Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations.

Area of Science:

• Genetics
• Evolutionary Biology
• Bioinformatics

Background:

• Statistical genetics and phylogenetics use disparate methods to study trait correlations.
• Integrating data across species in medicine, conservation, and evolution necessitates bridging these fields.
• Existing methods for controlling genetic structure are not unified.

Purpose of the Study:

• To develop a general quantitative-genetic model for the covariance of genetic contributions to phenotypes.
• To demonstrate that standard models in statistical genetics and phylogenetics are special cases of this general model.
• To facilitate the integration of methods between statistical genetics and phylogenetics.

Main Methods:

• Developed a general model for the covariance between genetic contributions to quantitative phenotypes.
• Interpreted genome-wide association studies (GWAS) and phylogenetic regression as special cases of this model.
• Applied techniques from GWAS, including the genetic relatedness matrix (GRM) and its eigenvectors, to phylogenetic analyses.

Main Results:

• Standard models in statistical genetics and phylogenetics share a common core architecture.
• Methodological advances from statistical genetics can be applied to phylogenetic analyses to mitigate spurious correlations.
• Using eigenvectors of the covariance matrix in a fungal coevolution study decreased false positives and increased true positives.

Conclusions:

• A unified quantitative-genetic framework can reconcile statistical genetics and phylogenetics.
• Integrating methods enhances the understanding of genetic architecture and evolutionary processes.
• This approach provides a foundation for more integrative studies in genetics and evolutionary biology.