Indirect genetic effects of siblings.

Within-family designs are increasingly used to decompose genotype-trait associations into direct and indirect genetic effects. Many such designs, including trio designs or within-sibship designs, assume an absence of sibling indirect genetic effects. Here, we expand two well-known molecular genetic within-family designs, one variance component (genome-based restricted maximum likelihood) and one trait-based (structural equation modeling with polygenic indices) to estimate sibling indirect genetic effects along with direct genetic effects. We link the Norwegian mother, father, and child cohort study (MoBa) to Norway’s national education database to model genetic effects on national standardized testing results at ages 10, 13, and 14, and on parent-rated ADHD symptoms at ages 3 and 8, in up to 15,971 genotyped and phenotyped siblings. Results from the genome-based restricted maximum likelihood and the structural equation modeling with polygenic indices approaches converge, albeit with the variance component estimates of genetic effects typically an order of magnitude greater than the trait-based estimates. We observe no indirect genetic effects of siblings on educational performance at any age, and only slightly negative indirect genetic effects of siblings on ADHD symptoms at age 3. We argue that the latter effect might reflect parental contrasted rating. The results suggest that within-family models of educational performance are unlikely to be drastically biased by an assumption of absent sibling indirect genetic effects. Combining trait-based analyses with variance component analyses can benefit understanding of indirect genetic effects, especially when the effects are not specific to a particular mechanism.

Neurodevelopmental conditions are highly heritable. Recent studies have shown that genomic heritability estimates can be confounded by genetic effects mediated via the environment (indirect genetic effects). However, the relative importance of direct versus indirect genetic effects on early variability in traits related to neurodevelopmental conditions is unknown. The sample included up to 24,692 parent-offspring trios from the Norwegian MoBa cohort. We use Trio-GCTA to estimate latent direct and indirect genetic effects on mother-reported neurodevelopmental traits at age of 3 years (restricted and repetitive behaviors and interests, inattention, hyperactivity, language, social, and motor development). Further, we investigate to what extent direct and indirect effects are attributable to common genetic variants associated with autism, ADHD, developmental dyslexia, educational attainment, and cognitive ability using polygenic scores (PGS) in regression modeling. We find evidence for contributions of direct and indirect latent common genetic effects to inattention (direct: explaining 4.8% of variance, indirect: 6.7%) hyperactivity (direct: 1.3%, indirect: 9.6%), and restricted and repetitive behaviors (direct: 0.8%, indirect: 7.3%). Direct effects best explained variation in social and communication, language, and motor development (5.1%-5.7%). Direct genetic effects on inattention were captured by PGS for ADHD, educational attainment, and cognitive ability, whereas direct genetic effects on language development were captured by cognitive ability, educational attainment, and autism PGS. Indirect genetic effects on neurodevelopmental traits were primarily captured by educational attainment and/or cognitive ability PGS. Results were consistent with differential contributions to neurodevelopmental traits in early childhood from direct and indirect genetic effects. Indirect effects were particularly important for hyperactivity and restricted and repetitive behaviors and interests and may be linked to genetic variation associated with cognition and educational attainment. Our findings illustrate the importance of within-family methods for disentangling genetic processes that influence early neurodevelopmental traits, even when identifiable associations are small.

BackgroundTheoretical models of the development of childhood externalizing disorders emphasize the role of parents. Empirical studies have not been able to identify specific aspects of parental behaviors explaining a considerable proportion of the observed individual differences in externalizing problems. The problem is complicated by the contribution of genetic factors to externalizing problems, as parents provide both genes and environments to their children. We studied the joint contributions of direct genetic effects of children and the indirect genetic effects of parents through the environment on externalizing problems.MethodsThe study used genome‐wide single nucleotide polymorphism data from 9,675 parent–offspring trios participating in the Norwegian Mother Father and child cohort study. Based on genomic relatedness matrices, we estimated the contribution of direct genetic effects and indirect maternal and paternal genetic effects on ADHD, conduct and disruptive behaviors at 8 years of age.ResultsModels including indirect parental genetic effects were preferred for the ADHD symptoms of inattention and hyperactivity, and conduct problems, but not oppositional defiant behaviors. Direct genetic effects accounted for 11% to 24% of the variance, whereas indirect parental genetic effects accounted for 0% to 16% in ADHD symptoms and conduct problems. The correlation between direct and indirect genetic effects, or gene–environment correlations, decreased the variance with 16% and 13% for conduct and inattention problems, and increased the variance with 6% for hyperactivity problems.ConclusionsThis study provides empirical support to the notion that parents have a significant role in the development of childhood externalizing behaviors. The parental contribution to decrease in variation of inattention and conduct problems by gene–environment correlations would limit the number of children reaching clinical ranges in symptoms. Not accounting for indirect parental genetic effects can lead to both positive and negative bias when identifying genetic variants for childhood externalizing behaviors.

