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Essential tremor (ET) is one of the most common movement disorders. The etiology of ET remains largely unexplained. Whole genome sequencing (WGS) is likely to be of value in understanding a large proportion of ET with Mendelian and complex disease inheritance patterns. In ET families with Mendelian inheritance patterns, WGS may lead to gene identification where WES analysis failed to identify the causative single nucleotide variant (SNV) or indel due to incomplete coverage of the entire coding region of the genome, in addition to accurate detection of larger structural variants (SVs) and copy number variants (CNVs). Alternatively, in ET families with complex disease inheritance patterns with gene x gene and gene x environment interactions enrichment of functional rare coding and non-coding variants may explain the heritability of ET. We performed WGS in eight ET families (n = 40 individuals) enrolled in the Family Study of Essential Tremor. The analysis included filtering WGS data based on allele frequency in population databases, rare SNV and indel classification and association testing using the Mixed-Model Kernel Based Adaptive Cluster (MM-KBAC) test. A separate analysis of rare SV and CNVs segregating within ET families was also performed. Prioritization of candidate genes identified within families was performed using phenolyzer. WGS analysis identified candidate genes for ET in 5/8 (62.5%) of the families analyzed. WES analysis in a subset of these families in our previously published study failed to identify candidate genes. In one family, we identified a deleterious and damaging variant (c.1367G>A, p.(Arg456Gln)) in the candidate gene, CACNA1G, which encodes the pore forming subunit of T-type Ca(2+) channels, CaV3.1, and is expressed in various motor pathways and has been previously implicated in neuronal autorhythmicity and ET. Other candidate genes identified include SLIT3 which encodes an axon guidance molecule and in three families, phenolyzer prioritized genes that are associated with hereditary neuropathies (family A, KARS, family B, KIF5A and family F, NTRK1). Functional studies of CACNA1G and SLIT3 suggest a role for these genes in ET disease pathogenesis. [ABSTRACT FROM AUTHOR]

The incidence of stillbirth in Sweden has essentially remained constant since the 1980’s, and despite thorough investigation, many cases remain unexplained. It has been suggested that a proportion of stillbirth cases is caused by heart disease, mainly channelopathies. The aim of this study was to analyze DNA from 290 stillbirth cases without chromosomal abnormalities for pathogenic single nucleotide variants (SNVs) in 70 genes associated with cardiac channelopathies and cardiomyopathies. The HaloPlex Target Enrichment System (Agilent Technologies) was utilized to prepare sequencing libraries which were sequenced on the Illumina NextSeq platform. We found that 12.1% of the 290 investigated stillbirth cases had one (n = 31) or two (n = 4) variants with evidence supporting pathogenicity, i.e. loss-of-function variants (nonsense, frameshift, splice site substitutions), evidence from functional studies, or previous identification of the variants in affected individuals. Regarding identified putative pathogenic variants in genes associated with channelopathies, the prevalence was significantly higher in the stillbirth cohort (n = 23, 7.93%) than the corresponding prevalence of the same variants in the non-Finnish European population of the Exome Aggregation Consortium (2.70%, p<0.001) and SweGen, (2.30%, p<0.001). Our results give further support to the hypothesis that cardiac channelopathies might contribute to stillbirth. Screening for pathogenic SNVs in genes associated with heart disease might be a valuable complement for stillbirth cases where today’s conventional investigation does not reveal the underlying cause of fetal demise. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Many genes have been suggested as candidate genes for keratoconus based on their function, their proximity to associated polymorphisms or due to the identification of putative causative variants within the gene. However, very few of these genes have been assessed for rare variation in keratoconus more broadly. In contrast, VSX1 and SOD1 have been widely assessed, however, the vast majority of studies have been small and the findings conflicting. In a cohort of Australians of European descent, consisting of 385 keratoconus cases and 396 controls, we screened 21 keratoconus candidate genes: BANP, CAST, COL4A3, COL4A4, COL5A1, FOXO1, FNDC3B, HGF, IL1A, IL1B, ILRN, IMMP2L, MPDZ, NFIB, RAB3GAP1, RAD51, RXRA, SLC4A11, SOD1, TF and VSX1. The candidate genes were sequenced in these individuals by either whole exome sequencing or targeted gene sequencing. Variants were filtered to identify rare (minor allele frequency <1%), potentially pathogenic variants. A total of 164 such variants were identified across the two groups with no variants fulfilling these criteria in cases in IL1RN, BANP, IL1B, RAD51 or SOD1. The frequency of variants was compared between cases and controls using chi-square or Fishers’ Exact tests for each gene with at least one rare potentially pathogenic variant identified in the case cohort. The number of rare potentially pathogenic variants per gene ranged from three (RXRA) to 102 (MPDZ), however for all genes, there was no difference in the frequency between the cases and controls. We conclude that rare potentially pathogenic variation in the 21 candidate genes assessed do not play a major role in keratoconus susceptibility and pathogenesis. [ABSTRACT FROM AUTHOR]

