Investigation on the role of nsSNPs in HNPCC genes--a bioinformatics approach

Computational Exploration of Single-Nucleotide Polymorphisms in the Human hRAS Gene: Implications and Insights

Background A group of genes called oncogenes includes the Harvey rat sarcoma virus (hRAS) gene. Along with hRAS, Kirsten rat sarcoma viral oncogene homolog (kRAS) and neuroblastoma RAS viral oncogene homolog (nRAS) genes belong to the Rat sarcoma (Ras) family of oncogenes. These three genes result in Rho guanosine triphosphate hydrolases (GTPases) as their protein product. Instructions for producing the protein hRAS, which is mainly involved in controlling cell division, are provided by the hRAS gene. The hRAS protein transfers signals from outside through a process called signal transduction. Because the hRAS protein is a GTPase, it changes the chemical guanosine-5'-triphosphate (GTP) into guanosine diphosphate (GDP). GTP and GDP molecules operate as switches to turn on and off the hRAS. This study aimed to anticipate the structure and stability of the protein resulting from missense single-nucleotide polymorphisms (SNPs) in the human hRAS genes. Methodology To investigate the possible negative effects associated with these SNPs, bioinformatic analysis is typically essential. The following tools were employed for forecasting harmful SNPs: Scale-Invariant Feature Transform (SIFT), Protein Analysis Through Evolutionary Relationships (PANTHER), non-synonymous SNP by Protein Variation Effect Analyzer (PROVEAN), and non-synonymous SNP by Single Nucleotide Polymorphism Annotation Platform (SNAP). Results The present study identified a total of 11 SNPs using the SIFT approach, which were shown to have functional significance. Only two of these 11 SNPs were determined to be tolerable, whereas nine were shown to be detrimental. Among the 11 SNPs analyzed, seven (Q61H, Q99H, K117R, A121D, A146V, R169W, R169Q) were classified as possibly damaging,and four (G13V, Q22K, Q61K, Q13V) were categorized as probably benign according to the predictions made by PANTHER tools. Therefore, the seven SNPs were identified as high-risk SNPs. Conclusions Given that SNPs have the potential to be candidates for cellular alterations brought on by mutations that are associated with cancer, this study provides vital information about how SNPs might be utilized as a diagnostic marker for cancer.

Investigation of pathogenic germline variants in gastric cancer and development of "GasCanBase" database

BACKGROUND

Gastric cancer, which is also known as stomach cancer, can be influenced by both germline and somatic mutations. Non-synonymous Single Nucleotide Polymorphisms (nsSNPs) in germline have long been reported to play a pivotal role in cancer progression.

AIM

The aim of this study is to examine the nsSNP in GC-associated genes. The study also aims to develop a database with extensive information regarding the nsSNPs in the GC-associated genes and their impacts.

METHODS AND RESULTS

A total of 34,588 nsSNPs from 1,493,460 SNPs of the 40 genes were extracted from the available SNP database. Drug binding and energy minimization were examined by molecular docking and YASARA. To validate the existence of the germline CDH1 gene mutation (rs34466743) in the isolated blood DNA of gastric cancer (GC) patients, polymerase chain reaction (PCR) and DNA sequencing were performed. According to the results of the gene network analysis, 17 genes may interact with other types of cancer. A total of 11,363 nsSNPs were detected within the 40 GC genes. Among these, 474 nsSNPs were predicted to be damaging and 40 to be the most damaging. The SNPs in domain regions were thought to be strong candidates that alter protein functions. Our findings proposed that most of the selected nsSNPs were within the domains or motif regions. Free Energy Deviation calculation of protein structure pointed toward noteworthy changes in the structure of each protein that can demolish its natural function. Subsequently, drug binding confirmed the structural variation and the ineffectiveness of the drug against the mutant model in individuals with these germline variants. Furthermore, in vitro analysis of the rs34466743 germline variant from the CDH1 gene confirmed the strength and robustness of the pipeline that could expand the somatic alteration for causing cancer. In addition, a comprehensive gastric cancer polymorphism database named "GasCanBase" was developed to make data available to researchers.

CONCLUSION

The findings of this study and the "GasCanBase" database may greatly contribute to our understanding of molecular epidemiology and the development of precise therapeutics for gastric cancer. GasCanBase is available at: https://www.gascanbase.com/.

Increased diagnostic yield in a cohort of hearing loss families using a comprehensive stepwise strategy of molecular testing

Hearing loss is one of the most common sensory disorders in humans. This study proposes a stepwise strategy of deafness gene detection using multiplex PCR combined with high-throughput sequencing, Sanger sequencing, multiplex ligation-dependent probe amplification (MLPA), and whole-exome sequencing (WES) to explore its application in molecular diagnosis of hearing loss families. A total of 152 families with hearing loss were included in this study, the highest overall diagnosis rate was 73% (111/152). The diagnosis rate of multiplex PCR combined with high-throughput sequencing was 52.6% (80/152). One families was diagnosed by Sanger sequencing of GJB2 exon 1. Two families were diagnosed by MLPA analysis of the STRC gene. The diagnosis rate with additional contribution from WES was 18.4% (28/152). We identified 21 novel variants from 15 deafness genes by WES. Combining WES and deep clinical phenotyping, we diagnosed 11 patients with syndromic hearing loss (SHL). This study demonstrated improved diagnostic yield in a cohort of hearing loss families and confirmed the advantages of a stepwise strategy in the molecular diagnosis of hearing loss.

Neonatal congenital leukemia caused by several missense mutations and AFF1‑KMT2A fusion: A case report

Neonatal leukemia, a congenital form of leukemia, is a rare and fatal disease occurring in the neonatal period. Its etiology and pathogenesis have remained to be fully elucidated and the clinical manifestations differ due to age variability. Acute myeloid leukemia (AML) occurring after birth indicates genetic abnormalities and possibly intrauterine exposure to radiation, drugs or other toxins. The present report described the case of a premature neonate without phenotypic signs of Down syndrome, but with an elevated white blood cell count, mainly pertaining to the monocytes of peripheral blood. At 31 weeks of gestation, delivery by Caesarean section was performed due to fetal distress; however, the infant died three days after birth. Further laboratory examination indicated pediatric myeloid leukemia. The present case report described a case of fetal AML. According to the results of peripheral blood smear and targeted-panel sequencing, 5 missense mutations with clinical significance and a novel AFF1-KMT2A fusion gene were detected, which may be the main causes of AML and death.

Do non-pathogenic variants of DNA mismatch repair genes modify neurofibroma load in neurofibromatosis type 1?

