A Flexible Method for Genomics-Based Quantitative Genetics in Wild Study Systems-A Case Study on a House Sparrow...

Area of Science:

• Quantitative genetics
• Population genomics
• Bioinformatics

Background:

• Genomic data in wild populations necessitates advanced methods for estimating genetic parameters.
• Existing quantitative genetic methods face challenges with wild systems' complexity (large populations, environmental variation).
• Current approaches can be computationally inefficient or rely on multi-step procedures.

Purpose of the Study:

• To develop a computationally efficient and scalable method for estimating quantitative genetic parameters in wild populations.
• To adapt animal breeding techniques for the unique challenges of wild study systems.
• To provide a robust tool for predicting breeding values and assessing micro-evolutionary change.

Main Methods:

• Adapted animal breeding methods using Bayesian ridge regression on principal components (PCs).
• Developed Bayesian principal component ridge regression (BPCRR) for efficient approximation of breeding values.
• Applied BPCRR to a Norwegian house sparrow meta-population and simulations.

Main Results:

• BPCRR efficiently estimates additive genetic variance.
• BPCRR accurately predicts breeding values in wild populations.
• The method demonstrated scalability and computational efficiency.

Conclusions:

• BPCRR offers a powerful and accessible tool for quantitative genetic analysis in wild populations.
• The method effectively captures micro-evolutionary changes across space and time.
• BPCRR enhances the analysis of genomic data from complex wild systems.