Phasing quality assessment in a brown layer population through family- and population-based software.

BACKGROUND: Haplotype data contains more information than genotype data and provides possibilities such as imputing low frequency variants, inferring points of recombination, detecting recurrent mutations, mapping linkage disequilibrium (LD), studying selection signatures, estimating IBD probabilities, etc. In addition, haplotype structure is used to assess genetic diversity and expected accuracy in genomic selection programs. Nevertheless, the quality and efficiency of phasing has rarely been a subject of thorough study but was assessed mainly as a by-product in imputation quality studies. Moreover, phasing studies based on data of a poultry population are non-existent. The aim of this study was to evaluate the phasing quality of FImpute and Beagle, two of the most used phasing software. RESULTS: We simulated ten replicated samples of a layer population comprising 888 individuals from a real SNP dataset of 580 k and a pedigree of 12 generations. Chromosomes analyzed were 1, 7 and 20. We measured the percentage of SNPs that were phased equally between true and phased haplotypes (Eqp), proportion of individuals completely correctly phased, number of incorrectly phased SNPs or Breakpoints (Bkp) and the length of inverted haplotype segments. Results were obtained for three different groups of individuals, with no parents or offspring genotyped in the dataset, with only one parent, and with both parents, respectively. The phasing was performed with Beagle (v3.3 and v4.1) and FImpute v2.2 (with and without pedigree). Eqp values ranged from 88 to 100%, with the best results from haplotypes phased with Beagle v4.1 and FImpute with pedigree information and at least one parent genotyped. FImpute haplotypes showed a higher number of Bkp than Beagle. As a consequence, switched haplotype segments were longer for Beagle than for FImpute. CONCLUSION: We concluded that for the dataset applied in this study Beagle v4.1 or FImpute with pedigree information and at least one parent genotyped in the data set were the best alternatives for obtaining high quality phased haplotypes.

Analysis of porcine body size variation using re-sequencing data of miniature and large pigs.

BACKGROUND: Domestication has led to substantial phenotypic and genetic variation in domestic animals. In pigs, the size of so called minipigs differs by one order of magnitude compared to breeds of large body size. We used biallelic SNPs identified from re-sequencing data to compare various publicly available wild and domestic populations against two minipig breeds to gain better understanding of the genetic background of the extensive body size variation. We combined two complementary measures, expected heterozygosity and the composite likelihood ratio test implemented in "SweepFinder", to identify signatures of selection in Minipigs. We intersected these sweep regions with a measure of differentiation, namely FST, to remove regions of low variation across pigs. An extraordinary large sweep between 52 and 61 Mb on chromosome X was separately analyzed based on SNP-array data of F2 individuals from a cross of Goettingen Minipigs and large pigs. RESULTS: Selective sweep analysis identified putative sweep regions for growth and subsequent gene annotation provided a comprehensive set of putative candidate genes. A long swept haplotype on chromosome X, descending from the Goettingen Minipig founders was associated with a reduction of adult body length by 3% in F2 cross-breds. CONCLUSION: The resulting set of genes in putative sweep regions implies that the genetic background of body size variation in pigs is polygenic rather than mono- or oligogenic. Identified genes suggest alterations in metabolic functions and a possible insulin resistance to contribute to miniaturization. A size QTL located within the sweep on chromosome X, with an estimated effect of 3% on body length, is comparable to the largest known in pigs or other species. The androgen receptor AR, previously known to influence pig performance and carcass traits, is the most obvious potential candidate gene within this region.

Liver transcriptome analysis reveals important factors involved in the metabolic adaptation of the transition cow.