Non-pathogenic mismatch repair (MMR) gene variants can be associated with decreased MMR capacity in several settings. Due to an increased mutation rate, reduced MMR capacity leads to accumulation of somatic sequence changes in tumour suppressor genes such as in the neurofibromatosis type 1 (NF1) gene. Patients with autosomal dominant NF1 typically develop neurofibromas ranging from single to thousands. Concerning the number of neurofibromas NF1 patients face a situation that is still not predictable. A few studies suggested that germline non-pathogenic MMR gene variants modify the number of neurofibromas in NF1 and by this mechanism may promote the extent of neurofibroma manifestation. This review represents first evidence that specific non-pathogenic single nucleotide variants of MMR genes act as a modifier of neurofibroma manifestation in NF1, highlighting MSH2 re4987188 as the best analysed non-pathogenic variant so far. In summary, besides MSH2 promotor methylation, specific non-pathogenic germline MSH2 variants are associated with the extent of neurofibroma manifestation. Those variants can serve as a biomarker to facilitate better mentoring of NF1 patients at risk.

Investigation of discordant sibling pairs from hereditary breast cancer families and analysis of a rare PMS1 variant

BACKGROUND

It is likely that additional genes for hereditary breast cancer can be identified using a discordant sib pair design. Using this design we identified individuals harboring a rare PMS1 c.605G>A variant previously predicted to result in loss of function.

OBJECTIVES

A family-based design and predictive algorithms were used to prioritize candidate variants possibly associated with an increased risk of hereditary breast cancer. Functional analyses were performed for one of the candidate variants, PMS1 c.605G>A.

METHODS

1) 14 discordant sister-pairs from hereditary breast cancer families were identified. 2) Whole exome sequencing was performed and candidate risk variants identified. 3) A rare PMS variant was identified in 2 unrelated affected sisters but no unaffected siblings. 4) Functional analysis of this variant was carried out using targeted mRNA sequencing.

RESULTS

Genotype-phenotype correlation did not demonstrate tracking of the variant with cancer in the family. Functional analysis revealed no difference in exon 6 incorporation, which was validated by analyzing PMS1 allele specific expression.

CONCLUSIONS

The PMS1 c.605G>A variant did not segregate with disease, and there was no variant-dependent impact on PMS1 exon 6 splicing, supporting this variant is likely benign. Functional analyses are imperative to understanding the clinical significance of predictive algorithms.

Pathogenic nsSNPs that increase the risks of cancers among the Orang Asli and Malays

Single-nucleotide polymorphisms (SNPs) are the most common genetic variations for various complex human diseases, including cancers. Genome-wide association studies (GWAS) have identified numerous SNPs that increase cancer risks, such as breast cancer, colorectal cancer, and leukemia. These SNPs were cataloged for scientific use. However, GWAS are often conducted on certain populations in which the Orang Asli and Malays were not included. Therefore, we have developed a bioinformatic pipeline to mine the whole-genome sequence databases of the Orang Asli and Malays to determine the presence of pathogenic SNPs that might increase the risks of cancers among them. Five different in silico tools, SIFT, PROVEAN, Poly-Phen-2, Condel, and PANTHER, were used to predict and assess the functional impacts of the SNPs. Out of the 80 cancer-related nsSNPs from the GWAS dataset, 52 nsSNPs were found among the Orang Asli and Malays. They were further analyzed using the bioinformatic pipeline to identify the pathogenic variants. Three nsSNPs; rs1126809 (TYR), rs10936600 (LRRC34), and rs757978 (FARP2), were found as the most damaging cancer pathogenic variants. These mutations alter the protein interface and change the allosteric sites of the respective proteins. As TYR, LRRC34, and FARP2 genes play important roles in numerous cellular processes such as cell proliferation, differentiation, growth, and cell survival; therefore, any impairment on the protein function could be involved in the development of cancer. rs1126809, rs10936600, and rs757978 are the important pathogenic variants that increase the risks of cancers among the Orang Asli and Malays. The roles and impacts of these variants in cancers will require further investigations using in vitro cancer models.

Mechanistic insights on nsSNPs on binding site of renin and cytochrome P450 proteins: A computational perceptual study for pharmacogenomics evaluation

Past several decades, therapeutic investigations lead to the discovery of numerous antihypertensive drugs. Although it has been proved for their potency, altered efficacy is common norms in several conditions due to genetic variations. Cytochrome P450 plays a crucial role in drug metabolism and responsible for the pharmacokinetic and pharmacodynamic properties of the drug molecules. Here, we report the deleterious point mutations in the genes associated with the altered response of antihypertensive drug molecules and their metabolizers. Missense variants were filtered as potential nonsynonymous single nucleotide polymorphisms among the available data for the target genes (REN, CYP2D6, CYP3A4). The key objective of the work is to identify the deleterious single nucleotide polymorphisms (SNPs) responsible for the drug response and metabolism for the application of personalized medication. The molecular docking studies revealed that Aliskiren and other clinically approved drug molecules have a high binding affinity with both wild and mutant structures of renin, CYP2D6, and CYP3A4 proteins. The docking (Glide XP) score was observed to have in the range of -8.896 to -11.693 kcal/mol. The molecular dynamics simulation studies were employed to perceive the structural changes and conformational deviation through various analyses. Each studied SNPs was observed to have disparate scoring in the binding affinity to the specific drug molecules. As a prospective plan, we assume this study might be applied to identify the risky SNPs associated with hypertension from the patients to recommend the suitable drug for personalized hypertensive treatment. Further, extensive clinical pharmacogenomics studies are required to support the findings.

Relevance of pathogenicity prediction tools in human RYR1 variants of unknown significance

