Results 51 to 60 of about 7,071,839 (288)

Genetic predisposition, Aβ misfolding in blood plasma, and Alzheimer’s disease

Translational Psychiatry, 2021
Alzheimer’s disease is highly heritable and characterized by amyloid plaques and tau tangles in the brain. The aim of this study was to investigate the association between genetic predisposition, Aβ misfolding in blood plasma, a unique marker of ...
Hannah Stocker, Andreas Nabers, Laura Perna, Tobias Möllers, Dan Rujescu, Annette M. Hartmann, Bernd Holleczek, Ben Schöttker, Julia Stockmann, Klaus Gerwert, Hermann Brenner   +10 more
doaj   +1 more source

Polygenic transcriptome risk scores improve portability of polygenic risk scores across ancestries [PDF]

, 2020
Abstract Polygenic risk scores (PRS) are on course to translate the results of genome-wide association studies (GWAS) into clinical practice. To date, most GWAS have been based on individuals of European-ancestry, meaning that the utility of PRS for non-European populations is limited because SNP effects and LD patterns may not be ...
Yanyu Liang, Milton Pividori, Ani Manichaikul, Abraham A. Palmer, Nancy J. Cox, Heather Wheeler, Hae Kyung Im   +6 more
openaire   +1 more source

PGSXplorer: an integrated nextflow pipeline for comprehensive quality control and polygenic score model development [PDF]

PeerJ
The rapid development of next-generation sequencing technologies and genomic data sharing initiatives during the post-Human Genome Project-era has catalyzed major advances in individualized medicine research.
Tutku Yaraş, Yavuz Oktay, Gökhan Karakülah   +2 more
doaj   +2 more sources

Rapid Progression of Kidney Dysfunction in Swiss People Living with HIV: Contribution of Polygenic Risk Score and D:A:D Clinical Risk Score. [PDF]

, 2020
BACKGROUND In people with HIV (PWH), it is unknown whether genetic background associates with rapid progression of kidney dysfunction; i.e. eGFR decrease of >5mL/min/1.73m 2 per year for >3 consecutive years.
Tarr, Philip E, Ryom, Lene, Thurnheer, M.C., Fellay, J., Darling, KEA, Ledergerber, B., Bernasconi, Enos, Darling, Kathrine E. A., Swiss HIV Cohort Study, Dietrich, Lena G., Schmid, Patrick, Buvelot, H., Burkhalter, Felix, Hasse, Barbara, Weisser, Maja, Buvelot, Hélène, Schmid, P., Fellay, Jacques, Ryom, L., Tarr, Philip E., Thorball, C.W., Thorball, Christian W, Ledergerber, Bruno, Dietrich, L.G., Weisser, M., Hasse, B., Darling, Kathrine E A, Thorball, Christian W., Dietrich, Léna G, Tarr, P.E., Burkhalter, F., Thurnheer, Maria Christine, Bernasconi, E., Thorball CW, Fellay J, Dietrich LG, Darling KEA, Tarr PE, Ledergerber B, Thurnheer MC, Schmid P, Burkhalter F, Ryom L, Buvelot H, Bernasconi E, Weisser M, Hasse B, H Furrer, L Kaiser, M Cavassini, Maria Christine Thurnheer, B Martinez de Tejada, E Bernasconi, H Kovari, J Fehr, G Martinetti, L Elzi, M Battegay, A Ciuffi, G Pantaleo, Philip E Tarr, G Dollenmaier, T Klimkait, Bruno Ledergerber, H C Bucher, P Tarr, null null, C Marzolini, C R Kahlert, Patrick Schmid, Enos Bernasconi, P Schmid, D Haerry, D Nicca, Hélène Buvelot, K J Metzner, M Egger, N Muller, C A Fux, A U Scherrer, Léna G Dietrich, G Wandeler, P Vernazza, I Hosli, M Stockle, R Speck, D L Braun, Felix Burkhalter, R D Kouyos, A Rauch, S Yerly, H F Gunthard, H H Hirsch, Jacques Fellay, M Huber, Barbara Hasse, Christian W Thorball, Maja Weisser, J Fellay, C Rudin, R Weber, O Keiser, M Hoffmann, Lene Ryom, B Ledergerber, Kathrine E A Darling, M Perreau, A Calmy, J Boni, A Anagnostopoulos, P Paioni, B Hasse, A Trkola   +112 more
core   +2 more sources

