Reconstructing the blood metabolome and genotype using long-range chromatin interactions

Fadason, Tayaza; Schierding, William; Kolbenev, Nikolai; Liu, Jiamou; Ingram, John R.; O’Sullivan, Justin M.

doi:10.1016/j.metop.2020.100035

Cited by 8 publications

(5 citation statements)

References 61 publications

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“…Here we performed correlational analyses of experimentally derived data to identify eQTLs that physically connect Parkinson-SNPs to the genes that they control, in three dimensions, with the goal of understanding the putative functional impacts of known Parkinson-SNPs. 21 The integration of spatial and eQTL data allows for the identification of trans- eQTL–gene associations, 25 thereby nominating genes which have not previously been implicated in Parkinson’s disease. Our analysis identified 518 genes subject to regulation by 76 Parkinson-SNPs across 49 tissues.…”

Section: Introductionmentioning

confidence: 99%

Establishing gene regulatory networks from Parkinson’s disease risk loci

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Schierding

Gokuladhas

et al. 2022

Brain

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The latest meta-analysis of genome wide association studies (GWAS) identified 90 independent variants across 78 genomic regions associated with Parkinson’s disease, yet the mechanisms by which these variants influence the development of the disease remains largely elusive. To establish the functional gene regulatory networks associated with Parkinson’s disease risk variants, we utilised an approach combining spatial (chromosomal conformation capture) and functional (expression quantitative trait loci; eQTL) data. We identified 518 genes subject to regulation by 76 Parkinson’s-variants across 49 tissues, that encompass 36 peripheral and 13 CNS tissues. Notably, one third of these genes were regulated via trans- acting mechanisms (distal; risk locus-gene separated by > 1Mb, or on different chromosomes). Of particular interest is the identification of a novel trans-eQTL-gene connection between rs10847864 and SYNJ1 in the adult brain cortex, highlighting a convergence between familial studies and Parkinson’s disease GWAS loci for SYNJ1 (PARK20) for the first time. Furthermore, we identified 16 neuro-development specific eQTL-gene regulatory connections within the foetal cortex, consistent with hypotheses suggesting a neurodevelopmental involvement in the pathogenesis of Parkinson’s disease. Through utilising Louvain clustering we extracted nine significant and highly intra-connected clusters within the entire gene regulatory network. The nine clusters are enriched for specific biological processes and pathways, some of which have not previously been associated with Parkinson’s. Together, our results not only contribute to an overall understanding of the mechanisms and impact of specific combinations of Parkinson’s disease variants, but also highlight the potential impact gene regulatory networks may have when elucidating aetiological subtypes of Parkinson’s disease.

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Section: Introductionmentioning

confidence: 99%

Establishing gene regulatory networks from Parkinson’s disease risk loci

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Schierding

Gokuladhas

et al. 2022

Brain

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“…In some cases, computational prediction and text mining have been used to enrich experiments without inclusion in knowledgebases. In one study focused on finding associations across omic modalities, Fadason et al found interactions between metabolite-associated single nucleotide polymorphisms (SNPs), metabolites, and chromatin loops (i.e., physical contact between enhancers and promoters) in human blood by combining literature text mining, known drug interactions, Hi-C chromatin interactions, eQTL, and gene ontologies [279]. Additionally, computational predictions can be used to infer associations between analytes.…”

Section: Computationally Predicted Resourcesmentioning

confidence: 99%

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

et al. 2020

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As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

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“…Spatially constrained gene regulatory networks integrate chromatin interaction (Hi-C) data and eQTL analyses to associate variants with the genes they regulate. These networks represent one approach for assigning functions to da-SNPs (12,13).…”

Section: Introductionmentioning

confidence: 99%

De novo identification of complex traits associated with asthma

Zaied

Fadason

O’Sullivan

2023

Preprint

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Network analysis is a powerful approach for untangling the relationships between complex diseases. Here, we integrated information on physical contacts between common single nucleotide polymorphisms (SNPs) and gene expression with expression quantitative trait loci (eQTL) data from the lung and whole blood to construct two tissue-specific spatial gene regulatory networks (GRN). From these GRNs, we located the genes that are functionally affected by asthma-associated eQTLs to identify the asthma GRN. We then expanded outwards to identify curated protein-protein interactions occurring 1 to 4 edges away from the asthma GRN. The eQTLs spatially regulating the identified genes were used to interrogate the GWAS Catalog to identify enriched traits (hypergeometric test; FDR≤0.05) within the asthma GRN and each of the 4 levels. This led to the de novo identification of traits both previously reported (e.g., depressive symptoms and lung cancer) and unreported (e.g., reticulocyte count) to be comorbid or associated with asthma. It additionally pinpoints the variants and genes that link asthma to the identified asthma-associated traits, a subset of which was replicated in a comorbidity analysis using health records of 26,781 asthma patients in New Zealand. Our discovery approach identifies enriched traits in the regulatory space proximal to asthma, in the tissue of interest, without a priori selection of the interacting traits. The predictions it makes expand our understanding of possible shared molecular interactions and therapeutic targets for asthma, where no cure is currently available.

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Reconstructing the blood metabolome and genotype using long-range chromatin interactions

Cited by 8 publications

References 61 publications

Establishing gene regulatory networks from Parkinson’s disease risk loci

Establishing gene regulatory networks from Parkinson’s disease risk loci

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

De novo identification of complex traits associated with asthma

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