Persistency of accuracy of genomic breeding values for different simulated pig breeding programs in developing countries.

Genetic improvement of pigs in tropical developing countries has focused on imported exotic populations which have been subjected to intensive selection with attendant high population-wide linkage disequilibrium (LD). Presently, indigenous pig population with limited selection and low LD are being considered for improvement. Given that the infrastructure for genetic improvement using the conventional BLUP selection methods are lacking, a genome-wide selection (GS) program was proposed for developing countries. A simulation study was conducted to evaluate the option of using 60 K SNP panel and observed amount of LD in the exotic and indigenous pig populations. Several scenarios were evaluated including different size and structure of training and validation populations, different selection methods and long-term accuracy of GS in different population/breeding structures and traits. The training set included previously selected exotic population, unselected indigenous population and their crossbreds. Traits studied included number born alive (NBA), average daily gain (ADG) and back fat thickness (BFT). The ridge regression method was used to train the prediction model. The results showed that accuracies of genomic breeding values (GBVs) in the range of 0.30 (NBA) to 0.86 (BFT) in the validation population are expected if high density marker panels are utilized. The GS method improved accuracy of breeding values better than pedigree-based approach for traits with low heritability and in young animals with no performance data. Crossbred training population performed better than purebreds when validation was in populations with similar or a different structure as in the training set. Genome-wide selection holds promise for genetic improvement of pigs in the tropics.

Opportunities for genome-wide selection for pig breeding in developing countries.

Genetic improvement of exotic and indigenous pigs in tropical developing countries is desired. Implementations of traditional selection methods on tropical pig populations are limited by lack of data recording and analysis infrastructure. Genome-wide selection (GS) provides an approach for achieving faster genetic progress without developing a pedigree recording system. The implications of GS on long-term gain and inbreeding should be studied before actual implementation, especially where low linkage disequilibrium (LD) is anticipated in the target population. A simulation case study of this option was performed on the basis of the available 60,000 SNP panel for porcine genome. Computer simulation was used to explore the effects of various selection methods, trait heritability, and different breeding programs when applying GS. Genomic predictions were based on the ridge regression method. Genome-wide selection performed better than BLUP and phenotypic selection methods by increasing genetic gain and maintaining genetic variation while lowering inbreeding, especially for traits with low heritability. Indigenous pig populations with low LD can be improved by using GS if high-density marker panels are available. The combination of GS with repeated backcrossing of crossbreds to exotic pigs in developing countries promises to rapidly improve the genetic merit of the commercial population. Application of this novel method on a real population will need to be performed to validate these results.

Genetic, maternal, and environmental variance components for body weight and length of Atlantic cod at 2 points in life.

Variance components were estimated for 2 body size traits of Atlantic cod at 2 time points. Wild-caught founders from 3 regions off eastern North America were spawned and their progeny were reared at 2 locations in 2 consecutive years. Full-sib families (n = 148) were kept separate until individuals achieved a size large enough to be tagged. At that time (220 d of age), BW and length of 47,637 offspring from 90 sires and 89 dams were recorded. The juveniles were then transferred to sea cages at 3 sites, where they grew further for more than a year. A second set of measurements was collected on 11,839 fish (634 d of age). Dispersion parameters were estimated using REML in bivariate analyses. Models included fixed degree-days (covariate), year × location subclasses, and genetic groups composed of connected families within region of origin. Random factors were animal (additive genetic effects), considering known relationships among the fish; dam (maternal effects); and family (effects common to full-sibs). At tagging, heritability estimates were small to moderate (0.15 and 0.24 for BW and length, respectively; SE = 0.14), similar to or somewhat larger than the proportions of variation ascribed to dams and families (11 to 16%). Later, heritability estimates were greater (0.27 ± 0.08 and 0.31 ± 0.09 for BW and length, respectively), whereas dam and family variance proportions were very small (3 to 4%). Omitting maternal or family components substantially increased the values obtained for heritability at both time points. At the later point, failure to account for maternal effects inflated heritability estimates by about 24% for both traits; ignoring family effects had double the impact. These effects persisted even though endogenous feeding lasts only a couple of weeks in this species and the fish had been pooled since tagging. Discarding data from parents that were completely confounded with their mates decreased heritability estimates slightly (by 0.04, for both traits) at the second point, with no loss of precision despite 15% fewer records and 34% fewer parents; the improved design seemed to have more fully disentangled the additive genetic effects. Estimates of genetic correlations between traits and between time points were very large (>0.89). The results imply that genetic variation exists for body size of cod at both stages. Poor data structure and inadequate models can potentially lead to overstatement of heritability, and thus also of the predicted selection response.

