In silico prediction of the deleterious effect of a mutation: proceed with caution in clinical genetics

The significance of carrying MEFV variants in symptomatic and asymptomatic individuals

Familial Mediterranean fever (FMF) is an autoinflammatory disease characterized by recurrent attacks of fever, serositis (peritonitis, pleuritis, or synovitis), and erysipelas-like erythema. Genetic variants in the MEFV gene are associated with this disease. Familial Mediterranean fever is considered an autosomal recessive disease. However, in Middle Eastern countries, a third of the patients expressing FMF manifestations, carry a single mutation only. Moreover, some cases of pure dominant inheritance linked to specific single MEFV variants have also been described. This complex inheritance of MEFV-associated inflammatory diseases poses a serious challenge when interpreting the results of genetic testing in patients having recurrent fever syndromes. In addition, in certain situations, asymptomatic individuals may be incidentally found to carry MEFV variants. These cases pose the question of their exact diagnosis and whether they should be treated. Previous studies have focused on genetic results interpretations among symptomatic patients. In the current article, we would like to elaborate on the genetic interpretation in cases of symptomatic individuals suspected to have FMF and on asymptomatic individuals carrying MEFV variants. We aim to assist physicians unfamiliar with FMF to cope with genetic results interpretation when facing symptomatic and asymptomatic individuals carrying MEFV variants and suggest a management plan accordingly.

Assessing the relationship between the in silico predicted consequences of 97 missense mutations mapping to 68 genes related to lipid metabolism and their association with porcine fatness traits

In general, the relationship between the predicted functional consequences of missense mutations mapping to genes known to be involved in human diseases and the severity of disease manifestations is weak. In this study, we tested in pigs whether missense single nucleotide polymorphisms (SNPs), predicted to have consequences on the function of genes related to lipid metabolism are associated with lipid phenotypes. Association analysis demonstrated that nine out of 72 nominally associated SNPs were classified as "highly" or "very highly consistent" in silico-predicted functional mutations and did not show association with lipid traits expected to be affected by inactivation of the corresponding gene. Although the lack of endophenotypes and the limited sample size of certain genotypic classes might have limited to some extent the reach of the current study, our data indicate that present-day bioinformatic tools have a modest ability to predict the impact of missense mutations on complex phenotypes.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Evaluation of in silico predictors on short nucleotide variants in HBA1, HBA2, and HBB associated with haemoglobinopathies

Haemoglobinopathies are the commonest monogenic diseases worldwide and are caused by variants in the globin gene clusters. With over 2400 variants detected to date, their interpretation using the American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) guidelines is challenging and computational evidence can provide valuable input about their functional annotation. While many in silico predictors have already been developed, their performance varies for different genes and diseases. In this study, we evaluate 31 in silico predictors using a dataset of 1627 variants in HBA1, HBA2, and HBB. By varying the decision threshold for each tool, we analyse their performance (a) as binary classifiers of pathogenicity and (b) by using different non-overlapping pathogenic and benign thresholds for their optimal use in the ACMG/AMP framework. Our results show that CADD, Eigen-PC, and REVEL are the overall top performers, with the former reaching moderate strength level for pathogenic prediction. Eigen-PC and REVEL achieve the highest accuracies for missense variants, while CADD is also a reliable predictor of non-missense variants. Moreover, SpliceAI is the top performing splicing predictor, reaching strong level of evidence, while GERP++ and phyloP are the most accurate conservation tools. This study provides evidence about the optimal use of computational tools in globin gene clusters under the ACMG/AMP framework.

Improving the laboratory diagnosis of pyruvate kinase deficiency

Pyruvate kinase (PK) deficiency is an autosomal recessive disease caused by mutations in the PKLR gene, which reduce erythrocyte PK enzyme activity and result in decreased energy synthesis in red cells, causing haemolytic anaemia. Historically, the investigation into pyruvate kinase deficiency (PKD) has been led by a red cell enzyme assay determining PK enzyme activity per unit of haemoglobin. For our laboratory, the reference range was set by Beutler et al. in 1977 when the test was first established. The introduction of genetic testing permitted the creation of reference sample datasets, with positive controls having two pathogenic variants causing disease. This permitted re-assessment of the enzyme assay's sensitivity and specificity, and was used to reassess the reference range of the enzyme assay. Using sequenced samples, we have devised an enzyme assay, DNA testing workflow, which minimises false negative/positive results and improves the diagnostic efficiency. This combined enzyme-DNA testing strategy should improve the diagnostic accuracy whilst limiting the number of expensive DNA tests. During this evaluation, 10 novel genetic variants were identified and are described.

Blood-based test for diagnosis and functional subtyping of familial Mediterranean fever

BACKGROUND AND OBJECTIVE

Familial Mediterranean fever (FMF) is the most common monogenic autoinflammatory disease (AID) worldwide. The disease is caused by mutations in the MEFV gene encoding the inflammasome sensor Pyrin. Clinical diagnosis of FMF is complicated by overlap in symptoms with other diseases, and interpretation of genetic testing is confounded by the lack of a clear genotype-phenotype association for most of the 340 reported MEFV variants. In this study, the authors designed a functional assay and evaluated its potential in supporting FMF diagnosis.

METHODS

Peripheral blood mononuclear cells (PBMCs) were obtained from patients with Pyrin-associated autoinflammation with an FMF phenotype (n=43) or with autoinflammatory features not compatible with FMF (n=8), 10 asymptomatic carriers and 48 healthy donors. Sera were obtained from patients with distinct AIDs (n=10), and whole blood from a subset of patients and controls. The clinical, demographic, molecular genetic factors and other characteristics of the patient population were assessed for their impact on the diagnostic test read-out. Interleukin (IL)-1β and IL-18 levels were measured by Luminex assay.

