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Innovative strategies for annotating the "relationSNP" between variants and molecular phenotypes
Jason E Miller1, Yogasudha Veturi1, Marylyn D Ritchie1
1Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104 USA.
Biodata Mining
|May 23, 2019
Summary
Understanding single nucleotide polymorphisms (SNPs) is key to disease research. This review covers SNP annotation history, tools, and future directions for exploring genetic disease etiology computationally.
Area of Science:
- Genomics and Bioinformatics
- Human Genetics
- Computational Biology
Background:
- The study of genetic variation at the nucleotide level, particularly single nucleotide polymorphisms (SNPs), has gained prominence since the human genome project.
- Understanding how SNPs contribute to traits and diseases is crucial for developing diagnostics, therapeutics, and elucidating disease etiology.
- Numerous datasets and algorithms exist for SNP annotation, focusing on coding and non-coding regions.
Purpose of the Study:
- To provide an updated summary of SNP annotation strategies, tools, and their historical context.
- To discuss the impact of different SNP annotation methods on exploring the genetic and molecular basis of diseases computationally.
- To highlight current gaps in knowledge and suggest future research directions in the field of SNP annotation.
Main Methods:
- Review of historical developments in SNP annotation.
- Discussion of commonly used and newer computational tools for SNP annotation.
- Analysis of approaches for annotating coding and non-coding variants, including contributions from consortiums like ENCODE and GTEx.
Main Results:
- SNP annotation has evolved from focusing on coding SNPs to encompassing non-coding regions, driven by advancements in sequencing and large-scale consortium data.
- Various tools exist to dissect the effects of different variant types, including synonymous variants, on gene expression and protein sequence.
- The choice of SNP annotation method significantly influences the *in-silico* exploration of disease etiology.
Conclusions:
- SNP annotation is a critical technique in human genetics for understanding disease.
- Continuous advancements in tools and biological understanding necessitate updated summaries of the field.
- This review serves as a resource for understanding computational tools for functional SNP characterization and their impact on research.