SNAP: predict effect of non-synonymous polymorphisms on function

PON-P: integrated predictor for pathogenicity of missense variants

High-throughput sequencing data generation demands the development of methods for interpreting the effects of genomic variants. Numerous computational methods have been developed to assess the impact of variations because experimental methods are unable to cope with both the speed and volume of data generation. To harness the strength of currently available predictors, the Pathogenic-or-Not-Pipeline (PON-P) integrates five predictors to predict the probability that nonsynonymous variations affect protein function and may consequently be disease related. Random forest methodology-based PON-P shows consistently improved performance in cross-validation tests and on independent test sets, providing ternary classification and statistical reliability estimate of results. Applied to missense variants in a melanoma cancer cell line, PON-P predicts variants in 17 genes to affect protein function. Previous studies implicate nine of these genes in the pathogenesis of various forms of cancer. PON-P may thus be used as a first step in screening and prioritizing variants to determine deleterious ones for further experimentation.

Cardiomyopathy in patients with POMT1-related congenital and limb-girdle muscular dystrophy

Protein-o-mannosyl transferase 1 (POMT1) is a glycosyltransferase involved in α-dystroglycan (α-DG) glycosylation. Clinical phenotype in POMT1-mutated patients ranges from congenital muscular dystrophy (CMD) with structural brain abnormalities, to limb-girdle muscular dystrophy (LGMD) with microcephaly and mental retardation, to mild LGMD. No cardiac involvement has until now been reported in POMT1-mutated patients. We report three patients who harbored compound heterozygous POMT1 mutations and showed left ventricular (LV) dilation and/or decrease in myocardial contractile force: two had a LGMD phenotype with a normal or close-to-normal cognitive profile and one had CMD with mental retardation and normal brain MRI. Reduced or absent α-DG immunolabeling in muscle biopsies were identified in all three patients. Bioinformatic tools were used to study the potential effect of POMT1-detected mutations. All the detected POMT1 mutations were predicted in silico to interfere with protein folding and/or glycosyltransferase function. The report on the patients described here has widened the clinical spectrum associated with POMT1 mutations to include cardiomyopathy. The functional impact of known and novel POMT1 mutations was predicted with a bioinformatics approach, and results were compared with previous in vitro studies of protein-o-mannosylase function.

A single mutation in MCCC1 or MCCC2 as a potential cause of positive screening for 3-methylcrotonyl-CoA carboxylase deficiency

Isolated 3-Methylcrotonyl-CoA carboxylase deficiency (MCC deficiency) is an organic aciduria presenting with a highly variable phenotype and has been part of newborn screening programs in various countries, in particular in the US. Here we present enzymatic and genetic characterisation of 22 individuals with increased 3-hydroxyisovalerylcarnitine and/or 3-methylcrotonylglycine suggesting MCC deficiency, but only partially reduced 3-methylcrotonyl-CoA carboxylase activity. Among these, 21 carried a single mutant allele in either MCCC1 (n=20) or MCCC2 (n=1). Our results suggest that heterozygosity for such a single deleterious mutation may lead to misdiagnosis of MCC deficiency.

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

Background

In this study, instead of current biochemical methods, the effects of deleterious amino acid substitutions in F8 and F9 gene upon protein structure and function were assayed by means of computational methods and information from the databases. Deleterious substitutions of F8 and F9 are responsible for Haemophilia A and Haemophilia B which is the most common genetic disease of coagulation disorders in blood. Yet, distinguishing deleterious variants of F8 and F9 from the massive amount of nonfunctional variants that occur within a single genome is a significant challenge.

Methods

We performed an in silico analysis of deleterious mutations and their protein structure changes in order to analyze the correlation between mutation and disease. Deleterious nsSNPs were categorized based on empirical based and support vector machine based methods to predict the impact on protein functions. Furthermore, we modeled mutant proteins and compared them with the native protein for analysis of protein structure stability.

Results

Out of 510 nsSNPs in F8, 378 nsSNPs (74%) were predicted to be 'intolerant' by SIFT, 371 nsSNPs (73%) were predicted to be 'damaging' by PolyPhen and 445 nsSNPs (87%) as 'less stable' by I-Mutant2.0. In F9, 129 nsSNPs (78%) were predicted to be intolerant by SIFT, 131 nsSNPs (79%) were predicted to be damaging by PolyPhen and 150 nsSNPs (90%) as less stable by I-Mutant2.0. Overall, we found that I-Mutant which emphasizes support vector machine based method outperformed SIFT and PolyPhen in prediction of deleterious nsSNPs in both F8 and F9.

Conclusions

The models built in this work would be appropriate for predicting the deleterious amino acid substitutions and their functions in gene regulation which would be useful for further genotype-phenotype researches as well as the pharmacogenetics studies. These in silico tools, despite being helpful in providing information about the nature of mutations, may also function as a first-pass filter to determine the substitutions worth pursuing for further experimental research in other coagulation disorder causing genes.

Partition dataset according to amino acid type improves the prediction of deleterious non-synonymous SNPs

Many non-synonymous SNPs (nsSNPs) are associated with diseases, and numerous machine learning methods have been applied to train classifiers for sorting disease-associated nsSNPs from neutral ones. The continuously accumulated nsSNP data allows us to further explore better prediction approaches. In this work, we partitioned the training data into 20 subsets according to either original or substituted amino acid type at the nsSNP site. Using support vector machine (SVM), training classification models on each subset resulted in an overall accuracy of 76.3% or 74.9% depending on the two different partition criteria, while training on the whole dataset obtained an accuracy of only 72.6%. Moreover, the dataset was also randomly divided into 20 subsets, but the corresponding accuracy was only 73.2%. Our results demonstrated that partitioning the whole training dataset into subsets properly, i.e., according to the residue type at the nsSNP site, will improve the performance of the trained classifiers significantly, which should be valuable in developing better tools for predicting the disease-association of nsSNPs.

