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Background: Genomic selection (GS) using estimated breeding values (GS-EBV) based on dense marker data is a promising approach for genetic improvement. A simulation study was undertaken to illustrate the opportunities offered by GS for designing breeding programs. It consisted of a selection program for a sex-limited trait in layer chickens, which was developed by deterministic predictions under different scenarios. Later, one of the possible schemes was implemented in a real population of layer chicken., Methods: In the simulation, the aim was to double the response to selection per year by reducing the generation interval by 50 %, while maintaining the same rate of inbreeding per year. We found that GS with retraining could achieve the set objectives while requiring 75 % fewer reared birds and 82 % fewer phenotyped birds per year. A multi-trait GS scenario was subsequently implemented in a real population of brown egg laying hens. The population was split into two sub-lines, one was submitted to conventional phenotypic selection, and one was selected based on genomic prediction. At the end of the 3-year experiment, the two sub-lines were compared for multiple performance traits that are relevant for commercial egg production., Results: Birds that were selected based on genomic prediction outperformed those that were submitted to conventional selection for most of the 16 traits that were included in the index used for selection. However, although the two programs were designed to achieve the same rate of inbreeding per year, the realized inbreeding per year assessed from pedigree was higher in the genomic selected line than in the conventionally selected line., Conclusions: The results demonstrate that GS is a promising alternative to conventional breeding for genetic improvement of layer chickens.

The cost of feed is a serious issue in the pork industry, contributing about 65 to 75% of the total production cost. To prevent economic losses and decreased productivity of the herd, it is important to select for animals that eat less for the same lean gain, or more efficient animals. Residual feed intake (RFI) is the difference between observed feed intake and expected feed intake based on estimated maintenance and production requirements. Selection for decreased RFI, or more efficient animals, is a potential solution to higher feed costs in pig production. However, animals that are highly selected for decreased RFI may have reduced energy input to the immune system and fail to withstand diseases and stressors after infection that negatively impact profitability. The objective of this study was to evaluate differences in circulating blood cell profiles at a young age between 2 lines of Yorkshire pigs that were divergently selected for RFI as well as the heritability of these traits, to investigate effects of selection for RFI on immune system parameters, and to identify potential biomarkers for feed efficiency. Previous work has shown that the 2 lines had diverged for IGF-1 in serum in young pigs and, therefore, this stage was investigated for other potential physiological differences. Blood samples were drawn for a complete blood count (CBC) analysis from 517 gilts and barrows, ages 35 to 42 d, across the 2 lines. In general, the low-RFI line had lower numbers of specific types of white blood cells but higher hemoglobin concentration and red blood cell volume compared to the high-RFI line. No significant correlations were found between CBC traits and RFI across and within the lines (0.05 < < 0.1). Of the 15 CBC traits that were measured, 3 were highly heritable (0.56 < < 0.62), 9 were moderately heritable (0.12 < < 0.47), and 3 were lowly heritable ( < 0.12), suggesting a substantial genetic component for CBC traits and that selection for CBC traits could be effective. Our results also show that selection for RFI has significantly impacted the number of circulating blood cells. In this experiment, we studied only healthy animals that were not under known pathogen challenge; therefore, our results cannot be directly applied to a disease challenge situation. Future work will be to challenge the animals and determine the effect of challenge on CBC levels.

Background: Determining an animal's genetic merit using genomic information can improve estimated breeding value (EBV) accuracy; however, the magnitude of the accuracy improvement must be large enough to recover the costs associated with implementing genome-enabled selection. One way to reduce costs is to genotype nucleus herd selection candidates using a low-density chip and to use high-density chip genotyping for animals that are used as parents in the nucleus breeding herd. The objective of this study was to develop a tool to estimate the cost structure associated with incorporating genome-enabled selection into multi-level commercial breeding programs., Results: For the purpose of this deterministic study, it was assumed that a commercial pig is created from a terminal line sire and a dam that is a cross between two maternal lines. It was also assumed that all male and female selection candidates from the 1000 sow maternal line nucleus herds were genotyped at low density and all animals used for breeding at high density. With the assumptions used in this analysis, it was estimated that genome-enabled selection costs for a maternal line would be approximately US$0.082 per weaned pig in the commercial production system. A total of US$0.164 per weaned pig is needed to incorporate genome-enabled selection into the two maternal lines. Similarly, for a 600 sow terminal line nucleus herd and genotyping only male selection candidates with the low-density panel, the cost per weaned pig in the commercial herd was estimated to be US$0.044. This means that US$0.21 per weaned pig produced at the commercial level and sired by boars obtained from the nucleus herd breeding program needs to be added to the genetic merit value in order to break even on the additional cost required when genome-enabled selection is used in both maternal lines and the terminal line., Conclusions: By modifying the input values, such as herd size and genotyping strategy, a flexible spreadsheet tool developed from this work can be used to estimate the additional costs associated with genome-enabled selection. This tool will aid breeders in estimating the economic viability of incorporating genome-enabled selection into their specific breeding program.

