Many studies in human genetics compare informativeness of single-nucleotide polymorphisms (SNPs) and microsatellites (single sequence repeats; SSR) in genome scans, but it is difficult to transfer the results directly to livestock because of different population structures. The aim of this study was to determine the number of SNPs needed to obtain the same differentiation power as with a given standard set of microsatellites. Eight chicken breeds were genotyped for 29 SSRs and 9216 SNPs. After filtering, only 2931 SNPs remained. The differentiation power was evaluated using two methods: partitioning of the Euclidean distance matrix based on a principal component analysis (PCA) and a Bayesian model-based clustering approach. Generally, with PCA-based partitioning, 70 SNPs provide a comparable resolution to 29 SSRs. In model-based clustering, the similarity coefficient showed significantly higher values between repeated runs for SNPs compared to SSRs. For the membership coefficients, reflecting the proportion to which a fraction segment of the genome belongs to the ith cluster, the highest values were obtained for 29 SSRs and 100 SNPs respectively. With a low number of loci (29 SSRs or ≤100 SNPs), neither marker types could detect the admixture in the Gödöllö Nhx population. Using more than 250 SNPs allowed a more detailed insight into the genetic architecture. Thus, the admixed population could be detected. It is concluded that breed differentiation studies will substantially gain power even with moderate numbers of SNPs.
BackgroundIn breeding programs for layers, selection of hens and cocks is based on recording phenotypic data from hens in different housing systems. Genomic information can provide additional information for selection and/or allow for a strong reduction in the generation interval. In this study, a typical conventional layer breeding program using a four-line cross was modeled and the expected genetic progress was derived deterministically with the software ZPLAN+. This non-genomic reference scenario was compared to two genomic breeding programs to determine the best strategy for implementing genomic information in layer breeding programs.ResultsIn scenario I, genomic information was used in addition to all other information available in the conventional breeding program, so the generation interval was the same as in the reference scenario, i.e. 14.5 months. Here, we assumed that either only young cocks or young cocks and hens were genotyped as selection candidates. In scenario II, we assumed that breeders of both sexes were used at the biologically earliest possible age, so that at the time of selection only performance data of the parent generation and genomic information of the selection candidates were available. In this case, the generation interval was reduced to eight months. In both scenarios, the number of genotyped male selection candidates was varied between 800 and 4800 males and two sizes of the calibration set (500 or 2000 animals) were considered. All genomic scenarios increased the expected genetic gain and the economic profit of the breeding program. In scenario II, the increase was much more pronounced and even in the most conservative implementation led to a 60% improvement in genetic gain and economic profit. This increase was in all cases associated with higher breeding costs.ConclusionsWhile genomic selection is shown to have the potential to improve genetic gain in layer breeding programs, its implementation remains a business decision of the breeding company; the possible extra profit for the breeding company depends on whether the customers of breeding stock are willing to pay more for improved genetic quality.
The availability of genomic information demands proper evaluation on how the kind (phenotypic versus genomic) and the amount of information influences the interplay of heritability (h(2)), genetic correlation (r(GiGj)) and economic weighting of traits with regard to the standard deviation of the index (σI). As σI is directly proportional to response to selection, it was the chosen parameter for comparing the indices. Three selection indices incorporating conventional and genomic information for a two trait (i and j) breeding goal were compared. Information sources were chosen corresponding to pig breeding applications. Index I incorporating an own performance in trait j served as reference scenario. In index II, additional information in both traits was contributed by a varying number of full-sibs (2, 7, 50). In index III, the conventional own performance in trait j was combined with genomic information for both traits. The number of animals in the reference population (NP = 1000, 5000, 10,000) and thus the accuracy of GBVs were varied. With more information included in the index, σI became more independent of r(GiGj), h(j)(2) and relative economic weighting. This applied for index II (more full-sibs) and for index III (more accurate GBVs). Standard deviations of index II with seven full-sibs and index III with NP = 1000 were similar when both traits had the same heritability. If the heritability of trait j was reduced (h(j)(2) = 0.1), σI of index III with NP = 1000 was clearly higher than for index II with seven full-sibs. When enhancing the relative economic weight of trait j, the decrease in σI of the conventional full-sib index was much stronger than for index III. Our results imply that NP = 1000 can be considered a minimum size for a reference population in pig breeding. These conclusions also hold for comparing the accuracies of the indices.
In many livestock breeding programmes, the development of inbreeding is of critical importance. Thus, the assessment of the expected development of inbreeding should be an essential element in the design of breeding programmes. We propose a new method to deterministically predict the rate of inbreeding based on the gene-flow method in well-defined complex and dynamic breeding programmes. In the suggested approach, a breeding programme has to be structured in homogeneous age-sex-groups, so called cohorts, with a defined origin of genes. Starting from an initial setup (usually an unrelated and non-inbred base population), transition rules to calculate the kinship within and between cohorts originating from reproduction or ageing, respectively, are defined. Using this approach recursively provides the expected development of kinship within and between all cohorts over time, which can be combined into average kinships for the whole population or defined subsets. From these quantities, relevant parameters like the inbreeding rate or the effective population size are easily derived. We illustrate the method with a simple static example breeding programme in sheep. Based on this reference breeding programme, we demonstrate the use of our approach for dynamic breeding programmes, in which cohort sizes or vectors of gene origin change over time: here, we model the situation of exponential population growth and a bottleneck situation, respectively. The suggested approach does not account for the effect of selection on the development of inbreeding, but ideas to overcome this limitation are discussed.
The Göttingen Minipig (GMP) developed at the University of Göttingen is a synthetic breed that is widely used in medical research and toxicology. It combines the high fertility of the Vietnamese potbellied pig, the low body weight of the Minnesota Minipig and the white coat colour of the German Landrace pig. The aim of this study was to find genomic regions that may have undergone selection since the creation of the breed in the 1960s. Therefore, the whole genome was screened for footprints of recent selection based on single nucleotide polymorphism (SNP) genotypes from the Illumina Porcine SNP60 BeadChip using two methods: the extended haplotype homozygosity (EHH) test and the estimation of the genomic proportion of the three original breeds at each SNP using a Bayesian approach. Local deviations from the average genome-wide breed composition were tested with a permutation-based empirical test. Results for a comprehensive whole-genome scan for both methods are presented. Several regions showing the highest P-values in the EHH test are related to breeding goals relevant in the GMP, such as growth (SOCS2, TXN, DDR2 and GRB10 genes) and white colour (PRLR gene). Additionally, the calculated proportion of the founder breeds diverged significantly in many regions from the pedigree-based expectations and the genome average. The results provide a genome-wide map of selection signatures in the GMP, which leads to a better understanding of selection that took place over the last decades in GMP breed development.
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