Genetic influences on social dominance: cow wars

BackgroundBirthweight is heritable and strongly associated with epigenetic differences at birth. It is unclear whether a genetic birthweight score is associated with DNA methylation (DNAm) and, if so, whether through direct genetic effects (i.e., child genotype) or indirect genetic effects (i.e., parental genotype, independent of child genotype), which are suspected to be mediated by the prenatal environment (e.g. metabolic factors).MethodsWe constructed polygenic scores (PGS) for birthweight predisposition in mothers and children using an existing ‘pre-adjusted’ GWAS assessing maternal indirect effects (maternal genotype on offspring birthweight, correcting for offspring genotype) or fetal effects (offspring genotype on offspring birthweight, correcting for maternal genotype) in 2116 families from the Generation R Study. Offspring DNAm levels at 829 birthweight-related sites were then regressed on both maternal and fetal effect PGS. Additionally, we aggregated 829 DNAm sites into a methylation profile score for birthweight (MPS-BW) and tested which pregnancy health factors (n=13) might mediate genetic effects.FindingsWe identified six DNAm sites associated with maternal indirect effects and the birthweight MPS revealed indirect genetic associations, as well. Gestational age partly mediated maternal indirect effects on DNAm, but no other mediators were identified. Results did not depend on offspring sex.InterpretationThis study presents first evidence of maternal genetic effects on offspring birthweight being associated with offspring epigenetic patterns at birth. Paternal genotype was not associated with offspring methylation in this study but was limited by lower sample size and lack of existing GWAS on paternal indirect effects.FundingEuropean UnionResearch in contextEvidence before this studyBirthweight is a moderately heritable trait and prior genome-wide association studies have identified multiple genetic variants associated with fetal growth. Genetic variants may impact birthweight via direct and indirect genetic routes of transmission. Indirect genetic effects occur when parental genetics still associate with child outcomes independent of genetic transmission to offspring, suggesting involvement of environmental mechanisms. For example, maternal genetic predispositions may shape the intrauterine environment in ways that accelerate or slow fetal growth. Indirect genetic effect may explain up to 22% of birthweight variance. Additionally, epigenetic modifications, particularly differences in DNA methylation (DNAm) at birth, have been linked to birthweight. Recent studies have identified over 900 CpG sites associated with birthweight. However, it is unknown whether the direct and indirect genetic effects on birthweight are reflected in DNAm variation, as well.Added value of this studyWe found that especially indirect maternal genetic effects on offspring birthweight are associated with offspring DNAm patterns at birth. We have linked six DNAm sites with indirect maternal effects in contrast to only one site with direct genetic transmission. A methylation profile score for birthweight associated similarly with direct and indirect effects, with a trend for stronger association with indirect effects.Implications of all the available evidenceThe results shed new light on how the interplay between genetics and environment on epigenetics may impact birthweight. This study confirms that maternal genetics partly affect offspring birthweight indirectly, independent of direct transmission. Importantly, our results imply that DNAm levels in cord blood partly reflect these indirect maternal genetic effects. This in turn raises the possibility that DNAm is an important mechanism mediating indirect maternal genetic effects on birthweight, but more research is needed to investigate causality.

From a functionalist perspective, parenting behaviors have adaptive functions and are partly expressions of genetic variation. Maternal genes that have effects on children are often referred to as indirect maternal genetic effects. Indirect genetic effects provide a means for measuring the role of parenting without the need for specifying the relevant parental behaviors. We studied indirect maternal genetic effects to address both the importance and commonality of parenting across the internalizing-externalizing spectrum of behavior problems in childhood. We further addressed how indirect genetic effects impact our understanding of direct genetic effects if not accounted for. Utilizing data from the Norwegian Mother, Father, and Child Cohort Study (MoBa), our analyses involved 42,423 children and their mothers. Both pedigree and genotype data were used to infer genetic relationships. We applied multivariate latent variable models to distinguish indirect maternal genetic effects and direct offspring genetic effects on seven measures of internalizing-externalizing behaviors. Our findings indicate significant maternal genetic influences, explaining 7%-18% of the variance across internalizing-externalizing behaviors. A general maternal effect common across behaviors could adequately account for most of the variability. The analyses further indicate that direct child genetic effects appear smaller and more complex when indirect maternal genetic effects are modeled simultaneously. By summarizing the effects of parenting with indirect maternal genetic effects, we show a substantial contribution of parents with respect to internalizing-externalizing behaviors in childhood. Although parenting is multifaceted, the effects of parenting are general and can succinctly be described as a single common dimension. Further, our study demonstrates that direct genetic effects appear smaller and more complex when maternal genetic effects are accounted for, highlighting the confounding potential of parental effects in understanding the role of genetic differences in child psychopathology.

