Oral Administration of L-Arginine Improves the Growth and Survival of Sow-Reared Intrauterine Growth-Restricted Piglets

Neonatal piglets with intrauterine growth restriction (IUGR) exhibit reduced rates of growth and survival. The present study tested the hypothesis that L-arginine supplementation can mitigate this problem. One hundred and twelve (112) IUGR piglets (with a mean birth weight of 0.84 kg) from 28 sows (four IUGR piglets/sow) were assigned randomly into one of four groups. Piglets were nursed by sows and orally administered 0, 0.1, 0.2, or 0.4 g L-arginine (in the form of L-arginine-HCl) per kg body weight (BW) twice daily between 0 and 14 days of age. The total doses of L-arginine were 0, 0.2, 0.4, or 0.8 g/kg BW/day. Appropriate amounts of L-alanine were added to L-arginine solutions so that all groups of piglets received the same amount of nitrogen. Piglets were weighed on days 0, 7, and 14 of age. On day 14, blood samples (5 mL) were obtained from the jugular vein of piglets at 1 h after suckling, and their milk consumption was measured over a 10-h period using the weigh-suckle-weigh technique. Milk intake did not differ (p > 0.05) among the four groups of piglets. Oral administration of 0.4 g L-arginine/kg BW/day increased (p < 0.05) the circulating levels of arginine, creatine, and anabolic hormones (insulin, growth hormone, and insulin-like growth factor-I), but decreased (p < 0.05) plasma concentrations of ammonia and cortisol (a catabolic hormone). Compared to the control group, IUGR piglets administered 0.2 and 0.4 g L-arginine/kg BW/day increased (p < 0.05) weight gain by 19% and 31%, respectively. Growth did not differ (p > 0.05) between the control and 0.8 g L-arginine/kg BW/day groups. The survival rates of IUGR piglets were 50%, 75%, 89%, and 89%, respectively, for the 0, 0.2, 0.4, and 0.8 g L-arginine/kg BW/day groups. Collectively, these results indicate that the growth and survival of IUGR piglets can be improved through L-arginine supplementation.

Indirect genetic effects increase the heritable variation available to selection and are largest for behaviors: a meta-analysis

The evolutionary potential of traits is governed by the amount of heritable variation available to selection. While this is typically quantified based on genetic variation in a focal individual for its own traits (direct genetic effects, DGEs), when social interactions occur, genetic variation in interacting partners can influence a focal individual's traits (indirect genetic effects, IGEs). Theory and studies on domesticated species have suggested IGEs can greatly impact evolutionary trajectories, but whether this is true more broadly remains unclear. Here, we perform a systematic review and meta-analysis to quantify the amount of trait variance explained by IGEs and the contribution of IGEs to predictions of adaptive potential. We identified 180 effect sizes from 47 studies across 21 species and found that, on average, IGEs of a single social partner account for a small but statistically significant amount of phenotypic variation (0.03). As IGEs affect the trait values of each interacting group member and due to a typically positive-although statistically nonsignificant-correlation with DGEs (r DGE-IGE = 0.26), IGEs ultimately increase trait heritability substantially from 0.27 (narrow-sense heritability) to 0.45 (total heritable variance). This 66% average increase in heritability suggests IGEs can increase the amount of genetic variation available to selection. Furthermore, whilst showing considerable variation across studies, IGEs were most prominent for behaviors and, to a lesser extent, for reproduction and survival, in contrast to morphological, metabolic, physiological, and development traits. Our meta-analysis, therefore, shows that IGEs tend to enhance the evolutionary potential of traits, especially for those tightly related to interactions with other individuals, such as behavior and reproduction.

Genetic parameters of gestation length trait in dromedary camels (Camelus dromedarius)

Gestation length (GL) data of dromedary camels were analysed for the period from 2007 to 2018. The database of the largest dairy camel herds (Dubai, United Arab Emirates) was used in this study. The data of 4,084 camels included in the assessment were classified into six ecotypes (Emirati, Emirati cross, Black, Pakistani, Saudi-Sudanese and Saudi cross). The aim of the study was to describe the heritability of GL of camels and the breeding value (BV) of sires for this trait. The genetic parameters of GL were estimated by the General Linear Model method and two Best Linear Unbiased Prediction (BLUP) animal models as well. The mean (±SE) of GL of camels was 384.3 ± 0.2 days. The direct heritability of GL (0.26 ± 0.06-0.36 ± 0.08) was higher than the maternal heritability (0.00 ± 0.05-0.13 ± 0.06) obtained. The maternal permanent environmental effect (0.15 ± 0.05) was similar to the results estimated previously in dromedary camel, but higher than the data reported by relevant sources in other species. Based on the results of this study it can be concluded that the GL of dromedary camels is a species-specific value similar to that in cattle, which is less affected by the maternal influence. Considerable differences (16 days) exist among male dromedaries in their BV for the GL trait.

A combined GWAS approach reveals key loci for socially-affected traits in Yorkshire pigs

Socially affected traits in pigs are controlled by direct genetic effects and social genetic effects, which can make elucidation of their genetic architecture challenging. We evaluated the genetic basis of direct genetic effects and social genetic effects by combining single-locus and haplotype-based GWAS on imputed whole-genome sequences. Nineteen SNPs and 25 haplotype loci are identified for direct genetic effects on four traits: average daily feed intake, average daily gain, days to 100 kg and time in feeder per day. Nineteen SNPs and 11 haplotype loci are identified for social genetic effects on average daily feed intake, average daily gain, days to 100 kg and feeding speed. Two significant SNPs from single-locus GWAS (SSC6:18,635,874 and SSC6:18,635,895) are shared by a significant haplotype locus with haplotype alleles 'GGG' for both direct genetic effects and social genetic effects in average daily feed intake. A candidate gene, MT3, which is involved in growth, nervous, and immune processes, is identified. We demonstrate the genetic differences between direct genetic effects and social genetic effects and provide an anchor for investigating the genetic architecture underlying direct genetic effects and social genetic effects on socially affected traits in pigs.