Animals selected for residual feed intake (RFI) can be used as a model to elucidate molecular explanations for differences in growth efficiency. The objective of this study was to determine the extent to which the protein profile and posttranslational modifications of mitochondria from skeletal muscle and liver relate to feed efficiency gains in pigs divergently selected for RFI. Mitochondria were isolated from the longissimus dorsi (LD) muscle and the liver from pigs (n = 9 each for the high and low RFI line; BW = 95.8 kg). Mitochondria protein profile differences were determined using two-dimensional difference in gel electrophoresis. Proteins were identified using electrospray ionization mass spectrometry. In the line comparison, the β subunit of ATP synthase, heat shock protein (HSP) 60, and HSP70, were identified as being increased in mitochondria from the liver of the low RFI line (23 to 50%; P < 0.1). These differences were not observed in the other comparisons. In the LD, proteins identified as being different between RFI phenotypes included HSP70 and subunit 1 of the cytochrome bc1 complex. These data indicate that genetic selection for RFI tends to result in a consistent change in mitochondrial protein profile. In contrast, classification by phenotype demonstrates that phenotypic differences in RFI are not specifically associated with alterations of the mitochondria protein profile.

Microbes and microbial components potentially impact the performance of pigs through immune stimulation and altered metabolism. These immune modulating factors can include endotoxin from gram negative bacterial outer membrane component, commonly referred to as lipopolysaccharide (LPS). In this study, our objective was to examine the relationship between intestinal barrier integrity, endotoxin and inflammation with feed efficiency (FE), using pig lines divergently selected for residual feed intake (RFI) as a model. Twelve gilts (62 ± 3 kg BW) from the low RFI (LRFI, more efficient) and 12 from the high RFI (HRFI, less efficient) were used. Individual performance data was recorded for 5 wk. At the end of the experimental period, ADFI of LRFI pigs was less (P < 0.001), ADG not different between the 2 lines (P = 0.72) but the G:F of LRFI pigs was greater than for HRFI pigs (P = 0.019). Serum endotoxin concentration (P < 0.01) and the acute phase protein haptoglobin (P < 0.05) were greater in HRFI pigs. Transepithelial resistance of the ileum, transport of fluorescein isothiocyanate labeled-Dextran and-LPS in ileum and colon, as well as tight junction protein mRNA expression in ileum, did not differ between the lines, indicating the 2 lines did not differ in transport characteristics at the intestinal level. Ileum inflammatory markers, myeloperoxidase (P < 0.05) and IL-8 (P < 0.10), were found to be greater in HRFI pigs. Alkaline phosphatase (ALP) activity was significantly increased in the LRFI pigs in ileum and liver tissues and negatively correlated with blood endotoxin (P < 0.05). Lysozyme activity in the liver was not different between the lines; however, the LRFI pigs had a twofold greater lysozyme activity in ileum (P < 0.05). Despite the difference in their activity, ALP or lysozyme mRNA expression was not different between the lines in either tissue. Decreased endotoxin and inflammatory markers and the enhanced activities of antimicrobial enzymes in the LRFI line may not fully explain the difference in the FE between the lines, but they have the potential to prevent the growth potential in HRFI pigs. Further studies are needed to identify the other mechanisms that may contribute to the greater endotoxin and acute phase proteins in the HRFI pigs and the greater FE in the LRFI pigs.

The objective of this study was to determine the extent to which genetic selection for residual feed intake (RFI) impacts electron leakage and reactive oxygen species (ROS) production in mitochondria from muscle and liver tissue. Understanding how genetic selection for RFI impacts animal physiology and growth efficiency is of the utmost importance as the world population increases. Production efficiency is tied directly to energy use. Mitochondria were used in this study because they produce 90% of the ATP in the body and use a large majority of dietary energy. Mitochondria were isolated from both muscle and liver tissue from pigs genetically selected for RFI (n = 8 per RFI line; 34 ± 4 kg). A 2,7-dichlorofluorscein diacetate assay was used to detect differences in hydrogen peroxide production between the more efficient low RFI line and the less efficient high RFI line. Our hypothesis was that greater efficiency would be linked to less ROS production from the mitochondria. There was less ROS production in mitochondria from the white portion of the semitendinosus in the low RFI line compared with the high RFI line, when both NADH and Flavin Adenine Dinucleotide (FADH2) energy substrates were used (glutamate and succinate, respectively). Additionally, mitochondria from the red portion of the semitendinosus in the low RFI line had less ROS production when succinate was used as an energy substrate (P < 0.05). A positive correlation was observed between RFI and ROS in mitochondria from the LM. These data indicate genetic selection for RFI may influence mitochondrial ROS production and efficiency of pork production.