RESULTS

The ex vivo colchicine assay may be performed on whole blood or PBMC. The functional assay robustly segregated patients with FMF from healthy controls and patients with related clinical disorders. The diagnostic test distinguished patients with classical FMF mutations (M694V, M694I, M680I, R761H) from patients with other MEFV mutations and variants (K695R, P369S, R202Q, E148Q) that are considered benign or of uncertain clinical significance.

CONCLUSION

The ex vivo colchicine assay may support diagnosis of FMF and functional subtyping of Pyrin-associated autoinflammation.

In silico analysis on the functional and structural impact of Rad50 mutations involved in DNA strand break repair

BACKGROUND

DNA double strand break repair is important to preserve the fidelity of our genetic makeup after DNA damage. Rad50 is one of the components in MRN complex important for DNA repair mechanism. Rad50 mutations can lead to microcephaly, mental retardation and growth retardation in human. However, Rad50 mutations in human and other organisms have never been gathered and heuristically compared for their deleterious effects. It is important to assess the conserved region in Rad50 and its homolog to identify vital mutations that can affect functions of the protein.

METHOD

In this study, Rad50 mutations were retrieved from SNPeffect 4.0 database and literature. Each of the mutations was analyzed using various bioinformatic analyses such as PredictSNP, MutPred, SNPeffect 4.0, I-Mutant and MuPro to identify its impact on molecular mechanism, biological function and protein stability, respectively.

RESULTS

We identified 103 mostly occurred mutations in the Rad50 protein domains and motifs, which only 42 mutations were classified as most deleterious. These mutations are mainly situated at the specific motifs such as Walker A, Q-loop, Walker B, D-loop and signature motif of the Rad50 protein. Some of these mutations were predicted to negatively affect several important functional sites that play important roles in DNA repair mechanism and cell cycle signaling pathway, highlighting Rad50 crucial role in this process. Interestingly, mutations located at non-conserved regions were predicted to have neutral/non-damaging effects, in contrast with previous experimental studies that showed deleterious effects. This suggests that software used in this study may have limitations in predicting mutations in non-conserved regions, implying further improvement in their algorithm is needed. In conclusion, this study reveals the priority of acid substitution associated with the genetic disorders. This finding highlights the vital roles of certain residues such as K42E, C681A/S, CC684R/S, S1202R, E1232Q and D1238N/A located in Rad50 conserved regions, which can be considered for a more targeted future studies.

Missense PALB2 germline variant disrupts nuclear localization of PALB2 in a patient with breast cancer

The PALB2 protein is essential to RAD51-mediated homologous recombination (HR) repair. Germline monoallelic PALB2 pathogenic variants confer significant risks for breast cancer. However, the majority of PALB2 variants remain classified as variants of unknown significance (VUS). We aim to functionally and mechanistically evaluate three novel PALB2 VUS. Patient-derived lymphoblastoid cell lines containing the VUS were analyzed for nuclear localization and foci formation of RAD51 as a measure of HR efficiency. To understand the mechanism underlying the HR deficiency, PALB2 nuclear localization was assessed using immunofluorescence studies. Among these VUS, c.3251C>T (p.Ser1084Leu) occurred in a patient with metastatic breast cancer while c.1054G>C (p.Glu352Gln) and c.1057A>G (p.Lys353Glu) were seen in patients with squamous cell carcinoma of skin and renal cell carcinoma respectively. Variant c.3251C>T was located within the WD40 domain which normally masked the nuclear export signal sequence responsible for nuclear delocalization of PALB2. Correspondingly, c.3251C>T displayed aberrant cytoplasmic localization of PALB2 which led to an impaired RAD51 nuclear localization and foci formation. On the other hand, both c.1054G>C and c.1057A>G showed intact HR functions and nuclear localization of PALB2, consistent with their locations within domains of no known function. Additionally, the prevalence of c.1054G>C was similar among healthy controls and patients with breast cancer (as seen in other studies), suggestive of its non-pathogenicity. In conclusion, our studies provided the functional evidence showing the deleterious effect of c.3251C>T, and non-deleterious effects of c.1054G>C and c.1057A>G. Using the ClinGen Pathogenicity calculator, c.3251C>T remains a VUS while c.1054G>C and c.1057A>G may be classified as likely benign variants.

In Silico Analysis of the Effects of Point Mutations on α-Globin: Implications for α-Thalassemia

Hemoglobinopathies are inherited diseases that impair the structure and function of the oxygen-carrying pigment hemoglobin (Hb). Adult Hb consists of two α and two β subunits. α-Thalassemia (α-thal) affects the genes that code for the α-globin chains, HBA1 and HBA2. Mutations can result in asymptomatic, mild or severe outcomes depending on several factors, such as mutation type, number of mutations and the location at which they occur. PredictSNP was used to estimate whether every possible single nucleotide polymorphism (SNP) would have a neutral or deleterious effect on the protein. These results were then used to create a plot of predicted tolerance to change for each residue in the protein. Tolerance to change was negatively correlated with the residue's sequence conservation score. The PredictSNP data were compared to clinical reports of 110 selected variants in the literature. There were 29 disagreements between the two data types. Some of these could be resolved by considering the role of the affected residue in binding other molecules. The three-dimensional structures of some of these variant proteins were modeled. These models helped explain variants which affect heme binding. We predict that where a point mutation alters a residue that is intolerant to change, is well conserved and or involved in interactions, it is likely to be associated with disease. Overall, the data from this study could be used alongside biochemical and clinical data to assess novel α-globin variants.

