Substantial Genetic Progress in the International Apis mellifera carnica Population Since the Implementation of Genetic Evaluation

The Apis mellifera carnica subspecies of the honeybee has long been praised for its gentleness and good honey yield before systematic breeding efforts began in the early 20th century. However, before the introduction of modern techniques of genetic evaluation (best linear unbiased prediction, BLUP) and a computerized data management in the mid 1990s, genetic progress was slow. Here, the results of the official breeding value estimation in BeeBreed.eu are analyzed to characterize breeding progress and inbreeding. From about the year 2000 onward, the genetic progression accelerated and resulted in a considerable gain in honey yield and desirable properties without increased inbreeding coefficients. The prognostic quality of breeding values is demonstrated by a retrospective analysis. The success of A. m. carnica breeding shows the potential of BLUP-based breeding values and serves as an example for a large-scale breeding program.

Download Full-text

Predicting survival and longevity of sows using purebred and crossbred data1

Translational Animal Science ◽

10.1093/tas/txaa073 ◽

2020 ◽

Vol 4 (2) ◽

pp. 993-1005

Author(s):

Maja W Iversen ◽

Øyvind Nordbø ◽

Eli Gjerlaug-Enger ◽

Eli Grindflek ◽

Theodorus H E Meuwissen

Keyword(s):

Genetic Correlation ◽

Prediction Accuracy ◽

Genetic Evaluation ◽

Breeding Value ◽

Breeding Values ◽

Breeding Goal ◽

Definition Of ◽

F1 Cross ◽

Economic Standpoint ◽

Selection Of

Abstract Survival and longevity are very important traits in pig breeding. From an economic standpoint, it is favorable to keep the sows for another parity instead of replacing them and, from the animal’s perspective, better welfare is achieved if they do not experience health problems. It is challenging to record longevity in purebred (PB) nucleus herds because animals are more likely to be replaced based on breeding value and high replacement rates rather than inability to produce. Crossbred (CB) sows are, however, submitted to lower replacement rates and are more likely to be kept in the farm longer if they can produce large and robust litters. Therefore, the objective of this study was to investigate whether the use of CB phenotypes could improve prediction accuracy of longevity for PBs. In addition, a new definition of survival was investigated. The analyzed data included phenotypes from two PB dam lines and their F1 cross. Three traits were evaluated: 1) whether or not the sow got inseminated for a second litter within 85 d of first farrowing (Longevity 1–2), 2) how many litters the sow can produce within 570 d of first farrowing [Longevity 1–5 (LGY15)], and 3) a repeatability trait that indicates whether or not the sow survived until the next parity (Survival). Traits were evaluated both as the same across breeds and as different between breeds. Results indicated that longevity is not the same trait in PB and CB animals (low genetic correlation). In addition, there were differences between the two PB lines in terms of which trait definition gave the greatest prediction accuracy. The repeatability trait (Survival) gave the greatest prediction accuracy for breed B, but LGY15 gave the greatest prediction accuracy for breed A. Prediction accuracy for CBs was generally poor. The Survival trait is recorded earlier in life than LGY15 and seemed to give a greater prediction accuracy for young animals than LGY15 (until own phenotype was available). Thus, for selection of young animals for breeding, Survival would be the preferred trait definition. In addition, results indicated that lots of data were needed to get accurate estimates of breeding values and that, if CB performance is the breeding goal, CB phenotypes should be used in the genetic evaluation.

Download Full-text

Genetic analysis of feet and leg traits of Australian Angus cattle using linear and threshold models

Animal Production Science ◽

10.1071/an11153 ◽

2012 ◽

Vol 52 (1) ◽

pp. 1 ◽

Cited By ~ 5

Author(s):

Gilbert Jeyaruban ◽

Bruce Tier ◽

David Johnston ◽

Hans Graser

Keyword(s):