Malignant hyperthermia (MH) is a pharmacogenetic disorder of skeletal muscle metabolism characterized by generalized muscle rigidity, increased body temperature, rhabdomyolysis, hyperkalemia and severe metabolic acidosis. The underlying mechanism of MH involves excessive Ca²⁺ release from myotubes via the ryanodine receptor type 1 (RYR1) and the voltage-dependent L-type calcium channel (CACNA1S). As more than 300 variants of unknown significance have been detected to date, we examined whether freely available pathogenicity prediction tools are able to detect relevant MH causing variants. In this diagnostic accuracy study, blood samples from 235 individuals with a history of a clinical malignant hyperthermia or their close relatives were genetically screened for RYR1 variants of all 106 RYR1 exons and additionally for known variants of CACNA1S. In vitro contracture tests were conducted on muscle biopsies obtained from all individuals, independently of whether a pathogenic variant, a variant of unknown significance or no variant was detected. Comparisons were made to three established bioinformatic pathogenicity detection tools to identify the clinical impact of the variants of unknown significance. All detected genetic variants were tested for pathogenicity by three in silico approaches and compared to the in vitro contracture test. Sensitivity and specificity of exon screening of all individuals listed in our MH database was analyzed. Exon screening identified 97 (41%) of the 235 individuals as carriers of pathogenic variants. Variants of unknown significance were detected in 21 individuals. Variants of unknown significance were subdivided into 19 malignant-hyperthermia-susceptible individuals and 2 non-malignant-hyperthermia-susceptible individuals. All pathogenic variants as well as the malignant-hyperthermia-suspectible variants were correctly identified by the bioinformatic prediction tools. Sensitivity of in silico approaches ranged between 0.71 and 0.98 (Polyphen 0.94 [CI 95% 0.75; 0.99]; Sift 0.98 [CI 95% 0.81; 0.99]; MutationTaster 0.92 [CI 95% 0.75; 0.99]). Specificity differed depending on the used tool (Polphen 0.98 [CI 95% 0.32; 0.99]; Sift 0.98 [CI 95% 0.32; 0.99]; MutationTaster 0.00 [CI 95% 0.00; 0.60]). All pathogenic variants and variants of unknown significance were scored as probably damaging in individuals, demonstrating a high sensitivity. Specificity was very low in one of the three tested programs. However, due to potential genotype–phenotype discordance, bioinformatic prediction tools are currently of limited value in diagnosing pathogenicity of MH-susceptible variants.

DNA Mismatch Repair Gene Variants in Sporadic Solid Cancers

The phenotypic effects of single nucleotide polymorphisms (SNPs) in the development of sporadic solid cancers are still scarce. The aim of this review was to summarise and analyse published data on the associations between SNPs in mismatch repair genes and various cancers. The mismatch repair system plays a unique role in the control of the genetic integrity and it is often inactivated (germline and somatic mutations and hypermethylation) in cancer patients. Here, we focused on germline variants in mismatch repair genes and found the outcomes rather controversial: some SNPs are sometimes ascribed as protective, while other studies reported their pathological effects. Regarding the complexity of cancer as one disease, we attempted to ascertain if particular polymorphisms exert the effect in the same direction in the development and treatment of different malignancies, although it is still not straightforward to conclude whether polymorphisms always play a clear positive role or a negative one. Most recent and robust genome-wide studies suggest that risk of cancer is modulated by variants in mismatch repair genes, for example in colorectal cancer. Our study shows that rs1800734 in MLH1 or rs2303428 in MSH2 may influence the development of different malignancies. The lack of functional studies on many DNA mismatch repair SNPs as well as their interactions are not explored yet. Notably, the concerted action of more variants in one individual may be protective or harmful. Further, complex interactions of DNA mismatch repair variations with both the environment and microenvironment in the cancer pathogenesis will deserve further attention.

Prediction of Deleterious Non-synonymous SNPs of Human STK11 Gene by Combining Algorithms, Molecular Docking, and Molecular Dynamics Simulation

Serine-threonine kinase11 (STK11) is a tumor suppressor gene which plays a key role in regulating cell growth and apoptosis. It is widely known as a multitasking kinase and engaged in cell polarity, cell cycle arrest, chromatin remodeling, energy metabolism, and Wnt signaling. The substitutions of single amino acids in highly conserved regions of the STK11 protein are associated with Peutz-Jeghers syndrome (PJS), which is an autosomal dominant inherited disorder. The abnormal function of the STK11 protein is still not well understood. In this study, we classified disease susceptible single nucleotide polymorphisms (SNPs) in STK11 by using different computational algorithms. We identified the deleterious nsSNPs, constructed mutant protein structures, and evaluated the impact of mutation by employing molecular docking and molecular dynamics analysis. Our results show that W239R and W308C variants are likely to be highly deleterious mutations found in the catalytic kinase domain, which may destabilize structure and disrupt the activation of the STK11 protein as well as reduce its catalytic efficiency. The W239R mutant is likely to have a greater impact on destabilizing the protein structure compared to the W308C mutant. In conclusion, these mutants can help to further realize the large pool of disease susceptibilities linked with catalytic kinase domain activation of STK11 and assist to develop an effective drug for associated diseases.

Molecular insights of the G2019S substitution in LRRK2 kinase domain associated with Parkinson's disease: A molecular dynamics simulation approach

The G2019S substitution in the Leucine-rich repeat kinase 2 (LRRK2) is significantly associated with Parkinson's disease (PD). This substitution was identified in both familial and sporadic forms of PD with a higher frequency. Few computational studies have reported the impact of G2019S substitution on inhibitors of the kinase domain of LRRK2. However, no computational study deeply investigated the possible impact of the G2019S substitution on the kinase domain in its Apo conformation. Therefore, in this study, we used 200 ns molecular dynamic simulation using the GROMACS 5.1.4 package software to investigate the impact of the G2019S substitution on the structure of the kinase domain of LRRK2. Our results indicate that the G2019S substitution affects the dynamics and stability of LRRK2 by decreasing the flexibility and increasing the compactness of the kinase domain and showing its tendency to be in an active conformation for long time interval because of the high energy barrier between active and inactive conformation. This study predicts the molecular pathogenicity mechanism of the G2019S on patients with PD and provides a potential platform for developing therapeutics for patients with PD that harbor this amino acid substitution.

Computational insights of K1444N substitution in GAP-related domain of NF1 gene associated with neurofibromatosis type 1 disease: a molecular modeling and dynamics approach

The NF1 gene encodes for neurofibromin protein, which is ubiquitously expressed, but most highly in the central nervous system. Non-synonymous SNPs (nsSNPs) in the NF1 gene were found to be associated with Neurofibromatosis Type 1 disease, which is characterized by the growth of tumors along nerves in the skin, brain, and other parts of the body. In this study, we used several in silico predictions tools to analyze 16 nsSNPs in the RAS-GAP domain of neurofibromin, the K1444N (K1423N) mutation was predicted as the most pathogenic. The comparative molecular dynamic simulation (MDS; 50 ns) between the wild type and the K1444N (K1423N) mutant suggested a significant change in the electrostatic potential. In addition, the RMSD, RMSF, Rg, hydrogen bonds, and PCA analysis confirmed the loss of flexibility and increase in compactness of the mutant protein. Further, SASA analysis revealed exchange between hydrophobic and hydrophilic residues from the core of the RAS-GAP domain to the surface of the mutant domain, consistent with the secondary structure analysis that showed significant alteration in the mutant protein conformation. Our data concludes that the K1444N (K1423N) mutant lead to increasing the rigidity and compactness of the protein. This study provides evidence of the benefits of the computational tools in predicting the pathogenicity of genetic mutations and suggests the application of MDS and different in silico prediction tools for variant assessment and classification in genetic clinics.

