Genetic Covariance Structure Research Articles

Evaluation and selection of cassava clones and exploitation of genetic covariance across multiple environments

AbstractClonal evaluation trials of cassava (Manihot esculenta Crantz), where the main selection of this crop takes place, are usually carried out in multiple environments. This study investigated the influence of genotype–environment (GE) interaction on selection and how to explore genetic information across environments in a mixed model approach by modeling different genetic covariance structures. Approximately 240 cassava clones were assessed in an augmented block design during the 2020/2021 growing season in Brazil. The unstructured model was the best suited and used to investigate several strategies of selection. The predicted genetic gains based on individual analyses varied greatly among environments (5.52%–12.62% for root yield; 1.00%–6.09% for dry matter content; and 4.01%–9.42% for dry matter yield), although the clones mean was similar. Moreover, most of the selected clones in each environment outperformed the best check (>80%), except for root yield and dry matter yield in one environment. By multi‐environment analysis, greater local gains were detected in each environment (means of 16.87% for root yield, 5.56% for dry matter content, and 17.27% for dry matter yield) and for mean heritability (0.52 for root yield, 0.76 for dry matter content, and 0.55 for dry matter yield). The coincidence of clones selected by individual and multi‐environment analyses was 64% for root yield, 73% for dry matter content, and 66% for dry matter yield. The best scenario for selection is when all environments are considered simultaneously, for which regional genetic gains of 16.71% were predicted for root yield, 5.40% for dry matter content, and 17.06% for dry matter yield.

Strong selection is poorly aligned with genetic variation in Ipomoea hederacea: implications for divergence and constraint.

The multivariate evolution of populations is the result of the interactions between natural selection, drift, and the underlying genetic structure of the traits involved. Covariances among traits bias responses to selection, and the multivariate axis which describes the greatest genetic variation is expected to be aligned with patterns of divergence across populations. An exception to this expectation is when selection acts on trait combinations lacking genetic variance, which limits evolutionary change. Here we used a common garden field experiment of individuals from 57 populations of Ipomoea hederacea to characterize linear and nonlinear selection on 5 quantitative traits in the field. We then formally compare patterns of selection to previous estimates of within population genetic covariance structure (the G-matrix) and population divergence in these traits. We found that selection is poorly aligned with previous estimates of genetic covariance structure and population divergence. In addition, the trait combinations favored by selection were generally lacking genetic variation, possessing approximately 15%-30% as much genetic variation as the most variable combination of traits. Our results suggest that patterns of population divergence are likely the result of the interplay between adaptive responses, correlated responses, and selection favoring traits lacking genetic variation.

The heritability of vocal tract structures estimated from structural MRI in a large cohort of Dutch twins

While language is expressed in multiple modalities, including sign, writing, or whistles, speech is arguably the most common. The human vocal tract is capable of producing the bewildering diversity of the 7000 or so currently spoken languages, but relatively little is known about its genetic bases, especially in what concerns normal variation. Here, we capitalize on five cohorts totaling 632 Dutch twins with structural magnetic resonance imaging (MRI) data. Two raters placed clearly defined (semi)landmarks on each MRI scan, from which we derived 146 measures capturing the dimensions and shape of various vocal tract structures, but also aspects of the head and face. We used Genetic Covariance Structure Modeling to estimate the additive genetic, common environmental or non-additive genetic, and unique environmental components, while controlling for various confounds and for any systematic differences between the two raters. We found high heritability, h2, for aspects of the skull and face, the mandible, the anteroposterior (horizontal) dimension of the vocal tract, and the position of the hyoid bone. These findings extend the existing literature, and open new perspectives for understanding the complex interplay between genetics, environment, and culture that shape our vocal tracts, and which may help explain cross-linguistic differences in phonetics and phonology.

Experimental evolution of a pheromone signal.

Sexual signals are important in speciation, but understanding their evolution is complex as these signals are often composed of multiple, genetically interdependent components. To understand how signals evolve, we thus need to consider selection responses in multiple components and account for the genetic correlations among components. One intriguing possibility is that selection changes the genetic covariance structure of a multicomponent signal in a way that facilitates a response to selection. However, this hypothesis remains largely untested empirically. In this study, we investigate the evolutionary response of the multicomponent female sex pheromone blend of the moth Heliothis subflexa to 10 generations of artificial selection. We observed a selection response of about three‐quarters of a phenotypic standard deviation in the components under selection. Interestingly, other pheromone components that are biochemically and genetically linked to the components under selection did not change. We also found that after the onset of selection, the genetic covariance structure diverged, resulting in the disassociation of components under selection and components not under selection across the first two genetic principle components. Our findings provide rare empirical support for an intriguing mechanism by which a sexual signal can respond to selection without possible constraints from indirect selection responses.

