Inferred expression regulator activities suggest genes mediating cardiometabolic genetic signals

Expression QTL (eQTL) analyses have suggested many genes mediating genome-wide association study (GWAS) signals but most GWAS signals still lack compelling explanatory genes. We have leveraged an adipose-specific gene regulatory network to infer expression regulator activities and phenotypic master regulators (MRs), which were used to detect activity QTLs (aQTLs) at cardiometabolic trait GWAS loci. Regulator activities were inferred with the VIPER algorithm that integrates enrichment of expected expression changes among a regulator’s target genes with confidence in their regulator-target network interactions and target overlap between different regulators (i.e., pleiotropy). Phenotypic MRs were identified as those regulators whose activities were most important in predicting their respective phenotypes using random forest modeling. While eQTLs were typically more significant than aQTLs in cis, the opposite was true among candidate MRs in trans. Several GWAS loci colocalized with MR trans-eQTLs/aQTLs in the absence of colocalized cis-QTLs. Intriguingly, at the 1p36.1 BMI GWAS locus the EPHB2 cis-aQTL was stronger than its cis-eQTL and colocalized with the GWAS signal and 35 BMI MR trans-aQTLs, suggesting the GWAS signal may be mediated by effects on EPHB2 activity and its downstream effects on a network of BMI MRs. These MR and aQTL analyses represent systems genetic methods that may be broadly applied to supplement standard eQTL analyses for suggesting molecular effects mediating GWAS signals.

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Identification of key tissue-specific, biological processes by integrating enhancer information in maize gene regulatory networks

10.1101/2020.06.16.155481 ◽

2020 ◽

Author(s):

Maud Fagny ◽

Marieke Lydia Kuijjer ◽

Maike Stam ◽

Johann Joets ◽

Olivier Turc ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factor ◽

Binding Sites ◽

Regulatory Network ◽

Tissue Specificity ◽

Target Genes ◽

Specific Gene ◽

Tissue Specific ◽

Genome Wide ◽

Gene Regulatory

AbstractEnhancers are important regulators of gene expression during numerous crucial processes including tissue differentiation across development. In plants, their recent molecular characterization revealed their capacity to activate the expression of several target genes through the binding of transcription factors. Nevertheless, identifying these target genes at a genome-wide level remains a challenge, in particular in species with large genomes, where enhancers and target genes can be hundreds of kilobases away. Therefore, the contribution of enhancers to regulatory network is still poorly understood in plants. In this study, we investigate the enhancer-driven regulatory network of two maize tissues at different stages: leaves at seedling stage and husks (bracts) at flowering. Using a systems biology approach, we integrate genomic, epigenomic and transcriptomic data to model the regulatory relationship between transcription factors and their potential target genes. We identify regulatory modules specific to husk and V2-IST, and show that they are involved in distinct functions related to the biology of each tissue. We evidence enhancers exhibiting binding sites for two distinct transcription factor families (DOF and AP2/ERF) that drive the tissue-specificity of gene expression in seedling immature leaf and husk. Analysis of the corresponding enhancer sequences reveals that two different transposable element families (TIR transposon Mutator and MITE Pif/Harbinger) have shaped the regulatory network in each tissue, and that MITEs have provided new transcription factor binding sites that are involved in husk tissue-specificity.SignificanceEnhancers play a major role in regulating tissue-specific gene expression in higher eukaryotes, including angiosperms. While molecular characterization of enhancers has improved over the past years, identifying their target genes at the genome-wide scale remains challenging. Here, we integrate genomic, epigenomic and transcriptomic data to decipher the tissue-specific gene regulatory network controlled by enhancers at two different stages of maize leaf development. Using a systems biology approach, we identify transcription factor families regulating gene tissue-specific expression in husk and seedling leaves, and characterize the enhancers likely to be involved. We show that a large part of maize enhancers is derived from transposable elements, which can provide novel transcription factor binding sites crucial to the regulation of tissue-specific biological functions.