During early lactation, dairy cows experience a severe metabolic load often resulting in the development of various diseases. The inevitable deficiency in nutrients and energy at the onset of lactation requires an optimal adaptation of the hepatic metabolism to overcome metabolic stress. We conducted a whole-liver transcriptome analysis for the transition cow to identify novel factors crucial for metabolic adaptation. Liver samples were obtained from 6 Red Holstein dairy cows (parity 2 to 7, mean ± standard deviation: 3.7 ± 2.3) at 3 time points: T1 = 22 ± 4 d antepartum, T2 = 10 ± 2 d postpartum, and T3 = 17 ± 2 d postpartum. Using RNA sequencing (RNA-seq), we studied the transcriptomic profile of the transition cow before and after parturition. We performed a differential gene expression analysis (DGEA) and gene-set enrichment analysis (GSEA) for biological processes (gene ontology, GO) and pathways (Kyoto Encyclopedia of Genes and Genomes, KEGG). Among the 10,186 expressed genes, we discovered 1,063 differentially expressed genes (false discovery rate = 5%). The GSEA revealed 16 biological processes and 7 pathways significantly (false discovery rate = 5%) associated with the hepatic changes of the transition cow. Our results confirm that major hepatic changes are related to energy mobilization after parturition; in particular, they are related to fatty acid oxidation/metabolism, cholesterol metabolism, and gluconeogenesis. Using the STRING database (https://string-db.org/), we investigated interactions between significant genes and identified 9 key genes (CYP7A1, APOA1, CREM, LOC522146, CYP2C87, HMGCR, FDFT1, SGLE, and CYP26A1) through which the different processes involved in the metabolic adaptation interact. Comparing our main results with the literature, we could identify further genes that have not yet been associated with the transition period (e.g., CPT1B, ADIPOR2, LEPR, CREB3L3, and CCND1) and that are mainly involved in processes controlled by AMP-activated protein kinase, an important regulator of energy homeostasis.

A reaction norm sire model to study the effect of metabolic challenge in early lactation on the functional longevity of dairy cows.

Due to the discrepancy of the high energy demand for rapidly increasing milk production and limited feed intake in the transition period around parturition, dairy cows require considerable metabolic adaptations. We hypothesize that some cows are genetically less suited to cope with these metabolic needs than others, leading to adverse follow-up effects on longevity. To test this, we designed a reaction norm model in which functional lifetime was linked to the metabolic challenge in the beginning of the first lactation. As challenge variables, we used either the sum of milk yield or the accumulated fat-to-protein ratio of the first 3 test-days (<120 d in milk), pre-adjusted for herd-test-day variance. We defined a random regression sire model, in which a random slope was estimated for each sire to assess whether a bull had robust (neutral or positive slopes) or non-robust (negative slopes) daughters. We fitted the model to data of ∼580,000 daughters of ∼5,000 Brown Swiss bulls with suitable observations available (≥10 daughters per bull). To validate our proposed model and assess the reliability of the estimated (co)variance components, we conducted an extensive bootstrap approach. For both challenge variables, we found the sire variance for the slope of the random regression to be significantly different from zero, suggesting a genetic component for metabolic adaptability. The results of the study show that the ability to cope with metabolic stress in the transition period has a genetic component, which can be used to breed metabolically robust dairy cows.

Accuracy of genomic breeding values revisited: Assessment of two established approaches and a novel one to determine the accuracy in two-step genomic prediction.

Selection decisions in genomic selection schemes are made based on genomic breeding values (GBV) of candidates. Thus, the accuracy of GBV is a relevant parameter, as it reflects the stability of prediction and the possibility that the GBV might change when more information becomes available. Accuracy of genomic prediction defined as the correlation between GBV and true breeding values (TBV), however, is difficult to assess, considering TBV of the candidates are not available in reality. In previous studies, several methods were proposed to assess the accuracy of GBV including methods using population parameters or parameters inferred from mixed-model equations. In practice, most approaches tended to overestimate the accuracy of genomic prediction. We thus tested approaches used in previous studies in order to assess the magnitude of bias. Analyses were performed based on simulated data under a variety of scenarios mimicking different livestock breeding programmes. Furthermore, we proposed a novel method and tested it both with simulated data and in a real Holstein data set. The new method provided a better prediction for the accuracy of GBV in the simulated scenarios.

