Genomic network analysis characterizes genetic architecture and identifies trait-specific biology

Abstract Pervasive genetic overlap across human complex traits necessitates developing multivariate methods that can parse pleiotropic and trait-specific genetic signals. Here, we introduce Genomic Network Analysis (GNA), an analytic framework that applies the principles of network modelling to estimates of genetic overlap derived from genome-wide association study (GWAS) summary statistics. The result is a genomic network that describes the conditionally independent genetic associations between traits that remain when controlling for shared signal with the broader network of traits. Graph theory metrics provide added insight by formally quantifying the most important traits in the genomic network. GNA can discover additional trait-specific pathways by incorporating gene expression or genetic variants into the network to estimate their conditional associations with each trait. Extensive simulations establish GNA is well-powered for most GWAS. Application to a diverse set of traits demonstrate that GNA yields critical insight into the genetic architecture that demarcate genetically overlapping traits at varying levels of biological granularity. Competing Interest Statement The authors have declared no competing interest. Funding Statement JGT is supported by an Australian National Health and Medical Research Council (NHMRC) EL1 Investigator Grant (2027002). WRR is supported by an Australian NHMRC EL1 Investigator Grant (2025671). EMD is supported by an Australian NHMRC L1 Investigator Grant (2026364). ADG is supported by NIH Grants R01MH120219 and RF1AG073593. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The data used in this study are all publicly available or can be requested for access. Specific download links for various datasets are directly below. Summary statistics for cardiometabolic traits in East Asian ancestry are available to download on GWAS catalog: https://www.ebi.ac.uk/gwas/ Psychiatric disorder summary statistics for data from the psychiatric genomics consortium (PGC) can be downloaded here: https://www.med.unc.edu/pgc/download-results/ Links to the GTEx v8 gene expression reference weights used for univariate TWAS imputation in FUSION can be found here: http://gusevlab.org/projects/fusion/ Links to the functional reference weights from PsychENCODE also used for univariate TWAS can be found here: http://resource.psychencode.org/ I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability The data used in this study are all publicly available or can be requested for access. Specific download links for various datasets are directly below. Summary statistics for cardiometabolic traits in East Asian ancestry are available to download on GWAS catalog: https://www.ebi.ac.uk/gwas/ Psychiatric disorder summary statistics for data from the psychiatric genomics consortium (PGC) can be downloaded here: https://www.med.unc.edu/pgc/download-results/ Links to the GTEx v8 gene expression reference weights used for univariate TWAS imputation in FUSION can be found here: http://gusevlab.org/projects/fusion/ Links to the functional reference weights from PsychENCODE also used for univariate TWAS can be found here: http://resource.psychencode.org/ https://www.med.unc.edu/pgc/download-results/