Genetic Covariance Research Articles

Genetic Complexity of Cortical Structure: Differences in Genetic and Environmental Factors Influencing Cortical Surface Area and Thickness.

Quantifying the genetic architecture of the cerebral cortex is necessary for understanding disease and changes to the brain across the lifespan. Prior work shows that both surface area (SA) and cortical thickness (CT) are heritable. However, we do not yet understand the extent to which region-specific genetic factors (i.e., independent of global effects) play a dominant role in the regional patterning or inter-regional associations across the cortex. Using a population sample of young adult twins (N = 923), we show that the heritability of SA and CT varies widely across regions, generally independent of measurement error. When global effects are controlled for, we detected a complex pattern of genetically mediated clusters of inter-regional associations, which varied between hemispheres. There were generally weak associations between the SA of different regions, except within the occipital lobe, whereas CT was positively correlated within lobar divisions and negatively correlated across lobes, mostly due to genetic covariation. These findings were replicated in an independent sample of twins and siblings (N = 698) from the Human Connectome Project. The different genetic contributions to SA and CT across regions reveal the value of quantifying sources of covariation to appreciate the genetic complexity of cortical structures.

Shared Genes but Not Shared Genetic Variation: Legume Colonization by Two Belowground Symbionts

AbstractMutualisms between hosts and multiple symbionts can generate diffuse coevolution if genetic covariance exists between host traits governing multiple interactions. Rhizobia and arbuscular mycorrhizal fungi (AMF) both interact with legume hosts, providing complementary nutrients (nitrogen and phosphorous). Molecular approaches have revealed extensive pleiotropy in the plant genetic pathways required for colonization of both symbionts; however, a quantitative genetic approach is required to understand whether this pleiotropy shapes evolution in natural populations. In a greenhouse experiment with 75 families of Chamaecrista fasciculata grown in two phosphorous (P) environments (fertilized and unfertilized), positive covariance between nodule number and plant aboveground biomass within and across environments indicates selection for increased colonization by rhizobia. Genetic variation for host restriction of AMF colonization in response to P suggests that this aspect of context dependency can evolve ...

Understanding variation in metabolic rate.

Metabolic rate reflects an organism's capacity for growth, maintenance and reproduction, and is likely to be a target of selection. Physiologists have long sought to understand the causes and consequences of within-individual to among-species variation in metabolic rates - how metabolic rates relate to performance and how they should evolve. Traditionally, this has been viewed from a mechanistic perspective, relying primarily on hypothesis-driven approaches. A more agnostic, but ultimately more powerful tool for understanding the dynamics of phenotypic variation is through use of the breeder's equation, because variation in metabolic rate is likely to be a consequence of underlying microevolutionary processes. Here we show that metabolic rates are often significantly heritable, and are therefore free to evolve under selection. We note, however, that 'metabolic rate' is not a single trait: in addition to the obvious differences between metabolic levels (e.g. basal, resting, free-living, maximal), metabolic rate changes through ontogeny and in response to a range of extrinsic factors, and is therefore subject to multivariate constraint and selection. We emphasize three key advantages of studying metabolic rate within a quantitative genetics framework: its formalism, and its predictive and comparative power. We make several recommendations when applying a quantitative genetics framework: (i) measuring selection based on actual fitness, rather than proxies for fitness; (ii) considering the genetic covariances between metabolic rates throughout ontogeny; and (iii) estimating genetic covariances between metabolic rates and other traits. A quantitative genetics framework provides the means for quantifying the evolutionary potential of metabolic rate and why variance in metabolic rates within populations might be maintained.

Quantitative Genetic Modeling of the Parental Care Hypothesis for the Evolution of Endothermy.

There are two heuristic explanations proposed for the evolution of endothermy in vertebrates: a correlated response to selection for stable body temperatures, or as a correlated response to increased activity. Parental care has been suggested as a major driving force in this context given its impact on the parents' activity levels and energy budgets, and in the offspring's growth rates due to food provisioning and controlled incubation temperature. This results in a complex scenario involving multiple traits and transgenerational fitness benefits that can be hard to disentangle, quantify and ultimately test. Here we demonstrate how standard quantitative genetic models of maternal effects can be applied to study the evolution of endothermy, focusing on the interplay between daily energy expenditure (DEE) of the mother and growth rates of the offspring. Our model shows that maternal effects can dramatically exacerbate evolutionary responses to selection in comparison to regular univariate models (breeder's equation). This effect would emerge from indirect selection mediated by maternal effects concomitantly with a positive genetic covariance between DEE and growth rates. The multivariate nature of selection, which could favor a higher DEE, higher growth rates or both, might partly explain how high turnover rates were continuously favored in a self-reinforcing process. Overall, our quantitative genetic analysis provides support for the parental care hypothesis for the evolution of endothermy. We contend that much has to be gained from quantifying maternal and developmental effects on metabolic and thermoregulatory variation during adulthood.