Using the variability of linkage disequilibrium between subpopulations to infer sweeps and epistatic selection in a diverse panel of chickens.

A whole-genome scan for identifying selection acting on pairs of linked loci is proposed and implemented. The scan is based on , one of Ohta's 1982 measures of between-population linkage disequilibrium (LD). An approximate empirical null distribution for the statistic is suggested. Although the partitioning of LD into between-population components was originally used to investigate epistatic selection, we demonstrate that values of may also be influenced by single-locus selective sweeps with linkage but no epistasis. The proposed scan is implemented in a diverse panel of chickens including 72 distinct breeds genotyped at 538 298 single-nucleotide polymorphisms. In all, 1723 locus pairs are identified as putatively corresponding to a selective sweep or epistatic selection. These pairs of loci generally cluster to form overlapping or neighboring signals of selection. Known variants that were expected to have been under selection in the panel are identified, as well as an assortment of novel regions that have putatively been under selection in chickens. Notably, a promising pair of genes located 8 MB apart on chromosome 9 are identified based on as demonstrating strong evidence of dispersive epistatic selection between populations.

Properties of different selection signature statistics and a new strategy for combining them.

Identifying signatures of recent or ongoing selection is of high relevance in livestock population genomics. From a statistical perspective, determining a proper testing procedure and combining various test statistics is challenging. On the basis of extensive simulations in this study, we discuss the statistical properties of eight different established selection signature statistics. In the considered scenario, we show that a reasonable power to detect selection signatures is achieved with high marker density (>1 SNP/kb) as obtained from sequencing, while rather small sample sizes (~15 diploid individuals) appear to be sufficient. Most selection signature statistics such as composite likelihood ratio and cross population extended haplotype homozogysity have the highest power when fixation of the selected allele is reached, while integrated haplotype score has the highest power when selection is ongoing. We suggest a novel strategy, called de-correlated composite of multiple signals (DCMS) to combine different statistics for detecting selection signatures while accounting for the correlation between the different selection signature statistics. When examined with simulated data, DCMS consistently has a higher power than most of the single statistics and shows a reliable positional resolution. We illustrate the new statistic to the established selective sweep around the lactase gene in human HapMap data providing further evidence of the reliability of this new statistic. Then, we apply it to scan selection signatures in two chicken samples with diverse skin color. Our analysis suggests that a set of well-known genes such as BCO2, MC1R, ASIP and TYR were involved in the divergent selection for this trait.

Maternal genealogical patterns of chicken breeds sampled in Europe.

The aim of this study was to investigate the maternal genealogical pattern of chicken breeds sampled in Europe. Sequence polymorphisms of 1256 chickens of the hypervariable region (D-loop) of mitochondrial DNA (mtDNA) were used. Median-joining networks were constructed to establish evolutionary relationships among mtDNA haplotypes of chickens, which included a wide range of breeds with different origin and history. Chicken breeds which have had their roots in Europe for more than 3000 years were categorized by their founding regions, encompassing Mediterranean type, East European type and Northwest European type. Breeds which were introduced to Europe from Asia since the mid-19th century were classified as Asian type, and breeds based on crossbreeding between Asian breeds and European breeds were classified as Intermediate type. The last group, Game birds, included fighting birds from Asia. The classification of mtDNA haplotypes was based on Liu et al.'s (2006) nomenclature. Haplogroup E was the predominant clade among the European chicken breeds. The results showed, on average, the highest number of haplotypes, highest haplotype diversity, and highest nucleotide diversity for Asian type breeds, followed by Intermediate type chickens. East European and Northwest European breeds had lower haplotype and nucleotide diversity compared to Mediterranean, Intermediate, Game and Asian type breeds. Results of our study support earlier findings that chicken breeds sampled in Europe have their roots in the Indian subcontinent and East Asia. This is consistent with historical and archaeological evidence of chicken migration routes to Europe.

Simulation, prediction, and genetic analyses of daily methane emissions in dairy cattle.

