Biology subjects

Wenric, S.

Publications and source records attributed to Wenric, S..

5 recordsLinked to original sources

bioRxiv2021

Privacy Preserving RNA-Model Validation Across Laboratories

Reproducibility of results obtained using RNA data across labs remains a major hurdle in cancer research. Often, molecular predictors trained on one dataset cannot be applied to another due to differences in RNA library preparation and quantification. While current RNA correction algorithms may overcome these differences, they require access to all patient-level data, which necessitates the sharing of training data for predictors when sharing predictors. Here, we describe SpinAdapt, an unsupervised RNA correction algorithm that enables the transfer of molecular models without requiring access to patient-level data. It computes data corrections only via aggregate statistics of each dataset, thereby maintaining patient data privacy. Furthermore, SpinAdapt can correct new samples, thereby enabling evaluation of validation cohorts. Despite an inherent tradeoff between privacy and performance, SpinAdapt outperforms current correction methods that require patient-level data access. We expect this novel correction paradigm to enhance research reproducibility and patient privacy. Finally, SpinAdapt lays a mathematical framework that can be extended to other -omics modalities.

bioinformatics↗

bioRxiv2019

Towards a Fine-scale Population Health Monitoring System

Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens specific to sub-populations. Here we propose a framework for repurposing data from Electronic Health Records (EHRs) in concert with genomic data to explore enrichment of disease within sub-populations. Using data from a diverse biobank in New York City, we genetically identified 17 sub-populations, and noted the presence of genetic founder effects in 7. By then linking community membership to the EHR, we were able to identify over 600 health outcomes that were statistically enriched within a specific population, with many representing known associations, and many others being novel. This work reinforces the utility of linking genomic data to EHRs, and provides a framework towards fine-scale monitoring of population health.

genomics↗

bioRxiv2019

Rapid response to the Alpha-1 Adrenergic Agent Phenylephrine in the Perioperative Period is Impacted by Genomics and Ancestry

BackgroundThe emergence of genomic data in biobanks and health systems offers new ways to derive medically important phenotypes, including acute phenotypes that occur during in-patient clinical care. We hypothesized that there is a genetic underpinning to the magnitude of the response to phenylephrine, an 1-adrenergic receptor agonist commonly used to treat hypotension during anesthesia and surgery.\n\nMethodsWe quantified the response to phenylephrine by determining the delta between the minimum blood pressure (BP) within five minutes before and the maximum BP within five minutes after bolus administration. We then performed a genome-wide association study (GWAS) adjusted for genetic ancestry, demographics, and relevant clinical covariates to investigate genetic factors underlying individual differences systolic BP response to phenylephrine ({Delta}SBP), as well as mean arterial pressure ({Delta}MAP) and diastolic BP ({Delta}DBP), for both the entire study cohort as well as for each of 3 ancestry sub-cohorts; European American(EA), African American(AA), and Hispanic American(HA).\n\nResults4,317 patients met inclusion criteria, of which 3,699 were genotyped. Average {Delta}BP values over the entire cohort were {Delta}SBP=17(+-25) mmHg, {Delta}MAP=14(+-18) mmHg, {Delta}DBP=11(+-14) mmHg. The largest difference between populations was observed for {Delta}SBP ({Delta}SBPEA=20(+-24) mmHg; {Delta}SBPHA=16(+-25) mmHg; {Delta}SBPAA=15(+-25) mmHg). The differences remained after adjusting for clinical covariates and ancestry (EA vs. HA: {Delta}SBP, p<0.032;{Delta}MAP, p<0.021;{Delta}DBP,p<0.008);(EA vs. AA:{Delta}SBP,p<5.13x10-5;{Delta}MAP,p<2.1x10-4;{Delta}DBP,p<3.3x10-4). GWAS revealed significant associations between loci and BP response in 5 different genome regions (p<5x10-8) in the entire cohort, and suggestive associations in 2 different regions in EAs (p<6x10-8,p<7x10-8). We observed non-random enrichment in association with SBP drug response in 165 loci previously reported to be associated with systolic blood pressure. Finally, we discovered rare variants, rs188427942 and rs147664194 present at [~]1% in EAs and rs146535276 present at [~]1% in AAs respectively, where patients carrying one copy of these variants show no response to phenylephrine.\n\nConclusionsIt is possible to derive a quantitative phenotype suited for comparative statistics and genome-wide association studies from routinely collected perioperative data. There are population differences in rapid response to phenylephrine, large effect alleles and novel genes affecting pharmaceutical response, and phenylephrine non-responders, with implications for personalized treatment during surgery.

genomics↗

bioRxiv2018

Using supervised learning methods for gene selection in RNA-Seq case-control studies

Whole transcriptome studies typically yield large amounts of data, with expression values for all genes or transcripts of the genome. The search for genes of interest in a particular study setting can thus be a daunting task, usually relying on automated computational methods. Moreover, most biological questions imply that such a search should be performed in a multivariate setting, to take into account the inter-genes relationships.\n\nDifferential expression analysis commonly yields large lists of genes deemed significant, even after adjustment for multiple testing, making the subsequent study possibilities extensive.\n\nHere, we explore the use of supervised learning methods to rank large ensembles of genes defined by their expression values measured with RNA-Seq in a typical 2 classes sample set. First, we use one of the variable importance measures generated by the random forests classification algorithm as a metric to rank genes. Second, we define the EPS (extreme pseudo-samples) pipeline, making use of VAEs (Variational Autoencoders) and regressors to extract a ranking of genes while leveraging the feature space of both virtual and comparable samples.\n\nWe show that, on 12 cancer RNA-Seq data sets ranging from 323 to 1210 samples, using either a random forests based gene selection method or the EPS pipeline outperforms differential expression analysis for 9 and 8 out of the 12 datasets respectively, in terms of identifying subsets of genes associated with survival.\n\nThese results demonstrate the potential of supervised learning-based gene selection methods in RNA-Seq studies and highlight the need to use such multivariate gene selection methods alongside the widely used differential expression analysis.

bioinformatics↗

bioRxiv2017

Transcriptome wide analysis of natural antisense transcripts shows their potential role in breast cancer.

Non-coding RNAs (ncRNA) represent at least 1/5 of the mammalian transcript amount, and about 90% of the genome length is actively transcribed. Many ncRNAs have been demonstrated to play a role in cancer. Among them, natural antisense transcripts (NAT) are RNA sequences which are complementary and overlapping to those of protein-coding transcripts (PCT). NATs were punctually described as regulating gene expression, and are expected to act more frequently in cis than other ncRNAs that commonly function in trans. In this work, 22 breast cancers expressing estrogen receptors and their paired healthy tissues were analyzed by strand-specific RNA sequencing. To highlight the potential role of NATs in gene regulations occurring in breast cancer, three different gene extraction methods were used: differential expression analysis of NATs between tumor and healthy tissues, differential correlation analysis of paired NAT/PCT between tumor and healthy tissues, and NAT/PCT read count ratio variation between tumor and healthy tissues. Each of these methods yielded lists of NAT/PCT pairs that were demonstrated to be enriched in survival-associated genes on an independent cohort (TCGA). This work allows to highlight NAT lists that display a strong potential to affect the expression of genes involved in the breast cancer pathology.

genomics↗