scholarly journals Cell type–specific genetic regulation of gene expression across human tissues

Science ◽  
2020 ◽  
Vol 369 (6509) ◽  
pp. eaaz8528 ◽  
Author(s):  
Sarah Kim-Hellmuth ◽  
François Aguet ◽  
Meritxell Oliva ◽  
Manuel Muñoz-Aguirre ◽  
Silva Kasela ◽  
...  
Keyword(s):  
Complex Traits ◽  
Functional Characterization ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Human Tissues ◽  
Genetic Associations ◽  
Cell Type ◽  
Wide Range ◽  
Type Interaction ◽  
Bulk Tissue

The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci in cis (QTLs) for the majority of genes across a wide range of human tissues. However, the functional characterization of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. We mapped interactions between computational estimates of cell type abundance and genotype to identify cell type–interaction QTLs for seven cell types and show that cell type–interaction expression QTLs (eQTLs) provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations with 87 complex traits show a contribution from cell type–interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.

scholarly journals Cell type specific genetic regulation of gene expression across human tissues

2019 ◽  
Author(s):  
Sarah Kim-Hellmuth ◽  
François Aguet ◽  
Meritxell Oliva ◽  
Manuel Muñoz-Aguirre ◽  
Valentin Wucher ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Regulation Of Gene Expression ◽  
Human Tissues ◽  
Cell Type ◽  
Wide Range ◽  
Trait Loci ◽  
Type Interaction ◽  
Bulk Tissue

AbstractThe Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci (cis-QTLs) for the majority of genes across a wide range of human tissues. However, the interpretation of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. Here, we map interactions between computational estimates of cell type abundance and genotype to identify cell type interaction QTLs for seven cell types and show that cell type interaction eQTLs provide finer resolution to tissue specificity than bulk tissuecis-eQTLs. Analyses of genetic associations to 87 complex traits show a contribution from cell type interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.One Sentence SummaryEstimated cell type abundances from bulk RNA-seq across tissues reveal the cellular specificity of quantitative trait loci.

scholarly journals Dynamic genetic regulation of gene expression during cellular differentiation

Science ◽  
2019 ◽  
Vol 364 (6447) ◽  
pp. 1287-1290 ◽  
Author(s):  
B. J. Strober ◽  
R. Elorbany ◽  
K. Rhodes ◽  
N. Krishnan ◽  
K. Tayeb ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Specific Gene ◽  
Genetic Associations ◽  
Sequencing Data ◽  
Using Data ◽  
Induced Pluripotent

Genetic regulation of gene expression is dynamic, as transcription can change during cell differentiation and across cell types. We mapped expression quantitative trait loci (eQTLs) throughout differentiation to elucidate the dynamics of genetic effects on cell type–specific gene expression. We generated time-series RNA sequencing data, capturing 16 time points during the differentiation of induced pluripotent stem cells to cardiomyocytes, in 19 human cell lines. We identified hundreds of dynamic eQTLs that change over time, with enrichment in enhancers of relevant cell types. We also found nonlinear dynamic eQTLs, which affect only intermediate stages of differentiation and cannot be found by using data from mature tissues. These fleeting genetic associations with gene regulation may explain some of the components of complex traits and disease. We highlight one example of a nonlinear eQTL that is associated with body mass index.

scholarly journals On Using Local Ancestry to Characterize the Genetic Architecture of Human Phenotypes: Genetic Regulation of Gene Expression in Multiethnic or Admixed Populations as a Model

2018 ◽  
Author(s):  
Yizhen Zhong ◽  
Minoli Perera ◽  
Eric R. Gamazon
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Type I ◽  
Eqtl Mapping ◽  
Entire Genome ◽  
Local Ancestry ◽  
Heritability Estimation

AbstractBackgroundUnderstanding the nature of the genetic regulation of gene expression promises to advance our understanding of the genetic basis of disease. However, the methodological impact of use of local ancestry on high-dimensional omics analyses, including most prominently expression quantitative trait loci (eQTL) mapping and trait heritability estimation, in admixed populations remains critically underexplored.ResultsHere we develop a statistical framework that characterizes the relationships among the determinants of the genetic architecture of an important class of molecular traits. We estimate the trait variance explained by ancestry using local admixture relatedness between individuals. Using National Institute of General Medical Sciences (NIGMS) and Genotype-Tissue Expression (GTEx) datasets, we show that use of local ancestry can substantially improve eQTL mapping and heritability estimation and characterize the sparse versus polygenic component of gene expression in admixed and multiethnic populations respectively. Using simulations of diverse genetic architectures to estimate trait heritability and the level of confounding, we show improved accuracy given individual-level data and evaluate a summary statistics based approach. Furthermore, we provide a computationally efficient approach to local ancestry analysis in eQTL mapping while increasing control of type I and type II error over traditional approaches.ConclusionOur study has important methodological implications on genetic analysis of omics traits across a range of genomic contexts, from a single variant to a prioritized region to the entire genome. Our findings highlight the importance of using local ancestry to better characterize the heritability of complex traits and to more accurately map genetic associations.