A profound computational study to prioritize the disease-causing mutations in PRPS1 gene

Charcot-Marie-Tooth disease (CMT) is one of the most commonly inherited congenital neurological disorders, affecting approximately 1 in 2500 in the US. About 80 genes were found to be in association with CMT. The phosphoribosyl pyrophosphate synthetase 1 (PRPS1) is an essential enzyme in the primary stage of de novo and salvage nucleotide synthesis. The mutations in the PRPS1 gene leads to X-linked Charcot-Marie-Tooth neuropathy type 5 (CMTX5), PRS super activity, Arts syndrome, X-linked deafness-1, breast cancer, and colorectal cancer. In the present study, we obtained 20 missense mutations from UniProt and dbSNP databases and applied series of comprehensive in silico prediction methods to assess the degree of pathogenicity and stability. In silico tools predicted four missense mutations (D52H, M115 T, L152P, and D203H) to be potential disease causing mutations. We further subjected the four mutations along with native protein to 50 ns molecular dynamics simulation (MDS) using Gromacs package. The resulting trajectory files were analyzed to understand the stability differences caused by the mutations. We used the Root Mean Square Deviation (RMSD), Radius of Gyration (Rg), solvent accessibility surface area (SASA), Covariance matrix, Principal Component Analysis (PCA), Free Energy Landscape (FEL), and secondary structure analysis to assess the structural changes in the protein upon mutation. Our study suggests that the four mutations might affect the PRPS1 protein function and stability of the structure. The proposed study may serve as a platform for drug repositioning and personalized medicine for diseases that are caused by the PRPS1 deficiency.

MERRF Classification: Implications for Diagnosis and Clinical Trials

BACKGROUND

Given the etiologic heterogeneity of disease classification using clinical phenomenology, we employed contemporary criteria to classify variants associated with myoclonic epilepsy with ragged-red fibers (MERRF) syndrome and to assess the strength of evidence of gene-disease associations. Standardized approaches are used to clarify the definition of MERRF, which is essential for patient diagnosis, patient classification, and clinical trial design.

METHODS

Systematic literature and database search with application of standardized assessment of gene-disease relationships using modified Smith criteria and of variants reported to be associated with MERRF using modified Yarham criteria.

RESULTS

Review of available evidence supports a gene-disease association for two MT-tRNAs and for POLG. Using modified Smith criteria, definitive evidence of a MERRF gene-disease association is identified for MT-TK. Strong gene-disease evidence is present for MT-TL1 and POLG. Functional assays that directly associate variants with oxidative phosphorylation impairment were critical to mtDNA variant classification. In silico analysis was of limited utility to the assessment of individual MT-tRNA variants. With the use of contemporary classification criteria, several mtDNA variants previously reported as pathogenic or possibly pathogenic are reclassified as neutral variants.

CONCLUSIONS

MERRF is primarily an MT-TK disease, with pathogenic variants in this gene accounting for ~90% of MERRF patients. Although MERRF is phenotypically and genotypically heterogeneous, myoclonic epilepsy is the clinical feature that distinguishes MERRF from other categories of mitochondrial disorders. Given its low frequency in mitochondrial disorders, myoclonic epilepsy is not explained simply by an impairment of cellular energetics. Although MERRF phenocopies can occur in other genes, additional data are needed to establish a MERRF disease-gene association. This approach to MERRF emphasizes standardized classification rather than clinical phenomenology, thus improving patient diagnosis and clinical trial design.

An Integrated Computational Framework to Assess the Mutational Landscape of α-L-Iduronidase IDUA Gene

Mucopolysaccharidosis type I is a lysosomal genetic disorder caused due to the deficiency of the α-L-iduronidase enzyme (IDUA). Mutations associated with IDUA lead to mild to severe forms of diseases characterized by different clinical features. In the present study, we first performed a comprehensive analysis using various in silico prediction tools to screen and prioritize the missense mutations or nonsynonymous SNPs (nsSNPs) associated with IDUA. Subsequently, statistical analysis was empowered to examine the predictive ability and accuracy of the in silico prediction tool results supporting the disease phenotype ranging from mild to severe. Till date, no study has been carried out in IDUA in analyzing the impact of the nsSNPs at the structural level. In this context with the aid of pathogenic and stability prediction in silico tools, we identified nsSNPs R89Q, R89W, and P533R to be most deleterious and disease-causing having impact on the function of the protein. Extensive molecular dynamics analysis was performed using Gromacs to understand the deleterious nature of the mutants. Variations observed between the trajectory files of native and mutants R89Q, R89W, and P533R using Gromacs utilities enabled us to measure the adverse effects on the protein and could be the underlying reasons for the disease pathogenesis. These findings may be helpful in understanding the genotype-phenotype relationship and molecular basis of the disease to design drugs for better treatment. J. Cell. Biochem. 119: 555-565, 2018. © 2017 Wiley Periodicals, Inc.

Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes

Missense variants represent a significant proportion of variants identified in clinical genetic testing. In the absence of strong clinical or functional evidence, the American College of Medical Genetics recommends that these findings be classified as variants of uncertain significance (VUS). VUSs may be reclassified to better inform patient care when new evidence is available. It is critical that the methods used for reclassification are robust in order to prevent inappropriate medical management strategies and unnecessary, life-altering surgeries. In an effort to provide evidence for classification, several in silico algorithms have been developed that attempt to predict the functional impact of missense variants through amino acid sequence conservation analysis. We report an analysis comparing internally derived, evidence-based classifications with the results obtained from six commonly used algorithms. We compiled a dataset of 1118 variants in BRCA1, BRCA2, MLH1, and MSH2 previously classified by our laboratory’s evidence-based variant classification program. We compared internally derived classifications with those obtained from the following in silico tools: Align-GVGD, CONDEL, Grantham Analysis, MAPP-MMR, PolyPhen-2, and SIFT. Despite being based on similar underlying principles, all algorithms displayed marked divergence in accuracy, specificity, and sensitivity. Overall, accuracy ranged from 58.7 to 90.8% while the Matthews Correlation Coefficient ranged from 0.26–0.65. CONDEL, a weighted average of multiple algorithms, did not perform significantly better than its individual components evaluated here. These results suggest that the in silico algorithms evaluated here do not provide reliable evidence regarding the clinical significance of missense variants in genes associated with hereditary cancer.

