Genome-wide association study reveals novel loci for litter size and its variability in a Large White pig population.

BACKGROUND: In many traits, not only individual trait levels are under genetic control, but also the variation around that level. In other words, genotypes do not only differ in mean, but also in (residual) variation around the genotypic mean. New statistical methods facilitate gaining knowledge on the genetic architecture of complex traits such as phenotypic variability. Here we study litter size (total number born) and its variation in a Large White pig population using a Double Hierarchical Generalized Linear model, and perform a genome-wide association study using a Bayesian method. RESULTS: In total, 10 significant single nucleotide polymorphisms (SNPs) were detected for total number born (TNB) and 9 SNPs for variability of TNB (varTNB). Those SNPs explained 0.83 % of genetic variance in TNB and 1.44 % in varTNB. The most significant SNP for TNB was detected on Sus scrofa chromosome (SSC) 11. A possible candidate gene for TNB is ENOX1, which is involved in cell growth and survival. On SSC7, two possible candidate genes for varTNB are located. The first gene is coding a swine heat shock protein 90 (HSPCB = Hsp90), which is a well-studied gene stabilizing morphological traits in Drosophila and Arabidopsis. The second gene is VEGFA, which is activated in angiogenesis and vasculogenesis in the fetus. Furthermore, the genetic correlation between additive genetic effects on TNB and on its variation was 0.49. This indicates that the current selection to increase TNB will also increase the varTNB. CONCLUSIONS: To the best of our knowledge, this is the first study reporting SNPs associated with variation of a trait in pigs. Detected genomic regions associated with varTNB can be used in genomic selection to decrease varTNB, which is highly desirable to avoid very small or very large litters in pigs. However, the percentage of variance explained by those regions was small. The SNPs detected in this study can be used as indication for regions in the Sus scrofa genome involved in maintaining low variability of litter size, but further studies are needed to identify the causative loci.

Supervised independent component analysis as an alternative method for genomic selection in pigs.

The objective of this work was to evaluate the efficiency of the supervised independent component regression (SICR) method for the estimation of genomic values and the SNP marker effects for boar taint and carcass traits in pigs. The methods were evaluated via the agreement between the predicted genetic values and the corrected phenotypes observed by cross-validation. These values were also compared with other methods generally used for the same purposes, such as RR-BLUP, SPCR, SPLS, ICR, PCR and PLS. The SICR method was found to have the most accurate prediction values.

Estimation of genetic parameters for BW and body measurements in Brahman cattle.

Body weight and body measurements are commonly used to represent growth and measured at several growth stages in beef cattle. Those economically important traits should be genetically improved. To achieve breeding programs, genetic parameters are prerequisite, as they are needed for designing and predicting outcomes of breeding programs, as well as estimating of breeding values. (Co)variance components were estimated for BW and body measurements on Brahman cattle born between 1990 and 2016 from 17 research herds across Thailand. The traits measured were BW, heart girth (GR), hip height (HH) and body length (BL) and were measured at birth, 200 days, 400 days and 600 days of age. The number of records varied between traits from 18 890 for birth BW to 876 for GR at 600 days. Estimation of variance components was performed using restricted maximum likelihood using univariate and multivariate animal models. Pre-weaning traits were influenced by genetic and/or permanent environmental effects of the dam, except for BL. Heritability estimates from birth to 600 days of age ranged from 0.28±0.01 to 0.50±0.06 for BW, 0.27±0.01 to 0.43±0.09 for GR, 0.28±0.01 to 0.58±0.08 for HH and 0.34±0.01 to 0.51±0.08 for BL using univariate analysis. Heritability estimates for the traits studied increased with age. A similar trend was observed for the phenotypic and genetic correlations between subsequent BW and body measurements. A positive correlation was observed between different traits measured at a similar age, ranging from 0.22±0.01 to 0.72±0.01 for the phenotypic correlation and 0.25±0.04 to 0.97±0.11 for the genetic correlation. Also, a positive correlation was observed for similar traits across different age classes ranging from 0.07±0.03 to 0.76±0.02 for the phenotypic correlation and 0.24±0.11 to 0.92±0.05 for the genetic correlation. Therefore, all correlations between body measurements at the same age and across age classes were positive. The results show the potential improvement of growth traits in Brahman cattle, and those traits can be improved simultaneously under the same breeding program.

Heritability of the backtest response in piglets and its genetic correlations with production traits.

