GWAS is a powerful approach to identify genomic regions and genetic variants associated with phenotypes. Whether you are investigating alleles that are common in humans with disease or finding single nucleotide variations that are optimal for cattle breeding, for whatever desirable trait you may be searching for, it is important to analyze SNPs to explain the variation that exists within that trait. Once the SNPs are identified that are associated with the phenotype of interest, the natural next question that comes to mind is, “What is the genotype that best predicts the desired phenotype?” Using genomic prediction to answer this question can help accurately estimate the genotypes that will improve selection for increased production yield or optimal breeding values for example. In this webcast recording, Golden Helix Field Application Scientist, Julia Love, demonstrates the following: How SVS can be used to perform GWAS and genomic prediction on a cattle dataset; Analyze high-quality SNPs by performing the association test with and without quality control filters; Quality control metrics, including evaluating sample statistics to check for outliers, identifying samples that are poor in quality, identifying SNPs that are in linkage disequilibrium, and identifying samples that depart from expected population stratification; Genomic prediction with K-Fold for both gBLUP and Bayes C-pi to estimate which genotypes best predict our desired phenotype.