Pleiotropic Variability Score: A Genome Interpretation Metric to Quantify Phenomic Associations of Genomic Variants

Abstract

AbstractA more complete understanding of phenomic space is critical for elucidating genome-phenome relationships and for assessing disease risk from genome sequencing. To incorporate knowledge of how related a variant’s associations are, we developed a new genome interpretation metric called Pleiotropic Variability Score (PVS). PVS uses semantic reasoning to score the relatedness of a genetic variant’s associated phenotypes based on those phenotypes’ relationships in the human phenotype ontology (HPO) and disease ontology (DO). We tested 78 unique semantic similarity methods and integrated six robust metrics to define the pleiotropy score of SNPs. We computed PVS for 12,541 SNPs which were mapped to 382 HPO and 317 DO unique phenotype terms in a genotype-phenotype catalog (10,021 SNPs mapped to DO phenotypes and 8,569 SNPs mapped to HPO phenotypes). We validated the utility of PVS by computing pleiotropy using an electronic health record linked genomic database (BioME, n=11,210). Further we demonstrate the application of PVS in personalized medicine using “personalized pleiotropy score” reports for individuals with genomic data that could potentially aid in variant interpretation. We further developed a software framework to incorporate PVS into VCF files and to consolidate pleiotropy assessment as part of genome interpretation pipelines. As the genome-phenome catalogs are growing, PVS will be a useful metric to assess genetic variation to find SNPs with highly pleiotropic effects. Additionally, variants with varying degree of pleiotropy can be prioritized for explorative studies to understand specific roles of SNPs and pleiotropic hubs in mediating novel phenotypes and drug development.