Multi-ancestry Transcriptome-wide Association Analyses Yield Insights into Tobacco Use Biology and Drug Repurposing

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

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References

Liu M, Jiang Y, Wedow R, Li Y, Brazel D, Chen F . Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019; 51(2):237-244. PMC: 6358542. DOI: 10.1038/s41588-018-0307-5. View

Taliun D, Harris D, Kessler M, Carlson J, Szpiech Z, Torres R . Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature. 2021; 590(7845):290-299. PMC: 7875770. DOI: 10.1038/s41586-021-03205-y. View

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

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

Gamazon E, Wheeler H, Shah K, Mozaffari S, Aquino-Michaels K, Carroll R . A gene-based association method for mapping traits using reference transcriptome data. Nat Genet. 2015; 47(9):1091-8. PMC: 4552594. DOI: 10.1038/ng.3367. View

Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat S . A statistical framework for cross-tissue transcriptome-wide association analysis. Nat Genet. 2019; 51(3):568-576. PMC: 6788740. DOI: 10.1038/s41588-019-0345-7. View

Gusev A, Mancuso N, Won H, Kousi M, Finucane H, Reshef Y . Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet. 2018; 50(4):538-548. PMC: 5942893. DOI: 10.1038/s41588-018-0092-1. View

Wu L, Shi W, Long J, Guo X, Michailidou K, Beesley J . A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer. Nat Genet. 2018; 50(7):968-978. PMC: 6314198. DOI: 10.1038/s41588-018-0132-x. View

Hall L, Medway C, Pain O, Pardinas A, Rees E, Escott-Price V . A transcriptome-wide association study implicates specific pre- and post-synaptic abnormalities in schizophrenia. Hum Mol Genet. 2019; 29(1):159-167. PMC: 7416679. DOI: 10.1093/hmg/ddz253. View

10.

Bhattacharya A, Garcia-Closas M, Olshan A, Perou C, Troester M, Love M . A framework for transcriptome-wide association studies in breast cancer in diverse study populations. Genome Biol. 2020; 21(1):42. PMC: 7033948. DOI: 10.1186/s13059-020-1942-6. View

11.

Peterson R, Kuchenbaecker K, Walters R, Chen C, Popejoy A, Periyasamy S . Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations. Cell. 2019; 179(3):589-603. PMC: 6939869. DOI: 10.1016/j.cell.2019.08.051. View

12.

Lam M, Chen C, Li Z, Martin A, Bryois J, Ma X . Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat Genet. 2019; 51(12):1670-1678. PMC: 6885121. DOI: 10.1038/s41588-019-0512-x. View

13.

Marigorta U, Navarro A . High trans-ethnic replicability of GWAS results implies common causal variants. PLoS Genet. 2013; 9(6):e1003566. PMC: 3681663. DOI: 10.1371/journal.pgen.1003566. View

14.

de Leeuw C, Mooij J, Heskes T, Posthuma D . MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015; 11(4):e1004219. PMC: 4401657. DOI: 10.1371/journal.pcbi.1004219. View

15.

Qian W, Huang P, Shen Z, Wang C, Yang Y, Zhang M . Brain Gray Matter Volume and Functional Connectivity Are Associated With Smoking Cessation Outcomes. Front Hum Neurosci. 2019; 13:361. PMC: 6803765. DOI: 10.3389/fnhum.2019.00361. View

16.

Miquel M, Toledo R, Garcia L, Coria-Avila G, Manzo J . Why should we keep the cerebellum in mind when thinking about addiction?. Curr Drug Abuse Rev. 2009; 2(1):26-40. DOI: 10.2174/1874473710902010026. View

17.

Gil-Miravet I, Guarque-Chabrera J, Carbo-Gas M, Olucha-Bordonau F, Miquel M . The role of the cerebellum in drug-cue associative memory: functional interactions with the medial prefrontal cortex. Eur J Neurosci. 2018; 50(3):2613-2622. DOI: 10.1111/ejn.14187. View

18.

Klein A, Ulmer J, Quinet S, Mathews V, Mark L . Nonmotor Functions of the Cerebellum: An Introduction. AJNR Am J Neuroradiol. 2016; 37(6):1005-9. PMC: 7963530. DOI: 10.3174/ajnr.A4720. View

19.

DAngelo E . The cerebellum gets social. Science. 2019; 363(6424):229. DOI: 10.1126/science.aaw2571. View

20.

Moulton E, Elman I, Becerra L, Goldstein R, Borsook D . The cerebellum and addiction: insights gained from neuroimaging research. Addict Biol. 2014; 19(3):317-31. PMC: 4031616. DOI: 10.1111/adb.12101. View