Analysis of DNA sequence variants detected by high-throughput sequencing

The Undiagnosed Diseases Program at the National Institutes of Health uses high-throughput sequencing (HTS) to diagnose rare and novel diseases. HTS techniques generate large numbers of DNA sequence variants, which must be analyzed and filtered to find candidates for disease causation. Despite the publication of an increasing number of successful exome-based projects, there has been little formal discussion of the analytic steps applied to HTS variant lists. We present the results of our experience with over 30 families for whom HTS sequencing was used in an attempt to find clinical diagnoses. For each family, exome sequence was augmented with high-density SNP-array data. We present a discussion of the theory and practical application of each analytic step and provide example data to illustrate our approach. The article is designed to provide an analytic roadmap for variant analysis, thereby enabling a wide range of researchers and clinical genetics practitioners to perform direct analysis of HTS data for their patients and projects.

Non-neutral nonsynonymous single nucleotide polymorphisms in human ABC transporters: the first comparison of six prediction methods

Nonsynonymous single nucleotide polymorphisms (nsSNPs) in coding regions that can lead to amino acid changes may cause alteration of protein function and account for susceptibility to disease and altered drug/xenobiotic response. Abundant nsSNPs have been found in genes coding for human ATP-binding cassette (ABC) transporters, but there is little known about the relationship between the genotype and phenotype of nsSNPs in these membrane proteins. In addition, it is unknown which prediction method is better suited for the prediction of non-neutral nsSNPs of ABC transporters. We have identified 2,172 validated nsSNPs in 49 human ABC transporter genes from the Ensembl genome database and the NCBI SNP database. Using six different algorithms, 41 to 52% of nsSNPs in ABC transporter genes were predicted to have functional impacts on protein function. Predictions largely agreed with the available experimental annotations. Overall, 78.5% of non-neutral nsSNPs were predicted correctly as damaging by SNAP, which together with SIFT and PolyPhen, was superior to the prediction methods Pmut, PhD-SNP, and Panther. This study also identified any amino acids that were likely to be functionally critical but have not yet been studied experimentally. There was significant concordance between the predicted results of SIFT and PolyPhen. Evolutionarily non-neutral (destabilizing) amino acid substitutions are predicted to be the basis for the pathogenic alteration of ABC transporter activity that is associated with disease susceptibility and altered drug/xenobiotic response.

A combined functional annotation score for non-synonymous variants

AIMS

Next-generation sequencing has opened the possibility of large-scale sequence-based disease association studies. A major challenge in interpreting whole-exome data is predicting which of the discovered variants are deleterious or neutral. To address this question in silico, we have developed a score called Combined Annotation scoRing toOL (CAROL), which combines information from 2 bioinformatics tools: PolyPhen-2 and SIFT, in order to improve the prediction of the effect of non-synonymous coding variants.

METHODS

We used a weighted Z method that combines the probabilistic scores of PolyPhen-2 and SIFT. We defined 2 dataset pairs to train and test CAROL using information from the dbSNP: 'HGMD-PUBLIC' and 1000 Genomes Project databases. The training pair comprises a total of 980 positive control (disease-causing) and 4,845 negative control (non-disease-causing) variants. The test pair consists of 1,959 positive and 9,691 negative controls.

RESULTS

CAROL has higher predictive power and accuracy for the effect of non-synonymous variants than each individual annotation tool (PolyPhen-2 and SIFT) and benefits from higher coverage.

CONCLUSION

The combination of annotation tools can help improve automated prediction of whole-genome/exome non-synonymous variant functional consequences.

Bioinformatics for personal genome interpretation

An international consortium released the first draft sequence of the human genome 10 years ago. Although the analysis of this data has suggested the genetic underpinnings of many diseases, we have not yet been able to fully quantify the relationship between genotype and phenotype. Thus, a major current effort of the scientific community focuses on evaluating individual predispositions to specific phenotypic traits given their genetic backgrounds. Many resources aim to identify and annotate the specific genes responsible for the observed phenotypes. Some of these use intra-species genetic variability as a means for better understanding this relationship. In addition, several online resources are now dedicated to collecting single nucleotide variants and other types of variants, and annotating their functional effects and associations with phenotypic traits. This information has enabled researchers to develop bioinformatics tools to analyze the rapidly increasing amount of newly extracted variation data and to predict the effect of uncharacterized variants. In this work, we review the most important developments in the field--the databases and bioinformatics tools that will be of utmost importance in our concerted effort to interpret the human variome.

A trans-specific polymorphism in ZC3HAV1 is maintained by long-standing balancing selection and may confer susceptibility to multiple sclerosis

The human ZC3HAV1 gene encodes an antiviral protein. The longest splicing isoform of ZC3HAV1 contains a C-terminal PARP-like domain, which has evolved under positive selection in primates. We analyzed the evolutionary history of this same domain in humans and in Pan troglodytes. We identified two variants that segregate in both humans and chimpanzees; one of them (rs3735007) does not occur at a hypermutable site and accounts for a nonsynonymous substitution (Thr851Ile). The probability that the two trans-specific polymorphisms have occurred independently in the two lineages was estimated to be low (P = 0.0054), suggesting that at least one of them has arisen before speciation and has been maintained by selection. Population genetic analyses in humans indicated that the region surrounding the shared variants displays strong evidences of long-standing balancing selection. Selection signatures were also observed in a chimpanzee population sample. Inspection of 1000 Genomes data confirmed these findings but indicated that search for selection signatures using low-coverage whole-genome data may need masking of repetitive sequences. A case–control study of more than 1,000 individuals from mainland Italy indicated that the Thr851Ile SNP is significantly associated with susceptibility to multiple sclerosis (MS) (odds ratio [OR] = 1.47, 95% confidence intervals [CI]: 1.08–1.99, P = 0.011). This finding was confirmed in a larger sample of 4,416 Sardinians cases/controls (OR = 1.18, 95% CI: 1.037–1.344, P = 0.011), but not in a population from Belgium. We provide one of the first instances of human/chimpanzee trans-specific coding variant located outside the major histocompatibility complex region. The selective pressure is likely to be virus driven; in modern populations, this variant associates with susceptibility to MS, possibly via the interaction with environmental factors.

