Genetic analysis of environmental variation

Assessing genotype-environment interactions in Atlantic salmon reared in freshwater loch and recirculating systems

The interest in recirculating aquaculture systems (RAS) is growing due to their benefits such as increased productivity, better control over animal care, reduced environmental effects, and less water consumption. However, in some regions of the world, traditional aquaculture methods remain prevalent, and selective breeding has often been designed for performance within these systems. Therefore, it is important to evaluate how current fish populations fare in RAS to guide future breeding choices. In a commercial setting, we explore the genetic structure of growth characteristics, measure genotype-environment interactions (GxE) in salmon smolts, and examine genetic markers related to growth in freshwater lochs and RAS. Young salmon were raised together until they reached the parr stage, after which they were divided equally between freshwater net-pens and RAS. After an 8-week period, we sampled fish from each environment and genotyped them. Our findings revealed that fish reared in RAS were generally smaller in weight and length but exhibited a higher condition factor and uniformity. We found a notably smaller component of unexplained variance in the RAS, leading to higher heritability estimates. We observed a low GxE effect for length and condition factor, but significant re-ranking for whole-body weight, as well as noticeable differences in trait associations across environments. Specifically, a segment of chromosome 22 was found to be linked with the condition factor in the RAS population only. Results suggests that if the use of RAS continues to expand, the efficiency of existing commercial populations may not reach its full potential unless breeding programs specific to RAS are implemented.

Breeding for resilience in finishing pigs can decrease tail biting, lameness and mortality

BACKGROUND

Previous research showed that deviations in longitudinal data are heritable and can be used as a proxy for pigs' general resilience. However, only a few studies investigated the relationship between these resilience traits and other traits related to resilience and welfare. Therefore, this study investigated the relationship between resilience traits derived from deviations in longitudinal data and traits related to animal resilience, health and welfare, such as tail and ear biting wounds, lameness and mortality.

RESULTS

In our experiment, 1919 finishing pigs with known pedigree (133 Piétrain sires and 266 crossbred dams) were weighed every 2 weeks and scored for physical abnormalities, such as lameness and ear and tail biting wounds (17,066 records). Resilience was assessed via deviations in body weight, deviations in weighing order and deviations in observed activity during weighing. The association between these resilience traits and physical abnormality traits was investigated and genetic parameters were estimated. Deviations in body weight had moderate heritability estimates (h2 = 25.2 to 36.3%), whereas deviations in weighing order (h2 = 4.2%) and deviations in activity during weighing (h2 = 12.0%) had low heritability estimates. Moreover, deviations in body weight were positively associated and genetically correlated with tail biting wounds (rg = 0.22 to 0.30), lameness (rg = 0.15 to 0.31) and mortality (rg = 0.19 to 0.33). These results indicate that events of tail biting, lameness and mortality are associated with deviations in pigs' body weight evolution. This relationship was not found for deviations in weighing order and activity during weighing. Furthermore, individual body weight deviations were positively correlated with uniformity at the pen level, providing evidence that breeding for these resilience traits might increase both pigs' resilience and within-family uniformity.

CONCLUSIONS

In summary, our findings show that breeding for resilience traits based on deviations in longitudinal weight data can decrease pigs' tail biting wounds, lameness and mortality while improving uniformity at the pen level. These findings are valuable for pig breeders, as they offer evidence that these resilience traits are an indication of animals' general health, welfare and resilience. Moreover, these results will stimulate the quantification of resilience via longitudinal body weights in other species.

Genetics of environmental sensitivity to psychiatric and neurodevelopmental phenotypes: evidence from GWAS of monozygotic twins

Individual sensitivity to environmental exposures may be genetically influenced. This genotype-by-environment interplay implies differences in phenotypic variance across genotypes. However, environmental sensitivity genetic variants have proven challenging to detect. GWAS of monozygotic twin differences is a family-based variance analysis method, which is more robust to systemic biases that impact population-based methods. We combined data from up to 21,792 monozygotic twins (10,896 pairs) from 11 studies to conduct the largest GWAS meta-analysis of monozygotic phenotypic differences in children and adolescents/adults for seven psychiatric and neurodevelopmental phenotypes: attention deficit hyperactivity disorder (ADHD) symptoms, autistic traits, anxiety and depression symptoms, psychotic-like experiences, neuroticism, and wellbeing. The SNP-heritability of variance in these phenotypes were estimated (h2: 0% to 18%), but were imprecise. We identified a total of 13 genome-wide significant associations (SNP, gene, and gene-set), including genes related to stress-reactivity for depression, growth factor-related genes for autistic traits and catecholamine uptake-related genes for psychotic-like experiences. Monozygotic twins are an important new source of evidence about the genetics of environmental sensitivity.

The genetics of resilience and its relationships with egg production traits and antibody traits in chickens

BACKGROUND

Resilience is the capacity of an animal to be minimally affected by disturbances or to rapidly return to its initial state before exposure to a disturbance. Resilient livestock are desired because of their improved health and increased economic profit. Genetic improvement of resilience may also lead to trade-offs with production traits. Recently, resilience indicators based on longitudinal data have been suggested, but they need further evaluation to determine whether they are indeed predictive of improved resilience, such as disease resilience. This study investigated different resilience indicators based on deviations between expected and observed egg production (EP) by exploring their genetic parameters, their possible trade-offs with production traits, and their relationships with antibody traits in chickens.

