Indirect genetic effects: a key component of the genetic architecture of behaviour.
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ABSTRACT:
SUBMITTER: Santostefano F
PROVIDER: S-EPMC5578976 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Indirect genetic effects (IGEs) occur when genes expressed in one individual alter the phenotype of an interacting partner. IGEs can dramatically affect the expression and evolution of social traits. However, the interacting phenotype(s) through which they are transmitted are often unknown, or cryptic, and their detection would enhance our ability to accurately predict evolutionary change. To illustrate this challenge and possible solutions to it, we assayed male leg-tapping behavior using inbred lines of Drosophila melanogaster paired with a common focal male strain. The expression of tapping in focal males was dependent on the genotype of their interacting partner, but this strong IGE was cryptic. Using a multiple-regression approach, we identified male startle response as a candidate interacting phenotype: the longer it took interacting males to settle after being startled, the less focal males tapped them. A genome-wide association analysis identified approximately a dozen candidate protein-coding genes potentially underlying the IGE, of which the most significant was slowpoke. Our methodological framework provides information about candidate phenotypes and candidate single-nucleotide polymorphisms that underpin a strong yet cryptic IGE. We discuss how this approach can facilitate the detection of cryptic IGEs contributing to unusual evolutionary dynamics in other study systems.
| S-EPMC4257566 | biostudies-literature
Project description:BackgroundFamily members resemble each other in their propensity for aggression. In twin studies, approximately 50% of the variance in aggression can be explained by genetic influences. However, if there are genotype-environment correlation mechanisms, such as environmental manifestations of parental and sibling genotypes, genetic influences may partly reflect environmental influences. In this study, we investigated the importance of indirect polygenic score (PGS) effects on aggression.MethodsWe modeled the effect of PGSs based on 3 genome-wide association studies: early-life aggression, educational attainment, and attention-deficit/hyperactivity disorder (ADHD). The associations with aggression were tested in a within- and between-family design (37,796 measures from 7740 individuals, ages 3-86 years [mean = 14.20 years, SE = 12.03], from 3107 families, 55% female) and in a transmitted/nontransmitted PGS design (42,649 measures from 6653 individuals, ages 3-61 years [mean = 11.81 years, SE = 8.68], from 3024 families, 55% female). All participants are enrolled in the Netherlands Twin Register.ResultsWe found no evidence for contributions of indirect PGS effects on aggression in either a within- and between-family design or a transmitted/nontransmitted PGS design. Results indicate significant direct effects on aggression for the PGSs based on early-life aggression, educational attainment, and ADHD, although explained variance was low (within- and between-family: early-life aggression R2 = 0.3%, early-life ADHD R2 = 0.6%, educational attainment R2 = 0.7%; transmitted/nontransmitted PGSs: early-life aggression R2 = 0.2%, early-life ADHD R2 = 0.9%, educational attainment R2 = 0.5%).ConclusionsPGSs included in the current study had a direct (but no indirect) effect on aggression, consistent with results of previous twin and family studies. Further research involving other PGSs for aggression and related phenotypes is needed to determine whether this conclusion generalizes to overall genetic influences on aggression.
| S-EPMC10593934 | biostudies-literature
Project description: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.
| S-EPMC6057005 | biostudies-literature
Project description:Estimating effects of parental and sibling genotypes (indirect genetic effects) can provide insight into how the family environment influences phenotypic variation. There is growing molecular genetic evidence for effects of parental phenotypes on their offspring (e.g. parental educational attainment), but the extent to which siblings affect each other is currently unclear. Here we used data from samples of unrelated individuals, without (singletons) and with biological full-siblings (non-singletons), to investigate and estimate sibling effects. Indirect genetic effects of siblings increase (or decrease) the covariance between genetic variation and a phenotype. It follows that differences in genetic association estimates between singletons and non-singletons could indicate indirect genetic effects of siblings if there is no heterogeneity in other sources of genetic association between singletons and non-singletons. We used UK Biobank data to estimate polygenic score (PGS) associations for height, BMI and educational attainment in self-reported singletons (N = 50,143) and non-singletons (N = 328,549). The educational attainment PGS association estimate was 12% larger (95% C.I. 3%, 21%) in the non-singleton sample than in the singleton sample, but the height and BMI PGS associations were consistent. Birth order data suggested that the difference in educational attainment PGS associations was driven by individuals with older siblings rather than firstborns. The relationship between number of siblings and educational attainment PGS associations was non-linear; PGS associations were 24% smaller in individuals with 6 or more siblings compared to the rest of the sample (95% C.I. 11%, 38%). We estimate that a 1 SD increase in sibling educational attainment PGS corresponds to a 0.025 year increase in the index individual's years in schooling (95% C.I. 0.013, 0.036). Our results suggest that older siblings may influence the educational attainment of younger siblings, adding to the growing evidence that effects of the environment on phenotypic variation partially reflect social effects of germline genetic variation in relatives.
