Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Genetic-Proteomic Integration Identifies Predictive Plasma Proteins for Multiple Sclerosis.

Annals of neurology - 1 Aug 2026

Ding Yuan, Hamitouche Dylan, Thebault Simon, Kearns Patrick, Abdelhak Ahmed, Harroud Adil

Abstract excerpt

OBJECTIVE: Multiple sclerosis (MS) develops after a prolonged preclinical phase. Identifying circulating biomarkers that capture this early biology can improve risk stratification and guide intervention. We aimed to identify plasma proteins driving MS susceptibility using large-scale proteogenomic integration and to evaluate their prediagnostic predictive value and effects on severity. METHODS: We used cis-acting...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Biomarkers
Share to Blood Proteins
Share to Genetic Predisposition to Disease

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genome-Wide Association Study

Share to Multiple Sclerosis

Share to Predictive Value of Tests

Share to Proteomics

Share to Quantitative Trait Loci

Source

Open in PubMed Open full text

Identifiers

PMID: 42171268
DOI: 10.1002/ana.78256
PMCID: PMC13387974

Genes

DKKL1

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Genetic-Proteomic Integration Identifies Predictive Plasma Proteins for Multiple Sclerosis.PMID 42171268Read ↗

Multiomics integration prioritizes potential drug targets for multiple sclerosisSemantic proximity Read ↗

Multi-omics integration prioritizes potential drug targets for multiple sclerosisSemantic proximity Read ↗

Identification of novel drug targets for multiple sclerosis by integrating plasma genetics and proteomes.Semantic proximity Read ↗

Novel plasma and brain proteins that are implicated in multiple sclerosisSemantic proximity Read ↗

The genetic architecture of multiple sclerosis in 2026: From susceptibility to disease progression.Semantic proximity Read ↗

Integrated multi-omics and machine learning prioritize key immune genes for multiple sclerosis risk prediction.Semantic proximity Read ↗

Human CSF proteogenomics links genetic variation to neurodegenerative disease proteinsSemantic proximity Read ↗

Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traitsSemantic proximity Read ↗

From genome to phenome via the proteome: broad capture, antibody-based proteomics to explore disease mechanismsSemantic proximity Read ↗

Proteome-Wide Mendelian Randomization Identifies Causal Proteins for Multiple Sclerosis Susceptibility and Severity.Semantic proximity Read ↗

Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosisSemantic proximity Read ↗

Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association dataSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.