A practical introduction to random forest for genetic association studies in ecology and evolution

Large genomic studies are becoming increasingly common with advances in sequencing technology, and our ability to understand how genomic variation influences phenotypic variation between individuals has never been greater. The exploration of such relationships first requires the identification of associations between molecular markers and phenotypes. Here we explore the use of Random Forest (RF), a powerful machine learning algorithm, in genomic studies to discern loci underlying both discrete and quantitative traits, particularly when studying wild or non-model organisms. RF is becoming increasingly used in ecological and population genetics because, unlike traditional methods, it can efficiently analyze thousands of loci simultaneously and account for non-additive interactions. However, understanding both the power and limitations of Random Forest is important for its proper implementation and the interpretation of results. We therefore provide a practical introduction to the algorith...