2017
Li, Xin; Kim, Yungil; Tsang, Emily K; Davis, Joe R; Damani, Farhan N; Chiang, Colby; Hess, Gaelen T; Zappala, Zachary; Strober, Benjamin J; Scott, Alexandra J; Li, Amy; Ganna, Andrea; Bassik, Michael C; Merker, Jason D; GTEx Consortium, incl. Michael Sammeth
The impact of rare variation on gene expression across tissues Journal Article
In: Nature, vol. 550, no. 7675, pp. 239–243, 2017, ISSN: 1476-4687.
@article{pmid29022581,
title = {The impact of rare variation on gene expression across tissues},
author = {Xin Li and Yungil Kim and Emily K Tsang and Joe R Davis and Farhan N Damani and Colby Chiang and Gaelen T Hess and Zachary Zappala and Benjamin J Strober and Alexandra J Scott and Amy Li and Andrea Ganna and Michael C Bassik and Jason D Merker and {GTEx Consortium}, {incl. Michael Sammeth}},
doi = {10.1038/nature24267},
issn = {1476-4687},
year = {2017},
date = {2017-10-01},
urldate = {2017-10-01},
journal = {Nature},
volume = {550},
number = {7675},
pages = {239--243},
abstract = {Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.
2016
Schwartze, Volker U; Winter, Sascha; Shelest, Ekaterina; Marcet-Houben, Marina; Horn, Fabian; Wehner, Stefanie; Linde, Jörg; Valiante, Vito; Sammeth, Michael; Riege, Konstantin; Nowrousian, Minou; Kaerger, Kerstin; Jacobsen, Ilse D; Marz, Manja; Brakhage, Axel A; Gabaldón, Toni; Böcker, Sebastian; Voigt, Kerstin
2016, ISSN: 1553-7404.
@misc{pmid27918580,
title = {Correction: Gene Expansion Shapes Genome Architecture in the Human Pathogen Lichtheimia corymbifera: An Evolutionary Genomics Analysis in the Ancient Terrestrial Mucorales (Mucoromycotina)},
author = {Volker U Schwartze and Sascha Winter and Ekaterina Shelest and Marina Marcet-Houben and Fabian Horn and Stefanie Wehner and Jörg Linde and Vito Valiante and Michael Sammeth and Konstantin Riege and Minou Nowrousian and Kerstin Kaerger and Ilse D Jacobsen and Manja Marz and Axel A Brakhage and Toni Gabaldón and Sebastian Böcker and Kerstin Voigt},
doi = {10.1371/journal.pgen.1006491},
issn = {1553-7404},
year = {2016},
date = {2016-12-01},
journal = {PLoS Genet},
volume = {12},
number = {12},
pages = {e1006491},
abstract = {[This corrects the article DOI: 10.1371/journal.pgen.1004496.].},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
[This corrects the article DOI: 10.1371/journal.pgen.1004496.].
Ferreira, Pedro G; Oti, Martin; Barann, Matthias; Wieland, Thomas; Ezquina, Suzana; Friedländer, Marc R; Rivas, Manuel A; Esteve-Codina, Anna; ; Rosenstiel, Philip; Strom, Tim M; Lappalainen, Tuuli; Guigó, Roderic; Sammeth, Michael
Sequence variation between 462 human individuals fine-tunes functional sites of RNA processing Journal Article
In: Sci Rep, vol. 6, pp. 32406, 2016, ISSN: 2045-2322.