METHODS

Egg production in a nucleus breeding herd environment based on 1-week-, 2-week-, or 3-week-intervals of two purebred chicken lines, a white egg-laying (33,825 chickens) and a brown egg-laying line (34,397 chickens), were used to determine deviations between observed EP and expected average batch EP, and between observed EP and expected individual EP. These deviations were used to calculate three types of resilience indicators for two life periods of each individual: natural logarithm-transformed variance (ln(variance)), skewness, and lag-one autocorrelation (autocorrelation) of deviations from 25 to 83 weeks of age and from 83 weeks of age to end of life. Then, we estimated their genetic correlations with EP traits and with two antibody traits.

RESULTS

The most promising resilience indicators were those based on 1-week-intervals, as they had the highest heritability estimates (0.02-0.12) and high genetic correlations (above 0.60) with the same resilience indicators based on longer intervals. The three types of resilience indicators differed genetically from each other, which indicates that they possibly capture different aspects of resilience. Genetic correlations of the resilience indicator traits based on 1-week-intervals with EP traits were favorable or zero, which means that trade-off effects were marginal. The resilience indicator traits based on 1-week-intervals also showed no genetic correlations with the antibody traits, which suggests that they are not informative for improved immunity or vice versa in the nucleus environment.

CONCLUSIONS

This paper gives direction towards the evaluation and implementation of resilience indicators, i.e. to further investigate resilience indicator traits based on 1-week-intervals, in breeding programs for selecting genetically more resilient layer chickens.

Macro- and micro-genetic environmental sensitivity of yearling weight in Angus beef cattle

Australian beef cattle experience variable conditions, which may give rise to genotype-by-environment interactions depending on the genotypes' macro- and/or micro-genetic environmental sensitivity (GES). Macro-GES gives rise to genotype-by-environment interactions across definable and shared environments, while micro-GES causes heritable variation of phenotypes, e.g., the performance of progeny from one sire may be more variable than other sires. Yearling weight (YW) is a key trait in Australian Angus cattle that may be impacted by both macro- and micro-GES. Current models for genetic evaluation of YW attempt to account for macro-GES by fitting sire-by-herd interactions (S × H). Variation in micro-GES had not yet been estimated for YW in Australian Angus. The aim of this study was to estimate genetic variation due to macro- and micro-GES in YW of Australian Angus cattle. A reaction norm with contemporary group effects as the environmental covariate was fitted either as an alternative to or in combination with a random S × H effect to account for macro-GES. Double hierarchical generalised linear models (DHGLM), fitted as sire models, were used to estimate the genetic variance of the dispersion as a measure of micro-GES. Variation due to both macro- and micro-GES were found in YW. The variance of the slope of the reaction norm was 0.02-0.03 (SEs 0.00), while the S × H variance accounted for 7% of the phenotypic variance in all models. Results showed that both a random S × H effect and a reaction norm should be included to account for both macro-GES and the additional variation captured by an S × H effect. The heritability of the dispersion on the measurement scale ranged from 0.06 to 0.10 (SEs 0.00) depending on which model was used. It should therefore be possible to alter both macro- and micro-GES of YW in Australian Angus through selection. However, care should be taken to ensure an appropriate data structure when including sire-by-herd interactions in the mean part of a DHGLM; otherwise, it can cause biased estimates of micro-GES.

Estimation of genotype by environmental interaction for litter traits by reaction norm model in Taiwan Landrace sows

The negative effects of heat stress on swine reproduction have been well documented and the recent global warming trend caused by climate change is leading to more days with high temperatures every year. This has caused a reduction in litter trait performance of Landrace sows in Taiwan, a country extending across tropical and subtropical oceanic zones. Therefore, this study developed a modified model to determine which stages of pregnancy, before, early, middle, and late, had the largest impacts of heat stress on litter traits. A reaction norm model (RNM) was used to identify sows with high resilience to heat stress for litter traits followed by analysis of the modified model. Data from Landrace sows were collected from 2 farms in Taiwan between 2008 and 2021. A total of 11,059 records were collected for total number born (TNB), number born alive (NBA), and stillborn rate (STBR). The results showed that the heritabilities of TNB, NBA, and STBR were 0.170, 0.115, and 0.077, respectively. These results were similar between the conventional model and the modified model. In the modified model, the before and early stages of sow pregnancy were the significant periods for TNB and NBA (P < 0.05), while the early and middle stages were significant for STBR (P < 0.05). According to the RNM results, the heritability estimates for TNB, NBA, and STBR were 0.23 to 0.11, 0.18 to 0.08, and 0.10 to 0.04, respectively, showing a decrease from low temperature-humidity index (THI) to high THI. The minimum genetic correlations between the highest and the lowest THI for TNB, NBA, and STBR were 0.85, 0.64, and 0.80, respectively. The results of the RNM for breeding value showed re-ranking across THI values. In conclusion, similar results were obtained for heritability when the model was modified for heat stress estimation. Yet re-ranking of breeding values across THI could help farmers to select not only for improved litter trait performance but also for heat stress resilience of Landrace sows in Taiwan.

