Towards precision medicine: advances in computational approaches for the analysis of human variants

Abstract

Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.

Keywords: GWAS; Genome Wide Association Studies; HGVS; Human Genome Variation Society; LSDB; NCBI; National Center for Biotechnology Information; SVM; databases for human variants; function prediction; human disease variants; locus-specific database; support vector machine.

Figure 1. Coverage comparison of genes, exonic missense variants, and indels between variant resources

Comparison of OMIM, UniProt/Swiss-Prot, ClinVar, HGMD Public, and HGMD Professional^™ for the genes (1A and 1B), the exonic missense variants (1C and 1D), and the insertions/deletions (1E and 1F) annotated with disease variants in each database. The area of each section of the venn diagrams is not proportional to size and the colors are for aesthetic purposes only.

Figure 2. Indel and splice site variant comparison between variant resources

Comparison of the OMIM, ClinVar, HGMD Public, and HGMD Professional^™ resources for genes annotated with disease variants that effect splicing sites (2A and 2B) and genes annotated with stop-loss or stop-gain disease variants (2C and 2D).

Figure 3. Heatmap of amino acid variants in human diseases

Depiction of the observed frequency of wild type to mutated type transitions implicated in human diseases. The missense variants analyzed were a non-redundant collection compiled using the OMIM, HGMD, UniProt/Swiss-Prot, and ClinVar resources.

Figure 4. Comparison of features utilized by techniques for predicting deleterious variants

Depiction of the types of information used by different methods for predicting deleterious variants.