Estimation of social genetic effects on feeding behaviour and production traits in pigs

Pigs are housed in groups during the test period. Social effects between pen mates may affect average daily gain (ADG), backfat thickness (BF), feed conversion rate (FCR), and the feeding behaviour traits of pigs sharing the same pen. The aim of our study was to estimate the genetic parameters of feeding behaviour and production traits with statistical models that include social genetic effects (SGEs). The data contained 3075 Finnish Yorkshire, 3351 Finnish Landrace, and 968 F1-crossbred pigs. Feeding behaviour traits were measured as the number of visits per day (NVD), time spent in feeding per day (TPD), daily feed intake (DFI), time spent in feeding per visit (TPV), feed intake per visit (FPV), and feed intake rate (FR). The test period was divided into five periods of 20 days. The number of pigs per pen varied from 8 to 12. Two model approaches were tested, i.e. a fixed group size model and a variable group size model. For the fixed group size model, eight random pigs per pen were included in the analysis, while all pigs in a pen were included for the variable group size model. The linear mixed-effects model included sex, breed, and herd*year*season as fixed effects and group (batch*pen), litter, the animal itself (direct genetic effect (DGE)), and pen mates (SGEs) as random effects. For feeding behaviour traits, estimates of the total heritable variation (T²± SE) and classical heritability (h²± SE, values given in brackets) from the variable group size model (e.g. period 1) were 0.34 ± 0.13 (0.30 ± 0.04) for NVD, 0.41 ± 0.10 (0.37 ± 0.04) for TPD, 0.40 ± 0.15 (0.14 ± 0.03) for DFI, 0.53 ± 0.15 (0.28 ± 0.04) for TPV, 0.66 ± 0.17 (0.28 ± 0.04) for FPV, and 0.29 ± 0.13 (0.22 ± 0.03) for FR. The effect of social interaction was minimal for ADG (T² = 0.29 ± 0.11 and h² = 0.29 ± 0.04), BF (T² = 0.48 ± 0.12 and h² = 0.38 ± 0.07), and FCR (T² = 0.37 ± 0.12 and h² = 0.29 ± 0.04) using the variable group size model. In conclusion, the results indicate that social interactions have a considerable indirect genetic effect on the feeding behaviour and FCR of pigs but not on ADG and BF.

Estimation of Variance Components and Genomic Prediction for Individual Birth Weight Using Three Different Genome-Wide SNP Platforms in Yorkshire Pigs

Simple Summary

The individual birth weight (IBW) of pigs is an important trait regarding its relevance to mortality at weaning, sow prolificacy, and growth performance. This study investigates the variance component estimation, informative window regions, and the efficiency of genomic predictions associated with IBW traits in Yorkshire pigs. The low heritability (0.13) is estimated on the basis of a full model including individual genetic, sow genetic, and common environmental effects. Two common window regions of the genome are identified under three different genotyping platforms found within the ARAP2 and TSN genes concerning the IBW trait. The genomic prediction ability is improved using deregressed estimated breeding values including parental information as a response variable despite Bayesian methods and genotyping platforms for the IBW trait in Korean Yorkshire pigs.

Abstract

This study estimates the individual birth weight (IBW) trait heritability and investigates the genomic prediction efficiency using three types of high-density single nucleotide polymorphism (SNP) genotyping panels in Korean Yorkshire pigs. We use 38,864 IBW phenotypic records to identify a suitable model for statistical genetics, where 698 genotypes match our phenotypic records. During our genomic analysis, the deregressed estimated breeding values (DEBVs) and their reliabilities are used as derived response variables from the estimated breeding values (EBVs). Bayesian methods identify the informative regions and perform the genomic prediction using the IBW trait, in which two common significant window regions (SSC8 27 Mb and SSC15 29 Mb) are identified using the three genotyping platforms. Higher prediction ability is observed using the DEBV-including parent average as a response variable, regardless of the SNP genotyping panels and the Bayesian methods, relative to the DEBV-excluding parent average. Hence, we suggest that fine-mapping studies targeting the identified informative regions in this study are necessary to find the causal mutations to improve the IBW trait’s prediction ability. Furthermore, studying the IBW trait using a genomic prediction model with a larger genomic dataset may improve the genomic prediction accuracy in Korean Yorkshire pigs.

Prediction of genetic merit for growth rate in pigs using animal models with indirect genetic effects and genomic information

Background

Several studies have found that the growth rate of a pig is influenced by the genetics of the group members (indirect genetic effects). Accounting for these indirect genetic effects in a selection program may increase genetic progress for growth rate. However, indirect genetic effects are small and difficult to predict accurately. Genomic information may increase the ability to predict indirect genetic effects. Thus, the objective of this study was to test whether including indirect genetic effects in the animal model increases the predictive performance when genetic effects are predicted with genomic relationships. In total, 11,255 pigs were phenotyped for average daily gain between 30 and 94 kg, and 10,995 of these pigs were genotyped. Two relationship matrices were used: a numerator relationship matrix (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{A}}$$\end{document}A) and a combined pedigree and genomic relationship matrix (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{H}}$$\end{document}H); and two different animal models were used: an animal model with only direct genetic effects and an animal model with both direct and indirect genetic effects. The predictive performance of the models was defined as the Pearson correlation between corrected phenotypes and predicted genetic levels. The predicted genetic level of a pig was either its direct genetic effect or the sum of its direct genetic effect and the indirect genetic effects of its group members (total genetic effect).

