Social Genetic Effects Research Articles

Disentangling Social-Genetic From Rearing-Environment Effects for Alcohol Use Disorder Using Swedish National Data

Investigations of social-genetic effects, whereby a social partner’s genotype affects another’s outcomes, can be confounded by the influence of the social partner’s rearing environment. We used marital information on more than 300,000 couples from Swedish national data to disentangle social-genetic from rearing-environment effects for alcohol use disorder (AUD). Using observational and extended-family designs, we found that (a) marriage to a spouse with a predisposition toward AUD (as indexed by a parental history of AUD) increased risk for developing AUD; (b) this increased risk was not explained by socioeconomic status, the spouse’s AUD status, or contact with the spouse’s parents; and (c) this increased risk reflected the psychological consequences of the spouse having grown up with an AUD-affected parent (i.e., a rearing-environment effect) rather than a social-genetic effect. Findings illustrate that a spouse’s rearing-environment exposures may confer risk for AUD.

Not only in my genes: The effects of peers’ genotype on obesity

We use data from three waves of Add Health to study the short- and long-run effects of high school peers’ genetic predisposition to high BMI—measured by grade-mates’ average BMI polygenic scores—on adolescent and adult obesity in the U.S. We find that, in the short-run, a one standard deviation increase in peers’ average BMI polygenic scores raises the probability of obesity for females by 2.8% points, about half the size of the effect induced by a one standard deviation increase in one's own polygenic score. No significant effect is found for males. In the long-run, however, the social-genetic effect fades away, while the effect of one's own genetic risk for BMI increases substantially. We suggest that mechanisms explaining the short-run effect for females include changes in nutrition habits and a distorted perception of body size.

It takes two: Heritable male effects on reproductive timing but not clutch size in a wild bird population.

Within-population variation in the traits underpinning reproductive output has long been of central interest to biologists. Since they are strongly linked to lifetime reproductive success, these traits are expected to be subject to strong selection and, if heritable, to evolve. Despite the formation of durable pair bonds in many animal taxa, reproductive traits are often regarded as female-specific, and estimates of quantitative genetic variation seldom consider a potential role for heritable male effects. Yet reliable estimates of such social genetic effects are important since they influence the amount of heritable variation available to selection. Based on a 52-year study of a nestbox-breeding great tit (Parus major) population, we apply "extended" bivariate animal models in which the heritable effects of both sexes are modeled to assess the extent to which males contribute to heritable variation in seasonal reproductive timing (egg laying date) and clutch size, while accommodating the covariance between the two traits. Our analyses show that reproductive timing is a jointly expressed trait in this species, with (positively covarying) heritable variation for laydate being expressed in both members of a breeding pair, such that the total heritable variance is 50% larger than estimated by traditional models. This result was robust to explicit consideration of a potential male-biased environmental confound arising through sexually dimorphic dispersal. In contrast to laydate, males' contribution to heritable variation in clutch size was limited. Our study thus highlights the contrasting extent of social determination for two major components of annual reproductive success, and emphasizes the need to consider the social context of what are often considered individual-level traits.

Corrigendum to: Estimation of indirect social genetic effects for skin lesion count in group-housed pigs by quantifying behavioral interactions.

Corrigendum to: Estimation of indirect social genetic effects for skin lesion count in group-housed pigs by quantifying behavioral interactions.

No Evidence for Social Genetic Effects or Genetic Similarity Among Friends Beyond that Due to Population Stratification: A Reappraisal of Domingue et al (2018).

Using data from 5500 adolescents from the National Longitudinal Study of Adolescent to Adult Health, Domingue et al. (Proc Natl Acad Sci 25:256., 2018) claimed to show that friends are genetically more similar to one another than randomly selected peers, beyond the confounding effects of population stratification by ancestry. The authors also claimed to show 'social-genetic' effects, whereby individuals' educational attainment (EA) is influenced by their friends' genes. We argue that neither claim is justified by the data. Mathematically we show that (1) the genetic similarity reported between friends is far larger than theoretically possible if it was caused by phenotypic assortment as the authors claim; uncontrolled population stratification is a likely reason for the genetic similarity they observed, and (2) significant association between individuals' EA and their friends' polygenic scores for EA is a necessary consequence of EA similarity among friends, and does not provide evidence for social-genetic effects. Going forward, we urge caution in the analysis and interpretation of data at the intersection of human genetics and the social sciences.

