Imputation of non-genotyped F1 dams to improve genetic gain in swine crossbreeding programs.

This study investigated using imputed genotypes from non-genotyped animals which were not in the pedigree for the purpose of genetic selection and improving genetic gain for economically relevant traits. Simulations were used to mimic a 3-breed crossbreeding system that resembled a modern swine breeding scheme. The simulation consisted of three purebred (PB) breeds A, B, and C each with 25 and 425 mating males and females, respectively. Males from A and females from B were crossed to produce AB females (n = 1,000), which were crossed with males from C to produce crossbreds (CB; n = 10,000). The genome consisted of three chromosomes with 300 quantitative trait loci and ~9,000 markers. Lowly heritable reproductive traits were simulated for A, B, and AB (h2 = 0.2, 0.2, and 0.15, respectively), whereas a moderately heritable carcass trait was simulated for C (h2 = 0.4). Genetic correlations between reproductive traits in A, B, and AB were moderate (rg = 0.65). The goal trait of the breeding program was AB performance. Selection was practiced for four generations where AB and CB animals were first produced in generations 1 and 2, respectively. Non-genotyped AB dams were imputed using FImpute beginning in generation 2. Genotypes of PB and CB were used for imputation. Imputation strategies differed by three factors: 1) AB progeny genotyped per generation (2, 3, 4, or 6), 2) known or unknown mates of AB dams, and 3) genotyping rate of females from breeds A and B (0% or 100%). PB selection candidates from A and B were selected using estimated breeding values for AB performance, whereas candidates from C were selected by phenotype. Response to selection using imputed genotypes of non-genotyped animals was then compared to the scenarios where true AB genotypes (trueGeno) or no AB genotypes/phenotypes (noGeno) were used in genetic evaluations. The simulation was replicated 20 times. The average increase in genotype concordance between unknown and known sire imputation strategies was 0.22. Genotype concordance increased as the number of genotyped CB increased with little additional gain beyond 9 progeny. When mates of AB were known and more than 4 progeny were genotyped per generation, the phenotypic response in AB did not differ (P > 0.05) from trueGeno yet was greater (P < 0.05) than noGeno. Imputed genotypes of non-genotyped animals can be used to increase performance when 4 or more progeny are genotyped and sire pedigrees of CB animals are known.

In swine breeding, phenotypic information is often gathered from elite purebred (PB) breeding stock and occasionally terminal crossbred animals (CB). Using economically relevant traits expressed by dams of CB (F1) in genetic evaluations is not common due to the lack of pedigree and/or genomic relationships to relate phenotypes of F1 to PB selection candidates. Since swine often have large litters, this study aimed to develop strategies to incorporate phenotypes of F1 into genetic evaluations by imputing F1 genotypes. Using simulation, we investigated the impact of CB pedigree completeness, the number of CB genotyped progeny, the number of parities (and thus mates) a F1 had, and genomic diversity in PB breeds on imputation accuracy and the response to selection in F1 performance. When mates of F1 were in the pedigree and 4 or more CB progeny were genotyped per generation, imputation accuracy was high and the phenotypic response in F1 did not differ compared to when true F1 genotypes were used. Our results show that imputed genotypes can be used to increase performance in swine breeding programs, but the magnitude depends upon the number of CB progeny genotyped, the number of F1 mates, and the completeness of the pedigree.

Bias in variance component estimation in swine crossbreeding schemes using selective genotyping and phenotyping strategies.

Selective genotyping of crossbred (CB) animals to include in traditionally purebred (PB) dominated genetic evaluations has been shown to provide an increase in the response to selection for CB performance. However, the inclusion of phenotypes from selectively genotyped CB animals, without the phenotypes of their non-genotyped cohorts, could cause bias in estimated variance components (VC) and subsequent estimated breeding values (EBV). The objective of the study was to determine the impact of selective CB genotyping on VC estimates and subsequent bias in EBV when non-genotyped CB animals are not included in genetic evaluations. A swine crossbreeding scheme producing 3-way CB animals was simulated to create selectively genotyped datasets. The breeding scheme consisted of three PB breeds each with 25 males and 450 females, F1 crosses with 1200 females and 12,000 CB progeny. Eighteen chromosomes each with 100 QTL and 4k SNP markers were simulated. Both PB and CB performances were considered to be moderately heritable (h2 = 0.4). Factors evaluated were as follows: 1) CB phenotype and genotype inclusion of 15% (n = 1800) or 35% (n = 4200), 2) genetic correlation between PB and CB performance (rpc = 0.1, 0.5, or 0.7), and 3) selective genotyping strategy. Genotyping strategies included the following: 1) Random: random CB selection, 2) Top: highest CB phenotype, and 3) Extreme: half highest and half lowest CB phenotypes. Top and Extreme selective genotyping strategies were considered by selecting animals in full-sib (FS) families or among the CB population (T). In each generation, 4320 PB selection candidates contributed phenotypic and genotypic records. Each scenario was replicated 15 times. VC were estimated for PB and CB performance utilizing bivariate models using pedigree relationships with dams of CB animals considered to be unknown. Estimated values of VC for PB performance were not statistically different from true values. Top selective genotyping strategies produced deflated estimates of phenotypic VC for CB performance compared to true values. When using estimated VC, Top_T and Extreme_T produced the most biased EBV, yet EBV of PB selection candidates for CB performance were most accurate when using Extreme_T. Results suggest that randomly selecting CB animals to genotype or selectively genotyping Top or Extreme CB animals within full-sib families can lead to accurate estimates of additive genetic VC for CB performance and unbiased EBV.