Results

The highest predictive performance was achieved when total genetic effects were predicted with genomic information (21.2 vs. 14.7%). In general, the predictive performance was greater for total genetic effects than for direct genetic effects (0.1 to 0.5% greater; not statistically significant). Both types of genetic effects had greater predictive performance when they were predicted with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{H}}$$\end{document}H rather than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{A}}$$\end{document}A (5.9 to 6.3%). The difference between predictive performances of total genetic effects and direct genetic effects was smaller when \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{H}}$$\end{document}H was used rather than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{A}}$$\end{document}A.

Conclusions

This study provides evidence that: (1) corrected phenotypes are better predicted with total genetic effects than with direct genetic effects only; (2) both direct genetic effects and indirect genetic effects are better predicted with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{H}}$$\end{document}H than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{A}}$$\end{document}A; (3) using \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{H}}$$\end{document}H rather than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{A}}$$\end{document}A primarily improves the predictive performance of direct genetic effects.

Understanding the Social Dynamics of Breeding Phenology: Indirect Genetic Effects and Assortative Mating in a Long-Distance Migrant

AbstractPhenological traits, such as the timing of reproduction, are often influenced by social interactions between paired individuals. Such partner effects may occur when pair members affect each other's prebreeding environment. Partner effects can be environmentally and/or genetically determined, and quantifying direct and indirect genetic effects is important for understanding the evolutionary dynamics of phenological traits. Here, using 26 years of data from a pedigreed population of a migratory seabird, the common tern (Sterna hirundo), we investigate male and female effects on female laying date. We find that female laying date harbors both genetic and environmental variation and is additionally influenced by the environmental and, to a lesser extent, genetic component of its mate. We demonstrate this partner effect to be largely explained by male arrival date. Interestingly, analyses of mating patterns with respect to arrival date show mating to be strongly assortative, and using simulations we show that assortative mating leads to overestimation of partner effects. Our study provides evidence for partner effects on breeding phenology in a long-distance migrant while uncovering the potential causal pathways underlying the observed effects and raising awareness for confounding effects resulting from assortative mating or other common environmental effects.

Single-step genome-wide association study for social genetic effects and direct genetic effects on growth in Landrace pigs

In livestock social interactions, social genetic effects (SGE) represent associations between phenotype of one individual and genotype of another. Such associations occur when the trait of interest is affected by transmissible phenotypes of social partners. The aim of this study was to estimate SGE and direct genetic effects (DGE, genetic effects of an individual on its own phenotype) on average daily gain (ADG) in Landrace pigs, and to conduct single-step genome-wide association study using SGE and DGE as dependent variables to identify quantitative trait loci (QTLs) and their positional candidate genes. A total of 1,041 Landrace pigs were genotyped using the Porcine SNP 60K BeadChip. Estimates of the two effects were obtained using an extended animal model. The SGE contributed 16% of the total heritable variation of ADG. The total heritability estimated by the extended animal model including both SGE and DGE was 0.52. The single-step genome-wide association study identified a total of 23 QTL windows for the SGE on ADG distributed across three chromosomes (i.e., SSC1, SSC2, and SSC6). Positional candidate genes within these QTL regions included PRDM13, MAP3K7, CNR1, HTR1E, IL4, IL5, IL13, KIF3A, EFHD2, SLC38A7, mTOR, CNOT1, PLCB2, GABRR1, and GABRR2, which have biological roles in neuropsychiatric processes. The results of biological pathway and gene network analyses also support the association of the neuropsychiatric processes with SGE on ADG in pigs. Additionally, a total of 11 QTL windows for DGE on ADG in SSC2, 3, 6, 9, 10, 12, 14, 16, and 17 were detected with positional candidate genes such as ARL15. We found a putative pleotropic QTL for both SGE and DGE on ADG on SSC6. Our results in this study provide important insights that can help facilitate a better understanding of the molecular basis of SGE for socially affected traits.

Genetic parameters of birth weight trait in dromedary camels (Camelus dromedarius)

Birth weight data of dromedary calves from the database of one of the world’s largest dairy herds (Dubai, UAE) were analyzed for the period from 2007 to 2018. The assessment included the data of 4124 camel calves that were classified into six ecotypes (Emirate, Emirate crossed, Black, Pakistanian, Saudi-Sudanian, and Saudi crossed). The aim of the study was to describe the heritability of birth weight of calves and the breeding value of sires. Genetic parameters of birth weight were estimated by ANOVA model and two BLUP animal models as well. The mean value of the camel calves’ birth weight was 34.75 ± 5.67 kg. The direct heritability of birth weight (h²_d = 0.09 ± 0.04–0.11 ± 0.03) was rather low, so was the maternal heritability (h²_m = 0.23 ± 0.10–0.50 ± 0.06). The maternal effect from environmental origin (c² = 0.23 ± 0.08) far exceeded the results previously calculated in cattle. There was no difference in reliability between BLUP1 and BLUP2 models, and both of them were more accurate than the ANOVA model. Based on the results of this study, we conclude that the birth weight of dromedary calves was more influenced by the dam’s intrauterine rearing capacity and by the environment, management, and feeding of the pregnant female camels than the hereditary growth potential. Considerable differences were found among male dromedaries in their breeding values for the birth weight trait.

