Correction: Sherloc: a comprehensive refinement of the ACMG-AMP variant classification criteria.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Assessing computational predictions of the phenotypic effect of cystathionine-beta-synthase variants.

Accurate prediction of the impact of genomic variation on phenotype is a major goal of computational biology and an important contributor to personalized medicine. Computational predictions can lead to a better understanding of the mechanisms underlying genetic diseases, including cancer, but their adoption requires thorough and unbiased assessment. Cystathionine-beta-synthase (CBS) is an enzyme that catalyzes the first step of the transsulfuration pathway, from homocysteine to cystathionine, and in which variations are associated with human hyperhomocysteinemia and homocystinuria. We have created a computational challenge under the CAGI framework to evaluate how well different methods can predict the phenotypic effect(s) of CBS single amino acid substitutions using a blinded experimental data set. CAGI participants were asked to predict yeast growth based on the identity of the mutations. The performance of the methods was evaluated using several metrics. The CBS challenge highlighted the difficulty of predicting the phenotype of an ex vivo system in a model organism when classification models were trained on human disease data. We also discuss the variations in difficulty of prediction for known benign and deleterious variants, as well as identify methodological and experimental constraints with lessons to be learned for future challenges.

Matching phenotypes to whole genomes: Lessons learned from four iterations of the personal genome project community challenges.

The advent of next-generation sequencing has dramatically decreased the cost for whole-genome sequencing and increased the viability for its application in research and clinical care. The Personal Genome Project (PGP) provides unrestricted access to genomes of individuals and their associated phenotypes. This resource enabled the Critical Assessment of Genome Interpretation (CAGI) to create a community challenge to assess the bioinformatics community's ability to predict traits from whole genomes. In the CAGI PGP challenge, researchers were asked to predict whether an individual had a particular trait or profile based on their whole genome. Several approaches were used to assess submissions, including ROC AUC (area under receiver operating characteristic curve), probability rankings, the number of correct predictions, and statistical significance simulations. Overall, we found that prediction of individual traits is difficult, relying on a strong knowledge of trait frequency within the general population, whereas matching genomes to trait profiles relies heavily upon a small number of common traits including ancestry, blood type, and eye color. When a rare genetic disorder is present, profiles can be matched when one or more pathogenic variants are identified. Prediction accuracy has improved substantially over the last 6 years due to improved methodology and a better understanding of features.

Sherloc: a comprehensive refinement of the ACMG-AMP variant classification criteria.

PurposeThe 2015 American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) guidelines were a major step toward establishing a common framework for variant classification. In practice, however, several aspects of the guidelines lack specificity, are subject to varied interpretations, or fail to capture relevant aspects of clinical molecular genetics. A simple implementation of the guidelines in their current form is insufficient for consistent and comprehensive variant classification.MethodsWe undertook an iterative process of refining the ACMG-AMP guidelines. We used the guidelines to classify more than 40,000 clinically observed variants, assessed the outcome, and refined the classification criteria to capture exceptions and edge cases. During this process, the criteria evolved through eight major and minor revisions.ResultsOur implementation: (i) separated ambiguous ACMG-AMP criteria into a set of discrete but related rules with refined weights; (ii) grouped certain criteria to protect against the overcounting of conceptually related evidence; and (iii) replaced the "clinical criteria" style of the guidelines with additive, semiquantitative criteria.ConclusionSherloc builds on the strong framework of 33 rules established by the ACMG-AMP guidelines and introduces 108 detailed refinements, which support a more consistent and transparent approach to variant classification.

In silico comparative characterization of pharmacogenomic missense variants.

BACKGROUND: Missense pharmacogenomic (PGx) variants refer to amino acid substitutions that potentially affect the pharmacokinetic (PK) or pharmacodynamic (PD) response to drug therapies. The PGx variants, as compared to disease-associated variants, have not been investigated as deeply. The ability to computationally predict future PGx variants is desirable; however, it is not clear what data sets should be used or what features are beneficial to this end. Hence we carried out a comparative characterization of PGx variants with annotated neutral and disease variants from UniProt, to test the predictive power of sequence conservation and structural information in discriminating these three groups. RESULTS: 126 PGx variants of high quality from PharmGKB were selected and two data sets were created: one set contained 416 variants with structural and sequence information, and, the other set contained 1,265 variants with sequence information only. In terms of sequence conservation, PGx variants are more conserved than neutral variants and much less conserved than disease variants. A weighted random forest was used to strike a more balanced classification for PGx variants. Generally structural features are helpful in discriminating PGx variant from the other two groups, but still classification of PGx from neutral polymorphisms is much less effective than between disease and neutral variants. CONCLUSIONS: We found that PGx variants are much more similar to neutral variants than to disease variants in the feature space consisting of residue conservation, neighboring residue conservation, number of neighbors, and protein solvent accessibility. Such similarity poses great difficulty in the classification of PGx variants and polymorphisms.

