Despite advances in high-throughput sequencing that have revolutionized the discovery of gene defects in rare Mendelian diseases, there are still gaps in translating individual genome variation to observed phenotypic outcomes. While we continue to improve genomics approaches to identify primary disease-causing variants, it is evident that no genetic variant acts alone. In other words, some other variants in the genome (genetic modifiers) may alleviate (suppress) or exacerbate (enhance) the severity of the disease, resulting in the variability of phenotypic outcomes. Thus, to truly understand the disease, we need to consider how the disease-causing variants interact with the rest of the genome in an individual. Here, we review the current state-of-the-field in the identification of genetic modifiers in rare Mendelian diseases and discuss the potential for future approaches that could bridge the existing gap.

Genetic modifiers are variants modulating phenotypic outcomes of a primary detrimental variant. They contribute to rare diseases phenotypic variability, but their identification is challenging. Genetic screening with model organisms is a widely used method for demystifying genetic modifiers. Forward genetics screening followed by whole genome sequencing (WGS) allows the detection of variants throughout the genome, but typically produces thousands of candidate variants making the interpretation and prioritization process very time consuming and tedious. Despite WGS being more time and cost efficient, usage of computational pipelines specific to modifier identification remains a challenge for biological-experiment-focused laboratories doing research with model organisms. To facilitate a broader implementation of WGS in genetic screens, we have developed MOM, a Model Organism Modifier pipeline as a user-friendly Galaxy workflow. MOM analyses raw short-read WGS data and implements tailored filtering to provide a Candidate Variant List (CVL) short enough to be further manually curated. We provide a detailed tutorial to run the Galaxy workflow MOM and guidelines to manually curate the CVLs. We have tested MOM on published and validated C. elegans modifiers screening datasets. As WGS facilitates high-throughput identification of genetic modifiers in model organisms, MOM provides a user-friendly solution to implement the bioinformatics analysis of the short-read datasets in laboratories without expertise or support in Bioinformatics.