Whole-genome re-sequencing association study for direct genetic effects and social genetic effects of six growth traits in Large White pigs

Socially affected traits are affected by direct genetic effects (DGE) and social genetic effects (SGE). DGE and SGE of an individual directly quantify the genetic influence of its own phenotypes and the phenotypes of other individuals, respectively. In the current study, a total of 3,276 Large White pigs from different pens were used, and each pen contained 10 piglets. DGE and SGE were estimated for six socially affected traits, and then a GWAS was conducted to identify SNPs associated with DGE and SGE. Based on the whole-genome re-sequencing, 40 Large White pigs were genotyped and 10,501,384 high quality SNPs were retained for single-locus and multi-locus GWAS. For single-locus GWAS, a total of 54 SNPs associated with DGE and 33 SNPs with SGE exceeded the threshold (P < 5.00E-07) were detected for six growth traits. Of these, 22 SNPs with pleiotropic effects were shared by DGE and SGE. For multi-locus GWAS, a total of 72 and 110 putative QTNs were detected for DGE and SGE, respectively. Of these, 5 SNPs with pleiotropic effects were shared by DGE and SGE. It is noteworthy that 2 SNPs (SSC8: 16438396 for DGE and SSC17: 9697454 for SGE) were detected in single-locus and multi-locus GWAS. Furthermore, 15 positional candidate genes shared by SGE and DGE were identified because of their roles in behaviour, health and disease. Identification of genetic variants and candidate genes for DGE and SGE for socially affected traits will provide a new insight to understand the genetic architecture of socially affected traits in pigs.

Enhancing genetic disease control by selecting for lower host infectivity and susceptibility

Infectious diseases have a huge impact on animal health, production and welfare, and human health. Understanding the role of host genetics in disease spread is important for developing disease control strategies that efficiently reduce infection incidence and risk of epidemics. While heritable variation in disease susceptibility has been targeted in livestock breeding, emerging evidence suggests that there is additional genetic variation in host infectivity, but the potential benefits of including infectivity into selection schemes are currently unknown. A Susceptible-Infected-Recovered epidemiological model incorporating polygenic genetic variation in both susceptibility and infectivity was combined with quantitative genetics selection theory to assess the non-linear impact of genetic selection on field measures of epidemic risk and severity. Response to 20 generations of selection was calculated in large simulated populations, exploring schemes differing in accuracy and intensity. Assuming moderate genetic variation in both traits, 50% selection on susceptibility required seven generations to reduce the basic reproductive number R₀ from 7.64 to the critical threshold of <1, below which epidemics die out. Adding infectivity in the selection objective accelerated the decline towards R₀ < 1, to 3 generations. Our results show that although genetic selection on susceptibility reduces disease risk and prevalence, the additional gain from selection on infectivity accelerates disease eradication and reduces more efficiently the risk of new outbreaks, while it alleviates delays generated by unfavourable correlations. In conclusion, host infectivity was found to be an important trait to target in future genetic studies and breeding schemes, to help reducing the occurrence and impact of epidemics.

Transcriptomic basis and evolution of the ant nurse-larval social interactome

Development is often strongly regulated by interactions among close relatives, but the underlying molecular mechanisms are largely unknown. In eusocial insects, interactions between caregiving worker nurses and larvae regulate larval development and resultant adult phenotypes. Here, we begin to characterize the social interactome regulating ant larval development by collecting and sequencing the transcriptomes of interacting nurses and larvae across time. We find that the majority of nurse and larval transcriptomes exhibit parallel expression dynamics across larval development. We leverage this widespread nurse-larva gene co-expression to infer putative social gene regulatory networks acting between nurses and larvae. Genes with the strongest inferred social effects tend to be peripheral elements of within-tissue regulatory networks and are often known to encode secreted proteins. This includes interesting candidates such as the nurse-expressed giant-lens, which may influence larval epidermal growth factor signaling, a pathway known to influence various aspects of insect development. Finally, we find that genes with the strongest signatures of social regulation tend to experience relaxed selective constraint and are evolutionarily young. Overall, our study provides a first glimpse into the molecular and evolutionary features of the social mechanisms that regulate all aspects of social life.

Transcriptomic basis and evolution of the ant nurse-larval social interactome

Development is often strongly regulated by interactions among close relatives, but the underlying molecular mechanisms are largely unknown. In eusocial insects, interactions between caregiving worker nurses and larvae regulate larval development and resultant adult phenotypes. Here, we begin to characterize the social interactome regulating ant larval development by collecting and sequencing the transcriptomes of interacting nurses and larvae across time. We find that the majority of nurse and larval transcriptomes exhibit parallel expression dynamics across larval development. We leverage this widespread nurse-larva gene co-expression to infer putative social gene regulatory networks acting between nurses and larvae. Genes with the strongest inferred social effects tend to be peripheral elements of within-tissue regulatory networks and are often known to encode secreted proteins. This includes interesting candidates such as the nurse-expressed giant-lens, which may influence larval epidermal growth factor signaling, a pathway known to influence various aspects of insect development. Finally, we find that genes with the strongest signatures of social regulation tend to experience relaxed selective constraint and are evolutionarily young. Overall, our study provides a first glimpse into the molecular and evolutionary features of the social mechanisms that regulate all aspects of social life.

