Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score, Condel

Mutation analysis of the ERCC4/FANCQ gene in hereditary breast cancer

The ERCC4 protein forms a structure-specific endonuclease involved in the DNA damage response. Different cancer syndromes such as a subtype of Xeroderma pigmentosum, XPF, and recently a subtype of Fanconi Anemia, FA-Q, have been attributed to biallelic ERCC4 gene mutations. To investigate whether monoallelic ERCC4 gene defects play some role in the inherited component of breast cancer susceptibility, we sequenced the whole ERCC4 coding region and flanking untranslated portions in a series of 101 Byelorussian and German breast cancer patients selected for familial disease (set 1, n = 63) or for the presence of the rs1800067 risk haplotype (set 2, n = 38). This study confirmed six known and one novel exonic variants, including four missense substitutions but no truncating mutation. Missense substitution p.R415Q (rs1800067), a previously postulated breast cancer susceptibility allele, was subsequently screened for in a total of 3,698 breast cancer cases and 2,868 controls from Germany, Belarus or Russia. The Gln415 allele appeared protective against breast cancer in the German series, with the strongest effect for ductal histology (OR 0.67; 95%CI 0.49; 0.92; p = 0.003), but this association was not confirmed in the other two series, with the combined analysis yielding an overall Mantel-Haenszel OR of 0.94 (95% CI 0.81; 1.08). There was no significant effect of p.R415Q on breast cancer survival in the German patient series. The other three detected ERCC4 missense mutations included two known rare variants as well as a novel substitution, p.E17V, that we identified on a p.R415Q haplotype background. The p.E17V mutation is predicted to be probably damaging but was present in just one heterozygous patient. We conclude that the contribution of ERCC4/FANCQ coding mutations to hereditary breast cancer in Central and Eastern Europe is likely to be small.

PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations

Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing

We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somatic mutations in cancer may disrupt pre-mRNA splicing. For inherited disease, the main mechanism responsible for the splicing defect is splice site loss, whereas for cancer the predominant mechanism of splicing disruption is predicted to be exon skipping via loss of exonic splicing enhancers or gain of exonic splicing silencer elements. MutPred Splice is available at http://mutdb.org/mutpredsplice.

AIPL1 implicated in the pathogenesis of two cases of autosomal recessive retinal degeneration

PURPOSE

To localize and identify the gene and mutations causing autosomal recessive retinal dystrophy in two consanguineous Pakistani families.

METHODS

Consanguineous families from Pakistan were ascertained to be affected with autosomal recessive retinal degeneration. All affected individuals underwent thorough ophthalmologic examinations. Blood samples were collected, and genomic DNA was extracted using a salting out procedure. Genotyping was performed using microsatellite markers spaced at approximately 10 cM intervals. Two-point linkage analysis was performed with the lod score method. Direct DNA sequencing of amplified genomic DNA was performed for mutation screening of candidate genes.

RESULTS

Genome-wide linkage scans yielded a lod score of 3.05 at θ=0 for D17S1832 and 3.82 at θ=0 for D17S938, localizing the disease gene to a 12.22 cM (6.64 Mb) region flanked by D17S1828 and D17S1852 for family 61032 and family 61227, which contains aryl hydrocarbon receptor interacting protein-like 1 (AIPL1), a gene previously implicated in recessive Leber congenital amaurosis and autosomal dominant cone-rod dystrophy. Sequencing of AIPL1 showed a homozygous c.773G>C (p.Arg258Pro) sequence change in all affected individuals of family 61032 and a homozygous c.465G>T (p.(H93_Q155del)) change in all affected members of family 61227.

CONCLUSIONS

The results strongly suggest that the c.773G>C (p.R258P) and c.465G>T (p.(H93_Q155del)) mutations in AIPL1 cause autosomal recessive retinal degeneration in these consanguineous Pakistani families.

SNPdryad: predicting deleterious non-synonymous human SNPs using only orthologous protein sequences

MOTIVATION

The recent advances in genome sequencing have revealed an abundance of non-synonymous polymorphisms among human individuals; subsequently, it is of immense interest and importance to predict whether such substitutions are functional neutral or have deleterious effects. The accuracy of such prediction algorithms depends on the quality of the multiple-sequence alignment, which is used to infer how an amino acid substitution is tolerated at a given position. Because of the scarcity of orthologous protein sequences in the past, the existing prediction algorithms all include sequences of protein paralogs in the alignment, which can dilute the conservation signal and affect prediction accuracy. However, we believe that, with the sequencing of a large number of mammalian genomes, it is now feasible to include only protein orthologs in the alignment and improve the prediction performance.

RESULTS

We have developed a novel prediction algorithm, named SNPdryad, which only includes protein orthologs in building a multiple sequence alignment. Among many other innovations, SNPdryad uses different conservation scoring schemes and uses Random Forest as a classifier. We have tested SNPdryad on several datasets. We found that SNPdryad consistently outperformed other methods in several performance metrics, which is attributed to the exclusion of paralogous sequence. We have run SNPdryad on the complete human proteome, generating prediction scores for all the possible amino acid substitutions.

AVAILABILITY AND IMPLEMENTATION

The algorithm and the prediction results can be accessed from the Web site: http://snps.ccbr.utoronto.ca:8080/SNPdryad/ CONTACT: Zhaolei.Zhang@utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online.

