Processes | Genetics | PCA for Population Stratification

PCA for Population Stratification
Principal components analysis (PCA) is a useful tool for exploring and revealing population structure based on SNP genotypes for a sample. It can also be used to adjust for population stratification and allele frequency variation due to ancestral differences, in association tests between SNP genotypes and a binary or continuous trait via the EIGENSTRAT method (Price et al ., 2006).
The PCA for Population Stratification process performs PCA on the rows (individuals) of the input data set to infer axes of genetic variation and adjust the association test accordingly. It produces several plots (like those created by the Principal Components Analysis process), and also outputs p-value plots like those created by the other Association Testing processes, that include the EIGENSTRAT adjustment for population stratification.
What do I need?
Two data sets are needed to run this process. The first, the Input Data Set , contains all of the marker data. The sample data set used in the following example, the samplegmdata_numgeno data set, is partially shown below.
The original samplegmdata data set described in Data Sets Used in JMP Genomics Processes was computer generated and consists of 1000 rows of individuals with 130 columns corresponding to data on these individuals. Marker data is presented in the two-column allelic format. This data set was recoded using the Recode Genotypes process to generate samplegmdata_numgeno data set. The recoded data set consists of 611 rows with 70 columns. Marker data is presented as numeric variables in the one-column genotypic format.
Note : The marker variables in the input data set must contain numerically coded genotypes; a data set containing these can be obtained by checking the Create data set with numerically coded genotypes option when you run Marker Properties or by running the Recode Genotypes process.
The second data set is the Annotation Data Set . This optional 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.
Both the samplegmdata and samplemap data sets are included in the Sample Data folder that comes 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 .
The output generated by this process is summarized in a Tabbed report. Refer to the PCA for Population Stratification output documentation for detailed descriptions and guides to interpreting your results.