procedure to test for significant trait-specific trans-association patterns in the background of highly polygenic regulation of gene-expression. By applying ARCHIE to available trans-eQTL summary statistics reported by the eQTLGen consortium, we identify 71 gene networks which have significant evidence of trans-association with groups of known genetic variants across 29 complex traits. A majority (50.7%) of the genes do not have any strong trans-associations and could not have been detected by standard trans-eQTL mapping. We provide further evidence for causal basis of the target genes through a series of follow-up analyses. These results show ARCHIE is a powerful tool for identifying sets of genes whose trans regulation may be related to specific complex traits.


2021 ◽  
Author(s):  
Diptavo Dutta ◽  
Yuan He ◽  
Ashis Saha ◽  
Marios Arvanitis ◽  
Alexis Battle ◽  
...  

Abstract Large scale genetic association studies have identified many trait-associated variants and understanding the role of these variants in downstream regulation of gene-expressions can uncover important mediating biological mechanisms. In this study, we propose Aggregative tRans assoCiation to detect pHenotype specIfic gEne-sets (ARCHIE), as a method to establish links between sets of known genetic variants associated with a trait and sets of co-regulated gene-expressions through trans associations. ARCHIE employs sparse canonical correlation analysis based on summary statistics from trans-eQTL mapping and genotype and expression correlation matrices constructed from external data sources. A resampling based procedure is then used to test for significant trait-specific trans-association patterns in the background of highly polygenic regulation of gene-expression. Simulation studies show that compared to standard trans-eQTL analysis, ARCHIE is better suited to identify “core”-like genes through which effects of many other genes may be mediated and which can explain disease specific patterns of genetic associations. By applying ARCHIE to available trans-eQTL summary statistics reported by the eQTLGen consortium, we identify 71 gene networks which have significant evidence of trans-association with groups of known genetic variants across 29 complex traits. Around half (50.7%) of the selected genes do not have any strong trans-associations and could not have been detected by standard trans-eQTL mapping. We provide further evidence for causal basis of the target genes through a series of follow-up analyses. These results show ARCHIE is a powerful tool for identifying sets of genes whose trans regulation may be related to specific complex traits. The method has potential for broader applications for identification of networks of various types of molecular traits which mediates complex traits genetic associations.


2019 ◽  
Author(s):  
Yuhua Zhang ◽  
Corbin Quick ◽  
Ketian Yu ◽  
Alvaro Barbeira ◽  
Francesca Luca ◽  
...  

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.


2019 ◽  
Author(s):  
Xiangying Sun ◽  
Zhezhen Wang ◽  
Carlos Perez-Cervantes ◽  
Alex Ruthenburg ◽  
Ivan Moskowitz ◽  
...  

AbstractLong noncoding RNAs (lncRNAs) localize in the cell nucleus and influence gene expression through a variety of molecular mechanisms. RNA sequencing of two biochemical fractions of nuclei reveals a unique class of lncRNAs, termed chromatin-enriched nuclear RNAs (cheRNAs) that are tightly bound to chromatin and putatively function to cis-activate gene expression. Until now, a rigorous analytic pipeline for nuclear RNA-seq has been lacking. In this study, we survey four computational strategies for nuclear RNA-seq data analysis and show that a new pipeline, Tuxedo, outperforms other approaches. Tuxedo not only assembles a more complete transcriptome, but also identifies cheRNA with higher accuracy. We have used Tuxedo to analyze gold-standard K562 cell datasets and further characterize the genomic features of intergenic cheRNA (icheRNA) and their similarity to those of enhancer RNA (eRNA). Moreover, we quantify the transcriptional correlation of icheRNA and adjacent genes, and suggest that icheRNA may be the cis-acting transcriptional regulator that is more positively associated with neighboring gene expression than eRNA predicted by state-of-art method or CAGE signal. We also explore two novel genomic associations, suggesting cheRNA may have diverse functions. A possible new role of H3K9me3 modification coincident with icheRNA may be associated with active enhancer derived from ancient mobile elements, while a potential cis-repressive function of antisense cheRNA (as-cheRNA) is likely to be involved in transiently modulating cell type-specific cis-regulation.Author SummaryChromatin-enriched nuclear RNA (cheRNA) is a class of gene regulatory non-coding RNAs. CheRNA provides a powerful way to profile the nuclear transcriptional landscape, especially to profile the noncoding transcriptome. The computational framework presented here provides a reliable approach to identifying cheRNA, and for studying cell-type specific gene regulation. We found that intergenic cheRNA, including intergenic cheRNA with high levels of H3K9me3 (a mark associated with closed/repressed chromatin), may act as a transcriptional activator. In contrast, antisense cheRNA, which originates from the complementary strand of the protein-coding gene, may interact with diverse chromatin modulators to repress local transcription. With our new pipeline, one future challenge will be refining the functional mechanisms of these noncoding RNA classes through exploring their regulatory roles, which are involved in diverse molecular and cellular processes in human and other organisms.


2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Kevin J. Gleason ◽  
Fan Yang ◽  
Brandon L. Pierce ◽  
Xin He ◽  
Lin S. Chen

Abstract To provide a comprehensive mechanistic interpretation of how known trait-associated SNPs affect complex traits, we propose a method, Primo, for integrative analysis of GWAS summary statistics with multiple sets of omics QTL summary statistics from different cellular conditions or studies. Primo examines association patterns of SNPs to complex and omics traits. In gene regions harboring known susceptibility loci, Primo performs conditional association analysis to account for linkage disequilibrium. Primo allows for unknown study heterogeneity and sample correlations. We show two applications using Primo to examine the molecular mechanisms of known susceptibility loci and to detect and interpret pleiotropic effects.