BackgroundSeveral 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 ({mathbf{A}}) and a combined pedigree and genomic relationship matrix ({mathbf{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).ResultsThe 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 {mathbf{H}} rather than {mathbf{A}} (5.9 to 6.3%). The difference between predictive performances of total genetic effects and direct genetic effects was smaller when {mathbf{H}} was used rather than {mathbf{A}}.ConclusionsThis 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 {mathbf{H}} than {mathbf{A}}; (3) using {mathbf{H}} rather than {mathbf{A}} primarily improves the predictive performance of direct genetic effects.

Additive genetic variance in a trait reflects its potential to respond to selection, which is key for adaptive evolution in the wild. Social interactions contribute to this genetic variation through indirect genetic effects-the effect of an individual's genotype on the expression of a trait in a conspecific. However, our understanding of the evolutionary importance of indirect genetic effects in the wild and of their strength relative to direct genetic effects is limited. In this study, we assessed how indirect genetic effects contribute to genetic variation of behavioral, morphological, and life-history traits in a wild Eastern chipmunk population. We also compared the contribution of direct and indirect genetic effects to traits evolvabilities and related these effects to selection strength across traits. We implemented a novel approach integrating the spatial structure of social interactions in quantitative genetic analyses, and supported the reliability of our results with power analyses. We found indirect genetic effects for trappability and relative fecundity, little direct genetic effects in all traits and a large role for direct and indirect permanent environmental effects. Our study highlights the potential evolutionary role of social permanent environmental effects in shaping phenotypes of conspecifics through adaptive phenotypic plasticity.

Phenotypic variability of a genotype is relevant both in natural and domestic populations. In the past two decades, variability has been studied as a heritable quantitative genetic trait in its own right, often referred to as inherited variability or environmental canalization. So far, studies on inherited variability have only considered genetic effects of the focal individual, that is, direct genetic effects on inherited variability. Observations from aquaculture populations and some plants, however, suggest that an additional source of genetic variation in inherited variability may be generated through competition. Social interactions, such as competition, are often a source of Indirect Genetic Effects (IGE). An IGE is a heritable effect of an individual on the trait value of another individual. IGEs may substantially affect heritable variation underlying the trait, and the direction and magnitude of response to selection. To understand the contribution of IGEs to evolution of environmental canalization in natural populations, and to exploit such inherited variability in animal and plant breeding, we need statistical models to capture this effect. To our knowledge, it is unknown to what extent the current statistical models commonly used for IGE and inherited variability capture the effect of competition on inherited variability. Here, we investigate the potential of current statistical models for inherited variability and trait values, to capture the direct and indirect genetic effects of competition on variability. Our results show that a direct model of inherited variability almost entirely captures the genetic sensitivity of individuals to competition, whereas an indirect model of inherited variability captures the cooperative genetic effects of individuals on their partners. Models for trait levels, however, capture only a small part of the genetic effects of competition. The estimation of direct and indirect genetic effects of competition, therefore, is possible with models for inherited variability but may require a two‐step analysis.

Traditional quantitative genetics assumes that an individual's phenotype is determined by both genetic and environmental factors. For many animals, part of the environment is social and provided by parents and other interacting partners. When expression of genes in social partners affects trait expression in a focal individual, indirect genetic effects occur. In this study, we explore the effects of indirect genetic effects on the magnitude and range of phenotypic values in a focal individual in a multi-member model analyzing three possible classes of interactions between individuals. We show that social interactions may not only cause indirect genetic effects but can also modify direct genetic effects. Furthermore, we demonstrate that both direct and indirect genetic effects substantially alter the range of phenotypic values, particularly when a focal trait can influence its own expression via interactions with traits in other individuals. We derive a function predicting the relative importance of direct versus indirect genetic effects. Our model reveals that both direct and indirect genetic effects can depend to a large extent on both group size and interaction strength, altering group mean phenotype and variance. This may lead to scenarios where between group variation is much higher than within group variation despite similar underlying genetic properties, potentially affecting the level of selection. Our analysis highlights key properties of indirect genetic effects with important consequences for trait evolution, the level of selection and potentially speciation.

