Predicting phenotypic severity of uncertain gene variants in the RET Proto-Oncogene

pre-printAlthough reported gene variants in the RET oncogene have been directly associated with multiple endocrine neoplasia type 2 and hereditary medullary thyroid carcinoma, other mutations are classified as variants of uncertain significance (VUS) until the associated clinical phenotype is made clear. Currently, some 46 non-synonymous VUS entries exist in curated archives. In the absence of a gold standard method for predicting phenotype outcomes, this follow up study applies feature selected amino acid physical and chemical properties feeding a Bayes classifier to predict disease association of uncertain gene variants into categories of benign and pathogenic. Algorithm performance and VUS predictions were compared to established phylogenetic based mutation prediction algorithms. Curated outcomes and unpublished RET gene variants with known disease association were used to benchmark predictor performance. Reliable classification of RET uncertain gene variants will augment current clinical information of RET mutations and assist in improving prediction algorithms as knowledge increases

In Silico Analysis of Single Nucleotide Polymorphism (SNPs) in Human β-Globin Gene

Single amino acid substitutions in the globin chain are the most common forms of genetic variations that produce hemoglobinopathies- the most widespread inherited disorders worldwide. Several hemoglobinopathies result from homozygosity or compound heterozygosity to beta-globin (HBB) gene mutations, such as that producing sickle cell hemoglobin (HbS), HbC, HbD and HbE. Several of these mutations are deleterious and result in moderate to severe hemolytic anemia, with associated complications, requiring lifelong care and management. Even though many hemoglobinopathies result from single amino acid changes producing similar structural abnormalities, there are functional differences in the generated variants. Using in silico methods, we examined the genetic variations that can alter the expression and function of the HBB gene. Using a sequence homology-based Sorting Intolerant from Tolerant (SIFT) server we have searched for the SNPs, which showed that 200 (80%) non-synonymous polymorphism were found to be deleterious. The structure-based method via PolyPhen server indicated that 135 (40%) non-synonymous polymorphism may modify protein function and structure. The Pupa Suite software showed that the SNPs will have a phenotypic consequence on the structure and function of the altered protein. Structure analysis was performed on the key mutations that occur in the native protein coded by the HBB gene that causes hemoglobinopathies such as: HbC (E→K), HbD (E→Q), HbE (E→K) and HbS (E→V). Atomic Non-Local Environment Assessment (ANOLEA), Yet Another Scientific Artificial Reality Application (YASARA), CHARMM-GUI webserver for macromolecular dynamics and mechanics, and Normal Mode Analysis, Deformation and Refinement (NOMAD-Ref) of Gromacs server were used to perform molecular dynamics simulations and energy minimization calculations on β-Chain residue of the HBB gene before and after mutation. Furthermore, in the native and altered protein models, amino acid residues were determined and secondary structures were observed for solvent accessibility to confirm the protein stability. The functional study in this investigation may be a good model for additional future studies

Improved Detection of Rare Genetic Variants for Diseases

Technology advances have promoted gene-based sequencing studies with the aim of identifying rare mutations responsible for complex diseases. A complication in these types of association studies is that the vast majority of non-synonymous mutations are believed to be neutral to phenotypes. It is thus critical to distinguish potential causative variants from neutral variation before performing association tests. In this study, we used existing predicting algorithms to predict functional amino acid substitutions, and incorporated that information into association tests. Using simulations, we comprehensively studied the effects of several influential factors, including the sensitivity and specificity of functional variant predictions, number of variants, and proportion of causative variants, on the performance of association tests. Our results showed that incorporating information regarding functional variants obtained from existing prediction algorithms improves statistical power under certain conditions, particularly when the proportion of causative variants is moderate. The application of the proposed tests to a real sequencing study confirms our conclusions. Our work may help investigators who are planning to pursue gene-based sequencing studies

In Silico profiling of deleterious amino acid substitutions of potential pathological importance in haemophlia A and haemophlia B

<p>Abstract</p> <p>Background</p> <p>In this study, instead of current biochemical methods, the effects of deleterious amino acid substitutions in <it>F8 and F9 </it>gene upon protein structure and function were assayed by means of computational methods and information from the databases. Deleterious substitutions of <it>F8 and F9 </it>are responsible for Haemophilia A and Haemophilia B which is the most common genetic disease of coagulation disorders in blood. Yet, distinguishing deleterious variants of <it>F8 and F9 </it>from the massive amount of nonfunctional variants that occur within a single genome is a significant challenge.</p> <p>Methods</p> <p>We performed an <it>in silico </it>analysis of deleterious mutations and their protein structure changes in order to analyze the correlation between mutation and disease. Deleterious nsSNPs were categorized based on empirical based and support vector machine based methods to predict the impact on protein functions. Furthermore, we modeled mutant proteins and compared them with the native protein for analysis of protein structure stability.</p> <p>Results</p> <p>Out of 510 nsSNPs in <it>F8</it>, 378 nsSNPs (74%) were predicted to be 'intolerant' by SIFT, 371 nsSNPs (73%) were predicted to be 'damaging' by PolyPhen and 445 nsSNPs (87%) as 'less stable' by I-Mutant2.0. In <it>F9</it>, 129 nsSNPs (78%) were predicted to be intolerant by SIFT, 131 nsSNPs (79%) were predicted to be damaging by PolyPhen and 150 nsSNPs (90%) as less stable by I-Mutant2.0. Overall, we found that I-Mutant which emphasizes support vector machine based method outperformed SIFT and PolyPhen in prediction of deleterious nsSNPs in both <it>F8 </it>and <it>F9</it>.</p> <p>Conclusions</p> <p>The models built in this work would be appropriate for predicting the deleterious amino acid substitutions and their functions in gene regulation which would be useful for further genotype-phenotype researches as well as the pharmacogenetics studies. These <it>in silico </it>tools, despite being helpful in providing information about the nature of mutations, may also function as a first-pass filter to determine the substitutions worth pursuing for further experimental research in other coagulation disorder causing genes.</p