Many automatic classifiers were introduced to aid inference of phenotypical effects of uncategorised nsSNVs (nonsynonymous Single Nucleotide Variations) in theoretical and medical applications. Lately, several meta-estimators have been proposed that combine different predictors, such as PolyPhen and SIFT, to integrate more information in a single score. Although many advances have been made in feature design and machine learning algorithms used, the shortage of high-quality reference data along with the bias towards intensively studied in vitro models call for improved generalisation ability in order to further increase classification accuracy and handle records with insufficient data. Since a meta-estimator basically combines different scoring systems with highly complicated nonlinear relationships, we investigated how deep learning (supervised and unsupervised), which is particularly efficient at discovering hierarchies of features, can improve classification performance. While it is believed that one should only use deep learning for high-dimensional input spaces and other models (logistic regression, support vector machines, Bayesian classifiers, etc) for simpler inputs, we still believe that the ability of neural networks to discover intricate structure in highly heterogenous datasets can aid a meta-estimator. We compare the performance with various popular predictors, many of which are recommended by the American College of Medical Genetics and Genomics (ACMG), as well as available deep learning-based predictors. Thanks to hardware acceleration we were able to use a computationally expensive genetic algorithm to stochastically optimise hyper-parameters over many generations. Overfitting was hindered by noise injection and dropout, limiting coadaptation of hidden units. Although we stress that this work was not conceived as a tool comparison, but rather an exploration of the possibilities of deep learning application in ensemble scores, our results show that even relatively simple modern neural networks can significantly improve both prediction accuracy and coverage. We provide open-access to our finest model via the web-site: . [ABSTRACT FROM AUTHOR]

The deleterious genetic variants contributing to certain diseases may differ in terms of number and allele frequency from population to population depending on their evolutionary background. Here, we prioritize the deleterious variants from Pakistani population in manually curated gene list already reported to be associated with common, Mendelian, and congenital cardiovascular diseases (CVDs) using the genome/exome sequencing data of Pakistani individuals publically available in 1000 Genomes Project (PJL), and Exome Aggregation Consortium (ExAC) South Asia. By applying a set of tools such as Combined Annotation Dependent Depletion (CADD), ANNOVAR, and Variant Effect Predictor (VEP), we highlighted 561 potentially detrimental variants from PJL data, and 7374 variants from ExAC South Asian data. Likewise, filtration from ClinVar for CVDs revealed 03 pathogenic and 02 likely pathogenic variants from PJL and 112 pathogenic and 42 likely pathogenic variants from ExAC South Asians. The comparison of derived allele frequencies (DAF) revealed many of these prioritized variants having two fold and higher DAF in Pakistani individuals than in other populations. The highest number of deleterious variants contributing to common CVDs in descending order includes hypertension, atherosclerosis, heart failure, aneurysm, and coronary heart disease, and for Mendelian and congenital CVDs cardiomyopathies, cardiac arrhythmias, and atrioventricular septal defects. [ABSTRACT FROM AUTHOR]

