Understanding Tissue-Specific Gene Regulation

Sonawane, Abhijeet R.; Platig, John; Fagny, Maud; Chen, Cho-Yi; Paulson, Joseph N.; Lopes‐Ramos, Camila M.; DeMeo, Dawn L.; Quackenbush, John; Glass, Kimberly; Kuijjer, Marieke L.

doi:10.1016/j.celrep.2017.10.001

Cited by 331 publications

(355 citation statements)

References 43 publications

(52 reference statements)

Supporting

Mentioning

326

Contrasting

Order By: Relevance

“…This gap may imply that many risk variants affect expression only in narrowly defined cell types or under precise conditions such as immune stimulation (Alasoo et al, 2017). When disease risk is mediated through multiple cell types, or highly specialized cell types, we anticipate that the cellular networks would vary across cell types (Price et al, 2011, Sonawane et al, 2017). The quantitative effect of any given variant would then be an average of its effect size in each cell type, weighted by cell type importance.…”

Section: An Extended Model For Complex Traitsmentioning

confidence: 99%

An Expanded View of Complex Traits: From Polygenic to Omnigenic

Boyle

Pritchard

2017

Cell

2,508

149

2,374

View full text Add to dashboard Cite

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.

show abstract

Section: An Extended Model For Complex Traitsmentioning

confidence: 99%

An Expanded View of Complex Traits: From Polygenic to Omnigenic

Boyle

Pritchard

2017

Cell

2,508

149

2,374

View full text Add to dashboard Cite

show abstract

“…Human tissues and organs are distinguished by the genes that they express and those that they do not 1,2 . Tissues have transcriptomes of different complexities in terms of uniquely-expressed genes, as well as those genes expressed at differential levels [3][4][5][6] .…”

Section: Main Textmentioning

confidence: 99%

Widespread transcriptional scanning in the testis modulates gene evolution rates

Xia

Yan

et al. 2018

Preprint

View full text Add to dashboard Cite

Abstract:A long-standing question in molecular biology relates to why the testes express the largest number of genes relative to all other organs. Here, we report a detailed gene expression map of human spermatogenesis using single-cell RNA-Seq. Surprisingly, we found that 20 spermatogenesis-expressed genes contain significantly fewer germline mutations than unexpressed genes, with the lowest mutation rates on the transcribed DNA strands. These results suggest a model of 'transcriptional scanning' to reduce germline mutations by correcting DNA damage. This model also explains the rapid evolution in sensory-and immune-defense related genes, as well as in male reproduction genes. Collectively, our results indicate that widespread 25 expression in the testes achieves a dual mechanism for maintaining the DNA integrity of most genes, while selectively promoting variation of other genes.. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/282129 doi: bioRxiv preprint first posted online Mar. 14, 2018; 2 Main Text:Human tissues and organs are distinguished by the genes that they express and those that they do not 1,2 . Tissues have transcriptomes of different complexities in terms of uniquely-expressed genes, as well as those genes expressed at differential levels [3][4][5][6] . One overarching goal in the life sciences is to characterize the specific transcriptomic signatures of all human tissues, and 5 ultimately each different cell type at the single-cell level 7 .In males, the testis is unique in comparison with somatic tissues in that it contains germ cells which pass the genetic information on to the next generation 8 . Interestingly, it has been known for many years that the testis stands out as having the most complex transcriptome with the highest number of expressed genes [9][10][11][12] . Widespread transcription in the testes has been 10 reported to account for an amazing expression of over 80% of all our protein-coding genes 10,11,13 , as well as across many other mammals 3,10 .Several hypotheses have been proposed to explain this observation. Widespread expression may represent a functional requirement for the gene-products in question 12 .However, other more complex organs such as the brain do not exhibit a corresponding number of 15 expressed genes despite the fact that they consist of a substantially greater number of distinct cell types 3,10,14-16 . Moreover, recent animal studies have shown that many testis-enriched and evolutionarily-conserved genes are not required for male fertility in mice 17 . A second hypothesis implicates leaky transcription during the massive chromatin remodeling that occurs throughout spermatogenesis 12,18,19 . However, this model predicts more expression during later stages of 20 spermatogenesis -when the genome is undergoing the most chromatin changes -contradicting ...

show abstract

“…We obtained tissue-specific gene co-expression networks inferred based on bulk RNAseq data collected on 38 tissues in the GTEx project [38] from https://zenodo.org/record/838734#.XALkry3MxTZ. Details on how these tissue-specific gene coexpression networks were constructed are described in [39]. Briefly, these networks were constructed using the software PANDA (Passing Attributes between Networks for Data Assimilation) [40], relying on information extracted from both gene expression measurements and an existing database that contains both known transcription factor (TF)-target gene interactions and known protein-protein interactions.…”

Section: Tissue Specific Gene Co-expression Network From Gtexmentioning

confidence: 99%

Leveraging Gene Co-expression Patterns to Infer Trait-Relevant Tissues in Genome-wide Association Studies

Shang

Smith

Zhou

2019

Preprint

View full text Add to dashboard Cite

AbstractGenome-wide association studies (GWASs) have identified many SNPs associated with various common diseases. Understanding the biological functions of these identified SNP associations requires identifying disease/trait relevant tissues or cell types. Here, we develop a network method, CoCoNet, to facilitate the identification of trait-relevant tissues or cell types. Different from existing approaches, CoCoNet incorporates tissue-specific gene co-expression networks constructed from either bulk or single cell RNA sequencing (RNAseq) studies with GWAS data for trait-tissue inference. In particular, CoCoNet relies on a covariance regression network model to express gene-level effect sizes for the given GWAS trait as a function of the tissue-specific co-expression adjacency matrix. With a composite likelihood-based inference algorithm, CoCoNet is scalable to tens of thousands of genes. We validate the performance of CoCoNet through extensive simulations. We apply CoCoNet for an in-depth analysis of four neurological disorders and four autoimmune diseases, where we integrate the corresponding GWASs with bulk RNAseq data from 38 tissues and single cell RNAseq data from 10 cell types. In the real data applications, we show how CoCoNet can help identify specific glial cell types relevant for neurological disorders and identify disease-targeted colon tissues as relevant for autoimmune diseases. Our results also provide empirical evidence supporting one hypothesis of the omnigenic model: that trait-relevant gene co-expression networks underlie disease etiology.

show abstract

Understanding Tissue-Specific Gene Regulation

Cited by 331 publications

References 43 publications

An Expanded View of Complex Traits: From Polygenic to Omnigenic

An Expanded View of Complex Traits: From Polygenic to Omnigenic

Widespread transcriptional scanning in the testis modulates gene evolution rates

Leveraging Gene Co-expression Patterns to Infer Trait-Relevant Tissues in Genome-wide Association Studies

Contact Info

Product

Resources

About