The backtest response of a pig gives an indication of its coping style, that is, its preferred strategy to cope with stressful situations, which may in turn be related to production traits. The objective of this study was therefore to estimate the heritability of the backtest response and estimate genetic correlations with production traits (birth weight, growth, fat depth and loin depth). The backtest was performed by placing the piglet on its back for 60 s and the number of struggles (NrS) and vocalizations (NrV), and the latency to struggle and vocalize (LV) was recorded. In total, 992 piglets were subjected to the backtest. Heritability estimates for backtest traits were statistically moderate (although high for behavioral traits), with LV having the highest heritability estimate (0.56±0.10, P<0.001) and NrS having the lowest estimate (0.37±0.09, P<0.001). Backtest traits also had high genetic correlations with each other, with vocalization traits (NrV and LV) having the highest (-0.94±0.03, P<0.001), and NrS with NrV the lowest correlation (0.70±0.09, P<0.001). No significant correlations were found between backtest traits and production traits, but correlations between NrS and birth weight (-0.38±0.25), and NrV and loin depth (-0.28±0.19) approached significance (P=0.07). More research into genotype-by-environment interactions may be needed to assess possible connections between backtest traits and production traits, as this may depend on the circumstances (environment, experiences, etc.). In conclusion, heritability estimates of backtest traits are high and it would therefore be possible to select for them. The high genetic correlations between backtest traits indicate that it may be possible to only consider one or two traits for characterization and selection purposes. There were no significant genetic correlations found between backtest traits and production traits, although some of the correlations approached significance and hence warrant further research.

Boar taint in entire male pigs: a genomewide association study for direct and indirect genetic effects on androstenone.

Androstenone is one of the compounds causing boar taint of pork and is highly heritable (approximately 0.6). Recently, indirect genetic effects (IGE; also known as associative effects or social genetic effects) were found for androstenone, meaning that pen mates (boars) affect each other's androstenone level genetically. Similar to estimating variance components with a direct-indirect animal model, direct and indirect genetic SNP effects can be estimated for androstenone. This study aims to detect SNP with significant direct genetic effects and IGE on androstenone. The dataset consisted of 1,282 noncastrated boars (993 boars genotyped) from 184 groups of pen members. After quality control, 46,421 SNP were included in the analysis. One model for single-SNP regression was fitted, where both the direct SNP effect of the individual itself and the indirect SNP effects of its pen mates were included. None of the SNP (direct or indirect) were found genomewide significant. One QTL on SSC6 was chromosome-wide significant for the direct effect. A single SNP on SSC9 and 2 regions and a single SNP on SSC14 were found for the indirect effect. A backwards elimination method and haplotype analysis were used to quantify the variance explained by the SNP. The backwards elimination method identified 4 independent regions affecting androstenone. The QTL on SSC6 explained 2.1 and 2.6% of the phenotypic variance using the backwards elimination method or the haplotype analysis. The QTL on SSC14 explained 3.4 and 2.7% of the phenotypic variance using the backwards elimination method or the haplotype analysis. The single association on SSC9 explained 2.2% of the phenotypic variance. All significant QTL together explained 7 to 8% of phenotypic variance and 40 to 44% of the total genetic variance available for response to selection. Besides the newly discovered QTL and the confirmation of known QTL, this study also presents a methodology to model SNP for IGE.

Growth performance and carcass traits in pigs selected for indirect genetic effects on growth rate in two environments.

Production traits such as growth rate may depend on the social interactions between group members. These social interactions may be partly heritable and are referred to as indirect genetic effects (IGE) or social, associative, or competitive genetic effects. Indirect genetic effects may contribute to heritable variation in traits and can therefore be used to increase the response to selection. This, however, has hardly been tested by selection experiments. Our objective was to determine the effects of 1 generation of selection on IGE for growth (IGEg) in pigs on ADG, BW, ADFI, feed efficiency, and postmortem measurements. Sires (n = 24) and dams (n = 64) were selected to create a high vs. low contrast for IGEg in the offspring (n = 480). The IGE difference was 2.8 g ADG per pen mate, corresponding to 14 g higher ADG in high IGEg offspring compared to low IGEg offspring when housed in groups of 6 (i.e., (6 - 1) × 2.8 = 14). Male (barrows) and female (gilts) offspring were housed in groups of 6 of the same IGEg classification, in either barren concrete pens or pens enriched with straw and wood shavings (n = 80 pens). Pigs were followed from birth to slaughter. Data were analyzed in a mixed model with pen as random factor. There was no difference in ADG between high and low IGEg pigs during the finishing period (wk 10 to 23). Opposite to expectations, high IGEg tended to have a 17 g lower ADG from weaning to slaughter (P = 0.08), which was caused by a higher BW of low IGEg pigs in wk 5 (P = 0.008). This led to a 2.3 kg lower carcass weight (P = 0.02) and 2.2 mm less muscle depth for high IGEg pigs (P = 0.03). High IGEg pigs had a higher stomach wall damage score (P = 0.01). Pigs on straw had a 25 g lower ADG during finishing (P = 0.03) and less stomach wall damage (P < 0.001). Fewer interventions against harmful behavior were required in high IGEg pigs. The unexpected results regarding IGEg may be due to several reasons. Despite initial power calculations showing good power, the IGEg contrast between groups may have been too small. Moreover, measures that were taken to limit harmful behavior may have had a substantial role. Harmful behavior such as tail biting may affect ADG and might underlie the effects of selection on IGEg in pigs. Research under commercial circumstances, where harmful behavior is likely to be more profound, may give more accurate insight into the benefits of selecting for IGEg.

