The statistical practice of the GTEx Project: from single to multiple tissues

Abstract

Background: The Genotype-Tissue Expression (GTEx) Project has collected genetic and transcriptome profiles from a wide spectrum of tissues in nearly 1,000 ceased individuals, providing an opportunity to study the regulatory roles of genetic variants in transcriptome activities from both cross-tissue and tissue-specific perspectives. Moreover, transcriptome activities (e.g., transcript abundance and alternative splicing) can be treated as mediators between genotype and phenotype to achieve phenotypic alteration. Knowing the genotype associated transcriptome status, researchers can better understand the biological and molecular mechanisms of genetic risk variants in complex traits.

Results: In this article, we first explore the genetic architecture of gene expression traits, and then review recent methods on quantitative trait locus (QTL) and co-expression network analysis. To further exemplify the usage of associations between genotype and transcriptome status, we briefly review methods that either directly or indirectly integrate expression/splicing QTL information in genome-wide association studies (GWASs).

Conclusions: The GTEx Project provides the largest and useful resource to investigate the associations between genotype and transcriptome status. The integration of results from the GTEx Project and existing GWASs further advances our understanding of roles of gene expression changes in bridging both the genetic variants and complex traits.

Author summary

In the genetic area, people have made extensive efforts to investigate the associations between genetic variants and disease traits. However, we are lacking the knowledge of underlying biological mechanisms through which the genetic factors could affect the phenotypic outcome. Genotype-Tissue Expression (GTEx) Project provided us several angles to think about this question, including quantitative trait locus, alternative spicing patterns, and tissue-specific effect of genetic variants, and so on. In this article, we are providing a comprehensive review of their methods and results, and also suggest several down-stream analysis methods (e.g., TWAS, co-expression network) by which we can go deeper into the regulatory mechanisms triggered by genetic factors.

Graphical abstract

Keywords

the Genotype-Tissue Expression Project / quantitative trait loci (QTL) / transcriptome-wide association studies / genome-wide association studies

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ACKNOWLEDGEMENTS

We would like to thank the two anonymous reviewers whose constructive comments have greatly improved this manuscript. This work was supported by grant R-913-200-098-263 from the Duke-NUS Medical School, and AcRF Tier 2 (MOE2016-T2-2-029, MOE2018T2-1-046 and MOE2018-T2-2-006) from the Ministry of Education, Singapore. The computational work for this article was partially performed using resources from the National Supercomputing Centre, Singapore (https://www.nscc.sg).

COMPLIANCE WITH ETHICS GUIDELINES

The authors Xu Liao, Xiaoran Chai, Xingjie Shi, Lin S. Chen and Jin Liu declare that they have no conflict of interests.

The article is a review article and does not contain any human or animal subjects performed by any of the authors.

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