A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics

Despite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits' genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and biological interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses, we demonstrate that our method provides accurate covariance estimates, thereby enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Among 50 complex traits with publicly accessible GWAS summary statistics (Ntotal≈ 4.5 million), we identified more than 170 pairs with statistically significant genetic covariance. In particular, we found strong genetic covariance between late-onset Alzheimer disease (LOAD) and amyotrophic lateral sclerosis (ALS), two major neurodegenerative diseases, in single-nucleotide polymorphisms (SNPs) with high minor allele frequencies and in SNPs located in the predicted functional genome. Joint analysis of LOAD, ALS, and other traits highlights LOAD's correlation with cognitive traits and hints at an autoimmune component for ALS.

Genotypic covariance between the performance of a resident species and community assembly in the field

Abstract Genetic variation in resident species can influence the assembly and dynamics of communities, but the potential for these genetic effects to persist across generations is largely unresolved. In principle, persistent, directional changes in communities are only predicted when community properties covary genetically with the fitness of resident species. Estimates of genetic covariance between the fitness of a resident species and its community are therefore necessary to “close the eco‐evolutionary loop” in studies of community genetics, but such estimates are rare. Emulating community genetics experiments in plants, we used clonal replicates of 21 genotypes of a resident species (the encrusting bryozoan, Hippopodina) to investigate the magnitude of genotypic variance contributing to assembly of a marine benthic community. Genotypes explained up to 35% of variation in community assembly. Critically, the performance of Hippopodina genotypes covaried both with the evenness of communities and with the abundances of some individual species, representing an indirect genetic effect that creates the potential for multigenerational interactions between Hippopodina and co‐existing species. Our results suggest that different genotypes will associate with different community members consistently across generations, and such non‐random associations can give rise to specialization. Further interactions between species other than Hippopodina itself may also be altered by effects of genetic variation in the focal species. Furthermore, species in the community other than Hippopodina itself will interact more commonly in the presence of some genotypes over others. Our results support the potential for genetic variation in one species to have deterministic effects on the dynamics of ecological communities. A plain language summary is available for this article.

Genetic structured antedependence and random regression models applied to the longitudinal feed conversion ratio in growing Large White pigs.

The objective of the present study was to compare a random regression model, usually used in genetic analyses of longitudinal data, with the structured antedependence (SAD) model to study the longitudinal feed conversion ratio (FCR) in growing Large White pigs and to propose criteria for animal selection when used for genetic evaluation. The study was based on data from 11,790 weekly FCR measures collected on 1,186 Large White male growing pigs. Random regression (RR) using orthogonal polynomial Legendre and SAD models was used to estimate genetic parameters and predict FCR-based EBV for each of the 10 wk of the test. The results demonstrated that the best SAD model (1 order of antedependence of degree 2 and a polynomial of degree 2 for the innovation variance for the genetic and permanent environmental effects, i.e., 12 parameters) provided a better fit for the data than RR with a quadratic function for the genetic and permanent environmental effects (13 parameters), with Bayesian information criteria values of -10,060 and -9,838, respectively. Heritabilities with the SAD model were higher than those of RR over the first 7 wk of the test. Genetic correlations between weeks were higher than 0.68 for short intervals between weeks and decreased to 0.08 for the SAD model and -0.39 for RR for the longest intervals. These differences in genetic parameters showed that, contrary to the RR approach, the SAD model does not suffer from border effect problems and can handle genetic correlations that tend to 0. Summarized breeding values were proposed for each approach as linear combinations of the individual weekly EBV weighted by the coefficients of the first or second eigenvector computed from the genetic covariance matrix of the additive genetic effects. These summarized breeding values isolated EBV trajectories over time, capturing either the average general value or the slope of the trajectory. Finally, applying the SAD model over a reduced period of time suggested that similar selection choices would result from the use of the records from the first 8 wk of the test. To conclude, the SAD model performed well for the genetic evaluation of longitudinal phenotypes.