Background: Genomic selection is an appealing method to select purebreds for crossbred performance. In the case of crossbred records, single nucleotide polymorphism (SNP) effects can be estimated using an additive model or a breed-specific allele model. In most studies, additive gene action is assumed. However, dominance is the likely genetic basis of heterosis. Advantages of incorporating dominance in genomic selection were investigated in a two-way crossbreeding program for a trait with different magnitudes of dominance. Training was carried out only once in the simulation., Results: When the dominance variance and heterosis were large and overdominance was present, a dominance model including both additive and dominance SNP effects gave substantially greater cumulative response to selection than the additive model. Extra response was the result of an increase in heterosis but at a cost of reduced purebred performance. When the dominance variance and heterosis were realistic but with overdominance, the advantage of the dominance model decreased but was still significant. When overdominance was absent, the dominance model was slightly favored over the additive model, but the difference in response between the models increased as the number of quantitative trait loci increased. This reveals the importance of exploiting dominance even in the absence of overdominance. When there was no dominance, response to selection for the dominance model was as high as for the additive model, indicating robustness of the dominance model. The breed-specific allele model was inferior to the dominance model in all cases and to the additive model except when the dominance variance and heterosis were large and with overdominance. However, the advantage of the dominance model over the breed-specific allele model may decrease as differences in linkage disequilibrium between the breeds increase. Retraining is expected to reduce the advantage of the dominance model over the alternatives, because in general, the advantage becomes important only after five or six generations post-training., Conclusion: Under dominance and without retraining, genomic selection based on the dominance model is superior to the additive model and the breed-specific allele model to maximize crossbred performance through purebred selection.

Little is known of the consequences of divergent selection for residual feed intake (RFI) on intestinal digestion capacity, particularly during immune system stimulation (ISS). Our objective was to evaluate the impact of ISS and divergent selection for RFI on apparent ileal digestibility (AID) and apparent fecal digestibility (AFD) of nutrients and intestinal nutrient active transport and barrier function. Twenty-eight gilts (63 ± 4 kg BW) from low RFI (LRFI; n = 14) and high RFI (HRFI; n = 14) Yorkshire lines were randomly selected from the Iowa State University RFI herd. Following adaptation, 8 pigs in each line were injected intramuscularly and every 48 h for 7 d with increasing amounts of Escherichia coli lipopolysaccharide (ISS+). Remaining pigs were injected with saline (ISS-). Pigs were then euthanized and ileal digesta was collected for measuring AID of nutrients. Fecal samples were collected on a daily basis and pooled for measuring AFD of nutrients. A segment of ileum was used to measure nutrient transport and transepithelial resistance (TER) and/or barrier integrity by Ussing chambers. No effects of line or its interaction with ISS on AID of CP (N × 6.25) and OM, TER, and active nutrients transport were observed. However, ISS decreased (P < 0.05) and tended to (P < 0.1) decrease AID of CP and OM, respectively. Decrease in AFD of CP as result of ISS was greater in the LRFI line compared to the HRFI line (P < 0.05). Relative to ISS-, active glucose and P transport was greater in ISS+ pigs (P < 0.05). Genetic selection for LRFI increases the AFD but has no effect on AID of nutrients. It also reduces the total tract digestive capacity of growing pigs during ISS. Immune system stimulation affects both AID and AFD of dietary CP.

Residual feed intake (RFI) is a measure of feed efficiency defined as the difference between observed and predicted feed intake based on average requirements for growth and maintenance. The objective of this study was to evaluate the effect of selection for decreased RFI on feeding behavior traits and to estimate their relationships with RFI. Three data sets from the 4th and 5th generations of a selection experiment with a line selected for reduced RFI (LRFI) and a randomly selected control line (CTRL) were analyzed. Lines were mixed in pens of 16 and evaluated for feeding behavior traits obtained from a single-space electronic feeder over a growing period of ~3 mo before ~115 kg. The following traits were evaluated as averages over the entire test period and over the first and second half of the test period: number of visits per day and hour; occupation time per day, visit, and hour; feed intake (FI) per day, visit, and hour; and FI rate per visit. Models used included fixed effects of line and feeder, covariates of on-test age and FI per day, and random effects of pen, on-test group, sire, and litter. Repeated measures models were used to analyze feeding patterns during the day. The LRFI pigs had significantly less FI per day than CTRL pigs for all 3 data sets. With adjustment for FI per day, line differences of all traits were in the same direction for all 3 data sets but differed in significance and size. Feed intake per visit and hour and visits per day and hour did not differ between lines, but the trend was for LRFI pigs to have fewer visits, in particular during peak eating times. The LRFI pigs had a greater feeding rate and less occupation time per day, visit, and hour than CTRL pigs, but this was not significant for all data sets. Correlations of RFI with FI per day and visit and visits per day were positive. Average daily gain was positively correlated with FI per day and visit and occupation time per visit but negatively correlated with visits per day. Feed intake per day was positively correlated with backfat. In conclusion, feed efficiency may be affected by FI behavior because selection for decreased RFI has resulted in pigs that spend less time eating and eat faster.

