Mechanistic Modelling of Recessive Disease through Allelic Integration of Variant Effects

Interpreting the effects of genetic variants remains a major challenge in recessive diseases, where clinical outcomes often depend on interactions between alleles. Multiplex assays of variant effects (MAVEs) measure variant function at scale, but nonlinear relationships with biochemical activity complicate the interpretation of MAVE scores. Here, we describe a broadly applicable approach to estimate enzymatic activities for thousands of genetic variants using a pair of fitness assays conducted at different expression levels, by modelling the nonlinear relationship between activity and fitness. Activity scores from two alleles are then combined into a single pathogenicity metric that captures their joint effect. We applied this approach to adenylosuccinate lyase (ADSL), a purine biosynthesis enzyme mutated in the autosomal recessive disorder ADSL deficiency. Using a yeast-based MAVE, we quantified the functional impact of over 8,000 coding variants. Our framework distinguished pathogenic from benign alleles based on estimated activity, and the integrated pathogenicity score correlated strongly with biochemical measurements from patient-derived cells, outperforming existing computational predictors. This dual innovation--the mechanistic transformation of MAVE data and allelic integration--offers a generalizable strategy for probing enzyme function and interpreting genetic variation in recessive disorders.