The Genetics Q-K Analysis Workflow process runs a workflow for calculating Q and K matrices to adjust for population structure and relatedness between individuals, respectively, and then runs a Q-K mixed model (Yu et al. 2006) to test for association between individual SNPs and one or more traits. The workflow contains the following processes:
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Recode Genotypes or Expand Multiallelic Genotypes - The former process is run if the columns of marker variables do not contain numeric genotypes, and by default converts multiallelic markers to biallelic markers before performing any calculations or analysis. The latter process, run when the option to perform multiallelic analyses on multiallelic markers is enabled, creates Q and K matrices using expanded genotypes.
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PCA for Population Stratification - This process is run to calculate the Q matrix using principal components (Zhao et al. 2007),
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Relationship Matrix - This process is run to calculate the root of the IBD (K) matrix representing relatedness between individuals,
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K Matrix Compression - This process can be run to create a smaller-dimension K matrix using an optimized clustering algorithm (Zhang et al. 2010),
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Q-K Mixed Model or Marker-Trait Association - The former process uses the Q and K matrices as fixed and random components, respectively, in a mixed models to test for association between each SNP and the traits of interest. The latter process, run when the option to perform multiallelic analyses on multiallelic markers is enabled, uses the Q and K matrices derived from expanded genotypes along with the original genotypes with all alleles included in the model simultaneously.
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One wide-formatted input SAS data set, containing all of the marker data, is required to run the Genetics Q-K Analysis Workflow process. The input data set should have the NxM wide format with:
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N samples (individuals) as rows, and
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M markers as columns (or 2M columns for marker variables in Allele format).
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The samplegmdata_numgeno.sas7bdat data set serves as an example. It was computer generated and consists of 1000 rows of individuals with 70 columns corresponding to data on these individuals. In this data set, genotypes for 60 markers (numgeno1 - numgeno60), are presented in the one-column format. This data set is partially shown below. Note that this is a wide data set; markers are listed in columns, whereas individuals are listed in rows.
An Annotation Data Set is optional. This data set contains information such as gene identity or chromosomal location, for each of the markers. The samplemap_numgeno.sas7bdat data set serves as an example, and is partially shown below. It identifies markers, location, and gene identities. This data set is a tall data set; each row corresponds to a different marker.
Note: The top-to-bottom order of the rows in the annotation data set matches the left-to-right order of the columns in the input data set. This correspondence is required for markers to be matched appropriately.
Both the samplegmdata_numgeno.sas7bdat input data set and the samplemap_numgeno.sas7bdat annotation data set are located in the Sample Data\Genetics directory included with JMP Genomics.
For detailed information about the files and data sets used or created by JMP Life Sciences software, see Files and Data Sets.
When you click , the Genetics Q-K Analysis Workflow process begins by opening the Workflow Builder. The Workflow Builder builds a settings file for each process, containing the information from the data sets and parameters specified in the Genetics Q-K Analysis Workflow dialog. Once the setting files are generated and saved, the individual processes in the workflow are sequentially opened, populated, and run. The results of the processes are saved in the specified output folder. Finally, a JMP journal, providing links to the workflow dialog and the results of each process, is generated.
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Click .
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The Workflow Builder dialog shows the settings for each of the processes in the workflow. You can select and edit individual settings to adjust your analysis.
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Clicking each of the buttons on the journal brings up the output of each of the processes. This enables you to examine each set of output so that adjustments can be made to the individual settings, as needed. For your convenience, links to the default Genetics Q-K Analysis Workflow processes are given below.
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