Transcriptome-wide association studies: recent advances in methods, applications and available databases

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Abstract

Genome-wide association study has identified fruitful variants impacting heritable traits. Nevertheless, identifying critical genes underlying those significant variants has been a great task. Transcriptome-wide association study (TWAS) is an instrumental post-analysis to detect significant gene-trait associations focusing on modeling transcription-level regulations, which has made numerous progresses in recent years. Leveraging from expression quantitative loci (eQTL) regulation information, TWAS has advantages in detecting functioning genes regulated by disease-associated variants, thus providing insight into mechanisms of diseases and other phenotypes. Considering its vast potential, this review article comprehensively summarizes TWAS, including the methodology, applications and available resources.

Abstract

This review provides a comprehensive summary of transcriptome-wide association study (TWAS) methods, applications and available resources.

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MAGMA: Generalized Gene-Set Analysis of GWAS Data

Christiaan de Leeuw, Joris Mooij, Tom Heskes … (2015)

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn’s Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn’s Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn’s Disease data was found to be considerably faster as well.

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Integrative approaches for large-scale transcriptome-wide association studies.

Alexander Gusev, Arthur Ko, Huwenbo Shi … (2016)

Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance of one or multiple proteins. Here we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated with complex traits. We leverage expression imputation from genetic data to perform a transcriptome-wide association study (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in ∼ 3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 new genes significantly associated with obesity-related traits (BMI, lipids and height). Many of these genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.

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Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets.

Zhihong Zhu, Futao Zhang, Wen-Han Hu … (2016)

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human complex traits. However, the genes or functional DNA elements through which these variants exert their effects on the traits are often unknown. We propose a method (called SMR) that integrates summary-level data from GWAS with data from expression quantitative trait locus (eQTL) studies to identify genes whose expression levels are associated with a complex trait because of pleiotropy. We apply the method to five human complex traits using GWAS data on up to 339,224 individuals and eQTL data on 5,311 individuals, and we prioritize 126 genes (for example, TRAF1 and ANKRD55 for rheumatoid arthritis and SNX19 and NMRAL1 for schizophrenia), of which 25 genes are new candidates; 77 genes are not the nearest annotated gene to the top associated GWAS SNP. These genes provide important leads to design future functional studies to understand the mechanism whereby DNA variation leads to complex trait variation.

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Author and article information

Contributors

Jingyao Zeng:

ORCID: http://orcid.org/0000-0001-7364-9677

zengjy@big.ac.cn

Jingfa Xiao:

ORCID: http://orcid.org/0000-0002-2835-4340

xiaojingfa@big.ac.cn

Journal

Journal ID (nlm-ta): Commun Biol

Journal ID (iso-abbrev): Commun Biol

Title: Communications Biology

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2399-3642

Publication date (Electronic): 1 September 2023

Publication date PMC-release: 1 September 2023

Publication date Collection: 2023

Volume: 6

Electronic Location Identifier: 899

Affiliations

[1 ]GRID grid.9227.e, ISNI 0000000119573309, National Genomics Data Center, Beijing Institute of Genomics, , Chinese Academy of Sciences and China National Center for Bioinformation, ; Beijing, 100101 China

[2 ]GRID grid.9227.e, ISNI 0000000119573309, CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, , Chinese Academy of Sciences and China National Center for Bioinformation, ; Beijing, 100101 China

[3 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, University of Chinese Academy of Sciences, ; Beijing, 100049 China

Author information

Jingyao Zeng http://orcid.org/0000-0001-7364-9677

Jingfa Xiao http://orcid.org/0000-0002-2835-4340

Article

Publisher ID: 5279

DOI: 10.1038/s42003-023-05279-y

PMC ID: 10474133

PubMed ID: 37658226

SO-VID: 309093eb-0943-43b2-b3cc-db40357747f9

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 21 March 2023

Date accepted : 24 August 2023

Funding

Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);

Award ID: 31970634, 32170669

Award Recipient : Jingfa Xiao

Funded by: FundRef https://doi.org/10.13039/501100004739, Youth Innovation Promotion Association of the Chinese Academy of Sciences (Youth Innovation Promotion Association CAS);

Award ID: 2022098

Award Recipient : Jingfa Xiao

Funded by: Strategic Priority Research Program of the Chinese Academy of Sciences (XDB38030400); National Key Research Program of China (2020YFA0907001)

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Keywords: statistical methods,genetics research

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Keywords: statistical methods, genetics research

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Transcriptome-wide association studies: recent advances in methods, applications and available databases

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Most cited references 54

MAGMA: Generalized Gene-Set Analysis of GWAS Data

Integrative approaches for large-scale transcriptome-wide association studies.

Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets.

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