Genetic and context-specific effects on individual inhibitory control performance in the guppy (Poecilia reticulata)

Among-individual variation in cognitive traits, widely assumed to have evolved under adaptive processes, is increasingly being demonstrated across animal taxa. As variation among individuals is required for natural selection, characterizing individual differences and their heritability is important to understand how cognitive traits evolve. Here, we use a quantitative genetic study of wild-type guppies repeatedly exposed to a 'detour task' to test for genetic variance in the cognitive trait of inhibitory control. We also test for genotype-by-environment interactions (GxE) by testing related fish under alternative experimental treatments (transparent vs. semi-transparent barrier in the detour-task). We find among-individual variation in detour task performance, consistent with differences in inhibitory control. However, analysis of GxE reveals that heritable factors only contribute to performance variation in one treatment. This suggests that the adaptive evolutionary potential of inhibitory control (and/or other latent variables contributing to task performance) may be highly sensitive to environmental conditions. The presence of GxE also implies that the plastic response of detour task performance to treatment environment is genetically variable. Our results are consistent with a scenario where variation in individual inhibitory control stems from complex interactions between heritable and plastic components.

Micro-genetic environmental sensitivity across macro-environments of chickens reared in Burkina Faso and France

BACKGROUND

Commercial poultry production systems follow a pyramidal structure with a nucleus of purebred animals under controlled conditions at the top and crossbred animals under commercial production conditions at the bottom. Genetic correlations between the same phenotypes on nucleus and production animals can therefore be influenced by differences both in purebred-crossbred genotypes and in genotype-by-environment interactions across the two environments, known as macro-genetic environmental sensitivity (GES). Within each environment, genotype-by-environment interactions can also occur due to so-called micro-GES. Micro-GES causes heritable variation in phenotypes and decreases uniformity. In this study, genetic variances of body weight (BW) and of micro-GES of BW and the impacts of purebred-crossbred differences and macro-environmental differences on micro-GES of BW were estimated. The dataset contained three subpopulations of slow-growing broiler chickens: purebred chickens (PB) reared in France, and crossbred chickens reared in France (FR) under the same conditions as PB or reared in Burkina Faso (BF) under local conditions. The crossbred chickens were offspring of the same dam line and had PB as their sire line.

RESULTS

Estimates of heritability of BW and micro-GES of BW were 0.54 (SE of 0.02) and 0.06 (0.01), 0.67 (0.03) and 0.03 (0.01), and 0.68 (0.04) and 0.02 (0.01) for the BF, FR, and PB subpopulations, respectively. Estimates of the genetic correlations for BW between the three subpopulations were moderately positive (0.37 to 0.53) and those for micro-GES were weakly to moderately positive (0.01 to 0.44).

CONCLUSIONS

The results show that the heritability of the micro-GES of BW varies with macro-environment, which indicates that responses to selection are expected to differ between macro-environments. The weak to moderate positive genetic correlations between subpopulations indicate that both macro-environmental differences and purebred-crossbred differences can cause re-ranking of sires based on their estimated breeding values for micro-GES of BW. Thus, the sire that produces the most variable progeny in one macro-environment may not be the one that produces the most variable offspring in another. Similarly, the sire that produces the most variable purebred progeny may not produce the most variable crossbred progeny. The results highlight the need for investigating micro-GES for all subpopulations included in the selection scheme, to ensure optimal genetic gain in all subpopulations.

The evolutionary ecology of variation in labile traits: selection on its among- and within-individual components

Closer integration between behavioral ecology and quantitative genetics has resulted in a recent increase in studies partitioning sources of variation in labile traits. Repeatable between-individual differences are commonly documented, and their existence is generally explained using adaptive arguments, implying that selection has shaped variation at the among- and within-individual level. However, predicting the expected pattern of non-adaptive phenotypic variation around an optimal phenotypic value is difficult, hampering our ability to provide quantitative assessments of the adaptive nature of observed patterns of phenotypic variation within a population. We argue that estimating the strength of selection on trait variation among and within individuals provides a way to test adaptive theory concerned with phenotypic variation. To achieve this aim, we describe a nonlinear selection analysis that enables the study of the selective pressures on trait means and their among- and within-individual variation. By describing an integrative approach for studying the strength of selection on phenotypic variation at different levels, we hope to stimulate empirical studies investigating the ecological factors that can shape the repeatability, heritability, and coefficients of variation of labile and other repeatedly expressed traits.

Environmental variance in male mating success modulates the positive versus negative impacts of sexual selection on genetic load

Sexual selection on males is predicted to increase population fitness, and delay population extinction, when mating success negatively covaries with genetic load across individuals. However, such benefits of sexual selection could be counteracted by simultaneous increases in genome-wide drift resulting from reduced effective population size caused by increased variance in fitness. Resulting fixation of deleterious mutations could be greatest in small populations, and when environmental variation in mating traits partially decouples sexual selection from underlying genetic variation. The net consequences of sexual selection for genetic load and population persistence are therefore likely to be context dependent, but such variation has not been examined. We use a genetically explicit individual-based model to show that weak sexual selection can increase population persistence time compared to random mating. However, for stronger sexual selection such positive effects can be overturned by the detrimental effects of increased genome-wide drift. Furthermore, the relative strengths of mutation-purging and drift critically depend on the environmental variance in the male mating trait. Specifically, increasing environmental variance caused stronger sexual selection to elevate deleterious mutation fixation rate and mean selection coefficient, driving rapid accumulation of drift load and decreasing population persistence times. These results highlight an intricate balance between conflicting positive and negative consequences of sexual selection on genetic load, even in the absence of sexually antagonistic selection. They imply that environmental variances in key mating traits, and intrinsic genetic drift, should be properly factored into future theoretical and empirical studies of the evolution of population fitness under sexual selection.

