Characterising RAG1 and RAG2 with predictive genomics

Abstract

While widespread genome sequencing ushers in a new era of preventive medicine, the tools for predictive genomics are still lacking. Time and resource limitations mean that human diseases remain uncharacterised because of an inability to predict clinically relevant genetic variants. The structural or functional impact of a coding variant is mirrored by allele frequencies amongst the general population. Studies in protein function frequently target sites that are evolutionarily preserved. However, rare diseases are often attributable to variants in genes that are highly conserved. An immunological disorder exemplifying this challenge occurs through damaging mutations in RAG1 and RAG2. RAG deficiency presents at an early age with a distinct phenotype of life-threatening immunodeficiency or autoimmunity. Many tools exist for variant pathogenicity prediction but these cannot account for the probability of variant occurrence. We present variants in RAG1 and RAG2 proteins which are most likely to be seen clinically as disease-causing. Our method of mutation rate residue frequency builds a map of most probable mutations allowing preemptive functional analysis. We compare the accuracy of our predicted probabilities to functional measurements and provide the method for application to any monogenic disorder.