A Facial Foundation Model for Clinical Biomarker Prediction and Real‐World Mobile Deployment

Advanced Science, EarlyView.
MedicalFaceFound is a facial foundation model developed through three‐stage progressive pre‐training, adapting representations from general vision to facial structure and clinical phenotypes. Using a single facial image, it estimates 62 biomarkers across multiple physiological systems and supports downstream risk prediction for cardiovascular ...
Tingfeng Xu, Huixuan Xu, Li Lin, Fei Wang, Yaodong Ding, Shuangqiao Liao, Xingyu Chen, Jichen Wen, Hongqiang Zhao, Jing Hao, Weixiang Zhao, Gong Zhang, Shiguang Shan, Yong Zeng, Hu Han, Minxian Wang   +15 more
wiley   +1 more source

Polygenic risk score for Alzheimer’s disease and trajectories of cardiometabolic risk factors in children

, 2019
Polygenic risk score for Alzheimer’s disease and trajectories of cardiometabolic risk factors in ...
Roxanna Korologou-linden
core   +1 more source

Structural Variants in the Human Genome Associated With Gut Microbiome and Blood Metabolic Traits With Potential Causal Links

Advanced Science, EarlyView.
Based on a multi‐omics cohort of 2002 individuals comprising high‐depth whole‐genome and metagenomic sequencing data, blood metabolites, and detailed questionnaires, we identified 138 859 high‐confidence SVs, uncovering 36 significant SV–microbiome associations and 48 SV–blood metabolite associations.
Qun Zhou, Keqing Wang, Junhong Chen, Haoyu Wang, Xinjiang Tan, Xuanlin Huang, Zhiyin Lin, Xun Xu, Xin Jin, Shaoqiao Zhang, Liang Xiao, Yuanqiang Zou, Tao Zhang, Xiaomin Liu   +13 more
wiley   +1 more source

Admixture-informed polygenic risk reporting using the ePRS framework

Nature Communications
Polygenic risk score values vary with genetic ancestry due to differences in population-specific allele frequencies and linkage disequilibrium patterns. We present a framework to calibrate polygenic risk scores based on ancestral makeup.
Yu-Jyun Huang, Nuzulul Kurniansyah, Matthew O. Goodman, Brian W. Spitzer, Jiongming Wang, Adrienne Stilp, Cecelia Laurie, Paul S. de Vries, Han Chen, Yuan-I Min, Mario Sims, Gina M. Peloso, Xiuqing Guo, Joshua C. Bis, Jennifer A. Brody, Laura M. Raffield, Jennifer A. Smith, Wei Zhao, Jerome I. Rotter, Stephen S. Rich, Susan Redline, Myriam Fornage, Robert Kaplan, Nora Franceschini, Daniel Levy, Alanna C. Morrison, Eric Boerwinkle, Nicholas L. Smith, Charles Kooperberg, Bruce M. Psaty, Sebastian Zöllner, the Trans-Omics for Precision Medicine Consortium, Tamar Sofer   +32 more
doaj   +1 more source

Distribution of polygenic risk scores in the PGSNPS cohort.

, 2016
A) Non-weighted polygenic risk score. B) Weighted polygenic risk score.
Chris Twelves (3156528), Siddhartha Kar (510873), Jean E. Abraham (3156513), Paul D. P. Pharoah (146906), Anne-Laure Vallier (3156534), Leila Dorling (3156525), Christopher J. Poole (3156516), Louise Hiller (39742), Kyriaki Michailidou (205130), Janet A. Dunn (3156531), Carlos Caldas (1042), Susan Ingle (3156522), Sarah J. Bowden (3156519), Richard Hardy (3156537), Helena M. Earl (695366)   +14 more
core   +1 more source

Information on the 21 SNPs used in the definition of Hodgkin lymphoma associated polygenic risk score.

, 2023
Information on the 21 SNPs used in the definition of Hodgkin lymphoma associated polygenic risk score.
Han Zhang (110653), Jing Qin (41397), Mark P. Purdue (7802537), Sheng Fu (247124), Sonja I. Berndt (6943262), Lei Song (30203), Kai Yu (50223)   +6 more
core   +1 more source