The road from next-generation sequencing to personalized medicine

Moving from a traditional medical model of treating pathologies to an individualized predictive and preventive model of personalized medicine promises to reduce the healthcare cost on an overburdened and overwhelmed system. Next-generation sequencing (NGS) has the potential to accelerate the early detection of disorders and the identification of pharmacogenetics markers to customize treatments. This review explains the historical facts that led to the development of NGS along with the strengths and weakness of NGS, with a special emphasis on the analytical aspects used to process NGS data. There are solutions to all the steps necessary for performing NGS in the clinical context where the majority of them are very efficient, but there are some crucial steps in the process that need immediate attention.

Amino acid changes in disease-associated variants differ radically from variants observed in the 1000 genomes project dataset

The 1000 Genomes Project data provides a natural background dataset for amino acid germline mutations in humans. Since the direction of mutation is known, the amino acid exchange matrix generated from the observed nucleotide variants is asymmetric and the mutabilities of the different amino acids are very different. These differences predominantly reflect preferences for nucleotide mutations in the DNA (especially the high mutation rate of the CpG dinucleotide, which makes arginine mutability very much higher than other amino acids) rather than selection imposed by protein structure constraints, although there is evidence for the latter as well. The variants occur predominantly on the surface of proteins (82%), with a slight preference for sites which are more exposed and less well conserved than random. Mutations to functional residues occur about half as often as expected by chance. The disease-associated amino acid variant distributions in OMIM are radically different from those expected on the basis of the 1000 Genomes dataset. The disease-associated variants preferentially occur in more conserved sites, compared to 1000 Genomes mutations. Many of the amino acid exchange profiles appear to exhibit an anti-correlation, with common exchanges in one dataset being rare in the other. Disease-associated variants exhibit more extreme differences in amino acid size and hydrophobicity. More modelling of the mutational processes at the nucleotide level is needed, but these observations should contribute to an improved prediction of the effects of specific variants in humans.

In this paper we compare the differences between ‘natural’ and disease-associated amino acid variants at both sequence as well as structural levels. We used data from the 1000 Genomes Project (1 kG), the OMIM database and UniProtKB Humsavar. The results highlight the complex interplay of features from the level of the DNA up to protein sequence and structure. The codon CpG dinucleotide content plays a large role in determining which amino acids mutate. This in turn affects the mutability of amino acids and a clear difference was seen between non-disease and disease variants where amino acids that are naturally very mutable show the opposite trend in the disease-associated data. The current results show evidence for some selection, mainly in that the variants occur slightly more often on the surface of the protein and are much less likely to be annotated as functional than expected by chance. However we should note that even the best definition of functional, taken from structural data, is limited. Even with these caveats, it is clear that the 1 kG variants eschew functional residues as defined here, a trend which is surprisingly even stronger in the OMIM data.

Computational approaches to identify functional genetic variants in cancer genomes

The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype.

Exome sequencing finds a novel PCSK1 mutation in a child with generalized malabsorptive diarrhea and diabetes insipidus

OBJECTIVES

Congenital diarrhea disorders are a group of genetically diverse and typically autosomal recessive disorders that have yet to be well characterized phenotypically or molecularly. Diagnostic assessments are generally limited to nutritional challenges and histologic evaluation, and many subjects eventually require a prolonged course of intravenous nutrition. Here we describe next-generation sequencing techniques to investigate a child with perplexing congenital malabsorptive diarrhea and other presumably unrelated clinical problems; this method provides an alternative approach to molecular diagnosis.

METHODS

We screened the diploid genome of an affected individual, using exome sequencing, for uncommon variants that have observed protein-coding consequences. We assessed the functional activity of the mutant protein, as well as its lack of expression using immunohistochemistry.

RESULTS

Among several rare variants detected was a homozygous nonsense mutation in the catalytic domain of the proprotein convertase subtilisin/kexin type 1 gene. The mutation abolishes prohormone convertase 1/3 endoprotease activity as well as expression in the intestine. These primary genetic findings prompted a careful endocrine reevaluation of the child at 4.5 years of age, and multiple significant problems were subsequently identified consistent with the known phenotypic consequences of proprotein convertase subtilisin/kexin type 1 (PCSK1) gene mutations. Based on the molecular diagnosis, alternate medical and dietary management was implemented for diabetes insipidus, polyphagia, and micropenis.

CONCLUSIONS

Whole-exome sequencing provides a powerful diagnostic tool to clinicians managing rare genetic disorders with multiple perplexing clinical manifestations.

wKinMut: an integrated tool for the analysis and interpretation of mutations in human protein kinases

Background

Protein kinases are involved in relevant physiological functions and a broad number of mutations in this superfamily have been reported in the literature to affect protein function and stability. Unfortunately, the exploration of the consequences on the phenotypes of each individual mutation remains a considerable challenge.

Results

The wKinMut web-server offers direct prediction of the potential pathogenicity of the mutations from a number of methods, including our recently developed prediction method based on the combination of information from a range of diverse sources, including physicochemical properties and functional annotations from FireDB and Swissprot and kinase-specific characteristics such as the membership to specific kinase groups, the annotation with disease-associated GO terms or the occurrence of the mutation in PFAM domains, and the relevance of the residues in determining kinase subfamily specificity from S3Det. This predictor yields interesting results that compare favourably with other methods in the field when applied to protein kinases.

Together with the predictions, wKinMut offers a number of integrated services for the analysis of mutations. These include: the classification of the kinase, information about associations of the kinase with other proteins extracted from iHop, the mapping of the mutations onto PDB structures, pathogenicity records from a number of databases and the classification of mutations in large-scale cancer studies. Importantly, wKinMut is connected with the SNP2L system that extracts mentions of mutations directly from the literature, and therefore increases the possibilities of finding interesting functional information associated to the studied mutations.