In Silico Analysis of Missense Mutations as a First Step in Functional Studies: Examples from Two Sphingolipidoses

In order to delineate a better approach to functional studies, we have selected 23 missense mutations distributed in different domains of two lysosomal enzymes, to be studied by in silico analysis. In silico analysis of mutations relies on computational modeling to predict their effects. Various computational platforms are currently available to check the probable causality of mutations encountered in patients at the protein and at the RNA levels. In this work we used four different platforms freely available online (Protein Variation Effect Analyzer- PROVEAN, PolyPhen-2, Swiss-model Expert Protein Analysis System—ExPASy, and SNAP2) to check amino acid substitutions and their effect at the protein level. The existence of functional studies, regarding the amino acid substitutions, led to the selection of the distinct protein mutants. Functional data were used to compare the results obtained with different bioinformatics tools. With the advent of next-generation sequencing, it is not feasible to carry out functional tests in all the variants detected. In silico analysis seems to be useful for the delineation of which mutants are worth studying through functional studies. Therefore, prediction of the mutation impact at the protein level, applying computational analysis, confers the means to rapidly provide a prognosis value to genotyping results, making it potentially valuable for patient care as well as research purposes. The present work points to the need to carry out functional studies in mutations that might look neutral. Moreover, it should be noted that single nucleotide polymorphisms (SNPs), occurring in coding and non-coding regions, may lead to RNA alterations and should be systematically verified. Functional studies can gain from a preliminary multi-step approach, such as the one proposed here.

Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance

BACKGROUND

An emerging standard-of-care for long-QT syndrome uses clinical genetic testing to identify genetic variants of the KCNQ1 potassium channel. However, interpreting results from genetic testing is confounded by the presence of variants of unknown significance for which there is inadequate evidence of pathogenicity.

METHODS AND RESULTS

In this study, we curated from the literature a high-quality set of 107 functionally characterized KCNQ1 variants. Based on this data set, we completed a detailed quantitative analysis on the sequence conservation patterns of subdomains of KCNQ1 and the distribution of pathogenic variants therein. We found that conserved subdomains generally are critical for channel function and are enriched with dysfunctional variants. Using this experimentally validated data set, we trained a neural network, designated Q1VarPred, specifically for predicting the functional impact of KCNQ1 variants of unknown significance. The estimated predictive performance of Q1VarPred in terms of Matthew's correlation coefficient and area under the receiver operating characteristic curve were 0.581 and 0.884, respectively, superior to the performance of 8 previous methods tested in parallel. Q1VarPred is publicly available as a web server at http://meilerlab.org/q1varpred.

CONCLUSIONS

Although a plethora of tools are available for making pathogenicity predictions over a genome-wide scale, previous tools fail to perform in a robust manner when applied to KCNQ1. The contrasting and favorable results for Q1VarPred suggest a promising approach, where a machine-learning algorithm is tailored to a specific protein target and trained with a functionally validated data set to calibrate informatics tools.

Polymorphisms of TNF-α -308 G/A and IL-8 -251 T/A Genes Associated with Urothelial Carcinoma: A Case-Control Study

Cigarette smoking and exposure to environmental tobacco smoke are well-known risk factors for urothelial carcinoma (UC). We conducted a hospital-based case-control study involving 287 UC cases and 574 cancer-free controls to investigate the joint effects of cigarette smoking and polymorphisms of inflammatory genes on UC risk. Tumor necrosis factor alpha (TNF-α) -308 G/A and interleukin-8 (IL-8) -251 T/A polymorphisms were determined using a polymerase chain reaction-restriction fragment length polymorphism method. People who had ever smoked and those who were exposed to environmental tobacco smoke had significantly increased UC odds ratios (ORs) of 1.65 and 1.68, respectively. Participants who had smoked more than 18 pack-years had a significantly increased UC OR of 2.64. People who had ever smoked and who carried the A/A genotype of the TNF-α -308 G/A polymorphism had a significantly higher UC OR (10.25) compared to people who had never smoked and who carried the G/G or G/A genotype. In addition, people who had ever smoked and who carried the IL-8 -251 T/T genotype had a significantly increased UC OR (3.08) compared to people who had never smoked and who carried the T/A or A/A genotype. In a combined analysis of three major risk factors (cumulative cigarette smoking, the TNF-α -308 A/A genotype, and the IL-8 -251 T/T genotype), subjects with any one, any two, and all three risk factors experienced significantly increased UC ORs of 1.55, 2.89, and 3.77, respectively, compared to individuals with none of the risk factors. Conclusions. Our results indicate that the combined effects of cumulative cigarette exposure and the TNF-α -308 A/A genotype and/or the IL-8 -251 T/T genotype on UC OR showed a significant dose-response relationship.

New workflow for classification of genetic variants' pathogenicity applied to hereditary recurrent fevers by the International Study Group for Systemic Autoinflammatory Diseases (INSAID)

BACKGROUND

Hereditary recurrent fevers (HRFs) are rare inflammatory diseases sharing similar clinical symptoms and effectively treated with anti-inflammatory biological drugs. Accurate diagnosis of HRF relies heavily on genetic testing.

OBJECTIVES

This study aimed to obtain an experts' consensus on the clinical significance of gene variants in four well-known HRF genes: MEFV, TNFRSF1A, NLRP3 and MVK.

METHODS

We configured a MOLGENIS web platform to share and analyse pathogenicity classifications of the variants and to manage a consensus-based classification process. Four experts in HRF genetics submitted independent classifications of 858 variants. Classifications were driven to consensus by recruiting four more expert opinions and by targeting discordant classifications in five iterative rounds.

RESULTS

Consensus classification was reached for 804/858 variants (94%). None of the unsolved variants (6%) remained with opposite classifications (eg, pathogenic vs benign). New mutational hotspots were found in all genes. We noted a lower pathogenic variant load and a higher fraction of variants with unknown or unsolved clinical significance in the MEFV gene.

CONCLUSION

Applying a consensus-driven process on the pathogenicity assessment of experts yielded rapid classification of almost all variants of four HRF genes. The high-throughput database will profoundly assist clinicians and geneticists in the diagnosis of HRFs. The configured MOLGENIS platform and consensus evolution protocol are usable for assembly of other variant pathogenicity databases. The MOLGENIS software is available for reuse at http://github.com/molgenis/molgenis; the specific HRF configuration is available at http://molgenis.org/said/. The HRF pathogenicity classifications will be published on the INFEVERS database at https://fmf.igh.cnrs.fr/ISSAID/infevers/.

