Dataset Information

BetaScan2: Standardized Statistics to Detect Balancing Selection Utilizing Substitution Data.

ABSTRACT: Long-term balancing selection results in a build-up of alleles at similar frequencies and a deficit of substitutions when compared with an outgroup at a locus. The previously published ?(1) statistics detect balancing selection using only polymorphism data. We now propose the ?(2) statistic which detects balancing selection using both polymorphism and substitution data. In addition, we derive the variance of all ? statistics, allowing for their standardization and thereby reducing the influence of parameters which can confound other selection tests. The standardized ? statistics outperform existing summary statistics in simulations, indicating ? is a well-powered and widely applicable approach for detecting balancing selection. We apply the ?(2) statistic to 1000 Genomes data and report two missense mutations with high ? scores in the ACSBG2 gene. An implementation of all ? statistics and their standardization are available in the BetaScan2 software package at https://github.com/ksiewert/BetaScan.

SUBMITTER: Siewert KM

PROVIDER: S-EPMC7058154 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

BetaScan2: Standardized Statistics to Detect Balancing Selection Utilizing Substitution Data.

Siewert Katherine M KM Voight Benjamin F BF

Genome biology and evolution 20200201 2

Long-term balancing selection results in a build-up of alleles at similar frequencies and a deficit of substitutions when compared with an outgroup at a locus. The previously published β(1) statistics detect balancing selection using only polymorphism data. We now propose the β(2) statistic which detects balancing selection using both polymorphism and substitution data. In addition, we derive the variance of all β statistics, allowing for their standardization and thereby reducing the influence ...[more]

PMID: 32011695

Similar Datasets

Project description:BackgroundAtheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study.MethodsThe study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators.ResultsAfter the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001).ConclusionThe systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.

Dataset Information

BetaScan2: Standardized Statistics to Detect Balancing Selection Utilizing Substitution Data.

Publications

BetaScan2: Standardized Statistics to Detect Balancing Selection Utilizing Substitution Data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets