Results 61 to 70 of about 46,677 (295)
Integrating Single‐Cell Transcriptome‐Wide Mendelian Randomization and Differentially Expressed Gene Analyses to Prioritize Dynamic Immune‐Related Drug Targets for Cancers
Advanced Science, EarlyView.Inspired by evidence triangulation, a new sensitivity method called MR‐DEG is developed, which uses the differentially expressed gene (DEG) results as additional evidence to minimize pleiotropic effects and strengthen Mendelian randomization (MR) causal estimates. Using dynamic single‐cell expression quantitative trait loci (eQTLs) as an example, it is Jie Zheng, Qian Yang, Haoyu Liu, Huiling Zhao, Shuangyuan Wang, Yi Liu, Xueyan Wu, Yilan Ding, Hui Ying, Youqiu Ye, Xi Huang, Lei Ye, Ruizhi Zheng, Hong Lin, Mian Li, Tiange Wang, Zhiyun Zhao, Min Xu, Yi Duan, Hao Guo, Zhongshang Yuan, Philip Haycock, George Davey Smith, Richard M Martin, Guang Ning, Fang Hu, Weiqing Wang, Tom R. Gaunt, Jieli Lu, Yufang Bi +29 morewiley +1 more sourceIPGCA: A Comprehensive Single Cell Atlas of 1 074 127 Porcine Intestinal Cells Revealing Cellular Dynamics, Genetic Regulation, and Cross‐Species Conservation
Advanced Science, EarlyView.A high resolution integrated cell atlas of the pig intestine provides insights into the genetic mechanisms of complex traits (Created in BioRender. Yu, P. (2025) https://BioRender.com/o14c563) Abstract
The porcine intestinal tract is vital for nutrient absorption, immune regulation, and various physiological processes.Pengfei Yu, Qinqin Xie, Xiaodian Cai, Zhanming Zhong, Ran Wei, He Han, Shuang Liu, Zhenyang Zhang, Lingyao Xu, Zitao Chen, Zhe Zhang, Qishan Wang, Yuchun Pan, Zhen Wang, Zhe Zhang +14 morewiley +1 more sourceGenetic Influences on Brain Gene Expression in Rats Selected for
Tameness and Aggression
, 2014 Inter-individual differences in many behaviors are partly due to genetic
differences, but the identification of the genes and variants that influence
behavior remains challenging.Alexander Cagan, Belyaev, Darvasi, Enrico Petretto, Frank W. Albert, François Besnier, Henrike O. Heyne, Irina Z. Plyusnina, Leonid Kruglyak, Lyudmila Trut, Maxime Rotival, Rimma Kozhemyakina, Ronald Nelson, Susann Lautenschläger, Svante Pääbo, Torsten Schöneberg, Örjan Carlborg +16 morecore +1 more sourceGenetic determinants of co-accessible chromatin regions in activated T cells across humans. [PDF]
, 2018 Over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease.A Barrie, A Battle, A Franke, AA Shabalin, AM Klein, AR Quinlan, Atsede Siba, Aviv Regev, Aviva P. Aiden, B Li, BE Stranger, C Hou, Christine S. Cheng, Christophe Benoist, Chun J. Ye, CJ Ye, CK Stroud, D Hnisz, D Lee, D Sakata, DE Speiser, Dmytro Lituiev, E Elinav, E Splinter, EM Schmidt, Erez Lieberman Aiden, EZ Macosko, G Jun, G McVicker, H Kilpinen, H Li, H Li, HK Finucane, HM Kang, Howard Y. Chang, Ido Machol, Ivo Wortman, J Yang, JD Buenrostro, JD Buenrostro, JD Storey, JE Phillips, JF Degner, JN Hirschhorn, JS Delisle, K Enjyoji, Kendrick L. Hougen, KK Farh, L Chen, L Plesner, M Feuerer, M Ghandi, M Kasowski, M Kronenberg, M Kurachi, M. Grace Gordon, Marcin Tabaka, MB Gerstein, Meena Subramaniam, MI Love, MI McCarthy, Michael A. Beer, MN Lee, MT Maurano, Muhammad Shamim, MY Donath, N Kumasaka, NC Durand, Neva C. Durand, NP Restifo, P Cauchy, P Li, PC Hollenhorst, Philip L. De Jager, PM Visscher, PS Ohashi, R Satija, Rachel E. Gate, RE Thurman, RM Samstein, Roadmap Epigenomics Consortium,, S Deaglio, S Heinz, S Neph, SM Waszak, SS Rao, Su-Chen Huang, T Lappalainen, T Raj, The ENCODE Project Consortium., Ting Feng, TL Murphy, UM Marigorta, WA Whyte, WJ Astle, X Chen, X Sun, Y Belkaid, Y Zhang, YY Fan +99 morecore +2 more sourcesIntegrative Omics Defines Metabolic Biomarkers and Genetic Regulatory Mechanisms of Mortality Risk
