Social Genetic Effects Research Articles

Peer Social Genetic Effects and the Etiology of Substance Use Disorders, Major Depression, and Anxiety Disorder in a Swedish National Sample.

There is growing interest in how peers' genotypes may influence health (i.e., peer social genetic effects). The authors sought to clarify the nature of peer social genetic effects on risk for drug use disorder, alcohol use disorder (AUD), major depression, and anxiety disorder. Cox models were used with data from a population-based Swedish cohort (N=655,327). Outcomes were drug use disorder, AUD, major depression, and anxiety disorder registrations between ages 17 and 30 from medical, criminal, and pharmacy registries. The authors indexed peer social genetic effects with peers' family genetic risk scores (FGRSs) for the same disorders, which are personalized measures of genetic risk inferred from diagnoses in first- to fifth-degree relatives. Across disorders, peer FGRSs predicted increased risks of proband registration (hazard ratio range, 1.01-1.59), with stronger effects for drug use disorder and AUD than for major depression and anxiety disorder. Peer social genetic effects were stronger for school classmates than for geographically proximal peers, and for peers from upper secondary school (ages 16-19) versus peers from lower secondary school (ages 7-16). Peer social genetic effects remained significant following statistical control for sociodemographic confounders, whether peers were affected, and peers' FGRS for educational attainment. Peer social genetic effects were more pronounced for probands at higher genetic risk. The genetic makeup of adolescents' peers has long-reaching consequences on risks for drug use disorder, AUD, major depression, and anxiety disorder. Individuals at high genetic risk are more sensitive to social genetic effects. Alternative hypotheses such as sociodemographic stratification, exposure to affected peers, and genetic predispositions for educational attainment did not explain the risk associated with peer social genetic effects for substance use and psychiatric disorders.

Unravelling the mechanisms underlying the social interaction effects on the feed efficiency of rabbits

The feed consumption and feeding patterns of group-housed livestock and their productive outputs can be affected by interactions among group members, such as competition for feeder access and aggression. The impact of competition is especially significant when feed restriction is implemented during the fattening stage, a common practice in rabbit farms to reduce mortality from digestive disorders during this period. The overall aim was to quantify the importance of social interaction effects at the genetic and environmental level on feed efficiency, feeding behaviour and social ranking traits in rabbit populations having access to electronic feeders whilst they were housed in groups during fattening. This was done by quantifying novel feeding behaviour (FB) and social ranking (SR) traits and by estimating the genetic parameters and genetic correlations between all combinations of direct and social effects of these traits and residual feed intake (RFI), an indicator of feed efficiency. These effects were estimated in animals fed either ad libitum (AL) or on a time-based feed restriction (R) and were selected for feed efficiency on these feeding regimes for six generations. We found that there were substantial social genetic and social litter effects on RFI and FB traits, manifesting as antagonistic relationships between direct and social genetic effects. These effects were stronger amongst the R, as opposed to the AL animals. Despite these antagonistic relationships, the large social genetic effects resulted in substantial heritable variation available for selection of these traits, and therefore feeding behaviour could be changed by genetic selection, especially for R animals. The indicators of an animal’s SR were also heritable and subject to social genetic effects and therefore may respond to selection. However, the phenotypic population mean would not change, because such traits are context−specific. The animals that took most of the feeding resources and had priority access to the feeder had a higher RFI, that is, they were less efficient, but they made their cage mates more efficient. This is the first time that the genetic correlations between direct effects of RFI and direct effects of FB and SR traits, as well as between social effects of RFI and direct effects of FB traits and SR traits, have been quantified. Results could help to understand the mechanisms by which an animal exerts its influence on the feed efficiency of its cage mates under different feeding regimes and assist in the development of breeding strategies.

