Regional issues on animal genetic resources: trends, policies and networking in Europe

European countries are individually and in collaboration carrying out active work on animal genetic resources (AnGR). The region has a very good starting point for work on AnGR: The breed concept was developed in Europe; current European mainstream breeds are derived from local breeds and, in many species, have further formed the core of the international breeds; there has always been very active research in Europe on farm animal genetics and breeding, including sustainable utilization and management of variation. Since the 1970s and 1980s many European countries have been paying attention to local breeds and have saved many of them from total extinction. In quite a few countries, the conservation work has been supported by cryopreservation. In the Food and Agriculture Organization of the United Nations (FAO) coordinated process, Europe has actively contributed to assessing the State of the World's Animal Genetic Resources and will continue to implement the Global Plan of Action. There are now national action plans in most of the European countries. The European consumption of animal products has changed very little over recent decades. At the same time, production has become very intensive. Among other driving forces, the development of agriculture is steered by the EU policies. The last decade has seen new kind of thinking and measures directed towards an overall consideration of rural development. This has given room for the revitalization of many local breeds. The aim is to have schemes that promote the self-sustainability of local breeds. The EU also has a very ambitious research programme to support these aims while enhancing the overall sustainable production and management of biological resources. The European Regional Focal Point for Animal Genetic Resources (ERFP) is a common forum for the coordinators of European national programmes on AnGR. There are also many non-governmental organizations (NGOs) working in the animal sector. These NGOs and networks are most relevant to raising awareness about the importance of values of AnGR and in enhancing activities that contribute to conservation and sustainable use of AnGR

Genotypic covariance matrices and their inverses for models allowing dominance and inbreeding

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Molecular genetic polymorphism at the κ-casein and β-lactoglobulin loci in Finnish dairy hulls

Dairy bulls have been genotyped for K-casein and P-lactoglobulin from semen samples by methodology based on a polymerase chain reaction (PCR), In this study, a previously described method for κ-casein A and B variants was extended to cover also the detection of the E variant. For β-lactoglobulin the variants A and B were genotyped by another PCR-based method. The frequencies of the κ-casein and β-lactoglobulin alleles were determined from 308 and 291 Finnish Ayrshire and 42 and 44 Finnish Friesian bulls, respectively. The bulls had been born between 1973 and 1988. There was no noticeable trend in the differences between allele frequencies over the years, the overall frequencies of κ-casein A, B and E being 0.62, 0.09 and 0.29 in the Finnish Ayrshires and 0.85, 0.14 and 0.01 in Finnish Friesians. The overall frequencies of β-lactoglobulin A and B alleles were 0.25 and 0.75 in Ayrshires and 0.56 and 0.44 in Friesian

Mapping of serum amylase-1 and quantitative trait loci for milk production traits to cattle chromosome 4

The present study was undertaken to confirm and refine the mapping of a quantitative trait locus in cattle for milk fat percentage that had earlier been reported to be linked to the serum amylase-1 locus, AM1. Five half-sib families from the previous study and 7 new ones were genotyped for nine microsatellite markers spanning chromosome 4. AM1 was mapped between the microsatellite markers BMS648 and BR6303. In a granddaughter design, interval mapping based on multiple-marker regression was utilized for an analysis of five milk production traits: milk yield, fat percentage and yield, and protein percentage and yield. In the families reported on previously, significant effects for fat and protein percentages were detected. In the new families, an effect on milk and fat yields was found. The most likely positions of the quantitative trait locus in both groups of families were in the same area of chromosome 4 in the vicinity of the obese locus. Direct effects of the obese locus were tested for using polymorphism in two closely linked microsatellites located 2.5 and 3.6 top downstream of the coding sequence. No firm evidence was found for an association between the obese locus and the tested traits

Multiple marker mapping of quantitative trait loci of Finnish dairy cattle by regression

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Potential of gene drives with genome editing to increase genetic gain in livestock breeding programs

Abstract Background This paper uses simulation to explore how gene drives can increase genetic gain in livestock breeding programs. Gene drives are naturally occurring phenomena that cause a mutation on one chromosome to copy itself onto its homologous chromosome. Methods We simulated nine different breeding and editing scenarios with a common overall structure. Each scenario began with 21 generations of selection, followed by 20 generations of selection based on true breeding values where the breeder used selection alone, selection in combination with genome editing, or selection with genome editing and gene drives. In the scenarios that used gene drives, we varied the probability of successfully incorporating the gene drive. For each scenario, we evaluated genetic gain, genetic variance ( \u3c3 A 2 ) , rate of change in inbreeding ( \u394 F ), number of distinct quantitative trait nucleotides (QTN) edited, rate of increase in favourable allele frequencies of edited QTN and the time to fix favourable alleles. Results Gene drives enhanced the benefits of genome editing in seven ways: (1) they amplified the increase in genetic gain brought about by genome editing; (2) they amplified the rate of increase in the frequency of favourable alleles and reduced the time it took to fix them; (3) they enabled more rapid targeting of QTN with lesser effect for genome editing; (4) they distributed fixed editing resources across a larger number of distinct QTN across generations; (5) they focussed editing on a smaller number of QTN within a given generation; (6) they reduced the level of inbreeding when editing a subset of the sires; and (7) they increased the efficiency of converting genetic variation into genetic gain. Conclusions Genome editing in livestock breeding results in short-, medium- and long-term increases in genetic gain. The increase in genetic gain occurs because editing increases the frequency of favourable alleles in the population. Gene drives accelerate the increase in allele frequency caused by editing, which results in even higher genetic gain over a shorter period of time with no impact on inbreeding

Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens

Background: Genome-wide association studies in humans have found enrichment of trait-associated single nucleotide polymorphisms (SNPs) in coding regions of the genome and depletion of these in intergenic regions. However, a recent release of the ENCyclopedia of DNA elements showed that ~80 % of the human genome has a biochemical function. Similar studies on the chicken genome are lacking, thus assessing the relative contribution of its genic and non-genic regions to variation is relevant for biological studies and genetic improvement of chicken populations. Methods: A dataset including 1351 birds that were genotyped with the 600K Affymetrix platform was used. We partitioned SNPs according to genome annotation data into six classes to characterize the relative contribution of genic and non-genic regions to genetic variation as well as their predictive power using all available quality-filtered SNPs. Target traits were body weight, ultrasound measurement of breast muscle and hen house egg production in broiler chickens. Six genomic regions were considered: intergenic regions, introns, missense, synonymous, 5′ and 3′ untranslated regions, and regions that are located 5 kb upstream and downstream of coding genes. Genomic relationship matrices were constructed for each genomic region and fitted in the models, separately or simultaneously. Kernelbased ridge regression was used to estimate variance components and assess predictive ability. Contribution of each class of genomic regions to dominance variance was also considered. Results: Variance component estimates indicated that all genomic regions contributed to marked additive genetic variation and that the class of synonymous regions tended to have the greatest contribution. The marked dominance genetic variation explained by each class of genomic regions was similar and negligible (~0.05). In terms of prediction mean-square error, the whole-genome approach showed the best predictive ability. Conclusions: All genic and non-genic regions contributed to phenotypic variation for the three traits studied. Overall, the contribution of additive genetic variance to the total genetic variance was much greater than that of dominance variance. Our results show that all genomic regions are important for the prediction of the targeted traits, and the whole-genome approach was reaffirmed as the best tool for genome-enabled prediction of quantitative traits