The objectives of this study were to determine the effect of selection for improved residual feed intake on behavior, activity, and lesion scores in gilts in their home pen. A total of 192 gilts were used, 96 from a line that had been selected for decreased residual feed intake over 5 generations (LRFI) and 96 from a randomly bred control line. Gilts were housed in 12 pens (16 gilts/pen; 0.82 m(2)/gilt) containing 8 gilts from each line in a conventional grow-finish unit. Twelve hours of video footage were collected on the day of placement and then every 4 wk for 3 more observational periods. Video was scored using a 10-min instantaneous scan sampling technique for 4 postures (standing, lying, sitting, and locomotion) and 1 behavior (at drinker). Categories of active (standing, locomotion, and at drinker) and inactive (sitting and lying) were also created. Lesion scores were collected 24 h after behavior collection had begun. The body of a gilt was divided into 4 regions, with each region receiving a score of 0 (0 lesions) to 3 (5+ lesions). All statistical analyses used Proc Mixed of SAS. Data were analyzed separately for the day of placement and the subsequent 3 rounds. General activity was summarized on a percentage basis by each posture and behavior and subjected to an arcsine square root transformation to normalize data and stabilize variance. Analysis was performed on each behavior and posture. Lesion scores for each region of the body were analyzed as repeated measures. There were no differences (P > 0.05) between genetic lines for all postures and the behavior at drinker on the day of placement. However, over subsequent rounds it was observed that LRFI gilts spent less (P = 0.03) time standing, more time sitting (P = 0.05), and were less active (P = 0.03) overall. Gilts from the LRFI line had decreased (P < 0.045) lesion scores on the day after placement. However, over subsequent rounds there were no (P > 0.05) differences between the genetic lines. In conclusion, on the day of placement there were no postural, behavior, or general activity differences between genetic lines, but LRFI gilts had decreased lesion scores. Behavioral differences were observed between genetic lines over subsequent rounds, with LRFI gilts becoming less active, but there were no differences in lesion scores.

Data from a selection experiment for residual feed intake (RFI) were used to estimate genetic correlations between measures of efficiency and performance traits with juvenile IGF-I, and to demonstrate direct and correlated responses to selection. The heritability of IGF-I was 0.28 +/- 0.06 and genetic correlations of IGF-I with feed intake (0.26 +/- 0.17), backfat (0.52 +/- 0.11), and feed conversion ratio (0.78 +/- 0.14) were moderate to large. The estimated and realized genetic correlations between RFI and IGF-I were 0.63 +/- 0.15 and 0.84. In contrast, genetic correlations between IGF-I and lifetime or test period growth did not differ (P > 0.05) significantly from zero (0.06 +/- 0.14 and -0.19 +/- 0.14). Selection for decreased RFI produced a direct response in RFI, as expected, and was accompanied by downward correlated responses in ADFI, juvenile IGF-I, backfat, and growth traits, listed in order of decreasing relative magnitude, and an increased loin muscle area. The correlated response in IGF-I to selection on RFI demonstrates that this physiological measure is genetically associated with efficiency, and is thus useful as an early information source to estimate genetic merit for efficiency before performance testing. Decreased juvenile IGF-I is associated with leaner, more efficient animals.

In livestock, genomic selection (GS) has primarily been investigated by simulation of purebred populations. Traits of interest are, however, often measured in crossbred or mixed populations with uncertain breed composition. If such data are used as the training data for GS without accounting for breed composition, estimates of marker effects may be biased due to population stratification and admixture. To investigate this, a genome of 100 cM was simulated with varying marker densities (5 to 40 segregating markers per cM). After 1,000 generations of random mating in a population of effective size 500, 4 lines with effective size 100 were isolated and mated for another 50 generations to create 4 pure breeds. These breeds were used to generate combined, F(1), F(2), 3- and 4-way crosses, and admixed training data sets of 1,000 individuals with phenotypes for an additive trait controlled by 100 segregating QTL and heritability of 0.30. The validation data set was a sample of 1,000 genotyped individuals from one pure breed. Method Bayes-B was used to simultaneously estimate the effects of all markers for breeding value estimation. With 5 (40) markers per cM, the correlation of true with estimated breeding value of selection candidates (accuracy) was greatest, 0.79 (0.85), when data from the same pure breed were used for training. When the training data set consisted of crossbreds, the accuracy ranged from 0.66 (0.79) to 0.74 (0.83) for the 2 marker densities, respectively. The admixed training data set resulted in nearly the same accuracies as when training was in the breed to which selection candidates belonged. However, accuracy was greatly reduced when genes from the target pure breed were not included in the admixed or crossbred population. This implies that, with high-density markers, admixed and crossbred populations can be used to develop GS prediction equations for all pure breeds that contributed to the population, without a substantial loss of accuracy compared with training on purebred data, even if breed origin has not been explicitly taken into account. In addition, using GS based on high-density marker data, purebreds can be accurately selected for crossbred performance without the need for pedigree or breed information. Results also showed that haplotype segments with strong linkage disequilibrium are shorter in crossbred and admixed populations than in purebreds, providing opportunities for QTL fine mapping.