@article{pmid27617755,
title = {Sequence variation between 462 human individuals fine-tunes functional sites of RNA processing},
author = {Pedro G Ferreira and Martin Oti and Matthias Barann and Thomas Wieland and Suzana Ezquina and Marc R Friedländer and Manuel A Rivas and Anna Esteve-Codina and and Philip Rosenstiel and Tim M Strom and Tuuli Lappalainen and Roderic Guigó and Michael Sammeth},
doi = {10.1038/srep32406},
issn = {2045-2322},
year = {2016},
date = {2016-09-01},
journal = {Sci Rep},
volume = {6},
pages = {32406},
abstract = {Recent advances in the cost-efficiency of sequencing technologies enabled the combined DNA- and RNA-sequencing of human individuals at the population-scale, making genome-wide investigations of the inter-individual genetic impact on gene expression viable. Employing mRNA-sequencing data from the Geuvadis Project and genome sequencing data from the 1000 Genomes Project we show that the computational analysis of DNA sequences around splice sites and poly-A signals is able to explain several observations in the phenotype data. In contrast to widespread assessments of statistically significant associations between DNA polymorphisms and quantitative traits, we developed a computational tool to pinpoint the molecular mechanisms by which genetic markers drive variation in RNA-processing, cataloguing and classifying alleles that change the affinity of core RNA elements to their recognizing factors. The in silico models we employ further suggest RNA editing can moonlight as a splicing-modulator, albeit less frequently than genomic sequence diversity. Beyond existing annotations, we demonstrate that the ultra-high resolution of RNA-Seq combined from 462 individuals also provides evidence for thousands of bona fide novel elements of RNA processing-alternative splice sites, introns, and cleavage sites-which are often rare and lowly expressed but in other characteristics similar to their annotated counterparts.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recent advances in the cost-efficiency of sequencing technologies enabled the combined DNA- and RNA-sequencing of human individuals at the population-scale, making genome-wide investigations of the inter-individual genetic impact on gene expression viable. Employing mRNA-sequencing data from the Geuvadis Project and genome sequencing data from the 1000 Genomes Project we show that the computational analysis of DNA sequences around splice sites and poly-A signals is able to explain several observations in the phenotype data. In contrast to widespread assessments of statistically significant associations between DNA polymorphisms and quantitative traits, we developed a computational tool to pinpoint the molecular mechanisms by which genetic markers drive variation in RNA-processing, cataloguing and classifying alleles that change the affinity of core RNA elements to their recognizing factors. The in silico models we employ further suggest RNA editing can moonlight as a splicing-modulator, albeit less frequently than genomic sequence diversity. Beyond existing annotations, we demonstrate that the ultra-high resolution of RNA-Seq combined from 462 individuals also provides evidence for thousands of bona fide novel elements of RNA processing-alternative splice sites, introns, and cleavage sites-which are often rare and lowly expressed but in other characteristics similar to their annotated counterparts.
Yang, Jialiang; Huang, Tao; Petralia, Francesca; Long, Quan; Zhang, Bin; Argmann, Carmen; Zhao, Yong; Mobbs, Charles V; Schadt, Eric E; Zhu, Jun; Tu, Zhidong; GTEx Consortium, incl. Michael Sammeth
2016, ISSN: 2045-2322.
@misc{pmid26795431,
title = {Corrigendum: Synchronized age-related gene expression changes across multiple tissues in human and the link to complex diseases},
author = {Jialiang Yang and Tao Huang and Francesca Petralia and Quan Long and Bin Zhang and Carmen Argmann and Yong Zhao and Charles V Mobbs and Eric E Schadt and Jun Zhu and Zhidong Tu and {GTEx Consortium}, {incl. Michael Sammeth}},
doi = {10.1038/srep19384},
issn = {2045-2322},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {Sci Rep},
volume = {6},
pages = {19384},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
2015
Yang, Jialiang; Huang, Tao; Petralia, Francesca; Long, Quan; Zhang, Bin; Argmann, Carmen; Zhao, Yong; Mobbs, Charles V; Schadt, Eric E; Zhu, Jun; Tu, Zhidong; GTEx Consortium, incl. Michael Sammeth
Synchronized age-related gene expression changes across multiple tissues in human and the link to complex diseases Journal Article
In: Sci Rep, vol. 5, pp. 15145, 2015, ISSN: 2045-2322.
@article{pmid26477495,
title = {Synchronized age-related gene expression changes across multiple tissues in human and the link to complex diseases},
author = {Jialiang Yang and Tao Huang and Francesca Petralia and Quan Long and Bin Zhang and Carmen Argmann and Yong Zhao and Charles V Mobbs and Eric E Schadt and Jun Zhu and Zhidong Tu and {GTEx Consortium}, {incl. Michael Sammeth}},
doi = {10.1038/srep15145},
issn = {2045-2322},
year = {2015},
date = {2015-10-01},
urldate = {2015-10-01},
journal = {Sci Rep},
volume = {5},
pages = {15145},
abstract = {Aging is one of the most important biological processes and is a known risk factor for many age-related diseases in human. Studying age-related transcriptomic changes in tissues across the whole body can provide valuable information for a holistic understanding of this fundamental process. In this work, we catalogue age-related gene expression changes in nine tissues from nearly two hundred individuals collected by the Genotype-Tissue Expression (GTEx) project. In general, we find the aging gene expression signatures are very tissue specific. However, enrichment for some well-known aging components such as mitochondria biology is observed in many tissues. Different levels of cross-tissue synchronization of age-related gene expression changes are observed, and some essential tissues (e.g., heart and lung) show much stronger "co-aging" than other tissues based on a principal component analysis. The aging gene signatures and complex disease genes show a complex overlapping pattern and only in some cases, we see that they are significantly overlapped in the tissues affected by the corresponding diseases. In summary, our analyses provide novel insights to the co-regulation of age-related gene expression in multiple tissues; it also presents a tissue-specific view of the link between aging and age-related diseases.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Aging is one of the most important biological processes and is a known risk factor for many age-related diseases in human. Studying age-related transcriptomic changes in tissues across the whole body can provide valuable information for a holistic understanding of this fundamental process. In this work, we catalogue age-related gene expression changes in nine tissues from nearly two hundred individuals collected by the Genotype-Tissue Expression (GTEx) project. In general, we find the aging gene expression signatures are very tissue specific. However, enrichment for some well-known aging components such as mitochondria biology is observed in many tissues. Different levels of cross-tissue synchronization of age-related gene expression changes are observed, and some essential tissues (e.g., heart and lung) show much stronger "co-aging" than other tissues based on a principal component analysis. The aging gene signatures and complex disease genes show a complex overlapping pattern and only in some cases, we see that they are significantly overlapped in the tissues affected by the corresponding diseases. In summary, our analyses provide novel insights to the co-regulation of age-related gene expression in multiple tissues; it also presents a tissue-specific view of the link between aging and age-related diseases.