SNPdbe: constructing an nsSNP functional impacts database

Summary: Many existing databases annotate experimentally characterized single nucleotide polymorphisms (SNPs). Each non-synonymous SNP (nsSNP) changes one amino acid in the gene product (single amino acid substitution;SAAS). This change can either affect protein function or be neutral in that respect. Most polymorphisms lack experimental annotation of their functional impact. Here, we introduce SNPdbe—SNP database of effects, with predictions of computationally annotated functional impacts of SNPs. Database entries represent nsSNPs in dbSNP and 1000 Genomes collection, as well as variants from UniProt and PMD. SAASs come from >2600 organisms; ‘human’ being the most prevalent. The impact of each SAAS on protein function is predicted using the SNAP and SIFT algorithms and augmented with experimentally derived function/structure information and disease associations from PMD, OMIM and UniProt. SNPdbe is consistently updated and easily augmented with new sources of information. The database is available as an MySQL dump and via a web front end that allows searches with any combination of organism names, sequences and mutation IDs.

Availability:http://www.rostlab.org/services/snpdbe

Contact:schaefer@rostlab.org; snpdbe@rostlab.org

Protein-protein interaction sites are hot spots for disease-associated nonsynonymous SNPs

Many nonsynonymous single nucleotide polymorphisms (nsSNPs) are disease causing due to effects at protein-protein interfaces. We have integrated a database of the three-dimensional (3D) structures of human protein/protein complexes and the humsavar database of nsSNPs. We analyzed the location of nsSNPS in terms of their location in the protein core, at protein-protein interfaces, and on the surface when not at an interface. Disease-causing nsSNPs that do not occur in the protein core are preferentially located at protein-protein interfaces rather than surface noninterface regions when compared to random segregation. The disruption of the protein-protein interaction can be explained by a range of structural effects including the loss of an electrostatic salt bridge, the destabilization due to reduction of the hydrophobic effect, the formation of a steric clash, and the introduction of a proline altering the main-chain conformation.

PRO-MINE: A bioinformatics repository and analytical tool for TARDBP mutations

TDP-43 is a multifunctional RNA-binding protein found to be a major protein component of intracellular inclusions found in neurodegenerative disorders such as Fronto Temporal Lobar Degeneration, Amyotrophic Lateral Sclerosis, and Alzheimer Disease. PRO-MINE (PROtein Mutations In NEurodegeneration) is a database populated with manually curated data from the literature regarding all TDP-43/TDP43/TARDBP gene disease-associated mutations identified to date. A web server interface has been developed to query the database and to provide tools for the analysis of already reported or novel TDP-43 gene mutations. As is usually the case with genetic association studies, assessing the potential impact of identified mutations is of crucial importance, and in order to avoid prediction biases it is essential to compare the prediction results. However, in most cases mutations have to be submitted separately to various prediction tools and the individual results manually merged together afterwards. The implemented web server aims to overcome the problem by providing simultaneous access to several prediction tools and by displaying the results into a single output. Furthermore, the results are displayed together in a comprehensive output for a more convenient analysis and are enriched with additional information about mutations. In addition, our web server can also display the mutation(s) of interest within an alignment of annotated TDP-43 protein sequences from different vertebrate species. In this way, the degree of sequence conservation where the mutation(s) occur can be easily tracked and visualized. The web server is freely available to researchers and can be accessed at http://bioinfo.hr/pro-mine. © 2010 Wiley-Liss, Inc.

An update on molecular genetics of Alkaptonuria (AKU)

Alkaptonuria (AKU) is an autosomal recessive disorder caused by a deficiency of homogentisate 1,2 dioxygenase (HGD) and characterized by homogentisic aciduria, ochronosis, and ochronotic arthritis. The defect is caused by mutations in the HGD gene, which maps to the human chromosome 3q21-q23. AKU shows a very low prevalence (1:100,000-250,000) in most ethnic groups, but there are countries such as Slovakia and the Dominican Republic in which the incidence of this disorder rises to as much as 1:19,000. In this work, we summarize the genetic aspects of AKU in general and the distribution of all known disease-causing mutations reported so far. We focus on special features of AKU in Slovakia, which is one of the countries with an increased incidence of this rare metabolic disorder.

SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants

Single nucleotide variants (SNVs) are, together with copy number variation, the primary source of variation in the human genome and are associated with phenotypic variation such as altered response to drug treatment and susceptibility to disease. Linking structural effects of non-synonymous SNVs to functional outcomes is a major issue in structural bioinformatics. The SNPeffect database (http://snpeffect.switchlab.org) uses sequence- and structure-based bioinformatics tools to predict the effect of protein-coding SNVs on the structural phenotype of proteins. It integrates aggregation prediction (TANGO), amyloid prediction (WALTZ), chaperone-binding prediction (LIMBO) and protein stability analysis (FoldX) for structural phenotyping. Additionally, SNPeffect holds information on affected catalytic sites and a number of post-translational modifications. The database contains all known human protein variants from UniProt, but users can now also submit custom protein variants for a SNPeffect analysis, including automated structure modeling. The new meta-analysis application allows plotting correlations between phenotypic features for a user-selected set of variants.

