Results 221 to 230 of about 92,622 (257)

SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning. [PDF]

Nat Commun
Parrish RL, Buchman AS, Tasaki S, Wang Y, Avey D, Xu J, De Jager PL, Bennett DA, Epstein MP, Yang J.   +9 more
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A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer. [PDF]

Am J Hum Genet
Gao G, McClellan J, Barbeira AN, Fiorica PN, Li JL, Mu Z, Olopade OI, Huo D, Im HK.   +8 more
europepmc   +1 more source

Transcriptome-Wide Association Study Reveals Potentially Candidate Genes Responsible for Milk Production Traits in Buffalo. [PDF]

Int J Mol Sci
Wei K, Lu Y, Ma X, Duan A, Lu X, Abdel-Shafy H, Deng T.   +6 more
europepmc   +1 more source

A distinct class of pan-cancer susceptibility genes revealed by an alternative polyadenylation transcriptome-wide association study. [PDF]

Nat Commun
Chen H, Wang Z, Gong L, Wang Q, Chen W, Wang J, Ma X, Ding R, Li X, Zou X, Plass M, Lian C, Ni T, Wei GH, Li W, Deng L, Li L.   +16 more
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Transcriptome-wide association study and Mendelian randomization in pancreatic cancer identifies susceptibility genes and causal relationships with type 2 diabetes and venous thromboembolism. [PDF]

EBioMedicine
Tan MCB, Isom CA, Liu Y, Trégouët DA, INVENT Consortium, Wu L, Zhou D, Gamazon ER.   +7 more
europepmc   +1 more source

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Multitrait transcriptome‐wide association study (TWAS) tests

Genetic Epidemiology, 2021
AbstractMultitrait tests can improve power to detect associations between individual single‐nucleotide polymorphisms (SNPs) and several related traits. Here, we develop methods for multi‐SNP transcriptome‐wide association (TWAS) tests to test the association between predicted gene expression levels and multiple phenotypes.
Helian Feng, Nicholas Mancuso, Bogdan Pasaniuc, Peter Kraft   +3 more
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Transcriptome wide association studies: general framework and methods

Quantitative Biology, 2021
BackgroundGenome‐wide association studies (GWAS) have succeeded in identifying tens of thousands of genetic variants associated with complex human traits during the past decade, however, they are still hampered by limited statistical power and difficulties in biological interpretation.
Yuhan Xie, Nayang Shan, Hongyu Zhao, Lin Hou   +3 more
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Transcriptome‐Wide Association Studies (TWAS): Methodologies, Applications, and Challenges

Current Protocols
AbstractTranscriptome‐wide association study (TWAS) methodologies aim to identify genetic effects on phenotypes through the mediation of gene transcription. In TWAS, in silico models of gene expression are trained as functions of genetic variants and then applied to genome‐wide association study (GWAS) data.
Patrick Evans, Taylor Nagai, Anuar Konkashbaev, Dan Zhou, Ela W. Knapik, Eric R. Gamazon   +5 more
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Transcriptome-wide Association Study Identifies Genetically Dysregulated Genes in Diabetic Neuropathy

Combinatorial Chemistry & High Throughput Screening, 2021
Background: Complications are the main cause of the disease burden of diabetes. Genes determining the development and progression of diabetic complications remain to be identified. Diabetic neuropathy is the most common and debilitating complication and mainly affects the nerves of legs and feet.
Danfeng, Lan, Hong-Yan, Jiang, Xiaoyang, Su, Yan, Zhao, Sicheng, Du, Ying, Li, Rui, Bi, Deng-Feng, Zhang, Qiuping, Yang   +8 more
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TRANSCRIPTOME-WIDE ASSOCIATION STUDIES: CHALLENGES, SOLUTIONS, AND FUTURE DIRECTIONS

2021
Genome-wide association studies (GWAS) have identified hundreds of thousands ofvariants associated with a broad range of complex traits. However, the biological mechanismsunderlying these associations remain largely unknown. Transcriptome-wide associationstudies (TWAS) are a gene-based method which can provide insight into these mechanismsby leveraging
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