Imperial College London
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DrJohnLees

Faculty of Medicine, School of Public Health

Visiting Researcher
 
 
 
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Contact

 

+44 (0)20 7594 2939j.lees Website

 
 
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Location

 

UG4Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Lees:2019:10.1101/852426,
author = {Lees, JA and Tien, Mai T and Galardini, M and Wheeler, NE and Corander, J},
doi = {10.1101/852426},
title = {Improved inference and prediction of bacterial genotype-phenotype associations using pangenome-spanning regressions},
url = {http://dx.doi.org/10.1101/852426},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:title>ABSTRACT</jats:title><jats:p>Discovery of influential genetic variants and prediction of phenotypes such as antibiotic resistance are becoming routine tasks in bacterial genomics. Genome-wide association study (GWAS) methods can be applied to study bacterial populations, with a particular emphasis on alignment-free approaches, which are necessitated by the more plastic nature of bacterial genomes. Here we advance bacterial GWAS by introducing a computationally scalable joint modeling framework, where genetic variants covering the entire pangenome are compactly represented by unitigs, and the model fitting is achieved using elastic net penalization. In contrast to current leading GWAS approaches, which test each genotype-phenotype association separately for each variant, our joint modelling approach is shown to lead to increased statistical power while maintaining control of the false positive rate. Our inference procedure also delivers an estimate of the narrow-sense heritability, which is gaining considerable interest in studies of bacteria. Using an extensive set of state-of-the-art bacterial population genomic datasets we demonstrate that our approach performs accurate phenotype prediction, comparable to popular machine learning methods, while retaining both interpretability and computational efficiency. We expect that these advances will pave the way for the next generation of high-powered association and prediction studies for an increasing number of bacterial species.</jats:p>
AU  - Lees,JA
AU  - Tien,Mai T
AU  - Galardini,M
AU  - Wheeler,NE
AU  - Corander,J
DO  - 10.1101/852426
PY  - 2019///
TI  - Improved inference and prediction of bacterial genotype-phenotype associations using pangenome-spanning regressions
UR  - http://dx.doi.org/10.1101/852426
ER  -
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