Characterizing common loss-of-function genes and their potential utility in assessing population variability and chemical susceptibility

Abstract

Inter-individual and population variability in susceptibility to chemical exposures confounds determination of threshold exposure levels to protect the most vulnerable. Current risk assessment frameworks, in the absence of empiric chemical-specific data, generally recommend default or probabilistic adjustment factors to account for such variability. We present an experimental approach to incorporate common genetic variants potentially impacting population-level differences in toxicant susceptibility into human cell-based models for any cellular apical endpoint of interest. We focus on the genes with the most common aggregate loss-of-function (LoF) alleles in the gnomAD v3.0 data which we designated as the PopVarLoF set. Unexpectedly, enrichment analysis of these genes found significant overrepresentation of gene products playing important functional roles in toxicology. Interrogation of GWAS and PheWAS databases found that these genes are associated with diverse metabolic phenotypes consistent with the relevance of the PopVarLoF set in studying variability of toxicant response in human populations. We further characterized the PopVarLoF set by developing custom lentiviral CRISPR knockout libraries targeting the PopVarLoF genes to assess their functional essentiality in the HepG2/C3A cell line. Functional disruption of 14 of the PopVarLoF genes ([~]1 %) without toxicant exposure resulted in significant growth defects in this cell line, consistent with the majority of PopVarLoF gene products having non-essential roles. The development of human cell-based toxicity assays or other NAMs which include the empiric assessment of common genetic sources of population variability in susceptibility to chemical exposure could contribute to more robust risk assessment which protects vulnerable populations while reducing uncertainty. Impact statementWe characterize common loss of function genetic variants which could impact toxicant susceptibility and describe an approach to incorporate them into NAMs to enable empiric estimates of the contribution of genetic variability to diverse toxicity endpoints.