Towards Uniform Gene Bank Documentation In Europe – The Experience From The EFABISnet Project

In the EFABISnet project, a collaborative effort of EAAP, FAO and partners from 14 European countries, in cooperation with the European Regional Focal Point for Animal Genetic Resources (ERFP), national information systems for monitoring the animal genetic resources on breed level were established in Austria, Cyprus, Estonia, Georgia, Iceland, Ireland, Italy, Netherlands, Slovakia, Slovenia, Switzerland, and United Kingdom. The network was soon extended beyond the project plans, with the establishment of EFABIS databases in Finland, Greece, and Hungary. The network was then complemented by a set of inventories of national gene bank collections to strengthen the documentation of ex situ conservation programmes. These documentation systems were established by the National Focal Points for management of farm animal genetic resources. Here we present the experience gained in establishment of these national inventories of gene banks and their relevance to the Strategic Priority Areas of the Global Plan of Action which could be useful for other areas in the world

A note on mate allocation for dominance handling in genomic selection

Estimation of non-additive genetic effects in animal breeding is important because it increases the accuracy of breeding value prediction and the value of mate allocation procedures. With the advent of genomic selection these ideas should be revisited. The objective of this study was to quantify the efficiency of including dominance effects and practising mating allocation under a whole-genome evaluation scenario. Four strategies of selection, carried out during five generations, were compared by simulation techniques. In the first scenario (MS), individuals were selected based on their own phenotypic information. In the second (GSA), they were selected based on the prediction generated by the Bayes A method of whole-genome evaluation under an additive model. In the third (GSD), the model was expanded to include dominance effects. These three scenarios used random mating to construct future generations, whereas in the fourth one (GSD + MA), matings were optimized by simulated annealing. The advantage of GSD over GSA ranges from 9 to 14% of the expected response and, in addition, using mate allocation (GSD + MA) provides an additional response ranging from 6% to 22%. However, mate selection can improve the expected genetic response over random mating only in the first generation of selection. Furthermore, the efficiency of genomic selection is eroded after a few generations of selection, thus, a continued collection of phenotypic data and re-evaluation will be required

Exploring farmer attitudes and preferences to inform the development and implementation of breeding (abstract)

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Mapping of multiple quantitative trait loci by regression in half sib designs.

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Mapping of quantitative trait loci affecting quality and production traits in egg layers

A mapping population segregating for egg quality traits was created by a line cross between two egg layer lines and screened by a genome scan. The F2 generation consisted of 307 hens, which were scored for egg quality and production traits. The mapping population was genotyped for 99 microsatellite loci, spanning nine macrochromosomes and five small linkage groups. The linkage maps were used in mapping QTL affecting 14 traits, by using multiple markers and a least-squares approach. We detected 14 genomewide significant and six suggestive QTL that were located on chromosomes 2, 3, 4, 5, and, 8 and sex chromosome Z. A significant QTL affecting egg white thinning was found on chromosome 2. For eggshell strength, a significant QTL was found on chromosome Z. For production traits, the most interesting area was on chromosome 4, where highly significant QTL effects were detected for BW, egg weight, and feed intake in the same area. The most significant QTL explains 25.8% of the phenotypic variance in F2 of body weight. An area affecting the age at first egg, egg weight, and the number of eggs was located on chromosome

Mapping of multiple quantitative trait loci by simple regression in half-sib designs

Detection of QTL in outbred half-sib family structures has mainly been based on interval mapping of single QTL on individual chromosomes. Methods to account for linked and unlinked QTL have been developed, but most of them are only applicable in designs with inbred species or pose great demands on computing facilities. This study describes a strategy that allows for rapid analysis, involving multiple QTL, of complete genomes. The methods combine information from individual analyses after which trait scores for a specific linkage group are adjusted for identified QTL at other linkage groups. Regression methods are used to estimate QTL positions and effects; permutation tests are used to obtain empirical threshold values. The description of the methods is complemented by an example of the combined analysis of 28 bovine chromosomes and their associations with milk yield in Finnish Ayrshire cattle. In this example, the individual analysis revealed five suggestive QTL affecting milk yield. Following the strategy presented in this paper, the final combined analysis showed eight significant QTL affecting milk yield. This clearly demonstrates the potential gain of using the combined analysis. The use of regression methods, with low demands on computing resources, makes this approach very practical for total genome scan

Effect of selection and selective genotyping for creation of reference on bias and accuracy of genomic prediction

Reference populations for genomic selection usually involve selected individuals, which may result in biased prediction of estimated genomic breeding values (GEBV). In a simulation study, bias and accuracy of GEBV were explored for various genetic models with individuals selectively genotyped in a typical nucleus breeding program. We compared the performance of three existing methods, that is, Best Linear Unbiased Prediction of breeding values using pedigree-based relationships (PBLUP), genomic relationships for genotyped animals only (GBLUP) and a Single-Step approach (SSGBLUP) using both. For a scenario with no-selection and random mating (RR), prediction was unbiased. However, lower accuracy and bias were observed for scenarios with selection and random mating (SR) or selection and positive assortative mating (SA). As expected, bias disappeared when all individuals were genotyped and used in GBLUP. SSGBLUP showed higher accuracy compared to GBLUP, and bias of prediction was negligible with SR. However, PBLUP and SSGBLUP still showed bias in SA due to high inbreeding. SSGBLUP and PBLUP were unbiased provided that inbreeding was accounted for in the relationship matrices. Selective genotyping based on extreme phenotypic contrasts increased the prediction accuracy, but prediction was biased when using GBLUP. SSGBLUP could correct the biasedness while gaining higher accuracy than GBLUP. In a typical animal breeding program, where it is too expensive to genotype all animals, it would be appropriate to genotype phenotypically contrasting selection candidates and use a Single-Step approach to obtain accurate and unbiased prediction of GEBV