Identification of 11 Novel Homogentisate 1,2 Dioxygenase Variants in Alkaptonuria Patients and Establishment of a Novel LOVD-Based HGD Mutation Database

Enzymatic loss in alkaptonuria (AKU), an autosomal recessive disorder, is caused by mutations in the homogentisate 1,2 dioxygenase (HGD) gene, which decrease or completely inactivate the function of the HGD protein to metabolize homogentisic acid (HGA). AKU shows a very low prevalence (1:100,000-250,000) in most ethnic groups, but there are countries with much higher incidence, such as Slovakia and the Dominican Republic. In this work, we report 11 novel HGD mutations identified during analysis of 36 AKU patients and 41 family members from 27 families originating from 9 different countries, mainly from Slovakia and France. In Slovak patients, we identified two additional mutations, thus a total number of HGD mutations identified in this small country is 12. In order to record AKU-causing mutations and variants of the HGD gene, we have created a HGD mutation database that is open for future submissions and is available online ( http://hgddatabase.cvtisr.sk/ ). It is founded on the Leiden Open (source) Variation Database (LOVD) system and includes data from the original AKU database ( http://www.alkaptonuria.cib.csic.es ) and also all so far reported variants and AKU patients. Where available, HGD-haplotypes associated with the mutations are also presented. Currently, this database contains 148 unique variants, of which 115 are reported pathogenic mutations. It provides a valuable tool for information exchange in AKU research and care fields and certainly presents a useful data source for genotype-phenotype correlations and also for future clinical trials.

Protein stability and in vivo concentration of missense mutations in phenylalanine hydroxylase

A previous computational analysis of missense mutations linked to monogenic disease found a high proportion of missense mutations affect protein stability, rather than other aspects of protein structure and function. The purpose of this study is to relate the presence of such stability damaging missense mutations to the levels of a particular protein present under "in vivo" like conditions, and to test the reliability of the computational methods. Experimental data on a set of missense mutations of the enzyme phenylalanine hydroxylase (PAH) associated with the monogenic disease phenylketonuria (PKU) have been compared with the expected in vivo impact on protein function, obtained using SNPs3D, an in silico analysis package. A high proportion of the PAH mutations are predicted to be destabilizing. The overall agreement between predicted stability impact and experimental evidence for lower protein levels is in accordance with the estimated error rates of the methods. For these mutations, destabilization of protein three-dimensional structure is the major molecular mechanism leading to PKU, and results in a substantial reduction of in vivo PAH protein concentration. Although of limited scale, the results support the view that destabilization is the most common mechanism by which missense mutations cause monogenic disease. In turn, this conclusion suggests the general therapeutic strategy of developing drugs targeted at restoring wild type stability.

Generation of recombinant antibodies and means for increasing their affinity

Highly specific interaction with foreign molecules is a unique feature of antibodies. Since 1975, when Keller and Milstein proposed the method of hybridoma technology and prepared mouse monoclonal antibodies, many antibodies specific to various antigens have been obtained. Recent development of methods for preparation of recombinant DNA libraries and in silico bioinformatics approaches for protein structure analysis makes possible antibody preparation using gene engineering approaches. The development of gene engineering methods allowed creating recombinant antibodies and improving characteristics of existing antibodies; this significantly extends the applicability of antibodies. By modifying biochemical and immunochemical properties of antibodies by changing their amino acid sequences it is possible to create antibodies with properties optimal for certain tasks. For example, application of recombinant technologies resulted in antibody preparation of high affinity significantly exceeding the initial affinity of natural antibodies. In this review we summarize information about the structure, modes of preparation, and application of recombinant antibodies and their fragments and also consider the main approaches used to increase antibody affinity.

Computational and statistical approaches to analyzing variants identified by exome sequencing

New sequencing technology has enabled the identification of thousands of single nucleotide polymorphisms in the exome, and many computational and statistical approaches to identify disease-association signals have emerged.

A novel CRYAB mutation resulting in multisystemic disease

Mutations in the CRYAB gene, encoding alpha-B crystallin, cause distinct clinical phenotypes including isolated posterior polar cataract, myofibrillar myopathy, cardiomyopathy, or a multisystemic disorder combining all these features. Genotype/phenotype correlations are still unclear. To date, multisystemic involvement has been reported only in kindred harboring the R120G substitution. We report a novel CRYAB mutation, D109H, associated with posterior polar cataract, myofibrillar myopathy and cardiomyopathy in a two-generation family with five affected individuals. Age of onset, clinical presentation, and muscle abnormalities were very similar to those described in the R120G family. Alpha-B crystallin may form dimers and acts as a chaperone for a number of proteins. It has been suggested that the phenotypic diversity could be related to the various interactions between target proteins of individual mutant residues. Molecular modeling indicates that residues D109 and R120 interact with each other during dimerization of alpha-B crystallin; interestingly, the two substitutions affecting these residues (D109H and R120G) are associated with the same clinical phenotype, thus suggesting a similar pathogenic mechanism. We propose that impairment of alpha-B crystallin dimerization may also be relevant to the pathogenesis of these disorders.

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.

Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data

Genome and exome sequencing yield extensive catalogues of human genetic variation. However, pinpointing the few phenotypically causal variants among the many variants present in human genomes remains a major challenge, particularly for rare and complex traits wherein genetic information alone is often insufficient. Here, we review approaches to estimate the deleteriousness of single nucleotide variants (SNVs), which can be used to prioritize disease-causal variants. We describe recent advances in comparative and functional genomics that enable systematic annotation of both coding and non-coding variants. Application and optimization of these methods will be essential to find the genetic answers that sequencing promises to hide in plain sight.

