IPCAPS: an R package for iterative pruning to capture population structure

Resolving population genetic structure is challenging, especially when dealing with closely related populations. Although Principal Component Analysis (PCA)-based methods and genomic var- iation with single nucleotide polymorphisms (SNPs) are widely used to describe shared genetic an- cestry, improvements can be made targeting fine-level population structure. This work presents an R package called IPCAPS, which uses SNP information for resolving possibly fine-level population structure. The IPCAPS routines are built on the iterative pruning Principal Component Analysis (ipP- CA) framework to systematically assign individuals to genetically similar subgroups. Our tool is able to detect and eliminate outliers in each iteration to avoid misclassification. It can be extended to de- tect subtle subgrouping in patients as well. In addition, IPCAPS supports different measurement scales for variables used to identify substructure. Hence, panels of gene expression and methylation data can be accommodated.