TL;DR: 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, and shows that competitive effects can be included in breeding programs, without measuring new traits, so that costs of the breeding program need not increase.
Abstract: 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.
TL;DR: This paper reviews models for phenotypic plasticity in evolutionary genetics and animal breeding and shows how those models are connected, including separate selection on overall performance and on sensitivity to the environment.
TL;DR: In this article, the authors address the problem of automatically adjusting the physical organization of a data base to optimize its performance as its access requirements change and present a heuristic algorithm for selecting indices to match projected access requirements.
Abstract: We address the problem of automatically adjusting the physical organization of a data base to optimize its performance as its access requirements change. We describe the principles of the automatic index selection facility of a prototype self-adaptive data base management system that is currently under development. The importance of accurate usage model acquisition and data characteristics estimation is stressed. The statistics gathering mechanisms that are being incorporated into our prototype system are discussed. Exponential smoothing techniques are used for averaging statistics observed over different periods of time in order to predict future characteristics. An heuristic algorithm for selecting indices to match projected access requirements is presented. The cost model on which the decision procedure is based is flexible enough to incorporate the overhead costs of index creation, index storage and application program recompilation.