Genomic selection (GS) using high-density single-nucleotide polymorphisms (SNPs) is promising to improve response to selection in populations that are under artificial selection. High-density SNP genotyping of all selection candidates each generation, however, may not be cost effective. Smaller panels with SNPs that show strong associations with phenotype can be used, but this may require separate SNPs for each trait and each population. As an alternative, we propose to use a panel of evenly spaced low-density SNPs across the genome to estimate genome-assisted breeding values of selection candidates in pedigreed populations. The principle of this approach is to utilize cosegregation information from low-density SNPs to track effects of high-density SNP alleles within families. Simulations were used to analyze the loss of accuracy of estimated breeding values from using evenly spaced and selected SNP panels compared to using all high-density SNPs in a Bayesian analysis. Forward stepwise selection and a Bayesian approach were used to select SNPs. Loss of accuracy was nearly independent of the number of simulated quantitative trait loci (QTL) with evenly spaced SNPs, but increased with number of QTL for the selected SNP panels. Loss of accuracy with evenly spaced SNPs increased steadily over generations but was constant when the smaller number individuals that are selected for breeding each generation were also genotyped using the high-density SNP panel. With equal numbers of low-density SNPs, panels with SNPs selected on the basis of the Bayesian approach had the smallest loss in accuracy for a single trait, but a panel with evenly spaced SNPs at 10 cM was only slightly worse, whereas a panel with SNPs selected by forward stepwise selection was inferior. Panels with evenly spaced SNPs can, however, be used across traits and populations and their performance is independent of the number of QTL affecting the trait and of the methods used to estimate effects in the training data and are, therefore, preferred for broad applications in pedigreed populations under artificial selection.

We compared the accuracies of four genomic-selection prediction methods as affected by marker density, level of linkage disequilibrium (LD), quantitative trait locus (QTL) number, sample size, and level of replication in populations generated from multiple inbred lines. Marker data on 42 two-row spring barley inbred lines were used to simulate high and low LD populations from multiple inbred line crosses: the first included many small full-sib families and the second was derived from five generations of random mating. True breeding values (TBV) were simulated on the basis of 20 or 80 additive QTL. Methods used to derive genomic estimated breeding values (GEBV) were random regression best linear unbiased prediction (RR-BLUP), Bayes-B, a Bayesian shrinkage regression method, and BLUP from a mixed model analysis using a relationship matrix calculated from marker data. Using the best methods, accuracies of GEBV were comparable to accuracies from phenotype for predicting TBV without requiring the time and expense of field evaluation. We identified a trade-off between a method's ability to capture marker-QTL LD vs. marker-based relatedness of individuals. The Bayesian shrinkage regression method primarily captured LD, the BLUP methods captured relationships, while Bayes-B captured both. Under most of the study scenarios, mixed-model analysis using a marker-derived relationship matrix (BLUP) was more accurate than methods that directly estimated marker effects, suggesting that relationship information was more valuable than LD information. When markers were in strong LD with large-effect QTL, or when predictions were made on individuals several generations removed from the training data set, however, the ranking of method performance was reversed and BLUP had the lowest accuracy.

The purpose of this study was to develop and investigate selection strategies that aim at maximizing long-term genetic response while conserving gene diversity and controlling inbreeding in populations of limited effective size, assuming complete knowledge of all genes affecting a quantitative trait. Three selection strategies were proposed to select on 100 quantitative trait loci (QTL) and compared with truncation selection on breeding value. Alternative selection strategies aimed at maximizing the average breeding value of parents with a penalty on (1) the number of unfavourable QTL genotypes among parents (OS-I), (2) the negative of the logarithm of the frequency of the favourable allele at each QTL among parents (OS-II), and (3) the average pedigree relationship among parents (OS-III). When all QTL and their effects were known, the strategies examined were able to obtain extra long-term responses, conserve QTL diversity and reduce inbreeding, compared with truncation selection. Strategy OS-II was the most effective in conserving QTL diversity and OS-III in reducing inbreeding. By changing the magnitude of the penalties applied, the impact on long-term response, inbreeding and diversity can be controlled. Extra long-term responses over truncation selection of OS-I and OS-II were even greater when effects of QTL were estimated rather than assumed known, indicating the applicability of results to practical strategies for marker-assisted selection. Extra responses are expected to be reduced for larger population sizes.

