IconCalPen Getting Started About

Icon Penetrance Calculator

Getting Started

The following is an illustration of how the application works:

In order to calculate penetrance for a mutation, five types of data are needed:

  1. The number of mutations identified in a patient sample
  2. The number of patients studied
  3. The number of mutations identified in the control sample
  4. The number of controls studied
  5. The general incidence of the disease under investigation in the population from which patients and controls are sampled.

Using these values, a Bayesian probabilistic method is employed to calculate penetrance. This involves simulation using the Python Scipy package and extraction of 2.5, 50 and 97.5% quantiles to obtain the median penetrance, and its ~95% credible intervals.

An example of this process is given as default values in the above fields.

This method is described in more detail in the papers written by Vassos et al.

  1. E. Vassos, D. A. Collier, S. Holden, C. Patch, D. Rujescu, D. St Clair, and C. M. Lewis, “Penetrance for copy number variants associated with schizophrenia,” Human molecular genetics, vol. 19, no. 17, pp. 3477–3481, 2010.
  2. G. Kirov, E. Rees, J. T. Walters, V. Escott-Price, L. Georgieva, A. L. Richards, K. D. Chambert, G. Davies, S. E. Legge, J. L. Moran, et al., “The penetrance of copy number variations for schizophrenia and developmental delay,” Biological psychiatry, vol. 75, no. 5, pp. 378–385, 2014.


KNM lab is supported by OPERA award from BITS Pilani and by the Centre for Human Diseases.

AA developed the software which was analysed by KNM and DB.