Biology subjects

Sebastiani, P.

Publications and source records attributed to Sebastiani, P..

23 records · Page 2Linked to original sources

bioRxiv2019

Estimating the Relative Probability of Direct Transmission between Infectious Disease Patients

BackgroundEstimating infectious disease parameters such as the serial interval (time between symptom onset in primary and secondary cases) and reproductive number (average number of secondary cases produced by a primary case) are important to understand infectious disease dynamics. Many estimation methods require linking cases by direct transmission, a difficult task for most diseases. MethodsUsing a subset of cases with detailed genetic or contact investigation data to develop a training set of probable transmission events, we build a model to estimate the relative transmission probability for all case-pairs from demographic, spatial and clinical data. Our method is based on naive Bayes, a machine learning classification algorithm which uses the observed frequencies in the training dataset to estimate the probability that a pair is linked given a set of covariates. ResultsIn simulations we find that the probabilities estimated using genetic distance between cases to define training transmission events are able to distinguish between truly linked and unlinked pairs with high accuracy (area under the receiver operating curve value of 95%). Additionally only a subset of the cases, 10-50% depending on sample size, need to have detailed genetic data for our method to perform well. We show how these probabilities can be used to estimate the average effective reproductive number and apply our method to a tuberculosis outbreak in Hamburg, Germany. ConclusionsOur method is a novel way to infer transmission dynamics in any dataset when only a subset of cases has rich contact investigation and/or genetic data. KEY MESSAGESO_LIThis method provides a way to calculate the relative probability that two infectious disease patients are connected by direct transmission using clinical, demographic, geographic, and genetic characteristics. C_LIO_LIWe use a naive Bayes, a machine learning technique to estimate these probabilities using a training set of probable links defined by contact investigation or pathogen WGS data on a subset of cases. C_LIO_LIThese probabilities can be used to explore possible transmission chains, rule out transmission events, and estimate the reproductive number. C_LI

epidemiology↗

bioRxiv2019

iEDGE: integration of Epi-DNA and Gene Expression and applications to the discovery of somatic copy number-associated drivers in cancer

The emergence of large-scale multi-omics data warrants method development for data integration. Genomic studies from cancer patients have identified epigenetic and genetic regulators - such as methylation marks, somatic mutations, and somatic copy number alterations (SCNAs), among others - as predictive features of cancer outcome. However, identification of \"driver genes\" associated with a given alteration remains a challenge. To this end, we developed a computational tool, iEDGE, to model cis and trans effects of (epi-)DNA alterations and identify potential cis driver genes, where cis and trans genes denote those genes falling within and outside the genomic boundaries of a given (epi-)genetic alteration, respectively.\n\nFirst, iEDGE identifies the cis and trans genes associated with the presence/absence of a particular epi-DNA alteration across samples. Tests of statistical mediation are then performed to determine the cis genes predictive of the trans gene expression. Finally, cis and trans effects are annotated by pathway enrichment analysis to gain insights into the underlying regulatory networks.\n\nWe used iEDGE to perform integrative analysis of SCNAs and gene expression data from breast cancer and 18 additional cancer types included in The Cancer Genome Atlas (TCGA). Notably, cis gene drivers identified by iEDGE were found to be significantly enriched for known driver genes from multiple compendia of validated oncogenes and tumor suppressors, suggesting that the remainder are of equal importance. Furthermore, predicted drivers were enriched for functionally relevant cancer genes with amplification-driven dependencies, which are of potential prognostic and therapeutic value. All the analyses results are accessible at https://montilab.bu.edu/iEDGE.

bioinformatics↗

bioRxiv2018

DNA methylation modules associate with incident cardiovascular disease and cumulative risk factor exposure

1 AbstractO_ST_ABSBackgroundC_ST_ABSEpigenome-wide association studies using DNA methylation have the potential to uncover novel biomarkers and mechanisms of cardiovascular disease (CVD) risk. However, the direction of causation for these associations is not always clear, and investigations to-date have generally failed to replicate at the level of individual loci. Here, we undertook module- and region-based DNA methylation analyses of incident CVD in the Womens Health Initiative (WHI) and Framingham Heart Study Offspring Cohort (FHS) in order to find more robust epigenetic biomarkers for cardiovascular risk.\n\nMethods and FindingsWe applied weighted gene correlation network analysis (WGCNA) and the Comb-p algorithm to find methylation modules and regions associated with incident CVD in the WHI dataset. We discovered two modules whose activation correlated with CVD risk and replicated across cohorts. One of these modules was enriched for development-related processes and overlaps strongly with epigenetic aging sites. For the other, we showed preliminary evidence for monocyte-specific effects and statistical links to cumulative exposure to traditional cardiovascular risk factors. Additionally, we found three regions (associated with the genes SLC9A1, SLC1A5, and TNRC6C) whose methylation associates with CVD risk.\n\nConclusionsIn sum, we present several epigenetic associations with incident CVD that reveal disease mechanisms related to development and monocyte biology. Furthermore, we show that epigenetic modules may act as a molecular readout of cumulative cardiovascular risk factor exposure, with implications for the improvement of clinical risk prediction.

genomics↗

bioRxiv2018

Cis- and Trans-Acting Expression Quantitative Trait Loci Differentially Regulate Gamma-Globin Gene Expression

Genetic association studies have detected two trans-acting quantitative trait loci (QTL) on chromosomes 2, 6 and one cis-acting QTL on chromosome 11 that were associated with fetal hemoglobin (HbF) levels. In these studies, HbF was expressed as a percentage of total hemoglobin or the number of erythrocytes that contain HbF (F-cells). As the{gamma} -globin chains of HbF are encoded by two non-allelic genes (HBG2, HBG1) that are expressed at different levels we used normalized gene expression and genotype data from The Genotype-Tissue Expression (GTEx)-project to study the effects of cis- and trans-acting HbF expression or eQTL. This allowed us to examine mRNA expression of HBG2 and HBG1individually. In addition to studying eQTL for globin genes we examined genes co-expressed with HBG1, studied upstream regulators of HBG1 co-expressed genes and performed a correlation analysis between HBG2 and HBG1 and known HbF regulators. Our results suggest differential effect of cis and trans-acting QTL on HBG and HBG1 expression. Trans-acting eQTLs have the same magnitude of effect on the expression of both HBG2 and HBG1 while the sole cis-acting eQTL affected only HBG2. Furthermore, the analysis of upstream regulators and the correlation analysis suggested that BCL2L1 might be a new potential trans-acting HbF activator. HbF is the major modulator of the phenotype of sickle cell anemia and {beta} thalassemia. Depending on the effect size, modification of trans-acting elements might have a greater impact on HbF levels than cis-acting elements alone.

genetics↗

bioRxiv2017

CaDrA: A computational framework for performing candidate driver analyses using binary genomic features

Identifying complementary genetic drivers of a given phenotypic outcome is a challenging task that is important to gaining new biological insight and discovering targets for disease therapy. Existing methods aimed at achieving this task lack analytical flexibility. We developed Candidate Driver Analysis or CaDrA, a framework to identify functionally-relevant subsets of binary genomic features that, together, are associated with a specific outcome of interest. We evaluate CaDrAs sensitivity and specificity for typically-sized multi-omic datasets, and demonstrate CaDrAs ability to identify both known and novel drivers of oncogenic activity in cancer cell lines and primary tumors.

bioinformatics↗