Selective genotyping and phenotypic data inclusion strategies of crossbred progeny for combined crossbred and purebred selection in swine breeding.

Inclusion of crossbred (CB) data into traditionally purebred (PB) genetic evaluations has been shown to increase the response in CB performance. Currently, it is unrealistic to collect data on all CB animals in swine production systems, thus, a subset of CB animals must be selected to contribute genomic/phenotypic information. The aim of this study was to evaluate selective genotyping strategies in a simulated 3-way swine crossbreeding scheme. The swine crossbreeding scheme was simulated and produced 3-way CB animals for 6 generations with 3 distinct PB breeds each with 25 and 175 mating males and females, respectively. F1 crosses (400 mating females) produced 4,000 terminal CB progeny which were subjected to selective genotyping. The genome consisted of 18 chromosomes with 1,800 QTL and 72k SNP markers. Selection was performed using estimated breeding values (EBV) for CB performance. It was assumed that both PB and CB performance was moderately heritable (h2=0.4). Several scenarios altering the genetic correlation between PB and CB performance (rpc=0.1, 0.3, 0.5, 0.7,or 0.9) were considered. CB animals were chosen based on phenotypes to select 200, 400, or 800 CB animals to genotype per generation. Selection strategies included: (1) Random: random selection, (2) Top: highest phenotype, (3) Bottom: lowest phenotype, (4) Extreme: half highest and half lowest phenotypes, and (5) Middle: average phenotype. Each selective genotyping strategy, except for Random, was considered by selecting animals in half-sib (HS) or full-sib (FS) families. The number of PB animals with genotypes and phenotypes each generation was fixed at 1,680. Each unique genotyping strategy and rpc scenario was replicated 10 times. Selection of CB animals based on the Extreme strategy resulted in the highest (P < 0.05) rates of genetic gain in CB performance (ΔG) when rpc<0.9. For highly correlated traits (rpc=0.9) selective genotyping did not impact (P > 0.05) ΔG. No differences (P > 0.05) were observed in ΔG between top, bottom, or middle when rpc>0.1. Higher correlations between true breeding values (TBV) and EBV were observed using Extreme when rpc<0.9. In general, family sampling method did not impact ΔG or the correlation between TBV and EBV. Overall, the Extreme genotyping strategy produced the greatest genetic gain and the highest correlations between TBV and EBV, suggesting that 2-tailed sampling of CB animals is the most informative when CB performance is the selection goal.

Impact of inclusion rates of crossbred phenotypes and genotypes in nucleus selection programs.

Numerous methods have been suggested to incorporate crossbred (CB) phenotypes and genotypes into swine selection programs, yet little research has focused on the implicit trade-off decisions between generating data at the nucleus or commercial level. The aim of this study was to investigate the impact of altering the proportion of purebred (PB) and CB phenotypes and genotypes in genetic evaluations on the response to selection of CB performance. Assuming CB and PB performance with moderate heritabilities (h2=0.4), a three-breed swine crossbreeding scheme was simulated and selection was practiced for six generations, where the goal was to increase CB performance. Phenotypes, genotypes, and pedigrees for three PB breeds (25 and 175 mating males and females for each breed, respectively), F1 crosses (400 mating females), and terminal cross progeny (2,500) were simulated. The genome consisted of 18 chromosomes with 1,800 quantitative trait loci and 72k single nucleotide polymorphism (SNP) markers. Selection was performed in PB breeds using estimated breeding value for each phenotyping/genotyping strategy. Strategies investigated were: 1) increasing the proportion of CB with genotypes, phenotypes, and sire pedigree relationships, 2) decreasing the proportion of PB phenotypes and genotypes, and 3) altering the genetic correlation between PB and CB performance (rpc). Each unique rpc scenario and data collection strategy was replicated 10 times. Results showed that including CB data improved the CB performance regardless of  rpc or data collection strategy compared with when no CB data were included. Compared with using only PB information, including 10% of CB progeny per generation with sire pedigrees and phenotypes increased the response in CB phenotype by 134%, 55%, 33%, 23%, and 21% when rpc was 0.1, 0.3, 0.5, 0.7, and 0.9, respectively. When the same 10% of CB progeny were also genotyped, CB performance increased by 243%, 54%, 38%, 23%, and 20% when the rpc was 0.1, 0.3, 0.5, 0.7, and 0.9, respectively, compared with when no CB data were utilized. Minimal change was observed in the average CB phenotype when PB phenotypes were included or proportionally removed when CB were genotyped. Removal of both PB phenotypes and genotypes when CB were genotyped greatly reduced the response in CB performance. In practice, the optimal inclusion rate of CB and PB data depends upon the genetic correlation between CB and PB animals and the expense of additional CB data collection compared with the economic benefit associated with increased CB performance.