Conclusions

wKinMut facilitates the exploration of the information available about individual mutations by integrating prediction approaches with the automatic extraction of information from the literature (text mining) and several state-of-the-art databases.

wKinMut has been used during the last year for the analysis of the consequences of mutations in the context of a number of cancer genome projects, including the recent analysis of Chronic Lymphocytic Leukemia cases and is publicly available at http://wkinmut.bioinfo.cnio.es.

Quantifying rare, deleterious variation in 12 human cytochrome P450 drug-metabolism genes in a large-scale exome dataset

The study of genetic influences on drug response and efficacy ('pharmacogenetics') has existed for over 50 years. Yet, we still lack a complete picture of how genetic variation, both common and rare, affects each individual's responses to medications. Exome sequencing is a promising alternative method for pharmacogenetic discovery as it provides information on both common and rare variation in large numbers of individuals. Using exome data from 2203 AA and 4300 Caucasian individuals through the NHLBI Exome Sequencing Project, we conducted a survey of coding variation within 12 Cytochrome P450 (CYP) genes that are collectively responsible for catalyzing nearly 75% of all known Phase I drug oxidation reactions. In addition to identifying many polymorphisms with known pharmacogenetic effects, we discovered over 730 novel nonsynonymous alleles across the 12 CYP genes of interest. These alleles include many with diverse functional effects such as premature stop codons, aberrant splicesites and mutations at conserved active site residues. Our analysis considering both novel, predicted functional alleles as well as known, actionable CYP alleles reveals that rare, deleterious variation contributes markedly to the overall burden of pharmacogenetic alleles within the populations considered, and that the contribution of rare variation to this burden is over three times greater in AA individuals as compared with Caucasians. While most of these impactful alleles are individually rare, 7.6-11.7% of individuals interrogated in the study carry at least one newly described potentially deleterious alleles in a major drug-metabolizing CYP.

Feature-based classification of amino acid substitutions outside conserved functional protein domains

There are more than 500 amino acid substitutions in each human genome, and bioinformatics tools irreplaceably contribute to determination of their functional effects. We have developed feature-based algorithm for the detection of mutations outside conserved functional domains (CFDs) and compared its classification efficacy with the most commonly used phylogeny-based tools, PolyPhen-2 and SIFT. The new algorithm is based on the informational spectrum method (ISM), a feature-based technique, and statistical analysis. Our dataset contained neutral polymorphisms and mutations associated with myeloid malignancies from epigenetic regulators ASXL1, DNMT3A, EZH2, and TET2. PolyPhen-2 and SIFT had significantly lower accuracies in predicting the effects of amino acid substitutions outside CFDs than expected, with especially low sensitivity. On the other hand, only ISM algorithm showed statistically significant classification of these sequences. It outperformed PolyPhen-2 and SIFT by 15% and 13%, respectively. These results suggest that feature-based methods, like ISM, are more suitable for the classification of amino acid substitutions outside CFDs than phylogeny-based tools.

DriverDB: an exome sequencing database for cancer driver gene identification

Exome sequencing (exome-seq) has aided in the discovery of a huge amount of mutations in cancers, yet challenges remain in converting oncogenomics data into information that is interpretable and accessible for clinical care. We constructed DriverDB (http://ngs.ym.edu.tw/driverdb/), a database which incorporates 6079 cases of exome-seq data, annotation databases (such as dbSNP, 1000 Genome and Cosmic) and published bioinformatics algorithms dedicated to driver gene/mutation identification. We provide two points of view, ‘Cancer’ and ‘Gene’, to help researchers to visualize the relationships between cancers and driver genes/mutations. The ‘Cancer’ section summarizes the calculated results of driver genes by eight computational methods for a specific cancer type/dataset and provides three levels of biological interpretation for realization of the relationships between driver genes. The ‘Gene’ section is designed to visualize the mutation information of a driver gene in five different aspects. Moreover, a ‘Meta-Analysis’ function is provided so researchers may identify driver genes in customer-defined samples. The novel driver genes/mutations identified hold potential for both basic research and biotech applications.

Towards precision medicine: advances in computational approaches for the analysis of human variants

Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.

CanDrA: cancer-specific driver missense mutation annotation with optimized features

Driver mutations are somatic mutations that provide growth advantage to tumor cells, while passenger mutations are those not functionally related to oncogenesis. Distinguishing drivers from passengers is challenging because drivers occur much less frequently than passengers, they tend to have low prevalence, their functions are multifactorial and not intuitively obvious. Missense mutations are excellent candidates as drivers, as they occur more frequently and are potentially easier to identify than other types of mutations. Although several methods have been developed for predicting the functional impact of missense mutations, only a few have been specifically designed for identifying driver mutations. As more mutations are being discovered, more accurate predictive models can be developed using machine learning approaches that systematically characterize the commonality and peculiarity of missense mutations under the background of specific cancer types. Here, we present a cancer driver annotation (CanDrA) tool that predicts missense driver mutations based on a set of 95 structural and evolutionary features computed by over 10 functional prediction algorithms such as CHASM, SIFT, and MutationAssessor. Through feature optimization and supervised training, CanDrA outperforms existing tools in analyzing the glioblastoma multiforme and ovarian carcinoma data sets in The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia project.

Analysis and annotation of whole-genome or whole-exome sequencing-derived variants for clinical diagnosis

Over the last several years, next-generation sequencing (NGS) has transformed genomic research through substantial advances in technology and reduction in the cost of sequencing, and also in the systems required for analysis of these large volumes of data. This technology is now being used as a standard molecular diagnostic test under particular circumstances in some clinical settings. The advances in sequencing have come so rapidly that the major bottleneck in identification of causal variants is no longer the sequencing but rather the analysis and interpretation. Interpretation of genetic findings in a clinical setting is scarcely a new challenge, but the task is increasingly complex in clinical genome-wide sequencing given the dramatic increase in dataset size and complexity. This increase requires the development of novel or repositioned analysis tools, methodologies, and processes. This unit provides an overview of these items. Specific challenges related to implementation in a clinical setting are discussed.