A genome-wide association study reveals a novel candidate gene for sperm motility in pigs.

Sperm motility is one of the most widely used parameters in order to evaluate boar semen quality. However, this trait can only be measured after puberty. Thus, the use of genomic information appears as an appealing alternative to evaluate and improve selection for boar fertility traits earlier in life. With this study we aimed to identify SNPs with significant association with sperm motility in two different commercial pig populations and to identify possible candidate genes within the identified QTL regions. We performed a single-SNP genome-wide association study using genotyped animals from a Landrace-based (L1) and a Large White-based (L2) pig populations. For L1, a total of 602 animals genotyped for 42,551 SNPs were used in the association analysis. For L2, a total of 525 animals genotyped for 40,890 SNPs were available. After the association analysis, a false discovery rate q-value ≤0.05 was used as the threshold for significant association. No SNPs were significantly associated with sperm motility in L1, while six SNPs on Sus scrofa chromosome 1 (position 117.26-119.56Mb) were significant in L2. The mitochondrial methionyl-tRNA formyltransferase (MTFMT) gene, which affects translation efficiency of proteins in sperm cells, was identified as a putative candidate gene. The significant markers identified in this study may be useful to enhance the genetic improvement of sperm motility by selection of boars at an earlier age under a marker assisted selection strategy.

Backtest and novelty behavior of female and castrated male piglets, with diverging social breeding values for growth.

Pigs housed together in a group influence each other's growth. Part of this effect is genetic and can be represented in a social breeding value. It is unknown, however, which traits are associated with social breeding values. The aim of this study was, therefore, to investigate whether personality and response to novelty could be associated with social breeding values for growth in piglets. Female and castrated male piglets from 80 litters, with either an estimated relative positive or negative social breeding value (+SBV or -SBV) for growth, were individually tested in a backtest and novel environment test, and group-wise in a novel object (i.e., a feeder with feed) test and human approach test. All tests were performed during the suckling period. No differences between +SBV and -SBV piglets were found for the frequency and latency of struggling and vocalizing in the backtest (at least, P > 0.30). In the novel object test, piglets with a +SBV for growth touched the feeder faster than piglets with -SBV for growth (P = 0.01) and were more frequently present near the person in the human approach test (P < 0.01). No behavioral differences between +SBV and -SBV piglets were found in the novel environment test (at least, P > 0.40), but piglets that struggled more in the backtest walked more in this test (P = 0.02). Behavior was affected by gender in each test. Female piglets were faster than castrated male piglets to start struggling in the backtest (P = 0.047). In the novel object test, females were faster than males to touch the feeder and sample the feed. In the human approach test, they were also faster than male piglets to touch a person (all, P < 0.001). Females were also more frequently present near the feeder (P < 0.001) and person (P = 0.03). In the novel environment test, female piglets explored the floor more (P = 0.046), produced less low- (P = 0.04) and high-pitched vocalizations (P = 0.02), and defecated (P = 0.08) and urinated less than male piglets (P < 0.01). It was concluded that +SBV and -SBV piglets do not differ in their response to the backtest, and only subtle differences were found in their response to novelty. More research is warranted to identify the traits underlying SBV for growth in pigs. Moreover, castrated male piglets seemed to react more fearfully to each test than female piglets.

Direct and associative effects for androstenone and genetic correlations with backfat and growth in entire male pigs.

In the pig industry, male piglets are surgically castrated early in life to prevent boar taint. Boar taint is mainly caused by androstenone and skatole. Androstenone is a pheromone that can be released from the salivary glands when the boar is sexually aroused. Boars are housed in groups and as a consequence boars can influence and be influenced by the phenotype of other boars by (non-)heritable social interactions. The influence of these social interactions on androstenone is not well understood. The objective of this study is to investigate whether androstenone concentrations are affected by (non-)heritable social interactions and estimate their genetic correlation with growth rate and backfat. The dataset contained 6,245 boars, of which 4,455 had androstenone observations (68%). The average number of animals per pen was 7 and boars were housed in 899 unique pen-groups (boars within a single pen) and 344 unique compartment-groups (boars within a unique 'room' within a barn during time). Four models including different random effects, were compared for androstenone. Direct genetic, associative (also known as social genetic or indirect genetic effects), group, compartment, common environment and residual effects were included as random effects in the full model (M3). Including random pen and compartment effects (non-heritable social effects) significantly improved the model (M2) compared with including only direct, common environment and residual as random effects (M1, P < 0.001), and including associative effects even more (M3, P < 0.001). The sum of the direct and associative variance components determines the total genetic variance of the trait. The associative effect explained 11.7% of the total genetic variance. Backfat thickness was analysed using M2 and growth using M3. The genetic correlation between backfat (direct genetic variance) and total genetic variance for androstenone was close to 0. Backfat and the direct and associative effects for androstenone had genetic correlations of 0.14 ± 0.08 and -0.25 ± 0.18, respectively. The genetic correlation between total genetic variances for growth rate and androstenone was 0.33 ± 0.18. The genetic correlation between direct effects was 0.11 ± 0.09 and between associative effects was 0.42 ± 0.31. The genetic correlations and current selection towards lower backfat and greater growth rate suggest that no major change in androstenone is expected when breeding goals are not changed. For selection against boar taint and therefore also against androstenone , results recommend that at least the social environment of the boars should be considered.

A single nucleotide polymorphism set for paternal identification to reduce the costs of trait recording in commercial pig breeding.

In animal breeding, recording of correct pedigrees is essential to achieve genetic progress. Markers on DNA are useful to verify the on-farm pedigree records (parental verification) but can also be used to assign parents retrospectively (parental identification). This approach could reduce the costs of recording for traits with low incidence, such as those related to diseases or mortality. In this study, SNP were used to assign the true sires of 368 purebred animals from a Duroc-based sire line and 140 crossbred offspring from a commercial pig population. Some of the sires were closely related. There were 3 full sibs and 17 half sibs among the true fathers and 4 full sibs and 35 half sibs among all putative fathers. To define the number of SNP necessary, 5 SNP panels (40, 60, 80, 100, and 120 SNP) were assembled from the Illumina PorcineSNP60 Beadchip (Illumina, San Diego, CA) based on minor allele frequency (>0.3), high genotyping call rate (≥90%), and equal spacing across the genome. For paternal identification considering only the 66 true sires in the data set, 60 SNP resulted in 100% correct assignment of the sire. By including additional putative sires (n = 304), 80 SNP were sufficient for 100% correct assignment of the sire. The following criteria were derived to identify the correct sire for the current data set: the logarithm of odds (LOD) score for assigning the correct sire was ≥5, the number of mismatches was ≤1, and the difference in the LOD score between the first and the second most likely sire was >5. If the correct sire was not present among all putative sires, the mean LOD for the most likely sire was close to zero or negative when using 100 SNP. More SNP would be needed for paternal identification if the number of putative sires increased and the degree of relatedness was greater than in the data set used here. The threshold for the number of mismatches can be adjusted according to the practical situation to account for the trade-off between false negatives and false positives. The latter can be avoided efficiently, ensuring that the correct father is being sampled. Nevertheless, a restriction on the number of putative sires is advisable to reduce the risk of assigning close relatives.

Maternal and social genetic effects on average daily gain of piglets from birth until weaning.

The aim of this study was to investigate whether there is heritable social variation in ADG from birth until weaning in piglets. Nursing and the establishment of teat order are sources of social interaction among suckling piglets nursed by the same sow. If a heritable social effect is present, but ignored, the selected animals might be the most competitive ones with negative effects on growth of their group mates, resulting in less response to selection than expected. The social interaction model was extended with a maternal component to estimate genetic maternal and social effects. Four different animal models were compared: a basic model with a direct heritable effect only; a social model accounting for direct and social heritable effects; a maternal model with a heritable maternal effect in addition to the basic model; and a social-maternal model accounting for direct, social, and maternal heritable effects. Estimates of direct, maternal, and social heritability were 0.07, 0.06, and around 0.0007 (not significantly different from zero, SE = 0.0005), respectively. Total heritable variance, including direct, social, and maternal heritable variance and their covariances ranged from 0.07 to 0.15 of the phenotypic variation. Both maternal models were significantly better than equivalent nonmaternal models (P