Germline mutations of the TMEM127 gene in patients with paraganglioma of head and neck and extraadrenal abdominal sites

BACKGROUND

Hereditary pheochromocytoma is associated with germline mutations of a set of susceptibility genes to which the TMEM127 gene has recently been added. Patients with TMEM127 mutations have been thus far exclusively identified with adrenal tumors.

PATIENTS AND METHODS

A population-based series of 48 consecutive individuals from the European-American Pheochromocytoma Paraganglioma Registry with multiple paraganglial tumors and, of these, one extraadrenal paraganglial tumor were selected for this study. They all had normal results when screened for germline mutations of the genes RET, VHL, SDHB, SDHC, and SDHD. Germline mutation analysis of the TMEM127 gene included a search for intragenic mutations and large rearrangements.

RESULTS

Of the 48 eligible patients with extraadrenal paraganglial tumors, two (4.2%) were found to have TMEM127 mutations. One patient had multiple head and neck paraganglioma and one retroperitoneal extraadrenal and adrenal tumor.

CONCLUSION

TMEM127 germline mutations confer risks of extraadrenal paraganglial tumors in addition to the documented adrenal pheochromocytoma. Thus, surveillance for extraadrenal and adrenal paraganglial tumors is likely warranted in TMEM127 mutation carriers, although the true prevalence should be evaluated in patients with extraadrenal paraganglial tumors.

In silico SNP analysis and bioinformatics tools: a review of the state of the art to aid drug discovery

SNPs can alter protein function and phenotype, leading to altered pharmacogenomic drug profiles. The exponential number of SNPs makes it impossible to perform wet laboratory experiments to determine the biological significance of each one. However, bioinformatics tools can be used to screen for potentially deleterious SNPs that might affect important drug targets before further investigation by wet laboratory techniques. As there are numerous web-based bioinformatics tools, much time and effort is needed to select the most appropriate tool to use. Here, we review state-of-the-art bioinformatics tools to help researchers analyze and select the most promising SNPs for drug discovery in the shortest time possible.

A mutation in VPS35, encoding a subunit of the retromer complex, causes late-onset Parkinson disease

To identify rare causal variants in late-onset Parkinson disease (PD), we investigated an Austrian family with 16 affected individuals by exome sequencing. We found a missense mutation, c.1858G>A (p.Asp620Asn), in the VPS35 gene in all seven affected family members who are alive. By screening additional PD cases, we saw the same variant cosegregating with the disease in an autosomal-dominant mode with high but incomplete penetrance in two further families with five and ten affected members, respectively. The mean age of onset in the affected individuals was 53 years. Genotyping showed that the shared haplotype extends across 65 kilobases around VPS35. Screening the entire VPS35 coding sequence in an additional 860 cases and 1014 controls revealed six further nonsynonymous missense variants. Three were only present in cases, two were only present in controls, and one was present in cases and controls. The familial mutation p.Asp620Asn and a further variant, c.1570C>T (p.Arg524Trp), detected in a sporadic PD case were predicted to be damaging by sequence-based and molecular-dynamics analyses. VPS35 is a component of the retromer complex and mediates retrograde transport between endosomes and the trans-Golgi network, and it has recently been found to be involved in Alzheimer disease.

Structural and functional impact of cancer-related missense somatic mutations

A number of large-scale cancer somatic genome sequencing projects are now identifying genetic alterations in cancers. Evaluation of the effects of these mutations is essential for understanding their contribution to tumorigenesis. We have used SNPs3D, a software suite originally developed for analyzing nonsynonymous germ-line variants, to identify single-base mutations with a high impact on protein structure and function. Two machine learning methods are used: one identifying mutations that destabilize protein three-dimensional structure and the other utilizing sequence conservation and detecting all types of effects on in vivo protein function. Incorporation of detailed structure information into the analysis allows detailed interpretation of the functional effects of mutations in specific cases.  Data from a set of breast and colorectal tumors were analyzed. In known cancer genes, mutations approaching 100% of mutations are found to impact protein function, supporting the view that these methods are appropriate for identifying driver mutations. Overall, 50-60% of all somatic missense mutations are predicted to have a high impact on structural stability or to more generally affect the function of the corresponding proteins. This value is similar to the fraction of all possible missense mutations that have a high impact and is much higher than the corresponding one for human population single-nucleotide polymorphisms, at about 30%. The majority of mutations in tumor suppressors destabilize protein structure, while mutations in oncogenes operate in more varied ways, including destabilization of less active conformational states. The set of high-impact mutations encompasses the possible drivers.

Predicting the functional impact of protein mutations: application to cancer genomics

As large-scale re-sequencing of genomes reveals many protein mutations, especially in human cancer tissues, prediction of their likely functional impact becomes important practical goal. Here, we introduce a new functional impact score (FIS) for amino acid residue changes using evolutionary conservation patterns. The information in these patterns is derived from aligned families and sub-families of sequence homologs within and between species using combinatorial entropy formalism. The score performs well on a large set of human protein mutations in separating disease-associated variants (∼19 200), assumed to be strongly functional, from common polymorphisms (∼35 600), assumed to be weakly functional (area under the receiver operating characteristic curve of ∼0.86). In cancer, using recurrence, multiplicity and annotation for ∼10 000 mutations in the COSMIC database, the method does well in assigning higher scores to more likely functional mutations ('drivers'). To guide experimental prioritization, we report a list of about 1000 top human cancer genes frequently mutated in one or more cancer types ranked by likely functional impact; and, an additional 1000 candidate cancer genes with rare but likely functional mutations. In addition, we estimate that at least 5% of cancer-relevant mutations involve switch of function, rather than simply loss or gain of function.

