Genetic variability and population diversity of the human SLCO (OATP) transporter family.

Organic anion transporting polypeptides (OATP) encoded by the SLCO gene family constitute clinically important transporters involved in the disposition of endogenous compounds and many commonly prescribed drugs, including statins, methotrexate and antihypertensive medications. Common genetic polymorphisms in SLCO genes are known to affect OATP function and modulate efficacy and safety of OATP substrates. However, current frequency data of these variants and haplotypes is generally based on few rather heterogenous populations of relatively small sample size. Furthermore, the genetic variability beyond these selected pharmacogenetic biomarkers has not been systematically analyzed. Here, we provide a global consolidated map of SLCO variability by leveraging fully compatible Next Generation Sequencing data from 138,632 unrelated individuals across seven major human populations. Overall, we find 9811 exonic single nucleotide variants and 155 copy number variations of which 99.3% were rare with frequencies <1%. Using orthogonal computational functionality predictors optimized for pharmacogenetic assessments, we find that four out of five individuals carry at least one deleterious variant in an SLCO transporter gene and rare variants contribute 23% to the genetically encoded functional variability. Moreover, 74.9% of all variants were found to be population-specific with important consequences for population-specific genotyping strategies and precision public health approaches. Combined, our analyses provide the most comprehensive data set of SLCO variability published to date and incentivize the integration of comprehensive NGS-based genotyping into personalized predictions of OATP substrate disposition.

Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA: a deep learning approach.

BACKGROUND: Since the US reported its first COVID-19 case on January 21, 2020, the science community has been applying various techniques to forecast incident cases and deaths. To date, providing an accurate and robust forecast at a high spatial resolution has proved challenging, even in the short term. METHOD: Here we present a novel multi-stage deep learning model to forecast the number of COVID-19 cases and deaths for each US state at a weekly level for a forecast horizon of 1-4 weeks. The model is heavily data driven, and relies on epidemiological, mobility, survey, climate, demographic, and SARS-CoV-2 variant frequencies data. We implement a rigorous and robust evaluation of our model-specifically we report on weekly performance over a one-year period based on multiple error metrics, and explicitly assess how our model performance varies over space, chronological time, and different outbreak phases. FINDINGS: The proposed model is shown to consistently outperform the CDC ensemble model for all evaluation metrics in multiple spatiotemporal settings, especially for the longer-term (3 and 4 weeks ahead) forecast horizon. Our case study also highlights the potential value of variant frequencies data for use in short-term forecasting to identify forthcoming surges driven by new variants. INTERPRETATION: Based on our findings, the proposed forecasting framework improves upon the available state-of-the-art forecasting tools currently used to support public health decision making with respect to COVID-19 risk. FUNDING: This work was funded the NSF Rapid Response Research (RAPID) grant Award ID 2108526 and the CDC Contract #75D30120C09570.

Variant profiling of evolving prokaryotic populations.

Genomic heterogeneity of bacterial species is observed and studied in experimental evolution experiments and clinical diagnostics, and occurs as micro-diversity of natural habitats. The challenge for genome research is to accurately capture this heterogeneity with the currently used short sequencing reads. Recent advances in NGS technologies improved the speed and coverage and thus allowed for deep sequencing of bacterial populations. This facilitates the quantitative assessment of genomic heterogeneity, including low frequency alleles or haplotypes. However, false positive variant predictions due to sequencing errors and mapping artifacts of short reads need to be prevented. We therefore created VarCap, a workflow for the reliable prediction of different types of variants even at low frequencies. In order to predict SNPs, InDels and structural variations, we evaluated the sensitivity and accuracy of different software tools using synthetic read data. The results suggested that the best sensitivity could be reached by a union of different tools, however at the price of increased false positives. We identified possible reasons for false predictions and used this knowledge to improve the accuracy by post-filtering the predicted variants according to properties such as frequency, coverage, genomic environment/localization and co-localization with other variants. We observed that best precision was achieved by using an intersection of at least two tools per variant. This resulted in the reliable prediction of variants above a minimum relative abundance of 2%. VarCap is designed for being routinely used within experimental evolution experiments or for clinical diagnostics. The detected variants are reported as frequencies within a VCF file and as a graphical overview of the distribution of the different variant/allele/haplotype frequencies. The source code of VarCap is available at https://github.com/ma2o/VarCap. In order to provide this workflow to a broad community, we implemeted VarCap on a Galaxy webserver, which is accessible at http://galaxy.csb.univie.ac.at.