Abstract
An increasing number of disorders have been identified for which two or more distinct alleles in two or more genes are required to either cause the disease or to significantly modify its onset, severity or phenotype. It is difficult to discover such interactions using existing approaches. The purpose of our work is to develop and evaluate a system that can identify combinations of alleles underlying digenic and oligogenic diseases in individual whole exome or whole genome sequences. Information that links patient phenotypes to databases of gene–phenotype associations observed in clinical or non-human model organism research can provide useful information and improve variant prioritization for genetic diseases. Additional background knowledge about interactions between genes can be utilized to identify sets of variants in different genes in the same individual which may then contribute to the overall disease phenotype. We have developed OligoPVP, an algorithm that can be used to prioritize causative combinations of variants in digenic and oligogenic diseases, using whole exome or whole genome sequences together with patient phenotypes as input. We demonstrate that OligoPVP has significantly improved performance when compared to state of the art pathogenicity detection methods in the case of digenic diseases. Our results show that OligoPVP can efficiently prioritize sets of variants in digenic diseases using a phenotype-driven approach and identify etiologically important variants in whole genomes. OligoPVP naturally extends to oligogenic disease involving interactions between variants in two or more genes. It can be applied to the identification of multiple interacting candidate variants contributing to phenotype, where the action of modifier genes is suspected from pedigree analysis or failure of traditional causative variant identification.
Highlights
Discrimination of causative genetic variants responsible for disease is a major challenge
We use the phenotypes associated with the combination of variants in disease database (DIDA) as phenotypes associated with the synthetic WGS, and we use PVP4 to prioritize variants, using an “unknown” mode of inheritance model
With the increasing appreciation of the relationship between complex and Mendelian diseases[54], the ability to discover multiple variants contributing to disease phenotype in the same genome provides a powerful tool to help understand the genetic architecture of diseases and the underlying physiological pathways
Summary
Discrimination of causative genetic variants responsible for disease is a major challenge. In a similar candidate approach for amyotrophic lateral sclerosis (ALS), affected individuals with proven or potentially pathogenic mutations in two or three known ALS genes are associated again with lower age of disease onset Congenital hypothyroidism has both rare monogenic recessive loss-of-function, and common, apparently sporadic, forms. Evidence has been provided for rare trigenic involvement of variants, such as in TSHR, SLC26A4 and GLIS321 In addition to these examples of genetic interaction, the observations suggesting digenic/triallelic inheritance in Bardet-Biedel syndrome (BBS)[22] continue to provoke interest and further research, and illustrate the challenges in establishing formal digenicity[23]. Other examples are critically discussed elsewhere[15]
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