Multiple Property Tolerance Analysis for the Evaluation of Missense Mutations

Computational prediction of the impact of a mutation on protein function is still not accurate enough for clinical diagnostics without additional human expert analysis. Sequence alignment-based methods have been extensively used but their results highly depend on the quality of the input alignments and the choice of sequences. Incorporating the structural information with alignments improves prediction accuracy. Here, we present a conservation of amino acid properties method for mutation prediction, Multiple Properties Tolerance Analysis (MuTA), and a new strategy, MuTA/S, to incorporate the solvent accessible surface (SAS) property into MuTA. Instead of combining multiple features by machine learning or mathematical methods, an intuitive strategy is used to divide the residues of a protein into different groups, and in each group the properties used is adjusted.

The results for LacI, lysozyme, and HIV protease show that MuTA performs as well as the widely used SIFT algorithm while MuTA/S outperforms SIFT and MuTA by 2%–25% in terms of prediction accuracy. By incorporating the SAS term alone, the alignment dependency of overall prediction accuracy is significantly reduced. MuTA/S also defines a new way to incorporate any structural features and knowledge and may lead to more accurate predictions.

Molecular basis of α-thalassemia

α-Thalassemia is an inherited, autosomal recessive, disorder characterized by a microcytic hypochromic anemia. It is one of the most common monogenic gene disorders in the world population. The clinical severity varies from almost asymptomatic, to mild microcytic hypochromic, and to a lethal hemolytic condition, called Hb Bart's Hydrops Foetalis Syndrome. The molecular basis are usually deletions and less frequently, point mutations affecting the expression of one or more of the duplicated α-genes. The clinical variation and increase in disease severity is directly related to the decreased expression of one, two, three or four copies of the α-globin genes. Deletions and point mutations in the α-globin genes and their regulatory elements have been studied extensively in carriers and patients and these studies have given insight into the α-globin genes are regulated. By looking at naturally occurring deletions and point mutations, our knowledge of globin-gene regulation and expression will continue to increase and will lead to new targets of therapy.

Development of pathogenicity predictors specific for variants that do not comply with clinical guidelines for the use of computational evidence

Background

Strict guidelines delimit the use of computational information in the clinical setting, due to the still moderate accuracy of in silico tools. These guidelines indicate that several tools should always be used and that full coincidence between them is required if we want to consider their results as supporting evidence in medical decision processes. Application of this simple rule certainly decreases the error rate of in silico pathogenicity assignments. However, when predictors disagree this rule results in the rejection of potentially valuable information for a number of variants. In this work, we focus on these variants of the protein sequence and develop specific predictors to help improve the success rate of their annotation.

Results

We have used a set of 59,442 protein sequence variants (15,723 pathological and 43,719 neutral) from 228 proteins to identify those cases for which pathogenicity predictors disagree. We have repeated this process for all the possible combinations of five known methods (SIFT, PolyPhen-2, PON-P2, CADD and MutationTaster2). For each resulting subset we have trained a specific pathogenicity predictor. We find that these specific predictors are able to discriminate between neutral and pathogenic variants, with a success rate different from random. They tend to outperform the constitutive methods but this trend decreases as the performance of the constitutive predictor improves (e.g. with PON-P2 and PolyPhen-2). We also find that specific methods outperform standard consensus methods (Condel and CAROL).

Conclusion

Focusing development efforts on the case of variants for which known methods disagree we may obtain pathogenicity predictors with improved performances. Although we have not yet reached the success rate that allows the use of this computational evidence in a clinical setting, the simplicity of the approach indicates that more advanced methods may reach this goal in a close future.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3914-0) contains supplementary material, which is available to authorized users.

Objective assessment of the evolutionary action equation for the fitness effect of missense mutations across CAGI-blinded contests

A major challenge in genome interpretation is to estimate the fitness effect of coding variants of unknown significance (VUS). Labor, limited understanding of protein functions, and lack of assays generally limit direct experimental assessment of VUS, and make robust and accurate computational approaches a necessity. Often, however, algorithms that predict mutational effect disagree among themselves and with experimental data, slowing their adoption for clinical diagnostics. To objectively assess such methods, the Critical Assessment of Genome Interpretation (CAGI) community organizes contests to predict unpublished experimental data, available only to CAGI assessors. We review here the CAGI performance of evolutionary action (EA) predictions of mutational impact. EA models the fitness effect of coding mutations analytically, as a product of the gradient of the fitness landscape times the perturbation size. In practice, these terms are computed from phylogenetic considerations as the functional sensitivity of the mutated site and as the magnitude of amino acid substitution, respectively, and yield the percentage loss of wild-type activity. In five CAGI challenges, EA consistently performed on par or better than sophisticated machine learning approaches. This objective assessment suggests that a simple differential model of evolution can interpret the fitness effect of coding variations, opening diverse clinical applications.

Screening of the Filamin C Gene in a Large Cohort of Hypertrophic Cardiomyopathy Patients

BACKGROUND

Recent exome sequencing studies identified filamin C (FLNC) as a candidate gene for hypertrophic cardiomyopathy (HCM). Our aim was to determine the rate of FLNC candidate variants in a large cohort of HCM patients who were also sequenced for the main sarcomere genes.

METHODS AND RESULTS

A total of 448 HCM patients were next generation-sequenced (semiconductor chip technology) for the MYH7, MYBPC3, TNNT2, TNNI3, ACTC1, TNNC1, MYL2, MYL3, TPM1, and FLNC genes. We also sequenced 450 healthy controls from the same population. Based on the reported population frequencies, bioinformatic criteria, and familial segregation, we identified 20 FLNC candidate variants (13 new; 1 nonsense; and 19 missense) in 22 patients. Compared with the patients, only 1 of the control's missense variants was nonreported (P=0.007; Fisher exact probability test). Based on the familial segregation and the reported functional studies, 6 of the candidate variants (in 7 patients) were finally classified as likely pathogenic, 10 as variants of uncertain significance, and 4 as likely benign.

