PopPAnTe: Population and Pedigree Association Testing for Quantitative Data

PopPAnTe is a user-friendly framework enable pairwise association testing of quantitative -omics variables in family-based study. Relationships between individuals can be either described by known family structures of any size and complexity, or by genetic similarity matrices (GSM) inferred from genome-wide genetic data. This approach is particularly useful when some degree of hidden relatedness (including population stratification) is expected, but extensive genealogical information is missing or incomplete. For instance, genealogical information going back more than three or four generations may be difficult to be retrieved for individuals recruited in large-scale biobank started in genetic isolates.

PopPAnTe models the data in a variance component framework to keep into account the resemblance among individuals, supports region-based testing, assesses the significance of the association through a formal likelihood ratio testing as well as through an adaptive permutation procedure, and performs basic data pre- and post-processing.

PopPAnTe is now at version 1.0.2 (released on March 20th, 2018)

If you use PopPAnTe for research purpose, please cite:
Visconti, Alessia, et al. “PopPAnTe: population and pedigree association testing for quantitative data.” BMC genomics 18.1 (2017): 150, DOI:10.1186/s12864-017-3527-7


MRC (MR/K01353X/1)