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Organization of gene programs revealed by unsupervised analysis of diverse gene-trait associations.

ABSTRACT: Genome wide association studies provide statistical measures of gene-trait associations that reveal how genetic variation influences phenotypes. This study develops an unsupervised dimensionality reduction method called UnTANGLeD (Unsupervised Trait Analysis of Networks from Gene Level Data) which organizes 16,849 genes into discrete gene programs by measuring the statistical association between genetic variants and 1,393 diverse complex traits. UnTANGLeD reveals 173 gene clusters enriched for protein-protein interactions and highly distinct biological processes governing development, signalling, disease, and homeostasis. We identify diverse gene networks with robust interactions but not associated with known biological processes. Analysis of independent disease traits shows that UnTANGLeD gene clusters are conserved across all complex traits, providing a simple and powerful framework to predict novel gene candidates and programs influencing orthogonal disease phenotypes. Collectively, this study demonstrates that gene programs co-ordinately orchestrating cell functions can be identified without reliance on prior knowledge, providing a method for use in functional annotation, hypothesis generation, machine learning and prediction algorithms, and the interpretation of diverse genomic data.

SUBMITTER: Mizikovsky D

PROVIDER: S-EPMC9410900 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Organization of gene programs revealed by unsupervised analysis of diverse gene-trait associations.

Mizikovsky Dalia D Naval Sanchez Marina M Nefzger Christian M CM Cuellar Partida Gabriel G Palpant Nathan J NJ

Nucleic acids research 20220801 15

Genome wide association studies provide statistical measures of gene-trait associations that reveal how genetic variation influences phenotypes. This study develops an unsupervised dimensionality reduction method called UnTANGLeD (Unsupervised Trait Analysis of Networks from Gene Level Data) which organizes 16,849 genes into discrete gene programs by measuring the statistical association between genetic variants and 1,393 diverse complex traits. UnTANGLeD reveals 173 gene clusters enriched for p ...[more]

PMID: 35716123

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Project description:BackgroundNasal (airway) epithelial methylation profiles have been associated with asthma, but the effects of such profiles on expression of distant cis-genes are largely unknown.Research questionTo identify genes whose expression is associated with proximal and distal CpG probes (within 1 Mb), and to assess whether and how such genes are differentially expressed in atopic asthma.Study design and methodsGenome-wide expression quantitative trait methylation (eQTM) analysis in nasal epithelium from Puerto Rican subjects (aged 9-20 years) with (n = 219) and without (n = 236) asthma. After the eQTM analysis, a Gene Ontology Enrichment analysis was conducted for the top 500 eQTM genes, and mediation analyses were performed to identify paths from DNA methylation to atopic asthma through gene expression. Asthma was defined as physician-diagnosed asthma and wheeze in the previous year, and atopy was defined as at least one positive IgE to allergens. Atopic asthma was defined as the presence of both atopy and asthma.ResultsWe identified 16,867 significant methylation-gene expression pairs (false-discovery rate-adjusted P < .01) in nasal epithelium from study participants. Most eQTM methylation probes were distant (average distance, ∼378 kb) from their target genes, and also more likely to be located in enhancer regions of their target genes in lung tissue than control probes. The top 500 eQTM genes were enriched in pathways for immune processes and epithelial integrity and were more likely to have been previously identified as differentially expressed in atopic asthma. In a mediation analysis, we identified 5,934 paths through which methylation markers could affect atopic asthma through gene expression in nasal epithelium.InterpretationPrevious epigenome-wide association studies of asthma have estimated the effects of DNA methylation markers on expression of nearby genes in airway epithelium. Our findings suggest that distant epigenetic regulation of gene expression in airway epithelium plays a role in atopic asthma.

Project description:Background & aimsGenome-wide association studies have greatly increased our understanding of intestinal disease. However, little is known about how genetic variations result in phenotypic changes. Some polymorphisms have been shown to modulate quantifiable phenotypic traits; these are called quantitative trait loci. Quantitative trait loci that affect levels of gene expression are called expression quantitative trait loci (eQTL), which can provide insight into the biological relevance of data from genome-wide association studies. We performed a comprehensive eQTL scan of intestinal tissue.MethodsTotal RNA was extracted from ileal biopsy specimens and genomic DNA was obtained from whole-blood samples from the same cohort of individuals. Cis- and trans-eQTL analyses were performed using a custom software pipeline for samples from 173 subjects. The analyses determined the expression levels of 19,047 unique autosomal genes listed in the US National Center for Biotechnology Information database and more than 580,000 variants from the Single Nucleotide Polymorphism database.ResultsThe presence of more than 15,000 cis- and trans-eQTL was detected with statistical significance. eQTL associated with the same expression trait were in high linkage disequilibrium. Comparative analysis with previous eQTL studies showed that 30% to 40% of genes identified as eQTL in monocytes, liver tissue, lymphoblastoid cell lines, T cells, and fibroblasts are also eQTL in ileal tissue. Conversely, most of the significant eQTL have not been previously identified and could be tissue specific. These are involved in many cell functions, including division and antigen processing and presentation. Our analysis confirmed that previously published cis-eQTL are single nucleotide polymorphisms associated with inflammatory bowel disease: rs2298428/UBE2L3, rs1050152/SLC22A4, and SLC22A5. We identified many new associations between inflammatory bowel disease susceptibility loci and gene expression.ConclusionseQTL analysis of intestinal tissue supports findings that some eQTL remain stable across cell types, whereas others are specific to the sampled location. Our findings confirm and expand the number of known genotypes associated with expression and could help elucidate mechanisms of intestinal disease.

Project description:BackgroundOur appreciation of the critical role of the genome's 3D organization in gene regulation is steadily increasing. Recent 3C-based deep sequencing techniques elucidated a hierarchy of structures that underlie the spatial organization of the genome in the nucleus. At the top of this hierarchical organization are chromosomal territories and the megabase-scale A/B compartments that correlate with transcriptional activity within cells. Below them are the relatively cell-type-invariant topologically associated domains (TADs), characterized by high frequency of physical contacts between loci within the same TAD, and are assumed to function as regulatory units. Within TADs, chromatin loops bring enhancers and target promoters to close spatial proximity. Yet, we still have only rudimentary understanding how differences in chromatin organization between different cell types affect cell-type-specific gene expression programs that are executed under basal and challenged conditions.ResultsHere, we carried out a large-scale meta-analysis that integrated Hi-C data from thirteen different cell lines and dozens of ChIP-seq and RNA-seq datasets measured on these cells, either under basal conditions or after treatment. Pairwise comparisons between cell lines demonstrate a strong association between modulation of A/B compartmentalization, differential gene expression and transcription factor (TF) binding events. Furthermore, integrating the analysis of transcriptomes of different cell lines in response to various challenges, we show that A/B compartmentalization of cells under basal conditions significantly correlates not only with gene expression programs and TF binding profiles that are active under the basal condition but also with those induced in response to treatment. Yet, in pairwise comparisons between different cell lines, we find that a large portion of differential TF binding and gene induction events occur in genomic loci assigned to A compartment in both cell types, underscoring the role of additional critical factors in determining cell-type-specific transcriptional programs.ConclusionsOur results further indicate the role of dynamic genome organization in regulation of differential gene expression between different cell types and the impact of intra-TAD enhancer-promoter interactions that are established under basal conditions on both the basal and treatment-induced gene expression programs.

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Organization of gene programs revealed by unsupervised analysis of diverse gene-trait associations.

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Organization of gene programs revealed by unsupervised analysis of diverse gene-trait associations.

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