Heritabilities and genetic correlations for honey yield, gentleness, calmness and swarming behaviour in Austrian honey bees
Brascamp, Evert ; Willam, Alfons ; Boigenzahn, Christian ; Bijma, Piter ; Veerkamp, Roel F. - \ 2016
Apidologie 47 (2016)6. - ISSN 0044-8435 - p. 739 - 748.
estimated breeding value - genetic correlation - genetic parameter - heritability - honey bee
Heritabilities and genetic correlations were estimated for honey yield and behavioural traits in Austrian honey bees using data on nearly 15,000 colonies of the bee breeders association Biene Österreich collected between 1995 and 2014. The statistical models used distinguished between the genetic effect of workers and that of the queen of the colony. Heritability estimates for worker effect were larger than those for queen effect. Genetic correlations between both effects were negative. Heritability estimates for the sum of both effects (i.e. selection criterion) were 0.27, 0.37, 0.38 and 0.06 for honey yield, gentleness, calmness and swarming behaviour, respectively, indicating that meaningful genetic improvement is possible. Genetic correlations between these traits were generally small to medium, with large standard errors, with the exception of the high genetic correlation between gentleness and calmness. The models we present here can be used to estimate breeding values in honey bees.
Genomic Selection for Fruit Quality Traits in Apple (Malus x domestica Borkh.)
Kumar, S. ; Chagné, D. ; Bink, M.C.A.M. ; Volz, R.K. ; Whitworth, C. ; Carlisle, C. - \ 2012
PLoS ONE 7 (2012)5. - ISSN 1932-6203
genetic-relationship information - estimated breeding value - linkage disequilibrium - status number - accuracy - values - prediction - markers - cattle - parameters
The genome sequence of apple (Malus×domestica Borkh.) was published more than a year ago, which helped develop an 8K SNP chip to assist in implementing genomic selection (GS). In apple breeding programmes, GS can be used to obtain genomic breeding values (GEBV) for choosing next-generation parents or selections for further testing as potential commercial cultivars at a very early stage. Thus GS has the potential to accelerate breeding efficiency significantly because of decreased generation interval or increased selection intensity. We evaluated the accuracy of GS in a population of 1120 seedlings generated from a factorial mating design of four females and two male parents. All seedlings were genotyped using an Illumina Infinium chip comprising 8,000 single nucleotide polymorphisms (SNPs), and were phenotyped for various fruit quality traits. Random-regression best liner unbiased prediction (RR-BLUP) and the Bayesian LASSO method were used to obtain GEBV, and compared using a cross-validation approach for their accuracy to predict unobserved BLUP-BV. Accuracies were very similar for both methods, varying from 0.70 to 0.90 for various fruit quality traits. The selection response per unit time using GS compared with the traditional BLUP-based selection were very high (>100%) especially for low-heritability traits. Genome-wide average estimated linkage disequilibrium (LD) between adjacent SNPs was 0.32, with a relatively slow decay of LD in the long range (r2 = 0.33 and 0.19 at 100 kb and 1,000 kb respectively), contributing to the higher accuracy of GS. Distribution of estimated SNP effects revealed involvement of large effect genes with likely pleiotropic effects. These results demonstrated that genomic selection is a credible alternative to conventional selection for fruit quality traits.