Genetic Modifiers and Rare Mendelian Disease

Abstract

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.

Keywords: GWAS; bioinformatics; expressivity; genetic interaction; genetic modifier; genome sequencing; mendelian disease; penetrance; phenotypic variability; rare disease.

Figure 1

Different genetic phenomena in combination with Mendelian disorders, can make disease characterization challenging. (A) This is a case of a digenic disorder. Two individually healthy variants combinedly produce the disease (e.g., Bardet-Biedl syndrome caused by BBS1 and BBS10 [35]). (B) Two different monogenic disorders may produce a blended or composite representation of both diseases (e.g., Mutation in NPL (causing sialic acid disorder) and GJB2 (causing deafness) creating composite disease phenotype for a patient [36]). (C) A case for a genetic modifier. Disease variant is modified by the modifier variant (circled) that enhances or suppresses the severity of the disease (i.e., Spinal Muscular Atrophy modified by SMN2 variants [37]). (D) An oligogenic disease can be modified by a modifier variant as well (i.e., Digenic Usher syndrome modified by PDZD7 [38]). More complex scenarios are also possible, such as multiple modifier alleles that can act independently or together (joint effect) [31].

Figure 2

Published literature on modifier studies from 1998 to 2019. Keyword-based literature search result was extracted from PubMed and WoS (See Figure S1). (A) Histogram showing a comparative view of published literature for each year. (B) Pie chart showing a comparison between common and rare disease studies from 846 literature records.

Figure 3

A comparative view of the usage of a SNP array and sequencing technology to study genetic modifiers. (A) Publication count between the years 1998–2019. (B) Recent (2015–2019) publications indicate increased interest in high throughput sequencing techniques. Two linear (dotted) lines are used to show the trend shift.

Figure 4

Typical analysis steps followed to identify modifiers using NGS based approaches in recent years.