Computational approaches for predicting variant impact: An overview from resources, principles to applications

Area of Science:

• Human Genetics
• Bioinformatics
• Computational Biology

Background:

• Next-generation sequencing (NGS) generates vast genomic data, increasing personal genetic information availability.
• Experimental validation of genotype-phenotype relationships is time-consuming and inefficient.
• A gap exists in comprehensive resources linking genetic variants to clinical/experimental evidence.

Purpose of the Study:

• To review and discuss computational approaches for predicting the impact of genetic variants on phenotype.
• To focus on methods for predicting the impact of non-synonymous variants (nsSNVs).

Main Methods:

• Categorization of nsSNV impact prediction approaches into six classes.
• Discussion of the underlying rationale, constraints, and comparative study findings for each category.
• Presentation of how predictive approaches are applied in various research contexts.

Main Results:

• Identification and classification of diverse computational methods for nsSNV impact prediction.
• Analysis of the strengths, weaknesses, and practical applications of these predictive tools.
• Highlighting the indispensable role of computational approaches in understanding genotype-phenotype relationships.

Conclusions:

• Computational variant impact prediction is crucial for interpreting large-scale genomic data.
• Despite existing constraints, these tools are essential for advancing human genetics research.
• Further development and validation of computational methods will enhance genotype-phenotype correlation studies.