Sequence variants in COL4A1 and COL4A2 genes in Ecuadorian families with keratoconus

PURPOSE

Keratoconus (KTCN) is a non-inflammatory, usually bilateral disorder of the eye which results in the conical shape and the progressive thinning of the cornea. Several studies have suggested that genetic factors play a role in the etiology of the disease. Several loci were previously described as possible candidate regions for familial KTCN; however, no causative mutations in any genes have been identified for any of these loci. The purpose of this study was to evaluate role of the collagen genes collagen type IV, alpha-1 (COL4A1) and collagen type IV, alpha-2 (COL4A2) in KTCN in Ecuadorian families.

METHODS

COL4A1 and COL4A2 in 15 Ecuadorian KTCN families were examined with polymerase chain reaction amplification, and direct sequencing of all exons, promoter and intron-exon junctions was performed.

RESULTS

Screening of COL4A1 and COL4A2 revealed numerous alterations in coding and non-coding regions of both genes. We detected three missense substitutions in COL4A1: c.19G>C (Val7Leu), c.1663A>C (Thr555Pro), and c.4002A>C (Gln1334His). Five non-synonymous variants were identified in COL4A2: c.574G>T (Val192Phe), c.1550G>A (Arg517Lys), c.2048G>C (Gly683Ala), c.2102A>G (Lys701Arg), and c.2152C>T (Pro718Ser). None of the identified sequence variants completely segregated with the affected phenotype. The Gln1334His variant was possibly damaging to protein function and structure.

CONCLUSIONS

This is the first mutation screening of COL4A1 and COL4A2 genes in families with KTCN and linkage to a locus close to these genes. Analysis of COL4A1 and COL4A2 revealed no mutations indicating that other genes are involved in KTCN causation in Ecuadorian families.

Germline fitness-based scoring of cancer mutations

A key goal in cancer research is to find the genomic alterations that underlie malignant cells. Genomics has proved successful in identifying somatic variants at a large scale. However, it has become evident that a typical cancer exhibits a heterogenous mutation pattern across samples. Cases where the same alteration is observed repeatedly seem to be the exception rather than the norm. Thus, pinpointing the key alterations (driver mutations) from a background of variations with no direct causal link to cancer (passenger mutations) is difficult. Here we analyze somatic missense mutations from cancer samples and their healthy tissue counterparts (germline mutations) from the viewpoint of germline fitness. We calibrate a scoring system from protein domain alignments to score mutations and their target loci. We show first that this score predicts to a good degree the rate of polymorphism of the observed germline variation. The scoring is then applied to somatic mutations. We show that candidate cancer genes prone to copy number loss harbor mutations with germline fitness effects that are significantly more deleterious than expected by chance. This suggests that missense mutations play a driving role in tumor suppressor genes. Furthermore, these mutations fall preferably onto loci in sequence neighborhoods that are high scoring in terms of germline fitness. In contrast, for somatic mutations in candidate onco genes we do not observe a statistically significant effect. These results help to inform how to exploit germline fitness predictions in discovering new genes and mutations responsible for cancer.

Performance of mutation pathogenicity prediction methods on missense variants

Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation in humans. The number of SNPs identified in the human genome is growing rapidly, but attaining experimental knowledge about the possible disease association of variants is laborious and time-consuming. Several computational methods have been developed for the classification of SNPs according to their predicted pathogenicity. In this study, we have evaluated the performance of nine widely used pathogenicity prediction methods available on the Internet. The evaluated methods were MutPred, nsSNPAnalyzer, Panther, PhD-SNP, PolyPhen, PolyPhen2, SIFT, SNAP, and SNPs&GO. The methods were tested with a set of over 40,000 pathogenic and neutral variants. We also assessed whether the type of original or substituting amino acid residue, the structural class of the protein, or the structural environment of the amino acid substitution, had an effect on the prediction performance. The performances of the programs ranged from poor (MCC 0.19) to reasonably good (MCC 0.65), and the results from the programs correlated poorly. The overall best performing methods in this study were SNPs&GO and MutPred, with accuracies reaching 0.82 and 0.81, respectively.

Using bioinformatics to predict the functional impact of SNVs

MOTIVATION

The past decade has seen the introduction of fast and relatively inexpensive methods to detect genetic variation across the genome and exponential growth in the number of known single nucleotide variants (SNVs). There is increasing interest in bioinformatics approaches to identify variants that are functionally important from millions of candidate variants. Here, we describe the essential components of bioinformatics tools that predict functional SNVs.

RESULTS

Bioinformatics tools have great potential to identify functional SNVs, but the black box nature of many tools can be a pitfall for researchers. Understanding the underlying methods, assumptions and biases of these tools is essential to their intelligent application.

Development and validation of a computational method for assessment of missense variants in hypertrophic cardiomyopathy

Assessing the significance of novel genetic variants revealed by DNA sequencing is a major challenge to the integration of genomic techniques with medical practice. Many variants remain difficult to classify by traditional genetic methods. Computational methods have been developed that could contribute to classifying these variants, but they have not been properly validated and are generally not considered mature enough to be used effectively in a clinical setting. We developed a computational method for predicting the effects of missense variants detected in patients with hypertrophic cardiomyopathy (HCM). We used a curated clinical data set of 74 missense variants in six genes associated with HCM to train and validate an automated predictor. The predictor is based on support vector regression and uses phylogenetic and structural features specific to genes involved in HCM. Ten-fold cross validation estimated our predictor's sensitivity at 94% (95% confidence interval: 83%-98%) and specificity at 89% (95% confidence interval: 72%-100%). This corresponds to an odds ratio of 10 for a prediction of pathogenic (95% confidence interval: 4.0-infinity), or an odds ratio of 9.9 for a prediction of benign (95% confidence interval: 4.6-21). Coverage (proportion of variants for which a prediction was made) was 57% (95% confidence interval: 49%-64%). This performance exceeds that of existing methods that are not specifically designed for HCM. The accuracy of this predictor provides support for the clinical use of automated predictions alongside family segregation and population frequency data in the interpretation of new missense variants and suggests future development of similar tools for other diseases.

