| Title |
FamAgg: an R package to evaluate familial aggregation of traits in large pedigrees
|
|---|---|
| Published in |
Bioinformatics, January 2016
|
| DOI | 10.1093/bioinformatics/btw019 |
| Pubmed ID | |
| Authors | |
| Abstract |
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 data set with more than 20,000 participants distributed across approximately 400 families. The FamAgg package is freely available at the Bioconductor repository, http://www.bioconductor.org/packages/FamAgg. [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 31 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 26% |
| Student > Ph. D. Student | 7 | 23% |
| Student > Master | 5 | 16% |
| Professor > Associate Professor | 2 | 6% |
| Student > Postgraduate | 2 | 6% |
| Other | 3 | 10% |
| Unknown | 4 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 11 | 35% |
| Agricultural and Biological Sciences | 4 | 13% |
| Veterinary Science and Veterinary Medicine | 2 | 6% |
| Computer Science | 2 | 6% |
| Social Sciences | 2 | 6% |
| Other | 6 | 19% |
| Unknown | 4 | 13% |