Results 11 to 20 of about 11,154 (181)

OTTERS: a powerful TWAS framework leveraging summary-level reference data [PDF]

, 2023
Most existing TWAS tools require individual-level eQTL reference data and thus are not applicable to summary-level reference eQTL datasets. The development of TWAS methods that can harness summary-level reference data is valuable to enable TWAS in ...
Stehouwer, Coen D.A.; id_orcid, Beutner, Frank, Marigorta, Urko M., Kirsten, Holger, Lee, Bernett, Montgomery, Grant W., Yang, Jinying, Ahsan, Habibul, Seppälä, Ilkka, Kasela, Silva, Kettunen, Johannes, Gibson, Greg, Andiappan, Anand Kumar, Jan Bonder, Marc, Christiansen, Mark W., Verlouw, Joost, Zhao, Hongyu, Jansen, Rick, Mei, Hailang, Milani, Lili, Awadalla, Philip, Boomsma, Dorret I., Esko, Tõnu, Kovacs, Peter, Lehtimäki, Terho, Kalnapenkis, Anette, Yang, Jian, Zhou, Geyu, Claringbould, Annique, Kim, Yungil, Arindrarto, Wibowo, Nauck, Matthias, Battle, Alexis, Franke, Lude, Kähönen, Mika, Frayling, Timothy, Hemani, Gibran, Kukushkina, Viktorija, Võsa, Urmo, Epstein, Michael P., Gharib, Sina A., Kutalik, Zoltan, eQTLGen Consortium, Raitakari, Olli T., Alves, Isabel, van Meurs, Joyce, Perola, Markus, Heijmans, Bastiaan T., Krohn, Knut, Agbessi, Mawussé, Favé, Marie Julie, Scholz, Markus, Müller-Nurasyid, Martina, Ripatti, Samuli, Pervjakova, Natalia, Teumer, Alexander, Deelen, Patrick, Rüeger, Sina, Kronberg, Jaanika, Yang, Jingjing, Dai, Qile, Loeffler, Markus, Thiery, Joachim, Rüeger, Sina,, Psaty, Bruce M., Beutner, Frank,, Slagboom, Eline P.,, Rotzschke, Olaf,, Zhao, Hongyu,, Westra, Harm-Jan,, Boomsma, Dorret I.; id_orcid, Kutalik, Zoltan,, Urmo Võsa, Lehtimäki Terho, Alexis Battle, Stehouwer, Coen D. A.,, Zhao Hongyu, Yang, Jian,, Mei, Hailang,, Battle, Alexis,, Ripatti, Samuli,, Lehtimäki, Terho,, Nivard, Michel G.,, Battle, A.,, Krohn, Knut,, Tõnu Esko, Yang, Jingjing,, Tönjes, Anke, Raitakari Olli T., Powell, Joseph, Stumvoll, Michael,, Geyu Zhou, Võsa, Urmo,, Zeng, Biao,, van Dongen, Jenny,, Kim, Yungil,, Saha, Ashis,, ‘t Hoen, Peter A. C.,, Stehouwer, Coen D. A., Loeffler, Markus,, Sullivan, Patrick, Zhang, Futao, Schramm, Katharina,, Battle, A., Agbessi, Mawussé,, Völker, Uwe,, Völker, Uwe, Lude Franke, Zeng, Biao, van Dongen, Jenny; id_orcid, Pierce, Brandon L., Michael P. Epstein, Nauck, Matthias,, Sullivan, Patrick,, Penninx, Brenda, Prokisch, Holger, Consortium eQTLGen, Deelen, Patrick,, Gibson, Greg,, van Iterson, Maarten, Nivard, Michel G., Visscher, Peter M.,, Franke Lude, Favé, Marie-Julie,, Pierce, Brandon L.,, Qile Dai, Lee, Bernett,, Kähönen, Mika,, Hongyu Zhao, Prokisch, Holger,, Penninx, Brenda,, Veldink, Jan H., van Dongen, Jenny, Powell, Joseph,, Christiansen, Mark W.,, Hemani, Gibran,, Schramm, Katharina, Gharib, Sina A.,, Dai, Qile,, Yaghootkar, Hanieh,, Võsa Urmo, Tong, Lin, Kovacs, Peter,, Kasela, Silva,, Frayling, Timothy,, Wijmenga, Cisca, Kronberg, Jaanika,, Saha, Ashis, Epstein, Michael P.,, Alves, Isabel,, Ahsan, Habibul,, Seppälä, Ilkka,, Claringbould, Annique,, ‘t Hoen, Peter A. C., Andiappan, Anand Kumar,, Pervjakova, Natalia,, Jansen, Rick,, Scholz, Markus,, Slagboom, Eline P., Kettunen, Johannes,, Zhou Geyu, Psaty, Bruce M.,, Kalnapenkis, Anette,, Awadalla, Philip,, Veldink, Jan H.,, Montgomery, Grant W.,, Esko Tõnu, Wijmenga, Cisca,, Esko, Tõnu,, Alexander Teumer, Heijmans, Bastiaan T.,, Teumer, Alexander,, Thiery, Joachim,, Milani, Lili,, Yaghootkar, Hanieh, Kirsten, Holger,, Rotzschke, Olaf, Arindrarto, Wibowo,, Jingjing Yang, Westra, Harm-Jan, Müller-Nurasyid, Martina,, Battle Alexis, Jan Bonder, Marc,, Epstein Michael P., Yang Jingjing, Stumvoll, Michael, Zhou, Geyu,, Verlouw, Joost,, Olli T. Raitakari, Kukushkina, Viktorija,, Favé, Marie-Julie, Marigorta, Urko M.,, Tong, Lin,, Dai Qile, Teumer Alexander, Perola, Markus,, Franke, Lude,, van Iterson, Maarten,, van Meurs, Joyce,, Tönjes, Anke,, Raitakari, Olli T.,, Visscher, Peter M., Boomsma, Dorret I.,, Nivard, Michel G.; id_orcid, Zhang, Futao,, Terho Lehtimäki   +204 more
core   +7 more sources