431 Awardee Talk: Phenomics and Phenotyping in Animal Breeding

Abstract Genomic prediction is now a mature technology for animal breeding and genetics and many breeders’ associations and livestock, and poultry genetics companies have adopted high-through genotyping for routine breeding value prediction and for assisting in selection decisions. In this context, more research efforts in breeding and genetics are focusing more on genome annotation and phenotyping. In animal breeding, the term phenomics is almost exchangeable with high-throughput phenotyping and automated phenotyping, i.e., the recording of high dimensional phenotypic data on an organism-wide scale. High throughput phenotyping is achieved through using a variety of sensors. The sensors generate raw data that must be captured, processed, cleaned, and modeled to produce meaningful phenotypes. Animal breeders are used to working with large amounts of data. However, high throughput phenomics usually produces data that may quickly overwhelm storage and processing capabilities of even the most prepared data analysis teams. Moreover, the black-box nature of many algorithms used to process raw sensor data into actionable phenotypes usually require thorough validation and checking. Animal breeders may build on prior lessons learned processing and modeling high-throughput genotypes and traditional phenotypes and we also need to adopt new practices commonly used in the field of machine learning and computer science. Finally, high throughput phenomics will produce novel phenotypes that will require different modeling or revisiting traditional models. In this talk I will share my views on all these points, and I will illustrate them using literature data and data from my own research related to computer vision, modeling of social genetic effects and modeling of social interactions.

Selection for social genetic effects in purebred pigs improves behaviour and handling of their crossbred progeny

BackgroundIn commercial pig production, reduction of harmful social behavioural traits, such as ear manipulation and tail biting, is of major interest. Moreover, farmers prefer animals that are easy to handle. The aim of this experiment was to determine whether selection on social breeding values (SBV) for growth rate in purebred pigs affects behaviour in a weighing crate, lesions from ear manipulation, and tail biting of their crossbred progeny. Data were collected on crossbred F1 pigs allocated to 274 pens, which were progeny of purebred Landrace sows and Yorkshire boars from a DanBred nucleus herd.ResultsBehaviour in the weighing crate scored on a three-level scale showed that groups of pigs with high SBV for growth rate were significantly calmer than groups of pigs with low SBV (P < 0.027). When the mean SBV in the group increased by 1 unit, the proportion of pigs that obtained a calmer score level was increased by 14%. A significant (p = 0.04), favourable effect of SBV was found on both the number of pigs with ear lesions in the group and the mean number of ear lesions per pig. For a 1 unit increase in mean SBV, the mean number of lesions per pig decreased by 0.06 from a mean of 0.98. Individual severity of ear lesions conditional upon the number of ear lesions was also significantly affected (p = 0.05) by the mean SBV in the group. In groups for which the mean SBV increased by 1 unit, the proportion of pigs that were observed with a lower severity score was increased by 20% on a three-level scale. Most pigs received no tail biting injuries and no effect of SBV was observed on the tail injury score.ConclusionsAfter 7 weeks in the finisher unit, crossbred progeny with high SBV were calmer in the weighing crate and had fewer ear lesions. These results indicate that selection of purebred parents for SBV for growth rate will increase welfare in their crossbred progeny by decreasing the number of ear lesions and making them easier to handle.

Using social interaction models for genetic analysis of skin damage in gilts.

Skin damage is a trait of economic and welfare importance that results from social interactions between animals. These interactions may produce wound signs on the gilt's skin as a result of damage behavior (i.e., fighting), biting syndromes (i.e., tail, vulva, or ear biting), and swine inflammation and necrosis syndrome. Although current selection for traits that are affected by social interactions primarily focuses on improving direct genetic effects, combined selection on direct and social genetic effects could increase genetic gain and avoid a negative response to selection in cases of competitive behavior. The objectives of this study were to (1) estimate variance components for combined skin damage (CSD), with or without accounting for social genetic effects, (2) investigate the impact of including genomic information on the prediction accuracy, bias, and dispersion of CSD estimated breeding values, and (3) perform a single-step genome-wide association study (ssGWAS) of CSD under a classical and a social interaction model. Our results show that CSD is heritable and affected by social genetic effects. Modeling CSD with social interaction models increased the total heritable variance relative to the phenotypic variance by three-fold compared to the classical model. Including genomic information increased the prediction accuracy of direct, social, and total estimated breeding values for purebred sires by at least 21.2%. Bias and dispersion of estimated breeding values were reduced by including genomic information in classical and social interaction models but remained present. The ssGWAS did not identify any single nucleotide polymorphism that was significantly associated with social or direct genetic effects for CSD. Combined skin damage is heritable, and genetic selection against this trait will increase the welfare of animals in the long term. Combined skin damage is affected by social genetic effects, and modeling this trait with a social interaction model increases the potential for genetic improvement. Including genomic information increases the prediction accuracy of estimated breeding values and reduces their bias and dispersion, although some biases persist. The results of the genome-wide association study indicate that CSD has a polygenic architecture and no major quantitative trait locus was detected.

