Footprints of recent selection and variability in breed composition in the Göttingen Minipig genome.

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.

Interplay between heritability, genetic correlation and economic weighting in a selection index with and without genomic information.

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.

The potential of embryo transfer in a German horse-breeding programme.

A reference horse-breeding programme with 13500 foals each year was modelled with ZPLAN+. This new software for the optimization of the structures in breeding programmes is based on ZPLAN. In two scenarios, the implementation of a rigorous selection of mares was implemented. In scenario I, the mare performance test was the point of selection, while in scenario II, further information on 20 competitions in two more years is available. These selected mares were used for embryo transfer (ET), partly in combination with multiple ovulation (MOET). The selection intensity and the number of foals out of (MO)ET were varied in both scenarios. It was expected that 250, 500 and 1000 mares are available for selecting 20, 50, 100 or 200 donor mares each year. The number of foals out of (MO)ET was varied between one and six foals per donor mare and year. Donor mares were used for ET for 4 years. It became clear that with high selection intensities of donor mares and high reproduction rates of them, the yearly genetic gain in a horse-breeding programme could increase over a large range. In scenario II, the additional information on 20 competitions increased the accuracy of the selection index to 0.85. With 200 selected donor mares of 1000 available mares and six foals per year, the genetic gain could almost be doubled compared to the reference scenario. The implementation of ET and a related higher usage of few selected mares entails rising costs and a reduction in the genetic variance. In the most extreme MOET scenario, the effective population size was reduced by 19% relative to the reference scenario. Only if the increase in genetic gain can be converted into higher return for the breeders, the implementation of (MO)ET schemes is a realistic and sensible option for horse-breeding programmes.

A recursive method for computing expected kinship and inbreeding in complex and dynamic breeding programmes.

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.

LDSO: a program to simulate pedigrees and molecular information under various evolutionary forces.

Simulations are a major tool to evaluate new statistical methods and optimize experimental designs in the genomic era. However, this can only be achieved when the simulations are close enough to reality, as well as diverse enough to be realistic. For mapping studies, it is thus critical to re-create as much as possible the forces generating linkage (mutation, random drift, changes in population sizes, selection and pedigree structure) and the mechanisms producing trait genetic architecture (additivity, dominance, epistasis). We present here a computer program (ldso) simulating these phenomena. Optional outputs provide statistics on the linkage disequilibrium (LD) structure and the identity by descent between chromosomal segments, facilitating further data analyses. Furthermore, ldso enables the simulation of genomic data in known pedigrees, which sticks as precisely as possible to recent population history and structures of the long-range LD, allowing optimization of fine-mapping strategies.

Comparison of SNPs and microsatellites for assessing the genetic structure of chicken populations.

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.

Power and robustness of three whole genome association mapping approaches in selected populations.

Selection is known to influence the linkage disequilibrium (LD) pattern in livestock populations. Spurious LD may lead to a higher number of false-positive signals in whole genome association mapping experiments. We compared three approaches for whole genome association mapping in a simulation study: single marker regression (SMR), a two-step approach, which analyses residuals corrected for family effects with an SMR (GRAMMAR), and a combined linkage and LD approach, which applies the quantitative transmission disequilibrium test to the Mendelian sampling term (MTDT). Three different scenarios were simulated: idealized random mating, limited number of parents and directional selection. The number of false-positive associations increased when the number of parents was limited. Mapping accuracy was the worst in the scenario with directional selection for all approaches. As SMR produced a high number of false-positive signals in small populations, results of whole genome scans in livestock analysed with SMR should be considered with caution. GRAMMAR was the most accurate approach, but also the least powerful one. The Bonferroni-corrected significance threshold seemed to be too stringent for this approach. Results obtained with MTDT changed only slightly with selected populations. MTDT combined sufficient power with a manageable number of false-positive associations in all scenarios.