Purpose: To determine survival in afatinib-treated patients after treatment with first-generation EGFR tyrosine kinase inhibitors (TKIs) and to study resistance mechanisms in afatinib-resistant tumors. Methods: Characteristics and survival of patients treated with afatinib after resistance to erlotinib or gefitinib in two large Dutch centers were collected. Whole exome sequencing (WES) and pathway analysis was performed on available pre- and post-afatinib tumor biopsies and normal tissue. Results: A total of 38 patients were treated with afatinib. T790M mutations were identified in 22/29 (76%) pre-afatinib treatment tumor samples. No difference in median progression-free-survival (2.8 months (95% CI 2.3–3.3) and 2.7 months (95% CI 0.9–4.6), p = 0.55) and median overall-survival (8.8 months (95% CI 4.2–13.4) and 3.6 months (95% CI 2.3–5.0), p = 0.14) were observed in T790M+ patients compared to T790M- mutations. Somatic mutations in TP53, ADAMTS2, CNN2 and multiple genes in the Wnt and PI3K-AKT pathway were observed in post-afatinib tumors of six afatinib-responding and in one non-responding patient. No new EGFR mutations were found in the post-afatinib samples of the six responding patients. Further analyses of post-afatinib progressive tumors revealed 28 resistant specific mutations in six genes (HLA-DRB1, AQP7, FAM198A, SEC31A, CNTLN, and ESX1) in three afatinib responding patients. No known EGFR-TKI resistant-associated copy number gains were acquired in the post-afatinib samples. Conclusion: No differences in survival were observed in patients with EGFR-T790M treated with afatinib compared to those without T790M. Tumors from patients who had progressive disease during afatinib treatment were enriched for mutations in genes involved in Wnt and PI3K-AKT pathways. [ABSTRACT FROM AUTHOR]

Background: High throughput sequencing technologies have revolutionized the identification of mutations responsible for genetic diseases such as hypertrophic cardiomyopathy (HCM). However, approximately 50% of individuals with a clinical diagnosis of HCM have no causal mutation identified. This may be due to the presence of pathogenic mutations located deep within the introns, which are not detected by conventional sequencing analysis restricted to exons and exon-intron boundaries. Objective: The aim of this study was to develop a whole-gene sequencing strategy to prioritize deep intronic variants that may play a role in HCM pathogenesis. Methods and results: The full genomic DNA sequence of 26 genes previously associated with HCM was analysed in 16 unrelated patients. We identified likely pathogenic deep intronic variants in VCL, PRKAG2 and TTN genes. These variants, which are predicted to act through disruption of either splicing or transcription factor binding sites, are 3-fold more frequent in our cohort of probands than in normal European populations. Moreover, we found a patient that is compound heterozygous for a splice site mutation in MYBPC3 and the deep intronic VCL variant. Analysis of family members revealed that carriers of the MYBPC3 mutation alone do not manifest the disease, while family members that are compound heterozygous are clinically affected. Conclusion: This study provides a framework for scrutinizing variation along the complete intronic sequence of HCM-associated genes and prioritizing candidates for mechanistic and functional analysis. Our data suggest that deep intronic variation contributes to HCM phenotype. [ABSTRACT FROM AUTHOR]

Aims: Dilated cardiomyopathy (DCM) is an important cause of heart failure with a strong familial component. We performed an exome-wide array-based association study (EWAS) to assess the contribution of missense variants to sporadic DCM. Methods and results: 116,855 single nucleotide variants (SNVs) were analyzed in 2796 DCM patients and 6877 control subjects from 6 populations of European ancestry. We confirmed two previously identified associations with SNVs in BAG3 and ZBTB17 and discovered six novel DCM-associated loci (Q-value<0.01). The lead-SNVs at novel loci are common and located in TTN, SLC39A8, MLIP, FLNC, ALPK3 and FHOD3. In silico fine mapping identified HSPB7 as the most likely candidate at the ZBTB17 locus. Rare variant analysis (MAF<0.01) demonstrated significant association for TTN variants only (P = 0.0085). All candidate genes but one (SLC39A8) exhibit preferential expression in striated muscle tissues and mutations in TTN, BAG3, FLNC and FHOD3 are known to cause familial cardiomyopathy. We also investigated a panel of 48 known cardiomyopathy genes. Collectively, rare (n = 228, P = 0.0033) or common (n = 36, P = 0.019) variants with elevated in silico severity scores were associated with DCM, indicating that the spectrum of genes contributing to sporadic DCM extends beyond those identified here. Conclusion: We identified eight loci independently associated with sporadic DCM. The functions of the best candidate genes at these loci suggest that proteostasis regulation might play a role in DCM pathophysiology. [ABSTRACT FROM AUTHOR]

