Results 81 to 90 of about 41,900,780 (178)

Integrative Analysis Uncover the Effects and Multi‐Omics Features of Thigh Muscle Fat Infiltration

Aging Cell, Volume 25, Issue 9, September 2026.
Thigh muscle fat infiltration (TMFI), estimated from MRI, is associated with increased mortality and diseases affecting multiple organ systems and mediates the effects of lifestyle factors on health. Multi‐omics analyses delineate the genetic, cellular, transcriptional, proteomic, and metabolic architecture of TMFI.
Yiwei Zhang, Qin Dang, Xizeng Zong, Haowei Chen, Simin Wen, Siqi Xu, Kunyan Wang, Xiaoshuai Wang, Jianwei Zhu, Zhaohua Zhu, Changhai Ding, Guangfeng Ruan, Yan Zhang   +12 more
wiley   +1 more source

Multi-tissue transcriptome-wide association studies. [PDF]

, 2020
A transcriptome-wide association study (TWAS) attempts to identify disease associated genes by imputing gene expression into a genome-wide association study (GWAS) using an expression quantitative trait loci (eQTL) data set and then testing for ...

core   +2 more sources

Analyzing and reconciling colocalization and transcriptome-wide association studies from the perspective of inferential reproducibility

, 2022
Transcriptome-wide association studies and colocalization analysis are popular computational approaches for integrating genetic-association data from molecular and complex traits.
Sampson M. G., Luca F., Wen X., Hukku, Abhay, Luca, Francesca, Pique-Regi, Roger, Wen, Xiaoquan, Hukku A., Pique-Regi R., Sampson, Matthew G.   +9 more
core   +1 more source

UTMOST, a single and cross-tissue TWAS (Transcriptome Wide Association Study), reveals new ASD (Autism Spectrum Disorder) associated genes [PDF]

, 2021
Cristina Rodriguez‐Fontenla, Ángel Carracedo   +1 more
openalex   +1 more source

Multiancestry and Multitrait GWAS Meta‐Analysis on Schizophrenia With a Sample of 322,321 Unveils Genetic Links to Chronic Lung Diseases

Genes, Brain and Behavior, Volume 25, Issue 4, August 2026.
This study integrated GWAS data for schizophrenia from European (PGC, FinnGen, iPSYCH) and East Asian cohorts with GWAS of chronic pulmonary diseases (COPD, asthma, ILD, pneumonia) and lung function traits, alongside transcriptomic and proteomic datasets (GEO, GTEx, CommonMind, pQTL).
Abudusalamu Ayoufu, Ayinigeer Abulimiti, Wusimanjiang Aierken, ZhouBin Wei, XueCheng Wang, HaiFeng Wang, Liang Zhao   +6 more
wiley   +1 more source

scPrediXcan integrates deep learning methods and single-cell data into a cell-type-specific transcriptome-wide association study framework

Cell Genomics
Summary: Transcriptome-wide association studies (TWASs) help identify disease-causing genes but often fail to pinpoint disease mechanisms at the cellular level because of the limited sample sizes and sparsity of cell-type-specific expression data.
Yichao Zhou, Temidayo Adeluwa, Lisha Zhu, Sofia Salazar-Magaña, Sarah Sumner, Hyunki Kim, Saideep Gona, Festus Nyasimi, Rohit Kulkarni, Joseph E. Powell, Ravi Madduri, Boxiang Liu, Mengjie Chen, Hae Kyung Im   +13 more
doaj   +1 more source

A large-scale transcriptome-wide association study (TWAS) of ten blood cell phenotypes reveals complexities of TWAS fine-mapping [PDF]

, 2021
Amanda L. Tapia, Bryce Rowland, Jonathan D. Rosen, Michael H Preuss, Kris Young, Mariaelisa Graff, Hélène Choquet, David J. Couper, Steven G. Buyske, Stephanie A. Bien, Eric Jorgenson, Charles L Kooperberg, Ruth J. F. Loos, Alanna C. Morrison, Kari E. North, Bing Yu, Alex P. Reiner, Yun Li, Laura M. Raffield   +18 more
openalex   +1 more source

Opportunities and challenges for transcriptome-wide association studies [PDF]

, 2019
Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and gene expression datasets to identify gene-trait associations.
Arno Ruusalepp, Pasaniuc, Bogdan, Rivas, Manuel A, Alvaro N. Barbeira, Quertermous, Thomas, Nasa Sinnott-Armstrong, Ke Hao, Anshul Kundaje, Wainberg, Michael, Raili Ermel, Bogdan Pasaniuc, Ruusalepp, Arno, Hao, Ke, Knowles, David A, Kundaje, Anshul, Nicholas Mancuso, Michael Wainberg, Mancuso, Nicholas, Golan, David, David A. Knowles, David Golan, Barbeira, Alvaro N, Ermel, Raili, Thomas Quertermous, Hae Kyung Im, Im, Hae Kyung, Sinnott-Armstrong, Nasa, Johan L. M. Björkegren, Manuel A. Rivas, Björkegren, Johan LM, Golan, D, Hao, K, Knowles, DA, Ruusalepp, A, Barbeira, AN, Sinnott-Armstrong, N, Wainberg, M, Mancuso, N, Ermel, R, Quertermous, T   +39 more
core   +1 more source

A pseudotime-dependent TWAS framework identifies disease genes along cell developmental paths

HGG Advances
Summary: Transcriptome-wide association studies (TWASs) link genes to disease risk by integrating gene expression with genome-wide association study (GWAS) data.
Rui Cao, Chunlin Li, Erjia Cui, Logan Spector, Andrew Raduski, Nathan Anderson, Weihua Guan, Peter Gordon, Cindy Im, Tianzhong Yang   +9 more
doaj   +1 more source

Multi-omic characterization of air pollution effects: Applications of AirSigOmniTWP Hub

Ecotoxicology and Environmental Safety
Air pollution, particularly fine particulate matter and gaseous pollutants including NO2 and NOx, presents significant public health challenges. While the harmful effects of these pollutants are well-documented, the molecular mechanisms underlying their ...
Wei Liu, Tong Liu, Xinxin Si, Jiaxing Liang, Xia Yan, Juexin Zhang, Bing Pang, Wenmin Luo, Junhong Liu, Huazhe Yang, Peng Shi   +10 more
doaj   +1 more source