Advanced Science, EarlyView.Understanding mortality mechanisms remains a fundamental challenge. Through multi‐omics analysis of a three‐generation chicken model, are identified 45,585 mQTLs and establish a 16‐metabolite predictor of mortality. An inflammation‐growth trade‐off and evolutionarily conserved pathways involving butyrate–microbiota interactions and L‐cysteine's dual ...Peihao Liu, Bingxing An, Jumei Zheng, Qiao Wang, Zhirui Yang, Zhengda Li, Dawei Liu, Fan Ying, Jie Wen, Lingzhao Fang, Guiping Zhao +10 morewiley +1 more sourceA common and unstable copy number variant is associated with differences in Glo1 expression and anxiety-like behavior [PDF]
, 2015 Glyoxalase 1 (Glo1) has been implicated in anxiety-like behavior in mice and in multiple psychiatric diseases in humans. We used mouse Affymetrix exon arrays to detect copy number variants (CNV) among inbred mouse strains and thereby identified a ...Borevitz, Justin O., Distler, Margaret G., Harr, Bettina, Lim, Jackie E., Palmer, Abraham A., Sokoloff, Greta, Su, Andrew I., Tarantino, Lisa M., Teschke, Meike, Walters, Ryan, Williams, Richard, Wiltshire, Tim, Wing, Claudia, Wu, Chunlei +13 morecore +2 more sourcesMendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits
Nature Communications, 2019 Genome-wide association studies (GWAS) have identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear.E. Porcu, S. Rüeger, Kaido Lepik, Mawussé Habibul Isabel Anand Wibowo Philip Alexis Frank Ma Agbessi Ahsan Alves Andiappan Arindrarto Awadalla , M. Agbessi, H. Ahsan, I. Alves, A. Andiappan, W. Arindrarto, P. Awadalla, A. Battle, F. Beutner, Marc Jan Bonder, D. Boomsma, M. Christiansen, A. Claringbould, Patrick Deelen, T. Esko, M. Favé, L. Franke, T. Frayling, Sina A. Gharib, G. Gibson, B. Heijmans, G. Hemani, R. Jansen, M. Kähönen, A. Kalnapenkis, S. Kasela, J. Kettunen, Yungil Kim, H. Kirsten, P. Kovacs, K. Krohn, J. Kronberg-Guzman, V. Kukushkina, Bernett Lee, T. Lehtimäki, M. Loeffler, U. Marigorta, Hailang Mei, L. Milani, G. Montgomery, M. Müller-Nurasyid, M. Nauck, M. Nivard, B. Penninx, M. Perola, N. Pervjakova, B. Pierce, J. Powell, H. Prokisch, B. Psaty, O. Raitakari, S. Ripatti, O. Rotzschke, A. Saha, Markus Scholz, K. Schramm, I. Seppälä, E. Slagboom, Coen D. A. Stehouwer, M. Stumvoll, Patrick F. Sullivan, Peter A. C. ’t Hoen, A. Teumer, J. Thiery, L. Tong, A. Tönjes, J. van Dongen, M. van Iterson, Joyce van Meurs, J. Veldink, J. Verlouw, P. Visscher, U. Völker, U. Võsa, H. Westra, C. Wijmenga, H. Yaghootkar, Jian Yang, B. Zeng, Futao Zhang, Wibowo Marian Dorret I. Jan Joris Patrick Lude Bastiaan T Arindrarto Beekman Boomsma Bot Deelen Deelen Frank, W. Arindrarto, M. Beekman, D. Boomsma, J. Bot, J. Deelen, B. Heijmans, Peter A. C. ’t Hoen, B. Hofman, J. Hottenga, A. Isaacs, M. Bonder, P. M. Jhamai, S. Kiełbasa, N. Lakenberg, R. Luijk, H. Mei, M. Moed, I. Nooren, R. Pool, C. Schalkwijk, P. Slagboom, H. Suchiman, M. Swertz, E. Tigchelaar, A. Uitterlinden, L. H. van den Berg, R. van der Breggen, C. V. D. van der Kallen, F. van Dijk, C. V. van Duijn, M. van Galen, M. V. van Greevenbroek, D. van Heemst, M. van Iterson, J. V. van Rooij, P. V. van‘t Hof, E. V. van Zwet, M. Vermaat, M. Verbiest, M. Verkerk, D. Zhernakova, S. Zhernakova, F. Santoni, A. Reymond, Z. Kutalik +128 moresemanticscholar +1 more sourceMainstream Artificial Intelligence Technologies in Contemporary Ophthalmology
Advanced Intelligent Systems, EarlyView.This review explores the latest artificial intelligence (AI) technologies in ophthalmology, focusing on four key data types: medical imaging, electronic health records, robotic‐assisted surgery, and genomics. It examines the structural features, use cases, clinical goals, and evaluation metrics of various AI algorithms, while also introducing emerging ...Shiqi Yin, Lei Wang, Shanjun Wu, Jiewei Jiang, Wei Qiang, Yangyang Wang, Shihong Wang, Yi Shao, Wei Chen, Zhongwen Li +9 morewiley +1 more source