scholarly journals The GTEx Consortium atlas of genetic regulatory effects across human tissues

Science ◽  
2020 ◽  
Vol 369 (6509) ◽  
pp. 1318-1330 ◽  
Author(s):  
Keyword(s):  
Complex Traits ◽  
Molecular Mechanisms ◽  
Tissue Expression ◽  
Genetic Effects ◽  
Regulatory Mechanisms ◽  
Human Tissues ◽  
Genetic Associations ◽  
Key Factor ◽  
Regulatory Effects ◽  
Almost All

The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

scholarly journals The GTEx Consortium atlas of genetic regulatory effects across human tissues

2019 ◽  
Author(s):  
François Aguet ◽  
Alvaro N Barbeira ◽  
Rodrigo Bonazzola ◽  
Andrew Brown ◽  
Stephane E Castel ◽  
...  
Keyword(s):  
Complex Traits ◽  
Molecular Mechanisms ◽  
Tissue Expression ◽  
Genetic Effects ◽  
Regulatory Mechanisms ◽  
Human Tissues ◽  
Genetic Associations ◽  
Key Factor ◽  
Regulatory Effects ◽  
Almost All

AbstractThe Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues, and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the v8 data, based on 17,382 RNA-sequencing samples from 54 tissues of 948 post-mortem donors. We comprehensively characterize genetic associations for gene expression and splicing incisandtrans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue-specificity of genetic effects, and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

scholarly journals Whole-organism eQTL mapping at cellular resolution with single-cell sequencing

2020 ◽  
Author(s):  
Eyal Ben-David ◽  
James Boocock ◽  
Longhua Guo ◽  
Stefan Zdraljevic ◽  
Joshua S Bloom ◽  
...  
Keyword(s):  
Single Cell ◽  
Complex Traits ◽  
Disease Risk ◽  
Single Cells ◽  
Large Population ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Cell Type ◽  
One Pot ◽  
Cellular Resolution

Genetic regulation of gene expression underlies variation in disease risk and other complex traits. The effect of expression quantitative trait loci (eQTLs) varies across cell types; however, the complexity of mammalian tissues makes studying cell-type eQTLs highly challenging. We developed a novel approach in the model nematode Caenorhabditis elegans that uses single cell RNA sequencing to map eQTLs at cellular resolution in a single one-pot experiment. We mapped eQTLs across cell types in an extremely large population of genetically distinct C. elegnas individuals. We found cell-type-specific trans-eQTL hotspots that affect the expression of core pathways in the relevant cell types. Finally, we found single-cell-specific eQTL effects in the nervous system, including an eQTL with opposite effects in two individual neurons. Our results show that eQTL effects can be specific down to the level of single cells.

scholarly journals Predicting cell-type-specific non-coding RNA transcription from genome sequence

2020 ◽  
Author(s):  
Masaru Koido ◽  
Chung-Chau Hon ◽  
Satoshi Koyama ◽  
Hideya Kawaji ◽  
Yasuhiro Murakawa ◽  
...  
Keyword(s):  
Complex Traits ◽  
Complex Trait ◽  
Cell Types ◽  
Dependent Manner ◽  
Genetic Associations ◽  
Cell Type ◽  
Non Coding Rna ◽  
Causal Variants ◽  
A Cell ◽  
Cell Type Specific

SUMMARYTranscription is regulated through complex mechanisms involving non-coding RNAs (ncRNAs). However, because transcription of ncRNAs, especially enhancer RNAs, is often low and cell type-specific, its dependency on genotype remains largely unexplored. Here, we developed mutation effect prediction on ncRNA transcription (MENTR), a quantitative machine learning framework reliably connecting genetic associations with expression of ncRNAs, resolved to the level of cell type. MENTR-predicted mutation effects on ncRNA transcription were concordant with estimates from previous genetic studies in a cell type-dependent manner. We inferred reliable causal variants from 41,223 GWAS variants, and proposed 7,775 enhancers and 3,548 long-ncRNAs as complex trait-associated ncRNAs in 348 major human primary cells and tissues, including plausible enhancer-mediated functional alterations in single-variant resolution in Crohn’s disease. In summary, we present new resources for discovering causal variants, the biological mechanisms driving complex traits, and the sequence-dependency of ncRNA regulation in relevant cell types.