Medium-chain acyl-CoA deficiency: outlines from newborn screening, in silico predictions, and molecular studies.

Medium-chain acyl-CoA dehydrogenase deficiency (MCADD) is a disorder of fatty acid oxidation characterized by hypoglycemic crisis under fasting or during stress conditions, leading to lethargy, seizures, brain damage, or even death. Biochemical acylcarnitines data obtained through newborn screening by liquid chromatography-tandem mass spectrometry (LC-MS/MS) were confirmed by molecular analysis of the medium-chain acyl-CoA dehydrogenase (ACADM) gene. Out of 324.000 newborns screened, we identified 14 MCADD patients, in whom, by molecular analysis, we found a new nonsense c.823G>T (p.Gly275∗) and two new missense mutations: c.253G>C (p.Gly85Arg) and c.356T>A (p.Val119Asp). Bioinformatics predictions based on both phylogenetic conservation and functional/structural software were used to characterize the new identified variants. Our findings confirm the rising incidence of MCADD whose existence is increasingly recognized due to the efficacy of an expanded newborn screening panel by LC-MS/MS making possible early specific therapies that can prevent possible crises in at-risk infants. We noticed that the "common" p.Lys329Glu mutation only accounted for 32% of the defective alleles, while, in clinically diagnosed patients, this mutation accounted for 90% of defective alleles. Unclassified variants (UVs or VUSs) are especially critical when considering screening programs. The functional and pathogenic characterization of genetic variants presented here is required to predict their medical consequences in newborns.

An excess of deleterious variants in VEGF-A pathway genes in Down-syndrome-associated atrioventricular septal defects.

About half of people with trisomy 21 have a congenital heart defect (CHD), whereas the remainder have a structurally normal heart, demonstrating that trisomy 21 is a significant risk factor but is not causal for abnormal heart development. Atrioventricular septal defects (AVSD) are the most commonly occurring heart defects in Down syndrome (DS), and ∼65% of all AVSD is associated with DS. We used a candidate-gene approach among individuals with DS and complete AVSD (cases = 141) and DS with no CHD (controls = 141) to determine whether rare genetic variants in genes involved in atrioventricular valvuloseptal morphogenesis contribute to AVSD in this sensitized population. We found a significant excess (p < 0.0001) of variants predicted to be deleterious in cases compared to controls. At the most stringent level of filtering, we found potentially damaging variants in nearly 20% of cases but fewer than 3% of controls. The variants with the highest probability of being damaging in cases only were found in six genes: COL6A1, COL6A2, CRELD1, FBLN2, FRZB, and GATA5. Several of the case-specific variants were recurrent in unrelated individuals, occurring in 10% of cases studied. No variants with an equal probability of being damaging were found in controls, demonstrating a highly specific association with AVSD. Of note, all of these genes are in the VEGF-A pathway, even though the candidate genes analyzed in this study represented numerous biochemical and developmental pathways, suggesting that rare variants in the VEGF-A pathway might contribute to the genetic underpinnings of AVSD in humans.

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.

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.

Pathogenic or not? And if so, then how? Studying the effects of missense mutations using bioinformatics methods.

Many gene defects are relatively easy to identify experimentally, but obtaining information about the effects of sequence variations and elucidation of the detailed molecular mechanisms of genetic diseases will be among the next major efforts in mutation research. Amino acid substitutions may have diverse effects on protein structure and function; thus, a detailed analysis of the mutations is essential. Experimental study of the molecular effects of mutations is laborious, whereas useful and reliable information about the effects of amino acid substitutions can readily be obtained by theoretical methods. Experimentally defined structures and molecular modeling can be used as a basis for interpretation of the mutations. The effects of missense mutations can be analyzed even when the 3D structure of the protein has not been determined, although structure-based analyses are more reliable. Structural analyses include studies of the contacts between residues, their implication for the stability of the protein, and the effects of the introduced residues. Investigations of steric and stereochemical consequences of substitutions provide insights on the molecular fit of the introduced residue. Mutations that change the electrostatic surface potential of a protein have wide-ranging effects. Analyses of the effects of mutations on interactions with ligands and partners have been performed for elucidation of functional mutations. We have employed numerous methods for predicting the effects of amino acid substitutions. We discuss the applicability of these methods in the analysis of genes, proteins, and diseases to reveal protein structure-function relationships, which is essential to gain insights into disease genotype-phenotype correlations.

Genome wide analysis of pathogenic SH2 domain mutations.