Estimation of indirect social genetic effects for skin lesion count in group-housed pigs by quantifying behavioral interactions1.

Mixing of pigs into new social groups commonly induces aggressive interactions that result in skin lesions on the body of the animals. The relationship between skin lesions and aggressive behavioral interactions in group-housed pigs can be analyzed within the framework of social genetic effects (SGE). This study incorporates the quantification of aggressive interactions between pairs of animals in the modeling of SGE for skin lesions in different regions of the body in growing pigs. The dataset included 792 pigs housed in 59 pens. Skin lesions in the anterior, central, and caudal regions of the body were counted 24 h after pig mixing. Animals were video-recorded for 9 h postmixing and trained observers recorded the type and duration of aggressive interactions between pairs of animals. The number of seconds that pairs of pigs spent engaged in reciprocal fights and unilateral attack behaviors were used to parametrize the intensity of social interactions (ISI). Three types of models were fitted: direct genetic additive model (DGE), traditional social genetic effect model (TSGE) assuming uniform interactions between dyads, and an intensity-based social genetic effect model (ISGE) that used ISI to parameterize SGE. All models included fixed effects of sex, replicate, lesion scorer, weight at mixing, premixing lesion count, and the total time that the animal spent engaged in aggressive interactions (reciprocal fights and unilateral attack behaviors) as a covariate; a random effect of pen; and a random direct genetic effect. The ISGE models recovered more direct genetic variance than DGE and TSGE, and the estimated heritabilities (h^D2) were highest for all traits (P < 0.01) for the ISGE with ISI parametrized with unilateral attack behavior. The TSGE produced estimates that did not differ significantly from DGE (P > 0.5). Incorporating the ISI into ISGE, even in a small dataset, allowed separate estimation of the genetic parameters for direct and SGE, as well as the genetic correlation between direct and SGE (r^ds), which was positive for all lesion traits. The estimates from ISGE suggest that if behavioral observations are available, selection incorporating SGE may reduce the consequences of aggressive behaviors after mixing pigs.

Application of single-step genomic evaluation using social genetic effect model for growth in pig

ObjectiveSocial genetic effects (SGE) are an important genetic component for growth, group productivity, and welfare in pigs. The present study was conducted to evaluate i) the feasibility of the single-step genomic best linear unbiased prediction (ssGBLUP) approach with the inclusion of SGE in the model in pigs, and ii) the changes in the contribution of heritable SGE to the phenotypic variance with different scaling ω constants for genomic relationships.MethodsThe dataset included performance tested growth rate records (average daily gain) from 13,166 and 21,762 pigs Landrace (LR) and Yorkshire (YS), respectively. A total of 1,041 (LR) and 964 (YS) pigs were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel. With the BLUPF90 software package, genetic parameters were estimated using a modified animal model for competitive traits. Giving a fixed weight to pedigree relationships (τ: 1), several weights (ωxx, 0.1 to 1.0; with a 0.1 interval) were scaled with the genomic relationship for best model fit with Akaike information criterion (AIC).ResultsThe genetic variances and total heritability estimates (T2) were mostly higher with ssGBLUP than in the pedigree-based analysis. The model AIC value increased with any level of ω other than 0.6 and 0.5 in LR and YS, respectively, indicating the worse fit of those models. The theoretical accuracies of direct and social breeding value were increased by decreasing ω in both breeds, indicating the better accuracy of ω0.1 models. Therefore, the optimal values of ω to minimize AIC and to increase theoretical accuracy were 0.6 in LR and 0.5 in YS.ConclusionIn conclusion, single-step ssGBLUP model fitting SGE showed significant improvement in accuracy compared with the pedigree-based analysis method; therefore, it could be implemented in a pig population for genomic selection based on SGE, especially in South Korean populations, with appropriate further adjustment of tuning parameters for relationship matrices.