A genome scan to detect quantitative trait loci for economically important traits in Holstein cattle using two methods and a dense single nucleotide polymorphism map.

Genome scans for detection of bovine quantitative trait loci (QTL) were performed via variance component linkage analysis and linkage disequilibrium single-locus regression (LDRM). Four hundred eighty-four Holstein sires, of which 427 were from 10 grandsire families, were genotyped for 9,919 single nucleotide polymorphisms (SNP) using the Affymetrix MegAllele GeneChip Bovine Mapping 10K SNP array. A hybrid of the grand-daughter and selective genotyping designs was applied. Four thousand eight hundred fifty-six of the 9,919 SNP were located to chromosomes in base-pairs and formed the basis for the analyses. The mean polymorphism information content of the SNP was 0.25. The SNP centimorgan position was interpolated from their base-pair position using a microsatellite framework map. Estimated breeding values were used as observations, and the following traits were analyzed: 305-d lactation milk, fat, and protein yield; somatic cell score; herd life; interval of calving to first service; and age at first service. The variance component linkage analysis detected 102 potential QTL, whereas LDRM analysis found 144 significant SNP associations after accounting for a 5% false discovery rate. Twenty potential QTL and 49 significant SNP associations were in close proximity to QTL cited in the literature. Both methods found significant regions on Bos taurus autosome (BTA) 3, 5, and 16 for milk yield; BTA 14 and 19 for fat yield; BTA 1, 3, 16, and 28 for protein yield; BTA 2 and 13 for calving to first service; and BTA 14 for age at first service. Both approaches were effective in detecting potential QTL with a dense SNP map. The LDRM was well suited for a first genome scan due to its approximately 8 times lower computational demands. Further fine mapping should be applied on the chromosomal regions of interest found in this study.

Modelling quality variations in commercial Ontario pork production.

This study explores the interactions of sensory and nutritional environment with genotype occurring in current commercial pork production in Ontario, Canada, which may interact to result in poor quality meat. The study focussed on identifying factors and signalling mechanisms that contribute to poor meat quality, in order to develop strategies to reduce the incidence of unacceptable product quality. In the first phase of the work reported here, animal behaviour and muscle metabolism studies were related to meat colour, tenderness and water-holding capacity measurements from commercially-produced pigs killed in a commercial packing plant. A partial least squares analysis was used to determine the most important of the principal production variables, peri-mortem biochemical measures and post-mortem carcass condition variables studied, in terms of their influence on water-holding, toughness and colour (L*-value). Variations between producer and kill day at the slaughterhouse were very strong contributors to variability in these three meat quality parameters, followed by pH variations. A second phase of the study is currently underway to characterize patterns of gene expression related to extremes of end-product quality and to reduce quality variations by nutritional and behavioural management strategies.

Estimation of genome-wide haplotype effects in half-sib designs.

Genome-wide estimated breeding values can be computed from the simultaneous estimates of the effects of small intervals of DNA throughout the genome on a trait or traits of interest. Small intervals or segments of DNA can be created by the use of thousands of single nucleotide polymorphisms (SNP) available in panels of 10, 25 and 50 thousand SNP. A simulation study was conducted to compare factors that could influence the accuracy of genome-wide selection. Factors studied were the heritability of the trait, dispersion of quantitative trait loci (QTL) across the genome and size of the QTL effects. A 100-cM genome was assumed with 100 equally spaced SNP markers and 10 QTL. A granddaughter design was constructed with 20 sires and 100 sons per sire. Population-wide linkage disequilibrium was assumed to be sufficient after 25 generations of random mating starting with 30 sires and 400 dams. Best linear unbiased prediction was used to simultaneously estimate the effects of 99 SNP intervals, based on determining the SNP haplotype of each son inherited from the sire. Indicator variables were used in the model to indicate haplotype transmission. A genome-wide estimated breeding value was calculated as the sum of the appropriate haplotype interval estimates for each son. Correlations between estimated and true breeding values ranged from 0.60 to 0.79. Situations with unequally sized QTL effects and randomly dispersed QTL gave higher correlations. QTL positions could be estimated to within 2 cM or less.

Impact of genetic selection on management of boar replacement.