Partners in Health: Investigating Social Genetic Effects Among Married and Cohabiting Couples

Partners resemble each other in health behaviors and outcomes such as alcohol use, smoking, physical activity, and obesity. While this is consistent with social contagion theory suggesting partner influence, it is notoriously difficult to establish causality because of assortative mating and contextual confounding. We offer a novel approach to studying social contagion in health in long-term partnerships by combining genetic data of both partners in married/cohabiting couples with longitudinal data on their health behaviors and outcomes. We examine the influence of the partner’s genetic predisposition for three health outcomes and behaviors (BMI, smoking, and drinking) among married/cohabiting couples. We use longitudinal data from the Health and Retirement Study and the English Longitudinal Study of Ageing with data on health outcomes and genotypes for both partners. Results show that changes over time in BMI, smoking, and drinking depend on the partner’s genetic predispositions to these traits. These findings underline the importance of people’s social surroundings for their health and highlight the potential of targeting health interventions at couples.

Hosts winnow symbionts with multiple layers of absolute and conditional discrimination mechanisms.

In mutualism, hosts select symbionts via partner choice and preferentially direct more resources to symbionts that provide greater benefits via sanctions. At the initiation of symbiosis, prior to resource exchange, it is not known how the presence of multiple symbiont options (i.e. the symbiont social environment) impacts partner choice outcomes. Furthermore, little research addresses whether hosts primarily discriminate among symbionts via sanctions, partner choice or a combination. We inoculated the legume, Acmispon wrangelianus, with 28 pairs of fluorescently labelled Mesorhizobium strains that vary continuously in quality as nitrogen-fixing symbionts. We find that hosts exert robust partner choice, which enhances their fitness. This partner choice is conditional such that a strain's success in initiating nodules is impacted by other strains in the social environment. This social genetic effect is as important as a strain's own genotype in determining nodulation and has both transitive (consistent) and intransitive (idiosyncratic) effects on the probability that a symbiont will form a nodule. Furthermore, both absolute and conditional partner choice act in concert with sanctions, among and within nodules. Thus, multiple forms of host discrimination act as a series of sieves that optimize host benefits and select for costly symbiont cooperation in mixed symbiont populations.

Social genetic effects for drug use disorder among spouses.

Preclinical and human studies suggest that a social partner's genotype may be associated with addiction-related outcomes. This study measured whether spousal genetic makeup is associated with risk of developing drug use disorder (DUD) during marriage and whether the risk associated with a spouse's genotype could be disentangled from potentially confounding rearing environmental effects. Univariable and multivariable logistic regression analyses. Sweden. Men and women born between 1960 and 1990 and in opposite-sex first marriages before age 35 (n = 294 748 couples). Outcome was DUD diagnosis (inclusive of opioids, sedatives/hypnotics/anxiolytics, cocaine, cannabis, amphetamine and other psychostimulants, hallucinogens, other drugs of abuse and combinations thereof) obtained from legal, medical and pharmacy registries. The focal predictor was family genetic risk scores for DUD (FGRS-DUD), which were inferred from diagnoses in first- through fifth-degree relatives and weighted by degree of genetic sharing. FGRS-DUD were calculated separately for each partner in a couple. Marriage to a spouse with a high FGRS-DUD was associated with increased risk of developing DUD during marriage, ORmales = 1.68 (95% CI = 1.50, 1.88) and ORfemales = 1.35 (1.16, 1.56), above and beyond the risk associated with one's own FGRS-DUD. The risk associated with a spouse's FGRS-DUD remained statistically significant after covarying for parental education. As indicated by a series of null interaction effects, there was no evidence that the risk associated with a spouse's FGRS-DUD differed depending on whether the spouse was DUD-affected, probands' probable contact with in-laws and whether the spouse was raised by his/her biological parents or in another home. There is relatively robust evidence that a person's risk for developing drug use disorder is associated with the genetic makeup of the person's spouse.

Examining social genetic effects on educational attainment via parental educational attainment, income, and parenting.

