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.

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.

Empirical comparison between different methods for genomic prediction of number of piglets born alive in moderate sized breeding populations.

Currently used multi-step methods to incorporate genomic information in the prediction of breeding values (BV) implicitly involve many assumptions which, if violated, may result in loss of information, inaccuracies and bias. To overcome this, single-step genomic best linear unbiased prediction (ssGBLUP) was proposed combining pedigree, phenotype and genotype of all individuals for genetic evaluation. Our objective was to implement ssGBLUP for genomic predictions in pigs and to compare the accuracy of ssGBLUP with that of multi-step methods with empirical data of moderately sized pig breeding populations. Different predictions were performed: conventional parent average (PA), direct genomic value (DGV) calculated with genomic BLUP (GBLUP), a GEBV obtained by blending the DGV with PA, and ssGBLUP. Data comprised individuals from a German Landrace (LR) and Large White (LW) population. The trait 'number of piglets born alive' (NBA) was available for 182,054 litters of 41,090 LR sows and 15,750 litters from 4534 LW sows. The pedigree contained 174,021 animals, of which 147,461 (26,560) animals were LR (LW) animals. In total, 526 LR and 455 LW animals were genotyped with the Illumina PorcineSNP60 BeadChip. After quality control and imputation, 495 LR (424 LW) animals with 44,368 (43,678) SNP on 18 autosomes remained for the analysis. Predictive abilities, i.e., correlations between de-regressed proofs and genomic BV, were calculated with a five-fold cross validation and with a forward prediction for young genotyped validation animals born after 2011. Generally, predictive abilities for LR were rather small (0.08 for GBLUP, 0.19 for GEBV and 0.18 for ssGBLUP). For LW, ssGBLUP had the greatest predictive ability (0.45). For both breeds, assessment of reliabilities for young genotyped animals indicated that genomic prediction outperforms PA with ssGBLUP providing greater reliabilities (0.40 for LR and 0.32 for LW) than GEBV (0.35 for LR and 0.29 for LW). Grouping of animals according to information sources revealed that genomic prediction had the highest potential benefit for genotyped animals without their own phenotype. Although, ssGBLUP did not generally outperform GBLUP or GEBV, the results suggest that ssGBLUP can be a useful and conceptually convincing approach for practical genomic prediction of NBA in moderately sized LR and LW populations.

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.

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.

Can multi-subpopulation reference sets improve the genomic predictive ability for pigs?

In most countries and for most livestock species, genomic evaluations are obtained from within-breed analyses. To achieve reliable breeding values, however, a sufficient reference sample size is essential. To increase this size, the use of multibreed reference populations for small populations is considered a suitable option in other species. Over decades, the separate breeding work of different pig breeding organizations in Germany has led to stratified subpopulations in the breed German Large White. Due to this fact and the limited number of Large White animals available in each organization, there was a pressing need for ascertaining if multi-subpopulation genomic prediction is superior compared with within-subpopulation prediction in pigs. Direct genomic breeding values were estimated with genomic BLUP for the trait "number of piglets born alive" using genotype data (Illumina Porcine 60K SNP BeadChip) from 2,053 German Large White animals from five different commercial pig breeding companies. To assess the prediction accuracy of within- and multi-subpopulation reference sets, a random 5-fold cross-validation with 20 replications was performed. The five subpopulations considered were only slightly differentiated from each other. However, the prediction accuracy of the multi-subpopulations approach was not better than that of the within-subpopulation evaluation, for which the predictive ability was already high. Reference sets composed of closely related multi-subpopulation sets performed better than sets of distantly related subpopulations but not better than the within-subpopulation approach. Despite the low differentiation of the five subpopulations, the genetic connectedness between these different subpopulations seems to be too small to improve the prediction accuracy by applying multi-subpopulation reference sets. Consequently, resources should be used for enlarging the reference population within subpopulation, for example, by adding genotyped females.

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.

