Improved Bayesian inference of hybrids using genome sequences

Area of Science:

• Population Genetics
• Genomic Inference
• Statistical Modeling

Background:

• Hybrid and backcross inference is crucial for understanding population structure and evolutionary processes.
• Existing methods may not fully account for uncertainties in population haplotype frequencies.
• Modeling linkage and recombination is essential for accurate genomic inference.

Purpose of the Study:

• To develop an improved Bayesian hybrid inference method.
• To account for uncertainty in population haplotype frequencies.
• To accurately model linkage and recombination across the genome.

Main Methods:

• A Bayesian hybrid inference framework was developed.
• The method incorporates uncertainty in population haplotype frequencies.
• It marginalizes over haplotypes while modeling genome-wide linkage and recombination.

Main Results:

• The new method produced comparable posterior probabilities to Chakraborty and Rannala (2023) with large sample sizes.
• For small sample sizes, posterior probabilities were lower due to accounting for additional uncertainties.
• Statistical performance, measured by the ROC curve, was equivalent to the previous method.

Conclusions:

• The developed Bayesian method offers robust hybrid and backcross inference.
• It provides a more comprehensive approach by incorporating haplotype frequency uncertainty.
• The method is applicable to diverse species, including kiwifruit, lizards, and pumas.