Higher parental educational attainment is associated with higher offspring educational attainment. In this study, we incorporated genotypic and phenotypic information from fathers, mothers, and offspring to disentangle the genetic and socioenvironmental pathways underlying this association. Data were drawn from a sample of individuals of European ancestry from the collaborative study on the genetics of alcoholism (n = 4,089; 51% female). Results from path analysis indicated that paternal and maternal educational attainment genome-wide polygenic scores were associated with offspring educational attainment, above and beyond the effect of offspring education polygenic score. Parental educational attainment, income, and parenting behaviors served as important socioenvironmental pathways that mediated the effect of parental education polygenic score on offspring educational attainment. Our study highlights the importance of using genetically informed family studies to disentangle the genetic and socioenvironmental pathways underlying parental influences on human development. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Genetic Parameter Estimation and Genome-Wide Association Analysis of Social Genetic Effects on Average Daily Gain in Purebreds and Crossbreds.

Simple SummaryAverage daily gain (ADG) is influenced by both an individual’s direct genetic effect (DGE) and by a social genetic effect (SGE) derived from pen mates. Therefore, identifying the DGE and SGE on ADG is essential for a better understanding of pig breeding systems. We conducted this study to elucidate the genetic characteristics and relationships of DGE and SGE on ADG using purebred and crossbred pigs. We found that the DGE and SGE both contributed to ADG in both populations. In addition, the SGE of purebred pigs was highly correlated with the DGE of crossbred pigs. Furthermore, we identified several genomic regions that may be associated with the DGE and SGE on ADG. Our findings will contribute to future genomic evaluation studies of socially affected traits.Average daily gain (ADG) is an important growth trait in the pig industry. The direct genetic effect (DGE) has been studied mainly to assess the association between genetic information and economic traits. The social genetic effect (SGE) has been shown to affect ADG simultaneously with the DGE because of group housing systems. We conducted this study to elucidate the genetic characteristics and relationships of the DGE and SGE of purebred Korean Duroc and crossbred pigs by single-step genomic best linear unbiased prediction and a genome-wide association study. We used the genotype, phenotype, and pedigree data of 1779, 6022, and 7904 animals, respectively. Total heritabilities on ADG were 0.19 ± 0.04 and 0.39 ± 0.08 for purebred and crossbred pigs, respectively. The genetic correlation was the greatest (0.77 ± 0.12) between the SGE of purebred and DGE of crossbred pigs. We found candidate genes located in the quantitative trait loci (QTLs) for the SGE that were associated with behavior and neurodegenerative diseases, and candidate genes in the QTLs for DGE that were related to body mass, size of muscle fiber, and muscle hypertrophy. These results suggest that the genomic selection of purebred animals could be applied for crossbred performance.

10 Deep Learning for Multi-Behavioral Video Classification of Interactive Behaviors of Pigs in Single-Spaced Automatic Feeders

Abstract Automatic pig feeders record individual feeding behavior traits that can be used in genetic selection. However, these feeders cannot measure how feeding traits are affected by behavioral interactions between animals. Moreover, recording behavioral interactions can improve the estimation of social genetic effects. This motivated our assessment of computer vision to classify 4 types of interactions at the feeder: head-to-body contact (HB) including head knocking, tail biting, pushing, gentle nosing and casual contact between head/ears of a pig with a feeding pig; levering (L) where the feeding pig was lifted from behind by another pig; mounting (M) where the feeding pig was mounted by another pig; and no-contact (NC) when a second pig entered the feeder without touching the feeding pig. Behavior at the feeder was filmed twice, 3 weeks apart, for 2 consecutive days each week in 6 groups of grow-finish pigs (10 per group) housed in pens equipped with FIRE feeders. Video segments that involved 2 pigs in the feeder were selected, and labeled as HB (n=10,114), L (n=925), M (n=1,242) and NC (n=3,398). Due to the sparse data available for training and the complex nature of the behavioral interactions, we utilized pretrained convolutional neural networks for automatic spatial feature extraction followed by long short-term memory for temporal feature extraction from sequences of frames. Focal loss, a loss function that assigns different weights to hard/easily misclassified examples to handle class imbalance problem, was used in this study. Accuracy, recall, and precision of behavior classification were obtained from 5 random cross-validations. The overall accuracy was 0.967±0.002. The average recalls for HB, L, M, and NC were 0.977±0.004, 0.866±0.047, 0.969±0.011, and 0.964±0.009 respectively, while average precisions were 0.976±0.002, 0.901±0.029, 0.956±0.018, and 0.963±0.012. The proposed algorithm accurately classified multiple interactive behaviors in an automated feeding stall from digital videos.

