Distant regulatory effects of genetic variation in multiple human tissues

Jo, Brian; He, Yuan; Bj, Strober; Parsana, Princy; Aguet, François; Brown, Andrew Anand; Se, Castel; Gamazon, Eric R.; Gewirtz, Ariel D. H.; Gliner, Genna; Han, Bingqian; He, AZ; Kang, E.Y.; Ic, McDowell; Li, X; Mohammadi, Pejman; Cb, Peterson; Quon, Gerald; Av, Segrè; Jh, Sul; Tj, Sullivan; Kg, Ardlie; Cd, Brown; Conrad, DF; Nj, Cox; Dermitzakis, E. T.; Eskin, Eleazar; Kellis, M; Lappalainen, Tuuli; Sabatti, Chiara; Be, Engelhardt; Battle, Alexis

doi:10.1101/074419

Cited by 12 publications

(14 citation statements)

References 81 publications

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“…Next, we evaluated the hub genes in each TSN, considering three thresholds of centrality: ≥ 5 edges (“small hubs”), ≥ 10 edges (“hubs”), and ≥ 50 edges (“large hubs”). Hubs were not enriched overall for cross-tissue transcription factors (TFs) (hypergeometric test across all TSNs, p ≤ 0.84; small and large hubs showed similar results), or for cross-tissue and tissue-specific TFs (hypergeometric test across all TSNs, p ≤ 0.90; small and large hubs showed similar results); this result echos previous work on transcription factor enrichment in genes with cis-eQTLs that appear to have broad regulatory effects on transcription (Jo et al, 2016; Weiser et al, 2014). However, hubs in several networks included genes known to play a role in tissue-specific function and disease.…”

Section: Resultssupporting

confidence: 78%

“…In the test described above, it is possible that artifactual correlations among gene expression levels, identified as edges in the networks, may also lead to artifactual trans-eQTL associations among cis-eVariants and the neighboring genes of the cis-eGene detected from the same data. However, large-scale, independent RNA-seq data sets are unavailable for most of the tissue types represented in GTEx, and identifying trans-eQTLs from a standard genome-wide association test has proven challenging (Jo et al, 2016), in part due to the large number of statistical tests. Restricting association tests to a plausible subset, according to prior knowledge (Jo et al, 2016) such as network relationships (Weiser et al, 2014) can increase statistical power substantially.…”

Section: Resultsmentioning

confidence: 99%

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Co-expression networks reveal the tissue-specific regulation of transcription and splicing

Kim

Gewirtz

et al. 2016

Preprint

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Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of regulatory genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single or small sets of tissues. Here, we have reconstructed networks that capture a much more complete set of regulatory relationships, specifically including regulation of relative isoform abundance and splicing, and tissue-specific connections unique to each of a diverse set of tissues. Using the Genotype-Tissue Expression (GTEx) project v6 RNA-sequencing data across 44 tissues in 449 individuals, we evaluated shared and tissue-specific network relationships. First, we developed a framework called Transcriptome Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the complex interplay between the regulation of splicing and transcription. We built TWNs for sixteen tissues, and found that hubs with isoform node neighbors in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome, and providing a set of candidate shared and tissue-specific regulatory hub genes. Next, we used a Bayesian biclustering model that identifies network edges between genes with co-expression in a single tissue to reconstruct tissue-specific networks (TSNs) for 27 distinct GTEx tissues and for four 1 . CC-BY-NC-ND4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/078741 doi: bioRxiv preprint first posted online Oct. 2, 2016;subsets of related tissues. Using both TWNs and TSNs, we characterized gene co-expression patterns shared across tissues. Finally, we found genetic variants associated with multiple neighboring nodes in our networks, supporting the estimated network structures and identifying 33 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships between genes in the human transcriptome, including tissue-specificity of gene co-expression, regulation of splicing, and the coordinated impact of genetic variation on transcription.

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Section: Resultssupporting

confidence: 78%

Section: Resultsmentioning

confidence: 99%

Co-expression networks reveal the tissue-specific regulation of transcription and splicing

Kim

Gewirtz

et al. 2016

Preprint

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“…Cis-eQTLs (cis-acting expression quantitative trait loci) may in turn affect mRNA or protein levels of other unlinked genes via the regulatory network (i.e., the variants would also be trans-acting eQTLs for genes elsewhere in the genome), but might also affect other functions such as post-translational modification or subcellular localization. At present, detection of trans-QTLs is challenging in current sample sizes (Westra et al, 2013, Jo et al, 2016), but it is estimated that ~70% of mRNA heritability is determined by trans-acting factors (Price et al, 2011). Moreover, many trans-QTLs may act through protein networks and thus not be detectable from RNA, though current data on trans-acting controls of proteins are very limited (Battle et al, 2015, Chick et al, 2016, Sun et al, 2017).…”

Section: An Extended Model For Complex Traitsmentioning

confidence: 99%

An Expanded View of Complex Traits: From Polygenic to Omnigenic

Boyle

Pritchard

2017

Cell

2,589

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Summary A central goal of genetics is to understand the links between genetic variation and disease. Intuitively, one might expect disease-causing variants to cluster into key pathways that drive disease etiology. But for complex traits, association signals tend to be spread across most of the genome–including near many genes without an obvious connection to disease. We propose that gene regulatory networks are sufficiently interconnected that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways. We refer to this hypothesis as an “omnigenic” model.

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“…Despite the overall importance of trans effects, trans-eQTLs are notoriously difficult to find in humans [43,25,42,44]. This is partly due to the extra multiple testing burden on trans-eQTLs, but is mainly due to the small effect sizes of trans-eQTLs.…”

Section: Core Gene Effects On Heritabilitymentioning

confidence: 99%

Trans effects on gene expression can drive omnigenic inheritance

Liu

Pritchard

2018

Preprint

145

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Early genome-wide association studies (GWAS) led to the surprising discovery that, for typical complex traits, the most significant genetic variants contribute only a small fraction of the estimated heritability. Instead, it has become clear that a huge number of common variants, each with tiny effects, explain most of the heritability. Previously, we argued that these patterns conflict with standard conceptual models, and that new models are needed. Here we provide a formal model in which genetic contributions to complex traits can be partitioned into direct effects from core genes, and indirect effects from peripheral genes acting as trans-regulators. We argue that the central importance of peripheral genes is a direct consequence of the large contribution of trans-acting variation to gene expression variation. In particular, we propose that if the core genes for a trait are co-regulated -as seems likely -then the effects of peripheral variation can be amplified by these co-regulated networks such that nearly all of the genetic variance is driven by peripheral genes. Thus our model proposes a framework for understanding key features of the architecture of complex traits.Key observations. As reviewed in our previous paper [10], a conceptual model of complex traits should allow for the following observations:

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Distant regulatory effects of genetic variation in multiple human tissues

Cited by 12 publications

References 81 publications

Co-expression networks reveal the tissue-specific regulation of transcription and splicing

Co-expression networks reveal the tissue-specific regulation of transcription and splicing

An Expanded View of Complex Traits: From Polygenic to Omnigenic

Trans effects on gene expression can drive omnigenic inheritance

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