A promising resilience parameter for breeding: the use of weight and feed trajectories in growing pigs

BACKGROUND

Increasing resilience is a priority in modern pig breeding. Recent research shows that general resilience can be quantified via variability in longitudinal data. The collection of such longitudinal data on weight, feed intake and feeding behaviour in pigs has been facilitated by the development of technologies such as automated feeding stations. The goal of this study was to investigate resilience traits, which were estimated as deviations from longitudinal weight, feed intake and feeding behaviour data during the finishing phase. A dataset with 324,207 records between the age of 95 and 155 days on 5,939 Piétrain pigs with known pedigree and genomic information was used. We provided guidelines for a rigid quality control of longitudinal body weight data, as we found that outliers can significantly affect results. Gompertz growth curve analysis, linear modelling and trajectory analyses were used for quantifying resilience traits.

RESULTS

To our knowledge, this is the first study comparing resilience traits from longitudinal body weight, feed intake and feeding behaviour data in pigs. We demonstrated that the resilience traits are lowly to moderately heritable for deviations in body weight (h² = 2.9%-20.2%), in feed intake (9.4%-23.3%) and in feeding behaviour (16.2%-28.3%). Additionally, these traits have good predictive abilities in cross-validation analyses. Deviations in individual body weight and feed intake trajectories are highly correlated (r_g = 0.78) with low to moderate favourable genetic correlations with feed conversion ratio (r_g = 0.39-0.49). Lastly, we showed that some resilience traits, such as the natural logarithm of variances of observed versus predicted body weights (lnvar_weight), are more robust to lower observation frequencies and are repeatable over three different time periods of the finishing phase.

CONCLUSIONS

Our results will help future studies investigating resilience traits and resilience-related traits. Moreover, our study provides first results on standardization of quality control and efficient data sampling from automated feeding station data. Our findings will be valuable for breeding organizations as they offer evidence that pigs' general resilience can be selected on with good accuracy. Moreover, this methodology might be extended to other species to quantify resilience based on longitudinal data.

Exploring the value of genomic predictions to simultaneously improve production potential and resilience of farmed animals

Sustainable livestock production requires that animals have a high production potential but are also highly resilient to environmental challenges. The first step to simultaneously improve these traits through genetic selection is to accurately predict their genetic merit. In this paper, we used simulations of sheep populations to assess the effect of genomic data, different genetic evaluation models and phenotyping strategies on prediction accuracies and bias for production potential and resilience. In addition, we also assessed the effect of different selection strategies on the improvement of these traits. Results show that estimation of both traits greatly benefits from taking repeated measurements and from using genomic information. However, the prediction accuracy for production potential is compromised, and resilience estimates tends to be upwards biased, when families are clustered in groups even when genomic information is used. The prediction accuracy was also found to be lower for both traits, resilience and production potential, when the environment challenge levels are unknown. Nevertheless, we observe that genetic gain in both traits can be achieved even in the case of unknown environmental challenge, when families are distributed across a large range of environments. Simultaneous genetic improvement in both traits however greatly benefits from the use of genomic evaluation, reaction norm models and phenotyping in a wide range of environments. Using models without the reaction norm in scenarios where there is a trade-off between resilience and production potential, and phenotypes are collected from a narrow range of environments may result in a loss for one trait. The study demonstrates that genomic selection coupled with reaction-norm models offers great opportunities to simultaneously improve productivity and resilience of farmed animals even in the case of a trade-off.

Technologies, strategies, and cautions when deconvoluting genome-wide association signals: FTO in focus

Genome-wide association studies have revealed a plethora of genetic variants that correlate with polygenic conditions. However, causal molecular mechanisms have proven challenging to fully define. Without such information, the associations are not physiologically useful or clinically actionable. By reviewing studies of the FTO locus in the genetic etiology of obesity, we wish to highlight advances in the field fueled by the evolution of technical and analytic strategies in assessing the molecular bases for genetic associations. Particular attention is drawn to extrapolating experimental findings from animal models and cell types to humans, as well as technical aspects used to identify long-range DNA interactions and their biological relevance with regard to the associated trait. A unifying model is proposed by which independent obesogenic pathways regulated by multiple FTO variants and genes are integrated at the primary cilium, a cellular antenna where signaling molecules that control energy balance convene.