Residual feed intake (RFI) is a measure of feed efficiency defined as the difference between the observed feed intake and that predicted from the average requirements for growth and maintenance. The objective of this study was to evaluate the response in a selection experiment consisting of a line selected for low RFI and a random control line and to estimate the genetic parameters for RFI and related production and carcass traits. Beginning with random allocation of purebred Yorkshire littermates, in each generation, electronically measured ADFI, ADG, and ultrasound backfat (BF) were evaluated during a approximately 40- to approximately 115-kg of BW test period on approximately 90 boars from first parity and approximately 90 gilts from second parity sows of the low RFI line. After evaluation of first parity boars, approximately 12 boars and approximately 70 gilts from the low RFI line were selected to produce approximately 50 litters for the next generation. Approximately 30 control line litters were produced by random selection and mating. Selection was on EBV for RFI from an animal model analysis of ADFI, with on-test group and sex (fixed), pen within group and litter (random), and covariates for interactions of on- and off-test BW, on-test age, ADG, and BF with generations. The RFI explained 34% of phenotypic variation in ADFI. After 4 generations of selection, estimates of heritability for RFI, ADFI, ADG, feed efficiency (FE, which is the reciprocal of the feed conversion ratio and equals ADG/ ADFI), and ultrasound-predicted BF, LM area (LMA), and intramuscular fat (IMF) were 0.29, 0.51, 0.42, 0.17, 0.68, 0.57, and 0.28, respectively; predicted responses based on average EBV in the low RFI line were -114, -202, and -39 g/d for RFI (= 0.9 phenotypic SD), ADFI (0.9 SD), and ADG (0.4 SD), respectively, and 1.56% for FE (0.5 SD), -0.37 mm for BF (0.1 SD), 0.35 cm(2) for LMA (0.1 SD), and -0.10% for IMF (0.3 SD). Direct phenotypic comparison of the low RFI and control lines based on 92 low RFI and 76 control gilts from the second parity of generation 4 showed that selection had significantly decreased RFI by 96 g/d (P = 0.002) and ADFI by 165 g/d (P < 0.0001). The low RFI line also had 33 g/d lower ADG (P = 0.022), 1.36% greater FE (P = 0.09), and 1.99 mm less BF (P = 0.013). There was not a significant difference in LMA and other carcass traits, including subjective marbling score, despite a large observed difference in ultrasound-predicted IMF (-1.05% with P < 0.0001). In conclusion, RFI is a heritable trait, and selection for low RFI has significantly decreased the feed required for a given rate of growth and backfat.

Selection index methods can be used for deterministic assessment of the potential benefit of including marker information in genetic improvement programmes using marker-assisted selection (MAS). By specifying estimates of breeding values derived from marker information (M-EBV) as a correlated trait with heritability equal to 1, it was demonstrated that marker information can be incorporated in standard software for selection index predictions of response and rates of inbreeding, which requires specifying phenotypic traits and their genetic parameters. Path coefficient methods were used to derive genetic and phenotypic correlations between M-EBV and the phenotypic data. Methods were extended to multi-trait selection and to the case when M-EBV are based on high-density marker genotype data, as in genomic selection. Methods were applied to several example scenarios, which confirmed previous results that MAS substantially increases response to selection but also demonstrated that MAS can result in substantial reductions in the rates of inbreeding. Although further validation by stochastic simulation is required, the developed methodology provides an easy means of deterministically evaluating the potential benefits of MAS and to optimize selection strategies with availability of marker data.

Efficiency of genomic selection with low-cost genotyping in a composite line from a cross between inbred lines was evaluated for a trait with heritability 0.10 or 0.25 using a low-density marker map. With genomic selection, selection was on the sum of estimates of effects of all marker intervals across the genome, fitted either as fixed (fixed GS) or random (random GS) effects. Reponses to selection over 10 generations, starting from the F(2), were compared with standard BLUP selection. Estimates of variance for each interval were assumed independent and equal. Both GS strategies outperformed BLUP selection, especially in initial generations. Random GS outperformed fixed GS in early generations and performed slightly better than fixed GS in later generations. Random GS gave higher genetic gain when the number of marker intervals was greater (180 or 10 cM intervals), whereas fixed GS gave higher genetic gain when the number of marker intervals was low (90 or 20 cM). Including interactions between generation and marker scores in the model resulted in lower genetic gains than models without interactions. When phenotypes were available only in the F(2) for GS, treating marker scores as fixed effects led to considerably lower genetic gain than random GS. Benefits of GS over standard BLUP were lower with high heritability. Genomic selection resulted in greater response than MAS based on only significant marker intervals (standard MAS) by increasing the frequency of QTL with both large and small effects. The efficiency of genomic selection over standard MAS depends on stringency of the threshold used for QTL detection. In conclusion, genomic selection can be effective in composite lines using a sparse marker map.