Carithers, Latarsha J; Ardlie, Kristin; Barcus, Mary; Branton, Philip A; Britton, Angela; Buia, Stephen A; Compton, Carolyn C; DeLuca, David S; Peter-Demchok, Joanne; Gelfand, Ellen T; Guan, Ping; Korzeniewski, Greg E; Lockhart, Nicole C; Rabiner, Chana A; Rao, Abhi K; Robinson, Karna L; Roche, Nancy V; Sawyer, Sherilyn J; Segrè, Ayellet V; Shive, Charles E; Smith, Anna M; Sobin, Leslie H; Undale, Anita H; Valentino, Kimberly M; Vaught, Jim; Young, Taylor R; Moore, Helen M; GTEx Consortium, incl. Michael Sammeth
A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project Journal Article
In: Biopreserv Biobank, vol. 13, no. 5, pp. 311–319, 2015, ISSN: 1947-5543.
@article{pmid26484571,
title = {A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project},
author = {Latarsha J Carithers and Kristin Ardlie and Mary Barcus and Philip A Branton and Angela Britton and Stephen A Buia and Carolyn C Compton and David S DeLuca and Joanne Peter-Demchok and Ellen T Gelfand and Ping Guan and Greg E Korzeniewski and Nicole C Lockhart and Chana A Rabiner and Abhi K Rao and Karna L Robinson and Nancy V Roche and Sherilyn J Sawyer and Ayellet V Segrè and Charles E Shive and Anna M Smith and Leslie H Sobin and Anita H Undale and Kimberly M Valentino and Jim Vaught and Taylor R Young and Helen M Moore and {GTEx Consortium}, {incl. Michael Sammeth}},
doi = {10.1089/bio.2015.0032},
issn = {1947-5543},
year = {2015},
date = {2015-10-01},
urldate = {2015-10-01},
journal = {Biopreserv Biobank},
volume = {13},
number = {5},
pages = {311--319},
abstract = {The Genotype-Tissue Expression (GTEx) project, sponsored by the NIH Common Fund, was established to study the correlation between human genetic variation and tissue-specific gene expression in non-diseased individuals. A significant challenge was the collection of high-quality biospecimens for extensive genomic analyses. Here we describe how a successful infrastructure for biospecimen procurement was developed and implemented by multiple research partners to support the prospective collection, annotation, and distribution of blood, tissues, and cell lines for the GTEx project. Other research projects can follow this model and form beneficial partnerships with rapid autopsy and organ procurement organizations to collect high quality biospecimens and associated clinical data for genomic studies. Biospecimens, clinical and genomic data, and Standard Operating Procedures guiding biospecimen collection for the GTEx project are available to the research community.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The Genotype-Tissue Expression (GTEx) project, sponsored by the NIH Common Fund, was established to study the correlation between human genetic variation and tissue-specific gene expression in non-diseased individuals. A significant challenge was the collection of high-quality biospecimens for extensive genomic analyses. Here we describe how a successful infrastructure for biospecimen procurement was developed and implemented by multiple research partners to support the prospective collection, annotation, and distribution of blood, tissues, and cell lines for the GTEx project. Other research projects can follow this model and form beneficial partnerships with rapid autopsy and organ procurement organizations to collect high quality biospecimens and associated clinical data for genomic studies. Biospecimens, clinical and genomic data, and Standard Operating Procedures guiding biospecimen collection for the GTEx project are available to the research community.