Associations with four generations of divergent selection for age at puberty in swine.

The objective was to evaluate 4 generations of divergent selection for age at puberty (young age at puberty = YOUNG; old age at puberty = OLD) in swine. Composite Landrace × Large White animals (n = 4,941) were reared at the North Carolina Department of Agriculture Tidewater Research Station. At 130 d of age, gilts were exposed to mature boars for 7 min daily. Estrous detection continued for 90 d. Puberty was defined as first observed standing reflex in the presence of a boar. Reproductive and performance traits included: age at puberty (AGEPUB), probability of a gilt reaching puberty by 220 d of age (PUB), puberty weight (PUBWT), pubertal estrus (LEN1), length of second estrus (LEN2), vulva width at puberty (VW1), vulva width at second estrus (VW2), gilt birth weight (BWT), gilt weaning weight (WWT), loin eye area (LEA), backfat depth (BF), and weight (WT178) were measured at 178 d of age on average. Variance components were estimated utilizing an animal model in ASReml 4.1. Models included fixed effects of generation and sex, a random common litter effect, and a random animal genetic effect. Covariates were fit for reproductive traits (age at boar exposure), LEA and BF (WT178) and WT178 (age weighed). In generation 4, YOUNG and OLD gilts had on average a PUB of 87% and 64%, respectively, and AGEPUB of 163 and 183 d, respectively. Heritability estimates for AGEPUB, PUB, PUBWT, LEN1, LEN2, VW1, VW2, BWT, WWT, LEA, BF, and WT178 were 0.40, 0.07, 0.39, 0.19, 0.17, 0.36, 0.48, 0.20, 0.12, 0.42, 0.43, and 0.37, respectively. Common litter effect estimates for AGEPUB, PUB, PUBWT, LEN1, LEN2, VW1, VW2, BWT, WWT, LEA, BF, and WT178 were 0.08, 0.14, 0.03, 0.00, 0.01, 0.05, 0.00, 0.03, 0.29, 0.02, 0.10, and 0.11, respectively. Genetic correlations between AGEPUB with PUBWT, LEN1, LEN2, VW1, VW2, BWT, WWT, LEA, BF, and WT178 were 0.83, -0.22, -0.31, 0.25, 0.19, -0.08, -0.29, 0.15, -0.21, and -0.43, respectively. Results suggest selection for reduced AGEPUB in swine would decrease AGEPUB and increase PUB.

Longitudinal analysis of weight showed little relationship with age at puberty in gilts.

Continued selection for increased gilt body weight could negatively impact selection for age at puberty (AP) in gilts. The purpose of this study was to compare the genetic potential for growth to that for reducing age of puberty in swine. Females utilized (n = 1,079) were produced over a 6-yr period from a population developed to determine the impact of energy restrictions and genetic influences on sow development and longevity. From 120 to 235 d of age, BW was collected every 14 d and attainment of puberty tested. Age at puberty was defined as the first observed standing estrus in the presence of a mature boar. All females were genotyped with the Porcine SNP60K BeadChip and genotypes were used to construct a genomic relationship matrix. Univariate (AP), repeatability (BW), and random regression (BW; RR) models were fitted. Univariate analysis included the fixed effects of contemporary group (CG) and age at first boar exposure, and random direct additive and common litter effects. Repeatability analysis included the fixed effects of CG and random effects of direct additive, common litter, and permanent environmental (PE) effects. Random regression analysis included fixed effects of CG, and random direct additive, common litter, and PE effects. Proportion of phenotypic variation due to direct additive and common litter variance for AP were 0.33 and 0.06, respectively. Proportion of phenotypic variation due to direct additive, common litter, and PE variance estimated from the repeatability model for BW were 0.26, 0.11, and 0.40, respectively. Proportion of phenotypic variation due to direct additive, common litter, and PE variance estimated from the RR for BW ranged from (mean) 0.19 to 0.30 (0.27), 0.08 to 0.31 (0.19), and 0.42 to 0.62 (0.50), respectively. Direct additive correlations between test days for BW from RR ranged from 0.30 to 0.99. Rank correlations between estimated breeding values (EBV) for AP and BW from the RR were near zero across all age points ranging from -0.03 to 0.09. Rank correlations were higher (0.63) when BW was considered at the age at which puberty was reached. Correlations between AP and BW EBV from the repeatability model were low (-0.11). Growth appears to be less related to AP than previously reported, suggesting the need to either directly measure AP or investigate alternative indicator traits. Selecting gilts with most desirable BW EBV alone would not result in improvement in AP, at least in the current population.