Background: Deep vein thrombosis (DVT) genetic predisposition is partially known. Objectives: This study aimed at assessing the functional impact of nine ADAMTS13 single nucleotide variants (SNVs) previously reported to be associated as a group with DVT in a burden test and the individual association of selected variants with DVT risk in two replication studies. Methods: Wild-type and mutant recombinant ADAMTS13 were transiently expressed in HEK293 cells. Antigen and activity of recombinant ADAMTS13 were measured by ELISA and FRETS-VWF73 assays, respectively. The replication studies were performed in an Italian case-control study (Milan study; 298/298 patients/controls) using a next-generation sequencing approach and in a Dutch case-control study (MEGA study; 4306/4887 patients/controls) by TaqMan assays. Results: In vitro results showed reduced ADAMTS13 activity for three SNVs (p.Val154Ile [15%; 95% confidence interval [CI] 14–16], p.Asp187His [19%; 95%[CI] 17–21], p.Arg421Cys [24%; 95%[CI] 22–26]) similar to reduced plasma ADAMTS13 levels of patients carriers for these SNVs. Therefore these three SNVs were interrogated for risk association. The first replication study identified 3 heterozygous carriers (2 cases, 1 control) of p.Arg421Cys (odds ratio [OR] 2, 95%[CI] 0.18–22.25). The second replication study identified 2 heterozygous carriers (1 case, 1 control) of p.Asp187His ([OR] 1.14, 95%[CI] 0.07–18.15) and 10 heterozygous carriers (4 cases, 6 controls) of p.Arg421Cys ([OR] 0.76, 95%[CI] 0.21–2.68). Conclusions: Three SNVs (p.Val154Ile, p.Asp187His and p.Arg421Cys) showed reduced ex vivo and in vitro ADAMTS13 levels. However, the low frequency of these variants makes it difficult to confirm their association with DVT. [ABSTRACT FROM AUTHOR]

Background: It has become common practice to analyse large scale sequencing data with statistical approaches based around the aggregation of rare variants within the same gene. We applied a novel approach to rare variant analysis by collapsing variants together using protein domain and family coordinates, regarded to be a more discrete definition of a biologically functional unit. Methods: Using Pfam definitions, we collapsed rare variants (Minor Allele Frequency ≤ 1%) together in three different ways 1) variants within single genomic regions which map to individual protein domains 2) variants within two individual protein domain regions which are predicted to be responsible for a protein-protein interaction 3) all variants within combined regions from multiple genes responsible for coding the same protein domain (i.e. protein families). A conventional collapsing analysis using gene coordinates was also undertaken for comparison. We used UK10K sequence data and investigated associations between regions of variants and lipid traits using the sequence kernel association test (SKAT). Results: We observed no strong evidence of association between regions of variants based on Pfam domain definitions and lipid traits. Quantile-Quantile plots illustrated that the overall distributions of p-values from the protein domain analyses were comparable to that of a conventional gene-based approach. Deviations from this distribution suggested that collapsing by either protein domain or gene definitions may be favourable depending on the trait analysed. Conclusion: We have collapsed rare variants together using protein domain and family coordinates to present an alternative approach over collapsing across conventionally used gene-based regions. Although no strong evidence of association was detected in these analyses, future studies may still find value in adopting these approaches to detect previously unidentified association signals. [ABSTRACT FROM AUTHOR]

Background: The success of collapsing methods which investigate the combined effect of rare variants on complex traits has so far been limited. The manner in which variants within a gene are selected prior to analysis has a crucial impact on this success, which has resulted in analyses conventionally filtering variants according to their consequence. This study investigates whether an alternative approach to filtering, using annotations from recently developed bioinformatics tools, can aid these types of analyses in comparison to conventional approaches. Methods & Results: We conducted a candidate gene analysis using the UK10K sequence and lipids data, filtering according to functional annotations using the resource CADD (Combined Annotation-Dependent Depletion) and contrasting results with ‘nonsynonymous’ and ‘loss of function’ consequence analyses. Using CADD allowed the inclusion of potentially deleterious intronic variants, which was not possible when filtering by consequence. Overall, different filtering approaches provided similar evidence of association, although filtering according to CADD identified evidence of association between ANGPTL4 and High Density Lipoproteins (P = 0.02, N = 3,210) which was not observed in the other analyses. We also undertook genome-wide analyses to determine how filtering in this manner compared to conventional approaches for gene regions. Results suggested that filtering by annotations according to CADD, as well as other tools known as FATHMM-MKL and DANN, identified association signals not detected when filtering by variant consequence and vice versa. Conclusion: Incorporating variant annotations from non-coding bioinformatics tools should prove to be a valuable asset for rare variant analyses in the future. Filtering by variant consequence is only possible in coding regions of the genome, whereas utilising non-coding bioinformatics annotations provides an opportunity to discover unknown causal variants in non-coding regions as well. This should allow studies to uncover a greater number of causal variants for complex traits and help elucidate their functional role in disease. [ABSTRACT FROM AUTHOR]