scholarly journals Whole-organism eQTL mapping at cellular resolution with single-cell sequencing

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Eyal Ben-David ◽  
James Boocock ◽  
Longhua Guo ◽  
Stefan Zdraljevic ◽  
Joshua S Bloom ◽  
...  
Keyword(s):  
Single Cell ◽  
Complex Traits ◽  
Disease Risk ◽  
Single Cells ◽  
Large Population ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Cell Type ◽  
One Pot ◽  
Cellular Resolution

Genetic regulation of gene expression underlies variation in disease risk and other complex traits. The effect of expression quantitative trait loci (eQTLs) varies across cell types; however, the complexity of mammalian tissues makes studying cell-type eQTLs highly challenging. We developed a novel approach in the model nematodeCaenorhabditis elegansthat uses single-cell RNA sequencing to map eQTLs at cellular resolution in a single one-pot experiment. We mapped eQTLs across cell types in an extremely large population of genetically distinctC. elegansindividuals. We found cell-type-specifictranseQTL hotspots that affect the expression of core pathways in the relevant cell types. Finally, we found single-cell-specific eQTL effects in the nervous system, including an eQTL with opposite effects in two individual neurons. Our results show that eQTL effects can be specific down to the level of single cells.

scholarly journals MicroRNAs Instruct and Maintain Cell Type Diversity in the Nervous System

2021 ◽  
Vol 14 ◽  
Author(s):  
Norjin Zolboot ◽  
Jessica X. Du ◽  
Federico Zampa ◽  
Giordano Lippi
Keyword(s):  
Gene Expression ◽  
Nervous System ◽  
Expression Profiles ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Neural Progenitor ◽  
Cell Type ◽  
Mrna Targets ◽  
Distinct Cell ◽  
Wide Range

Characterizing the diverse cell types that make up the nervous system is essential for understanding how the nervous system is structured and ultimately how it functions. The astonishing range of cellular diversity found in the nervous system emerges from a small pool of neural progenitor cells. These progenitors and their neuronal progeny proceed through sequential gene expression programs to produce different cell lineages and acquire distinct cell fates. These gene expression programs must be tightly regulated in order for the cells to achieve and maintain the proper differentiated state, remain functional throughout life, and avoid cell death. Disruption of developmental programs is associated with a wide range of abnormalities in brain structure and function, further indicating that elucidating their contribution to cellular diversity will be key to understanding brain health. A growing body of evidence suggests that tight regulation of developmental genes requires post-transcriptional regulation of the transcriptome by microRNAs (miRNAs). miRNAs are small non-coding RNAs that function by binding to mRNA targets containing complementary sequences and repressing their translation into protein, thereby providing a layer of precise spatial and temporal control over gene expression. Moreover, the expression profiles and targets of miRNAs show great specificity for distinct cell types, brain regions and developmental stages, suggesting that they are an important parameter of cell type identity. Here, we provide an overview of miRNAs that are critically involved in establishing neural cell identities, focusing on how miRNA-mediated regulation of gene expression modulates neural progenitor expansion, cell fate determination, cell migration, neuronal and glial subtype specification, and finally cell maintenance and survival.

scholarly journals Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arnaud Liehrmann ◽  
Guillem Rigaill ◽  
Toby Dylan Hocking
Keyword(s):  
Histone Modifications ◽  
Count Data ◽  
High Throughput Sequencing ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Basic Mechanism ◽  
R Package ◽  
Detection Accuracy ◽  
Full Potential ◽  
Segmentation Models

Abstract Background Histone modification constitutes a basic mechanism for the genetic regulation of gene expression. In early 2000s, a powerful technique has emerged that couples chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq). This technique provides a direct survey of the DNA regions associated to these modifications. In order to realize the full potential of this technique, increasingly sophisticated statistical algorithms have been developed or adapted to analyze the massive amount of data it generates. Many of these algorithms were built around natural assumptions such as the Poisson distribution to model the noise in the count data. In this work we start from these natural assumptions and show that it is possible to improve upon them. Results Our comparisons on seven reference datasets of histone modifications (H3K36me3 & H3K4me3) suggest that natural assumptions are not always realistic under application conditions. We show that the unconstrained multiple changepoint detection model with alternative noise assumptions and supervised learning of the penalty parameter reduces the over-dispersion exhibited by count data. These models, implemented in the R package CROCS (https://github.com/aLiehrmann/CROCS), detect the peaks more accurately than algorithms which rely on natural assumptions. Conclusion The segmentation models we propose can benefit researchers in the field of epigenetics by providing new high-quality peak prediction tracks for H3K36me3 and H3K4me3 histone modifications.

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