This study presents an approach combining phenotypes from novel traits, deterministic equations from cattle nutrition, and stochastic simulation techniques from animal breeding to generate test-day methane emissions (MEm) of dairy cows. Data included test-day production traits (milk yield, fat percentage, protein percentage, milk urea nitrogen), conformation traits (wither height, hip width, body condition score), female fertility traits (days open, calving interval, stillbirth), and health traits (clinical mastitis) from 961 first lactation Brown Swiss cows kept on 41 low-input farms in Switzerland. Test-day MEm were predicted based on the traits from the current data set and 2 deterministic prediction equations, resulting in the traits labeled MEm1 and MEm2. Stochastic simulations were used to assign individual concentrate intake in dependency of farm-type specifications (requirement when calculating MEm2). Genetic parameters for MEm1 and MEm2 were estimated using random regression models. Predicted MEm had moderate heritabilities over lactation and ranged from 0.15 to 0.37, with highest heritabilities around DIM 100. Genetic correlations between MEm1 and MEm2 ranged between 0.91 and 0.94. Antagonistic genetic correlations in the range from 0.70 to 0.92 were found for the associations between MEm2 and milk yield. Genetic correlations between MEm with days open and with calving interval increased from 0.10 at the beginning to 0.90 at the end of lactation. Genetic relationships between MEm2 and stillbirth were negative (0 to -0.24) from the beginning to the peak phase of lactation. Positive genetic relationships in the range from 0.02 to 0.49 were found between MEm2 with clinical mastitis. Interpretation of genetic (co)variance components should also consider the limitations when using data generated by prediction equations. Prediction functions only describe that part of MEm which is dependent on the factors and effects included in the function. With high probability, there are more important effects contributing to variations of MEm that are not explained or are independent from these functions. Furthermore, autocorrelations exist between indicator traits and predicted MEm. Nevertheless, this integrative approach, combining information from dairy cattle nutrition with dairy cattle genetics, generated novel traits which are difficult to record on a large scale. The simulated data basis for MEm was used to determine the size of a cow calibration group for genomic selection. A calibration group including 2,581 cows with MEm phenotypes was competitive with conventional breeding strategies.

Alternative strategies for genetic analyses of milk flow in dairy cattle.

Measurements for average milk flow (AMF) in kilograms of milk per minute of milking time from 629,161 Holstein cows from calving years 1990 to 2008 were used to estimate genetic covariance components using a variety of statistical models. For bivariate linear-threshold model applications, Gaussian-distributed AMF (linear sire model) was categorized into 2 distinct classes (threshold sire model) by setting arbitrary thresholds for extremely slow or extremely fast milking cows. In different bivariate runs with the 2 traits, Gaussian AMF and binary AMF, within a Bayesian framework, thresholds for the binary trait were 1.2, 1.6, 2.6, and 2.8 kg/min. Posterior heritabilities for AMF from the linear and the threshold models in all runs were in a narrow range and close to 0.26, and the posterior genetic correlation between AMF, defined as either a Gaussian or binary trait, was 0.99. A data subset was used to infer genetic and phenotypic relationships between AMF with test-day traits milk yield, fat percentage, protein percentage, somatic cell score (SCS), fat-to-protein ratio, and energy-corrected milk using recursive linear sire models, standard multiple trait linear sire models, and multiple trait linear sire models accounting for the effect of a trait 1 on a trait 2, and of trait 2 on trait 3, via linear regressions. The time-lagged 3-trait system focused on the first test-day trait after calving (trait 1), on AMF (trait 2), and on the test-day trait (trait 3) after the AMF measurement. Posterior means for heritabilities for AMF from linear and recursive linear models used for the reduced data set ranged between 0.29 and 0.38, and were slightly higher than heritabilities from the threshold models applied to the full data set. Genetic correlations from the recursive linear model and the linear model were similar for identical trait combinations including AMF and test-day traits 1 and 3. The largest difference was found for the genetic correlation between AMF and fat percentage from the first test day (i.e., -0.31 from the recursive linear model vs. -0.26 from the linear model). Genetic correlations from the linear model, including an additional regression coefficient, partly differed, especially when comparing correlations between AMF and SCS and between AMF and fat-to-protein ratio recorded after the AMF measurement data. Structural equation coefficients from the recursive linear model and corresponding regression coefficients from the linear model with additional regression, both depicting associations on the phenotypic scale, were quite similar. From a physiological perspective, all models confirmed the antagonistic relationship between SCS with AMF on genetic and phenotypic scales. A pronounced recursive relationship was also noted between productivity (milk yield and energy-corrected milk) and AMF, suggesting further research using physiological parameters as indicators for cow stress response (e.g., level of hormones) should be conducted.

