Constructing genotype and phenotype network helps reveal disease heritability and phenome-wide association studies

Analyses of a bipartite Genotype and Phenotype Network (GPN), linking the genetic variants and phenotypes based on statistical associations, provide an integrative approach to elucidate the complexities of genetic relationships across diseases and identify pleiotropic loci. In this study, we first assess contributions to constructing a well-defined GPN with a clear representation of genetic associations by comparing the network properties with a random network, including connectivity, centrality, and community structure. Next, we construct network topology annotations of genetic variants that quantify the possibility of pleiotropy and apply stratified linkage disequilibrium (LD) score regression to 12 highly genetically correlated phenotypes to identify enriched annotations. The constructed network topology annotations are informative for disease heritability after conditioning on a broad set of functional annotations from the baseline-LD model. Finally, we extend our discussion to include an application of bipartite GPN in phenome-wide association studies (PheWAS). The community detection method can be used to obtain a priori grouping of phenotypes detected from GPN based on the shared genetic architecture, then jointly test the association between multiple phenotypes in each network module and one genetic variant to discover the cross-phenotype associations and pleiotropy. Significance thresholds for PheWAS are adjusted for multiple testing by applying the false discovery rate (FDR) control approach. Extensive simulation studies and analyses of 633 electronic health record (EHR)-derived phenotypes in the UK Biobank GWAS summary dataset reveal that most multiple phenotype association tests based on GPN can well-control FDR and identify more significant genetic variants compared with the tests based on UK Biobank categories. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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 All data produced in the present work are contained in the manuscript