Bayesian adaptive experimental design for efficient microbial genome-wide association studies
Bacterial genome-wide association studies (GWAS) offer a powerful approach to identify the genetic basis of a trait measured in a set of sequenced isolates. As the number of sequenced isolates has grown, the limiting factor for GWAS has become phenotyping enough isolates to achieve statistical power. To overcome the need for large-scale phenotyping, we developed Bayesian Adaptive Sequential…
Bacterial genome-wide association studies (GWAS) are a powerful method for uncovering the genetic factors behind traits in sequenced bacterial isolates. However, as the number of sequenced isolates has increased, phenotyping enough isolates to attain statistical power has become the main hurdle. To address this, researchers have created Bayesian Adaptive Sequential Sampling GWAS (BASS-GWAS).
This innovative approach combines Bayesian adaptive experimental design with a sparse regression model to identify the most informative isolates for phenotypic testing.
A team of scientists applied BASS-GWAS to study three antimicrobial resistance traits in Neisseria gonorrhoeae. The result was that fewer phenotyped isolates were required compared to random sampling methods, making the process more efficient. Furthermore, BASS-GWAS was used to discover variants that enable gyrBD429N-dependent cross-resistance to the new topoisomerase inhibitors zoliflodacin and gepotidacin.
Remarkably, this was achieved after only testing fewer than 30 isolates. Through phenotyping these isolates, the researchers were able to identify and validate two significant factors: parCD86N and a gyrA-parE-based pathway. BASS-GWAS proves to be a practical and statistically robust solution for conducting efficient bacterial GWAS.
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