Supplemental Material for Examining Social Genetic Effects on Educational Attainment via Parental Educational Attainment, Income, and Parenting

Supplemental Material for Examining Social Genetic Effects on Educational Attainment via Parental Educational Attainment, Income, and Parenting

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.

Selection for social genetic effects in purebreds increases growth in crossbreds

BackgroundAverage daily gain (ADG) in pigs is affected by both direct and social genetic effects (SGE). However, selection for SGE in purebreds has not conclusively been shown to improve ADG in crossbreds, and it is unknown whether SGE in purebreds are equal to those in crossbreds. Moreover, SGE may reflect dominance related behaviour, which is affected by the variation in body weight within a group. Therefore, we hypothesized that (a) there is a positive effect of parent average SGE estimated in purebred pigs on phenotypic ADG in crossbred offspring, and (b) there is an interaction between SGE on ADG and standard deviation in starting weight of pigs within the group. We also hypothesized that (c) social genetic variance for ADG exists in crossbred pigs, and (d) there is a favourable genetic correlation between SGE on ADG in purebred and crossbred pigs.ResultsWe found a statistically significant interaction between the standard deviation in starting weight and SGE within groups, and conditioning on the mean standard deviation in starting weight, we found a favourable regression coefficient (0.37 ± 0.21) of ADG in crossbreds on SGE in purebreds. Variances for SGE were small in both Landrace (L) and Yorkshire (Y), and higher for SGE in both the dam and sire component of crossbred YL. The genetic correlations between SGE in purebreds and the dam or sire component of SGE in crossbreds were also favourable (0.52 ± 0.48 and 0.34 ± 0.42, respectively), although not significantly different from 0.ConclusionsWe confirmed that there is a positive effect of SGE estimated using purebred information on phenotypic ADG in crossbreds, and that the largest effect is achieved when the within-group variation in starting weight is small. Our results indicate that social genetic variance in crossbreds exists and that there is a favourable genetic correlation between social genetic effects in purebreds and crossbreds. Collectively, our results indicate that selection for SGE on ADG in purebreds in a nucleus farm environment with little competition for resources can improve ADG in crossbreds in a commercial environment.

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.

Contribution of social genetic effects in variance components estimation for body weight in Nigerian indigenous chickens raised in a tropical humid location.

Social interactions among chickens can have a great unfavourable effect on economic returns in a poultry farm. The purpose of this study was to use four models to examine the influence of social genetic influences on the variation in body weight of Nigerian indigenous chickens. Sex was treated as the fixed effect within the models. Direct additive genetic, social genetic, and family effects and covariance between direct and social genetic effects were used as random effects. Data were analysed using single-trait animal models which include or exclude social genetic effects. Model comparison revealed that inclusion of full-sib family effect in model 3 did not cause any change in residual and additive genetic variances relative to estimates obtained with model 2. In general, social genetic variance was lower than the estimate for additive genetic variance, but substantially added to the overall heritable variance. For direct hereditary, full-sib family, and residual effects, accounting for heritable social effect in model 4 had a marginal effect on the size of the variances measured. All the estimated residual, additive genetic, social genetic effect, and family variances increased in comparison with model 3. The relationship between direct and social additive effects was positive and not significantly different from 0 (P > 0.05), suggesting autonomy between the direct and social breeding values. In conclusion, the use of models that account for direct effect and social genetic effect of the individual on its group members would entail an optimal individual selection scheme to increase the body weight of chickens.

Statistical model and testing designs to increase response to selection with constrained inbreeding in genomic breeding programs for pigs affected by social genetic effects

