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OTTERS: a powerful TWAS framework leveraging summary-level reference data.
Dai Q, Zhou G, Zhao H, Võsa U, Franke L, Battle A, Teumer A, Lehtimäki T, Raitakari OT, Esko T; eQTLGen Consortium; Epstein MP, Yang J. Dai Q, et al. Among authors: yang j. Nat Commun. 2023 Mar 7;14(1):1271. doi: 10.1038/s41467-023-36862-w. Nat Commun. 2023. PMID: 36882394 Free PMC article.
SR-TWAS: Leveraging Multiple Reference Panels to Improve TWAS Power by Ensemble Machine Learning.
Parrish RL, Buchman AS, Tasaki S, Wang Y, Avey D, Xu J, De Jager PL, Bennett DA, Epstein MP, Yang J. Parrish RL, et al. Among authors: yang j. medRxiv [Preprint]. 2024 May 13:2023.06.20.23291605. doi: 10.1101/2023.06.20.23291605. medRxiv. 2024. Update in: Nat Commun. 2024 Aug 5;15(1):6646. doi: 10.1038/s41467-024-50983-w. PMID: 37425698 Free PMC article. Updated. Preprint.
Cis- and trans-eQTL TWAS of breast and ovarian cancer identify more than 100 risk associated genes in the BCAC and OCAC consortia.
Head ST, Dezem F, Todor A, Yang J, Plummer J, Gayther S, Kar S, Schildkraut J, Epstein MP. Head ST, et al. Among authors: yang j. bioRxiv [Preprint]. 2023 Nov 13:2023.11.09.566218. doi: 10.1101/2023.11.09.566218. bioRxiv. 2023. Update in: Am J Hum Genet. 2024 Jun 6;111(6):1084-1099. doi: 10.1016/j.ajhg.2024.04.012. PMID: 38014246 Free PMC article. Updated. Preprint.
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