On the Interpretation of Transcriptome-wide Association Studies

Overview

Journal PLoS Genet

Specialty Genetics

Date 2023 Sep 7

PMID 37676898

Authors

Christiaan de Leeuw

Josefin Werme

Jeanne E Savage

Wouter J Peyrot

Danielle Posthuma

Affiliations

Soon will be listed here.

Abstract

Transcriptome-wide association studies (TWAS) aim to detect relationships between gene expression and a phenotype, and are commonly used for secondary analysis of genome-wide association study (GWAS) results. Results from TWAS analyses are often interpreted as indicating a genetic relationship between gene expression and a phenotype, but this interpretation is not consistent with the null hypothesis that is evaluated in the traditional TWAS framework. In this study we provide a mathematical outline of this TWAS framework, and elucidate what interpretations are warranted given the null hypothesis it actually tests. We then use both simulations and real data analysis to assess the implications of misinterpreting TWAS results as indicative of a genetic relationship between gene expression and the phenotype. Our simulation results show considerably inflated type 1 error rates for TWAS when interpreted this way, with 41% of significant TWAS associations detected in the real data analysis found to have insufficient statistical evidence to infer such a relationship. This demonstrates that in current implementations, TWAS cannot reliably be used to investigate genetic relationships between gene expression and a phenotype, but that local genetic correlation analysis can serve as a potential alternative.

Citing Articles

Predicting the genetic component of gene expression using gene regulatory networks.

Mohammad G, Michoel T Bioinform Adv. 2024; 4(1):vbae180.

PMID: 39717201 PMC: 11665636. DOI: 10.1093/bioadv/vbae180.

Genetic analysis of psychosis Biotypes: shared Ancestry-adjusted polygenic risk and unique genomic associations.

Xia C, Alliey-Rodriguez N, Tamminga C, Keshavan M, Pearlson G, Keedy S Mol Psychiatry. 2024; .

PMID: 39709506 DOI: 10.1038/s41380-024-02876-z.

Genetic Analysis of Psychosis Biotypes: Shared Ancestry-Adjusted Polygenic Risk and Unique Genomic Associations.

Xia C, Alliey-Rodriguez N, Tamminga C, Keshavan M, Pearlson G, Keedy S medRxiv. 2024; .

PMID: 39677452 PMC: 11643284. DOI: 10.1101/2024.12.05.24318404.

Multiomic integration analysis identifies atherogenic metabolites mediating between novel immune genes and cardiovascular risk.

Carreras-Torres R, Galvan-Femenia I, Farre X, Cortes B, Diez-Obrero V, Carreras A Genome Med. 2024; 16(1):122.

PMID: 39449064 PMC: 11515386. DOI: 10.1186/s13073-024-01397-2.

The Genetic Architecture of the Human Corpus Callosum and its Subregions.

Bhatt R, Gadewar S, Shetty A, Ba Gari I, Haddad E, Javid S bioRxiv. 2024; .

PMID: 39091796 PMC: 11291056. DOI: 10.1101/2024.07.22.603147.

References

Li L, Chen Z, von Scheidt M, Li S, Steiner A, Guldener U . Transcriptome-wide association study of coronary artery disease identifies novel susceptibility genes. Basic Res Cardiol. 2022; 117(1):6. PMC: 8852935. DOI: 10.1007/s00395-022-00917-8. View

Bowden J, Del Greco M F, Minelli C, Zhao Q, Lawlor D, Sheehan N . Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption. Int J Epidemiol. 2018; 48(3):728-742. PMC: 6659376. DOI: 10.1093/ije/dyy258. View

Luningham J, Chen J, Tang S, De Jager P, Bennett D, Buchman A . Bayesian Genome-wide TWAS Method to Leverage both cis- and trans-eQTL Information through Summary Statistics. Am J Hum Genet. 2020; 107(4):714-726. PMC: 7536614. DOI: 10.1016/j.ajhg.2020.08.022. View

Nagpal S, Meng X, Epstein M, Tsoi L, Patrick M, Gibson G . TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits. Am J Hum Genet. 2019; 105(2):258-266. PMC: 6698804. DOI: 10.1016/j.ajhg.2019.05.018. View