Genomic prediction for grain yield and micro-environmental sensitivity in winter wheat

Individuals within a common environment experience variations due to unique and non-identifiable micro-environmental factors. Genetic sensitivity to micro-environmental variation (i.e. micro-environmental sensitivity) can be identified in residuals, and genotypes with lower micro-environmental sensitivity can show greater resilience towards environmental perturbations. Micro-environmental sensitivity has been studied in animals; however, research on this topic is limited in plants and lacking in wheat. In this article, we aimed to (i) quantify the influence of genetic variation on residual dispersion and the genetic correlation between genetic effects on (expressed) phenotypes and residual dispersion for wheat grain yield using a double hierarchical generalized linear model (DHGLM); and (ii) evaluate the predictive performance of the proposed DHGLM for prediction of additive genetic effects on (expressed) phenotypes and its residual dispersion. Analyses were based on 2,456 advanced breeding lines tested in replicated trials within and across different environments in Denmark and genotyped with a 15K SNP-Illumina-BeadChip. We found that micro-environmental sensitivity for grain yield is heritable, and there is potential for its reduction. The genetic correlation between additive effects on (expressed) phenotypes and dispersion was investigated, and we observed an intermediate correlation. From these results, we concluded that breeding for reduced micro-environmental sensitivity is possible and can be included within breeding objectives without compromising selection for increased yield. The predictive ability and variance inflation for predictions of the DHGLM and a linear mixed model allowing heteroscedasticity of residual variance in different environments (LMM-HET) were evaluated using leave-one-line-out cross-validation. The LMM-HET and DHGLM showed good and similar performance for predicting additive effects on (expressed) phenotypes. In addition, the accuracy of predicting genetic effects on residual dispersion was sufficient to allow genetic selection for resilience. Such findings suggests that DHGLM may be a good choice to increase grain yield and reduce its micro-environmental sensitivity.

Genetic parameters for fleece uniformity in alpacas

Fiber diameter is the main selection objective and criterion in alpaca breeding programs, but it can vary across anatomic regions of the animal. As fiber diameter is usually registered from a unique sample from the mid side of the body, fiber diameter variability within fleece is never addressed and phenotypic and genetic differences may exist for fleece uniformity in alpaca populations. The objective of this work was to estimate the genetic parameters of fleece uniformity in an alpaca population. Fiber diameters measured in three different locations were used as repeated records of the same animal and studied for fitting a model that considers heterogeneous the residual variance of the model. Also, the logarithm of the standard deviation of the three measures was used as a measure of the fleece variability. Estimate of the additive genetic variance of the environmental variability was 0.43±0.14, enough high to suggest the existence of wide room to select for fleece uniformity. Genetic correlation of the trait with its environmental variability was 0.76±0.13 showing that fleece uniformity will be indirectly selected when aiming to reduce the fiber diameter. In the light of these parameters, and due to the cost of registering and the cost of opportunity, it looks no worthy to include uniformity as a selection criterion in alpaca breeding programs.

Aggregate Trait Evolvability and Macroevolution in Two Sister Species of the Bryozoan Stylopoma

The study of trait evolution in modular animals is more complicated than that in solitary animals, because a single genotype of a modular colony can express an enormous range of phenotypic variation. Furthermore, traits can occur either at the module level or at the colony level. However, it is unclear how the traits at the colony level evolve. We test whether colony-level aggregate traits, defined as the summary statistics of a phenotypic distribution, can evolve. To quantify this evolutionary potential, we use parent-offspring pairs in two sister species of the bryozoan Stylopoma, grown and bred in a common garden breeding experiment. We find that the medians of phenotypic distributions are evolvable between generations of colonies. We also find that the structure of this evolutionary potential differs between these two species. Ancestral species align more closely with the direction of species divergence than the descendent species. This result indicates that aggregate trait evolvability can itself evolve.

A quantile integral linear model to quantify genetic effects on phenotypic variability

Detecting genetic variants associated with the variance of complex traits, that is, variance quantitative trait loci (vQTLs), can provide crucial insights into the interplay between genes and environments and how they jointly shape human phenotypes in the population. We propose a quantile integral linear model (QUAIL) to estimate genetic effects on trait variability. Through extensive simulations and analyses of real data, we demonstrate that QUAIL provides computationally efficient and statistically powerful vQTL mapping that is robust to non-Gaussian phenotypes and confounding effects on phenotypic variability. Applied to UK Biobank (n = 375,791), QUAIL identified 11 vQTLs for body mass index (BMI) that have not been previously reported. Top vQTL findings showed substantial enrichment for interactions with physical activities and sedentary behavior. Furthermore, variance polygenic scores (vPGSs) based on QUAIL effect estimates showed superior predictive performance on both population-level and within-individual BMI variability compared to existing approaches. Overall, QUAIL is a unified framework to quantify genetic effects on the phenotypic variability at both single-variant and vPGS levels. It addresses critical limitations in existing approaches and may have broad applications in future gene-environment interaction studies.

From Mendel to quantitative genetics in the genome era: the scientific legacy of W. G. Hill

The quantitative geneticist W. G. ('Bill') Hill, awardee of the 2018 Darwin Medal of the Royal Society and the 2019 Mendel Medal of the Genetics Society (United Kingdom), died on 17 December 2021 at the age of 81 years. Here, we pay tribute to his multiple key scientific contributions, which span population and evolutionary genetics, animal and plant breeding and human genetics. We discuss his theoretical research on the role of linkage disequilibrium (LD) and mutational variance in the response to selection, the origin of the widely used LD metric r² in genomic association studies, the genetic architecture of complex traits, the quantification of the variation in realized relationships given a pedigree relationship and much more. We demonstrate that basic theoretical research in quantitative and statistical genetics has led to profound insights into the genetics and evolution of complex traits and made predictions that were subsequently empirically validated, often decades later.