Melé, Marta; Ferreira, Pedro G; Reverter, Ferran; DeLuca, David S; Monlong, Jean; Sammeth, Michael; Young, Taylor R; Goldmann, Jakob M; Pervouchine, Dmitri D; Sullivan, Timothy J; Johnson, Rory; Segrè, Ayellet V; Djebali, Sarah; Niarchou, Anastasia; GTEx Consortium,; Wright, Fred A; Lappalainen, Tuuli; Calvo, Miquel; Getz, Gad; Dermitzakis, Emmanouil T; Ardlie, Kristin G; Guigó, Roderic
Human genomics. The human transcriptome across tissues and individuals Journal Article
In: Science, vol. 348, no. 6235, pp. 660–665, 2015, ISSN: 1095-9203.
@article{pmid25954002,
title = {Human genomics. The human transcriptome across tissues and individuals},
author = {Marta Melé and Pedro G Ferreira and Ferran Reverter and David S DeLuca and Jean Monlong and Michael Sammeth and Taylor R Young and Jakob M Goldmann and Dmitri D Pervouchine and Timothy J Sullivan and Rory Johnson and Ayellet V Segrè and Sarah Djebali and Anastasia Niarchou and {GTEx Consortium}, and Fred A Wright and Tuuli Lappalainen and Miquel Calvo and Gad Getz and Emmanouil T Dermitzakis and Kristin G Ardlie and Roderic Guigó},
doi = {10.1126/science.aaa0355},
issn = {1095-9203},
year = {2015},
date = {2015-05-01},
urldate = {2015-05-01},
journal = {Science},
volume = {348},
number = {6235},
pages = {660--665},
abstract = {Transcriptional regulation and posttranscriptional processing underlie many cellular and organismal phenotypes. We used RNA sequence data generated by Genotype-Tissue Expression (GTEx) project to investigate the patterns of transcriptome variation across individuals and tissues. Tissues exhibit characteristic transcriptional signatures that show stability in postmortem samples. These signatures are dominated by a relatively small number of genes—which is most clearly seen in blood—though few are exclusive to a particular tissue and vary more across tissues than individuals. Genes exhibiting high interindividual expression variation include disease candidates associated with sex, ethnicity, and age. Primary transcription is the major driver of cellular specificity, with splicing playing mostly a complementary role; except for the brain, which exhibits a more divergent splicing program. Variation in splicing, despite its stochasticity, may play in contrast a comparatively greater role in defining individual phenotypes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Transcriptional regulation and posttranscriptional processing underlie many cellular and organismal phenotypes. We used RNA sequence data generated by Genotype-Tissue Expression (GTEx) project to investigate the patterns of transcriptome variation across individuals and tissues. Tissues exhibit characteristic transcriptional signatures that show stability in postmortem samples. These signatures are dominated by a relatively small number of genes—which is most clearly seen in blood—though few are exclusive to a particular tissue and vary more across tissues than individuals. Genes exhibiting high interindividual expression variation include disease candidates associated with sex, ethnicity, and age. Primary transcription is the major driver of cellular specificity, with splicing playing mostly a complementary role; except for the brain, which exhibits a more divergent splicing program. Variation in splicing, despite its stochasticity, may play in contrast a comparatively greater role in defining individual phenotypes.
Rivas, Manuel A; Pirinen, Matti; Conrad, Donald F; Lek, Monkol; Tsang, Emily K; Karczewski, Konrad J; Maller, Julian B; Kukurba, Kimberly R; DeLuca, David S; Fromer, Menachem; Ferreira, Pedro G; Smith, Kevin S; Zhang, Rui; Zhao, Fengmei; Banks, Eric; Poplin, Ryan; Ruderfer, Douglas M; Purcell, Shaun M; Tukiainen, Taru; Minikel, Eric V; Stenson, Peter D; Cooper, David N; Huang, Katharine H; Sullivan, Timothy J; Nedzel, Jared; GTEx Consortium,; Bustamante, Carlos D; Li, Jin Billy; Daly, Mark J; Guigo, Roderic; Donnelly, Peter; Ardlie, Kristin; Sammeth, Michael; Dermitzakis, Emmanouil T; McCarthy, Mark I; Montgomery, Stephen B; Lappalainen, Tuuli; MacArthur, Daniel G
Human genomics. Effect of predicted protein-truncating genetic variants on the human transcriptome Journal Article
In: Science, vol. 348, no. 6235, pp. 666–669, 2015, ISSN: 1095-9203.
