Effectiveness Of Phenotypic Selection Research Articles

Effects of phenotypic selection on height-diameter ratio of Norway spruce and Scots pine in Sweden

Effects of phenotypic selection on height-diameter ratio of Norway spruce and Scots pine in Sweden

The effects of phenotypic selection for residual feed intake on growth performance and carcass traits in Angus and Braunvieh-sired cattle

The effects of phenotypic selection for residual feed intake on growth performance and carcass traits in Angus and Braunvieh-sired cattle

Relative Efficiency of Genomewide Selection for Testcross Performance of Doubled Haploid Lines in a Maize Breeding Program

ABSTRACTIn a simple scheme for genomewide selection in maize (Zea mays L.), a subset of n out of N doubled haploid (DH) lines from a biparental cross are genotyped and phenotyped in testcross combination. The n lines are then used as a training population to predict the performance of the remaining N − n lines. Our objectives were to (i) determine whether or not this scheme for genomewide selection is more efficient than phenotypic selection under a fixed budget and (ii) give recommendations for implementing this genomewide selection scheme during maize line development. From empirical data across multiple populations, testers, years, and locations, we assessed the observed and expected gains from phenotypic selection and genomewide selection under different budget scenarios. Total budgets ranged from $12,000 to $22,000, and standard costs per DH line were $90 for field trials and $15 for genotyping. The relative efficiency of genomewide selection (compared with phenotypic selection) depended on the (i) effectiveness of phenotypic selection and of genomewide selection for the trait in a given cross, (ii) sizes of the training and test populations that could be accommodated within a given budget, and (iii) number of selected lines. In general, the correlation between marker‐predicted values and phenotypic values had to exceed 0.50 for the genomewide selection scheme to be more efficient than phenotypic selection. Our results suggested that for the genomewide selection scheme to be effective, a training population of 60 to 80 lines and a test population of at least 100 lines are needed per biparental cross.

Effect of Phenotypic Selection on Stochastic Gene Expression

Genetically identical cells in the same population can take on phenotypically variable states, leading to differentiated responses to external signals, such as nutrients and drug-induced stress. Many models and experiments have focused on a description based on discrete phenotypic states. Here, we consider the effects of selection acting on a single trait, which we explicitly link to the variable number of proteins expressed by a gene. Considering different regulatory models for the gene under selection, we calculate the steady-state distribution of expression levels and show how the population adapts its expression to enhance its fitness. We quantitatively relate the overall fitness of the population to the heritability of expression levels and their diversity within the population. We show how selection can increase or decrease the variability in the population, alter the stability of bimodal states, and impact the switching rates between metastable attractors.

The effects of selection for gain in mice on the direct-maternal genetic correlation

Components of genetic variation for postweaning growth traits were estimated for both control and growth stocks of mice. The effect of phenotypic selection for gain, which genetically combines selection for additive direct and maternal effects, on additive genetic variance components, heritability, and additive genetic correlationsis discussed. Quantitative genetic theory predicts that simultaneous selection for two metric traits in the same direction will cause the genetic correlation between the two traits to become more negative. The results presented in this paper conflict with this theory. The direct-maternal additive genetic correlation was more negative in the control line (with 356 mice) than in the growth-selected line (with 320 mice) for the three traits analyzed (0.310 vs 0.999 for 21-day weight, 0.316 vs 1.000 for 42-day weight, and 0.506 vs 1.000 for gain from 21-42 days). Estimates were obtained by restricted maximum likelihood (REML) computed under a derivative free algorithm (DFREML).