Predictive Model and Software for Inbreeding-Purging Analysis of Pedigreed Populations

نویسندگان

  • Aurora García-Dorado
  • Jinliang Wang
  • Eugenio López-Cortegano
چکیده

The inbreeding depression of fitness traits can be a major threat for the survival of populations experiencing inbreeding. However, its accurate prediction requires taking into account the genetic purging induced by inbreeding, which can be achieved using a "purged inbreeding coefficient." We have developed a method to compute purged inbreeding at the individual level in pedigreed populations with overlapping generations. Furthermore, we derive the inbreeding depression slope for individual logarithmic fitness, which is larger than that for the logarithm of the population fitness average. In addition, we provide a new software PURGd based on these theoretical results that allows analyzing pedigree data to detect purging and to estimate the purging coefficient, which is the parameter necessary to predict the joint consequences of inbreeding and purging. The software also calculates the purged inbreeding coefficient for each individual, as well as standard and ancestral inbreeding. Analysis of simulation data show that this software produces reasonably accurate estimates for the inbreeding depression rate and for the purging coefficient that are useful for predictive purposes.

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Information to Users

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عنوان ژورنال:

دوره 6  شماره 

صفحات  -

تاریخ انتشار 2016