Assessing the degree of stratification between closely related Holstein-Friesian populations

Genomic information is an important part of the routine evaluation of dairy cattle and provides the wide availability of animals genotyped using single nucleotide polymorphism (SNP) microarrays. We analyzed 2243 Polish and 2294 German Holstein-Friesian bulls genotyped using the Illumina BovineSNP50 BeadChip. For each bull, estimated breeding values (EBVs) calculated from national routine genetic evaluation were available for production traits and for somatic cell score (SCS). Separately for each population, we estimated SNP haplotypes, pairwise linkage disequilibrium (LD), and SNP effects. The SNP genetic covariance between both populations was estimated using a bivariate mixed model. The average LD was lower in the Polish than in the German population and, with increasing genomic distance, LD decays 1.7 times more rapidly in German than in Polish cattle. The comparison of SNP allele frequencies for base populations estimated separately using Polish and German data revealed a very good agreement. The comparison of genetic effects corresponding to various window lengths defined in bp emerged a systematic pattern: regardless of the length of the compared region, few significant differences were found for production traits, while many were observed for SCS. For each trait, the German population had much higher SNP variances than the Polish population and the genetic covariance estimates were all positive. Depending on traits’ inheritance mode, the additive genetic variation can be stored in many genes following the infinitesimal model (like for SCS) or distributed between genes with high effects and the polygenic “background” (like for production traits). Accounting for those differences has implications on the prospective international genomic evaluation.

Maternal body composition in seedstock herds. 3. Multivariate analysis using factor analytic models and cluster analysis

Considerable information exists on genetic relationships of body composition and carcass quality of young and finished beef cattle. However, there is a dearth of information on genetic relationships of cow body composition over time and, also, relationships with young-animal body-composition measures. The aim of the present study is to understand genetic relationships among various cow body-composition traits of Angus cows over time, from yearling to weaning of a second calf at ~3.5 years. To determine genetic correlations among various composition traits over time, a multi-trait–multi-time analysis is required. For the Maternal Productivity Project, this necessitates modelling of five traits (namely weight and ultrasound measure for loin eye muscle area (EMA), rib fat, P8 rump fat and intramuscular fat) by five time combinations (recordings at yearling then pre-calving and weaning in first and second parity). The approach was based on including all 25 trait-by-time combinations in an analysis using factor analytic models to approximate the genetic covariance matrix. Various models for the residual covariance structure were investigated. The analyses yielded correlations that could be compared with those of past studies reported in the literature and, also, to a set of bivariate analyses. Clustering of the genetic multi-trait–multi-time correlation structure resulted in a separation of traits (weight and EMA, and the fat traits) and also of time effects into early (heifer = before first lactation) and late (cow = post-first lactation) measurements.

Genetic and environmental etiology of stability and changes in self-esteem linked to personality: A Japanese twin study

This study used a behavioral genetic approach to examine the genetic and environmental etiology of stability and changes in self-esteem in relation to personality. Multiple genetic analyses were conducted on a longitudinal dataset of self-esteem and Big Five personality scores among young adult Japanese twins over the course of a decade. There were 1221 individuals for whom data were available on both self-esteem and the Big Five personality test at Time 1 and 365 at Time 2. The mean interval between the two times was 9.95years. Genetic effects on self-esteem were robust, and the same genes were responsible for the stability of self-esteem in individuals over time. Nearly half of the variance in self-esteem was explained by a new genetic factor arising during the decade, suggesting that genetic innovation of self-esteem occurred in early adult life. The genetic and environmental covariance structure between personality and self-esteem in individuals was constant over a decade, providing evidence that the stability of self-esteem was largely attributable to personality. However, genetics for self-esteem independent of personality still contributed to stability over time, differentiating the concept of self-esteem from personality as a trait in terms of its genetic and environmental etiological levels.

The Integrated Genomic Architecture and Evolution of Dental Divergence in East African Cichlid Fishes (Haplochromis chilotes x H. nyererei).