Animal Model ◽

Genetic Correlation ◽

Genetic Parameters ◽

Genetic Evaluation ◽

The Other ◽

Breeding Value ◽

Value Analysis ◽

Breeding Values ◽

Angus Cattle ◽

Six Traits

The advantages of using a univariate threshold animal model (TAM) over the conventional linear animal model (AM) in the development of a genetic evaluation system for feet and leg traits of Angus cattle were explored. The traits were scored on a scale of 1–9 with scores 5 and 6 being the most desirable. The genetic parameters and estimated breeding values for front feet angle (FA), rear feet angle (RA), front feet claw set (FC), rear feet claw set (RC), rear leg hind view (RH) and rear leg side view (RS) were compared from AM and TAM. In order to predict breeding values to identify the animals with intermediate optimum, the scores were categorised to form three groups to differentiate the desirable group (5–6) from the other two groups with less desirable feet and leg appearances (1–4 and 7–9). The AM and TAM were used to estimate genetic parameters for the grouped data as well as the original score data. A TAM using the group data was used to predict the probability and breeding value for the desirable intermediate group. For the original score data, estimated heritabilities on the underlying scale, using TAM, were 0.50, 0.46, 0.35, 0.44, 0.32 and 0.22 for FA, FC, RA, RC, RH and RS, respectively, and were 0.01–0.18 higher than the heritabilities estimated using AM. Genetic correlation between the six traits using a bivariate TAM with all scores ranged from 0.02 to 0.50 with front and rear angles had the highest genetic correlation at 0.50. For all six traits, proportion in the intermediate desirable group was higher than the other two groups combined. The low annual genetic change observed for all six traits over the 10 years of data recording reflected the lack of directional selection to improve the traits in Angus cattle. For genetic evaluation of feet and leg traits with an intermediate optimum, TAM is a preferred method for estimating genetic parameters and predicting breeding values for the desirable category. The TAM has now been implemented for regular estimated breeding value analysis of feet and leg traits of Angus cattle.

Download Full-text

9 A comparison of different genomic prediction methods in the Canadian Landrace swine population

Journal of Animal Science ◽

10.1093/jas/skaa278.019 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 10-10

Author(s):

Siavash Salek Ardestani ◽

Mohsen Jafarikia ◽

Brian Sullivan ◽

Mehdi Sargolzaei ◽

Younes Miar

Keyword(s):

Genomic Prediction ◽

Performance Test ◽

Average Daily Gain ◽

Single Step ◽

Breeding Value ◽

Prediction Methods ◽

Nucleotide Polymorphisms ◽

Economic Traits ◽

Genetic Progress ◽

Breeding Values

Abstract Increasing the accuracy of breeding value prediction can lead to more profitability through accelerating genetic progress for economic traits. The objective of this study was to assess the predictive abilities and unbiasedness of best linear unbiased prediction (BLUP) and popular genomic prediction methods of BayesC, BayesC(π = 0.99), genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP). Genotypic information (50K and 60K) of 4,890 performance tested Landrace pigs before February 2019 and 471 validation Landrace pigs that both had phenotypic information on backfat thickness (BFT), average daily gain (ADG), and loin muscle depth (LMD) from two Canadian pig breeding companies (AlphaGene and Alliance Genetics Canada) were used. The de-regressed breeding values (DEBV) were employed in GBLUP and Bayesian methods. A total number of 48,580 single nucleotide polymorphisms remained after quality control and imputation steps. The prediction accuracies were calculated using the correlation between predicted breeding values before performance test and DEBVs after performance test. All employed genomic prediction methods showed higher prediction accuracies for BFT (50.80–52.68%), ADG (26.61–34.47%), and LMD (18.25–25.08%) compared to BLUP method (BFT = 28.54%, ADG = 16.41%, LMD = 17.15%). The highest prediction accuracies for BFT and ADG were obtained using ssGBLUP method, and for LMD it was obtained using BayesC(π = 0.99). The BayesC(π = 0.99) showed also the lowest prediction biases across the studied traits (+0.05 for BFT, 0.00 for AGD, and -0.10 for LMD). In conclusion, our results revealed the superiority of ssGBLUP (for BFT and ADG) and BayesC(π = 0.99) (for LMD) over other tested methods in this study. However, the prediction accuracies from the tested genomic prediction methods were not significantly different from each other. Thus, employing these methods can be helpful for accelerating the genetic improvement of BFT, ADG, and LMD in the moderate population size of Canadian Landrace.