Estimation of direct and indirect (i.e. parental and/or sibling) genetic effects on phenotypes is becoming increasingly important. We compare several multivariate methods that utilize summary results statistics from genome-wide association studies to determine how well they estimate direct and indirect genetic effects. Using data from the UK Biobank, we contrast point estimates and standard errors at individual loci compared to those obtained using individual level data. We show that Genomic structural equation modelling (SEM) outperforms the other methods in accurately estimating conditional genetic effects and their standard errors. We apply Genomic SEM to fertility data in the UK Biobank and partition the genetic effect into female and male fertility and a sibling specific effect. We identify a novel locus for fertility and genetic correlations between fertility and educational attainment, risk taking behaviour, autism and subjective well-being. We recommend Genomic SEM be used to partition genetic effects into direct and indirect components when using summary results from genome-wide association studies.

Indirect genetic effects describe phenotypic variation that results from differences in the genotypic composition of social partners. Such effects represent heritable sources of environmental variation in eusocial organisms because individuals are typically reared by their siblings. In the fire ant Solenopsis invicta, a social supergene exhibits striking indirect genetic effects on worker regulation of colony queen number, such that the genotypic composition of workers at the supergene determines whether colonies contain a single or multiple queens. We assessed the direct and indirect genetic effects of this supergene on gene expression in brains and abdominal tissues from laboratory-reared workers and compared these with previously published data from field-collected prereproductive queens. We found that direct genetic effects caused larger gene expression changes and were more consistent across tissue types and castes than indirect genetic effects. Indirect genetic effects influenced the expression of many loci but were generally restricted to the abdominal tissues. Further, indirect genetic effects were only detected when the genotypic composition of social partners differed throughout the development and adult life of focal workers, and were often only significant with relatively lenient statistical cutoffs. Our study provides insight into direct and indirect genetic effects of a social supergene on gene regulatory dynamics across tissues and castes in a complex society.

Indirect genetic effects (IGEs) are heritable effects of individuals on trait values of their conspecifics. IGEs may substantially affect response to selection, but empirical studies on IGEs are sparse and their magnitude and correlation with direct genetic effects are largely unknown in plants. Here we used linear mixed models to estimate genetic (co)variances attributable to direct and indirect effects for growth and foliar disease damage in a large pedigreed population of Eucalyptus globulus. We found significant IGEs for growth and disease damage, which increased with age for growth. The correlation between direct and indirect genetic effects was highly negative for growth, but highly positive for disease damage, consistent with neighbour competition and infection, respectively. IGEs increased heritable variation by 71% for disease damage, but reduced heritable variation by 85% for growth, leaving nonsignificant heritable variation for later age growth. Thus, IGEs are likely to prevent response to selection in growth, despite a considerable ordinary heritability. IGEs change our perspective on the genetic architecture and potential response to selection. Depending on the correlation between direct and indirect genetic effects, IGEs may enhance or diminish the response to natural or artificial selection compared with that predicted from ordinary heritability.

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

BackgroundCannibalism is an important welfare problem in the layer industry. Cannibalism is a social behavior where individual survival is affected by direct genetic effects (DGE) and indirect genetic effects (IGE). Previous studies analysed repeated binomial survival, instead of survival time, which improved accuracies of breeding value predictions. Our study aimed at identifying SNPs associated with DGE and IGE for survival time, and comparing results from models that analyse survival time and repeated binomial survival.MethodsSurvival data of three layer crosses (W1 * WA, W1 * WB, and W1 * WC) were used. Each individual had one survival time record and 13 monthly survival (0/1) records. Approximately 30,000 single nucleotide polymorphisms (SNPs) were included in the genome-wide association study (GWAS), using a linear mixed model for survival time, a linear mixed model and a generalized linear mixed model for repeated binomial survival (0/1). Backwards elimination was used to determine phenotypic and genetic variance explained by SNPs.ResultsThe same quantitative trait loci were identified with all models. A SNP associated with DGE was found in cross W1 * WA, with an allele substitution effect of 22 days. This SNP explained 3% of the phenotypic variance, and 36% of the total genetic variance. Four SNPs associated with DGE were found in cross W1 * WB, with effects ranging from 16 to 35 days. These SNPs explained 1 to 6% of the phenotypic variance and 9 to 44% of the total genetic variance. Our results suggest a link of DGE and IGE for survival time in layers with the gamma-aminobutyric acid (GABA) system, since a SNP located near a gene for a GABA receptor was associated with DGE and with IGE (not significant).ConclusionsThis is one of the first large studies investigating the genetic architecture of a socially-affected trait. The power to detect SNP associations was relatively low and thus we expect that many effects on DGE and IGE remained undetected. Yet, GWAS results revealed SNPs with large DGE and a link of DGE and IGE for survival time in layers with the GABAergic system, which supports existing evidence for the involvement of GABA in the development of abnormal behaviors.