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Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

Genome Biology ◽

10.1186/s13059-020-02252-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Alvaro N. Barbeira ◽

◽

Rodrigo Bonazzola ◽

Eric R. Gamazon ◽

Yanyu Liang ◽

...

Keyword(s):

Complex Traits ◽

Target Genes ◽

Genome Wide Association Study ◽

Data Driven ◽

Functional Interpretation ◽

Transcriptome Regulation ◽

Genome Wide ◽

Causal Genes ◽

Dose Dependent ◽

Single Approach

AbstractThe resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.

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Deciphering the genetic links between NAFLD and co-occurring conditions using a liver gene regulatory network

10.1101/2021.12.08.471841 ◽

2021 ◽

Author(s):

Sreemol Gokuladhas ◽

William Schierding ◽

Roan Eltigani Zaied ◽

Tayaza Fadason ◽

Murim Choi ◽

...

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Complex Traits ◽

Target Genes ◽

Specific Gene ◽

Fat Percentage ◽

Alcoholic Fatty Liver ◽

Regulatory Interactions ◽

Hepatic Diseases ◽

Gene Regulatory

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) is a multi-system metabolic disease that co-occurs with various hepatic and extra-hepatic diseases. The phenotypic manifestation of NAFLD is primarily observed in the liver. Therefore, identifying liver-specific gene regulatory interactions between variants associated with NAFLD and multimorbid conditions may help to improve our understanding of underlying shared aetiology. Methods: Here, we constructed a liver-specific gene regulatory network (LGRN) consisting of genome-wide spatially constrained expression quantitative trait loci (eQTLs) and their target genes. The LGRN was used to identify regulatory interactions involving NAFLD-associated genetic modifiers and their inter-relationships to other complex traits. Results and Conclusions: We demonstrate that MBOAT7 and IL32, which are associated with NAFLD progression, are regulated by spatially constrained eQTLs that are enriched for an association with liver enzyme levels. MBOAT7 transcript levels are also linked to eQTLs associated with cirrhosis, and other traits that commonly co-occur with NAFLD. In addition, genes that encode interacting partners of NAFLD-candidate genes within the liver-specific protein-protein interaction network were affected by eQTLs enriched for phenotypes relevant to NAFLD (e.g. IgG glycosylation patterns, OSA). Furthermore, we identified distinct gene regulatory networks formed by the NAFLD-associated eQTLs in normal versus diseased liver, consistent with the context-specificity of the eQTLs effects. Interestingly, genes targeted by NAFLD-associated eQTLs within the LGRN were also affected by eQTLs associated with NAFLD-related traits (e.g. obesity and body fat percentage). Overall, the genetic links identified between these traits expand our understanding of shared regulatory mechanisms underlying NAFLD multimorbidities.

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A compendium of uniformly processed human gene expression and splicing quantitative trait loci

Nature Genetics ◽

10.1038/s41588-021-00924-w ◽

2021 ◽

Vol 53 (9) ◽

pp. 1290-1299

Author(s):

Nurlan Kerimov ◽

James D. Hayhurst ◽

Kateryna Peikova ◽

Jonathan R. Manning ◽

Peter Walter ◽

...

Keyword(s):

Gene Expression ◽

Quantitative Trait ◽

Target Genes ◽

Genome Wide Association Study ◽

Cell Types ◽

Summary Statistics ◽

Genome Wide ◽

Cell Type Specific ◽

Trait Locus ◽

Complex Human Traits

AbstractMany gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue (https://www.ebi.ac.uk/eqtl), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.