Comparative Quantitative Genetics of the Pelvis in Four-Species of Rodents and the Conservation of Genetic Covariance and Correlation Structure

Comparative Quantitative Genetics of the Pelvis in Four-Species of Rodents and the Conservation of Genetic Covariance and Correlation Structure

Gene-by-Crisis Interaction for Optimism and Meaning in Life: The Effects of the COVID-19 Pandemic

The corona virus disease 2019 (COVID-19) pandemic and the restrictions to reduce the spread of the virus has had a large impact on daily life. We investigated the individual differences in the effect of the COVID-19 pandemic and first lockdown on optimism and meaning in life in a sample from the Netherlands Twin Register. Participants completed surveys before (N = 9964, Mean age: 48.2, SD = 14.4) and during the first months of the pandemic (i.e. April–May 2020, N = 17,464, Mean age: 44.6 SD = 14.8), with a subsample completing both surveys (N = 6461, Mean age T1: 48.8, SD = 14.5). We applied genetic covariance structure models to twin data to investigate changes in the genetic architecture of the outcome traits due to the pandemic and the interaction of genes with the environmental exposure. Although 56% and 35% of the sample was negatively affected by the pandemic in their optimism and meaning in life, many participants were stable (32% and 43%) or even showed increased optimism and meaning in life (11% and 22%). Subgroups, specifically women, higher educated people, and people with poorer health, experienced larger negative effects. During the first months of the pandemic, slightly lower heritability estimates for optimism and meaning in life (respectively 20% and 25%) were obtained compared to pre-pandemic (respectively 26% and 32%), although confidence intervals overlap. The lower than unity genetic correlations across time (.75 and .63) suggest gene-environment interactions, where the expression of genes that influence optimism and meaning in life differs before and during the pandemic. The COVID-19 pandemic is a strong exposure that leads to imbalanced effects on the well-being of individuals. Some people decrease in well-being, while others get more optimistic and consider their lives as more meaningful during the pandemic. These differences are partly explained by individual differences in genetic sensitivity to extreme environmental change. More knowledge on the person-specific response to specific environmental variables underlying these individual differences is urgently needed to prevent further inequality.

Genetically Informed Regression Analysis: Application to Aggression Prediction by Inattention and Hyperactivity in Children and Adults

We present a procedure to simultaneously fit a genetic covariance structure model and a regression model to multivariate data from mono- and dizygotic twin pairs to test for the prediction of a dependent trait by multiple correlated predictors. We applied the model to aggressive behavior as an outcome trait and investigated the prediction of aggression from inattention (InA) and hyperactivity (HA) in two age groups. Predictions were examined in twins with an average age of 10 years (11,345 pairs), and in adult twins with an average age of 30 years (7433 pairs). All phenotypes were assessed by the same, but age-appropriate, instruments in children and adults. Because of the different genetic architecture of aggression, InA and HA, a model was fitted to these data that specified additive and non-additive genetic factors (A and D) plus common and unique environmental (C and E) influences. Given appropriate identifying constraints, this ADCE model is identified in trivariate data. We obtained different results for the prediction of aggression in children, where HA was the more important predictor, and in adults, where InA was the more important predictor. In children, about 36% of the total aggression variance was explained by the genetic and environmental components of HA and InA. Most of this was explained by the genetic components of HA and InA, i.e., 29.7%, with 22.6% due to the genetic component of HA. In adults, about 21% of the aggression variance was explained. Most was this was again explained by the genetic components of InA and HA (16.2%), with 8.6% due to the genetic component of InA.