Assessment of two statistical approaches for variance genome-wide association studies in plants

Genomic loci that control the variance of agronomically important traits are increasingly important due to the profusion of unpredictable environments arising from climate change. The ability to identify such variance-controlling loci in association studies will be critical for future breeding efforts. Two statistical approaches that have already been used in the variance genome-wide association study (vGWAS) paradigm are the Brown-Forsythe test (BFT) and the double generalized linear model (DGLM). To ensure that these approaches are deployed as effectively as possible, it is critical to study the factors that influence their ability to identify variance-controlling loci. We used genome-wide marker data in maize (Zea mays L.) and Arabidopsis thaliana to simulate traits controlled by epistasis, genotype by environment (GxE) interactions, and variance quantitative trait nucleotides (vQTNs). We then quantified true and false positive detection rates of the BFT and DGLM across all simulated traits. We also conducted a vGWAS using both the BFT and DGLM on plant height in a maize diversity panel. The observed true positive detection rates at the maximum sample size considered (N = 2815) suggest that both of these vGWAS approaches are capable of identifying epistasis and GxE for sufficiently large sample sizes. We also noted that the DGLM decisively outperformed the BFT for simulated traits controlled by vQTNs at sample sizes of N = 500. Although we conclude that there are still certain aspects of vGWAS approaches that need further refinement, this study suggests that the BFT and DGLM are capable of identifying variance-controlling loci in current state-of-the-art plant or agronomic data sets.

Capturing indirect genetic effects on phenotypic variability: Competition meets canalization

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.

Impact of selection for birth weight variability on reproductive longevity: A mice model

Uniformity, understood as a similar performance in relevant livestock traits, such as birth weight within the litter, is being included as one of the selection objectives in breeding programmes, especially for polytocous livestock species. A divergent selection experiment for birth weight within‐litter variability in mice during 23 generations showed that homogeneous animals were better for litter size, survival and feed efficiency but less heavy than heterogeneous animals. The aim of this study was to compare the reproductive longevity in both divergent lines as time to the end of the reproductive period. Two generations from both lines with an initial number of 43 females and 43 males were mated one to one and stayed together to have consecutive parturitions until the end of the reproductive life. Females were discarded when the time elapsed from the last parturition was longer than 63 days. The time to the end of the reproductive period between both lines was compared by fitting a Cox proportional hazard regression model adjusting for line, generation and its interaction. The rate of parturitions in both lines was also compared using a Prentice–Williams–Peterson model adjusted for the same effects. The low variability line was associated with a higher parturition rate, e.g., adjusted hazard ratio was 2.93 (95% CI 2.17–3.94). The Cox model showed that the low variability females also presented benefits of time to the end of the reproductive period, with an adjusted hazard ratio of 0.26 (95% CI 0.16–0.41). The median of reproductive days was 55.50 in the high variability line whilst the median was 252.50 days for the low variability females. The homogeneous line presented important reproductive advantages suggesting higher robustness and animal welfare. Further research should confirm whether the findings presented here of a better performance in the low variability line could be properly applied to some livestock species.

Terminology use in animal personality research: a self-report questionnaire and a systematic review

Whether animal personality studies provide insights of broader evolutionary and ecological relevance to the field of behavioural ecology is frequently questioned. One of the sources of controversy is the vast, but often vague terminology present in the field. From a statistical perspective, animal personality is defined as among-individual variance in behaviour. However, numerous conceptual definitions of animal personality are available in the literature. Here, we performed (i) a self-report questionnaire and (ii) a systematic literature review to quantify how researchers interpreted conceptual and statistical definitions commonly used in animal personality research. We also compared whether data obtained from the questionnaire matched with data from the literature review. Among the 430 self-reported researchers that participated in our questionnaire, we observed discrepancies in key questions such as the conceptual definition of animal personality or the interpretation of repeatability. Furthermore, our literature review generally confirmed the global patterns revealed by the questionnaire. Overall, we identified common disagreements within the field of animal personality and discussed potential solutions. We advocate for adopting a terminology that avoids ambiguous interpretations and helps to make more explicit the widespread connotations implicit in the label 'animal personality'.

The quantitative genetics of the prevalence of infectious diseases: hidden genetic variation due to indirect genetic effects dominates heritable variation and response to selection

Infectious diseases have profound effects on life, both in nature and agriculture. However, a quantitative genetic theory of the host population for the endemic prevalence of infectious diseases is almost entirely lacking. While several studies have demonstrated the relevance of transmission of infections for heritable variation and response to selection, current quantitative genetics ignores transmission. Thus, we lack concepts of breeding value and heritable variation for endemic prevalence, and poorly understand response of endemic prevalence to selection. Here, we integrate quantitative genetics and epidemiology, and propose a quantitative genetic theory for the basic reproduction number R0 and for the endemic prevalence of an infection. We first identify the genetic factors that determine the prevalence. Subsequently, we investigate the population-level consequences of individual genetic variation, for both R0 and the endemic prevalence. Next, we present expressions for the breeding value and heritable variation, for endemic prevalence and individual binary disease status, and show that these depend strongly on the prevalence. Results show that heritable variation for endemic prevalence is substantially greater than currently believed, and increases strongly when prevalence decreases, while heritability of disease status approaches zero. As a consequence, response of the endemic prevalence to selection for lower disease status accelerates considerably when prevalence decreases, in contrast to classical predictions. Finally, we show that most heritable variation for the endemic prevalence is hidden in indirect genetic effects, suggesting a key role for kin-group selection in the evolutionary history of current populations and for genetic improvement in animals and plants.

