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FamAgg: an R package to evaluate familial aggregation of traits in large pedigrees.


ABSTRACT:

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Familial aggregation analysis is the first fundamental step to perform when assessing the extent of genetic background of a disease. However, there is a lack of software to analyze the familial clustering of complex phenotypes in very large pedigrees. Such pedigrees can be utilized to calculate measures that express trait aggregation on both the family and individual level, providing valuable directions in choosing families for detailed follow-up studies. We developed FamAgg, an open source R package that contains both established and novel methods to investigate familial aggregation of traits in large pedigrees. We demonstrate its use and interpretation by analyzing a publicly available cancer dataset with more than 20 000 participants distributed across approximately 400 families.

Availability and implementation

The FamAgg package is freely available at the Bioconductor repository, http://www.bioconductor.org/packages/FamAgg

Contact

Christian.Weichenberger@eurac.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Rainer J 

PROVIDER: S-EPMC4866523 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Publications

FamAgg: an R package to evaluate familial aggregation of traits in large pedigrees.

Rainer Johannes J   Taliun Daniel D   D'Elia Yuri Y   Pattaro Cristian C   Domingues Francisco S FS   Weichenberger Christian X CX  

Bioinformatics (Oxford, England) 20160122 10


<h4>Unlabelled</h4>Familial aggregation analysis is the first fundamental step to perform when assessing the extent of genetic background of a disease. However, there is a lack of software to analyze the familial clustering of complex phenotypes in very large pedigrees. Such pedigrees can be utilized to calculate measures that express trait aggregation on both the family and individual level, providing valuable directions in choosing families for detailed follow-up studies. We developed FamAgg,  ...[more]

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