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.

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.

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.

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.

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.

Assessing the impact of natural service bulls and genotype by environment interactions on genetic gain and inbreeding in organic dairy cattle genomic breeding programs.

The objective of the present study was to compare genetic gain and inbreeding coefficients of dairy cattle in organic breeding program designs by applying stochastic simulations. Evaluated breeding strategies were: (i) selecting bulls from conventional breeding programs, and taking into account genotype by environment (G×E) interactions, (ii) selecting genotyped bulls within the organic environment for artificial insemination (AI) programs and (iii) selecting genotyped natural service bulls within organic herds. The simulated conventional population comprised 148 800 cows from 2976 herds with an average herd size of 50 cows per herd, and 1200 cows were assigned to 60 organic herds. In a young bull program, selection criteria of young bulls in both production systems (conventional and organic) were either 'conventional' estimated breeding values (EBV) or genomic estimated breeding values (GEBV) for two traits with low (h 2=0.05) and moderate heritability (h 2=0.30). GEBV were calculated for different accuracies (r mg), and G×E interactions were considered by modifying originally simulated true breeding values in the range from r g=0.5 to 1.0. For both traits (h 2=0.05 and 0.30) and r mg⩾0.8, genomic selection of bulls directly in the organic population and using selected bulls via AI revealed higher genetic gain than selecting young bulls in the larger conventional population based on EBV; also without the existence of G×E interactions. Only for pronounced G×E interactions (r g=0.5), and for highly accurate GEBV for natural service bulls (r mg>0.9), results suggests the use of genotyped organic natural service bulls instead of implementing an AI program. Inbreeding coefficients of selected bulls and their offspring were generally lower when basing selection decisions for young bulls on GEBV compared with selection strategies based on pedigree indices.

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.

Efficiency of different selection strategies against boar taint in pigs.

The breeding scheme of a Swiss sire line was modeled to compare different target traits and information sources for selection against boar taint. The impact of selection against boar taint on production traits was assessed for different economic weights of boar taint compounds. Genetic gain and breeding costs were evaluated using ZPlan+, a software based on selection index theory, gene flow method and economic modeling. Scenario I reflected the currently practiced breeding strategy as a reference scenario without selection against boar taint. Scenario II incorporated selection against the chemical compounds of boar taint, androstenone (AND), skatole (SKA) and indole (IND) with economic weights of -2.74, -1.69 and -0.99 Euro per unit of the log transformed trait, respectively. As information sources, biopsy-based performance testing of live boars (BPT) was compared with genomic selection (GS) and a combination of both. Scenario III included selection against the subjectively assessed human nose score (HNS) of boar taint. Information sources were either station testing of full and half sibs of the selection candidate or GS against HNS of boar taint compounds. In scenario I, annual genetic gain of log-transformed AND (SKA; IND) was 0.06 (0.09; 0.02) Euro, which was because of favorable genetic correlations with lean meat percentage and meat surface. In scenario II, genetic gain increased to 0.28 (0.20; 0.09) Euro per year when conducting BPT. Compared with BPT, genetic gain was smaller with GS. A combination of BPT and GS only marginally increased annual genetic gain, whereas variable costs per selection candidate augmented from 230 Euro (BPT) to 330 Euro (GS) or 380 Euro (both). The potential of GS was found to be higher when selecting against HNS, which has a low heritability. Annual genetic gain from GS was higher than from station testing of 4 full sibs and 76 half sibs with one or two measurements. The most effective strategy to reduce HNS was selecting against chemical compounds by conducting BPT. Because of heritabilities higher than 0.45 for AND, SKA and IND and high genetic correlations to HNS, the (correlated) response in units of the trait could be increased by 62% compared with scenario III with GS and even by 79% compared with scenario III, with station testing of siblings with two measurements. Increasing the economic weights of boar taint compounds amplified negative effects on average daily gain, drip loss and intramuscular fat percentage.