Genomic prediction for broad and specific adaptation in sorghum accommodating differential variances of SNP effects

AbstractThis paper reports a first study exploring genomic prediction for adaptation of sorghum [Sorghum bicolor (L.) Moench] to drought‐stress (D‐ET) and nonstress (W‐ET) environment types. The objective was to evaluate the impact of both modeling genotype × environment interaction (G×E) and accounting for heterogeneous variances of marker effects on genomic prediction of parental breeding values for grain yield within and across environment types (ETs). For this aim, different genetic covariance structures and different weights for individual markers were investigated in best linear unbiased prediction (BLUP)‐based prediction models. The BLUP models used a kinship matrix combining pedigree and genomic information, termed K‐BLUP. The dataset comprised testcross yield performances under D‐ET and W‐ET as well as pedigree and genomic data. In general, modeling G×E increased predictive ability and reduced empirical bias of genomic predictions for broad adaptation across both ETs vs. models that ignored G×E by fitting a main genetic effect only. Genomic predictions for specific adaptation to D‐ET or W‐ET were also improved by K‐BLUP models that explicitly accommodated G×E and used data from both ETs relative to prediction models that used data from the targeted ET exclusively or models that used all the data but assumed no G×E. Allowing for heterogeneous marker variances through weighted K‐BLUP produced clear increments (43–72%) in predictive ability of genomic prediction for grain yield in all adaptation scenarios. We conclude that G×E as well as locus‐specific genetic variances should be accommodated in genomic prediction models to improve adaptability of sorghum to variable environmental conditions.

A Comparison of the ASEBA Adult Self Report (ASR) and the Brief Problem Monitor (BPM/18-59)

The adult self report (ASR) is a well-validated instrument with multiple scales relating to adult psychopathology. Recently, an 18-item version has been introduced, the brief problem monitor (BPM) to measure Internalizing behavior (INT), Externalizing behavior (EXT), and attention problems (ATT). The present study compared the BPM and ASR and investigated how well the BPM can serve as a supplement or an alternative for the ASR for specific clinical and scientific purposes. In a large sample of adult twins (N = 9.835) from the Netherlands Twin Register (NTR), we compared the internal consistency, clinical classification concordance, means, and variances of the ASR and BPM. Using the classical twin design, we investigated the genetic covariance structure. For external validation, the associations between subjective well-being and different subscales of the ASR and BPM were compared. The internal consistency of the BPM scales (around α = 0.75) was somewhat lower than the ASR (α ~ 0.85). The BPM Externalizing scale showed the lowest internal consistency (α = 0.63). ASR and BPM scores showed good clinical classification concordance (0.61–0.80) and high correlations (r > 0.88). A small reversed sex difference in the BPM Externalizing scale appeared (women > men). Genetic (0.34–0.54) and environmental components (0.46–0.66) explained the variance to a similar extent for the ASR and BPM. The phenotypic and genetic associations with well-being were comparable. In situations where sum scores are sufficient, the BPM performs as well as the longer ASR. Depending on the situation and goal, it is worth considering the BPM as an alternative for the ASR to reduce the participant burden.

The moderating role of SES on genetic differences in educational achievement in the Netherlands

Parental socioeconomic status (SES) is a strong predictor of children’s educational achievement (EA), with an increasing effect throughout development. Inequality in educational outcomes between children from different SES backgrounds exists in all Western countries. It has been proposed that a cause of this inequality lies in the interplay between genetic effects and SES on EA, which might depend on society and the equality of the education system. This study adopted two approaches, a classical twin design and polygenic score (PGS) approach, to address the effect of parental SES on EA in a large sample of 12-year-old Dutch twin pairs (2479 MZ and 4450 DZ twin pairs with PGSs for educational attainment available in 2335 children) from the Netherlands Twin Register (NTR). The findings of this study indicated that average EA increased with increasing parental SES. The difference in EA between boys and girls became smaller in the higher SES groups. The classical twin design analyses based on genetic covariance structure modeling pointed to lower genetic, environmental, and thus phenotypic variation in EA at higher SES. Independent from a child’s PGS, parental SES predicted EA. However, the strength of the association between PGS and EA did not depend on parental SES. In a within-family design, the twin with a higher PGS scored higher on EA than the co-twin, demonstrating that the effect of the PGS on EA was at least partly independent from parental SES. To conclude, EA depended on SES both directly and indirectly, and SES moderated the additive genetic and environmental components of EA. Adding information from PGS, in addition to parental SES, improved the prediction of children’s EA.

Protein deprivation facilitates the independent evolution of behavior and morphology.

