Familial aggregation analysis software tools | Population genomics
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.
Handles family data with a pedigree object. Pedigree plotting features have been updated to display features on complex pedigrees while adhering to pedigree plotting standards. Kinship matrices can now be calculated for the X chromosome. Other methods have been added to subset and trim pedigrees while maintaining the pedigree structure.
An efficient tool for mining complex inbred genealogies that identify clusters of individuals sharing the same expected amount of relatedness is described. Additionally Jenti allows for the reconstruction of sub-pedigrees suitable for genetic mapping in a systematic way. A graphical interface assists the user step-by-step in the selection process, from the exploration and cleaning of the whole genealogical data to the manual or semi-automatic clustering of individuals in homogeneous sub-groups.
An open source R package that contains both established and novel methods to investigate familial aggregation of traits in large pedigrees. FamAgg provides functions to sub-set pedigrees, to identify common ancestors for any given list of individuals, to identify matched controls within pedigrees and to convert pedigrees into graphs, which opens the whole world of graph-theory methods to pedigree analyses. 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.
Permits users to analyze patient populations and perform “what-if” analyses in real-time. TriNetX Live provides access to clinical data, allows determination of protocol design and feasibility, and also enables site identification. Each data point can be traced to healthcare organizations able to identify individual patients. The software also can be used to develop virtual patient cohorts that can be re-identified for potential recruitment into a clinical trial.