Predicting disease-associated substitution of a single amino acid by analyzing residue interactions

BACKGROUND

The rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues.

RESULTS

We found that SAPs can be well characterized by network topological features. Mutations are probably disease-associated when they occur at a site with a high centrality value and/or high degree value in a protein structure network. We also discovered that study of the neighboring residues around a mutation site can help to determine whether the mutation is disease-related or not. We compiled a dataset from the Swiss-Prot variant pages and constructed a model to predict disease-associated SAPs based on the random forest algorithm. The values of total accuracy and MCC were 83.0% and 0.64, respectively, as determined by 5-fold cross-validation. With an independent dataset, our model achieved a total accuracy of 80.8% and MCC of 0.59, respectively.

CONCLUSIONS

The satisfactory performance suggests that network topological features can be used as quantification measures to determine the importance of a site on a protein, and this approach can complement existing methods for prediction of disease-associated SAPs. Moreover, the use of this method in SAP studies would help to determine the underlying linkage between SAPs and diseases through extensive investigation of mutual interactions between residues.

Evaluation of the disease liability of CFTR variants

Over 1600 novel sequence variants in the CFTR gene have been reported to the CF Mutation Database (http://www.genet.sickkids.on.ca/cftr/Home.html). While about 25 mutations are well characterized by clinical studies and functional assays, the disease liability of most of the remaining mutations is either unclear or unknown. This gap in knowledge has implications for diagnosis, therapy selection, and counseling for patients and families carrying an uncharacterized CFTR mutation. This chapter will describe a critical approach to assessing the disease implications of CFTR mutations utilizing clinical data, literature review, functional testing, and bioinformatic in silico methods.

Algorithms and semantic infrastructure for mutation impact extraction and grounding

Background

Mutation impact extraction is a hitherto unaccomplished task in state of the art mutation extraction systems. Protein mutations and their impacts on protein properties are hidden in scientific literature, making them poorly accessible for protein engineers and inaccessible for phenotype-prediction systems that currently depend on manually curated genomic variation databases.

Results

We present the first rule-based approach for the extraction of mutation impacts on protein properties, categorizing their directionality as positive, negative or neutral. Furthermore protein and mutation mentions are grounded to their respective UniProtKB IDs and selected protein properties, namely protein functions to concepts found in the Gene Ontology. The extracted entities are populated to an OWL-DL Mutation Impact ontology facilitating complex querying for mutation impacts using SPARQL. We illustrate retrieval of proteins and mutant sequences for a given direction of impact on specific protein properties. Moreover we provide programmatic access to the data through semantic web services using the SADI (Semantic Automated Discovery and Integration) framework.

Conclusion

We address the problem of access to legacy mutation data in unstructured form through the creation of novel mutation impact extraction methods which are evaluated on a corpus of full-text articles on haloalkane dehalogenases, tagged by domain experts. Our approaches show state of the art levels of precision and recall for Mutation Grounding and respectable level of precision but lower recall for the task of Mutant-Impact relation extraction. The system is deployed using text mining and semantic web technologies with the goal of publishing to a broad spectrum of consumers.

Exploring polymorphisms and effects of candidate genes on milk fat quality in dairy sheep

The aim of the present study was to investigate the genetic control of the fatty acid (FA) composition in milk from 3 breeds of sheep: Altamurana, Gentile di Puglia, and Sarda. Single nucleotide polymorphisms within genes, encoding enzymes putatively involved in the synthesis and metabolism of milk fat, were selected for analysis, and the allele substitution effects were determined for 16 genes, which were polymorphic in the 3 sheep breeds, upon the milk fat composition. Four genes (alpha-1-antichymotrypsin-2; diacylglycerol O-acyltransferase homolog-2; propionyl Coenzyme A carboxylase, beta polypeptide; and insulin-like growth factor-I) play a role in the desaturation of stearic FA into polyunsaturated fatty acids. Furthermore, 2 genes (growth hormone receptor and zona pellucida glycoprotein-2) affect the variability of the total fat content in addition to the butyric and stearic FA profile, and the fatty acid synthetase gene has an influence on the medium-chain FA. Milk FA profiles play an important role in dairy sheep farming because they have a large effect on cheese characteristics and also because sheep milk may be marketed as a source of nutraceuticals because it contains higher levels of conjugated linoleic acid than milk from other ruminants. The current study evaluated the global effects of a large number of single nucleotide polymorphisms and haplotypes on traits that are not commonly investigated in sheep but that are potentially very useful for improving milk quality.

Rare melanocortin-3 receptor mutations with in vitro functional consequences are associated with human obesity

In contrast to the melanocortin 4 receptor, the possible role of the melanocortin 3 receptor (MC3R) in regulating body weight is still debated. We have previously reported three mutations in the MC3R gene showing association with human obesity, but these results were not confirmed in a study of severe obese North American adults. In this study, we evaluated the entire coding region of MC3R in 839 severely obese subjects and 967 lean controls of Italian and French origin. In vitro functional analysis of the mutations detected was also performed. The total prevalence of rare MC3R variants was not significantly different in obese subjects when compared with controls (P= 0.18). However, the prevalence of mutations with functional alterations was significantly higher in the obese group (P= 0.022). In conclusions, the results of this large study demonstrate that in the populations studied functionally significant MC3R variants are associated with obesity supporting the current hypothesis that rare variants might have a stronger impact on the individual susceptibility to gain weight. They also underline the importance of detailed in vitro functional studies in order to prove the pathogenic effect of such variants. Further investigations in larger cohorts will be needed in order to define the specific phenotypic characteristics potentially correlated with reduced MC3R signalling.