CONCLUSIONS

We provide a compelling evidence of the involvement of FLNC in the development of HCM. Most of the FLNC variants were associated with mild forms of HCM and a reduced penetrance, with few affected in the families to confirm the segregation. Our work, together with others who found FLNC variants among patients with dilated and restrictive cardiomyopathies, pointed to this gene as an important cause of structural cardiomyopathies.

Association of SRD5A2 gene mutations with risk of hypospadias in the Iranian population

BACKGROUND

Hypospadias is one of the most common forms of congenital malformation of the male external genitalia worldwide. The ratio in the Iranian population is one in 250 live male births. The conversion of testosterone to dihydrotestosterone (DHT) in the presence of steroid 5α-reductase 2, which is encoded by SRD5A2 gene, plays an important role in the normal development of the male reproductive system.

METHODS

We examined whether SRD5A2 gene mutations (V89L and A49T polymorphisms) are associated with the risk of hypospadias in the Iranian population. We performed exons sequencing for SRD5A2 gene in 109 hypospadias patients.

RESULTS

We identified two new mutations in the subgroups of affected cases: including a substitution of the nucleotide T > A in the codon 73 [c.219T > A (p.Leu73_Ser74insHisPro)] and an insertion of an extra A nucleotide in the codon 77 [c.229insA* (p.Gly77*)]. Additionally, we performed PCR-RFLP for the two identified polymorphisms and revealed that V89L [OR = 5.8, 95% CI (3.8-8.8), p value < 0.001] and A49T [OR = 10.16, 95% CI (3.94-26.25), p value < 0.001] are significantly associated with hypospadias occurrence in patients. Our haplotype analysis further indicated that the Leu-Ala haplotype increases risk of hypospadias; conversely, the Val-Ala haplotype decreases the risk of hypospadias in the studied patients.

CONCLUSIONS

This study demonstrates that polymorphisms in the SRD5A2 gene could be considered as a risk factor for hypospadias disease emergence.

Spectrum of DNA variants for non-syndromic deafness in a large cohort from multiple continents

Hearing loss is the most common sensory deficit in humans with causative variants in over 140 genes. With few exceptions, however, the population-specific distribution for many of the identified variants/genes is unclear. Until recently, the extensive genetic and clinical heterogeneity of deafness precluded comprehensive genetic analysis. Here, using a custom capture panel (MiamiOtoGenes), we undertook a targeted sequencing of 180 genes in a multi-ethnic cohort of 342 GJB2 mutation-negative deaf probands from South Africa, Nigeria, Tunisia, Turkey, Iran, India, Guatemala, and the United States (South Florida). We detected causative DNA variants in 25 % of multiplex and 7 % of simplex families. The detection rate varied between 0 and 57 % based on ethnicity, with Guatemala and Iran at the lower and higher end of the spectrum, respectively. We detected causative variants within 27 genes without predominant recurring pathogenic variants. The most commonly implicated genes include MYO15A, SLC26A4, USH2A, MYO7A, MYO6, and TRIOBP. Overall, our study highlights the importance of family history and generation of databases for multiple ethnically discrete populations to improve our ability to detect and accurately interpret genetic variants for pathogenicity.

PaPI: pseudo amino acid composition to score human protein-coding variants

Background

High throughput sequencing technologies are able to identify the whole genomic variation of an individual. Gene-targeted and whole-exome experiments are mainly focused on coding sequence variants related to a single or multiple nucleotides. The analysis of the biological significance of this multitude of genomic variant is challenging and computational demanding.

Results

We present PaPI, a new machine-learning approach to classify and score human coding variants by estimating the probability to damage their protein-related function. The novelty of this approach consists in using pseudo amino acid composition through which wild and mutated protein sequences are represented in a discrete model. A machine learning classifier has been trained on a set of known deleterious and benign coding variants with the aim to score unobserved variants by taking into account hidden sequence patterns in human genome potentially leading to diseases. We show how the combination of amphiphilic pseudo amino acid composition, evolutionary conservation and homologous proteins based methods outperforms several prediction algorithms and it is also able to score complex variants such as deletions, insertions and indels.

Conclusions

This paper describes a machine-learning approach to predict the deleteriousness of human coding variants. A freely available web application (http://papi.unipv.it) has been developed with the presented method, able to score up to thousands variants in a single run.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0554-8) contains supplementary material, which is available to authorized users.

An improved understanding of cancer genomics through massively parallel sequencing

DNA sequencing technology advances have enabled genetic investigation of more samples in a shorter time than has previously been possible. Furthermore, the ability to analyze and understand large sequencing datasets has improved due to concurrent advances in sequence data analysis methods and software tools. Constant improvements to both technology and analytic approaches in this fast moving field are evidenced by many recent publications of computational methods, as well as biological results linking genetic events to human disease. Cancer in particular has been the subject of intense investigation, owing to the genetic underpinnings of this complex collection of diseases. New massively-parallel sequencing (MPS) technologies have enabled the investigation of thousands of samples, divided across tens of different tumor types, resulting in new driver gene identification, mutagenic pattern characterization, and other newly uncovered features of tumor biology. This review will focus both on methods and recent results: current analytical approaches to DNA and RNA sequencing will be presented followed by a review of recent pan-cancer sequencing studies. This overview of methods and results will not only highlight the recent advances in cancer genomics, but also the methods and tools used to accomplish these advancements in a constantly and rapidly improving field.

The impact of next generation sequencing on the analysis of breast cancer susceptibility: a role for extremely rare genetic variation?