Bioinformatic Analysis of GJB2 Gene Missense Mutations

Gap junction beta 2 (GJB2) gene is the most commonly mutated connexin gene in patients with autosomal recessive and dominant hearing loss. According to Ensembl (release 74) database, 1347 sequence variations are reported in the GJB2 gene and about 13.5% of them are categorized as missense SNPs or nonsynonymous variant. Because of the high incidence of GJB2 mutations in hearing loss patients, revealing the molecular effect of GJB2 mutations on protein structure may also provide clear point of view regarding the molecular etiology of deafness. Hence, the aim of this study is to analyze structural and functional consequences of all known GJB2 missense variations to the Cx26 protein by applying multiple bioinformatics methods. Two-hundred and eleven nonsynonymous variants were collected from Ensembl release 74, Leiden Open Variation Database (LOVD) and The Human Gene Mutation Database (HGMD). A number of bioinformatic tools were utilized for predicting the effect of GJB2 missense mutations at the sequence, structural, and functional levels. Some of the mutations were found to locate highly conserved regions and have structural and functional properties. Moreover, GJB2 mutations were also found to affect Cx26 protein at the molecular level via loss or gain of disorder, catalytic site, and post-translational modifications, including methylation, glycosylation, and ubiquitination. Findings, presented here, demonstrated the application of bioinformatic algorithms to predict the effects of mutations causing hearing impairment. I expect, this type of analysis will serve as a start point for future experimental evaluation of the GJB2 gene mutations and it will also be helpful in evaluating other deafness-related gene mutations.

Influence of V54M mutation in giant muscle protein titin: a computational screening and molecular dynamics approach

Recent genetic studies have revealed the impact of mutations in associated genes for cardiac sarcomere components leading to dilated cardiomyopathy (DCM). The cardiac sarcomere is composed of thick and thin filaments and a giant muscle protein known as titin or connectin. Titin interacts with T-cap/telethonin in the Z-line region and plays a vital role in regulating sarcomere assembly. Initially, we screened all the variants associated with giant protein titin and analyzed their impact with the aid of pathogenicity and stability prediction methods. V54M mutation found in the hydrophobic core region of the protein associated with abnormal clinical phenotype leads to DCM was selected for further analysis. To address this issue, we mapped the deleterious mutant V54M, modeled the mutant protein complex, and deciphered the impact of mutation on binding with its partner telethonin in the titin crystal structure of PDB ID: 1YA5 with the aid of docking analysis. Furthermore, two run molecular dynamics simulation was initiated to understand the mechanistic action of V54M mutation in altering the protein structure, dynamics, and stability. According to the results obtained from the repeated 50 ns trajectory files, the overall effect of V54M mutation was destabilizing and transition of bend to coil in the secondary structure was observed. Furthermore, MMPBSA elucidated that V54M found in the Z-line region of titin decreases the binding affinity of titin to Z-line proteins T-cap/telethonin thereby hindering the protein-protein interaction.

Targeted next-generation sequencing of 22 mismatch repair genes identifies Lynch syndrome families

Causative germline mutations in mismatch repair (MMR) genes can only be identified in ~50% of families with a clinical diagnosis of the inherited colorectal cancer (CRC) syndrome hereditary nonpolyposis colorectal cancer (HNPCC)/Lynch syndrome (LS). Identification of these patients are critical as they are at substantially increased risk of developing multiple primary tumors, mainly colorectal and endometrial cancer (EC), occurring at a young age. This demonstrates the need to develop new and/or more thorough mutation detection approaches. Next‐generation sequencing (NGS) was used to screen 22 genes involved in the DNA MMR pathway in constitutional DNA from 14 HNPCC and 12 sporadic EC patients, plus 2 positive controls. Several softwares were used for analysis and functional annotation. We identified 5 exonic indel variants, 42 exonic nonsynonymous single‐nucleotide variants (SNVs) and 1 intronic variant of significance. Three of these variants were class 5 (pathogenic) or class 4 (likely pathogenic), 5 were class 3 (uncertain clinical relevance) and 40 were classified as variants of unknown clinical significance. In conclusion, we have identified two LS families from the sporadic EC patients, one without a family history of cancer, supporting the notion for universal MMR screening of EC patients. In addition, we have detected three novel class 3 variants in EC cases. We have, in addition discovered a polygenic interaction which is the most likely cause of cancer development in a HNPCC patient that could explain previous inconsistent results reported on an intronic EXO1 variant.

Genomic scan for identifying candidate genes for paratuberculosis resistance in sheep

Breeding objectives relating to health, functional traits and welfare need to receive priority in the research programs and selection schemes, but very few reports are available on natural resistant genotypes in livestock, where some important diseases cause severe economic losses and pose serious zoonotic threats. In this study, diagnosis of paratuberculosis was performed on 759 adult sheep, from a single flock, with the serum antibody enzyme-linked immunosorbent assay; 100 sheep were selected among the extreme divergent animals for the S/P ratio obtained from the test, and were genotyped on the Illumina Ovine SNP50K BeadChip. A genome-wide scan was then performed on the individual marker genotypes, in the attempt to identify genomic regions associated with disease resistance in sheep. For each marker, the allelic substitution effect was calculated by regressing the S/P value on the number of copies of the reference allele. The position on the OARv3.1 Genome Assembly was searched for 32 markers, which showed a statistically significant allelic substitution effect (Raw P < 0.0006 and FDR P < 0.09). All markers were located within, or close to, annotated genes. Five of these genes, SEMA3, CD109, PCP4, PRDM2 and ITFG2 are referred in literature to play a role in either disease resistance or cell-mediated immune response.

Functional and Structural Consequences of Damaging Single Nucleotide Polymorphisms in Human Prostate Cancer Predisposition Gene RNASEL

A commonly diagnosed cancer, prostate cancer (PrCa), is being regulated by the gene RNASEL previously known as PRCA1 codes for ribonuclease L which is an integral part of interferon regulated system that mediates antiviral and antiproliferative role of the interferons. Both somatic and germline mutations have been implicated to cause prostate cancer. With an array of available Single Nucleotide Polymorphism data on dbSNP this study is designed to sort out functional SNPs in RNASEL by implementing different authentic computational tools such as SIFT, PolyPhen, SNPs&GO, Fathmm, ConSurf, UTRScan, PDBsum, Tm-Align, I-Mutant, and Project HOPE for functional and structural assessment, solvent accessibility, molecular dynamics, and energy minimization study. Among 794 RNASEL SNP entries 124 SNPs were found nonsynonymous from which SIFT predicted 13 nsSNPs as nontolerable whereas PolyPhen-2 predicted 28. SNPs found on the 3' and 5' UTR were also assessed. By analyzing six tools having different perspectives an aggregate result was produced where nine nsSNPs were found to be most likely to exert deleterious effect. 3D models of mutated proteins were generated to determine the functional and structural effect of the mutations on ribonuclease L. The initial findings were reinforced by the results from I-Mutant and Project HOPE as these tools predicted significant structural and functional instability of the mutated proteins. Expasy-ProSit tool defined the mutations to be situated in the functional domains of the protein. Considering previous analysis this study revealed a conclusive result deducing the available SNP data on the database by identifying the most damaging three nsSNP rs151296858 (G59S), rs145415894 (A276V), and rs35896902 (R592H). As such studies involving polymorphisms of RNASEL were none to be found, the results of the current study would certainly be helpful in future prospects concerning prostate cancer in males.

