848 related articles for article (PubMed ID: 30926968)
1. Opportunities and challenges for transcriptome-wide association studies.
Wainberg M; Sinnott-Armstrong N; Mancuso N; Barbeira AN; Knowles DA; Golan D; Ermel R; Ruusalepp A; Quertermous T; Hao K; Björkegren JLM; Im HK; Pasaniuc B; Rivas MA; Kundaje A
Nat Genet; 2019 Apr; 51(4):592-599. PubMed ID: 30926968
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. Leveraging lung tissue transcriptome to uncover candidate causal genes in COPD genetic associations.
Lamontagne M; Bérubé JC; Obeidat M; Cho MH; Hobbs BD; Sakornsakolpat P; de Jong K; Boezen HM; ; Nickle D; Hao K; Timens W; van den Berge M; Joubert P; Laviolette M; Sin DD; Paré PD; Bossé Y
Hum Mol Genet; 2018 May; 27(10):1819-1829. PubMed ID: 29547942
[TBL] [Abstract][Full Text] [Related]
5. Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies.
Feng H; Mancuso N; Gusev A; Majumdar A; Major M; Pasaniuc B; Kraft P
PLoS Genet; 2021 Apr; 17(4):e1008973. PubMed ID: 33831007
[TBL] [Abstract][Full Text] [Related]
6. Integrating eQTL and GWAS data characterises established and identifies novel migraine risk loci.
Ghaffar A; ; Nyholt DR
Hum Genet; 2023 Aug; 142(8):1113-1137. PubMed ID: 37245199
[TBL] [Abstract][Full Text] [Related]
7. Transcriptome-wide association study and eQTL colocalization identify potentially causal genes responsible for human bone mineral density GWAS associations.
Al-Barghouthi BM; Rosenow WT; Du KP; Heo J; Maynard R; Mesner L; Calabrese G; Nakasone A; Senwar B; Gerstenfeld L; Larner J; Ferguson V; Ackert-Bicknell C; Morgan E; Brautigan D; Farber CR
Elife; 2022 Nov; 11():. PubMed ID: 36416764
[TBL] [Abstract][Full Text] [Related]
8. Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes.
Wang L; Khunsriraksakul C; Markus H; Chen D; Zhang F; Chen F; Zhan X; Carrel L; Liu DJ; Jiang B
Nat Commun; 2024 May; 15(1):4260. PubMed ID: 38769300
[TBL] [Abstract][Full Text] [Related]
9. Integrative analysis of transcriptome-wide association study and mRNA expression profile identified candidate genes and pathways associated with aortic aneurysm and dissection.
Zhang Y; Li L; Ma L
Gene; 2022 Jan; 808():145993. PubMed ID: 34626721
[TBL] [Abstract][Full Text] [Related]
10. webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study.
Cao C; Wang J; Kwok D; Cui F; Zhang Z; Zhao D; Li MJ; Zou Q
Nucleic Acids Res; 2022 Jan; 50(D1):D1123-D1130. PubMed ID: 34669946
[TBL] [Abstract][Full Text] [Related]
11. A powerful fine-mapping method for transcriptome-wide association studies.
Wu C; Pan W
Hum Genet; 2020 Feb; 139(2):199-213. PubMed ID: 31844974
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Accounting for nonlinear effects of gene expression identifies additional associated genes in transcriptome-wide association studies.
Lin Z; Xue H; Malakhov MM; Knutson KA; Pan W
Hum Mol Genet; 2022 Jul; 31(14):2462-2470. PubMed ID: 35043938
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Meta-Analysis of Transcriptome-Wide Association Studies across 13 Brain Tissues Identified Novel Clusters of Genes Associated with Nicotine Addiction.
Ye Z; Mo C; Ke H; Yan Q; Chen C; Kochunov P; Hong LE; Mitchell BD; Chen S; Ma T
Genes (Basel); 2021 Dec; 13(1):. PubMed ID: 35052378
[TBL] [Abstract][Full Text] [Related]
16. Transcriptome-wide association studies accounting for colocalization using Egger regression.
Barfield R; Feng H; Gusev A; Wu L; Zheng W; Pasaniuc B; Kraft P
Genet Epidemiol; 2018 Jul; 42(5):418-433. PubMed ID: 29808603
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults.
Li B; Veturi Y; Verma A; Bradford Y; Daar ES; Gulick RM; Riddler SA; Robbins GK; Lennox JL; Haas DW; Ritchie MD
PLoS Genet; 2021 Apr; 17(4):e1009464. PubMed ID: 33901188
[TBL] [Abstract][Full Text] [Related]
19. Some statistical consideration in transcriptome-wide association studies.
Xue H; Pan W;
Genet Epidemiol; 2020 Apr; 44(3):221-232. PubMed ID: 31821608
[TBL] [Abstract][Full Text] [Related]
20. Transcriptome-wide association study identifies novel candidate susceptibility genes for migraine.
Meyers TJ; Yin J; Herrera VA; Pressman AR; Hoffmann TJ; Schaefer C; Avins AL; Choquet H
HGG Adv; 2023 Jul; 4(3):100211. PubMed ID: 37415806
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