Women with a family history of breast cancer have an approximately twofold elevated risk of the disease. Even though an array of genes has been associated with breast cancer risk the past two decades, variants within these genes jointly explain at most 40% of this familial risk. Many explanations for this 'missing heritability' have been proposed, including the existence of many very rare variants, interactions between genetic and environmental factors and structural genetic variation. In this review, we discuss how next generation sequencing will teach us more about the genetic architecture of breast cancer, with a specific focus on very rare genetic variants. While such variants potentially explain a substantial proportion of familial breast cancer, assessing the breast cancer risks conferred by them remains challenging, even if this risk is relatively high. To assess more moderate risks, epidemiological approaches will require very large patient cohorts to be genotyped for the variant, only achievable through international collaboration. How well we will be able to eventually resolve the missing heritability for breast cancer in a clinically meaningful way crucially depends on the underlying complexity of the genetic architecture.

Molecular diagnostic testing for congenital disorders of glycosylation (CDG): detection rate for single gene testing and next generation sequencing panel testing

Congenital disorders of glycosylation (CDG) are comprised of over 60 disorders with the majority of defects residing within the N-glycosylation pathway. Approximately 20% of patients do not survive beyond five years of age due to widespread organ dysfunction. A diagnosis of CDG is based on abnormal glycosylation of transferrin but this method cannot identify the specific gene defect. For many individuals diagnosed with CDG the gene defect remains unknown. To improve the molecular diagnosis of CDG we developed molecular testing for 25 CDG genes including single gene testing and next generation sequencing (NGS) panel testing. From March 2010 through November 2012, a total of 94 samples were referred for single gene testing and 68 samples were referred for NGS panel testing. Disease causing mutations were identified in 24 patients resulting in a molecular diagnosis rate of 14.8%. Coverage of the 24 CDG genes using panel testing and whole exome sequencing (WES) was compared and it was determined that many exons of these genes were not adequately covered using a WES approach and a panel approach may be the preferred first option for CDG patients. A collaborative effort between physicians, researchers and diagnostic laboratories will be very important as NGS testing using panels and exome becomes more widespread. This technology will ultimately improve the molecular diagnosis of patients with CDG in hard to solve cases.

Next-generation sequencing in understanding complex neurological disease

Next-generation sequencing techniques have made vast quantities of data on human genomes and transcriptomes available to researchers. Huge progress has been made towards understanding the basis of many Mendelian neurological conditions, but progress has been considerably slower in complex neurological diseases (multiple sclerosis, migraine, Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and so on). The authors review current next-generation sequencing methodologies and present selected studies illustrating how these have been used to cast light on the genetic etiology of complex neurological diseases with specific focus on multiple sclerosis. The authors highlight particular pitfalls in next-generation sequencing experiments and speculate on both clinical and research applications of these sequencing platforms for complex neurological disorders in the future.

Genetic analysis of sudden unexplained death: a multidisciplinary approach

Each year infants, children and young adults die suddenly and unexpectedly. In many cases the cause of death can be elucidated by medico-legal autopsy, however, a significant number of these cases remain unexplained despite a detailed postmortem investigation and are labeled as sudden unexplained death (SUD). Post-mortem genetic testing, so called molecular autopsy, revealed that primary arrhythmogenic disorders including long QT syndrome and catecholaminergic polymorphic ventricular tachycardia (CPVT) may account for a certain number of these cases. Because of the inheritance of these diseases, close relatives of the deceased may also at potential risk of carrying fatal cardiac disorders. Therefore, advanced diagnostic analyses, genetic counseling and interdisciplinary collaboration should be integral parts of clinical and forensic practice. In the present study, we performed mutation analyses of the major genes causing cardiac channelopathies in 15 SUD cases. In four cases we found putative pathogenic mutations in cardiac ion channel genes. Clinical and genetic examination of family members of SUD victims was also performed and affected family members were identified. This study demonstrates that molecular genetic screening needs to become an inherent part of the postmortem examination. This will enhance the ability of screening family members of SUD victims who may be at risk. The present data also illustrate that detection and follow up of familial cases of sudden death is challenging and requires a close multidisciplinary collaboration between different medical disciplines, with great responsibility for the forensic pathologist.

Severe congenital RYR1-associated myopathy: the expanding clinicopathologic and genetic spectrum

OBJECTIVE

To report a series of 11 patients on the severe end of the spectrum of ryanodine receptor 1 (RYR1) gene-related myopathy, in order to expand the clinical, histologic, and genetic heterogeneity associated with this group of patients.

METHODS

Eleven patients evaluated in the neonatal period with severe neonatal-onset RYR1-associated myopathy confirmed by genetic testing were ascertained. Clinical features, molecular testing results, muscle imaging, and muscle histology are reviewed.

RESULTS

Clinical features associated with the severe neonatal presentation of RYR1-associated myopathy included decreased fetal movement, hypotonia, poor feeding, respiratory involvement, arthrogryposis, and ophthalmoplegia in 3 patients, and femur fractures or hip dislocation at birth. Four patients had dominant RYR1 mutations, and 7 had recessive RYR1 mutations. One patient had a cleft palate, and another a congenital rigid spine phenotype-findings not previously described in the literature in patients with early-onset RYR1 mutations. Six patients who underwent muscle ultrasound showed relative sparing of the rectus femoris muscle. Histologically, all patients with dominant mutations had classic central cores on muscle biopsy. Patients with recessive mutations showed great histologic heterogeneity, including fibrosis, variation in fiber size, skewed fiber typing, very small fibers, and nuclear internalization with or without ill-defined cores.

CONCLUSIONS

This series confirms and expands the clinical and histologic variability associated with severe congenital RYR1-associated myopathy. Both dominant and recessive mutations of the RYR1 gene can result in a severe neonatal-onset phenotype, but more clinical and histologic heterogeneity has been seen in those with recessive RYR1 gene mutations. Central cores are not obligatory histologic features in recessive RYR1 mutations. Sparing of the rectus femoris muscle on imaging should prompt evaluation for RYR1-associated myopathy in the appropriate clinical context.