62 Improved estimation of indirect social genetic effects in group-housed pigs by quantifying behavioral interactions

Abstract Traditional social genetic effects modeling assumes uniform intensity of interaction between group members. Tree breeders proposed relaxing this assumption by incorporating estimates of intensity of competition between pairs of individuals. Here, we incorporated the quantification of aggressive interactions between pairs of animals in the estimation of indirect social genetic effects on skin lesions in the anterior part of the body in growing pigs. The data consisted of 491 pigs (215 barrows and 276 gilts, mean of 66 ±5 days of age). Animals were housed in 37 pens (11 to 15 pigs by pen) over 7 replicates. Trained scorers counted the number of skin lesions immediately before and 24 hours after mixing pigs. Animals were video-recorded for 9 hours post mixing and trained observers quantified the type and duration of aggressive interactions between pairs of pigs. The number of skin lesions in the frontal part of the body 24 hours post-mixing was the response variable and the number of seconds that pairs of animals spent engaged in reciprocal fights was used to quantify the intensity of interaction. We compared three different models: A direct genetic additive model (DGE), a traditional social genetic effect model (TSGE) assuming uniform interactions, and an improved social genetic effect model (ISGE) that used intensity of interaction to parameterize social genetic effects. All models included fixed effects of sex, replicate, lesion scorer, initial weight and pre-mixing lesion count; a random effect of pen; and a random direct genetic effect. The model ISGE recovered the most variance (smallest σe2) and resulted in the highest estimated h2 (P &lt; 0.005). The model TSGE produced estimates that did not differ significantly from DGE (P = 1). Contrarily, incorporating the intensity of interaction into the modeling of ISGE allowed direct and indirect genetic effects to be estimated separately, even in a small dataset.

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.

Genetic association between sow longevity and social genetic effects on growth in pigs.

ObjectiveSow longevity is important for efficient and profitable pig farming. Recently, there has been an increasing interest in social genetic effect (SGE) of pigs on stress-tolerance and behavior. The present study aimed to estimate genetic correlations among average daily gain (ADG), stayability (STAY), and number of piglets born alive at the first parity (NBA1) in Korean Yorkshire pigs, using a model including SGE.MethodsThe phenotypic records of ADG and reproductive traits of 33,120 and 11,654 pigs, respectively, were evaluated. The variances and (co) variances of the studied traits were estimated by a multi-trait animal model applying the Bayesian with linear-threshold models using Gibbs sampling.ResultsThe direct and SGEs on ADG had a significantly negative (−0.30) and neutral (0.04) genetic relationship with STAY, respectively. In addition, the genetic correlation between the social effects on ADG and NBA1 tended to be positive (0.27), unlike the direct effects (−0.04). The genetic correlation of the total effect on ADG with that of STAY was negative (−0.23) but non-significant, owing to the social effect.ConclusionThese results suggested that total genetic effect on growth in the SGE model might reduce the negative effect on sow longevity because of the growth potential of pigs. We recommend including social effects as selection criteria in breeding programs to obtain satisfactory genetic changes in both growth and longevity.

Individual and Social Genomic Contributions to Educational and Neighborhood Attainments: Geography, Selection, and Stratification in the United States

Research on neighborhood effects draws suggestive links between local spatial environments and a range of social, economic, and public health outcomes. Here, we consider the potential role of genetics in the geography of social stratification in the United States using genomic data from the National Longitudinal Study of Adolescent to Adult Health. We find that those with genotypes related to higher educational attainment sort into neighborhoods that are better educated and have higher population densities, both descriptively and using formal school and sibling fixed-effects models. We identify four mechanisms through which this geographic sorting on genetic endowment can magnify social stratification: assortative mating, social-genetic effects, gene-by-environment interactions, and gene–by–social-genetic interactions. We examine the presence of the latter three in our data, finding provisional yet suggestive evidence for social-genetic effects that putatively amount to about one-third of the influence of one’s own genomic profile. We find no evidence, however, for the presence of interactions between environments and individual genetic background. Collectively, these findings highlight the potential for geographic sorting on genotype to emerge both as a key methodological concern in population genetics and social science research and also a potentially overlooked dimension of social stratification worthy of future study.