Ecological conditions such as nutrition can change genetic covariances between traits and accelerate or slow down trait evolution. As adaptive trait correlations can become maladaptive following rapid environmental change, poor or stressful environments are expected to weaken genetic covariances, thereby increasing the opportunity for independent evolution of traits. Here, we demonstrate the differences in genetic covariance among multiple behavioral and morphological traits (exploration, aggression, and body weight) between southern field crickets (Gryllus bimaculatus) raised in favorable (free-choice) versus stressful (protein-deprived) nutritional environments. We also quantify the extent to which differences in genetic covariance structures contribute to the potential for the independent evolution of these traits. We demonstrate that protein-deprived environments tend to increase the potential for traits to evolve independently, which is caused by genetic covariances that are significantly weaker for crickets raised on protein-deprived versus free-choice diets. The weakening effects of stressful environments on genetic covariances tended to be stronger in males than in females. The weakening of the genetic covariance between traits under stressful nutritional environments was expected to facilitate the opportunity for adaptive evolution across generations. Therefore, the multivariate gene-by-environment interactions revealed here may facilitate behavioral and morphological adaptations to rapid environmental change.

Evolutionary genetics of personality in the Trinidadian guppy II: sexual dimorphism and genotype-by-sex interactions

Sexual dimorphism in behaviour and personality has been identified in a number of species, but few studies have assessed the extent of shared genetic architecture across the sexes. Under sexually antagonistic selection, mechanisms are expected to evolve that reduce evolutionary conflict, resulting in genotype-by-sex (GxS) interactions. Here we assess the extent of sexual dimorphism in four risk-taking behaviour traits in the Trinidadian guppy, Poecilia reticulata, and apply a multivariate approach to test for GxS interactions. We also quantify the among-individual and genetic covariances between personality and size and growth, which are known a priori to differ between the sexes. We found significant sexual dimorphism in three of the four behaviours, although rmf between sex-specific homologous traits was significantly <+1 for only one behaviour. Using multivariate models, we then estimated sex-specific genetic (co)variance matrices (Gm and Gf) and tested for asymmetry of the cross-trait cross-sex genetic covariance structure (submatrix B). While Gm and Gf were not significantly different from each other overall, their respective leading eigenvectors were poorly aligned. Statistical support for asymmetry in B was found, but limited to a single trait pair for which the cross-sex covariances differed (i.e., COVA(m,f) ≠ COVA(f,m)). Thus, while single- and multi-trait perspectives evidence some GxS, the overall picture is one of similarity between the sexes in their genetic (co)variance structures. Our results suggest behavioural traits related to risk-taking may lack the sex-specific genetic architecture for further dimorphism to evolve under what is hypothesised to be antagonistic selection.

Social Support and Strain Across Close Relationships: A Twin Study

Social relationships play a critical role in health and well-being throughout life. We analyzed the genetic and environmental variance co-variance structure for social support and strain across four sets of relationships including with one’s co-twin, spouse/partner, family and friends. The sample included 5288 Norwegian twins aged 40–80. Older people reported less support from their co-twin and friends and less strain from their family and friends. Genetic influences contribute importantly to variation across all the measures, with estimates ranging from 0 to 58%; variance due to shared environmental influences was most important for the twin-relationship, ranging from 0.11 to 0.42%. Social support was negatively correlated with social strain across all sets of relationships. With the exception of the co-twin relationship, these associations were primarily mediated by genetic and non-shared environmental effects.

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.

Accounting for Sampling Error in Genetic Eigenvalues Using Random Matrix Theory.

The distribution of genetic variance in multivariate phenotypes is characterized by the empirical spectral distribution of the eigenvalues of the genetic covariance matrix. Empirical estimates of genetic eigenvalues from random effects linear models are known to be overdispersed by sampling error, where large eigenvalues are biased upward, and small eigenvalues are biased downward. The overdispersion of the leading eigenvalues of sample covariance matrices have been demonstrated to conform to the Tracy-Widom (TW) distribution. Here we show that genetic eigenvalues estimated using restricted maximum likelihood (REML) in a multivariate random effects model with an unconstrained genetic covariance structure will also conform to the TW distribution after empirical scaling and centering. However, where estimation procedures using either REML or MCMC impose boundary constraints, the resulting genetic eigenvalues tend not be TW distributed. We show how using confidence intervals from sampling distributions of genetic eigenvalues without reference to the TW distribution is insufficient protection against mistaking sampling error as genetic variance, particularly when eigenvalues are small. By scaling such sampling distributions to the appropriate TW distribution, the critical value of the TW statistic can be used to determine if the magnitude of a genetic eigenvalue exceeds the sampling error for each eigenvalue in the spectral distribution of a given genetic covariance matrix.

MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information.

Summary: We have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software based on the mixed model equation, our method is substantially faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including multi-trait models and random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss the advantages and limitations.Availability and implementation: MTG2 is available in https://sites.google.com/site/honglee0707/mtg2.Contact: hong.lee@une.edu.auSupplementary information: Supplementary data are available at Bioinformatics online.