CausalCellInfer: Prioritizing Cell-Type-Specific Disease Associations From Genetically Predicted Transcriptomes in Biobank Cohorts. [PDF]

Adv Sci (Weinh)
Bulk and genotype‐imputed transcriptomes obscure the cellular drivers of complex disease. CausalCellInfer resolves them by integrating invariant marker selection, deep learning deconvolution, and regularized matrix completion. Applied to ∼500 000 UK Biobank participants across 29 traits, it uncovers hallmark cell‐type proportion shifts, including ...
Yin L, Shi Y, Zhang R, Xiang Y, Qiu J, Sham PC, So HC.   +6 more
europepmc   +2 more sources

Transcriptome-wide association study identifies new susceptibility genes and pathways for spondyloarthritis

Journal of Orthopaedic Surgery and Research, 2023
Background Spondyloarthritis (SpA) is a group of multifactorial bone diseases influenced by genetic factors, the environment and lifestyle. However, current studies have found a limited number of SpA-related genes, and the genetic and pathogenic ...
Xiaochen Su, Anfa Chen, Menghao Teng, Wenchen Ji, Yingang Zhang   +4 more
doaj   +1 more source

Integrating Transcriptome-Wide Association Study and mRNA Expression Profiling Identifies Novel Genes Associated With Osteonecrosis of the Femoral Head

Frontiers in Genetics, 2021
ObjectiveThis study aims to identify novel candidate genes associated with osteonecrosis of the femoral head (ONFH).MethodsA transcriptome-wide association study (TWAS) was performed by integrating the genome-wide association study dataset of ...
Mei Ma, Peilin Li, Li Liu, Shiqiang Cheng, Bolun Cheng, Chu Jun Liang, Sijia Tan, Wenyu Li, Yan Wen, Xiong Guo, Cuiyan Wu   +10 more
doaj   +1 more source

Integrating transcriptome-wide association study and mRNA expression profile identified candidate genes related to hand osteoarthritis

Arthritis Research & Therapy, 2021
Background Osteoarthritis (OA) is a common skeletal system disease that has been partially attributed to genetic factors. The hand is frequently affected, which seriously affects the patient’s quality of life.
Jiawen Xu, Yi Zeng, Haibo Si, Yuan Liu, Mingyang Li, Junfeng Zeng, Bin Shen   +6 more
doaj   +1 more source

Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies.

PLoS Genetics, 2021
Transcriptome-wide association studies (TWAS) test the association between traits and genetically predicted gene expression levels. The power of a TWAS depends in part on the strength of the correlation between a genetic predictor of gene expression and ...
Helian Feng, Nicholas Mancuso, Alexander Gusev, Arunabha Majumdar, Megan Major, Bogdan Pasaniuc, Peter Kraft   +6 more
doaj   +1 more source

XingyiGuo/TF-TWAS: TF-TWAS

, 2022
TF-TWAS is a tool integrating susceptible transcription factor occupancy with transcriptome-wide association studies (TWAS) to identify susceptibility genes in human cancers and non-cancer ...
Guo Lab at VUMC
core   +1 more source

TWAS models for eQTLGen

, 2022
TWAS models based on eQTLGen summary statistics. Instructions on how these weights can be used for TWAS can be found here: TBC. Please cite: Study describing how the TWAS models were generated: TBC Original study for eQTLGen in which the eQTL ...
Pain
core   +1 more source

Identifying celiac disease-related chemicals by transcriptome-wide association study and chemical-gene interaction analyses

Frontiers in Genetics, 2022
Celiac disease (CeD) is one of the most common intestinal inflammatory diseases, and its incidence and prevalence have increased over time. CeD affects multiple organs and systems in the body, and environmental factors play a key role in its complex ...
Mengnan Lu, Ruoyang Feng, Yuesheng Liu, Yujie Qin, Hongyang Deng, Yanfeng Xiao, Chunyan Yin   +6 more
doaj   +1 more source

TWAS models for MetaBrain

, 2022
TWAS models based on MetaBrain summary statistics. Instructions on how these weights can be used for TWAS can be found here: TBC. Please cite: Study describing how the TWAS models were generated: TBC Original study for MetaBrain in which the eQTL ...
Pain, Oliver
core   +1 more source