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

BACKGROUND: With the advent of affordable and comprehensive sequencing technologies, access to molecular genetics for clinical diagnostics and research applications is increasing. However, variant interpretation remains challenging, and tools that close the gap between data generation and data interpretation are urgently required. Here we present a transferable approach to help address the limitations in variant annotation. METHODS: We develop a network of Bayesian logistic regression models that integrate multiple lines of evidence to evaluate the probability that a rare variant is the cause of an individual's disease. We present models for genes causing inherited cardiac conditions, though the framework is transferable to other genes and syndromes. RESULTS: Our models report a probability of pathogenicity, rather than a categorisation into pathogenic or benign, which captures the inherent uncertainty of the prediction. We find that gene- and syndrome-specific models outperform genome-wide approaches, and that the integration of multiple lines of evidence performs better than individual predictors. The models are adaptable to incorporate new lines of evidence, and results can be combined with familial segregation data in a transparent and quantitative manner to further enhance predictions. Though the probability scale is continuous, and innately interpretable, performance summaries based on thresholds are useful for comparisons. Using a threshold probability of pathogenicity of 0.9, we obtain a positive predictive value of 0.999 and sensitivity of 0.76 for the classification of variants known to cause long QT syndrome over the three most important genes, which represents sufficient accuracy to inform clinical decision-making. A web tool APPRAISE [http://www.cardiodb.org/APPRAISE] provides access to these models and predictions. CONCLUSIONS: Our Bayesian framework provides a transparent, flexible and robust framework for the analysis and interpretation of rare genetic variants. Models tailored to specific genes outperform genome-wide approaches, and can be sufficiently accurate to inform clinical decision-making

Melanoma epidemiology, prognosis and trends in Latvia

Background Melanoma incidence and mortality rates are increasing worldwide within the white population. Clinical and histological factors have been usually used for the prognosis and assessment of the risk for melanoma. Objectives The aim of the study was to describe the clinical and histopathological features of the cutaneous melanoma (CM) in the Latvian population, to test the association between melanoma features and patient survival, and to assess the time trends for melanoma incidence. Methods We undertook a descriptive, retrospective analysis of archive data of 984 melanoma patients treated at the largest oncological hospital of Latvia, Riga East University Hospital Latvian Oncology Centre (LOC), between 1998 and 2008. Cox proportional hazards model was used to analyse patient survival and autoregressive models were applied to detect trends in melanoma incidence over time for various categories of melanoma. Results The study showed a significant ascending trend in melanoma incidence in Latvia during the time period from 1998 to 2008 (ß = 1.83, 95% CI = 1.15-2.91, P = 0.011). Nodular melanoma was the most common tumour subtype with a frequency of 39.2%. Ulceration was present in 45.2% of melanomas. The mean Breslow thickness was 6.0 mm (6.8 mm) and no significant decline in median Breslow thickness was observed during the study period (P = 0.609). A better overall prognosis was detected for females in comparison with males (HR = 1.49; 95% CI = 1.22-1.81; P < 0.001). Conclusions There is a steady increase in melanoma incidence in Latvia with the majority of melanomas diagnosed at late stages with poor prognosis for survival.publishersversionPeer reviewe

Casual Compressive Sensing for Gene Network Inference

We propose a novel framework for studying causal inference of gene interactions using a combination of compressive sensing and Granger causality techniques. The gist of the approach is to discover sparse linear dependencies between time series of gene expressions via a Granger-type elimination method. The method is tested on the Gardner dataset for the SOS network in E. coli, for which both known and unknown causal relationships are discovered

Sequential Logic Model Deciphers Dynamic Transcriptional Control of Gene Expressions

Cellular signaling involves a sequence of events from ligand binding to membrane receptors through transcription factors activation and the induction of mRNA expression. The transcriptional-regulatory system plays a pivotal role in the control of gene expression. A novel computational approach to the study of gene regulation circuits is presented here.Based on the concept of finite state machine, which provides a discrete view of gene regulation, a novel sequential logic model (SLM) is developed to decipher control mechanisms of dynamic transcriptional regulation of gene expressions. The SLM technique is also used to systematically analyze the dynamic function of transcriptional inputs, the dependency and cooperativity, such as synergy effect, among the binding sites with respect to when, how much and how fast the gene of interest is expressed. expression and additional activities of binding sites are required. Further analyses suggest detailed mechanism of R switch activity where indirect dependency occurs in between UI activity and R switch during specification to differentiation stage. is a promising step for further application of the proposed method

Current approaches to gene regulatory network modelling

Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model

Platelet function is modified by common sequence variation in megakaryocyte super enhancers

Linking non-coding genetic variants associated with the risk of diseases or disease-relevant traits to target genes is a crucial step to realize GWAS potential in the introduction of precision medicine. Here we set out to determine the mechanisms underpinning variant association with platelet quantitative traits using cell type-matched epigenomic data and promoter long-range interactions. We identify potential regulatory functions for 423 of 565 (75%) non-coding variants associated with platelet traits and we demonstrate, through ex vivo and proof of principle genome editing validation, that variants in super enhancers play an important role in controlling archetypical platelet functions

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

10.1186/s13073-014-0120-4Genome Medicine71

The allelic landscape of human blood cell trait variation and links to common complex disease

Many common variants have been associated with hematological traits, but identification of causal genes and pathways has proven challenging. We performed a genome-wide association analysis in the UK Biobank and INTERVAL studies, testing 29.5 million genetic variants for association with 36 red cell, white cell, and platelet properties in 173,480 European-ancestry participants. This effort yielded hundreds of low frequency (&lt;5%) and rare (&lt;1%) variants with a strong impact on blood cell phenotypes. Our data highlight general properties of the allelic architecture of complex traits, including the proportion of the heritable component of each blood trait explained by the polygenic signal across different genome regulatory domains. Finally, through Mendelian randomization, we provide evidence of shared genetic pathways linking blood cell indices with complex pathologies, including autoimmune diseases, schizophrenia, and coronary heart disease and evidence suggesting previously reported population associations between blood cell indices and cardiovascular disease may be non-causal