Ablepharon macrostomia syndrome: A distinct genetic entity clinically related to the group of FRAS-FREM complex disorders

Ablepharon macrostomia syndrome (AMS; OMIM 200110) is an extremely rare congenital malformation syndrome. It overlaps clinically with Fraser syndrome (FS; OMIM 219000), which is known to be caused by mutations in either FRAS1, FREM2, or GRIP1, encoding components of a protein complex that plays a role in epidermal-dermal interactions during morphogenetic processes. We explored the hypothesis that AMS might be either allelic to FS or caused by mutations in other genes encoding known FRAS1 interacting partners. No mutation in either of these genes was found in a cohort of 11 patients with AMS from 10 unrelated families. These findings demonstrate that AMS is genetically distinct from FS. It is proposed that it constitutes a separate entity within the group of FRAS-FREM complex disorders.

Genotype and clinical care correlations in craniosynostosis: findings from a cohort of 630 Australian and New Zealand patients

Craniosynostosis is one of the most common craniofacial disorders encountered in clinical genetics practice, with an overall incidence of 1 in 2,500. Between 30% and 70% of syndromic craniosynostoses are caused by mutations in hotspots in the fibroblast growth factor receptor (FGFR) genes or in the TWIST1 gene with the difference in detection rates likely to be related to different study populations within craniofacial centers. Here we present results from molecular testing of an Australia and New Zealand cohort of 630 individuals with a diagnosis of craniosynostosis. Data were obtained by Sanger sequencing of FGFR1, FGFR2, and FGFR3 hotspot exons and the TWIST1 gene, as well as copy number detection of TWIST1. Of the 630 probands, there were 231 who had one of 80 distinct mutations (36%). Among the 80 mutations, 17 novel sequence variants were detected in three of the four genes screened. In addition to the proband cohort there were 96 individuals who underwent predictive or prenatal testing as part of family studies. Dysmorphic features consistent with the known FGFR1-3/TWIST1-associated syndromes were predictive for mutation detection. We also show a statistically significant association between splice site mutations in FGFR2 and a clinical diagnosis of Pfeiffer syndrome, more severe clinical phenotypes associated with FGFR2 exon 10 versus exon 8 mutations, and more frequent surgical procedures in the presence of a pathogenic mutation. Targeting gene hot spot areas for mutation analysis is a useful strategy to maximize the success of molecular diagnosis for individuals with craniosynostosis.

VarRanker: rapid prioritization of sequence variations associated with human disease

BACKGROUND

Identification of the genetic alterations responsible for human disease is a central challenge facing medical genetics. While many algorithms have been developed to predict the degree of damage caused by a given sequence alteration, few tools are able to incorporate information about a given phenotype of interest.

METHODS

Here, we describe an algorithm and web-based application which take into account both the probability that a variant damages the function of a gene as well as the relevance of the gene to a given phenotype. Phenotypes are described by a list of scored terms supplied by the user. These terms are then used to search a variety of public databases including NCBI gene summaries, PubMed abstracts, and Gene Ontology terms, and protein-protein interactions in String-DB to determine a relevance score. The overall ranking is determined by the product of the functional damage score and the relevance score, such that highly ranked variants are likely to be damaging and in genes of interest.

RESULTS

We demonstrate the method on several test cases including samples with Hereditary Hemorrhagic Telangiectasia (HHT) and Diamond-Blackfan Anemia (DBA). We have also implemented a web-based application which allows public access to the VarRanker algorithm.

CONCLUSIONS

Automated searching of public literature and online databases may substantially decrease the amount of time required to identify the mutations underlying human disease. However, several ad-hoc and subjective decisions must be made, and the results of such analyses are likely to depend on the researcher and the state of the literature and databases involved.

Genetic variants in CHI3L1 influencing YKL-40 levels: resequencing 900 individuals and genotyping 9000 individuals from the general population

BACKGROUND

Despite its important role in many serious diseases, the genetic background for plasma YKL-40 has still not been systematically catalogued. Therefore, we aimed at identifying genetic variants in CHI3L1 influencing plasma YKL-40 levels in the general population.

METHODS

We resequenced the promoter, all 10 exons and exon-flanking intron segments of CHI3L1 in 904 individuals from the Danish general population (n=8899) with extreme plasma YKL-40 levels, adjusted for age. To potentially identify clinically important genetic variants with elevated plasma YKL-40 levels, we included twice as many individuals with the highest plasma YKL-40 levels (n=603) compared with the lowest plasma YKL-40 levels (n=301). Next, we mapped linkage disequilibrium for all variants with a minor allele frequency (MAF)>0.005. Finally, all participants were genotyped for eight variants that had divergent MAFs in the two extreme plasma YKL-40 groups.

RESULTS

We identified 59 genetic variants in CHI3L1. Fifteen of the genetic variants were associated with plasma YKL-40 levels. Three promoter SNPs, 1 non-synonymous SNP, and four intronic SNPs in CHI3L1 were associated with plasma YKL-40 levels at or below genome-wide association significance levels (unadjusted p for trend: from 4 × 10(-8) to 6 × 10(-243); age adjusted percentiles p for trend: from 3 × 10(-12) to 2 × 10(-304)).

CONCLUSIONS

In a systematic search to identify genetic variants influencing plasma YKL-40 levels, we identified eight SNPs associated with plasma YKL-40 levels in the general population.