Indirect genetic effect model using feeding behaviour traits to define the degree of interaction between mates: an implementation in pigs growth rate

An alternative implementation of the animal model including indirect genetic effect (IGE) is presented considering pair-mate-specific interaction degrees to improve the performance of the model. Data consisted of average daily gain (ADG) records from 663 pigs kept in groups of 10 to 14 mates during the fattening period. Three types of models were used to fit ADG data: (i) animal model (AM); (ii) AM with classical IGE (AM-IGE); and (iii) AM fitting IGE with a specific degree of interaction between each pair of mates (AM-IGEi). Several feeding behavior phenotypes were used to define the pair-mate-specific degree of interaction in AM-IGEi: feeding rate (g/min), feeding frequency (min/day), the time between consecutive visits to the feeder (min/day), occupation time (min/day) and an index considering all these variables. All models included systematic effects batch, initial age (covariate), final age (covariate), number of pigs per pen (covariate), plus the random effect of the pen. Estimated posterior mean (posterior SD) of heritability was 0.47 (0.15) using AM. Including social genetic effects in the model, total heritable variance expressed as a proportion of total phenotypic variance (T2) was 0.54 (0.29) using AM-IGE, whereas it ranged from 0.51 to 0.55 (0.12 to 0.14) with AM-IGEi, depending on the behavior trait used to define social interactions. These results confirm the contribution of IGEs to the total heritable variation of ADG. Moreover, important differences between models were observed in EBV rankings. The percentage of coincidence of top 10% animals between AM and AM-IGEi ranged from 0.44 to 0.89 and from 0.41to 0.68 between AM-IGE and AM-IGEi. Based on the goodness of fit and predictive ability, social models are preferred for the genetic evaluation of ADG. Among models including IGEs, when the pair-specific degree of interaction was defined using feeding behavior phenotypes we obtained an increase in the accuracy of genetic parameters estimates, the better goodness of fit and higher predictive ability. We conclude that feeding behavior variables can be used to measure the interaction between pen mates and to improve the performance of models including IGEs.

Not Only in My Genes: The Effects of Peers' Genotype on Obesity

We use data from three waves of Add Health to study the short- and long-run effects of high school peers' genetic predisposition to high BMI – measured by grade-mates' average BMI polygenic scores – on adolescent and adult obesity in the U.S. We find that, in the short-run, a one standard deviation increase in peers' average BMI polygenic scores raises the probability of obesity for females by 2.8 percentage points, about half the size of the effect induced by a one standard deviation increase in one's own polygenic score. No significant effect is found for males. In the long-run, however, the social-genetic effect fades away, while the effect of one's own genetic risk for BMI increases substantially. We suggest that mechanisms explaining the short-run effect for females include changes in nutrition habits and a distorted perception of body size.

The Role of Emergence in Genetically Informed Relationships Research: A Methodological Analysis.

This paper provides a critical analysis of genetically informed research on relationships, with an emphasis on relationships among unrelated individuals (e.g., spouses). To date, research in this area has used traditional behavioral genetic frameworks to either partition the variance in relationship-related outcomes into genetic and environmental components, or to examine gene-environment interplay between relationship factors and other outcomes. However, this conventional approach is at odds with the long-standing understanding from the field of relationship science that both partners' characteristics matter when predicting shared outcomes-that is, outcomes that are emergent. We examine briefly the philosophical concept of emergence, and discuss ways to model dyadic outcomes in genetically informed relationships research. We also review the related topic of social genetic effects, which refer to the influence of a social partner's genotype on a proband's phenotype. A genetically informed dyadic perspective has potentially important consequences for our understanding of the pathways from genotype→shared or individual-level phenotypes, and more fully recognizes the complexity of how genetic and social/environmental factors come together to influence human behavior.