The independent evolution of the two toothed jaws of cichlid fishes is thought to have promoted their unparalleled ecological divergence and species richness. However, dental divergence in cichlids could exhibit substantial genetic covariance and this could dictate how traits like tooth numbers evolve in different African Lakes and on their two jaws. To test this hypothesis, we used a hybrid mapping cross of two trophically divergent Lake Victoria species (Haplochromis chilotes × Haplochromis nyererei) to examine genomic regions associated with cichlid tooth diversity. Surprisingly, a similar genomic region was found to be associated with oral jaw tooth numbers in cichlids from both Lake Malawi and Lake Victoria. Likewise, this same genomic location was associated with variation in pharyngeal jaw tooth numbers. Similar relationships between tooth numbers on the two jaws in both our Victoria hybrid population and across the phylogenetic diversity of Malawi cichlids additionally suggests that tooth numbers on the two jaws of haplochromine cichlids might generally coevolve owing to shared genetic underpinnings. Integrated, rather than independent, genomic architectures could be key to the incomparable evolutionary divergence and convergence in cichlid tooth numbers.

Activity level in the lab: Overlap with shyness indicates it is more than pure motoric activity.

The observation that children's activity level (AL) differs between novel and familiar situations is well established. What influences individual differences in how AL is different across these situations is less well understood. Drawing on animal literature, which links rats' AL when 1st placed in a novel setting with novelty seeking phenotypes, and child temperament literature, which links AL, novelty response, and shyness, we hypothesized that shyness would be an important component of children's AL in a novel situation. We examined this using mechanically assessed AL from 2 situations (the home and the lab) and 2 measures of shyness (1 parent-rated and 1 observer-rated) on up to 313 twin pairs (145 monozygotic and 168 dizygotic), at 2 and 3 years of age. Biometric genetic models removed from lab AL the variance shared with home AL, representing what was different in AL when the child entered the lab compared to the home. We report that almost half (43%) of the genetic component of AL in the lab was independent of AL in the home, and this unique genetic component shared genetic covariance with shyness. Shyness influences AL in a novel situation such as the lab, indicating that mechanically assessed AL represents more than global motoric activity and provides information on a child's temperamental response to novelty. (PsycINFO Database Record

Genetic co-variance functions for live weight, feed intake, and efficiency measures in growing pigs1

Genetic co-variance functions for live weight, feed intake, and efficiency measures in growing pigs1

The effects of stress and sex on selection, genetic covariance, and the evolutionary response.

The capacity of a population to adapt to selection (evolvability) depends on whether the structure of genetic variation permits the evolution of fitter trait combinations. Selection, genetic variance and genetic covariance can change under environmental stress, and males and females are not genetically independent, yet the combined effects of stress and dioecy on evolvability are not well understood. Here, we estimate selection, genetic (co)variance and evolvability in both sexes of Tribolium castaneum flour beetles under stressful and benign conditions, using a half-sib breeding design. Although stress uncovered substantial latent heritability, stress also affected genetic covariance, such that evolvability remained low under stress. Sexual selection on males and natural selection on females favoured a similar phenotype, and there was positive intersex genetic covariance. Consequently, sexual selection on males augmented adaptation in females, and intralocus sexual conflict was weak or absent. This study highlights that increased heritability does not necessarily increase evolvability, suggests that selection can deplete genetic variance for multivariate trait combinations with strong effects on fitness, and tests the recent hypothesis that sexual conflict is weaker in stressful or novel environments.

Multi-population Genomic Relationships for Estimating Current Genetic Variances Within and Genetic Correlations Between Populations.

Different methods are available to calculate multi-population genomic relationship matrices. Since those matrices differ in base population, it is anticipated that the method used to calculate genomic relationships affects the estimate of genetic variances, covariances, and correlations. The aim of this article is to define the multi-population genomic relationship matrix to estimate current genetic variances within and genetic correlations between populations. The genomic relationship matrix containing two populations consists of four blocks, one block for population 1, one block for population 2, and two blocks for relationships between the populations. It is known, based on literature, that by using current allele frequencies to calculate genomic relationships within a population, current genetic variances are estimated. In this article, we theoretically derived the properties of the genomic relationship matrix to estimate genetic correlations between populations and validated it using simulations. When the scaling factor of across-population genomic relationships is equal to the product of the square roots of the scaling factors for within-population genomic relationships, the genetic correlation is estimated unbiasedly even though estimated genetic variances do not necessarily refer to the current population. When this property is not met, the correlation based on estimated variances should be multiplied by a correction factor based on the scaling factors. In this study, we present a genomic relationship matrix which directly estimates current genetic variances as well as genetic correlations between populations.

