Predicting the combined effect of multiple genetic variants

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• The functional impact of single genetic variants is well-studied, but the combined effects of multiple variants within the same gene remain largely unexplored due to complexity and data limitations.
• Investigating joint variant effects is crucial for a comprehensive understanding of genetic disease mechanisms.

Purpose of the Study:

• To extend the HMMvar tool for analyzing the joint effects of multiple genetic variants.
• To assess the functional consequences of combined variants in tumor suppressor genes (TP53, PTEN) and cardiovascular-related genes (β-MHC, MyBP-C).

Main Methods:

• Utilized HMMvar, a hidden Markov model-based approach, to analyze multiple variants from the 1000 Genomes Project.
• Investigated the joint effects of compensatory indel variants in TP53 and PTEN.
• Examined compound mutations in β-MHC and MyBP-C genes.

Main Results:

• Demonstrated that the joint effect of multiple variants can significantly differ from the effect of a single variant.
• Observed that compensatory indels in TP53 and PTEN can alleviate the deleterious effects of single indel variants.
• Validated that compound mutations in β-MHC and MyBP-C lead to more severe cardiovascular disease than single mutations.

Conclusions:

• HMMvar is enhanced to quantify the functional effects of both single and multiple genetic variations on proteins.
• This represents the first tool capable of predicting functional effects for both single and general multiple variations.
• Precomputed scores for multiple variants and the HMMvar package are publicly available for research use.