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Classic genome-wide association methods are unlikely to identify causal variants in strongly clonal microbial populations

Peter E. Chen, View ORCID ProfileB. Jesse Shapiro
doi: https://doi.org/10.1101/2021.06.30.450606
Peter E. Chen
aDépartement de sciences biologiques, Université de Montréal, Montréal, QC H3C 3J7, Canada.
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  • For correspondence: peter.ethan@gmail.com peter.ethan@gmail.com
B. Jesse Shapiro
aDépartement de sciences biologiques, Université de Montréal, Montréal, QC H3C 3J7, Canada.
bDepartment of Microbiology & Immunology, McGill Genome Centre, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
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  • ORCID record for B. Jesse Shapiro
  • For correspondence: peter.ethan@gmail.com jesse.shapiro@mcgill.ca
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Abstract

Since the advent of genome-wide association studies (GWAS) in human genomes, an increasing sophistication of methods has been developed for more robust association detection. Currently, the backbone of human GWAS approaches is allele-counting-based methods where the signal of association is derived from alleles that are identical-by-state. Borrowing this approach from human GWAS, allele-counting-based methods have been popularized in microbial GWAS, notably the generalized linear model using either dimension reduction for fixed covariates and/or a genetic relationship matrix as a random effect in a mixed model to control for population stratification. In this work, we show how the effects of linkage disequilibrium (LD) can potentially obscure true-positive genotype-phenotype associations (i.e., genetic variants causally associated with the phenotype of interest) and also lead to unacceptably high rates of false-positive associations when applying these classical approaches to GWAS in weakly recombining microbial genomes. We developed a GWAS method called POUTINE (https://github.com/Peter-Two-Point-O/POUTINE), which relies on homoplastic mutation to both clarify the source of putative causal variants and reduce likely false-positive associations compared to traditional allele counting methods. Using datasets of M. tuberculosis genomes and antibiotic-resistance phenotypes, we show that LD can in fact render all association signals from allele counting methods to be fully indistinguishable from hundreds to thousands of sites scattered across an entire genome. These classic GWAS methods thus fail to pinpoint likely causal genotype-phenotype associations and separate them from background noise, even after applying methods to correct for population structure. We therefore urge caution when utilizing classical approaches, particularly in populations that are strongly clonal.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted July 01, 2021.
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Classic genome-wide association methods are unlikely to identify causal variants in strongly clonal microbial populations
Peter E. Chen, B. Jesse Shapiro
bioRxiv 2021.06.30.450606; doi: https://doi.org/10.1101/2021.06.30.450606
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Classic genome-wide association methods are unlikely to identify causal variants in strongly clonal microbial populations
Peter E. Chen, B. Jesse Shapiro
bioRxiv 2021.06.30.450606; doi: https://doi.org/10.1101/2021.06.30.450606

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