Several studies have shown that selection of purebreds for increased performance of their crossbred descendants under field conditions is hampered by low genetic correlations between purebred and commercial crossbred (CC) performance. Although this can be addressed by including phenotypic data from CC relatives for selection of purebreds through combined crossbred and purebred selection (CCPS), this also increases rates of inbreeding and requires comprehensive systems for collection of phenotypic data and pedigrees at the CC level. This study shows that both these limitations can be overcome with marker-assisted selection (MAS) by using estimates of the effects of markers on CC performance. To evaluate the potential benefits of CC-MAS, a model to incorporate marker information in selection strategies was developed based on selection index theory, which allows prediction of responses and rates of inbreeding by using standard deterministic selection theory. Assuming a genetic correlation between purebred and CC performance of 0.7 for a breeding program representing a terminal sire line in pigs, CC-MAS was shown to substantially increase rates of response and reduce rates of inbreeding compared with purebred selection and CCPS, with 60 CC half sibs available for each purebred selection candidate. When the accuracy of marker-based EBV was 0.6, CC-MAS resulted in 34 and 10% greater responses in CC performance than purebred selection and CCPS. Corresponding rates of inbreeding were 1.4% per generation for CC-MAS, compared with 2.1% for purebred selection and 3.0% for CCPS. For marker-based EBV with an accuracy of 0.9, CC-MAS resulted in 75 and 43% greater responses than purebred selection and CCPS, and further reduced rates of inbreeding to 1.0% per generation. Selection on marker-based EBV derived from purebred phenotypes resulted in substantially less response in CC performance than in CC-MAS. In conclusion, effective use of MAS requires estimates of the effect on CC performance, and MAS based on such estimates enables more effective selection for CC performance without the need for extensive pedigree recording and while reducing rates of inbreeding.

A method was developed to optimize simultaneous selection for a quantitative trait with a known QTL within a male and a female line to maximize crossbred performance from a two-way cross. Strategies to maximize cumulative discounted response in crossbred performance over ten generations were derived by optimizing weights in an index of a QTL and phenotype. Strategies were compared to selection on purebred phenotype. Extra responses were limited for QTL with additive and partial dominance effects, but substantial for QTL with over-dominance, for which optimal QTL selection resulted in differential selection in male and female lines to increase the frequency of heterozygotes and polygenic responses. For over-dominant QTL, maximization of crossbred performance one generation at a time resulted in similar responses as optimization across all generations and simultaneous optimal selection in a male and female line resulted in greater response than optimal selection within a single line without crossbreeding. Results show that strategic use of information on over-dominant QTL can enhance crossbred performance without crossbred testing.

Stochastic computer simulation was used to investigate the potential extra genetic gains obtained from gene-assisted selection (GAS) by combining 1) optimization of genetic contributions for maximizing gain, while restricting the rate of inbreeding with 2) optimization of the relative emphasis given to the QTL over generations. The genetic model assumed implied a mixed inheritance model in which a single quantitative trait locus (i.e., QTL) is segregating together with polygenes. When compared with standard GAS (i.e., fixed contributions and equal emphasis on the QTL and polygenic EBV), combined optimization of contributions of selection candidates and weights on the QTL across generations allowed substantial increases in gain at a fixed rate of inbreeding and avoided the conflict between short- and long-term responses in GAS schemes. Most of the increase of gain was produced by optimization of selection candidates' contributions. Optimization of the relative emphasis given to the QTL over generations had, however, a greater effect on avoiding the long-term loss usually observed in GAS schemes. Optimized contribution schemes led to lower gametic phase disequilibrium between the QTL and polygenes and to higher selection intensities both on the QTL and polygenes than with standard truncation selection with fixed contributions of selection candidates.

During the past few decades, advances in molecular genetics have led to the identification of multiple genes or genetic markers associated with genes that affect traits of interest in livestock, including genes for single-gene traits and QTL or genomic regions that affect quantitative traits. This has provided opportunities to enhance response to selection, in particular for traits that are difficult to improve by conventional selection (low heritability or traits for which measurement of phenotype is difficult, expensive, only possible late in life, or not possible on selection candidates). Examples of genetic tests that are available to or used in industry programs are documented and classified into causative mutations (direct markers), linked markers in population-wide linkage disequilibrium with the QTL (LD markers), and linked markers in population-wide equilibrium with the QTL (LE markers). In general, although molecular genetic information has been used in industry programs for several decades and is growing, the extent of use has not lived up to initial expectations. Most applications to date have been integrated in existing programs on an ad hoc basis. Direct markers are preferred for effective implementation of marker-assisted selection, followed by LD and LE markers, the latter requiring within-family analysis and selection. Ease of application and potential for extra-genetic gain is greatest for direct markers, followed by LD markers, but is antagonistic to ease of detection, which is greatest for LE markers. Although the success of these applications is difficult to assess, several have been hampered by logistical requirements, which are substantial, in particular for LE markers. Opportunities for the use of molecular information exist, but their successful implementation requires a comprehensive integrated strategy that is closely aligned with business goals. The current attitude toward marker-assisted selection is therefore one of cautious optimism.