Download Full-text

New Formula of Pollen Supplemental Diets to Study Honey Bee (Apis mellifera carnica) Attractiveness

Egyptian Academic Journal of Biological Sciences A Entomology ◽

10.21608/eajbsa.2014.12944 ◽

2014 ◽

Vol 7 (2) ◽

pp. 47-55

Author(s):

Mohamed Aly ◽

Khaled Osman ◽

Karem, Mohanny ◽

Walaa Elsayeh

Keyword(s):

Apis Mellifera ◽

Honey Bee ◽

Apis Mellifera Carnica ◽

New Formula

Download Full-text

GPS Coordinates for Modelling Correlated Herd Effects in Genomic Prediction Models Applied to Hanwoo Beef Cattle

Animals ◽

10.3390/ani11072050 ◽

2021 ◽

Vol 11 (7) ◽

pp. 2050

Author(s):

Beatriz Castro Dias Cuyabano ◽

Gabriel Rovere ◽

Dajeong Lim ◽

Tae Hun Kim ◽

Hak Kyo Lee ◽

...

Keyword(s):

Environmental Factors ◽

Genomic Prediction ◽

Prediction Models ◽

Phenotypic Expression ◽

Genetic Evaluation ◽

Genomic Breeding ◽

Breeding Values ◽

Korean Cattle ◽

Evaluation Programs ◽

The Impact

It is widely known that the environment influences phenotypic expression and that its effects must be accounted for in genetic evaluation programs. The most used method to account for environmental effects is to add herd and contemporary group to the model. Although generally informative, the herd effect treats different farms as independent units. However, if two farms are located physically close to each other, they potentially share correlated environmental factors. We introduce a method to model herd effects that uses the physical distances between farms based on the Global Positioning System (GPS) coordinates as a proxy for the correlation matrix of these effects that aims to account for similarities and differences between farms due to environmental factors. A population of Hanwoo Korean cattle was used to evaluate the impact of modelling herd effects as correlated, in comparison to assuming the farms as completely independent units, on the variance components and genomic prediction. The main result was an increase in the reliabilities of the predicted genomic breeding values compared to reliabilities obtained with traditional models (across four traits evaluated, reliabilities of prediction presented increases that ranged from 0.05 ± 0.01 to 0.33 ± 0.03), suggesting that these models may overestimate heritabilities. Although little to no significant gain was obtained in phenotypic prediction, the increased reliability of the predicted genomic breeding values is of practical relevance for genetic evaluation programs.

Download Full-text

Exploring Two Honey Bee Traits for Improving Resistance Against Varroa destructor: Development and Genetic Evaluation

Insects ◽

10.3390/insects12030216 ◽

2021 ◽

Vol 12 (3) ◽

pp. 216

Author(s):

Matthieu Guichard ◽

Benoît Droz ◽

Evert W. Brascamp ◽

Adrien von Virag ◽

Markus Neuditschko ◽

...

Keyword(s):

Apis Mellifera ◽

Honey Bee ◽

Honey Bees ◽

Varroa Destructor ◽

Field Trial ◽

Genetic Evaluation ◽

Phenotypic Correlation ◽

Apis Mellifera Mellifera ◽

Novel Traits ◽

Resistance Traits

For the development of novel selection traits in honey bees, applicability under field conditions is crucial. We thus evaluated two novel traits intended to provide resistance against the ectoparasitic mite Varroa destructor and to allow for their straightforward implementation in honey bee selection. These traits are new field estimates of already-described colony traits: brood recapping rate (‘Recapping’) and solidness (‘Solidness’). ‘Recapping’ refers to a specific worker characteristic wherein they reseal a capped and partly opened cell containing a pupa, whilst ‘Solidness’ assesses the percentage of capped brood in a predefined area. According to the literature and beekeepers’ experiences, a higher recapping rate and higher solidness could be related to resistance to V. destructor. During a four-year field trial in Switzerland, the two resistance traits were assessed in a total of 121 colonies of Apis mellifera mellifera. We estimated the repeatability and the heritability of the two traits and determined their phenotypic correlations with commonly applied selection traits, including other putative resistance traits. Both traits showed low repeatability between different measurements within each year. ‘Recapping’ had a low heritability (h2 = 0.04 to 0.05, depending on the selected model) and a negative phenotypic correlation to non-removal of pin-killed brood (r = −0.23). The heritability of ‘Solidness’ was moderate (h2 = 0.24 to 0.25) and did not significantly correlate with resistance traits. The two traits did not show an association with V. destructor infestation levels. Further research is needed to confirm the results, as only a small number of colonies was evaluated.