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.

Global diversity and genetic contributions of chicken populations from African, Asian and European regions.

Genetic diversity and population structure of 113 chicken populations from Africa, Asia and Europe were studied using 29 microsatellite markers. Among these, three populations of wild chickens and nine commercial purebreds were used as reference populations for comparison. Compared to commercial lines and chickens sampled from the European region, high mean numbers of alleles and a high degree of heterozygosity were found in Asian and African chickens as well as in Red Junglefowl. Population differentiation (FST ) was higher among European breeds and commercial lines than among African, Asian and Red Junglefowl populations. Neighbour-Net genetic clustering and structure analysis revealed two main groups of Asian and north-west European breeds, whereas African populations overlap with other breeds from Eastern Europe and the Mediterranean region. Broilers and brown egg layers were situated between the Asian and north-west European clusters. structure analysis confirmed a lower degree of population stratification in African and Asian chickens than in European breeds. High genetic differentiation and low genetic contributions to global diversity have been observed for single European breeds. Populations with low genetic variability have also shown a low genetic contribution to a core set of diversity in attaining maximum genetic variation present from the total populations. This may indicate that conservation measures in Europe should pay special attention to preserving as many single chicken breeds as possible to maintain maximum genetic diversity given that higher genetic variations come from differentiation between breeds.

Accuracy of direct genomic values for functional traits in Brown Swiss cattle.

In this study, direct genomic values for the functional traits general temperament, milking temperament, aggressiveness, rank order in herd, milking speed, udder depth, position of labia, and days to first heat in Brown Swiss dairy cattle were estimated based on ~777,000 (777 K) single nucleotide polymorphism (SNP) information from 1,126 animals. Accuracy of direct genomic values was assessed by a 5-fold cross-validation with 10 replicates. Correlations between deregressed proofs and direct genomic values were 0.63 for general temperament, 0.73 for milking temperament, 0.69 for aggressiveness, 0.65 for rank order in herd, 0.69 for milking speed, 0.71 for udder depth, 0.66 for position of labia, and 0.74 for days to first heat. Using the information of ~54,000 (54K) SNP led to only marginal deviations in the observed accuracy. Trying to predict the 20% youngest bulls led to correlations of 0.55, 0.77, 0.73, 0.55, 0.64, 0.59, 0.67, and 0.77, respectively, for the traits listed above. Using a novel method to estimate the accuracy of a direct genomic value (defined as correlation between direct genomic value and true breeding value and accounting for the correlation between direct genomic values and conventional breeding values) revealed accuracies of 0.37, 0.20, 0.19, 0.27, 0.48, 0.45, 0.36, and 0.12, respectively, for the traits listed above. These values are much smaller but probably also more realistic than accuracies based on correlations, given the heritabilities and samples sizes in this study. Annotation of the largest estimated SNP effects revealed 2 candidate genes affecting the traits general temperament and days to first heat.

An evaluation of a novel estimator of linkage disequilibrium.