Novel Resilience Phenotypes Using Feed Intake Data From a Natural Disease Challenge Model in Wean-to-Finish Pigs

The objective of this study was to extract novel phenotypes related to disease resilience using daily feed intake data from growing pigs under a multifactorial natural disease challenge that was designed to mimic a commercial environment with high disease pressure to maximize expression of resilience. Data used were the first 1,341 crossbred wean-to-finish pigs from a research facility in Québec, Canada. The natural challenge was established under careful veterinary oversight by seeding the facility with diseased pigs from local health-challenged farms, targeting various viral and bacterial diseases, and maintaining disease pressure by entering batches of 60–75 pigs in a continuous flow system. Feed intake (FI) is sensitive to disease, as pigs tend to eat less when they become ill. Four phenotypes were extracted from the individual daily FI data during finishing as novel measures of resilience. The first two were daily variability in FI or FI duration, quantified by the root mean square error (RMSE) from the within individual regressions of FI or duration at the feeder (DUR) on age (RMSE_FI and RMSE_DUR). The other two were the proportion of off-feed days, classified based on negative residuals from a 5% quantile regression (QR) of daily feed intake or duration data on age across all pigs (QR_FI and QR_DUR). Mortality and treatment rate had a heritability of 0.13 (±0.05) and 0.29 (±0.07), respectively. Heritability estimates for RMSE_FI, RMSE_DUR, QR_FI, and QR_DUR were 0.21 (±0.07) 0.26 (±0.07), 0.15 (±0.06), and 0.23 (±0.07), respectively. Genetic correlations of RMSE and QR measures with mortality and treatment rate ranged from 0.37 to 0.85, with QR measures having stronger correlations with both. Estimates of genetic correlations of RMSE measures with production traits were typically low, but often favorable (e.g., −0.31 between RMSE_FI and finishing ADG). Although disease resilience was our target, fluctuations in FI and duration can be caused by many factors other than disease and should be viewed as overall indicators of general resilience to a variety of stressors. In conclusion, daily variation in FI or duration at the feeder can be used as heritable measures of resilience.

Estimation of direct and maternal genetic parameters for individual birth weight, weaning weight, and probe weight in Yorkshire and Landrace pigs

As a result of selecting for increased litter size, newborn piglets are being born lighter and have a lower chance of survival. Raising fewer pigs to market weight would have a negative impact on the industry and farmer profitability; thus, understanding the genetics of individual growth performance traits will determine whether these traits will play an important role in pig breeding schemes. This study aimed to estimate genetic parameters for individual birth weight (BW), weaning weight (WW), and probe weight (PW) in Canadian-purebred Yorkshire and Landrace pigs. PW is a live weight taken at the time of the ultrasound measurements, when pigs weigh about 100 kg. Data were collected from 2 large and related breeding herds from 2003 to 2015. Four linear animal models were used, which included the following: Model 1-direct additive genetic effect; Model 2-direct additive genetic and maternal genetic effect; Model 3-direct additive genetic and common litter effect; and Model 4-direct additive genetic, maternal genetic, and common litter effect. The model which included all 3 random effects (Model 4) was determined to be the best fit to the data. Low to moderate direct heritability estimates were observed as follows: 0.15 ± 0.03 for BW, 0.04 ± 0.01 for WW, and 0.33 ± 0.03 for PW for the Yorkshire breed; and 0.05 ± 0.01 for BW, 0.01 ± 0.01 for WW, and 0.27 ± 0.03 for PW in the Landrace breed. As expected, the direct heritability estimates increased with age as a result of decreased maternal influence on the trait. Bivariate animal models were also used to estimate genetic and environmental correlations between traits. Strong direct genetic correlations were observed between BW and WW in both breeds. Based on the estimates of genetic parameters, individual BW could be evaluated and considered in breeding programs aiming to increase BW and improve subsequent performance. Different selection emphasis could also be applied on direct and maternal additive genetic effects on BW to optimize the breeding programs and improve selection efficiency.

Economic weights of maternal and direct traits of pigs calculated by applying gene flow methods

Multiple trait selection indexes in pig breeding programmes should take into account the population structure and time delay between parent selection and expressions of traits in all production levels next to the trait impacts on economic efficiency of production systems. Gene flow procedures could be used for the correct evaluation of maternal and direct traits of pig breeds involved in breeding or crossbreeding systems. Therefore, the aim of this study was to expand a previously developed bioeconomic model and computer program to calculate the marginal economic values by including a gene flow procedure to calculate the economic weights for maternal and direct traits in pig breeds. The new program was then applied to the three-way crossbreeding system of the Czech National Programme for Pig Breeding. Using this program, the marginal economic values of traits for dam breeds Czech Large White in the dam position and Czech Landrace in the sire position, and for the sire breed Pietrain were weighted by the number of discounted gene expressions of selected parents of each breed summarised within all links of the crossbreeding system during the 8-year investment period. Economic weights calculated in this way were compared with the approximate economic weights calculated previously without a gene flow procedure. Taking into account the time delay between parent selection and trait expression (using discounting with half-year discount rates of 2% or 5%) and including more than one generation of parent progeny had little impact on the relative economic importance of maternal and direct traits of breeds involved in the evaluated three-way crossbreeding system. These results indicated that this gene-flow method could be foregone when estimating the relative economic weights of traits in pig crossbreeding systems applying artificial insemination at all production levels.