Download Full-text

A SNP assay for assessing diversity in immune genes in the honey bee (Apis mellifera L.)

Scientific Reports ◽

10.1038/s41598-021-94833-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Dora Henriques ◽

Ana R. Lopes ◽

Nor Chejanovsky ◽

Anne Dalmon ◽

Mariano Higes ◽

...

Keyword(s):

Apis Mellifera ◽

Honey Bee ◽

Large Scale ◽

Medium Density ◽

Nucleotide Polymorphisms ◽

Functional Variation ◽

Immune Genes ◽

Wide Range ◽

Scale Range ◽

Innate Immune Genes

AbstractWith a growing number of parasites and pathogens experiencing large-scale range expansions, monitoring diversity in immune genes of host populations has never been so important because it can inform on the adaptive potential to resist the invaders. Population surveys of immune genes are becoming common in many organisms, yet they are missing in the honey bee (Apis mellifera L.), a key managed pollinator species that has been severely affected by biological invasions. To fill the gap, here we identified single nucleotide polymorphisms (SNPs) in a wide range of honey bee immune genes and developed a medium-density assay targeting a subset of these genes. Using a discovery panel of 123 whole-genomes, representing seven A. mellifera subspecies and three evolutionary lineages, 180 immune genes were scanned for SNPs in exons, introns (< 4 bp from exons), 3’ and 5´UTR, and < 1 kb upstream of the transcription start site. After application of multiple filtering criteria and validation, the final medium-density assay combines 91 quality-proved functional SNPs marking 89 innate immune genes and these can be readily typed using the high-sample-throughput iPLEX MassARRAY system. This medium-density-SNP assay was applied to 156 samples from four countries and the admixture analysis clustered the samples according to their lineage and subspecies, suggesting that honey bee ancestry can be delineated from functional variation. In addition to allowing analysis of immunogenetic variation, this newly-developed SNP assay can be used for inferring genetic structure and admixture in the honey bee.

Download Full-text

A note on genetic parameters and accuracy of estimated breeding values in honey bees

Genetics Selection Evolution ◽

10.1186/s12711-019-0510-6 ◽

2019 ◽

Vol 51 (1) ◽

Cited By ~ 6

Author(s):

Evert W. Brascamp ◽

Piter Bijma

Keyword(s):

Honey Bees ◽

Variance Components ◽

Genetic Parameters ◽

Additive Genetic Variance ◽

Breeding Value ◽

Phenotypic Variance ◽

Breeding Values ◽

Estimated Breeding Values ◽

Additive Genetic Relationship ◽

The Relationship

Abstract Background In honey bees, observations are usually made on colonies. The phenotype of a colony is affected by the average breeding value for the worker effect of the thousands of workers in the colony (the worker group) and by the breeding value for the queen effect of the queen of the colony. Because the worker group consists of multiple individuals, interpretation of the variance components and heritabilities of phenotypes observed on the colony and of the accuracy of selection is not straightforward. The additive genetic variance among worker groups depends on the additive genetic relationship between the drone-producing queens (DPQ) that produce the drones that mate with the queen. Results Here, we clarify how the relatedness between DPQ affects phenotypic variance, heritability and accuracy of the estimated breeding values of replacement queens. Second, we use simulation to investigate the effect of assumptions about the relatedness between DPQ in the base population on estimates of genetic parameters. Relatedness between DPQ in the base generation may differ considerably between populations because of their history. Conclusions Our results show that estimates of (co)variance components and derived genetic parameters were seriously biased (25% too high or too low) when assumptions on the relationship between DPQ in the statistical analysis did not agree with reality.

Download Full-text