DNA topoisomerases are enzymes responsible for the relaxation of DNA torsional strain, as well as for the untangling of DNA duplexes after replication, and are important cancer drug targets. One class of topoisomerase inhibitors, “poisons”, binds to the transient enzyme-DNA complex which occurs during the mechanism of action, and inhibits the religation of DNA. This ultimately leads to the accumulation of DNA double strand breaks and cell death. Different types of topoisomerases occur in human cells and several poisons of topoisomerase I and II are widely used clinically. However, their use is compromised by a variety of side effects. Recent studies confirm that the inhibition of the α-isoform of topoisomerase II is responsible for the cytotoxic effect, whereas the inhibition of the β-isoform leads to development of adverse drug reactions. Thus, the discovery of agents selective for topoisomerase IIα is an important strategy for the development of topoisomerase II poisons with improved clinical profiles. Here, we present a computer-aided drug design study leading to the identification of structurally novel topoisomerase IIα poisons. The study combines ligand- and structure-based drug design methods including pharmacophore models, homology modelling, docking, and virtual screening of the National Cancer Institute compound database. From the 8 compounds identified from the computational work, 6 were tested for their capacity to poison topoisomerase II in vitro: 4 showed selective inhibitory activity for the α- over the β-isoform and 3 of these exhibited cytotoxic activity. Thus, our study confirms the applicability of computer-aided methods for the discovery of novel topoisomerase II poisons, and presents compounds which could be investigated further as selective topoisomerase IIα inhibitors. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is a debilitating metabolic disorder and remains a significant threat to public health. Herbal medicines have been proven to be effective anti-diabetic agents compared to synthetic drugs in terms of side effects. However, the complexity in their chemical constituents and mechanism of action, hinder the effort to discover novel anti-diabetic drugs. Hence, understanding the biological and chemical basis of pharmacological action of phytochemicals is essential for the discovery of potential anti-diabetic drugs. Identifying important active compounds, their protein targets and the pathways involved in diabetes would serve this purpose. In this context, the present study was aimed at exploring the mechanism of action of anti-diabetic plants phytochemicals through network and chemical-based approaches. This study also involves a focused and constructive strategy for preparing new effective anti-diabetic formulations. Further, a protocol for target enrichment was proposed, to identify novel protein targets for important active compounds. Therefore, the successive use of network analysis combined with target enrichment studies would accelerate the discovery of potential anti-diabetic phytochemicals. [ABSTRACT FROM AUTHOR]

Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60–0.69 and 0.61–0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates. [ABSTRACT FROM AUTHOR]

Quantum mechanical (QM) calculations have been used to predict the binding affinity of a set of ligands towards HIV-1 RT associated RNase H (RNH). The QM based chelation calculations show improved binding affinity prediction for the inhibitors compared to using an empirical scoring function. Furthermore, full protein fragment molecular orbital (FMO) calculations were conducted and subsequently analysed for individual residue stabilization/destabilization energy contributions to the overall binding affinity in order to better understand the true and false predictions. After a successful assessment of the methods based on the use of a training set of molecules, QM based chelation calculations were used as filter in virtual screening of compounds in the ZINC database. By this, we find, compared to regular docking, QM based chelation calculations to significantly reduce the large number of false positives. Thus, the computational models tested in this study could be useful as high throughput filters for searching HIV-1 RNase H active-site molecules in the virtual screening process. [ABSTRACT FROM AUTHOR]