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AuthorVisconti, Alessia
AuthorAl-Shafai, Mashael
AuthorAl Muftah, Wadha A
AuthorZaghlool, Shaza B
AuthorMangino, Massimo
AuthorSuhre, Karsten
AuthorFalchi, Mario
Available date2017-12-21T11:06:57Z
Publication Date2017-02-01
Publication NameBMC Genomicsen_US
Identifierhttp://dx.doi.org/10.1186/s12864-017-3527-7
CitationVisconti et al. PopPAnTe: population and pedigree association testing for quantitative data.. BMC Genomics (2017) 18:150
URIhttp://hdl.handle.net/10576/6014
AbstractFamily-based designs, from twin studies to isolated populations with their complex genealogical data, are a valuable resource for genetic studies of heritable molecular biomarkers. Existing software for family-based studies have mainly focused on facilitating association between response phenotypes and genetic markers, and no user-friendly tools are at present available to straightforwardly extend association studies in related samples to large datasets of generic quantitative data, as those generated by current -omics technologies. We developed PopPAnTe, a user-friendly Java program, which evaluates the association of quantitative data in related samples. Additionally, PopPAnTe implements data pre and post processing, region based testing, and empirical assessment of associations. PopPAnTe is an integrated and flexible framework for pairwise association testing in related samples with a large number of predictors and response variables. It works either with family data of any size and complexity, or, when the genealogical information is unknown, it uses genetic similarity information between individuals as those inferred from genome-wide genetic data. It can therefore be particularly useful in facilitating usage of biobank data collections from population isolates when extensive genealogical information is missing.
Languageen
PublisherBioMed Central
Subject-omics data
SubjectAssociation studies
SubjectFamily data
SubjectHeritability
SubjectIsolated population
SubjectPopulation genetics
TitlePopPAnTe: population and pedigree association testing for quantitative data.
TypeArticle
Volume Number18
ESSN1471-2164


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