Identification and biochemical analysis of a novel APOB mutation that causes autosomal dominant hypercholesterolemia

Patients with autosomal dominant hypercholesterolemia (ADH) have a high risk of developing cardiovascular disease that can be effectively treated using statin drugs. Molecular diagnosis and family cascade screening is recommended for early identification of individuals at risk, but up to 40% of families have no mutation detected in known genes. This study combined linkage analysis and exome sequencing to identify a novel variant in exon 3 of APOB (Arg50Trp). Mass spectrometry established that low-density lipoprotein (LDL) containing Arg50Trp APOB accumulates in the circulation of affected individuals, suggesting defective hepatic uptake. Previously reported mutations in APOB causing ADH have been located in exon 26. This is the first report of a mutation outside this region causing this phenotype, therefore, more extensive screening of this large and highly polymorphic gene may be necessary in ADH families. This is now feasible due to the high capacity of recently available sequencing platforms.

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.

IL-12Rβ1 deficiency: mutation update and description of the IL12RB1 variation database

IL-12Rβ1 deficiency is an autosomal recessive disorder characterized by predisposition to recurrent and/or severe infections caused by otherwise poorly pathogenic mycobacteria and salmonella. IL-12Rβ1 is a receptor chain of both the IL-12 and the IL-23 receptor and deficiency of IL-12Rβ1 thus abolishes both IL-12 and IL-23 signaling. IL-12Rβ1 deficiency is caused by bi-allelic mutations in the IL12RB1 gene. Mutations resulting in premature stop codons, such as nonsense, frame shift, and splice site mutations, represent the majority of IL-12Rβ1 deficiency causing mutations (66%; 46/70). Also every other morbid mutation completely inactivates the IL-12Rβ1 protein. In addition to disease-causing mutations, rare and common variations with unknown functional effect have been reported in IL12RB1. All these variants have been deposited in the online IL12RB1 variation database (www.LOVD.nl/IL12RB1). In this article, we review the function of IL-12Rβ1 and molecular genetics of human IL12RB1.

Building a genome analysis pipeline to predict disease risk and prevent disease

Reduced costs and increased speed and accuracy of sequencing can bring the genome-based evaluation of individual disease risk to the bedside. While past efforts have identified a number of actionable mutations, the bulk of genetic risk remains hidden in sequence data. The biggest challenge facing genomic medicine today is the development of new techniques to predict the specifics of a given human phenome (set of all expressed phenotypes) encoded by each individual variome (full set of genome variants) in the context of the given environment. Numerous tools exist for the computational identification of the functional effects of a single variant. However, the pipelines taking advantage of full genomic, exomic, transcriptomic (and other) sequences have only recently become a reality. This review looks at the building of methodologies for predicting "variome"-defined disease risk. It also discusses some of the challenges for incorporating such a pipeline into everyday medical practice.

A gene-specific method for predicting hemophilia-causing point mutations

A fundamental goal of medical genetics is the accurate prediction of genotype-phenotype correlations. As an approach to develop more accurate in silico tools for prediction of disease-causing mutations of structural proteins, we present a gene- and disease-specific prediction tool based on a large systematic analysis of missense mutations from hemophilia A (HA) patients. Our HA-specific prediction tool, HApredictor, showed disease prediction accuracy comparable to other publicly available prediction software. In contrast to those methods, its performance is not limited to non-synonymous mutations. Given the role of synonymous mutations in disease and drug codon optimization, we propose that utilizing a gene- and disease-specific method can be highly useful to make functional predictions possible even for synonymous mutations. Incorporating computational metrics at both nucleotide and amino acid levels along with multiple protein sequence/structure alignment significantly improved the predictive performance of our tool. HApredictor is freely available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/HA_Predict/index.htm.

A recessive founder mutation in regulator of telomere elongation helicase 1, RTEL1, underlies severe immunodeficiency and features of Hoyeraal Hreidarsson syndrome

Dyskeratosis congenita (DC) is a heterogeneous inherited bone marrow failure and cancer predisposition syndrome in which germline mutations in telomere biology genes account for approximately one-half of known families. Hoyeraal Hreidarsson syndrome (HH) is a clinically severe variant of DC in which patients also have cerebellar hypoplasia and may present with severe immunodeficiency and enteropathy. We discovered a germline autosomal recessive mutation in RTEL1, a helicase with critical telomeric functions, in two unrelated families of Ashkenazi Jewish (AJ) ancestry. The affected individuals in these families are homozygous for the same mutation, R1264H, which affects three isoforms of RTEL1. Each parent was a heterozygous carrier of one mutant allele. Patient-derived cell lines revealed evidence of telomere dysfunction, including significantly decreased telomere length, telomere length heterogeneity, and the presence of extra-chromosomal circular telomeric DNA. In addition, RTEL1 mutant cells exhibited enhanced sensitivity to the interstrand cross-linking agent mitomycin C. The molecular data and the patterns of inheritance are consistent with a hypomorphic mutation in RTEL1 as the underlying basis of the clinical and cellular phenotypes. This study further implicates RTEL1 in the etiology of DC/HH and immunodeficiency, and identifies the first known homozygous autosomal recessive disease-associated mutation in RTEL1.

Prediction of disease causing non-synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP

We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further investigation. NetDiseaseSNP is publicly available as an online tool as well as a web service: http://www.cbs.dtu.dk/services/NetDiseaseSNP.

The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations

Background

Understanding and predicting the effects of mutations on protein structure and phenotype is an increasingly important area. Genes for many genetically linked diseases are now routinely sequenced in the clinic. Previously we focused on understanding the structural effects of mutations, creating the SAAPdb resource.

