254 related articles for article (PubMed ID: 33733352)
1. Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies.
Shi X; Yang C; Liu J
Methods Mol Biol; 2021; 2212():93-103. PubMed ID: 33733352
[TBL] [Abstract][Full Text] [Related]
2. TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits.
Nagpal S; Meng X; Epstein MP; Tsoi LC; Patrick M; Gibson G; De Jager PL; Bennett DA; Wingo AP; Wingo TS; Yang J
Am J Hum Genet; 2019 Aug; 105(2):258-266. PubMed ID: 31230719
[TBL] [Abstract][Full Text] [Related]
3. A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies.
Shi X; Chai X; Yang Y; Cheng Q; Jiao Y; Chen H; Huang J; Yang C; Liu J
Nucleic Acids Res; 2020 Nov; 48(19):e109. PubMed ID: 32978944
[TBL] [Abstract][Full Text] [Related]
4. Statistical power of transcriptome-wide association studies.
He R; Xue H; Pan W;
Genet Epidemiol; 2022 Dec; 46(8):572-588. PubMed ID: 35766062
[TBL] [Abstract][Full Text] [Related]
5. Influence of tissue context on gene prioritization for predicted transcriptome-wide association studies.
Li B; Veturi Y; Bradford Y; Verma SS; Verma A; Lucas AM; Haas DW; Ritchie MD
Pac Symp Biocomput; 2019; 24():296-307. PubMed ID: 30864331
[TBL] [Abstract][Full Text] [Related]
6. How powerful are summary-based methods for identifying expression-trait associations under different genetic architectures?
Veturi Y; Ritchie MD
Pac Symp Biocomput; 2018; 23():228-239. PubMed ID: 29218884
[TBL] [Abstract][Full Text] [Related]
7. Cis- and trans-eQTL TWASs of breast and ovarian cancer identify more than 100 susceptibility 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
Am J Hum Genet; 2024 Jun; 111(6):1084-1099. PubMed ID: 38723630
[TBL] [Abstract][Full Text] [Related]
8. CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies.
Yang Y; Shi X; Jiao Y; Huang J; Chen M; Zhou X; Sun L; Lin X; Yang C; Liu J
Bioinformatics; 2020 Apr; 36(7):2009-2016. PubMed ID: 31755899
[TBL] [Abstract][Full Text] [Related]
9. Meta-imputation of transcriptome from genotypes across multiple datasets by leveraging publicly available summary-level data.
Liu AE; Kang HM
PLoS Genet; 2022 Jan; 18(1):e1009571. PubMed ID: 35100255
[TBL] [Abstract][Full Text] [Related]
10. Bayesian Genome-wide TWAS Method to Leverage both cis- and trans-eQTL Information through Summary Statistics.
Luningham JM; Chen J; Tang S; De Jager PL; Bennett DA; Buchman AS; Yang J
Am J Hum Genet; 2020 Oct; 107(4):714-726. PubMed ID: 32961112
[TBL] [Abstract][Full Text] [Related]
11. Partitioning gene-based variance of complex traits by gene score regression.
Zhang W; Li SY; Liu T; Li Y
PLoS One; 2020; 15(8):e0237657. PubMed ID: 32817676
[TBL] [Abstract][Full Text] [Related]
12. Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits.
Mancuso N; Shi H; Goddard P; Kichaev G; Gusev A; Pasaniuc B
Am J Hum Genet; 2017 Mar; 100(3):473-487. PubMed ID: 28238358
[TBL] [Abstract][Full Text] [Related]
13. Simultaneous test and estimation of total genetic effect in eQTL integrative analysis through mixed models.
Wang T; Qiao J; Zhang S; Wei Y; Zeng P
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35212359
[TBL] [Abstract][Full Text] [Related]
14. InTACT: An adaptive and powerful framework for joint-tissue transcriptome-wide association studies.
Bae YE; Wu L; Wu C
Genet Epidemiol; 2021 Dec; 45(8):848-859. PubMed ID: 34255882
[TBL] [Abstract][Full Text] [Related]
15. A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer.
Gao G; McClellan J; Barbeira AN; Fiorica PN; Li JL; Mu Z; Olopade OI; Huo D; Im HK
Am J Hum Genet; 2024 Jun; 111(6):1100-1113. PubMed ID: 38733992
[TBL] [Abstract][Full Text] [Related]
16. TWAS pathway method greatly enhances the number of leads for uncovering the molecular underpinnings of psychiatric disorders.
Chatzinakos C; Georgiadis F; Lee D; Cai N; Vladimirov VI; Docherty A; Webb BT; Riley BP; Flint J; Kendler KS; Daskalakis NP; Bacanu SA
Am J Med Genet B Neuropsychiatr Genet; 2020 Dec; 183(8):454-463. PubMed ID: 32954640
[TBL] [Abstract][Full Text] [Related]
17. A Multi-tissue Transcriptome Analysis of Human Metabolites Guides Interpretability of Associations Based on Multi-SNP Models for Gene Expression.
Ndungu A; Payne A; Torres JM; van de Bunt M; McCarthy MI
Am J Hum Genet; 2020 Feb; 106(2):188-201. PubMed ID: 31978332
[TBL] [Abstract][Full Text] [Related]
18. Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits.
Zhao S; Crouse W; Qian S; Luo K; Stephens M; He X
Nat Genet; 2024 Feb; 56(2):336-347. PubMed ID: 38279041
[TBL] [Abstract][Full Text] [Related]
19. A joint transcriptome-wide association study across multiple tissues identifies candidate breast cancer susceptibility genes.
Gao G; Fiorica PN; McClellan J; Barbeira AN; Li JL; Olopade OI; Im HK; Huo D
Am J Hum Genet; 2023 Jun; 110(6):950-962. PubMed ID: 37164006
[TBL] [Abstract][Full Text] [Related]
20. SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification.
Zhang Z; Bae YE; Bradley JR; Wu L; Wu C
Nat Commun; 2022 Oct; 13(1):6336. PubMed ID: 36284135
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