Estimation of direct and maternal genetic parameters for individual birth weight and probe weight using cross-fostering information

A total of 246 357 measurements of birth (BW) and probe (PW) weights of purebred Yorkshire and Landrace pigs were used to compare the fitting of two alternate models including either common-litter effect or cross-fostering group effect to account for common environmental variation. PW, is a live ultrasonic weight measurement taken when the pigs are 100 ± 30 kg, following national standards. The common-litter effect was defined as piglets born into the same litter, and the group effect was used to account for cross-fostering, and defined as the effect common to piglets raised by the same nurse-sow, regardless of whether that piglet was born into that litter or not. It was found that the cross-fostering group explained 5% more environmental variation in BW when compared with the common-litter effect, indication that BW is a criterion in cross-fostering. Cross-fostering also explained 1% more environmental variation in PW in both the Yorkshire and Landrace. When the cross-fostering group effect was included in place of the common-litter effect, the direct and maternal genetic heritability estimates were similar, but residual variances were reduced. This study advanced the understanding of the effects of cross-fostering on PW, its association with BW and its implications in modern pig breeding programs.

Longitudinal analysis of direct and indirect effects on average daily gain in rabbits using a structured antedependence model

BACKGROUND

Indirect genetic effects (IGE) are important components of various traits in several species. Although the intensity of social interactions between partners likely vary over time, very few genetic studies have investigated how IGE vary over time for traits under selection in livestock species. To overcome this issue, our aim was: (1) to analyze longitudinal records of average daily gain (ADG) in rabbits subjected to a 5-week period of feed restriction using a structured antedependence (SAD) model that includes IGE and (2) to evaluate, by simulation, the response to selection when IGE are present and genetic evaluation is based on a SAD model that includes IGE or not.

RESULTS

The direct genetic variance for ADG (g/d) increased from week 1 to 3 [from 8.03 to 13.47 (g/d)²] and then decreased [6.20 (g/d)² at week 5], while the indirect genetic variance decreased from week 1 to 4 [from 0.43 to 0.22 (g/d)²]. The correlation between the direct genetic effects of different weeks was moderate to high (ranging from 0.46 to 0.86) and tended to decrease with time interval between measurements. The same trend was observed for IGE for weeks 2 to 5 (correlations ranging from 0.62 to 0.91). Estimates of the correlation between IGE of week 1 and IGE of the other weeks did not follow the same pattern and correlations were lower. Estimates of correlations between direct and indirect effects were negative at all times. After seven generations of simulated selection, the increase in ADG from selection on EBV from a SAD model that included IGE was higher (~ 30%) than when those effects were omitted.

CONCLUSIONS

Indirect genetic effects are larger just after mixing animals at weaning than later in the fattening period, probably because of the establishment of social hierarchy that is generally observed at that time. Accounting for IGE in the selection criterion maximizes genetic progress.

Direct and indirect genetic and fine-scale location effects on breeding date in song sparrows

Quantifying direct and indirect genetic effects of interacting females and males on variation in jointly expressed life-history traits is central to predicting microevolutionary dynamics. However, accurately estimating sex-specific additive genetic variances in such traits remains difficult in wild populations, especially if related individuals inhabit similar fine-scale environments. Breeding date is a key life-history trait that responds to environmental phenology and mediates individual and population responses to environmental change. However, no studies have estimated female (direct) and male (indirect) additive genetic and inbreeding effects on breeding date, and estimated the cross-sex genetic correlation, while simultaneously accounting for fine-scale environmental effects of breeding locations, impeding prediction of microevolutionary dynamics. We fitted animal models to 38 years of song sparrow (Melospiza melodia) phenology and pedigree data to estimate sex-specific additive genetic variances in breeding date, and the cross-sex genetic correlation, thereby estimating the total additive genetic variance while simultaneously estimating sex-specific inbreeding depression. We further fitted three forms of spatial animal model to explicitly estimate variance in breeding date attributable to breeding location, overlap among breeding locations and spatial autocorrelation. We thereby quantified fine-scale location variances in breeding date and quantified the degree to which estimating such variances affected the estimated additive genetic variances. The non-spatial animal model estimated nonzero female and male additive genetic variances in breeding date (sex-specific heritabilities: 0·07 and 0·02, respectively) and a strong, positive cross-sex genetic correlation (0·99), creating substantial total additive genetic variance (0·18). Breeding date varied with female, but not male inbreeding coefficient, revealing direct, but not indirect, inbreeding depression. All three spatial animal models estimated small location variance in breeding date, but because relatedness and breeding location were virtually uncorrelated, modelling location variance did not alter the estimated additive genetic variances. Our results show that sex-specific additive genetic effects on breeding date can be strongly positively correlated, which would affect any predicted rates of microevolutionary change in response to sexually antagonistic or congruent selection. Further, we show that inbreeding effects on breeding date can also be sex specific and that genetic effects can exceed phenotypic variation stemming from fine-scale location-based variation within a wild population.

Efficient Boar Semen Production and Genetic Contribution: The Impact of Low-Dose Artificial Insemination on Fertility

Diluting semen from high fertile breeding boars, and by that inseminating many sows, is the core business for artificial insemination (AI) companies worldwide. Knowledge about fertility results is the reason by which an AI company can lower the concentration of a dose. Efficient use of AI boars with high genetic merit by decreasing the number of sperm cells per insemination dose is important to maximize dissemination of the genetic progress made in the breeding nucleus. However, a potential decrease in fertility performance in the field should be weighed against the added value of improved genetics and, in general, is not tolerated in commercial production. This overview provides some important aspects that influence the impact of low-dose AI on fertility: (i) the importance of monitoring field fertility, (ii) the need for accurate and precise semen assessment, (iii) the parameters that are taken into account, (iv) the application of information from genetic and genomic selection and (v) the optimization when using different AI techniques. Efficient semen production, processing and insemination in combination with increasing use of genetic and genomic applications result in maximum impact of genetic trend.

