Results 101 to 110 of about 8,166 (222)

PTWAS: Investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis [PDF]

, 2020
We propose a new computational framework, probabilistic transcriptome-wide association study (PTWAS), to investigate causal relationships between gene expressions and complex traits.
Barbeira A., Kyung Im H., Luca F., Pique-Regi R., Quick C., Wen X., Yu K., Zhang Y.   +7 more
core   +1 more source

Integrative Transcriptome-Wide Analyses Uncover Novel Risk-Associated MicroRNAs in Hormone-Dependent Cancers

Frontiers in Genetics, 2021
BackgroundHormone-dependent cancers (HDC) are among the leading causes of death worldwide among both men and women. Some of the established risk factors of HDC include unhealthy lifestyles, environmental factors, and genetic influences.
Dulari K. Jayarathna, Dulari K. Jayarathna, Miguel E. Rentería, Miguel E. Rentería, Adil Malik, Adil Malik, Emilie Sauret, Jyotsna Batra, Jyotsna Batra, Neha S. Gandhi, Neha S. Gandhi   +10 more
doaj   +1 more source

Integrative GWAS and Mendelian Randomization Analysis Identifies IREB2 and CD27+ Memory B Cells as Core Drivers of COPD to Lung Cancer Progression

MedComm, Volume 6, Issue 12, December 2025.
This flowchart outlines our integrative pipeline. We linked COPD phenotypes and lung function with lung cancer in NHANES, then applied GWAS, Mendelian Randomization (MR), and colocalization to identify shared loci and driver genes. Candidate genes were refined by eQTL and single‐cell RNA‐seq analyses, and key findings validated in human tissues and B ...
Erkang Yi, Qingyang Li, Wenqian Wu, Chengshu Xie, Hairong Wang, Erping Long, Fan Wu, Xuanyi Lu, Yu Liu, Ruiting Sun, Xinqing Lin, Xiaohong Xie, Yumin Zhou, Chengzhi Zhou, Pixin Ran   +14 more
wiley   +1 more source

Multi‐Omics‐Augmented GWAS for Crop Improvement: From Mechanisms to Breeding

Modern Agriculture, Volume 3, Issue 2, December 2025.
This review presents an integrated framework that synergises multi‐omics with GWAS and machine learning to decode the genetic basis of complex agronomic traits, bridging biological discovery to precision breeding for climate‐resilient crops. ABSTRACT Global food security demands innovative strategies to enhance crop resilience and productivity amidst ...
Naimatullah Mangi, Qianli Zhao, Mengfan Li, Chunyang Li, Lulu Yan, Luyao Kong, Aizhi Qin, Yaping Zhou, Hao Liu, Yinpeng Zhang, Mengyu Liao, Jiani Long, Mengeng Zhou, Xiaoli Fan, Baozhen Wang, Wenxuan Kang, Zhixin Liu, Xuwu Sun   +17 more
wiley   +1 more source

Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults.

PLoS Genetics, 2021
As a type of relatively new methodology, the transcriptome-wide association study (TWAS) has gained interest due to capacity for gene-level association testing.
Binglan Li, Yogasudha Veturi, Anurag Verma, Yuki Bradford, Eric S Daar, Roy M Gulick, Sharon A Riddler, Gregory K Robbins, Jeffrey L Lennox, David W Haas, Marylyn D Ritchie   +10 more
doaj   +1 more source

Transcriptome‐Wide Association Uncovers LncRNAs Controlling Seed Weight in Soybean

Advanced Science, Volume 12, Issue 41, November 6, 2025.
This study employs transcriptome‐wide association studies (TWAS) to identify 201 long non‐coding RNAs (lncRNAs) associated with seed weight. Expression quantitative trait locus (eQTL) analysis reveals dynamic and static regulatory variants controlling lncRNA expression across development.
Xiang Wang, Xinyu Jiang, Xiaobo Yuan, Zhipeng Zhou, Zhenwei Zhao, Longfei Wang, Wu Jiao, Wenxue Ye, Qingxin Song   +8 more
wiley   +1 more source

