|Year : 2015 | Volume
| Issue : 1 | Page : 58-59
Gene mutations: Understanding the significance using in silico analysis
K Sanjana Pillay
Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
|Date of Web Publication||22-May-2015|
K Sanjana Pillay
CSIR-Institute of Genomics and Integrative Biology, Mathura Road, New Delhi - 110 025
Source of Support: None, Conflict of Interest: None
Several individuals, from families at risk for genetic diseases, undergoing genetic testing receive results reporting a variant of unknown significance (VUS) leading to issues in risk assessment, counseling and preventive care. These variants remain frequently unknown because of the lack of information that could establish their direct correlation with predisposition to a disease. In silico analyses in the past few years has emerged as an efficient tool to classify these variants as being lethal or neutral. Most of these techniques take into consideration the effect of these variants on the structure and function of the protein. The review below describes the significance of in silico analyses in classification of these unknown variants.
Keywords: Variance of unknown significance (VUS), in silico analyses, genetic testing
|How to cite this article:|
Pillay K S. Gene mutations: Understanding the significance using in silico analysis. J Pract Cardiovasc Sci 2015;1:58-9
The human body is very complicated in its composition and functioning. The body being composed of trillion of cells and each cell contains the genetic material, the deoxyribonucleic acid (DNA), which is replicated and apportioned equally to the daughter cells each time a cell divides. A gene is a given or specific stretch of DNA, which codes for a specific protein or in some cases only RNA. In most genes, coding regions called exons are interrupted by noncoding regions called introns.
A mutation is an alteration or change in the structure of a gene, resulting in a variant which can be transferred to the next generation. A mutation may cause a change in the amino acid sequence of a protein, thus, making either a nonfunctional or a partially functional protein. Such a mutation that results in a nonfunctional protein in one or more gene leads to a genetic disorder. However, a mutation or a variation whose association with disease risk is unknown is termed as a variance of unknown significance (VUS). These may include missense mutations or in-frame deletions, variants that affect messenger RNA (mRNA) splicing, and variants in regulatory sequences. Several groups worldwide are now involved in the analysis of these unknown variants with the aim of providing it's relation to a disease and consequently, unequivocally identify at risk families. With the completion of genome sequencing for most of the higher organism it has become easier to screen for such unknown variance and its contribution toward disease development.
Screening for mutations has been instrumental in the study of the function of a gene and consequently, its impact on disease development. Major approaches for screening of VUS's involve in vivo, in vitro and for the past few years, bioinformatic tools like in silico screening. In silico is an expression meaning "performed on the computer." The phrase was coined as an allusion to similar Latin phrases "in vivo" and "in vitro." A number of in vitro assays are being used to understand the effect of these variants on gene function. Functional analyses such as producing gene knockdowns, in which expression of an organism's gene is reduced in cell lines and zebrafish (in vitro assays) are some of the other approaches which are expensive and may have limited information due to the multi-functionality of the gene. Several in vitro analyses can identify those unknown variants that affect the stability and integrity of mRNA transcripts of the gene.
In silico programs have in the past few years emerged as a convincing approach to study unknown variances. It is thought to have the potential of speeding up the process of classifying these unknown variances into pathogenic or neutral. In silico is an expression for describing all computer-based techniques used for analysis and interpretation of dataset or, in other words, carrying out biological experiments entirely on a computer. Bioinformatics is one such interdisciplinary field that applies computational methods in understanding biological data. According to Spurdle et al., 2008,  the most clinically useful of these programs are the ones which cause sequence variations that may create cryptic or ectopic sites in intronic or exonic regions. They suggest the use of multiple bioinformatic prediction programs, which may accurately assess the clinical significance of these unknown variants. A study by Flanagan et al., 2010  tested the significance of two such tools, PolyPhen and SIFT, in predicting the effect of VUS on the function of a protein. These two tools predict the effect of a VUS, resulting in an amino acid substitution, on the structure and function of a protein. The study found the two tools to be effective in prioritizing these unknown variants depending on their impact on the function of a protein. Another computational analyses tool such as align - Grantham Variation and Grantham Deviation (GVGD) takes into account multiple protein sequence alignment and the biophysical properties of amino acids to predict whether a VUS is deleterious or has no effect. Karchin et al. in 2008  developed a computational method that produces a probabilistic likelihood ratio predictive of whether these variants impair protein function. They applied the method to a tumor suppressor gene, BRCA2, and computed the likelihood ratio for 229 VUS found in individuals from high-risk breast/ovarian cancer families. The tool further may be used by clinicians and counselors to communicate the risk of an unknown variant to a person having that variant. In 2011, Radice et al.  stressed on the importance of integrating the results from in vitro and in silico analyses in order to accurately classify these variants and hence, the families as being linked or unlinked to the genes. So far, most of the studies carried out have primarily focused on the effect of the variants on tumor suppressor genes such as the BRCA genes.Diagram 1 gives an overview of the guidelines, as put forward by Elliot et al., 2014 Eur Heart J,  for genetic screening in families at risk for hypertrophic cardiomyopathy.
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Spurdle AB, Couch FJ, Hogervorst FB, Radice P, Sinilnikova OM, IARC Unclassified Genetic Variants Working Group. Prediction and assessment of splicing alterations: Implications for clinical testing. Hum Mutat 2008;29:1304-13.
Flanagan SE, Patch AM, Ellard S. Using SIFT and PolyPhen to predict loss-of-function and gain-of-function mutations. Genet Test Mol Biomarkers 2010;14:533-7.
Karchin R, Agarwal M, Sali A, Couch F, Beattie MS. Classifying Variants of Undetermined Significance in BRCA2 with protein likelihood ratios. Cancer Inform 2008;6:203-16.
Radice P, De Summa S, Caleca L, Tommasi S. Unclassified variants in BRCA genes: Guidelines for interpretation. Ann Oncol 2011;22 Suppl 1:i18-23.
Authors/Task Force members, Elliott PM, Anastasakis A, Borger MA, Borggrefe M, Cecchi F, et al.
2014 ESC guidelines on diagnosis and management of hypertrophic cardiomyopathy: The Task Force for the Diagnosis and Management of Hypertrophic Cardiomyopathy of the European Society of Cardiology (ESC). Eur Heart J 2014;35:2733-79.