Results 211 to 220 of about 1,985,505 (274)

Harnessing confounding and genetic pleiotropy to identify causes of disease through proteomics and Mendelian randomisation – ‘MR Fish’

open access: yes
Warwick AN   +11 more
europepmc   +1 more source

Student–teacher relationships as a mediator of children's education‐linked genetics: results from the Norwegian Mother, Father and Child Cohort Study

open access: yesJournal of Child Psychology and Psychiatry, Volume 67, Issue 10, Page 1729-1740, October 2026.
Background Genetic differences are robustly associated with educational outcomes, but how they become linked is poorly understood. A plausible hypothesis, yet to be thoroughly empirically tested, is that the school environment mediates the association. Methods Using structural equation models, we tested whether the student–teacher relationship at age 5
Chloe Austerberry   +10 more
wiley   +1 more source

Unveiling the Diagnostic Value and Potential Therapeutic Targets of Phenylalanine Metabolism in Pancreatic Cancer via Integrated Multi‐Omics and Machine Learning

open access: yesThe FASEB Journal, Volume 40, Issue 18, 30 September 2026.
MR identified phenylalanine as a causal PC risk factor (OR 1.18). A 5‐gene RF model (AUC 0.955) highlighted SLC6A14 as the top biomarker. Single‐cell analysis revealed epithelial–immune crosstalk. Molecular docking identified genistein (−9.1 kcal/mol) as a lead SLC6A14‐targeting compound.
Xing Liu   +3 more
wiley   +1 more source

The Spatiotemporal Genetic Architecture of Seed Vigor in Upland Cotton

open access: yesAdvanced Science, Volume 13, Issue 50, 7 September 2026.
Leveraging the semi‐automated SeedRanger platform, we profiled the germination kinetics of 356 cotton accessions at a 30‐min interval. This high‐throughput phenomic approach delineated a temporal genetic network comprising 541 stage‐specific loci. Crucially, functional validation identified FLA2 as a pivotal, auxin‐modulated regulator that orchestrates
Luyao Wang   +32 more
wiley   +1 more source

Livestock Multi‐Omics Integration: A Systematic Framework From Statistical Association to Causal Interpretation

open access: yesAdvanced Science, Volume 13, Issue 50, 7 September 2026.
A three‐tier livestock multi‐omics framework resolves four typical analytical pitfalls. Moving from statistical association through machine learning preprocessing to triple‐modal causal inference, it converts omics results into genomic selection and gene editing strategies to achieve One Health, underpinned by multi‐omics data, multimodal sequencing ...
Jiying Wen   +5 more
wiley   +1 more source

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