| S-EPMC9262210 | biostudies-literature
Project description:Genetic variation can have important consequences for populations: high population genetic diversity is typically associated with ecological success. Some mechanisms that account for these benefits assume that local social groups with high genetic diversity are more successful than low-diversity groups. At the same time, active decision-making by individuals can influence group genetic diversity. Here, we examine how maternal decisions that determine group genetic diversity influence the viability of Drosophila melanogaster larvae. Our groups contained wild-type larvae, whose genetic diversity we manipulated, and genetically marked 'tester' larvae, whose genotype and frequency were identical in all trials. We measured wild-type and tester viability for each group. Surprisingly, the viability of wild-type larvae was neither augmented nor reduced when group genetic diversity was altered. However, the viability of the tester genotype was substantially depressed in large, high-diversity groups. Further, not all high-diversity groups produced this effect: certain combinations of wild-type genotypes were deleterious to tester viability, while other groups of the same diversity-but containing different wild-type genotypes-were not deleterious. These deleterious combinations of wild-type genotypes could not be predicted by observing the performance of the same tester and wild-type genotypes in low-diversity groups. Taken together, these results suggest that nonadditive interactions among genotypes, rather than genetic diversity per se, account for between-group differences in viability in D. melanogaster and that predicting the consequences of genetic diversity at the population level may not be straightforward.
| S-EPMC3531796 | biostudies-literature
Project description:The immune system is highly diverse, but characterization of its genetic architecture has lagged behind the vast progress made by genome-wide association studies (GWASs) of emergent diseases. Our GWAS for 54 functionally relevant phenotypes of the adaptive immune system in 489 healthy individuals identifies eight genome-wide significant associations explaining 6%-20% of variance. Coding and splicing variants in PTPRC and COMMD10 are involved in memory T cell differentiation. Genetic variation controlling disease-relevant T helper cell subsets includes RICTOR and STON2 associated with Th2 and Th17, respectively, and the interferon-lambda locus controlling regulatory T cell proliferation. Early and memory B cell differentiation stages are associated with variation in LARP1B and SP4. Finally, the latrophilin family member ADGRL2 correlates with baseline pro-inflammatory interleukin-6 levels. Suggestive associations reveal mechanisms of autoimmune disease associations, in particular related to pro-inflammatory cytokine production. Pinpointing these key human immune regulators offers attractive therapeutic perspectives.
| S-EPMC6205839 | biostudies-literature
Project description:BackgroundIndirect genetic effects (IGEs) occur when genes expressed in one individual alter the expression of traits in social partners. Previous studies focused on the evolutionary consequences and evolutionary dynamics of IGEs, using equilibrium solutions to predict phenotypes in subsequent generations. However, whether or not such steady states may be reached may depend on the dynamics of interactions themselves.ResultsIn our study, we focus on the dynamics of social interactions and indirect genetic effects and investigate how they modify phenotypes over time. Unlike previous IGE studies, we do not analyse evolutionary dynamics; rather we consider within-individual phenotypic changes, also referred to as phenotypic plasticity. We analyse iterative interactions, when individuals interact in a series of discontinuous events, and investigate the stability of steady state solutions and the dependence on model parameters, such as population size, strength, and the nature of interactions. We show that for interactions where a feedback loop occurs, the possible parameter space of interaction strength is fairly limited, affecting the evolutionary consequences of IGEs. We discuss the implications of our results for current IGE model predictions and their limitations.