Functional analysis of MSH2 unclassified variants found in suspected Lynch syndrome patients reveals pathogenicity due to attenuated mismatch repair

BACKGROUND

Lynch syndrome, an autosomal-dominant disorder characterised by high colorectal and endometrial cancer risks, is caused by inherited mutations in DNA mismatch repair (MMR) genes. Mutations fully abrogating gene function are unambiguously disease causing. However, missense mutations often have unknown functional implications, hampering genetic counselling. We have applied a novel approach to study three MSH2 unclassified variants (UVs) found in Dutch families with suspected Lynch syndrome.

METHODS

The three mutations were recreated in the endogenous Msh2 gene in mouse embryonic stem cells by oligonucleotide-directed gene modification. The effect of the UVs on MMR activity was then tested using a set of functional assays interrogating the main MMR functions.

RESULTS

We recreated and functionally tested three MSH2 UVs: MSH2-Y165D (c.493T>G), MSH2-Q690E (c.2068C>G) and MSH2-M813V (c.2437A>G). We observed reduced levels of MSH2-Y165D and MSH2-Q690E but not MSH2-M813V proteins. MSH2-M813V was able to support all MMR functions similar to wild-type MSH2, whereas MSH2-Y165D and MSH2-Q690E showed partial defects.

CONCLUSIONS

Based on the results from our functional assays, we conclude that the MSH2-M813V variant is not disease causing. The MSH2-Y165D and MSH2-Q690E variants affect MMR function and are therefore likely the underlying cause of familial cancer predisposition. Since the MMR defect is partial, these variants may represent low penetrance alleles.

Evaluation and identification of damaged single nucleotide polymorphisms in COL1A1 gene involved in osteoporosis

INTRODUCTION

Single-nucleotide polymorphisms (SNPs) are biomarkers for exploring the genetic basis of many complex human diseases. The prediction of SNPs is promising in modern genetic analysis but it is still a great challenge to identify the functional SNPs in a disease-related gene. The computational approach has overcome this challenge and an increase in the successful rate of genetic association studies and reduced cost of genotyping have been achieved. The objective of this study is to identify deleterious non-synonymous SNPs (nsSNPs) associated with the COL1A1 gene.

MATERIAL AND METHODS

The SNPs were retrieved from the Single Nucleotide Polymorphism Database (dbSNP). Using I-Mutant, protein stability change was calculated. The potentially functional nsSNPs and their effect on proteins were predicted by PolyPhen and SIFT respectively. FASTSNP was used for estimation of risk score.

RESULTS

Our analysis revealed 247 SNPs as non-synonymous, out of which 5 nsSNPs were found to be least stable by I-Mutant 2.0 with a DDG value of > -1.0. Four nsSNPs, namely rs17853657, rs17857117, rs57377812 and rs1059454, showed a highly deleterious tolerance index score of 0.00 with a change in their physicochemical properties by the SIFT server. Seven nsSNPs, namely rs1059454, rs8179178, rs17853657, rs17857117, rs72656340, rs72656344 and rs72656351, were found to be probably damaging with a PSIC score difference between 2.0 and 3.5 by the PolyPhen server. Three nsSNPs, namely rs1059454, rs17853657 and rs17857117, were found to be highly polymorphic with a risk score of 3-4 with a possible effect of non-conservative change and splicing regulation by FASTSNP.

CONCLUSIONS

Three nsSNPs, namely rs1059454, rs17853657 and rs17857117, are potential functional polymorphisms that are likely to have a functional impact on the COL1A1 gene.

Functional analysis in mouse embryonic stem cells reveals wild-type activity for three MSH6 variants found in suspected Lynch syndrome patients

Lynch syndrome confers an increased risk to various types of cancer, in particular early onset colorectal and endometrial cancer. Mutations in mismatch repair (MMR) genes underlie Lynch syndrome, with the majority of mutations found in MLH1 and MSH2. Mutations in MSH6 have also been found but these do not always cause a clear cancer predisposition phenotype and MSH6-defective tumors often do not show the standard characteristics of MMR deficiency, such as microsatellite instability. In particular, the consequences of MSH6 missense mutations are challenging to predict, which further complicates genetic counseling. We have previously developed a method for functional characterization of MSH2 missense mutations of unknown significance. This method is based on endogenous gene modification in mouse embryonic stem cells using oligonucleotide-directed gene targeting, followed by a series of functional assays addressing the MMR functions. Here we have adapted this method for the characterization of MSH6 missense mutations. We recreated three MSH6 variants found in suspected Lynch syndrome families, MSH6-P1087R, MSH6-R1095H and MSH6-L1354Q, and found all three to behave like wild type MSH6. Thus, despite suspicion for pathogenicity from clinical observations, our approach indicates these variants are not disease causing. This has important implications for counseling of mutation carriers.

Performance of in silico analysis in predicting the effect of non-synonymous variants in inherited steroid metabolic diseases

BACKGROUND

Unclassified genetic variants are commonly encountered in molecular diagnostic service. In silico analyses using web-based predictive programs may provide information on the nature of the genetic variants, and help to prioritize novel variants for in vitro functional characterization. The objective of this study was to compare the performance of three such programs in genes related to steroid metabolism.

METHODS

The effects of non-synonymous benign and pathogenic sequence variants in the CYP11A1, CYP11B1, CYP11B2, CYP17A1, CYP19A1, CYP21A2, DHCR7, HSD3B2, HSD11B2, HSD17B3, POR, and SRD5A2 genes listed in the Human Gene Mutation Database and dbSNP were tested by SIFT, PolyPhen-2 and PON-P. Their concordance, sensitivity, specificity, positive and negative predictive values and accuracy were assessed, using the reported phenotype and the in vitro functional data as gold standards.

RESULTS

797 sequence variants were tested. SIFT and PolyPhen-2 had high concordance, with PolyPhen-2 being slightly superior to SIFT in all assessments. PON-P behaved differently, with one-third of the variants unclassified.

CONCLUSIONS

SIFT and PolyPhen-2 behaved similarly while PON-P behaved differently in predicting pathogenicity in genes related to steroid metabolism. Molecular pathologists should verify the performance of these programs before considering them in clinical decision making, and be aware that these programmes cannot replace in vitro function studies. Clinicians and patients should also be informed about the limitations of genetic testing, particularly when a novel variant is encountered.