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Bayes Factor-Based Regulatory Gene Network Analysis of Genome-Wide Association Study of Economic Traits in a Purebred Swine Population

Genes ◽

10.3390/genes10040293 ◽

2019 ◽

Vol 10 (4) ◽

pp. 293 ◽

Cited By ~ 3

Author(s):

Lee ◽

Kang ◽

Kim

Keyword(s):

Association Study ◽

Gene Network ◽

Bayes Factor ◽

Target Genes ◽

Genome Wide Association Study ◽

Function Analysis ◽

Regulatory Gene ◽

Genome Wide Association ◽

Regulatory Sequence ◽

Genome Wide

: Early stage prediction of economic trait performance is important and directly linked to profitability of farm pig production. Genome-wide association study (GWAS) has been applied to find causative genomic regions of traits. This study established a regulatory gene network using GWAS for critical economic pig characteristics, centered on easily measurable body fat thickness in live animals. We genotyped 2,681 pigs using Illumina Porcine SNP60, followed by GWAS to calculate Bayes factors for 47,697 single nucleotide polymorphisms (SNPs) of seven traits. Using this information, SNPs were annotated with specific genes near genome locations to establish the association weight matrix. The entire network consisted of 226 nodes and 6,921 significant edges. For in silico validation of their interactions, we conducted regulatory sequence analysis of predicted target genes of transcription factors (TFs). Three key regulatory TFs were identified to guarantee maximum coverage: AT-rich interaction domain 3B (ARID3B), glial cell missing homolog 1 (GCM1), and GLI family zinc finger 2 (GLI2). We identified numerous genes targeted by ARID3B, associated with cellular processes. GCM1 and GLI2 were involved in developmental processes, and their shared target genes regulated multicellular organismal process. This system biology-based function analysis might contribute to enhancing understanding of economic pig traits.

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Beyond the ENCODE project: using genomics and epigenomics strategies to study enhancer evolution

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2013.0022 ◽

2013 ◽

Vol 368 (1632) ◽

pp. 20130022 ◽

Cited By ~ 13

Author(s):

Noboru Jo Sakabe ◽

Marcelo A. Nobrega

Keyword(s):

Gene Expression ◽

Target Genes ◽

Expression Patterns ◽

Dna Interaction ◽

Regulatory Elements ◽

Specific Gene ◽

Genome Wide ◽

Identify Transcription Factor ◽

Specific Proteins ◽

Species Specific

The complex expression patterns observed for many genes are often regulated by distal transcription enhancers. Changes in the nucleotide sequences of enhancers may therefore lead to changes in gene expression, representing a central mechanism by which organisms evolve. With the development of the experimental technique of chromatin immunoprecipitation (ChIP), in which discrete regions of the genome bound by specific proteins can be identified, it is now possible to identify transcription factor binding events (putative cis -regulatory elements) in entire genomes. Comparing protein–DNA binding maps allows us, for the first time, to attempt to identify regulatory differences and infer global patterns of change in gene expression across species. Here, we review studies that used genome-wide ChIP to study the evolution of enhancers. The trend is one of high divergence of cis -regulatory elements between species, possibly compensated by extensive creation and loss of regulatory elements and rewiring of their target genes. We speculate on the meaning of the differences observed and discuss that although ChIP experiments identify the biochemical event of protein–DNA interaction, it cannot determine whether the event results in a biological function, and therefore more studies are required to establish the effect of divergence of binding events on species-specific gene expression.

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Genetics of human gut microbiome composition

10.1101/2020.06.26.173724 ◽

2020 ◽

Cited By ~ 3

Author(s):

Alexander Kurilshikov ◽

Carolina Medina-Gomez ◽

Rodrigo Bacigalupe ◽

Djawad Radjabzadeh ◽

Jun Wang ◽

...

Keyword(s):

Association Study ◽

Gut Microbiome ◽

Genome Wide Association Study ◽

Food Preferences ◽

Microbial Composition ◽

Fecal Microbiome ◽

Microbiome Composition ◽

Genome Wide ◽

A Genome ◽

Gwas Signal

AbstractTo study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed whole-genome genotypes and 16S fecal microbiome data from 18,473 individuals (25 cohorts). Microbial composition showed high variability across cohorts: we detected only 9 out of 410 genera in more than 95% of the samples. A genome-wide association study (GWAS) of host genetic variation in relation to microbial taxa identified 30 loci affecting microbome taxa at a genome-wide significant (P<5×10-8) threshold. Just one locus, the lactase (LCT) gene region, reached study-wide significance (GWAS signal P=8.6×10−21); it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.94×10−10<P<5×10−8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization analyses identified enrichment of microbiome trait loci SNPs in the metabolic, nutrition and environment domains and indicated food preferences and diseases as mediators of genetic effects.

