Protein–protein interaction network architecture of human polygenic traits reveals domain-spanning connectivity and evolutionary pressures

Abstract Background Human polygenic phenotypes arise from the combined effects of many genes that interact within molecular networks. Yet, we know little about how the structure of these networks constrains or facilitates the evolution of complex traits. Here, we systematically examine the relationship between protein–protein interaction (PPI) network architecture and evolutionary signatures across 4,756 human polygenic phenotypes. Results We show that genes associated with polygenic phenotypes exhibit significantly higher connectivity within the global PPI network compared to matched random gene sets. Highly connected genes are enriched for immune-related biological processes, whereas genes with fewer interactions are preferentially associated with neurogenesis-related functions. Importantly, among trait-associated genes, greater network connectivity is associated with weaker selective constraint, indicating that evolutionary pressure varies according to network embedding. Conclusions Together, these findings provide a systems-level framework linking molecular interaction architecture to the evolution of human polygenic traits. To support this effort, we also develop an online portal enabling researchers to generate and explore hypotheses by identifying genes that are both highly associated and highly connected across thousands of polygenic phenotypes.

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Publication Details

Journal
Genome biology
Published
2026-08-27
DOI
https://doi.org/10.1186/s13059-026-04253-1
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

Protein–protein interaction network architecture of human polygenic traits reveals domain-spanning connectivity and evolutionary pressures

Jessica Bigge, Pouria Dasmeh, Ehsan Tamandeh, Adrian Serohijos et al.
Genome biology
Bioinformatics and Genomic Networks
article

Protein–protein interaction network architecture of human polygenic traits reveals domain-spanning connectivity and evolutionary pressures

Jessica Bigge, Pouria Dasmeh, Ehsan Tamandeh, Adrian Serohijos, Johannes Schumacher, Kiran Kunwar, Carlo Maj
article en

Abstract

Abstract Background Human polygenic phenotypes arise from the combined effects of many genes that interact within molecular networks. Yet, we know little about how the structure of these networks constrains or facilitates the evolution of complex traits. Here, we systematically examine the relationship between protein–protein interaction (PPI) network architecture and evolutionary signatures across 4,756 human polygenic phenotypes. Results We show that genes associated with polygenic phenotypes exhibit significantly higher connectivity within the global PPI network compared to matched random gene sets. Highly connected genes are enriched for immune-related biological processes, whereas genes with fewer interactions are preferentially associated with neurogenesis-related functions. Importantly, among trait-associated genes, greater network connectivity is associated with weaker selective constraint, indicating that evolutionary pressure varies according to network embedding. Conclusions Together, these findings provide a systems-level framework linking molecular interaction architecture to the evolution of human polygenic traits. To support this effort, we also develop an online portal enabling researchers to generate and explore hypotheses by identifying genes that are both highly associated and highly connected across thousands of polygenic phenotypes.

Genome biologyVol. 27(1)
Philipps University of Marburg (DE), Phillips University (US), Université de Montréal (CA)
Philipps-Universität Marburg, Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 18%
Bioinformatics and Genomic Networks
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Protein–protein interaction network architecture of human polygenic traits reveals domain-spanning connectivity and evolutionary pressures — Jessica Bigge, Pouria Dasmeh, et al. · Genome biology (2026) | TGRS Research Map | TGRS