Quantifying the contribution of genetic variation to healthcare expenditure across diverse healthcare systems

Abstract

Healthcare systems must balance rising costs with the delivery of effective care, yet the factors underlying large inter-individual differences in healthcare expenditure remain incompletely understood. Here we examine how genome-wide genetic variation contributes to healthcare costs, analysing inpatient, outpatient, primary care and prescription drug expenditure in up to 1,429,889 individuals from 11 studies across 7 countries. We identify hundreds of common genetic variants robustly associated with healthcare costs, revealing a reproducible polygenic architecture shared across healthcare systems. Individual common variants have modest effects, typically altering annual costs by [~]1-2% per allele, with the strongest signals arising from the HLA region, consistent with pleiotropic effects across autoimmune and inflammatory diseases. In contrast, putative loss-of-function (pLOF) variants (ClinVar/ENIGMA pathogenic variants or LOFTEE high-confidence pLOF) in clinically actionable genes, studied in UK Biobank, have large individual-level consequences: carriers of such variants in BRCA1, BRCA2, MSH2 and APC experience more than a two-fold increase in annual inpatient costs. Cost-associated signals colocalize extensively with autoimmune disorders, cardiometabolic risk factors, pain sensitivity, and depression amongst others. Polygenic scores derived for healthcare costs can predict up to 1.4% of drug-related healthcare expenditure in independent cohorts and retain their effects in within-family analyses, indicating largely direct genetic influences. By linking genetic risk to healthcare expenditure, this work provides a foundation for integrating human genetics into health economics, preventive strategies and population-level screening.