Identification of functional SNPs in BARD1 gene and in silico analysis of damaging SNPs: based on data procured from dbSNP database

BACKGROUND

The BARD1 gene encodes for the BRCA1-associated RING domain (BARD1) protein. Germ line and somatic mutations in BARD1 are found in sporadic breast, ovarian and uterine cancers. There is a plethora of single nucleotide polymorphisms (SNPs) which may or may not be involved in the onset of female cancers. Hence, before planning a larger population study, it is advisable to sort out the possible functional SNPs. To accomplish this goal, data available in the dbSNP database and different computer programs can be used. To the best of our knowledge, until now there has been no such study on record for the BARD1 gene. Therefore, this study was undertaken to find the functional nsSNPs in BARD1.

RESULT

2.85% of all SNPs in the dbSNP database were present in the coding regions. SIFT predicted 11 out of 50 nsSNPs as not tolerable and PolyPhen assessed 27 out of 50 nsSNPs as damaging. FastSNP revealed that the rs58253676 SNP in the 3' UTR may have splicing regulator and enhancer functions. In the 5' UTR, rs17489363 and rs17426219 may alter the transcriptional binding site. The intronic region SNP rs67822872 may have a medium-high risk level. The protein structures 1JM7, 3C5R and 2NTE were predicted by PDBSum and shared 100% similarity with the BARD1 amino acid sequence. Among the predicted nsSNPs, rs4986841, rs111367604, rs13389423 and rs139785364 were identified as deleterious and damaging by the SIFT and PolyPhen programs. Additionally, I-Mutant showed a decrease in stability for these nsSNPs upon mutation. Finally, the ExPASy-PROSIT program revealed that the predicted deleterious mutations are contained in the ankyrin ring and BRCT domains.

CONCLUSION

Using the available bioinformatics tools and the data present in the dbSNP database, the four nsSNPs, rs4986841, rs111367604, rs13389423 and rs139785364, were identified as deleterious, reducing the protein stability of BARD1. Hence, these SNPs can be used for the larger population-based studies of female cancers.

Classification of mismatch repair gene missense variants with PON-MMR

Numerous mismatch repair (MMR) gene variants have been identified in Lynch syndrome and other cancer patients, but knowledge about their pathogenicity is frequently missing. The diagnosis and treatment of patients would benefit from knowing which variants are disease related. Bioinformatic approaches are well suited to the problem and can handle large numbers of cases. Functional effects were revealed based on literature for 168 MMR missense variants. Performance of numerous prediction methods was tested with this dataset. Among the tested tools, only the results of tolerance prediction methods correlated to functional information, however, with poor performance. Therefore, a novel consensus-based predictor was developed. The novel prediction method, pathogenic-or-not mismatch repair (PON-MMR), achieved accuracy of 0.87 and Matthews correlation coefficient of 0.77 on the experimentally verified variants. When applied to 616 MMR cases with unknown effects, 81 missense variants were predicted to be pathogenic and 167 neutral. With PON-MMR, the number of MMR missense variants with unknown effect was reduced by classifying a large number of cases as likely pathogenic or benign. The results can be used, for example, to prioritize cases for experimental studies and assist in the classification of cases.

Screening and structural evaluation of deleterious Non-Synonymous SNPs of ePHA2 gene involved in susceptibility to cataract formation

Age-related cataract is clinically and genetically heterogeneous disorder affecting the ocular lens, and the leading cause of vision loss and blindness worldwide. Here we screened nonsynonymous single nucleotide polymorphisms (nsSNPs) of a novel gene, EPHA2 responsible for age related cataracts. The SNPs were retrieved from dbSNP. Using I-Mutant, protein stability change was calculated. The potentially functional nsSNPs and their effect on protein was predicted by PolyPhen and SIFT respectively. FASTSNP was used for functional analysis and estimation of risk score. The functional impact on the EPHA2 protein was evaluated by using SWISSPDB viewer and NOMAD-Ref server. Our analysis revealed 16 SNPs as nonsynonymous out of which 6 nsSNPs, namely rs11543934, rs2291806, rs1058371, rs1058370, rs79100278 and rs113882203 were found to be least stable by I-Mutant 2.0 with DDG value of > -1.0. nsSNPs, namely rs35903225, rs2291806, rs1058372, rs1058370, rs79100278 and rs113882203 showed a highly deleterious tolerance index score of 0.00 by SIFT server. Four nsSNPs namely rs11543934, rs2291806, rs1058370 and rs113882203 were found to be probably damaging with PSIC score of ≥ 2. 0 by Polyp hen server. Three nsSNPs namely, rs11543934, rs2291806 and rs1058370 were found to be highly polymorphic with a risk score of 3-4 with a possible effect of Non-conservative change and splicing regulation by FASTSNP. The total energy and RMSD value was higher for the mutant-type structure compared to the native type structure. We concluded that the nsSNP namely rs2291806 as the potential functional polymorphic that is likely to have functional impact on the EPHA2 gene.

The cumulative effects of polymorphisms in the DNA mismatch repair genes and tobacco smoking in oesophageal cancer risk

The DNA mismatch repair (MMR) enzymes repair errors in DNA that occur during normal DNA metabolism or are induced by certain cancer-contributing exposures. We assessed the association between 10 single-nucleotide polymorphisms (SNPs) in 5 MMR genes and oesophageal cancer risk in South Africans. Prior to genotyping, SNPs were selected from the HapMap database, based on their significantly different genotypic distributions between European ancestry populations and four HapMap populations of African origin. In the Mixed Ancestry group, the MSH3 rs26279 G/G versus A/A or A/G genotype was positively associated with cancer (OR = 2.71; 95% CI: 1.34–5.50). Similar associations were observed for PMS1 rs5742938 (GG versus AA or AG: OR = 1.73; 95% CI: 1.07–2.79) and MLH3 rs28756991 (AA or GA versus GG: OR = 2.07; 95% IC: 1.04–4.12). In Black individuals, however, no association between MMR polymorhisms and cancer risk was observed in individual SNP analysis. The interactions between MMR genes were evaluated using the model-based multifactor-dimensionality reduction approach, which showed a significant genetic interaction between SNPs in MSH2, MSH3 and PMS1 genes in Black and Mixed Ancestry subjects, respectively. The data also implies that pathogenesis of common polymorphisms in MMR genes is influenced by exposure to tobacco smoke. In conclusion, our findings suggest that common polymorphisms in MMR genes and/or their combined effects might be involved in the aetiology of oesophageal cancer.

