Incorporation of competitive effects in forest tree or animal breeding programs.
Competition among domesticated plants or animals can have a dramatic negative impact on yield of a stand or farm. The usual quantitative genetic model ignores these competitive interactions and could result in seriously incorrect breeding decisions and acerbate animal well-being. A general solution to this problem is given, for either forest tree breeding or penned animals, with mixed-model methodology (BLUP) utilized to separate effects on the phenotype due to the individuals' own genes (direct effects) and those from competing individuals (associative effects) and thereby to allow an optimum index selection on those effects. Biological verification was based on two lines of Japanese quail selected for 6-week weight; one line was selected only for direct effects (D-BLUP) while the other was selected on an optimal index for both direct and associative effects (C-BLUP). Results over 23 cycles of selection showed that C-BLUP produced a significant positive response to selection (b=0.52+/-0.25 g/hatch) whereas D-BLUP resulted in a nonsignificant negative response (b=-0.10+/-0.25 g/hatch). The regression of percentage of mortality on hatch number was significantly different between methods, decreasing with C-BLUP (b=-0.06+/-0.15 deaths/hatch) and increasing with D-BLUP (b=0.32+/-0.15 deaths/hatch). These results demonstrate that the traditional D-BLUP approach without associative effects not only is detrimental to response to selection but also compromises the well-being of animals. The differences in response show that competitive effects can be included in breeding programs, without measuring new traits, so that costs of the breeding program need not increase.
Muir
William M
WM
Department of Animal Science, Purdue University, West Lafayette, Indiana 47907-1151, USA. bmuir@purdue.edu
D000818:Animals; D001835:Body Weight / Q000235:genetics; D001947:Breeding / Q000379:methods*; D003370:Coturnix / Q000235:genetics* / Q000502:physiology; D017753:Ecosystem; D016468:Forestry / Q000379:methods*; D008957:Models, Genetic; D009026:Mortality; D010641:Phenotype; D012044:Regression Analysis; D012641:Selection, Genetic