Results

We have updated SAAPdb to include 41% more SNPs and 36% more PDs.

Introducing a hydrophobic residue on the surface, or a hydrophilic residue in the core, no longer shows significant differences between SNPs and PDs. We have improved some of the analyses significantly enhancing the analysis of clashes and of mutations to-proline and from-glycine. A new web interface has been developed allowing users to analyze their own mutations. Finally we have developed a machine learning method which gives a cross-validated accuracy of 0.846, considerably out-performing well known methods including SIFT and PolyPhen2 which give accuracies between 0.690 and 0.785.

Conclusions

We have updated SAAPdb and improved its analyses, but with the increasing rate with which mutation data are generated, we have created a new analysis pipeline and web interface. Results of machine learning using the structural analysis results to predict pathogenicity considerably outperform other methods.

Assessment of computational methods for predicting the effects of missense mutations in human cancers

BACKGROUND

Recent advances in sequencing technologies have greatly increased the identification of mutations in cancer genomes. However, it remains a significant challenge to identify cancer-driving mutations, since most observed missense changes are neutral passenger mutations. Various computational methods have been developed to predict the effects of amino acid substitutions on protein function and classify mutations as deleterious or benign. These include approaches that rely on evolutionary conservation, structural constraints, or physicochemical attributes of amino acid substitutions. Here we review existing methods and further examine eight tools: SIFT, PolyPhen2, Condel, CHASM, mCluster, logRE, SNAP, and MutationAssessor, with respect to their coverage, accuracy, availability and dependence on other tools.

RESULTS

Single nucleotide polymorphisms with high minor allele frequencies were used as a negative (neutral) set for testing, and recurrent mutations from the COSMIC database as well as novel recurrent somatic mutations identified in very recent cancer studies were used as positive (non-neutral) sets. Conservation-based methods generally had moderately high accuracy in distinguishing neutral from deleterious mutations, whereas the performance of machine learning based predictors with comprehensive feature spaces varied between assessments using different positive sets. MutationAssessor consistently provided the highest accuracies. For certain combinations metapredictors slightly improved the performance of included individual methods, but did not outperform MutationAssessor as stand-alone tool.

CONCLUSIONS

Our independent assessment of existing tools reveals various performance disparities. Cancer-trained methods did not improve upon more general predictors. No method or combination of methods exceeds 81% accuracy, indicating there is still significant room for improvement for driver mutation prediction, and perhaps more sophisticated feature integration is needed to develop a more robust tool.

Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers

Every malignant tumor has a unique spectrum of genomic alterations including numerous protein mutations. There are also hundreds of personal germline variants to be taken into account. The combinatorial diversity of potential cancer-driving events limits the applicability of statistical methods to determine tumor-specific "driver" alterations among an overwhelming majority of "passengers". An alternative approach to determining driver mutations is to assess the functional impact of mutations in a given tumor and predict drivers based on a numerical value of the mutation impact in a particular context of genomic alterations.Recently, we introduced a functional impact score, which assesses the mutation impact by the value of entropic disordering of the evolutionary conservation patterns in proteins. The functional impact score separates disease-associated variants from benign polymorphisms with an accuracy of ~80%. Can the score be used to identify functionally important non-recurrent cancer-driver mutations? Assuming that cancer-drivers are positively selected in tumor evolution, we investigated how the functional impact score correlates with key features of natural selection in cancer, such as the non-uniformity of distribution of mutations, the frequency of affected tumor suppressors and oncogenes, the frequency of concurrent alterations in regions of heterozygous deletions and copy gain; as a control, we used presumably non-selected silent mutations. Using mutations of six cancers studied in TCGA projects, we found that predicted high-scoring functional mutations as well as truncating mutations tend to be evolutionarily selected as compared to low-scoring and silent mutations. This result justifies prediction of mutations-drivers using a shorter list of predicted high-scoring functional mutations, rather than the "long tail" of all mutations.

Collective judgment predicts disease-associated single nucleotide variants

Background

In recent years the number of human genetic variants deposited into the publicly available databases has been increasing exponentially. The latest version of dbSNP, for example, contains ~50 million validated Single Nucleotide Variants (SNVs). SNVs make up most of human variation and are often the primary causes of disease. The non-synonymous SNVs (nsSNVs) result in single amino acid substitutions and may affect protein function, often causing disease. Although several methods for the detection of nsSNV effects have already been developed, the consistent increase in annotated data is offering the opportunity to improve prediction accuracy.

Results

Here we present a new approach for the detection of disease-associated nsSNVs (Meta-SNP) that integrates four existing methods: PANTHER, PhD-SNP, SIFT and SNAP. We first tested the accuracy of each method using a dataset of 35,766 disease-annotated mutations from 8,667 proteins extracted from the SwissVar database. The four methods reached overall accuracies of 64%-76% with a Matthew's correlation coefficient (MCC) of 0.38-0.53. We then used the outputs of these methods to develop a machine learning based approach that discriminates between disease-associated and polymorphic variants (Meta-SNP). In testing, the combined method reached 79% overall accuracy and 0.59 MCC, ~3% higher accuracy and ~0.05 higher correlation with respect to the best-performing method. Moreover, for the hardest-to-define subset of nsSNVs, i.e. variants for which half of the predictors disagreed with the other half, Meta-SNP attained 8% higher accuracy than the best predictor.