Rapid changes in genetic architecture of behavioural syndromes following colonization of a novel environment

Behavioural syndromes, that is correlated behaviours, may be a result from adaptive correlational selection, but in a new environmental setting, the trait correlation might act as an evolutionary constraint. However, knowledge about the quantitative genetic basis of behavioural syndromes, and the stability and evolvability of genetic correlations under different ecological conditions, is limited. We investigated the quantitative genetic basis of correlated behaviours in the freshwater isopod Asellus aquaticus. In some Swedish lakes, A. aquaticus has recently colonized a novel habitat and diverged into two ecotypes, presumably due to habitat-specific selection from predation. Using a common garden approach and animal model analyses, we estimated quantitative genetic parameters for behavioural traits and compared the genetic architecture between the ecotypes. We report that the genetic covariance structure of the behavioural traits has been altered in the novel ecotype, demonstrating divergence in behavioural correlations. Thus, our study confirms that genetic correlations behind behaviours can change rapidly in response to novel selective environments.

On the Design of Field Experiments with Correlated Treatment Effects

Large-scale field evaluation of genetic material forms an important part of the selection process in the early stages of plant breeding programs. These experiments are typically designed ignoring information on genetic relatedness, often available in the form of crossing history, or plant pedigree records. This paper considers the design of plant breeding experiments where the residuals may be correlated with an assumed autoregressive process, and there is a known genetic covariance structure among genotype effects. This structure is frequently more complex than simple nested family models, arising more generally from the pedigree, or possibly identity in state measures. It is widely accepted that the analysis of these data is improved using information on related individuals. The design of these experiments exploiting known genetic relatedness is considered using three case studies from industry that differ in selection goals, genetic complexity and scale.

Genetic analysis of resistance to Pseudomonas syringae pv. actinidiae (Psa) in a kiwifruit progeny test: an application of generalised linear mixed models (GLMMs).

Linear Mixed models (LMMs) that incorporate genetic and spatial covariance structures have been used for many years to estimate genetic parameters and to predict breeding values in animal and plant breeding. Although the theoretical aspects for extending LMM to generalised linear mixed models (GLMMs) have been around for some time, suitable software has been developed only within the last decade or so. The GLIMMIX procedure in SAS® is becoming popular for fitting GLMMs in various disciplines. Applications of GLMMs to genetic analysis have been limited, probably because of the complexity of the models used. This is particularly so for Proc GLIMMIX because, unlike ASReml software, it is not specifically tailored for analysis of breeding data and some pre-procedure coding is necessary. Binary data that fits the GLMM framework is commonly encountered in breeding experiments, such as when evaluating individuals for resistance by observing the presence or absence of disease. Bacterial canker (Psa) caused by Pseudomonas syringae pv. actinidiae is a serious disease of kiwifruit in New Zealand and other kiwifruit-producing countries. Data from a progeny test trial was available to identify parents with high breeding values for resistance. We successfully applied the GLIMMIX procedure for this purpose. Heritability for resistance was moderate, and we identified two parents and their family as having high potential for Psa resistance breeding. There are several potential pitfalls when using GLMMs with binary data and these are briefly discussed.

A MULTIVARIATE ANALYSIS OF GENETIC VARIATION IN THE ADVERTISEMENT CALL OF THE GRAY TREEFROG,HYLA VERSICOLOR

Genetic variation in sexual displays is crucial for an evolutionary response to sexual selection, but can be eroded by strong selection. Identifying the magnitude and sources of additive genetic variance underlying sexually selected traits is thus an important issue in evolutionary biology. We conducted a quantitative genetics experiment with gray treefrogs (Hyla versicolor) to investigate genetic variances and covariances among features of the male advertisement call. Two energetically expensive traits showed significant genetic variation: call duration, expressed as number of pulses per call, and call rate, represented by its inverse, call period. These two properties also showed significant genetic covariance, consistent with an energetic constraint to call production. Combining the genetic variance-covariance matrix with previous estimates of directional sexual selection imposed by female preferences predicts a limited increase in call duration but no change in call rate despite significant selection on both traits. In addition to constraints imposed by the genetic covariance structure, an evolutionary response to sexual selection may also be limited by high energetic costs of long-duration calls and by preferences that act most strongly against very short-duration calls. Meanwhile, the persistence of these preferences could be explained by costs of mating with males with especially unattractive calls.