Dataset Information

Prewas: data pre-processing for more informative bacterial GWAS.

ABSTRACT: While variant identification pipelines are becoming increasingly standardized, less attention has been paid to the pre-processing of variants prior to their use in bacterial genome-wide association studies (bGWAS). Three nuances of variant pre-processing that impact downstream identification of genetic associations include the separation of variants at multiallelic sites, separation of variants in overlapping genes, and referencing of variants relative to ancestral alleles. Here we demonstrate the importance of these variant pre-processing steps on diverse bacterial genomic datasets and present prewas, an R package, that standardizes the pre-processing of multiallelic sites, overlapping genes, and reference alleles before bGWAS. This package facilitates improved reproducibility and interpretability of bGWAS results. prewas enables users to extract maximal information from bGWAS by implementing multi-line representation for multiallelic sites and variants in overlapping genes. prewas outputs a binary SNP matrix that can be used for SNP-based bGWAS and will prevent the masking of minor alleles during bGWAS analysis. The optional binary gene matrix output can be used for gene-based bGWAS, which will enable users to maximize the power and evolutionary interpretability of their bGWAS studies. prewas is available for download from GitHub.

SUBMITTER: Saund K

PROVIDER: S-EPMC7371116 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

prewas: data pre-processing for more informative bacterial GWAS.

Saund Katie K Lapp Zena Z Thiede Stephanie N SN Pirani Ali A Snitkin Evan S ES

Microbial genomics 20200420 5

While variant identification pipelines are becoming increasingly standardized, less attention has been paid to the pre-processing of variants prior to their use in bacterial genome-wide association studies (bGWAS). Three nuances of variant pre-processing that impact downstream identification of genetic associations include the separation of variants at multiallelic sites, separation of variants in overlapping genes, and referencing of variants relative to ancestral alleles. Here we demonstrate t ...[more]

PMID: 32310745

Similar Datasets

Project description:BACKGROUND:EQ-5D is generic measure of health-related quality of life. Studies using EQ-5D generate ordinal data that are interpreted as categories ordered by severity. New analytic approaches taking into account the ordinal nature of the health dimension severity and leading to a better interpretation of EQ-5D data are needed to better elucidate differences in health-related quality of life. We propose utilizing the Improved RIDIT statistical method to analyze EQ-5D outcomes. METHODS:556 Moroccan participants aged over 18 years representing four chronic diseases: back pain (n = 158), renal insufficiency (n = 56), diabetes (n = 82) or hypertension (n = 80) and healthy subjects (n = 180). All participants received the two EQ-5D versions. Two other published data sets were included. The first was extracted from a diabetic Spain study and the second was extracted from a clinical trial study. The Improved RIDIT analyses were carried out using an R statistic program we developed. RESULTS:Applying the Improved RIDIT on the EQ-5D data allowed estimating for the first time the ordinal odds, the Absolute Risk Reduction (ARR) or the Absolute Risk Increase (ARI) and the Number Needed to Treat. The ARI values estimated for Moroccan patients showed that (i) hypertension increased anxiety/depression by 66% and reduced mobility by 65%; (ii) back pain increased pain/discomfort by 69%; (iii) renal insufficiency impacts mobility (ARI = 57%, oddsordinal = 9.95) and usual activities (ARI = 44%, oddsordinal = 6.41) and (iv) diabetes acts only on anxiety/depression (ARI = 50%, oddsordinal = 4.8). Also, we demonstrated that the approach works well in clinical trials. CONCLUSIONS:Improved RIDIT provides more intuitive and informative interpretation of the EQ-5D data by (1) taking into account the level severity; estimating (2) the odds ordinal, (3) the ARR/ARI and the NNT; (4) analyzing the five dimensions of the EQ-5D separately, which gives clinical teams more precision in understanding the treatment/pathology impacts on the health status and completes the EQ-5D data analysis based on score utilities.

Dataset Information

Prewas: data pre-processing for more informative bacterial GWAS.

Publications

prewas: data pre-processing for more informative bacterial GWAS.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets