Bayesian models for syndrome- and gene-specific probabilities of novel variant pathogenicity

Novel gene-specific Bayesian Gaussian mixture model to predict the missense variants pathogenicity of Sanfilippo syndrome

MPS III is an autosomal recessive lysosomal storage disease caused mainly by missense variants in the NAGLU, GNS, HGSNAT, and SGSH genes. The pathogenicity interpretation of missense variants is still challenging. We aimed to develop unsupervised clustering-based pathogenicity predictor scores using extracted features from eight in silico predictors to predict the impact of novel missense variants of Sanfilippo syndrome. The model was trained on a dataset consisting of 415 uncertain significant (VUS) missense NAGLU variants. Performance The SanfilippoPred tool was evaluated by validation and test datasets consisting of 197-labelled NAGLU missense variants, and its performance was compared versus individual pathogenicity predictors using receiver operating characteristic (ROC) analysis. Moreover, we tested the SanfilippoPred tool using extra-labelled 427 missense variants to assess its specificity and sensitivity threshold. Application of the trained machine learning (ML) model on the test dataset of labelled NAGLU missense variants showed that SanfilippoPred has an accuracy of 0.93 (0.86-0.97 at CI 95%), sensitivity of 0.93, and specificity of 0.92. The comparative performance of the SanfilippoPred showed better performance (AUC = 0.908) than the individual predictors SIFT (AUC = 0.756), Polyphen-2 (AUC = 0.788), CADD (AUC = 0.568), REVEL (AUC = 0.548), MetaLR (AUC = 0.751), and AlphMissense (AUC = 0.885). Using high-confidence labelled NAGLU variants, showed that SanfilippoPred has an 85.7% sensitivity threshold. The poor correlation between the Sanfilippo syndrome phenotype and genotype represents a demand for a new tool to classify its missense variants. This study provides a significant tool for preventing the misinterpretation of missense variants of the Sanfilippo syndrome-relevant genes. Finally, it seems that ML-based pathogenicity predictors and Sanfilippo syndrome-specific prediction tools could be feasible and efficient pathogenicity predictors in the future.

PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies

Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.

Genetic variant interpretation: a primer for clinicians

Clinicians beyond specialist genetic services are now able to order tests to interrogate the genetic basis of disease. Behind every genetic report lies a significant body of work that draws on decades of collaboration between clinicians, researchers and database curators. Understanding these advances in genetic variant interpretation may allow practising clinicians to develop a more nuanced appreciation of the role genetic variant interpretation can play in the diagnosis and management of heritable disorders. In this article, we consider genetic variant interpretation with reference to efforts to better understand variation in the mismatch repair genes and their relation to Lynch syndrome - the most common cause of hereditary colon cancer.

Change Point Analysis for Detecting Vaccine Safety Signals

It is important to detect signals of abrupt changes in adverse event reporting in order to notice public safety concerns and take prompt action, especially for vaccines under national immunization programs. In this study, we assessed the applicability of change point analysis (CPA) for signal detection in vaccine safety surveillance. The performances of three CPA methods, namely Bayesian change point analysis, Taylor’s change point analysis (Taylor-CPA), and environmental time series change point detection (EnvCpt), were assessed via simulated data with assumptions for the baseline number of events and degrees of change. The analysis was validated using the Korea Adverse Event Reporting System (KAERS) database. In the simulation study, the Taylor-CPA method exhibited better results for the detection of a change point (accuracy of 96% to 100%, sensitivity of 7% to 100%, specificity of 98% to 100%, positive predictive value of 25% to 85%, negative predictive value of 96% to 100%, and balanced accuracy of 53% to 100%) than the other two CPA methods. When the CPA methods were applied to reports of syncope or dizziness following human papillomavirus (HPV) immunization in the KAERS database, Taylor-CPA and EnvCpt detected a change point (Q2/2013), which was consistent with actual public safety concerns. CPA can be applied as an efficient tool for the early detection of vaccine safety signals.

Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies

For the last couple of decades, there has been a significant growth in sequencing data, leading to an extraordinary increase in the number of gene variants. This places a challenge on the bioinformatics research community to develop and improve computational tools for functional annotation of new variants. Genes coding for epigenetic regulators have important roles in cancer pathogenesis and mutations in these genes show great potential as clinical biomarkers, especially in hematologic malignancies. Therefore, we developed a model that specifically focuses on these genes, with an assumption that it would outperform general models in predicting the functional effects of amino acid substitutions. EpiMut is a standalone software that implements a sequence based alignment-free method. We applied a two-step approach for generating sequence based features, relying on the biophysical and biochemical indices of amino acids and the Fourier Transform as a sequence transformation method. For each gene in the dataset, the machine learning algorithm-Naïve Bayes was used for building a model for prediction of the neutral or disease-related status of variants. EpiMut outperformed state-of-the-art tools used for comparison, PolyPhen-2, SIFT and SNAP2. Additionally, EpiMut showed the highest performance on the subset of variants positioned outside conserved functional domains of analysed proteins, which represents an important group of cancer-related variants. These results imply that EpiMut can be applied as a first choice tool in research of the impact of gene variants in epigenetic regulators, especially in the light of the biomarker role in hematologic malignancies. EpiMut is freely available at https://www.vin.bg.ac.rs/180/tools/epimut.php.