Lean growth rate (LGR) in pigs is a nonlinear biological function of growth rate and lean quantity. According to animal breeding theory, genetic progress for LGR is maximized with selection on a linear index of its component traits, but selection on direct EBV for LGR is also common. In this study, the performance of five criteria for selection on estimated LGR in pigs was evaluated through simulation over five generations: linear indexes of multiple-trait EBV of component traits with or without updating index weights in each generation; a nonlinear index of multiple-trait EBV of component traits; and direct selection on EBV for LGR from a single-trait model or a multiple-trait model that included LGR and component traits. The nonlinear index yielded the highest response in LGR in Generation 5, but the linear index with updating performed almost as well. Not updating weights for the linear index reduced response in LGR by 1.1% in Generation 5 (P < 0.05). Direct selection on single-trait EBV for LGR yielded the lowest responses in Generation 5. Direct selection on EBV for LGR from a multiple-trait animal model yielded a 3.1% greater response in LGR in Generation 5 than direct selection on EBV for LGR based on a single-trait animal model (P < 0.05), but yielded a 1.9% lower response than the nonlinear index. Although differences in response in LGR were limited, alternative selection criteria resulted in substantially different responses in component traits. Linear index selection for LGR placed more emphasis on lean quantity, whereas direct selection for LGR emphasized growth rate. Based on the relative changes in the responses in LGR, selection for estimated LGR based on a nonlinear index or a linear index with updating is recommended for use in the swine industry.

A mathematical approach was developed to model and optimize selection on multiple known quantitative trait loci (QTL) and polygenic estimated breeding values in order to maximize a weighted sum of responses to selection over multiple generations. The model allows for linkage between QTL with multiple alleles and arbitrary genetic effects, including dominance, epistasis, and gametic imprinting. Gametic phase disequilibrium between the QTL and between the QTL and polygenes is modeled but polygenic variance is assumed constant. Breeding programs with discrete generations, differential selection of males and females and random mating of selected parents are modeled. Polygenic EBV obtained from best linear unbiased prediction models can be accommodated. The problem was formulated as a multiple-stage optimal control problem and an iterative approach was developed for its solution. The method can be used to develop and evaluate optimal strategies for selection on multiple QTL for a wide range of situations and genetic models.

A mathematical approach to optimize selection on multiple quantitative trait loci (QTL) and an estimate of residual polygenic effects was applied to selection on two linked or unlinked additive QTL. Strategies to maximize total or cumulative discounted response over ten generations were compared to standard QTL selection on the sum of breeding values for the QTL and an estimated breeding value for polygenes, and to phenotypic selection. Optimal selection resulted in greater response to selection than standard QTL or phenotypic selection. Tight linkage between the QTL (recombination rate 0.05) resulted in a slightly lower response for standard QTL and phenotypic selection but in a greater response for optimal selection. Optimal selection capitalized on linkage by emphasizing selection on favorable haplotypes. When the objective was to maximize total response after ten generations and QTL were unlinked, optimal selection increased QTL frequencies to fixation in a near linear manner. When starting frequencies were equal for the two QTL, equal emphasis was given to each QTL, regardless of the difference in effects of the QTL and regardless of the linkage, but the emphasis given to each of the two QTL was not additive. These results demonstrate the ability of optimal selection to capitalize on information on the complex genetic basis of quantitative traits that is forthcoming.

The potential extra response that can be obtained from the optimal use of a known QTL in selection by optimizing weights in an index of breeding value for the QTL and polygenic EBV was investigated for a range of parameters. Optimal strategies were derived for a deterministic model of simultaneous selection on a QTL and polygenic effects using optimal control theory. Responses over 10 generations to the following selection strategies were compared: 1) standard QTL selection, with QTL weights equal to 1, 2) optimal QTL selection, 3) stepwise single-generation optimal QTL selection, and 4) non-QTL selection based on phenotype. Cumulative discounted response with discount rates of 10 or 30% per generation were evaluated and used as objective for optimal selection strategies. Optimal selection balanced the conflict between short- and long-term responses and gave greater cumulative discounted response than standard QTL selection of up to 20%, but less than 5% for most cases. Discount rate had limited impact. For a QTL with an additive effect of one polygenic standard deviation, cumulative discounted response from optimal QTL selection was less than 5% greater than response for non-QTL selection for most cases. Exceptions were traits with low heritability and recessive QTL at low frequency, for which extra response was up to 55% greater. Stepwise optimal selection resulted in less cumulative discounted response than standard QTL selection for QTL with negative dominance. The benefit of optimal over stepwise optimal selection was limited (less than 4%) for most cases, except for overdominant QTL. These results indicate that optimizing selection on an identified QTL can result in greater responses to selection but that extra responses tend to be limited for the situations studied here of single-stage purebred selection on a single QTL for a trait observed on both sexes.