2021 ◽  
Author(s):  
David A Gallegos ◽  
Melyssa Minto ◽  
Fang Liu ◽  
Mariah F Hazlett ◽  
S Aryana Yousefzadeh ◽  
...  

Parvalbumin-expressing (PV+) interneurons of the nucleus accumbens (NAc) play an essential role in the addictive-like behaviors induced by psychostimulant exposure. To identify molecular mechanisms of PV+ neuron plasticity, we isolated interneuron nuclei from the NAc of male and female mice following acute or repeated exposure to amphetamine (AMPH) and sequenced for cell type-specific RNA expression and chromatin accessibility. AMPH regulated the transcription of hundreds of genes in PV+ interneurons, and this program was largely distinct from that regulated in other NAc GABAergic neurons. Chromatin accessibility at enhancers predicted cell-type specific gene regulation, identifying transcriptional mechanisms of differential AMPH responses. Finally, we observed dysregulation of multiple PV-specific, AMPH-regulated genes in an Mecp2 mutant mouse strain that shows heightened behavioral sensitivity to psychostimulants, suggesting the functional importance of this transcriptional program. Together these data provide novel insight into the cell-type specific programs of transcriptional plasticity in NAc neurons that underlie addictive-like behaviors.


2019 ◽  
Author(s):  
Kevin J Gleason ◽  
Fan Yang ◽  
Brandon L Pierce ◽  
Xin He ◽  
Lin S Chen

AbstractTo provide a comprehensive mechanistic interpretation of how known trait-associated SNPs affect complex traits, we propose a method – Primo – for integrative analysis of GWAS summary statistics with multiple sets of omics QTL summary statistics from different cellular conditions or studies. Primo examines SNPs’ association patterns to complex and omics traits. In gene regions harboring known susceptibility loci, Primo performs conditional association analysis to account for linkage disequilibrium. Primo allows for unknown study heterogeneity and sample correlations. We show two applications using Primo to examine the molecular mechanisms of known susceptibility loci and to detect and interpret pleiotropic effects.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ting Li ◽  
Zheng Ning ◽  
Zhijian Yang ◽  
Ranran Zhai ◽  
Chenqing Zheng ◽  
...  

AbstractQuantifying the overall magnitude of every single locus’ genetic effect on the widely measured human phenome is of great challenge. We introduce a unified modelling technique that can consistently provide a total genetic contribution assessment (TGCA) of a gene or genetic variant without thresholding genetic association signals. Genome-wide TGCA in five UK Biobank phenotype domains highlights loci such as the HLA locus for medical conditions, the bone mineral density locus WNT16 for physical measures, and the skin tanning locus MC1R and smoking behaviour locus CHRNA3 for lifestyle. Tissue-specificity investigation reveals several tissues associated with total genetic contributions, including the brain tissues for mental health. Such associations are driven by tissue-specific gene expressions, which share genetic basis with the total genetic contributions. TGCA can provide a genome-wide atlas for the overall genetic contributions in each particular domain of human complex traits.


2021 ◽  
Vol 12 ◽  
Author(s):  
Kazuko Miyazaki ◽  
Masaki Miyazaki

Cell type-specific gene expression is driven through the interplay between lineage-specific transcription factors (TFs) and the chromatin architecture, such as topologically associating domains (TADs), and enhancer-promoter interactions. To elucidate the molecular mechanisms of the cell fate decisions and cell type-specific functions, it is important to understand the interplay between chromatin architectures and TFs. Among enhancers, super-enhancers (SEs) play key roles in establishing cell identity. Adaptive immunity depends on the RAG-mediated assembly of antigen recognition receptors. Hence, regulation of the Rag1 and Rag2 (Rag1/2) genes is a hallmark of adaptive lymphoid lineage commitment. Here, we review the current knowledge of 3D genome organization, SE formation, and Rag1/2 gene regulation during B cell and T cell differentiation.


2019 ◽  
Author(s):  
Gabriel Cuellar-Partida ◽  
Mischa Lundberg ◽  
Pik Fang Kho ◽  
Shannon D’Urso ◽  
Luis F. Gutierrez-Mondragon ◽  
...  

AbstractBackgroundGenome-wide association studies (GWAS) are an important method for mapping genetic variation underlying complex traits and diseases. Tools to visualize, annotate and analyse results from these studies can be used to generate hypotheses about the molecular mechanisms underlying the associations.FindingsThe Complex-Traits Genetics Virtual Lab (CTG-VL) integrates over a thousand publicly-available GWAS summary statistics, a suite of analysis tools, visualization functions and diverse data sets for genomic annotations. CTG-VL also makes available results from gene, pathway and tissue-based analyses from over 1,500 complex-traits allowing to assess pleiotropy not only at the genetic variant level but also at the gene, pathway and tissue levels. In this manuscript, we showcase the platform by analysing GWAS summary statistics of mood swings derived from UK Biobank. Using analysis tools in CTG-VL we highlight hippocampus as a potential tissue involved in mood swings, and that pathways including neuron apoptotic process may underlie the genetic associations. Further, we report a negative genetic correlation with educational attainment rG = −0.41 ± 0.018 and a potential causal effect of BMI on mood swings OR = 1.01 (95% CI = 1.00–1.02). Using CTG-VL’s database, we show that pathways and tissues associated with mood swings are also associated with neurological traits including reaction time and neuroticism, as well as traits such age at menopause and age at first live birth.ConclusionsCTG-VL is a platform with the most complete set of tools to carry out post-GWAS analyses. The CTG-VL is freely available at https://genoma.io as an online web application.


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