Conclusions

Here we find that the Meta-SNP algorithm achieves better performance than the best single predictor. This result suggests that the methods used for the prediction of variant-disease associations are orthogonal, encoding different biologically relevant relationships. Careful combination of predictions from various resources is therefore a good strategy for the selection of high reliability predictions. Indeed, for the subset of nsSNVs where all predictors were in agreement (46% of all nsSNVs in the set), our method reached 87% overall accuracy and 0.73 MCC. Meta-SNP server is freely accessible at http://snps.biofold.org/meta-snp.

The power of meta-analysis in genome-wide association studies

Meta-analysis of multiple genome-wide association (GWA) studies has become common practice over the past few years. The main advantage of this technique is the maximization of power to detect subtle genetic effects for common traits. Moreover, one can use meta-analysis to probe and identify heterogeneity in the effect sizes across the combined studies. In this review, we systematically appraise and evaluate the characteristics of GWA meta-analyses with 10,000 or more subjects published up to June 2012. We provide an overview of the current landscape of variants discovered by GWA meta-analyses, and we discuss and assess with extrapolations from empirical data the value of larger meta-analyses for the discovery of additional genetic associations and new biology in the future. Finally, we discuss some emerging logistical and practical issues related to the conduct of meta-analysis of GWA studies.

Refining the role of PMS2 in Lynch syndrome: germline mutational analysis improved by comprehensive assessment of variants

BACKGROUND AND AIM

The majority of mismatch repair (MMR) gene mutations causing Lynch syndrome (LS) occur either in MLH1 or MSH2. However, the relative contribution of PMS2 is less well defined. The aim of this study was to evaluate the role of PMS2 in LS by assessing the pathogenicity of variants of unknown significance (VUS) detected in the mutational analysis of PMS2 in a series of Spanish patients.

METHODS

From a cohort of 202 LS suspected patients, 13 patients showing loss of PMS2 expression in tumours were screened for germline mutations in PMS2, using a long range PCR based strategy and multiplex ligation dependent probe amplification (MLPA). Pathogenicity assessment of PMS2 VUS was performed evaluating clinicopathological data, frequency in control population and in silico and in vitro analyses at the RNA and protein level.

RESULTS

Overall 25 different PMS2 DNA variants were detected. Fourteen were classified as polymorphisms. Nine variants were classified as pathogenic: seven alterations based on their molecular nature and two after demonstrating a functional defect (c.538-3C>G affected mRNA processing and c.137G>T impaired MMR activity). The c.1569C>G variant was classified as likely neutral while the c.384G>A remained as a VUS. We have also shown that the polymorphic variant c.59G>A is MMR proficient.

CONCLUSIONS

Pathogenic PMS2 mutations were detected in 69% of patients harbouring LS associated tumours with loss of PMS2 expression. In all, PMS2 mutations account for 6% of the LS cases identified. The comprehensive functional analysis shown here has been useful in the classification of PMS2 VUS and contributes to refining the role of PMS2 in LS.

Common and rare von Willebrand factor (VWF) coding variants, VWF levels, and factor VIII levels in African Americans: the NHLBI Exome Sequencing Project

Several rare European von Willebrand disease missense variants of VWF (including p.Arg2185Gln and p.His817Gln) were recently reported to be common in apparently healthy African Americans (AAs). Using data from the NHLBI Exome Sequencing Project, we assessed the association of these and other VWF coding variants with von Willebrand factor (VWF) and factor VIII (FVIII) levels in 4468 AAs. Of 30 nonsynonymous VWF variants, 6 were significantly and independently associated (P < .001) with levels of VWF and/or FVIII. Each additional copy of the common VWF variants encoding p.Thr789Ala or p.Asp1472His was associated with 6 to 8 IU/dL higher VWF levels. The VWF variant encoding p.Arg2185Gln was associated with 7 to 13 IU/dL lower VWF and FVIII levels. The type 2N-related VWF variant encoding p.His817Gln was associated with 17 IU/dL lower FVIII level but normal VWF level. A novel, rare missense VWF variant that predicts disruption of an O-glycosylation site (p.Ser1486Leu) and a rare variant encoding p.Arg2287Trp were each associated with 30 to 40 IU/dL lower VWF level (P < .001). In summary, several common and rare VWF missense variants contribute to phenotypic differences in VWF and FVIII among AAs.

PARADIGM-SHIFT predicts the function of mutations in multiple cancers using pathway impact analysis

Motivation: A current challenge in understanding cancer processes is to pinpoint which mutations influence the onset and progression of disease. Toward this goal, we describe a method called PARADIGM-SHIFT that can predict whether a mutational event is neutral, gain-or loss-of-function in a tumor sample. The method uses a belief-propagation algorithm to infer gene activity from gene expression and copy number data in the context of a set of pathway interactions.

Results: The method was found to be both sensitive and specific on a set of positive and negative controls for multiple cancers for which pathway information was available. Application to the Cancer Genome Atlas glioblastoma, ovarian and lung squamous cancer datasets revealed several novel mutations with predicted high impact including several genes mutated at low frequency suggesting the approach will be complementary to current approaches that rely on the prevalence of events to reach statistical significance.

Availability: All source code is available at the github repository http:github.org/paradigmshift.

Contact:jstuart@soe.ucsc.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Evaluation of autosomal dominant retinal dystrophy genes in an unaffected cohort suggests rare or private missense variants may often be benign

BACKGROUND

Many genes have been reported as harboring autosomal dominant mutations causing retinal dystrophy. As newly available gene panel sequencing and whole exome sequencing will open these genes up to greater scrutiny, we assess the rate of rare coding variation in these genes among unaffected individuals to provide context for variants that will be discovered when clinical subjects are sequenced.

METHODS

Publicly available data from the Exome Variant Project were analyzed, focusing on 36 genes known to harbor mutations causing autosomal dominant macular dystrophy.