Evaluation of the phenotypic and genomic background of variability based on litter size of Large White pigs

BACKGROUND

The genetic background of trait variability has captured the interest of ecologists and animal breeders because the genes that control it could be involved in buffering various environmental effects. Phenotypic variability of a given trait can be assessed by studying the heterogeneity of the residual variance, and the quantitative trait loci (QTL) that are involved in the control of this variability are described as variance QTL (vQTL). This study focuses on litter size (total number born, TNB) and its variability in a Large White pig population. The variability of TNB was evaluated either using a simple method, i.e. analysis of the log-transformed variance of residuals (LnVar), or the more complex double hierarchical generalized linear model (DHGLM). We also performed a single-SNP (single nucleotide polymorphism) genome-wide association study (GWAS). To our knowledge, this is only the second study that reports vQTL for litter size in pigs and the first one that shows GWAS results when using two methods to evaluate variability of TNB: LnVar and DHGLM.

RESULTS

Based on LnVar, three candidate vQTL regions were detected, on Sus scrofa chromosomes (SSC) 1, 7, and 18, which comprised 18 SNPs. Based on the DHGLM, three candidate vQTL regions were detected, i.e. two on SSC7 and one on SSC11, which comprised 32 SNPs. Only one candidate vQTL region overlapped between the two methods, on SSC7, which also contained the most significant SNP. Within this vQTL region, two candidate genes were identified, ADGRF1, which is involved in neurodevelopment of the brain, and ADGRF5, which is involved in the function of the respiratory system and in vascularization. The correlation between estimated breeding values based on the two methods was 0.86. Three-fold cross-validation indicated that DHGLM yielded EBV that were much more accurate and had better prediction of missing observations than LnVar.

CONCLUSIONS

The results indicated that the LnVar and DHGLM methods resulted in genetically different traits. Based on their validation, we recommend the use of DHGLM over the simpler method of log-transformed variance of residuals. These conclusions can be useful for future studies on the evaluation of the variability of any trait in any species.

Estimation of macro- and micro-genetic environmental sensitivity in unbalanced datasets

Genotype-by-environment interaction is caused by variation in genetic environmental sensitivity (GES), which can be subdivided into macro- and micro-GES. Macro-GES is genetic sensitivity to macro-environments (definable environments often shared by groups of animals), while micro-GES is genetic sensitivity to micro-environments (individual environments). A combined reaction norm and double hierarchical generalised linear model (RN-DHGLM) allows for simultaneous estimation of base genetic, macro- and micro-GES effects. The accuracy of variance components estimated using a RN-DHGLM has been explicitly studied for balanced data and recommendation of a data size with a minimum of 100 sires with at least 100 offspring each have been made. In the current study, the data size (numbers of sires and progeny) and structure requirements of the RN-DHGLM were investigated for two types of unbalanced datasets. Both datasets had a variable number of offspring per sire, but one dataset also had a variable number of offspring within macro-environments. The accuracy and bias of the estimated macro- and micro-GES effects and the estimated breeding values (EBVs) obtained using the RN-DHGLM depended on the data size. Reasonably accurate and unbiased estimates were obtained with data containing 500 sires with 20 offspring or 100 sires with 50 offspring, regardless of the data structure. Variable progeny group sizes, alone or in combination with an unequal number of offspring within macro-environments, had little impact on the dispersion of the EBVs or the bias and accuracy of variance component estimation, but resulted in lower accuracies of the EBVs. Compared to genetic correlations of zero, a genetic correlation of 0.5 between base genetic, macro- and micro-GES components resulted in a slight decrease in the percentage of replicates that converged out of 100 replicates, but had no effect on the dispersion and accuracy of variance component estimation or the dispersion of the EBVs. The results show that it is possible to apply the RN-DHGLM to unbalanced datasets to obtain estimates of variance due to macro- and micro-GES. Furthermore, the levels of accuracy and bias of variance estimates when analysing macro- and micro-GES simultaneously are determined by average family size, with limited impact from variability in family size and/or cohort size. This creates opportunities for the use of field data from populations with unbalanced data structures when estimating macro- and micro-GES.