Social and Genetic Pathways in Multigenerational Transmission of Educational Attainment

This study investigates the complex roles of the social environment and genes in the multigenerational transmission of educational attainment. Drawing on genome-wide data and educational attainment measures from the Framingham Heart Study (FHS) and the Health and Retirement Study (HRS), I conduct polygenic score analyses to examine genetic confounding in the estimation of parents’ and grandparents’ influences on their children’s and grandchildren’s educational attainment. I also examine social genetic effects (i.e., genetic effects that operate through the social environment) in the transmission of educational attainment across three generations. Two-generation analyses produce three important findings. First, about one-fifth of the parent-child association in education reflects genetic inheritance. Second, up to half of the association between parents’ polygenic scores and children’s education is mediated by parents’ education. Third, about one-third of the association between children’s polygenic scores and their educational attainment is attributable to parents’ genotypes and education. Three-generation analyses suggest that genetic confounding on the estimate of the direct effect of grandparents’ education on grandchildren’s education (net of parents’ education) may be inconsequential, and I find no evidence that grandparents’ genotypes significantly influence grandchildren’s education through non-biological pathways. The three-generation results are suggestive, and the results may change when different samples are used.

Social genetic effects for growth in pigs differ between boars and gilts

BackgroundAverage daily gain (ADG) in pigs is affected by the so-called social (or indirect) genetic effects (SGE). However, SGE may differ between sexes because boars grow faster than gilts and their social behaviours differ. We hypothesized that direct genetic effects (DGE) and SGE for ADG in pigs differ between boars and gilts and that accounting for these differences will improve the predictive ability of a social genetic effects model (SGM). Our data consisted of ADG from 30 to 94 kg for 32,212 uncastrated males (boars) and 48,252 gilts that were raised in sex-specific pens. Data were analyzed using a univariate model with sex as a fixed effect and a bivariate model with ADG in boars and gilts as separate traits using both a classical animal model (CM) and a SGM.ResultsWith the univariate model, the heritability for ADG was 0.22 ± 0.01 for the CM, while the estimate of the total heritable variance (T2) was 0.23 ± 0.01 with the SGM. With the bivariate model, the genetic variance for SGE was higher for boars (13.8 ± 5.8) than for gilts (9.3 ± 3.9). For the bivariate model, T2 was 0.32 ± 0.02 for boars and 0.27 ± 0.01 for gilts. Estimates of the genetic correlations between DGE (0.88 ± 0.02) and SGE (0.30 ± 0.30) for boars versus gilts indicated that ADG in boars and gilts are different traits. Moreover, the estimate of the genetic correlation between DGE and SGE indicated presence of genetic effects of competition among gilts but not among boars. Compared to a CM, the univariate SGM improved predictive ability significantly only for gilts and the bivariate SGM improved predictive ability significantly for both boars and gilts.ConclusionsWe found significant genetic variances of SGE for ADG. The covariance between DGE and SGE was much more negative for gilts than for boars when applying the bivariate model. Because the estimate of the genetic correlation for ADG between gilts and boars differed significantly from 1 and the predictive ability for boars and gilts was improved significantly with the bivariate model, we recommend the use of a bivariate model to estimate both SGE and DGE for ADG in pigs.

The social genome of friends and schoolmates in the National Longitudinal Study of Adolescent to Adult Health

Humans tend to form social relationships with others who resemble them. Whether this sorting of like with like arises from historical patterns of migration, meso-level social structures in modern society, or individual-level selection of similar peers remains unsettled. Recent research has evaluated the possibility that unobserved genotypes may play an important role in the creation of homophilous relationships. We extend this work by using data from 5,500 adolescents from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to examine genetic similarities among pairs of friends. Although there is some evidence that friends have correlated genotypes, both at the whole-genome level as well as at trait-associated loci (via polygenic scores), further analysis suggests that meso-level forces, such as school assignment, are a principal source of genetic similarity between friends. We also observe apparent social-genetic effects in which polygenic scores of an individual's friends and schoolmates predict the individual's own educational attainment. In contrast, an individual's height is unassociated with the height genetics of peers.

A genome-wide association study of social genetic effects in Landrace pigs.