A Bayesian framework for efficient and accurate variant prediction

There is a growing need to develop variant prediction tools capable of assessing a wide spectrum of evidence. We present a Bayesian framework that involves aggregating pathogenicity data across multiple in silico scores on a gene-by-gene basis and multiple evidence statistics in both quantitative and qualitative forms, and performs 5-tiered variant classification based on the resulting probability credible interval. When evaluated in 1,161 missense variants, our gene-specific in silico model-based meta-predictor yielded an area under the curve (AUC) of 96.0% and outperformed all other in silico predictors. Multifactorial model analysis incorporating all available evidence yielded 99.7% AUC, with 22.8% predicted as variants of uncertain significance (VUS). Use of only 3 auto-computed evidence statistics yielded 98.6% AUC with 56.0% predicted as VUS, which represented sufficient accuracy to rapidly assign a significant portion of VUS to clinically meaningful classifications. Collectively, our findings support the use of this framework to conduct large-scale variant prioritization using in silico predictors followed by variant prediction and classification with a high degree of predictive accuracy.

Bayesian genome- and epigenome-wide association studies with gene level dependence

High-throughput genetic and epigenetic data are often screened for associations with an observed phenotype. For example, one may wish to test hundreds of thousands of genetic variants, or DNA methylation sites, for an association with disease status. These genomic variables can naturally be grouped by the gene they encode, among other criteria. However, standard practice in such applications is independent screening with a universal correction for multiplicity. We propose a Bayesian approach in which the prior probability of an association for a given genomic variable depends on its gene, and the gene-specific probabilities are modeled nonparametrically. This hierarchical model allows for appropriate gene and genome-wide multiplicity adjustments, and can be incorporated into a variety of Bayesian association screening methodologies with negligible increase in computational complexity. We describe an application to screening for differences in DNA methylation between lower grade glioma and glioblastoma multiforme tumor samples from The Cancer Genome Atlas. Software is available via the package BayesianScreening for R: github.com/lockEF/BayesianScreening.

Designing expanded carrier screening panels: results of a qualitative study with European geneticists

Aim: To explore the views of clinical and molecular geneticists on the inclusion of disorders and specific pathogenic mutations into expanded carrier screening (ECS) tests for reproductive purposes. Materials & methods: In-depth semistructured interviews were conducted with 16 European geneticists between April and September 2014. Results: All participants supported carrier screening for severe, childhood-onset autosomal recessive disorders with known natural history. Some participants were also in favor of screening for late-onset and X-linked disorders. Regarding selection of specific pathogenic mutations, our participants argued that ECS should include highly penetrant pathogenic mutations with known genotype–phenotype associations. Conclusion: This study highlights main challenges surrounding the development of ECS panels and offers suggestions for future research in this rapidly advancing field.

Gene-set Analysis with CGI Information for Differential DNA Methylation Profiling

DNA methylation is a well-established epigenetic biomarker for many diseases. Studying the relationships among a group of genes and their methylations may help to unravel the etiology of diseases. Since CpG-islands (CGIs) play a crucial role in the regulation of transcription during methylation, including them in the analysis may provide further information in understanding the pathogenesis of cancers. Such CGI information, however, has usually been overlooked in existing gene-set analyses. Here we aimed to include both pathway information and CGI status to rank competing gene-sets and identify among them the genes most likely contributing to DNA methylation changes. To accomplish this, we devised a Bayesian model for matched case-control studies with parameters for CGI status and pathway associations, while incorporating intra-gene-set information. Three cancer studies with candidate pathways were analyzed to illustrate this approach. The strength of association for each candidate pathway and the influence of each gene were evaluated. Results show that, based on probabilities, the importance of pathways and genes can be determined. The findings confirm that some of these genes are cancer-related and may hold the potential to be targeted in drug development.

Massive parallel sequencing applied to the molecular autopsy in sudden cardiac death in the young

Sudden cardiac death in the young is a very traumatic event that occurs often in apparently healthy individuals without an explainable cause of death after a comprehensive medico-legal investigation. Knowledge about the pathologies with a risk of sudden death is increasingly showing a greater underlying genetic heterogeneity, which provides one of the main handicaps for molecular autopsy. On the other hand the enormous technological advances in sequencing technologies, allow us to analyse as many genes as we want at a cost increasingly reduced. The sum of these two factors (increased knowledge of genetics and available technologies) allow us to make an individualized study of the causes of sudden cardiac death in young adults, through massive sequencing of all potential genes involved in the process. We define this approach as massive genomic autopsy, and with this review we will try to explain the possible scenarios and methods available for its implementation.