Heritable Micro-environmental Variance Covaries with Fitness in an Outbred Population of Drosophila serrata.

The genetic basis of stochastic variation within a defined environment, and the consequences of such micro-environmental variance for fitness are poorly understood . Using a multigenerational breeding design in Drosophila serrata, we demonstrated that the micro-environmental variance in a set of morphological wing traits in a randomly mating population had significant additive genetic variance in most single wing traits. Although heritability was generally low (<1%), coefficients of additive genetic variance were of a magnitude typical of other morphological traits, indicating that the micro-environmental variance is an evolvable trait. Multivariate analyses demonstrated that the micro-environmental variance in wings was genetically correlated among single traits, indicating that common mechanisms of environmental buffering exist for this functionally related set of traits. In addition, through the dominance genetic covariance between the major axes of micro-environmental variance and fitness, we demonstrated that micro-environmental variance shares a genetic basis with fitness, and that the pattern of selection is suggestive of variance-reducing selection acting on micro-environmental variance.

The phenotypic correlates and quantitative genetics of masculinization in the rodent, Octodon degus.

This corrects the article DOI: 10.1038/hdy.2017.20.

Facial Trustworthiness is Associated with Heritable Aspects of Face Shape

Facial trustworthiness is thought to underlie social judgements in face perception, though it is unclear whether trustworthiness judgements are based on stable facial attributes. If this were the case, we could expect a genetic component of facial trustworthiness. From facial photographs of a large sample of identical and nonidentical twins and siblings (1320 individuals), we tested for genetic variation in facial trustworthiness and genetic covariation with several stable facial attributes, including facial attractiveness, two measures of masculinity, and facial width-to-height ratio. We found a significant genetic component of facial trustworthiness in men (but not women), and significant genetic correlations with the stable morphological facial traits of attractiveness (positive), perceived masculinity (negative), and facial width-to-height ratio (negative). However, there was no significant genetic or shared environmental correlation between facial trustworthiness and an objective masculinity score based on facial landmark coordinates, despite there being a significant phenotypic correlation. Our results suggest that heritable facial traits influence trustworthiness judgements.

Methodological approaches in analysing observational data: A practical example on how to address clustering and selection bias

BackgroundBecause not every scientific question on effectiveness can be answered with randomised controlled trials, research methods that minimise bias in observational studies are required. Two major concerns influence the internal validity of effect estimates: selection bias and clustering. Hence, to reduce the bias of the effect estimates, more sophisticated statistical methods are needed. AimTo introduce statistical approaches such as propensity score matching and mixed models into representative real-world analysis and to conduct the implementation in statistical software R to reproduce the results. Additionally, the implementation in R is presented to allow the results to be reproduced. MethodWe perform a two-level analytic strategy to address the problems of bias and clustering: (i) generalised models with different abilities to adjust for dependencies are used to analyse binary data and (ii) the genetic matching and covariate adjustment methods are used to adjust for selection bias. Hence, we analyse the data from two population samples, the sample produced by the matching method and the full sample. ResultsThe different analysis methods in this article present different results but still point in the same direction. In our example, the estimate of the probability of receiving a case conference is higher in the treatment group than in the control group. Both strategies, genetic matching and covariate adjustment, have their limitations but complement each other to provide the whole picture. ConclusionThe statistical approaches were feasible for reducing bias but were nevertheless limited by the sample used. For each study and obtained sample, the pros and cons of the different methods have to be weighted.

Modelling lactation curves of dairy goats by fitting random regression models using Legendre polynomials or B-splines

A total of 17 356 test-day milk yield (TDMY) records from 642 first lactations of Alpine goats were used to model variations in lactation curve using random regression models (RRM). Orthogonal Legendre polynomials and B-splines were evaluated to obtain adequate and parsimonious models for the estimation of genetic parameters. The analysis was performed using a single-trait RRM, including the additive genetic, permanent environmental, and residual effects. We estimated the mean trend of milk yield, and the additive genetic and permanent environmental covariance functions through random regression using different orders of orthogonal Legendre polynomial (three to six) and B-spline functions (linear, quadratic, and cubic, with three to six knots). This study further evaluated different number of classes of residual variances. The covariance components and the genetic parameters were estimated using the restricted maximum likelihood method. Heritability estimates presented similar trends for both functions. T...