RESULTS

Rates of rare (minor allele frequency ≤0.1%) and private missense variants within autosomal dominant retinal dystrophy genes were found to occur at a high frequency in unaffected individuals, while nonsense variants were not.

CONCLUSIONS

We conclude that rare missense variations in most of these genes identified in individuals with retinal dystrophy cannot be confidently classified as disease-causing in the absence of additional information such as linkage or functional validation.

Combined NGS approaches identify mutations in the intraflagellar transport gene IFT140 in skeletal ciliopathies with early progressive kidney Disease

Ciliopathies are genetically heterogeneous disorders characterized by variable expressivity and overlaps between different disease entities. This is exemplified by the short rib-polydactyly syndromes, Jeune, Sensenbrenner, and Mainzer-Saldino chondrodysplasia syndromes. These three syndromes are frequently caused by mutations in intraflagellar transport (IFT) genes affecting the primary cilia, which play a crucial role in skeletal and chondral development. Here, we identified mutations in IFT140, an IFT complex A gene, in five Jeune asphyxiating thoracic dystrophy (JATD) and two Mainzer-Saldino syndrome (MSS) families, by screening a cohort of 66 JATD/MSS patients using whole exome sequencing and targeted resequencing of a customized ciliopathy gene panel. We also found an enrichment of rare IFT140 alleles in JATD compared with nonciliopathy diseases, implying putative modifier effects for certain alleles. IFT140 patients presented with mild chest narrowing, but all had end-stage renal failure under 13 years of age and retinal dystrophy when examined for ocular dysfunction. This is consistent with the severe cystic phenotype of Ift140 conditional knockout mice, and the higher level of Ift140 expression in kidney and retina compared with the skeleton at E15.5 in the mouse. IFT140 is therefore a major cause of cono-renal syndromes (JATD and MSS). The present study strengthens the rationale for IFT140 screening in skeletal ciliopathy spectrum patients that have kidney disease and/or retinal dystrophy.

Functional and structural analysis of C-terminal BRCA1 missense variants

Germline inactivating mutations in BRCA1 and BRCA2 genes are responsible for Hereditary Breast and Ovarian Cancer Syndrome (HBOCS). Genetic testing of these genes is available, although approximately 15% of tests identify variants of uncertain significance (VUS). Classification of these variants into pathogenic or non-pathogenic type is an important challenge in genetic diagnosis and counseling. The aim of the present study is to functionally assess a set of 7 missense VUS (Q1409L, S1473P, E1586G, R1589H, Y1703S, W1718L and G1770V) located in the C-terminal region of BRCA1 by combining in silico prediction tools and structural analysis with a transcription activation (TA) assay. The in silico prediction programs gave discrepant results making its interpretation difficult. Structural analysis of the three variants located in the BRCT domains (Y1703S, W1718L and G1770V) reveals significant alterations of BRCT structure. The TA assay shows that variants Y1703S, W1718L and G1770V dramatically compromise the transcriptional activity of BRCA1, while variants Q1409L, S1473P, E1586G and R1589H behave like wild-type BRCA1. In conclusion, our results suggest that variants Y1703S, W1718L and G1770V can be classified as likely pathogenic BRCA1 mutations.

Mutations in HNF1A result in marked alterations of plasma glycan profile

A recent genome-wide association study identified hepatocyte nuclear factor 1-α (HNF1A) as a key regulator of fucosylation. We hypothesized that loss-of-function HNF1A mutations causal for maturity-onset diabetes of the young (MODY) would display altered fucosylation of N-linked glycans on plasma proteins and that glycan biomarkers could improve the efficiency of a diagnosis of HNF1A-MODY. In a pilot comparison of 33 subjects with HNF1A-MODY and 41 subjects with type 2 diabetes, 15 of 29 glycan measurements differed between the two groups. The DG9-glycan index, which is the ratio of fucosylated to nonfucosylated triantennary glycans, provided optimum discrimination in the pilot study and was examined further among additional subjects with HNF1A-MODY (n = 188), glucokinase (GCK)-MODY (n = 118), hepatocyte nuclear factor 4-α (HNF4A)-MODY (n = 40), type 1 diabetes (n = 98), type 2 diabetes (n = 167), and nondiabetic controls (n = 98). The DG9-glycan index was markedly lower in HNF1A-MODY than in controls or other diabetes subtypes, offered good discrimination between HNF1A-MODY and both type 1 and type 2 diabetes (C statistic ≥ 0.90), and enabled us to detect three previously undetected HNF1A mutations in patients with diabetes. In conclusion, glycan profiles are altered substantially in HNF1A-MODY, and the DG9-glycan index has potential clinical value as a diagnostic biomarker of HNF1A dysfunction.

POT1 mutations cause telomere dysfunction in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is the most frequent leukemia in adults. We have analyzed exome sequencing data from 127 individuals with CLL and Sanger sequencing data from 214 additional affected individuals, identifying recurrent somatic mutations in POT1 (encoding protection of telomeres 1) in 3.5% of the cases, with the frequency reaching 9% when only individuals without IGHV@ mutations were considered. POT1 encodes a component of the shelterin complex and is the first member of this telomeric structure found to be mutated in human cancer. Somatic mutation of POT1 primarily occurs in gene regions encoding the two oligonucleotide-/oligosaccharide-binding (OB) folds and affects key residues required to bind telomeric DNA. POT1-mutated CLL cells have numerous telomeric and chromosomal abnormalities that suggest that POT1 mutations favor the acquisition of the malignant features of CLL cells. The identification of POT1 as a new frequently mutated gene in CLL may facilitate novel approaches for the clinical management of this disease.