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Open access Jul 2026

Beyond the classical plasma secretome: genetic architecture and disease associations of the expanded human plasma proteome in 13,445 Europeans

Circulating plasma proteins are key biomarkers and therapeutic targets, now measurable at scale through high-throughput technologies, yet whether expanding proteomics platforms beyond the classical plasma secretome enhances genetic discovery and causal inference remains poorly understood. Here, we use an expanded SomaScan 7k platform to map the genetic architecture of a broader segment of the plasma proteome and to evaluate how proteome expansion affects pQTL discovery, causal inference and therapeutic target prioritisation. After quality control, we analysed 7,144 aptamers targeting 6,267 proteins in the harmonised dataset of two European cohorts: INTERVAL (n = 9,251 participants) and CHRIS (n = 4,194), and conducted genome-wide pQTL association analyses followed by meta-analysis. We identified 7,870 significant pQTLs (P-value < 1.26 x 10E-11; 1,784 cis, 6,086 trans), of which 2,704 (34%) associations were not reported in five prior large-scale pQTL studies. Newly assessed proteins, which accounted for 53% (1,422/2,704) of the novel associations, were less likely to harbour cis-pQTLs associations (15%) than those in the previous platform version (28%), consistent with their lower expected plasma concentrations and predominantly intracellular localisation. Colocalization analyses revealed widespread sharing of genetic signals across proteins and characterised 22 pleiotropic trans-regulatory hotspots accounting for 68% of all trans-pQTLs. Through two-sample Mendelian randomization analyses on 2,003 phenotypes from the Million Veteran Program, UK Biobank, and FinnGen (combined N > 1.2 million), we identified 6,340 genetically supported protein-trait associations, highlighting disease mechanisms and potential therapeutic opportunities beyond currently drug-targeted circulating proteins. Together, these findings provide a systematic view of the genetic architecture of the expanded plasma proteome and demonstrate that plasma proteome expansion reveals genetically anchored disease biology beyond the classical secretome, while exposing inherent biological and technical constraints of studying low-abundance intracellular proteins in circulation.

S. Cadiou, E. Konig, A. Mapelli et al. · 0 citations
Open access Nov 2025

SignifiKANTE: efficient P-value computation for gene regulatory networks

Gene regulatory networks (GRNs) are graph-based representations of regulatory relationships between transcription factors and target genes. Various tools exist to infer GRNs from gene expression data, but since this task is computationally intensive, statistical significance estimates are often omitted. While permutation-based empirical P-value computation methods are relatively straightforward to implement, they are prohibitively expensive when applied to popular regression-based GRN inference methods and realistically sized datasets. To address this bottleneck, we developed SignifiKANTE. SignifiKANTE is based on the key insight that the background count distributions of groups of target genes may be highly similar, even if their expression vectors show distinct behavior. Relying on this insight, SignifiKANTE employs gene clustering based on the 1-Wasserstein distance to create a small, constant number of background distributions which enables the simultaneous computation of approximate empirical P-values for multiple target genes. This reduces runtime by orders of magnitudes (for some datasets, from several weeks to few hours), without compromising faithfulness of the obtained P-values. SignifiKANTE extends the popular GRN inference package Arboreto and is available as a Python package on GitHub (https://github.com/bionetslab/SignifiKANTE) and PyPI (https://pypi.org/project/signifikante/).

F. Woller, Paul Martini, Souptik Sen et al. · 1 citation

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