Integrative Transcriptome‐Wide Association Study With Expression Quantitative Trait Loci Colocalization Identifies a Causal VAMP8 Variant for Nasopharyngeal Carcinoma Susceptibility

Advancement of science
Nasopharyngeal carcinoma (NPC) is an Asia‐prevalent malignancy, yet its genetic underpinnings remain incompletely understood. Here, a transcriptome‐wide association study (TWAS) is conducted on NPC, leveraging gene expression prediction models based on ...
Yan Liang, Xiang-Yu Xiong, G.-W. Lin, X. Bai, Fugui Li, J. M. Ko, Yun-He Zhou, A.-Y. Xu, Shu-Qiang Liu, Shuai He, P.-P. Wei, Qiu-Yan Chen, Lin-Quan Tang, Vivien Ya-Fan Wang, H. Mai, Chun-Ling Luo, Yanni Zeng, M. Lung, M. Ji, Jin-Xin Bei   +19 more
semanticscholar   +1 more source

Marker Metabolite‐Based Multi‐Omics Analysis Identifies New Loci Controlling Thousand Seed Weight in Brassica Napus

Advanced Science, Volume 12, Issue 42, November 13, 2025.
Marker metabolite‐based multi‐omics (genome, transcriptome, and metabolome) analysis is conducted firstly to unravel the genetic basis of thousand seed weight (TSW) in Brassica napus. The released metabolite‐QTL‐gene network represents a valuable genetic resource, and the newly validated BnaTGA6 is a promising target for the improvement of TSW in ...
Long Li, Zhitao Tian, Xiaowei Wu, Zengdong Tan, Xu Han, Yuyan Xiang, Jie Chen, Hu Zhao, Wei Chen, Liang Guo, Xuan Yao   +10 more
wiley   +1 more source

Integrative multi‐omics approaches identify molecular pathways and improve Alzheimer's disease risk prediction

Alzheimer's &Dementia, Volume 21, Issue 11, November 2025.
Abstract INTRODUCTION Alzheimer's disease (AD) is a complex neurodegenerative disorder with heterogeneous genetic and molecular underpinnings. Polygenic scores (PGS) capture little of this complexity. METHODS We conducted genome‐, transcriptome‐, and proteome‐wide association studies (G/T/PWAS) on 15,480 individuals from the Alzheimer's Disease ...
Rasika Venkatesh, Katie M. Cardone, Yuki Bradford, Anni K. Moore, Rachit Kumar, Jason H. Moore, Li Shen, Dokyoon Kim, Marylyn D. Ritchie   +8 more
wiley   +1 more source

Using Genome and Transcriptome Data From African-Ancestry Female Participants To Identify Putative Breast Cancer Susceptibility Genes [PDF]

African-ancestry (AA) participants are underrepresented in genetics research. Here, we conducted a transcriptome-wide association study (TWAS) in AA female participants to identify putative breast cancer susceptibility genes.
Adejumo, Prisca O, Ahearn, Thomas, Ambrosone, Christine, Ambs, Stefan, Barnard, Mollie E, Brewster, Abenaa M, Butler, Ebonee N, Cai, Qiuyin, Chen, Yu, Garcia-Closas, Montserrat, Gu, Jian, Guo, Xingyi, Haiman, Christopher A, Hennis, Anselm J M, Hu, Jennifer J, Huang, Maosheng, Huo, Dezheng, Jia, Guochong, John, Esther M, Li, Bingshan, Li, Christopher I, Long, Jirong, Makumbi, Timothy, Nathanson, Katherine, Ndom, Paul, Nemesure, Barbara, Ntekim, Atara, O\u27Brien, Katie M, Olopade, Olufunmilayo I, Olshan, Andrew F, Oluwasanu, Mojisola M, Pal, Tuya, Palmer, Julie R, Ping, Jie, Press, Michael F, Reid, Sonya, Sanderson, Maureen, Sandler, Dale P, Tao, Ran, Troester, Melissa A, Yao, Song, Yoshimatsu, Toshio, Zheng, Wei   +42 more
core   +5 more sources