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Identification of miRNA-eQTLs in maize mature leaf by GWAS

BMC Genomics ◽

10.1186/s12864-020-07073-0 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Shu-Yun Chen ◽

Mei-Hsiu Su ◽

Karl A. Kremling ◽

Nicholas K. Lepak ◽

M. Cinta Romay ◽

...

Keyword(s):

Mirna Expression ◽

Target Genes ◽

Genome Wide Association Study ◽

Developmental Stages ◽

Mature Leaf ◽

Plant Genome ◽

Expression Level ◽

Genome Wide ◽

Mirna Expression Level ◽

Next Generation Sequencing Ngs

Abstract Background MiRNAs play essential roles in plant development and response to biotic and abiotic stresses through interaction with their target genes. The expression level of miRNAs shows great variations among different plant accessions, developmental stages, and tissues. Little is known about the content within the plant genome contributing to the variations in plants. This study aims to identify miRNA expression-related quantitative trait loci (miR-QTLs) in the maize genome. Results The miRNA expression level from next generation sequencing (NGS) small RNA libraries derived from mature leaf samples of the maize panel (200 maize lines) was estimated as phenotypes, and maize Hapmap v3.2.1 was chosen as the genotype for the genome-wide association study (GWAS). A total of four significant miR-eQTLs were identified contributing to miR156k-5p, miR159a-3p, miR390a-5p and miR396e-5p, and all of them are trans-eQTLs. In addition, a strong positive coexpression of miRNA was found among five miRNA families. Investigation of the effects of these miRNAs on the expression levels and target genes provided evidence that miRNAs control the expression of their targets by suppression and enhancement. Conclusions These identified significant miR-eQTLs contribute to the diversity of miRNA expression in the maize penal at the developmental stages of mature leaves in maize, and the positive and negative regulation between miRNA and its target genes has also been uncovered.

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CNet: a multi-omics approach to detecting clinically associated, combinatory genomic signatures

Bioinformatics ◽

10.1093/bioinformatics/btz441 ◽

2019 ◽

Vol 35 (24) ◽

pp. 5207-5215

Author(s):

Peilin Jia ◽

Guangsheng Pei ◽

Zhongming Zhao

Keyword(s):

Patient Outcomes ◽

Genome Wide Association Study ◽

Cancer Genomics ◽

Study Data ◽

Supplementary Information ◽

Specific Gene ◽

Multiple Traits ◽

Response Data ◽

Genomic Signatures ◽

Genome Wide

Abstract Motivation Genome-wide multi-omics profiling of complex diseases provides valuable resources and opportunities to discover associations between various measures of genes and diseases. Currently, a pressing challenge is how to effectively detect functional genes associated with or causing phenotypic outcomes. We developed CNet to identify groups of genomic signatures whose combinatory effect is significantly associated with clinical and phenotypical outcomes. Results CNet builds on a generalized sequential feedforward method, augmented by a down-sampling bootstrap strategy to reduce random hitchhiking signatures. It further applies a dynamic trimming procedure to remove relatively less informative signatures at every step. CNet can manage heterogeneous genomic signature profiles simultaneously and select the best signature to represent a specific gene. To deal with various forms of clinical and phenotypical measurements, we introduced four models to deal with continuous, categorical and censored data. We tested CNet using drug-response data, multidimensional cancer genomics data and genome-wide association study data for multiple traits. Our results demonstrated that in various scenarios, CNet could effectively identify signatures that are associated with the outcomes. In addition, we applied CNet to identify likely disease-causing chains involving somatic mutations, pathway activities and patient outcomes. With appropriate setting, CNet can be applied in many biological conditions. Availability and implementation CNet can be downloaded at https://github.com/bsml320/CNet. Supplementary information Supplementary data are available at Bioinformatics online.

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