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Proteomics identify disease-associated variants in patients with rare diseases undiagnosed after genome sequencing

Item Type:Article
Title:Proteomics identify disease-associated variants in patients with rare diseases undiagnosed after genome sequencing
Creators: Carrasco-Zanini, Julia ORCID logoORCID: https://orcid.org/0000-0002-3988-7505, Andrade, Jorge ORCID logoORCID: https://orcid.org/0000-0002-4577-8620, Pietzner, Maik ORCID logoORCID: https://orcid.org/0000-0003-3437-9963, Kousathanas, Athanasios ORCID logoORCID: https://orcid.org/0000-0001-6265-6521, Jacobsen, Julius O.B. ORCID logoORCID: https://orcid.org/0000-0002-3265-1591, Lord, Jenny ORCID logoORCID: https://orcid.org/0000-0002-0539-9343, Telugu, Narasimha ORCID logoORCID: https://orcid.org/0000-0003-4509-7056, Diecke, Sebastian ORCID logoORCID: https://orcid.org/0000-0002-5219-5992, Mülleder, Michael ORCID logoORCID: https://orcid.org/0000-0001-9792-3861, Bierbaum, Dominik ORCID logoORCID: https://orcid.org/0000-0002-4639-3378, Vestito, Letizia ORCID logoORCID: https://orcid.org/0000-0003-0008-936X, Robinson, Peter N. ORCID logoORCID: https://orcid.org/0000-0002-0736-9199, Ralser, Markus ORCID logoORCID: https://orcid.org/0000-0001-9535-7413, Baralle, Diana, Wareham, Nicholas J. ORCID logoORCID: https://orcid.org/0000-0003-1422-2993, Elgar, Greg ORCID logoORCID: https://orcid.org/0000-0001-7323-1596, Potente, Michael ORCID logoORCID: https://orcid.org/0000-0002-5689-0036, Brown, Matthew A. ORCID logoORCID: https://orcid.org/0000-0003-0538-8211, Caulfield, Mark ORCID logoORCID: https://orcid.org/0000-0001-9295-3594, Smedley, Damian ORCID logoORCID: https://orcid.org/0000-0002-5836-9850 and Langenberg, Claudia ORCID logoORCID: https://orcid.org/0000-0002-5017-7344
Abstract:Despite the introduction of genome sequencing (GS) for rare disease diagnostics, a genetic cause is not identified in most patients. Here, we explored the potential of proteomics to improve the diagnostic yield in 424 patients with rare diseases from the 100,000 Genomes Project (100kGP) without a genetic diagnosis. Serum proteomic profiling was performed using the Olink Explore 1536 assay (N = 1463 proteins). For 13 patients without genetic diagnoses, detection of lower serum protein "outliers" (z-score < -2) led to confirmed genetic diagnoses by resolving variants of uncertain significance or prioritizing genes for targeted GS reanalysis. For 23 additional patients without genetic diagnoses (64% of findings), we identified candidate gene-disease links and variants through convergent evidence from lower protein outliers and variants ranked through the variant prioritization tool Exomiser. For example, we identified a candidate heterozygous missense variant [Genome Aggregation Database (gnomAD) minor allele frequency = 0.006%] in tyrosine kinase with immunoglobulin-like and epidermal growth factor homology domains 1 (TIE1) that was only present in a patient with lower TIE1 serum abundance (z-score = -5.12) and their father, both of whom were affected by the same monogenic cardiac disorder, but in no other individuals from the 100kGP. Missense (52.5%) and splice region (27.5%) variants accounted for most diagnostic or candidate variants prioritized. This proof-of-principle study demonstrated that serum proteomics can support rare disease diagnosis and identify disease-causing genes in patients undiagnosed after GS, although successful implementation will likely depend on tissue specificity of protein expression, detectability in blood, proteomic platform coverage, and sensitivity.
Keywords:Genetic Predisposition to Disease, Human Genome, Proteomics, Rare Diseases, Whole Genome Sequencing
Source:Science Translational Medicine
ISSN:1946-6234
Publisher:American Association for the Advancement of Science
Volume:18
Number:866
Page Range:eaeb1331
Date:9 September 2026
Official Publication:https://doi.org/10.1126/scitranslmed.aeb1331
PubMed:View item in PubMed
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