as Lj (j = 1, …, m) and the average GC percentage in the 1 Mb window around the marker, then fit a linear regression model: where the model parameters α and β are estimated by the least-squares method. To reduce the effect of markers within CNV regions on the regression coefficients, we restricted the analysis to markers with LRR between −2 and 1. After obtaining these estimated regression parameters, for each of the M marker in the genotyping array, we then calculate the expected signal intensity value based on the GC percentage in the 1 Mb window around the marker. The adjusted signal intensity value is then simply calculated as the observed LRR value minus the expected value (residual in the regression model). The procedures for signal adjustment are implemented in the PennCNV package, available at http://www.openbioinformatics.org/penncnv. The adjustment procedure is available as a stand-alone application that can be used outside of PennCNV, and has also been incorporated directly into the CNV calling procedure within PennCNV.
