Network reconstruction for trans acting genetic loci using multi-omics data and prior information

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Multi-omics data (genome, transcriptome, epigenome) offer deep biological insights.
• Integrating multi-omics data is key to understanding complex regulatory processes.
• Quantitative trait loci (QTL) link genetic variants to molecular traits, revealing regulatory network footprints (trans-QTL hotspots).

Purpose of the Study:

• To reconstruct regulatory networks underlying trans-QTL hotspots.
• To integrate human cohort multi-omics data with data-driven prior biological information.
• To improve network inference by incorporating biological priors.

Main Methods:

• Developed a novel strategy for integrating QTL with human population-scale multi-omics data.
• Applied state-of-the-art network inference methods (BDgraph, glasso).
• Utilized manually curated prior information from biological databases and benchmarked extensively with simulated and cross-cohort data.

Main Results:

• Prior-based network inference strategies outperformed methods without prior information in simulations and showed better cross-dataset replication.
• Applied the approach to human cohort data, identifying two novel regulatory networks.
• Generated novel functional hypotheses for networks associated with schizophrenia and lean body mass.

Conclusions:

• Existing biological knowledge significantly enhances the integrative analysis of networks underlying trans associations.
• The developed approach successfully generates novel hypotheses about complex regulatory mechanisms.
• Demonstrated the utility of integrating multi-omics data with prior biological information for network reconstruction.