Boars in an artificial insemination centre have been selected for their superior genetic potential, with 'superior' being defined as having traits the customer wants transmitted to his herd. The ability to meet the customers' needs depends on the heritability of the trait, the geneticist's success in devising a selection scheme for the trait in balance with other economically important traits, and the boar's ability to produce sperm that can fertilise oocytes. Genetic evaluation research over the past 20 years has greatly increased the number of traits for which a boar can be selected: currently in the Canadian national program, these include age at 100 kg, backfat at 100 kg, feed efficiency, lean yield and litter size. In the near future, traits that are very likely to be added to this selection list include piglet survival, marbling, loin eye area and structure traits. In Canada, sires are ranked on two estimated breeding value (EBV) indices; one, focused on development of terminal sire lines, is based on the growth and yield traits and another, primarily focused on maternal line development, de-emphasises these traits and incorporates litter size. Boars that are in Canadian AI centres because of their excellent growth traits are typically in the top 5-10% of the national population for terminal sire line index, but they may be only average or substandard for litter size. Conversely, boars selected to be in the top 5-10% for conveying such reproductive traits as litter size may only be in the top 33% for growth traits. The more offspring from a superior boar in either of these indices, the faster the population average for the trait improves. The original sire gets knocked out of the elite group, is culled and replaced by a higher ranked young boar from the now improved general population. Although genetic superiority should govern an AI centre's selection and culling of boars, decision-making in real life is seldom that simple. Selection criteria may be contradictory as above, or a boar with truly superior traits may be excluded because a newly-developed molecular genetics test determines he carries an undesirable gene such as PSS, RN or others being developed. Selection for terminal sire or maternal line traits can ignore important practical factors that affect an AI centre--boars with superior genetics may not produce good semen because skeletal or penile problems prevent ejaculation, or because sperm production is poor due to a genetic flaw, disease, or some other cause. Interestingly, selection pressure for one trait may inadvertently select for a trait that is linked but whose linkage is unrecognised, and such unintentionally selected genes could benefit, harm, or have no effect on production traits. An AI centre serving a variety of customers must select boars in anticipation of their customers' needs (including new, foreign and niche markets). A centre should also review its genetic evaluation results and progeny records, both to critique its own selection success and to try to detect unexpected linkages. Finally, an AI centre needs to predict its own future, selecting not just for production traits for the swine producer, but also for factors that enhance the centre's efficiency including boar conformation and temperament, and sperm quantity, quality and hardiness. Can we select for efficiency? Our colleagues in dairy cattle AI evaluate bull performance--should the swine industry consider evaluation of male fertility traits?

A high-resolution radiation hybrid map of porcine chromosome 6.

A high-resolution comprehensive map was constructed for porcine chromosome (SSC) 6, where quantitative trait loci (QTL) for reproduction and meat quality traits have been reported to exist. A radiation hybrid (RH) map containing 105 gene-based markers and 15 microsatellite markers was constructed for this chromosome using a 3000-rad porcine/hamster RH panel. In total, 40 genes from human chromosome (HSA) 1p36.3-p22, 29 from HSA16q12-q24, 17 from HSA18p11.3-q12 and 19 from HSA19q13.1-q13.4 were assigned to SSC6. All primers for these gene markers were designed based on porcine gene or EST sequences, and the orthologous status of the gene markers was confirmed by direct sequencing of PCR products amplified from separate Meishan and Large White genomic DNA pools. The RH map spans SSC6 and consists of six linkage groups created by using a LOD score threshold of 4. The boundaries of the conserved segments between SSC6 and HSA1, 16, 18 and 19 were defined more precisely than previously reported. This represents the most comprehensive RH map of SSC6 reported to date. Polymorphisms were detected for 38 of 105 gene-based markers placed on the RH map and these are being exploited in ongoing chromosome wide scans for QTL and eventual fine mapping of genes associated with prolificacy in a Meishan x Large White multigenerational commercial population.

No detectable association of the ESR PvuII mutation with sow productivity in a Meishan x Large White F2 population.

The polymorphism at the PvuII recognition site in the ESR gene showed no statistically significant association with sow productivity traits in a Meishan x Large White F2 population. Estimates of the effect on litter size were, however, in the opposite direction and statistically different from previously published estimates. Taken together with results from other publications, results here indicate that this PvuII polymorphism displays different degrees of linkage disequilibrium with a gene or genes controlling litter size in different populations.