Computational refinement of functional single nucleotide polymorphisms associated with ATM gene

BACKGROUND

Understanding and predicting molecular basis of disease is one of the major challenges in modern biology and medicine. SNPs associated with complex disorders can create, destroy, or modify protein coding sites. Single amino acid substitutions in the ATM gene are the most common forms of genetic variations that account for various forms of cancer. However, the extent to which SNPs interferes with the gene regulation and affects cancer susceptibility remains largely unknown.

PRINCIPAL FINDINGS

We analyzed the deleterious nsSNPs associated with ATM gene based on different computational methods. An integrative scoring system and sequence conservation of amino acid residues was adapted for a priori nsSNP analysis of variants associated with cancer. We further extended our approach on SNPs that could potentially influence protein Post Translational Modifications in ATM gene.

SIGNIFICANCE

In the lack of adequate prior reports on the possible deleterious effects of nsSNPs, we have systematically analyzed and characterized the functional variants in both coding and non coding region that can alter the expression and function of ATM gene. In silico characterization of nsSNPs affecting ATM gene function can aid in better understanding of genetic differences in disease susceptibility.

Screening and Evaluation of Deleterious SNPs in APOE Gene of Alzheimer's Disease

Introduction. Apolipoprotein E (APOE) is an important risk factor for Alzheimer's disease (AD) and is present in 30–50% of patients who develop late-onset AD. Several single-nucleotide polymorphisms (SNPs) are present in APOE gene which act as the biomarkers for exploring the genetic basis of this disease. The objective of this study is to identify deleterious nsSNPs associated with APOE gene. Methods. The SNPs were retrieved from dbSNP. Using I-Mutant, protein stability change was calculated. The potentially functional nonsynonymous (ns) SNPs and their effect on protein was predicted by PolyPhen and SIFT, respectively. FASTSNP was used for functional analysis and estimation of risk score. The functional impact on the APOE protein was evaluated by using Swiss PDB viewer and NOMAD-Ref server. Results. Six nsSNPs were found to be least stable by I-Mutant 2.0 with DDG value of >−1.0. Four nsSNPs showed a highly deleterious tolerance index score of 0.00. Nine nsSNPs were found to be probably damaging with position-specific independent counts (PSICs) score of ≥2.0. Seven nsSNPs were found to be highly polymorphic with a risk score of 3-4. The total energies and root-mean-square deviation (RMSD) values were higher for three mutant-type structures compared to the native modeled structure. Conclusion. We concluded that three nsSNPs, namely, rs11542041, rs11542040, and rs11542034, to be potentially functional polymorphic.

Path to facilitate the prediction of functional amino acid substitutions in red blood cell disorders--a computational approach

Background

A major area of effort in current genomics is to distinguish mutations that are functionally neutral from those that contribute to disease. Single Nucleotide Polymorphisms (SNPs) are amino acid substitutions that currently account for approximately half of the known gene lesions responsible for human inherited diseases. As a result, the prediction of non-synonymous SNPs (nsSNPs) that affect protein functions and relate to disease is an important task.

Principal Findings

In this study, we performed a comprehensive analysis of deleterious SNPs at both functional and structural level in the respective genes associated with red blood cell metabolism disorders using bioinformatics tools. We analyzed the variants in Glucose-6-phosphate dehydrogenase (G6PD) and isoforms of Pyruvate Kinase (PKLR & PKM2) genes responsible for major red blood cell disorders. Deleterious nsSNPs were categorized based on empirical rule 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 evaluation of protein structure stability.

Significance

We argue here that bioinformatics tools can play an important role in addressing the complexity of the underlying genetic basis of Red Blood Cell disorders. Based on our investigation, we report here the potential candidate SNPs, for future studies in human Red Blood Cell disorders. Current study also demonstrates the presence of other deleterious mutations and also endorses with in vivo experimental studies. Our approach will present the application of computational tools in understanding functional variation from the perspective of structure, expression, evolution and phenotype.

In silico searching for disease-associated functional DNA variants

Experimental analyses of disease-associated DNA variants have provided significant insights into the functional implications of sequence variation. However, such experiment-based approaches for identifying functional DNA variants from a pool with a large number of neutral variants are challenging. Computational biology has the opportunity to play an important role in the identification of functional DNA variants in large-scale genotyping studies, ultimately yielding new drug targets and biomarkers. This chapter outlines in silico methods to predict disease-associated functional DNA variants so that the number of DNA variants screened for association with disease can be reduced to those that are most likely to alter gene function. To explore possible relationships between genetic mutations and phenotypic variation, different computational methods like Sorting Intolerant from Tolerant (SIFT, an evolutionary-based approach), Polymorphism Phenotyping (PolyPhen, a structure-based approach) and PupaSuite are discussed for prioritization of high-risk DNA variants. The PupaSuite tool aims to predict the phenotypic effect of DNA variants on the structure and function of the affected protein as well as the effect of variants in the non-coding regions of the same genes. To further investigate the possible causes of disease at the molecular level, deleterious nonsynonymous variants can be mapped to 3D protein structures. An analysis of solvent accessibility and secondary structure can also be performed to understand the impact of a mutation on protein function and stability. This chapter demonstrates a 'real-world' application of some existing bioinformatics tools for DNA variant analysis.

Using SIFT and PolyPhen to predict loss-of-function and gain-of-function mutations

CONTEXT

The interpretation of novel missense variants is a challenge with increasing numbers of such variants being identified and a responsibility to report the findings in the context of all available scientific evidence. Various in silico bioinformatic tools have been developed that predict the likely pathogenicity of missense variants; however, their utility within the diagnostic setting requires further investigation.

AIM

The aim of our study was to test the predictive value of two of these tools, sorting intolerant from tolerant (SIFT) and polymorphism phenotyping (PolyPhen), in a set of 141 missense variants (131 pathogenic, 8 benign) identified in the ABCC8, GCK, and KCNJ11 genes.

METHODS

Sixty-six of the mutations caused a gain of protein function, while 67 were loss-of-function mutations. The evolutionary conservation at each residue was also investigated using multiple sequence alignments from the UCSC genome browser.

RESULTS

The sensitivity of SIFT and PolyPhen was reasonably high (69% and 68%, respectively), but their specificity was low (13% and 16%). Both programs were significantly better at predicting loss-of-function mutations than gain-of-function mutations (SIFT, p = 0.001; PolyPhen, p < or = 0.0001). The most reliable method for assessing the likely pathogenicity of a missense variant was to investigate the degree of conservation at the affected residue. Eighty-eight percent of the mutations affected highly conserved residues, while all of the benign variants occurred at residues that were polymorphic across multiple species.

CONCLUSIONS

Although SIFT and PolyPhen may be useful in prioritizing changes that are likely to cause a loss of protein function, their low specificity means that their predictions should be interpreted with caution and further evidence to support/refute pathogenicity should be sought before reporting novel missense changes.