ObjectiveThe genetic effects of an individual on the phenotypes of its social partners, such as its pen mates, are known as social genetic effects. This study aims to identify the candidate genes for social (pen-mates’) average daily gain (ADG) in pigs by using the genome-wide association approach.MethodsSocial ADG (sADG) was the average ADG of unrelated pen-mates (strangers). We used the phenotype data (16,802 records) after correcting for batch (week), sex, pen, number of strangers (1 to 7 pigs) in the pen, full-sib rate (0% to 80%) within pen, and age at the end of the test. A total of 1,041 pigs from Landrace breeds were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel, which comprised 61,565 single nucleotide polymorphism (SNP) markers. After quality control, 909 individuals and 39,837 markers remained for sADG in genome-wide association study.ResultsWe detected five new SNPs, all on chromosome 6, which have not been associated with social ADG or other growth traits to date. One SNP was inside the prostaglandin F2α receptor (PTGFR) gene, another SNP was located 22 kb upstream of gene interferon-induced protein 44 (IFI44), and the last three SNPs were between 161 kb and 191 kb upstream of the EGF latrophilin and seven transmembrane domain-containing protein 1 (ELTD1) gene. PTGFR, IFI44, and ELTD1 were never associated with social interaction and social genetic effects in any of the previous studies.ConclusionThe identification of several genomic regions, and candidate genes associated with social genetic effects reported here, could contribute to a better understanding of the genetic basis of interaction traits for ADG. In conclusion, we suggest that the PTGFR, IFI44, and ELTD1 may be used as a molecular marker for sADG, although their functional effect was not defined yet. Thus, it will be of interest to execute association studies in those genes.

Interaction of direct and social genetic effects with feeding regime in growing rabbits

BackgroundMost rabbit production farms apply feed restriction at fattening because of its protective effect against digestive diseases that affect growing rabbits. However, it leads to competitive behaviour between cage mates, which is not observed when animals are fed ad libitum. Our aim was to estimate the contribution of direct (d) and social (s) genetic effects (also known as indirect genetic effects) to total heritable variance of average daily gain ({text{ADG}}) in rabbits on different feeding regimens (FR), and the magnitude of the interaction between genotype and FR (G × FR).MethodsA total of 6264 contemporary kits were housed in cages of eight individuals and raised on full (F) or restricted (R) feeding to 75% of the ad libitum intake. A Bayesian analysis of weekly records of {text{ADG}} (from 32 to 60 days of age) in rabbits on F and R was performed with a two-trait model including d and s.ResultsThe ratio between total heritable variance and phenotypic variance (T^{2}) was low (<0.10) and did not differ significantly between FR. However, the ratio between h^{2} (i.e. variance of d relative to phenotypic variance) and T^{2} was ~0.52 and 0.86 for animals on R and F, respectively, thus s contributed more to the heritable variance of animals on R than on F. Feeding regimen also affected the sign and magnitude of the correlation between d and s, i.e. −0.5 and ~0 for animals on R and F, respectively. The posterior mean (posterior sd) of the correlation between estimated total breeding values (ETBV) of animals on R and F was 0.26 (0.20), indicating very strong G × FR interactions. The correlations between d and s in rabbits on F and R ranged from −0.47 (d on F and s on R) to 0.64.ConclusionsOur results suggest that selection of rabbits for {text{ADG}} under F may completely fail to improve {text{ADG}} in rabbits on R. Social genetic effects contribute substantially to ETBV of rabbits on R but not on F. Selection for {text{ADG}} should be performed under production conditions regarding the FR, by accounting for s if the amount of food is limited.

Genetic Variation in the Social Environment Contributes to Health and Disease.

Assessing the impact of the social environment on health and disease is challenging. As social effects are in part determined by the genetic makeup of social partners, they can be studied from associations between genotypes of one individual and phenotype of another (social genetic effects, SGE, also called indirect genetic effects). For the first time we quantified the contribution of SGE to more than 100 organismal phenotypes and genome-wide gene expression measured in laboratory mice. We find that genetic variation in cage mates (i.e. SGE) contributes to variation in organismal and molecular measures related to anxiety, wound healing, immune function, and body weight. Social genetic effects explained up to 29% of phenotypic variance, and for several traits their contribution exceeded that of direct genetic effects (effects of an individual’s genotypes on its own phenotype). Importantly, we show that ignoring SGE can severely bias estimates of direct genetic effects (heritability). Thus SGE may be an important source of “missing heritability” in studies of complex traits in human populations. In summary, our study uncovers an important contribution of the social environment to phenotypic variation, sets the basis for using SGE to dissect social effects, and identifies an opportunity to improve studies of direct genetic effects.