A practical introduction to Random Forest for genetic association studies in ecology and evolution

Area of Science:

• Genomics
• Machine Learning
• Population Genetics

Background:

• Genomic studies are expanding due to sequencing advancements.
• Understanding genotype-phenotype relationships is crucial.
• Wild and nonmodel organisms present unique challenges in genomic analysis.

Purpose of the Study:

• To explore the application of Random Forest (RF) for identifying genomic loci associated with phenotypic variation.
• To provide a practical guide for using RF in genomic studies, especially for wild or nonmodel organisms.
• To highlight the capabilities and limitations of RF in analyzing complex genetic data.

Main Methods:

• Utilizing the Random Forest (RF) machine-learning algorithm.
• Analyzing thousands of genetic loci simultaneously.
• Accounting for nonadditive genetic interactions.

Main Results:

• RF can efficiently discern loci underlying discrete and quantitative traits.
• The algorithm is suitable for large-scale genomic datasets.
• Practical implementation and interpretation guidelines are provided.

Conclusions:

• Random Forest is a powerful tool for identifying trait-associated genetic markers in diverse populations.
• Understanding RF's strengths and weaknesses is key for accurate genomic research.
• This work serves as an entry point for molecular ecologists using RF for trait association studies.