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Predicting the genomic resolution of bulk segregant analysis
Runxi Shen1, Philipp W Messer1
1Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA.
G3 (Bethesda, Md.)
|February 9, 2022
Summary
Bulk segregant analysis (BSA) identifies genetic loci for trait differences by comparing allele frequencies in pooled samples. This study models BSA resolution, revealing key factors like recombination and population size that influence mapping accuracy.
Area of Science:
- Genetics
- Genomics
- Population Genetics
Background:
- Bulk segregant analysis (BSA) is a powerful technique for mapping quantitative trait loci (QTLs) in various organisms.
- Previous BSA studies often faced limitations in mapping resolution, with a need for better understanding of influencing experimental parameters.
Purpose of the Study:
- To theoretically calculate the expected genomic resolution of BSA for a monogenic trait using coalescence theory.
- To investigate the impact of experimental parameters on BSA mapping resolution and provide a predictive framework for experimental design.
Main Methods:
- Utilized coalescence theory to model the genomic resolution of BSA in both infinite and finite populations.
- Incorporated an effective population size parameter to account for coalescence events in finite populations.
- Validated model predictions through numerical simulations and assessed robustness to pool contamination.
Main Results:
- In infinite populations, BSA resolution is inversely proportional to recombination rate, generations, and sample size.
- Coalescence events in finite populations increase the mapped region length, modeled by effective population size.
- The model's predictions closely matched simulations and showed robustness to moderate pool contamination.
Conclusions:
- The developed theoretical framework accurately predicts BSA mapping resolution.
- Researchers can use this framework to optimize experimental designs for enhanced mapping power and resolution.
- The model is adaptable to different crossing schemes, offering broad applicability in genetic mapping studies.