Social genetic effects influence reproductive performance of group-housed sows

Group housing of gestating sows has implications for reproductive performance due to detrimental interactions between sows within groups. Reproductive records ( = 10,748) were obtained for 8,444 pedigreed nucleus sows housed in a single facility, formed into 1,827 static groups during gestation. Only data from complete groups were used to estimate genetic parameters for total born (TB), number born alive (NBA), and gestation length (GL) and to compare models extended to account for group effects. Censored data for sows which did not farrow (0.8% of records) were augmented with biologically meaningful values. Group sizes ranged from 2 to 10, in pens designed to hold 4, 8, or 10 sows per pen. Sows were grouped by parity, line, and mating date after d 35 of pregnancy. Heritability estimates were generally constant across all model alternatives at 0.11 ± 0.02 for TB and NBA and 0.32 ± 0.03 for GL. However, models for all traits were significantly ( < 0.05) improved through inclusion of terms for nongenetic group and social genetic effects (SGE). Group effects were no longer significant in models containing both terms. The proportional contributions of SGE () to phenotypic variances were very low (≤0.002 across traits), but their contributions to calculated total genetic variance (T) were significant. The differences between h and T ranged between 3 and 5% under simple models, increasing to 8 to 14% in models accounting for both covariances between additive direct (A) and SGE and the effects of varying group size on the magnitude of estimates for SGE. Estimates of covariance between A and SGE were sensitive to the modeling of dilution factors for group size. The models of best fit for litter size traits used a customized dilution based on sows/pen relative to the maximum sows/pen. The best model supported a reduction in SGE with increased space per sow, independent of maximum group size, and no significant correlation between A and SGE. The latter is expected if A and SGE reflect different trait complexes. It is suggested that the SGE estimated for reproductive traits represented the expression of an unobserved phenotype, such as sow aggression, of an individual on its pen mates. Further investigation into the use of competitive effects models for genetic evaluation of reproductive traits for group-housed sows could be considered a strategy to improve welfare and performance of group-housed sows.

Direct and social genetic effects on body weight at 270 days and carcass and ham quality traits in heavy pigs

The aims of this study were to estimate covariance components for BW at 270 d (BW270) and carcass and ham quality traits in heavy pigs using models accounting for social effects and to compare the ability of such models to fit the data relative to models ignoring social interactions. Phenotypic records were from 9,871 pigs sired by 293 purebred boars mated to 456 crossbred sows. Piglets were born and reared at the same farm and randomly assigned at 60 d of age to groups (6.1 pigs per group on average) housed in finishing pens, each having an area of 6 m(2). The average additive genetic relationship among group mates was 0.11. Pigs were slaughtered at 277 ± 3 d of age and 169.7 ± 13.9 kg BW in groups of nearly 70 animals each. Four univariate animal models were compared: a basic model (M1) including only direct additive genetic effects, a model (M2) with nonheritable social group (pen) effects in addition to effects in M1, a model (M3) accounting for litter effects in addition to M2, and a model (M4) accounting for social genetic effects in addition to effects in M3. Restricted maximum likelihood estimates of covariance components were obtained for BW270; carcass backfat depth; carcass lean meat content (CLM); iodine number (IOD); and linoleic acid content (LIA) of raw ham subcutaneous fat; subcutaneous fat depth in the proximity of semimembranosus muscle (SFD1) and quadriceps femoris muscle (SFD2); and linear scores for ham round shape (RS), subcutaneous fat (SF), and marbling. Likelihood ratio tests indicated that, for all traits, M2 fit the data better than M1 and that M3 was superior to M2 except for SFD1 and SFD2. Model M4 was significantly better than M3 for BW270 (P < 0.001) and CLM, IOD, RS, and SF (P < 0.05). The contribution of social genetic effects to the total heritable variance was large for CLM and BW270, ranging from 33.2 to 35%, whereas the one for ham quality traits ranged from 6.8 (RS) to 11.2% (SF). Direct and social genetic effects on BW270 were uncorrelated, whereas there was a negative genetic covariance between direct and social effects on CLM, IOD, RS, and SF, which reduced the total heritable variance. This variance, measured relative to phenotypic variance, ranged from 21 (CLM) to 54% (BW270). Results indicate that social genetic effects affect variation in traits relevant for heavy pigs used in dry-cured hams manufacturing. Such effects should be exploited and taken into account in design of breeding programs for heavy pigs.

The prospects of selection for social genetic effects to improve welfare and productivity in livestock

Social interactions between individuals living in a group can have both positive and negative effects on welfare, productivity, and health of these individuals. Negative effects of social interactions in livestock are easier to observe than positive effects. For example, laying hens may develop feather pecking, which can cause mortality due to cannibalism, and pigs may develop tail biting or excessive aggression. Several studies have shown that social interactions affect the genetic variation in a trait. Genetic improvement of socially-affected traits, however, has proven to be difficult until relatively recently. The use of classical selection methods, like individual selection, may result in selection responses opposite to expected, because these methods neglect the effect of an individual on its group mates (social genetic effects). It has become clear that improvement of socially-affected traits requires selection methods that take into account not only the direct effect of an individual on its own phenotype but also the social genetic effects, also known as indirect genetic effects, of an individual on the phenotypes of its group mates. Here, we review the theoretical and empirical work on social genetic effects, with a focus on livestock. First, we present the theory of social genetic effects. Subsequently, we evaluate the evidence for social genetic effects in livestock and other species, by reviewing estimates of genetic parameters for direct and social genetic effects. Then we describe the results of different selection experiments. Finally, we discuss issues concerning the implementation of social genetic effects in livestock breeding programs. This review demonstrates that selection for socially-affected traits, using methods that target both the direct and social genetic effects, is a promising, but sometimes difficult to use in practice, tool to simultaneously improve production and welfare in livestock.