The authors have made a genome-wide analysis of mutations in Src homology 2 (SH2) domains associated with human disease. Disease-causing mutations have been detected in the SH2 domains of cytoplasmic signaling proteins Bruton tyrosine kinase (BTK), SH2D1A, Ras GTPase activating protein (RasGAP), ZAP-70, SHP-2, STAT1, STAT5B, and the p85alpha subunit of the PIP3. Mutations in the BTK, SH2D1A, ZAP70, STAT1, and STAT5B genes have been shown to cause diverse immunodeficiencies, whereas the mutations in RASA1 and PIK3R1 genes lead to basal carcinoma and diabetes, respectively. PTPN11 mutations cause Noonan sydrome and different types of cancer, depending mainly on whether the mutation is inherited or sporadic. We collected and analyzed all known pathogenic mutations affecting human SH2 domains by bioinformatics methods. Among the investigated protein properties are sequence conservation and covariance, structural stability, side chain rotamers, packing effects, surface electrostatics, hydrogen bond formation, accessible surface area, salt bridges, and residue contacts. The majority of the mutations affect positions essential for phosphotyrosine ligand binding and specificity. The structural basis of the SH2 domain diseases was elucidated based on the bioinformatic analysis.

Expression of sterol regulatory element-binding transcription factor (SREBF) 2 and SREBF cleavage-activating protein (SCAP) in human atheroma and the association of their allelic variants with sudden cardiac death.

BACKGROUND: Disturbed cellular cholesterol homeostasis may lead to accumulation of cholesterol in human atheroma plaques. Cellular cholesterol homeostasis is controlled by the sterol regulatory element-binding transcription factor 2 (SREBF-2) and the SREBF cleavage-activating protein (SCAP). We investigated whole genome expression in a series of human atherosclerotic samples from different vascular territories and studied whether the non-synonymous coding variants in the interacting domains of two genes, SREBF-2 1784G>C (rs2228314) and SCAP 2386A>G, are related to the progression of coronary atherosclerosis and the risk of pre-hospital sudden cardiac death (SCD). METHODS: Whole genome expression profiling was completed in twenty vascular samples from carotid, aortic and femoral atherosclerotic plaques and six control samples from internal mammary arteries. Three hundred sudden pre-hospital deaths of middle-aged (33-69 years) Caucasian Finnish men were subjected to detailed autopsy in the Helsinki Sudden Death Study. Coronary narrowing and areas of coronary wall covered with fatty streaks or fibrotic, calcified or complicated lesions were measured and related to the SREBF-2 and SCAP genotypes. RESULTS: Whole genome expression profiling showed a significant (p = 0.02) down-regulation of SREBF-2 in atherosclerotic carotid plaques (types IV-V), but not in the aorta or femoral arteries (p = NS for both), as compared with the histologically confirmed non-atherosclerotic tissues. In logistic regression analysis, a significant interaction between the SREBF-2 1784G>C and the SCAP 2386A>G genotype was observed on the risk of SCD (p = 0.046). Men with the SREBF-2 C allele and the SCAP G allele had a significantly increased risk of SCD (OR 2.68, 95% CI 1.07-6.71), compared to SCAP AA homologous subjects carrying the SREBF-2 C allele. Furthermore, similar trends for having complicated lesions and for the occurrence of thrombosis were found, although the results were not statistically significant. CONCLUSION: The results suggest that the allelic variants (SREBF-2 1784G>C and SCAP 2386A>G) in the cholesterol homeostasis regulating SREBF-SCAP pathway may contribute to SCD in early middle-aged men.

Bioinformatic analysis of protein structure-function relationships: case study of leukocyte elastase (ELA2) missense mutations.

Cyclic and congenital neutropenia are caused by mutations in the human neutrophil elastase (HNE) gene (ELA2), leading to an immunodeficiency characterized by decreased or oscillating levels of neutrophils in the blood. The HNE mutations presumably cause loss of enzyme activity, consequently leading to compromised immune system function. To understand the structural basis for the disease, we implemented methods from bioinformatics to analyze all the known HNE missense mutations at both the sequence and structural level. Our results demonstrate that the 32 different mutations have diverse effects on HNE structure and function, affecting structural disorder and aggregation tendencies, stability maintaining contacts, and electrostatic properties. A large proportion of the mutations are located at conserved amino acids, which are usually essential in determining protein structure and function. The majority of the disease-causing HNE missense mutations lead to major structural changes and loss of stability in the protein. A few mutations also affect functional residues, leading into decreased catalytic activity or altered ligand binding. Our analysis reveals the putative effects of all known missense mutations in HNE, thus allowing the structural basis of cyclic and congenital neutropenia to be elucidated. We have employed and analyzed a set of some 30 different methods for predicting the effects of amino acid substitutions. We present results and experience from the analysis of the applicability of these methods in the analysis of numerous genes, proteins, and diseases to reveal protein structure-function relationships and disease genotype-phenotype correlations.