@article{pmid25954003,
title = {Human genomics. Effect of predicted protein-truncating genetic variants on the human transcriptome},
author = {Manuel A Rivas and Matti Pirinen and Donald F Conrad and Monkol Lek and Emily K Tsang and Konrad J Karczewski and Julian B Maller and Kimberly R Kukurba and David S DeLuca and Menachem Fromer and Pedro G Ferreira and Kevin S Smith and Rui Zhang and Fengmei Zhao and Eric Banks and Ryan Poplin and Douglas M Ruderfer and Shaun M Purcell and Taru Tukiainen and Eric V Minikel and Peter D Stenson and David N Cooper and Katharine H Huang and Timothy J Sullivan and Jared Nedzel and {GTEx Consortium}, and Carlos D Bustamante and Jin Billy Li and Mark J Daly and Roderic Guigo and Peter Donnelly and Kristin Ardlie and Michael Sammeth and Emmanouil T Dermitzakis and Mark I McCarthy and Stephen B Montgomery and Tuuli Lappalainen and Daniel G MacArthur},
doi = {10.1126/science.1261877},
issn = {1095-9203},
year = {2015},
date = {2015-05-01},
urldate = {2015-05-01},
journal = {Science},
volume = {348},
number = {6235},
pages = {666--669},
abstract = {Accurate prediction of the functional effect of genetic variation is critical for clinical genome interpretation. We systematically characterized the transcriptome effects of protein-truncating variants, a class of variants expected to have profound effects on gene function, using data from the Genotype-Tissue Expression (GTEx) and Geuvadis projects. We quantitated tissue-specific and positional effects on nonsense-mediated transcript decay and present an improved predictive model for this decay. We directly measured the effect of variants both proximal and distal to splice junctions. Furthermore, we found that robustness to heterozygous gene inactivation is not due to dosage compensation. Our results illustrate the value of transcriptome data in the functional interpretation of genetic variants.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Accurate prediction of the functional effect of genetic variation is critical for clinical genome interpretation. We systematically characterized the transcriptome effects of protein-truncating variants, a class of variants expected to have profound effects on gene function, using data from the Genotype-Tissue Expression (GTEx) and Geuvadis projects. We quantitated tissue-specific and positional effects on nonsense-mediated transcript decay and present an improved predictive model for this decay. We directly measured the effect of variants both proximal and distal to splice junctions. Furthermore, we found that robustness to heterozygous gene inactivation is not due to dosage compensation. Our results illustrate the value of transcriptome data in the functional interpretation of genetic variants.
GTEx Consortium, incl. Michael Sammeth
Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans Journal Article
In: Science, vol. 348, no. 6235, pp. 648–660, 2015, ISSN: 1095-9203.
@article{pmid25954001,
title = {Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans},
author = {{GTEx Consortium}, {incl. Michael Sammeth}},
doi = {10.1126/science.1262110},
issn = {1095-9203},
year = {2015},
date = {2015-05-01},
urldate = {2015-05-01},
journal = {Science},
volume = {348},
number = {6235},
pages = {648--660},
abstract = {Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues.
Pierson, Emma; GTEx Consortium, incl. Michael Sammeth; Koller, Daphne; Battle, Alexis; Mostafavi, Sara; Ardlie, Kristin G; Getz, Gad; Wright, Fred A; Kellis, Manolis; Volpi, Simona; Dermitzakis, Emmanouil T
Sharing and Specificity of Co-expression Networks across 35 Human Tissues Journal Article
In: PLoS Comput Biol, vol. 11, no. 5, pp. e1004220, 2015, ISSN: 1553-7358.
@article{pmid25970446,
title = {Sharing and Specificity of Co-expression Networks across 35 Human Tissues},
author = {Emma Pierson and {GTEx Consortium}, {incl. Michael Sammeth} and Daphne Koller and Alexis Battle and Sara Mostafavi and Kristin G Ardlie and Gad Getz and Fred A Wright and Manolis Kellis and Simona Volpi and Emmanouil T Dermitzakis},
doi = {10.1371/journal.pcbi.1004220},
issn = {1553-7358},
year = {2015},
date = {2015-05-01},
urldate = {2015-05-01},
journal = {PLoS Comput Biol},
volume = {11},
number = {5},
pages = {e1004220},
abstract = {To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity for deriving shared and tissue specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved across tissues are especially likely to have functions common to all tissues, while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function. Finally, we provide a web tool, available at mostafavilab.stat.ubc.ca/GNAT, which allows exploration of gene function and regulation in a tissue-specific manner.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity for deriving shared and tissue specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved across tissues are especially likely to have functions common to all tissues, while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function. Finally, we provide a web tool, available at mostafavilab.stat.ubc.ca/GNAT, which allows exploration of gene function and regulation in a tissue-specific manner.