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Genetic markers applied in regression tree prediction models
Author(s) -
Hizer S. E.,
Wright T. M.,
Garcia D. K.
Publication year - 2004
Publication title -
animal genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.756
H-Index - 81
eISSN - 1365-2052
pISSN - 0268-9146
DOI - 10.1046/j.1365-2052.2003.01068.x
Subject(s) - biology , cart , rapd , regression , tree (set theory) , genetic marker , regression analysis , predictive modelling , categorical variable , statistics , genetics , gene , mathematics , genetic diversity , population , mechanical engineering , mathematical analysis , demography , sociology , engineering
Summary Classification and regression tree (CART) modelling was used to determine infectious hypodermal and haematopoietic necrosis virus (IHHNV) resistance and susceptibility in Penaeus stylirostris . In a previous study, eight random amplified polymorphic DNA (RAPD) markers and viral load values using real‐time quantitative PCR were obtained and used as the training data set in order to create numerous regression tree models. Specifically, the genetic markers were used as categorical predictor variables and viral load values as the dependent response variable. To determine which model has the highest predictive accuracy for future samples, RAPD fingerprint data was generated from new Penaues stylirostris IHHNV resistant and susceptible individuals and used to test the regression models. The best performing tree was a four terminal node tree with three genetic markers as significant variables. Marker‐assisted breeding practices may benefit from the creation of regression tree models that apply genetic markers as predictive factors. To our knowledge this is the first study to use RAPD markers as predictors within a CART prediction model to determine viral susceptibility.