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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorZomer, Aldert
dc.contributor.authorVermeulen, Sander
dc.date.accessioned2023-05-23T00:01:01Z
dc.date.available2023-05-23T00:01:01Z
dc.date.issued2023
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/43914
dc.description.abstractGenome-wide association studies (GWAS) have proven to be a successful method for identifying associations between human genotypes and phenotypes. Due to advances in sequencing technologies and the subsequent growth of bacterial datasets, bacterial GWAS is increasingly becoming a viable research method for identifying bacterial genotype-phenotype associations. However, bacterial GWAS cannot be performed using established methods used in human GWAS due to genomic differences. Specialized software to perform bacterial GWAS has been developed, utilizing regression models, phylogenetic trees, and machine learning to overcome the unique genomic challenges. Here, we will discuss these challenges of bacterial GWAS, the software methods that have been developed and our recommendations on their usage, and discuss alternative methods for identifying genotype-phenotype associations in bacteria.
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectAn assessment of current methods and computer programs in use for bacterial GWAS, the unique challenges bacterial GWAS faces compared to human GWAS and alternative methods to find phenotype-genotype relations in bacteria.
dc.titleBacterial GWAS: A Comprehensive Assessment of Challenges, Methods and Alternatives
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsGWAS; bacterial GWAS; bGWAS; genome-wide association studies;
dc.subject.courseuuBioinformatics and Biocomplexity
dc.thesis.id16862


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