Genetic data sets are often quite large and analyses with these large data sets require significant computational resources and time. It is often useful, particularly for preliminary analyses of the major features in the data, to reduce the number of variables by using only a representative set.
The LD tagSNP Selection process uses the linkage disequilibrium measure R2 (the square of the correlation coefficient) between pairs of SNPs to separate the SNPs into bins, using the binning algorithm developed by Carlson et al. (2004), and identifies tagSNPs that can be used to represent all the SNPs in a bin. This procedure effectively reduces the total number of SNPs (and hence, the number of columns) in the main data set to a more manageable number, increasing the efficiency of association studies.
One Input Data Set is needed for this process. The samplegmdata.sas7bdat data set used in the following example was computer generated and consists of 1000 rows of individuals with 130 columns corresponding to data on these individuals. There are 2 categorical phenotypic variables (sex and disease status) and 4 quantitative phenotypic variables (Qtrt1, Qtrt2, Qtrt3, and Qtrt4). Genotypes for 60 different markers are presented in the two-column allelic format (ma1 — ma120). This data set is partially shown below.
Note that this is a wide data set; phenotypes and markers are listed in columns, whereas individuals are listed in rows.
The second, optional, data set is the Annotation Data Set. This data set contains information, such as gene identity or chromosomal location, for each of the markers. The annotation data set used in this example, the samplemap data set, was computer generated and identifies markers, location and gene identities. A portion of this data set is illustrated below. 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 data sets are described in Data Sets Used in JMP Genomics Processes and are included in the Sample Data folder.
For detailed information about the files and data sets used or created by JMP Life Sciences software, see Files and Data Sets.
The output generated by this process is summarized in a Tabbed report. Refer to the LD tagSNP Selection output documentation for detailed descriptions and guides to interpreting your results.