BackgroundSocial genetic effects (SGE) are the effects of the genotype of one animal on the phenotypes of other animals within a social group. Because SGE contribute to variation in economically important traits for pigs, the inclusion of SGE in statistical models could increase responses to selection (RS) in breeding programs. In such models, increasing the relatedness of members within groups further increases RS when using pedigree-based relationships; however, this has not been demonstrated with genomic-based relationships or with a constraint on inbreeding. In this study, we compared the use of statistical models with and without SGE and compared groups composed at random versus groups composed of families in genomic selection breeding programs with a constraint on the rate of inbreeding.ResultsWhen SGE were of a moderate magnitude, inclusion of SGE in the statistical model substantially increased RS when SGE were considered for selection. However, when SGE were included in the model but not considered for selection, the increase in RS and in accuracy of predicted direct genetic effects (DGE) depended on the correlation between SGE and DGE. When SGE were of a low magnitude, inclusion of SGE in the model did not increase RS, probably because of the poor separation of effects and convergence issues of the algorithms. Compared to a random group composition design, groups composed of families led to higher RS. The difference in RS between the two group compositions was slightly reduced when using genomic-based compared to pedigree-based relationships.ConclusionsThe use of a statistical model that includes SGE can substantially improve response to selection at a fixed rate of inbreeding, because it allows the heritable variation from SGE to be accounted for and capitalized on. Compared to having random groups, family groups result in greater response to selection in the presence of SGE but the advantage of using family groups decreases when genomic-based relationships are used.

297 GWAS for complex models accounting for populations structure with GBLUP and ssGBLUP

Abstract Genomic Best Linear Unbiased Prediction (GBLUP) is the method of choice for incorporating genomic information into the genetic evaluation of livestock species. Furthermore, single step GBLUP (ssGBLUP) is adopted by many breeders’ associations and private entities managing large scale breeding programs. While prediction of breeding values remains the primary use of genomic markers in animal breeding, a secondary interest focuses on performing genome-wide association studies (GWAS). The goal of GWAS is to uncover genomic regions that harbor variants that explain a large proportion of the phenotypic variance, and thus become candidates for discovering and studying causative variants. Several methods have been proposed and successfully applied for embedding GWAS into genomic prediction models. Most methods commonly avoid formal hypothesis testing and resort to estimation of SNP effects, relying on visual inspection of graphical outputs to determine candidate regions. However, with the advent of high throughput phenomics and transcriptomics, a more formal testing approach with automatic discovery thresholds is more appealing. In this work we present the methodological details of a method for performing formal hypothesis testing for GWAS in GBLUP models. First, we present the method and its equivalencies and differences with other GWAS methods. Moreover, we demonstrate through simulation analyses that the proposed method controls type I error rate at the nominal level. Second, we demonstrate two possible computational implementations based on mixed model equations for ssGBLUP and based on the generalized least square equations (GLS). We show that ssGBLUP can deal with datasets with extremely large number of animals and markers and with multiple traits. GLS implementations are well suited for dealing with smaller number of animals with tens of thousands of phenotypes. Third, we show several useful extensions, such as: testing multiple markers at once, testing pleiotropic effects and testing association of social genetic effects.

Selection for feed efficiency using the social effects animal model in growing Duroc pigs: evaluation by simulation

BackgroundTraits recorded on animals that are raised in groups can be analysed with the social effects animal model (SAM). For multiple traits, this model specifies the genetic correlation structure more completely than the animal model (AM). Our hypothesis was that by using the SAM for genetic evaluation of average daily gain (ADG) and backfat thickness (BF), a high rate of improvement in feed conversion ratio (FCR) might be achieved, since unfavourable genetic correlations between ADG and BF reported in a Duroc pig line could be partially avoided. We estimated genetic and non-genetic correlations between BF, ADG and FCR on 1144 pigs using Bayesian methods considering the SAM; and responses to selection indexes that combine estimates of indirect (IGE) and direct (DGE) genetic effects for ADG and BF by stochastic simulation.ResultsEstimates of the ratio of the variance of DGE to the phenotypic variance were 0.31, 0.39 and 0.25 and those of the total genetic variance to the phenotypic variance were 0.63, 0.74 and 0.93 for ADG, BF and FCR, respectively. In spite of this, when the SAM was used to generate data and for the genetic evaluations, the average economic response was worse than that obtained when BV predictions from the AM were considered. The achieved economic response was due to a direct reduction in BF and not to an improvement in FCR.ConclusionsOur results show that although social genetic effects play an important role in the traits studied, their proper consideration in pig breeding programs to improve FCR indirectly is still difficult. The correlations between IGE and DGE that could help to overcome the unfavourable genetic correlations between DGE did not reach sufficiently high magnitudes; also, the genetic parameters estimates from the SAM have large errors. These two factors penalize the average response under the SAM compared to the AM.

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.