The analysis of systems involving many loci is important in population and quantitative genetics. An important problem is the study of linkage disequilibrium (LD), a concept relevant in genome-enabled prediction of quantitative traits and in exploration of marker-phenotype associations. This article introduces a new estimator of a LD parameter (ρ(2)) that is much easier to compute than a maximum likelihood (or Bayesian) estimate of a tetra-choric correlation. We examined the conjecture that the sampling distribution of the estimator of ρ(2) could be less frequency dependent than that of the estimator of r(2), a widely used metric for assessing LD. This was done via an empirical evaluation of LD in 806 Holstein-Friesian cattle using 771 single-nucleotide polymorphism (SNP) markers and of HapMap III data on 21,991 SNPs (chromosome 3) observed in 88 unrelated individuals from Tuscany. Also, 1600 haplotypes over a region of 1 Mb simulated under the coalescent were used to estimate LD using the two measures. Subsequently, a simulation study compared the new estimator with that of r(2) using several scenarios of LD and allelic frequencies. From these studies, it is concluded that ρ(2) provides a useful metric for the study of LD as the distribution of its estimator is less frequency dependent than that of the standard estimator of r(2).

Estimation of genetic parameters for individual udder quarter milk content traits in Brown Swiss cattle.

The aim of this study was to estimate genetic parameters and accuracies of breeding values for milk content traits of individual udder quarters in Brown Swiss cattle. Data of 1,799 phenotyped cows from 40 Swiss dairy herds were analyzed, taking the complete pedigree into account. Fat, protein, lactose, and urea contents, somatic cell score (SCS), and information about hyperkeratosis were available for each udder quarter. The milk of rear udder quarters was found to have significantly higher lactose content and significantly lower fat content than milk of the front udder quarters. The same trend found for fat content was observed for protein content, whereas no differences between the udder quarters were observed for urea content, SCS, or hyperkeratosis. Heritabilities for each udder quarter were in the following ranges: fat content 0.09±0.06 to 0.14±0.06, protein content 0.20±0.09 to 0.33±0.07, lactose content 0.04±0.03 to 0.16±0.07, urea content 0.13±0.07 to 0.22±0.08, SCS 0.18±0.06 to 0.32±0.07, and hyperkeratosis 0.12±0.04 to 0.26±0.05. In our study, hyperkeratosis, protein content, and SCS showed higher heritabilities in the front udder quarters, fat content had higher heritabilities in the rear udder quarters, and no systematic pattern in heritability was observed for lactose content or urea content. Additive genetic correlations between all udder quarters were >0.90 for protein and urea contents, whereas they were remarkably low (<0.60) for SCS. For fat and lactose contents, the genetic correlations between the 2 front or between the 2 rear quarters, respectively, were notably higher than correlations between 1 front and 1 rear quarter, suggesting that the front and the rear udders could be considered as partly genetically different organs. The variability within the udder as such was found to be of low heritability (<0.10) in general, but repeatability was moderate to high for some traits (lactose content: 0.33±0.05, protein content: 0.53±0.05). Some of these findings can be explained by differences in the physiological background of the traits.

Estimation of genetic parameters for novel functional traits in Brown Swiss cattle.

The aim of this study was to estimate genetic parameters and accuracies of breeding values for a set of functional, behavior, and conformation traits in Brown Swiss cattle. These traits were milking speed, udder depth, position of labia, rank order in herd, general temperament, aggressiveness, milking temperament, and days to first heat. Data of 1,799 phenotyped Brown Swiss cows from 40 Swiss dairy herds were analyzed taking the complete pedigree into account. Estimated heritabilities were within the ranges reported in literature, with results at the high end of the reported values for some traits (e.g., milking speed: 0.42±0.06, udder depth: 0.42±0.06), whereas other traits were of low heritability and heritability estimates were of low accuracy (e.g., milking temperament: 0.04±0.04, days to first heat: 0.02±0.04). For most behavior traits, we found relatively high heritabilities (general temperament: 0.38±0.07, aggressiveness: 0.12±0.08, and rank order in herd: 0.16±0.06). Position of labia, arguably an indicator trait for pathological urovagina, was genetically analyzed in this study for the first time, and a moderate heritability (0.28±0.06) was estimated.

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.

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.

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.

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.