| S-EPMC4436347 | biostudies-literature
Project description:Genetic variance of a phenotypic trait can originate from direct genetic effects, or from indirect effects, i.e., through genetic effects on other traits, affecting the trait of interest. This distinction is often of great importance, for example, when trying to improve crop yield and simultaneously control plant height. As suggested by Sewall Wright, assessing contributions of direct and indirect effects requires knowledge of (1) the presence or absence of direct genetic effects on each trait, and (2) the functional relationships between the traits. Because experimental validation of such relationships is often unfeasible, it is increasingly common to reconstruct them using causal inference methods. However, most current methods require all genetic variance to be explained by a small number of quantitative trait loci (QTL) with fixed effects. Only a few authors have considered the "missing heritability" case, where contributions of many undetectable QTL are modeled with random effects. Usually, these are treated as nuisance terms that need to be eliminated by taking residuals from a multi-trait mixed model (MTM). But fitting such an MTM is challenging, and it is impossible to infer the presence of direct genetic effects. Here, we propose an alternative strategy, where genetic effects are formally included in the graph. This has important advantages: (1) genetic effects can be directly incorporated in causal inference, implemented via our PCgen algorithm, which can analyze many more traits; and (2) we can test the existence of direct genetic effects, and improve the orientation of edges between traits. Finally, we show that reconstruction is much more accurate if individual plant or plot data are used, instead of genotypic means. We have implemented the PCgen-algorithm in the R-package pcgen.
| S-EPMC7153926 | biostudies-literature
Project description:BackgroundThe phenotype of an individual can be affected not only by the individual's own genotypes, known as direct genetic effects (DGE), but also by genotypes of interacting partners, indirect genetic effects (IGE). IGE have been detected using polygenic models in multiple species, including laboratory mice and humans. However, the underlying mechanisms remain largely unknown. Genome-wide association studies of IGE (igeGWAS) can point to IGE genes, but have not yet been applied to non-familial IGE arising from "peers" and affecting biomedical phenotypes. In addition, the extent to which igeGWAS will identify loci not identified by dgeGWAS remains an open question. Finally, findings from igeGWAS have not been confirmed by experimental manipulation.ResultsWe leverage a dataset of 170 behavioral, physiological, and morphological phenotypes measured in 1812 genetically heterogeneous laboratory mice to study IGE arising between same-sex, adult, unrelated mice housed in the same cage. We develop and apply methods for igeGWAS in this context and identify 24 significant IGE loci for 17 phenotypes (FDR < 10%). We observe no overlap between IGE loci and DGE loci for the same phenotype, which is consistent with the moderate genetic correlations between DGE and IGE for the same phenotype estimated using polygenic models. Finally, we fine-map seven significant IGE loci to individual genes and find supportive evidence in an experiment with a knockout model that Epha4 gives rise to IGE on stress-coping strategy and wound healing.ConclusionsOur results demonstrate the potential for igeGWAS to identify IGE genes and shed light into the mechanisms of peer influence.
| S-EPMC8311926 | biostudies-literature
Project description:Chemical contaminants (e.g. metals, pesticides, pharmaceuticals) are changing ecosystems via effects on wildlife. Indeed, recent work explicitly performed under environmentally realistic conditions reveals that chemical contaminants can have both direct and indirect effects at multiple levels of organization by influencing animal behaviour. Altered behaviour reflects multiple physiological changes and links individual- to population-level processes, thereby representing a sensitive tool for holistically assessing impacts of environmentally relevant contaminant concentrations. Here, we show that even if direct effects of contaminants on behavioural responses are reasonably well documented, there are significant knowledge gaps in understanding both the plasticity (i.e. individual variation) and evolution of contaminant-induced behavioural changes. We explore implications of multi-level processes by developing a conceptual framework that integrates direct and indirect effects on behaviour under environmentally realistic contexts. Our framework illustrates how sublethal behavioural effects of contaminants can be both negative and positive, varying dynamically within the same individuals and populations. This is because linkages within communities will act indirectly to alter and even magnify contaminant-induced effects. Given the increasing pressure on wildlife and ecosystems from chemical pollution, we argue there is a need to incorporate existing knowledge in ecology and evolution to improve ecological hazard and risk assessments.
| S-EPMC6125903 | biostudies-literature