JMP Genomics has analytical pipelines or Workflows to perform a series of analyses on a data set. The Basic Genetics Workflow is a quick and easy method for gaining a deeper understanding of your data before beginning a more in-depth analysis. This workflow recodes marker data to a numeric format, imputes missing genotypic data, and creates data subsets based on minor allele frequency (MAF), missing genotype proportions, and Hardy-Weinberg Equilibrium (HWE). The workflow then gives Marker Properties output and performs a Case-Control Association to identify markers of interest. Note that in this workflow, the case-control analysis is only useful for a binary phenotypic variable (affected vs. unaffected). For continuous or discrete phenotypic variables, other analyses such as the SNP-Trait Association (which will be covered in a later post) are more appropriate. In this example, we explore ~22,000 markers in a sample of 474 Bernese Mountain Dogs that are either affected or unaffected for histiocytic sarcomas (tumors).
Workflows in JMP Genomics are a great way to perform multiple analyses on a single data set. This Basic Genetics Workflow is an effective way to quickly learn the ins and outs of your data, set filtering criteria for both markers and individuals, and get an initial identification of markers of interest. From the Drill Downs menu, there are options for more subsetting, plotting traits by genotype, and exploring linked databases via the gene accession numbers from the annotation data set. This workflow includes a Case-Control Analysis, which is only appropriate for binary traits. For continuous traits, a SNP-Trait association will work (which will be covered later in the series). Now that we have a good understanding of our data, and we have eliminated unwanted rows and columns, we can begin more robust analysis on our subsets.