The Holy Grail problem in the field of genetics is to understand the genotype to phenotype map. In the recent years, there has been a quantum leap for data available in the field of quantitative genetics due to the advent of next generation sequencing methods. This enables us to better understand the genetic elements that affect the phenotype of the organism. In particular, GWAS (Genome Wide Association Studies) and QTL (Quantitative Trait Loci) mapping have enabled the identification of novel loci or regions in the genome that play an important role in determining the phenotype of the organism. However, while these methods are reliable at identifying associated regions, they are unable to elucidate the genes responsible and the mechanism by which these regions affect the phenotype. Elucidation of the same, requires a lot of experimental work which can be both time consuming and expensive, especially when the number of associated genes is large. Therefore, reliable tools that can predict the genes that are responsible for the phenotype of the organism are the need of the hour. In this presentation, we summarize our work on the building and validating a machine learning model to predict the growth rate of yeast under different chemical growth conditions and report our findings.