Genetic purgatory and the cardiac channelopathies: Exposing the variants of uncertain/unknown significance issue

Merriam-Webster's online dictionary defines purgatory as "an intermediate state after death for expiatory purification" or more specifically as "a place or state of punishment wherein according to Roman Catholic doctrine the souls of those who die in God׳s grace may make satisfaction for past sins and so become fit for heaven." Alternatively, it is defined as "a place or state of temporary suffering or misery." Either way, purgatory is a place where you are stuck, and you don't want to be stuck there. It is in this context that the term genetic purgatory is introduced. Genetic purgatory is a place where the genetic test-ordering physician and patients and their families are stuck when a variant of uncertain/unknown significance (VUS) has been elucidated. It is in this dark place where suffering and misery are occurring because of unenlightened handling of a VUS, which includes using the VUS for predictive genetic testing and making radical treatment recommendations based on the presence or absence of a so-called maybe mutation. Before one can escape from this miserable place, one must first recognize that one is stuck there. Hence, the purpose of this review article is to fully expose the VUS issue as it relates to the cardiac channelopathies and make the cardiologists/geneticists/genetic counselors who order such genetic tests believers in genetic purgatory. Only then can one meaningfully attempt to get out of that place and seek to promote a VUS to disease-causative mutation status or demote it to an utterly innocuous and irrelevant variant.

Enhanced Classification of Brugada Syndrome-Associated and Long-QT Syndrome-Associated Genetic Variants in the SCN5A-Encoded Na(v)1.5 Cardiac Sodium Channel

BACKGROUND

A 2% to 5% background rate of rare SCN5A nonsynonymous single nucleotide variants (nsSNVs) among healthy individuals confounds clinical genetic testing. Therefore, the purpose of this study was to enhance interpretation of SCN5A nsSNVs for clinical genetic testing using estimated predictive values derived from protein-topology and 7 in silico tools.

METHODS AND RESULTS

Seven in silico tools were used to assign pathogenic/benign status to nsSNVs from 2888 long-QT syndrome cases, 2111 Brugada syndrome cases, and 8975 controls. Estimated predictive values were determined for each tool across the entire SCN5A-encoded Na(v)1.5 channel as well as for specific topographical regions. In addition, the in silico tools were assessed for their ability to correlate with cellular electrophysiology studies. In long-QT syndrome, transmembrane segments S3-S5+S6 and the DIII/DIV linker region were associated with high probability of pathogenicity. For Brugada syndrome, only the transmembrane spanning domains had a high probability of pathogenicity. Although individual tools distinguished case- and control-derived SCN5A nsSNVs, the composite use of multiple tools resulted in the greatest enhancement of interpretation. The use of the composite score allowed for enhanced interpretation for nsSNVs outside of the topological regions that intrinsically had a high probability of pathogenicity, as well as within the transmembrane spanning domains for Brugada syndrome nsSNVs.

CONCLUSIONS

We have used a large case/control study to identify regions of Na(v)1.5 associated with a high probability of pathogenicity. Although topology alone would leave the variants outside these identified regions in genetic purgatory, the synergistic use of multiple in silico tools may help promote or demote a variant's pathogenic status.

Enhancing the Predictive Power of Mutations in the C-Terminus of the KCNQ1-Encoded Kv7.1 Voltage-Gated Potassium Channel

Despite the overrepresentation of Kv7.1 mutations among patients with a robust diagnosis of long QT syndrome (LQTS), a background rate of innocuous Kv7.1 missense variants observed in healthy controls creates ambiguity in the interpretation of LQTS genetic test results. A recent study showed that the probability of pathogenicity for rare missense mutations depends in part on the topological location of the variant in Kv7.1's various structure-function domains. Since the Kv7.1's C-terminus accounts for nearly 50 % of the overall protein and nearly 50 % of the overall background rate of rare variants falls within the C-terminus, further enhancement in mutation calling may provide guidance in distinguishing pathogenic long QT syndrome type 1 (LQT1)-causing mutations from rare non-disease-causing variants in the Kv7.1's C-terminus. Therefore, we have used conservation analysis and a large case-control study to generate topology-based estimative predictive values to aid in interpretation, identifying three regions of high conservation within the Kv7.1's C-terminus which have a high probability of LQT1 pathogenicity.

Variant interpretation through Bayesian fusion of frequency and genomic knowledge

Variant interpretation is a central challenge in genomic medicine. A recent study demonstrates the power of Bayesian statistical approaches to improve interpretation of variants in the context of specific genes and syndromes. Such Bayesian approaches combine frequency (in the form of observed genetic variation in cases and controls) with biological annotations to determine a probability of pathogenicity. These Bayesian approaches complement other efforts to catalog human variation.

See related Research; 10.1186/s13073-014-0120-4