Mutation screening of the DYT6/THAP1 gene in Serbian patients with primary dystonia

Primary dystonia (PrD) is characterized by sustained muscle contractions, causing twisting and repetitive movements and abnormal postures. Besides DYT1/TOR1A gene, DYT6/THAP1 gene is the second gene known to cause primary pure dystonia. We screened 281 Serbian primary dystonia patients and 106 neurologically healthy control individuals for the GAG deletion in TOR1A gene and for mutations in THAP1 gene by direct sequencing. Nine subjects were found to have the GAG deletion in TOR1A gene. Four coding mutations, including two novel mutations, were identified in the THAP1 gene in five unrelated patients. Two mutations were missense, one was nonsense, and one was 24 bp duplication. None of the coding mutations were seen in 106 control individuals. In addition, one novel nucleotide change in the 5'UTR region of THAP1 gene was detected in two unrelated patients. The mutation frequency of THAP1 gene in Serbian patients with primary dystonia was 1.8 %, similar to the mutation frequency in other populations. Most of the patients reported here with THAP1 mutations had the clinical features of predominantly laryngeal or oromandibular dystonia. Our data expand the genotypic spectrum of THAP1 and strengthen the association with upper body involvement, including the cranial and cervical regions that are usually spared in DYT1-PrD.

Identification of Novel Mutations in FAH Gene and Prenatal Diagnosis of Tyrosinemia in Indian Family

Carrier of tyrosinemia type I was diagnosed by sequencing FAH (fumarylacetoacetate hydrolase) gene. It leads to the identification of heterozygous status for both c.648C>G (p.Ile216Met) and c.1159G>A (p.Gly387Arg) mutations in exons 8 and 13, respectively, in the parents. The experimental program PolyPhen, SIFT, and MT predicts former missense point mutation as “benign” that creates a potential donor splice site and later one as “probably damaging” which disrupts secondary structure of protein.

An informatics approach to analyzing the incidentalome

PURPOSE

Next-generation sequencing has transformed genetic research and is poised to revolutionize clinical diagnosis. However, the vast amount of data and inevitable discovery of incidental findings require novel analytic approaches. We therefore implemented for the first time a strategy that utilizes an a priori structured framework and a conservative threshold for selecting clinically relevant incidental findings.

METHODS

We categorized 2,016 genes linked with Mendelian diseases into "bins" based on clinical utility and validity, and used a computational algorithm to analyze 80 whole-genome sequences in order to explore the use of such an approach in a simulated real-world setting.

RESULTS

The algorithm effectively reduced the number of variants requiring human review and identified incidental variants with likely clinical relevance. Incorporation of the Human Gene Mutation Database improved the yield for missense mutations but also revealed that a substantial proportion of purported disease-causing mutations were misleading.

CONCLUSION

This approach is adaptable to any clinically relevant bin structure, scalable to the demands of a clinical laboratory workflow, and flexible with respect to advances in genomics. We anticipate that application of this strategy will facilitate pretest informed consent, laboratory analysis, and posttest return of results in a clinical context.

THAP1 mutations and dystonia phenotypes: genotype phenotype correlations

THAP1 mutations have been shown to be the cause of DYT6. A number of different mutation types and locations in the THAP1 gene have been associated with a range of severity and dystonia phenotypes, but, as yet, it has been difficult to identify clear genotype phenotype patterns. Here, we screened the THAP1 gene in a further series of dystonia cases and evaluated the mutation pathogenicity in this series as well as previously reported mutations to investigate possible phenotype-genotype correlations. THAP1 mutations have been identified throughout the coding region of the gene, with the greatest concentration of variants localized to the THAP1 domain. In the additional cases analyzed here, a further two mutations were found. No obvious, indisputable genotype-phenotype correlation emerged from these data. However, we managed to find a correlation between the pathogenicity of mutations, distribution, and age of onset of dystonia. THAP1 mutations are an important cause of dystonia, but, as yet, no clear genotype-phenotype correlations have been identified. Greater mutation numbers in different populations will be important and mutation-specific functional studies will be essential to identify the pathogenicity of the various THAP1 mutations. © 2012 Movement Disorder Society

Novel Autosomal Recessive c10orf2 Mutations Causing Infantile-Onset Spinocerebellar Ataxia

Recessive mutations in genes encoding mitochondrial DNA replication machinery lead to mitochondrial DNA depletion syndromes. This genetically and phenotypically heterogeneous group includes infantile onset spinocerebellar ataxia (OMIM# 271245) a neurodegenerative disease caused by mutations in the mtDNA helicase gene, c10orf2, with an increased frequency in the Finnish population due to a founder mutation. We describe a child of English descent who presented with a severe phenotype of IOSCA as a result of two-novel mutations in the c10orf2 gene. This paper expands the phenotypic spectrum of IOSCA and adds further evidence for the presence of a genotype-phenotype correlation among patients with recessive mutations in this gene.

Whole-genome and whole-exome sequencing in neurological diseases

Genetic risk factors that underlie many rare and common neurological disorders remain poorly understood because of the multifactorial and heterogeneous nature of these complex traits. With the decreasing cost of massively parallel sequencing technologies, whole-genome and whole-exome sequencing will soon allow the characterization of the full spectrum of sequence and structural variants present in each individual. Methods are being developed to parse the huge amount of genomic data and to sift out which variants are associated with diseases. Numerous challenges are inherent in the identification of rare and common variants that have a role in complex neurological diseases, and tools are being developed to overcome these challenges. Given that genomic data will soon be the main driver towards the goal of personalized medicine, future developments in the production and interpretation of data, as well as in ethics and counselling, will be needed for whole-genome and whole-exome sequencing to be used as informative tools in a clinical setting.

Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting

Accurate interpretation of gene testing is a key component in customizing patient therapy. Where confirming evidence for a gene variant is lacking, computational prediction may be employed. A standardized framework, however, does not yet exist for quantitative evaluation of disease association for uncertain or novel gene variants in an objective manner. Here, complementary predictors for missense gene variants were incorporated into a weighted Consensus framework that includes calculated reference intervals from known disease outcomes. Data visualization for clinical reporting is also discussed.

An integrated approach for classifying mitochondrial DNA variants: one clinical diagnostic laboratory's experience

PURPOSE

The mitochondrial genome is highly polymorphic. A unique feature of deleterious mitochondrial DNA (mtDNA) mutations is heteroplasmy. Genetic background and variable penetrance also play roles in the pathogenicity for a mtDNA variant. Clinicians are increasingly interested in requesting mtDNA testing. However, interpretation of uncharacterized mtDNA variants is a great challenge. We suggest a stepwise interpretation procedure for clinical service.

METHODS

We describe the algorithms used to interpret novel and rare mtDNA variants. mtDNA databases and in silico predictive algorithms are used to evaluate the pathogenic potential of novel and/or rare mtDNA variants.

RESULTS

mtDNA variants can be classified into three categories: benign variants, unclassified variants, and deleterious mutations based on database search and in silico prediction. Targeted DNA sequence analysis of matrilineal relatives, heteroplasmy quantification, and functional studies are useful to classify mtDNA variants.

CONCLUSION

Clinical significance of a novel or rare variant is critical in the diagnosis of the disease and counseling of the family. Based on the results from clinical, biochemical, and molecular genetic studies of multiple family members, proper interpretation of mtDNA variants is important for clinical laboratories and for patient care.

Variable expressivity of a novel mutation in the SCN1A gene leading to an autosomal dominant seizure disorder

Mutations in the SCN1A gene can cause a variety of dominantly inherited epilepsy syndromes. Severe phenotypes usually result from loss of function mutations, whereas missense mutations cause a milder phenotype by altering the sodium channel activity. We report on a novel missense variant (p.Val1379Leu) in the SCN1A gene segregating in an autosomal dominant pattern in a family exhibiting a variable epilepsy phenotype ranging from generalized epilepsy with febrile seizures during infancy to a well controlled seizure disorder in adulthood. This report supports the importance of SCN1A mutation analysis in families in which seizure disorders segregate in an autosomal dominant fashion.

Utility of gene-specific algorithms for predicting pathogenicity of uncertain gene variants

The rapid advance of gene sequencing technologies has produced an unprecedented rate of discovery of genome variation in humans. A growing number of authoritative clinical repositories archive gene variants and disease phenotypes, yet there are currently many more gene variants that lack clear annotation or disease association. To date, there has been very limited coverage of gene-specific predictors in the literature. Here the evaluation is presented of "gene-specific" predictor models based on a naïve Bayesian classifier for 20 gene-disease datasets, containing 3986 variants with clinically characterized patient conditions. The utility of gene-specific prediction is then compared with "all-gene" generalized prediction and also with existing popular predictors. Gene-specific computational prediction models derived from clinically curated gene variant disease datasets often outperform established generalized algorithms for novel and uncertain gene variants.

Next-generation sequencing entering the clinical arena

Over the last decade the genetic etiology of many heritable diseases could be resolved. For heart muscle diseases, so called cardiomyopathies, mutations in more than 40 different genes have been identified. Due to this large genetic heterogeneity and missing of adequate gene-diagnostic tools, most patients are not genetically characterized, which would be important for individualized patient care. Currently, next-generation sequencing technologies are revolutionizing genetic and epigenetic research, since they are capable to produce billions of bases of sequence information in a single experiment. Accordingly, this powerful technology can now also open avenues for genetic diagnostics. The scope of this article is to illustrate technical approaches, clinical applications, and yet unsolved problems of next-generation sequencing entering the clinical arena.

Analysis of the coding genome of diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common form of human lymphoma. Although a number of structural alterations have been associated with the pathogenesis of this malignancy, the full spectrum of genetic lesions that are present in the DLBCL genome, and therefore the identity of dysregulated cellular pathways, remains unknown. By combining next-generation sequencing and copy number analysis, we show that the DLBCL coding genome contains, on average, more than 30 clonally represented gene alterations per case. This analysis also revealed mutations in genes not previously implicated in DLBCL pathogenesis, including those regulating chromatin methylation (MLL2; 24% of samples) and immune recognition by T cells. These results provide initial data on the complexity of the DLBCL coding genome and identify novel dysregulated pathways underlying its pathogenesis.

Homology modeling and functional testing of an ABCA1 mutation causing Tangier disease

OBJECTIVE

To investigate the impact of the p.R1068H mutation on the structure and function of the ATP-binding cassette A1 (ABCA1) protein.

METHODS

A homology model of the nucleotide binding domains of ABCA1 was constructed to identify the three-dimensional orientation of R1068. Cholesterol efflux assays were performed on fibroblasts obtained from members of a Tangier disease (TD) family carrying the p.R1068H mutation and in HEK293 cells transfected with a p.R1068H mutant cDNA vector. Confocal microscopy was used to investigate the localisation of the wildtype and mutant p.R1068H protein in HEK293 cells.

RESULTS

Sequence alignments and modeling indicated residue R1068 to be located in an α-helix downstream of the Walker B motif in the first nucleotide binding domain (NBD-1), in a position to form ionic interactions with D1092 and E1093. Cholesterol efflux studies showed the efflux from TD fibroblasts and HEK293 cells expressing the mutant p.R1068H protein to be markedly reduced compared to wildtype. Localisation of the mutant p.R1068H protein in HEK293 cells showed intracellular retention of the protein indicating a defect in trafficking to the plasma membrane.

CONCLUSION

Homology modeling of the ABCA1 protein showed that the p.R1068H mutation would likely disrupt the conformation of NBD-1. Functional studies of p.R1068H showed a lack of cholesterol efflux function due to defective trafficking to the plasma membrane, most likely caused by impaired oligomerisation.