Giambartolomei C, Vukcevic D, Schadt E, Franke L, Hingorani A, Wallace C . Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014; 10(5):e1004383. PMC: 4022491. DOI: 10.1371/journal.pgen.1004383. View

Strunz T, Lauwen S, Kiel C, Hollander A, Weber B . A transcriptome-wide association study based on 27 tissues identifies 106 genes potentially relevant for disease pathology in age-related macular degeneration. Sci Rep. 2020; 10(1):1584. PMC: 6994629. DOI: 10.1038/s41598-020-58510-9. View

Su Y, Di C, Bien S, Huang L, Dong X, Abecasis G . A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics. Am J Hum Genet. 2018; 102(5):904-919. PMC: 5986723. DOI: 10.1016/j.ajhg.2018.03.019. View

Zhang J, Xie S, Gonzales S, Liu J, Wang X . A fast and powerful eQTL weighted method to detect genes associated with complex trait using GWAS summary data. Genet Epidemiol. 2020; 44(6):550-563. DOI: 10.1002/gepi.22297. View

Porcu E, Rueger S, Lepik K, Santoni F, Reymond A, Kutalik Z . Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits. Nat Commun. 2019; 10(1):3300. PMC: 6656778. DOI: 10.1038/s41467-019-10936-0. View

10.

Zhao B, Shan Y, Yang Y, Yu Z, Li T, Wang X . Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits. Nat Commun. 2021; 12(1):2878. PMC: 8128893. DOI: 10.1038/s41467-021-23130-y. View

11.

Bhattacharya A, Li Y, Love M . MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies. PLoS Genet. 2021; 17(3):e1009398. PMC: 7971899. DOI: 10.1371/journal.pgen.1009398. View

12.

Asefa N, Kamali Z, Pereira S, Vaez A, Jansonius N, Bergen A . Bioinformatic Prioritization and Functional Annotation of GWAS-Based Candidate Genes for Primary Open-Angle Glaucoma. Genes (Basel). 2022; 13(6). PMC: 9222386. DOI: 10.3390/genes13061055. View

13.

Cheng W, van der Meer D, Parker N, Hindley G, OConnell K, Wang Y . Shared genetic architecture between schizophrenia and subcortical brain volumes implicates early neurodevelopmental processes and brain development in childhood. Mol Psychiatry. 2022; 27(12):5167-5176. DOI: 10.1038/s41380-022-01751-z. View

14.

Pulit S, Stoneman C, Morris A, Wood A, Glastonbury C, Tyrrell J . Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet. 2018; 28(1):166-174. PMC: 6298238. DOI: 10.1093/hmg/ddy327. View

15.

Xu Z, Wu C, Wei P, Pan W . A Powerful Framework for Integrating eQTL and GWAS Summary Data. Genetics. 2017; 207(3):893-902. PMC: 5676241. DOI: 10.1534/genetics.117.300270. View

16.

Zhu Z, Zhang F, Hu H, Bakshi A, Robinson M, Powell J . Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016; 48(5):481-7. DOI: 10.1038/ng.3538. View

17.

Mancuso N, Freund M, Johnson R, Shi H, Kichaev G, Gusev A . Probabilistic fine-mapping of transcriptome-wide association studies. Nat Genet. 2019; 51(4):675-682. PMC: 6619422. DOI: 10.1038/s41588-019-0367-1. View

18.

Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx B . Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016; 48(3):245-52. PMC: 4767558. DOI: 10.1038/ng.3506. View

19.

Chen X, Yao T, Cai J, Zhang Q, Li S, Li H . A novel genetic variant potentially altering the expression of in the cerebellum associated with attention deficit hyperactivity disorder in Han Chinese children. World J Biol Psychiatry. 2021; 23(7):548-559. DOI: 10.1080/15622975.2021.2014248. View

20.

Yuan Z, Zhu H, Zeng P, Yang S, Sun S, Yang C . Testing and controlling for horizontal pleiotropy with probabilistic Mendelian randomization in transcriptome-wide association studies. Nat Commun. 2020; 11(1):3861. PMC: 7395774. DOI: 10.1038/s41467-020-17668-6. View