Open Collections

Yang, Hsin Jou

University of British Columbia

Genome-wide association studies (GWAS) identify relationships between genetic variants and observed phenotypes in the population. These studies have been recently applied to bacterial genomes with several new genes identified to be associated with molecular epidemiology, antimicrobial resistance, and virulence. However, in Pseudomonas aeruginosa, there have been no published studies using GWAS algorithms to show the association of sequence variants with different physiological traits. In addition, no studies to my knowledge have applied and compared different GWAS methods to analyze Pseudomonas aeruginosa genomes. In the work presented here, two separate algorithms were used to perform comprehensive GWAS analyses in P. aeruginosa. The overall aim was to search for gene variants amongst a population of genomes that were linked to phenotypes for ciprofloxacin resistance, biofilm formation, and swarming motility. Furthermore, I analyzed the outcomes and accuracy of these GWAS methods. After generating a database of P. aeruginosa genomes representing 512 unique isolates, two GWAS analysis algorithms were tested, TreeWAS and De Bruijn Graph GWAS (DBGWAS). TreeWAS is a phylogenetic tree-based tool built for the specific purpose of GWAS studies in bacteria. DBGWAS is a newly designed method for rapid bacterial GWAS study using De Bruijn graphs. Both methods performed well by identifying known genetic loci conferring ciprofloxacin resistance. Furthermore, TreeWAS identified three gene variants related to biofilm formation and two single nucleotide polymorphisms (SNPs) related to swarming motility. This includes the known gene glyA, and hypothetical proteins (PA14_07430, PA14_21750, PA14_52010, and PA14_01400). DBGWAS identified more than 10 k-mers significantly associated with each of the ciprofloxacin resistance phenotype, biofilm formation and swarming motility including leuC, pslG, and other hypothetical proteins (PA14_11630, PA15080, and PA14_31690). This work provides the basis for a standard GWAS pipeline that researchers could use to identify genetic causes of phenotypic variations in a population and identify potential therapeutic targets in P. aeruginosa.

Text

2022-03-22

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/80997

Bioinformatics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/