|
Activity Number:
|
244
- Statistical methods for microbiome data analysis and beyond
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
|
|
Sponsor:
|
Section on Statistics in Genomics and Genetics
|
|
Abstract #318433
|
|
|
Title:
|
A Comparison of Selection Identification Methods in Viral Genomes Using a Range of Statistical Techniques
|
|
Author(s):
|
Carly Elizabeth Middleton* and Laura Kubatko
|
|
Companies:
|
The Ohio State University and The Ohio State University
|
|
Keywords:
|
SARS-CoV-2;
GISAID;
transition-transversion ratio;
Bayesian inference
|
|
Abstract:
|
Understanding which regions of a genome are under selection is crucially important in enabling effective responses to rapidly-evolving viruses. The need for such tools has been highlighted by the recent Covid-19 pandemic. We compare methods for identifying selection given a collection of viral genomes. The methods we consider range from those based on the estimated transition-transversion ratio along a phylogeny to more recently developed methods based on allele frequencies. In all cases, we compare the methods with respect to their statistical power when applied in sliding windows along the genome. The methods are applied to empirical data for SARS-CoV-2 obtained from the public GISAID repository.
|
Authors who are presenting talks have a * after their name.