Female and male genetic effects on offspring paternity: additive genetic (co)variances in female extra-pair reproduction and male paternity success in song sparrows (Melospiza melodia)

Ongoing evolution of polyandry, and consequent extra-pair reproduction in socially monogamous systems, is hypothesized to be facilitated by indirect selection stemming from cross-sex genetic covariances with components of male fitness. Specifically, polyandry is hypothesized to create positive genetic covariance with male paternity success due to inevitable assortative reproduction, driving ongoing coevolution. However, it remains unclear whether such covariances could or do emerge within complex polyandrous systems. First, we illustrate that genetic covariances between female extra-pair reproduction and male within-pair paternity success might be constrained in socially monogamous systems where female and male additive genetic effects can have opposing impacts on the paternity of jointly reared offspring. Second, we demonstrate nonzero additive genetic variance in female liability for extra-pair reproduction and male liability for within-pair paternity success, modeled as direct and associative genetic effects on offspring paternity, respectively, in free-living song sparrows (Melospiza melodia). The posterior mean additive genetic covariance between these liabilities was slightly positive, but the credible interval was wide and overlapped zero. Therefore, although substantial total additive genetic variance exists, the hypothesis that ongoing evolution of female extra-pair reproduction is facilitated by genetic covariance with male within-pair paternity success cannot yet be definitively supported or rejected either conceptually or empirically.

The quantitative genetics of indirect genetic effects: a selective review of modelling issues

Indirect genetic effects (IGE) occur when the genotype of an individual affects the phenotypic trait value of another conspecific individual. IGEs can have profound effects on both the magnitude and the direction of response to selection. Models of inheritance and response to selection in traits subject to IGEs have been developed within two frameworks; a trait-based framework in which IGEs are specified as a direct consequence of individual trait values, and a variance-component framework in which phenotypic variance is decomposed into a direct and an indirect additive genetic component. This work is a selective review of the quantitative genetics of traits affected by IGEs, with a focus on modelling, estimation and interpretation issues. It includes a discussion on variance-component vs trait-based models of IGEs, a review of issues related to the estimation of IGEs from field data, including the estimation of the interaction coefficient Ψ (psi), and a discussion on the relevance of IGEs for response to selection in cases where the strength of interaction varies among pairs of individuals. An investigation of the trait-based model shows that the interaction coefficient Ψ may deviate considerably from the corresponding regression coefficient when feedback occurs. The increasing research effort devoted to IGEs suggests that they are a widespread phenomenon, probably particularly in natural populations and plants. Further work in this field should considerably broaden our understanding of the quantitative genetics of inheritance and response to selection in relation to the social organisation of populations.

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.

Indirect genetic effects for survival in domestic chickens (Gallus gallus) are magnified in crossbred genotypes and show a parent-of-origin effect

Through social interactions, individuals can affect one another's phenotype. The heritable effect of an individual on the phenotype of a conspecific is known as an indirect genetic effect (IGE). Although IGEs can have a substantial impact on heritable variation and response to selection, little is known about the genetic architecture of traits affected by IGEs. We studied IGEs for survival in domestic chickens (Gallus gallus), using data on two purebred lines and their reciprocal cross. Birds were kept in groups of four. Feather pecking and cannibalism caused mortality, as beaks were kept intact. Survival time was shorter in crossbreds than in purebreds, indicating outbreeding depression and the presence of nonadditive genetic effects. IGEs contributed the majority of heritable variation in crossbreds (87 and 72%) and around half of heritable variation in purebreds (65 and 44%). There was no evidence of dominance variance, neither direct nor indirect. Absence of dominance variance in combination with considerable outbreeding depression suggests that survival is affected by many loci. Direct-indirect genetic correlations were moderately to highly negative in crossbreds (-0.37 ± 0.17 and -0.83 ± 0.10), but low and not significantly different from zero in purebreds (0.20 ± 0.21 and -0.28 ± 0.18). Consequently, unlike purebreds, crossbreds would fail to respond positively to mass selection. The direct genetic correlation between both crosses was high (0.95 ± 0.23), whereas the indirect genetic correlation was moderate (0.41 ± 0.26). Thus, for IGEs, it mattered which parental line provided the sire and which provided the dam. This indirect parent-of-origin effect appeared to be paternally transmitted and is probably Z chromosome linked.

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.

Group selection and social evolution in domesticated animals

Social interactions, especially those involving competition among individuals, are important in domesticated livestock and in natural populations. The heritability of traits affected by such interactions has two components, one originating in the individual like that of classical traits (direct effects) and the other originating in other group members (indirect effects). The latter type of trait represents a significant source of 'hidden heritability' and it requires population structure and knowledge from relatives in order to access it for selective breeding. When ignored, competitive interactions may increase as an indirect response to direct selection, resulting in diminished yields. We illustrate how population genetic structure affects the response to selection of traits with indirect genetic effects using population genetic and quantitative genetic theory. Population genetic theory permits us to connect our results to the existing body of theory on kin and group selection in natural populations. The quantitative genetic perspective allows us to see how breeders have used knowledge from relatives and family selection in the domestication of plants and animals to improve the welfare and production of livestock by incorporating social genetic effects in the breeding program. We illustrate the central features of these models by reviewing empirical studies from domesticated chickens.