In silico functional profiling of human disease-associated and polymorphic amino acid substitutions

An important challenge in translational bioinformatics is to understand how genetic variation gives rise to molecular changes at the protein level that can precipitate both monogenic and complex disease. To this end, we compiled datasets of human disease-associated amino acid substitutions (AAS) in the contexts of inherited monogenic disease, complex disease, functional polymorphisms with no known disease association, and somatic mutations in cancer, and compared them with respect to predicted functional sites in proteins. Using the sequence homology-based tool SIFT to estimate the proportion of deleterious AAS in each dataset, only complex disease AAS were found to be indistinguishable from neutral polymorphic AAS. Investigation of monogenic disease AAS predicted to be nondeleterious by SIFT were characterized by a significant enrichment for inherited AAS within solvent accessible residues, regions of intrinsic protein disorder, and an association with the loss or gain of various posttranslational modifications. Sites of structural and/or functional interest were therefore surmised to constitute useful additional features with which to identify the molecular disruptions caused by deleterious AAS. A range of bioinformatic tools, designed to predict structural and functional sites in protein sequences, were then employed to demonstrate that intrinsic biases exist in terms of the distribution of different types of human AAS with respect to specific structural, functional and pathological features. Our Web tool, designed to potentiate the functional profiling of novel AAS, has been made available at http://profile.mutdb.org/.

Mutations in GRIN2A and GRIN2B encoding regulatory subunits of NMDA receptors cause variable neurodevelopmental phenotypes

N-methyl-D-aspartate (NMDA) receptors mediate excitatory neurotransmission in the mammalian brain. Two glycine-binding NR1 subunits and two glutamate-binding NR2 subunits each form highly Ca²(+)-permeable cation channels which are blocked by extracellular Mg²(+) in a voltage-dependent manner. Either GRIN2B or GRIN2A, encoding the NMDA receptor subunits NR2B and NR2A, was found to be disrupted by chromosome translocation breakpoints in individuals with mental retardation and/or epilepsy. Sequencing of GRIN2B in 468 individuals with mental retardation revealed four de novo mutations: a frameshift, a missense and two splice-site mutations. In another cohort of 127 individuals with idiopathic epilepsy and/or mental retardation, we discovered a GRIN2A nonsense mutation in a three-generation family. In a girl with early-onset epileptic encephalopathy, we identified the de novo GRIN2A mutation c.1845C>A predicting the amino acid substitution p.N615K. Analysis of NR1-NR2A(N615K) (NR2A subunit with the p.N615K alteration) receptor currents revealed a loss of the Mg²(+) block and a decrease in Ca²(+) permeability. Our findings suggest that disturbances in the neuronal electrophysiological balance during development result in variable neurological phenotypes depending on which NR2 subunit of NMDA receptors is affected.

Molecular insight into human platelet antigens: structural and evolutionary conservation analyses offer new perspective to immunogenic disorders

BACKGROUND

Human platelet antigens (HPAs) are polymorphisms in platelet membrane glycoproteins (GPs) that can stimulate production of alloantibodies once exposed to foreign platelets (PLTs) with different HPAs. These antibodies can cause neonatal alloimmune thrombocytopenia, posttransfusion purpura, and PLT transfusion refractoriness. Most HPAs are localized on the main PLT receptors: 1) integrin αIIbβ3, known as the fibrinogen receptor; 2) the GPIb-IX-V complex that functions as the receptor for von Willebrand factor; and 3) integrin α2β1, which functions as the collagen receptor.

STUDY DESIGN AND METHODS

We analyzed the structural location and the evolutionary conservation of the residues associated with the HPAs to characterize the features that induce immunologic responses but do not cause inherited diseases.

RESULTS

We found that all HPAs reside in positions located on the protein surface, apart from the ligand-binding site, and are evolutionary variable.

CONCLUSION

Disease-causing mutations often reside in highly conserved and buried positions. In contrast, the HPAs affect residues on the protein surface that were not conserved throughout evolution; this explains their naive effect on the protein function. Nonetheless, the HPAs involve substitutions of solvent-exposed positions that lead to altered interfaces on the surface of the protein and might present epitopes foreign to the immune system.

Prediction of deleterious non-synonymous SNPs based on protein interaction network and hybrid properties

Non-synonymous SNPs (nsSNPs), also known as Single Amino acid Polymorphisms (SAPs) account for the majority of human inherited diseases. It is important to distinguish the deleterious SAPs from neutral ones. Most traditional computational methods to classify SAPs are based on sequential or structural features. However, these features cannot fully explain the association between a SAP and the observed pathophysiological phenotype. We believe the better rationale for deleterious SAP prediction should be: If a SAP lies in the protein with important functions and it can change the protein sequence and structure severely, it is more likely related to disease. So we established a method to predict deleterious SAPs based on both protein interaction network and traditional hybrid properties. Each SAP is represented by 472 features that include sequential features, structural features and network features. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) were applied to obtain the optimal feature set and the prediction model was Nearest Neighbor Algorithm (NNA). In jackknife cross-validation, 83.27% of SAPs were correctly predicted when the optimized 263 features were used. The optimized predictor with 263 features was also tested in an independent dataset and the accuracy was still 80.00%. In contrast, SIFT, a widely used predictor of deleterious SAPs based on sequential features, has a prediction accuracy of 71.05% on the same dataset. In our study, network features were found to be most important for accurate prediction and can significantly improve the prediction performance. Our results suggest that the protein interaction context could provide important clues to help better illustrate SAP's functional association. This research will facilitate the post genome-wide association studies.