Estimation of genetic parameter for feed efficiency and resilience traits in three pig breeds

Recently, automatic feeders have become popular for collecting daily feed intake data in the pig industry, making it possible to evaluate genetic effects on feed efficiency and resilience traits, expressed as day-to-day fluctuations in feeding records. This study aimed to understand the influence of genetic factors on feed efficiency traits, including residual intake and BW gain (RIG), and resilience traits, as well as to compare the differences in genetic parameter estimates among three purebred pig breeds. A total of 6 103 pigs from three breeds (Large White: 1 193 pigs, Landrace: 3 010 pigs, and Duroc: 1 900 pigs) were raised in a specific pathogen-free environment. The growth and feed intake records during the testing period were obtained using automatic feeders, and the average daily gain (ADG) and average feed intake (AFI) were calculated. Feed conversion ratio (FCR), residual feed intake (RFI), residual gain, and RIG were calculated as feed efficiency traits, and the log-transformed variance of deviation for the daily feed intake (LnVar_FI), daily occupation time (LnVar_OC), and the daily number of visits to the feeder (LnVar_VT) was calculated as resilience traits. After estimating the genetic parameters for each breed, a meta-analysis was performed to obtain the weighted mean of heritability estimates (h_m²) and genetic correlation estimates (GC_m) for the three breeds. The h_m² were moderate and ranged from 0.31 to 0.39 for feed efficiency traits and 0.31 to 0.40 for resilience traits, and there were no significant differences in heritability estimates among the three breeds except for AFI, RFI, and RIG. For feed efficiency traits, the FCR and RIG showed favourably moderate GC_m with AFI (0.29 and -0.33, respectively) and ADG (-0.39 and 0.31, respectively). For resilience traits, the LnVar_FI and LnVar_VT showed favourably low to moderate GC_m with FCR (0.33 and 0.28, respectively) and RIG (-0.37 and 0.28, respectively), and there were no genetic relationships of LnVar_OC with FCR and RIG (the absolute value of GC_m was 0.01). There was no significant difference in the genetic correlation estimates among the three breeds for feed efficiency and resilience traits. Our results suggest that feed efficiency and resilience traits were heritable, and resilience traits showed favourable or no genetic correlation with feed efficiency traits. In addition, the influence of genetic factors on feed efficiency and resilience traits could be the same among breeds.

Albatrosses respond adaptively to climate variability by changing variance in a foraging trait

The ability of individuals and populations to adapt to a changing climate is a key determinant of population dynamics. While changes in mean behaviour are well studied, changes in trait variance have been largely ignored, despite being assumed to be crucial for adapting to a changing environment. As the ability to acquire resources is essential to both reproduction and survival, changes in behaviours that maximize resource acquisition should be under selection. Here, using foraging trip duration data collected over 7 years on black-browed albatrosses (Thalassarche melanophris) on the Kerguelen Islands in the southern Indian Ocean, we examined the importance of changes in the mean and variance in foraging behaviour, and the associated effects on fitness, in response to the El Niño Southern Oscillation (ENSO). Using double hierarchical models, we found no evidence that individuals change their mean foraging trip duration in response to a changing environment, but found strong evidence of changes in variance. Younger birds showed greater variability in foraging trip duration in poor conditions as did birds with higher fitness. However, during brooding, birds showed greater variability in foraging behaviour under good conditions, suggesting that optimal conditions allow the alteration between chick provisioning and self-maintenance trips. We found weak correlations between sea surface temperature and the ENSO, but stronger links with sea-level pressure. We suggest that variability in behavioural traits affecting resource acquisition is under selection and offers a mechanism by which individuals can adapt to a changing climate. Studies which look only at effects on mean behaviour may underestimate the effects of climate change and fail to consider variance in traits as a key evolutionary force.

Selection for litter size and litter birthweight in Large White pigs: Maximum, mean and variability of reproduction traits

Gradually increasing trend of litter size poses a challenge to pig farmers in terms of managing larger litters. Therefore, it seems that a balanced approach that optimises litter size, litter birthweight, and uniformity of those traits is needed in order to address animal welfare and farm management concerns. This study aimed to investigate this issue by defining several traits for total number born (TNB), number born alive (NBA) and litter birthweight (LW). First, the highest value from at least five records per sow was selected as maximum (max) value for each reproduction trait. Second, a mean (mean) for each reproduction trait was calculated per sow. Last, the variability of reproduction traits between parities of the sow was calculated as log-transformed variance of residuals of all observations per sow for each reproduction trait (LnVar). In total, 23 193 Large White sows from Topigs Norsvin with 152 282 litter records were used for analysis in ASReml 4.1. Also, a simulation of breeding schemes was performed with the use of SelAction 2.1 and estimates from genetic analysis. Maximum value of reproductive traits had a much higher heritability than repeated observations or mean of reproduction traits, e.g., 0.31 for maxTNB vs. 0.12 for TNB and 0.07 for meanTNB, which allows for a faster response under selection. The maximum value traits, however, were found to carry more risks, i.e. higher ratio of stillborn (not for maxNBA) and increased variability of traits. Thus, using them in breeding programme should be carefully considered. The genetic coefficient of variation on SD level estimated to indicate the genetic magnitude for variability phenotypes indicated a maximum change of 6-9% in genetic SD of TNB, NBA and LW. The genetic correlations between mean and corresponding variability traits varied from 0.66 to 0.74, whereas the correlation between other mean and variability traits ranged from 0.33 to 0.99. The simulation indicated that even with selection targeted against the variability of reproduction traits, a very limited change should be expected due to a complex genetic and phenotypic relationship between the traits. In the scenarios with selection against LnVarTNB and LnVarLW, this was a decrease of 0.1-0.6% per year, whereas in scenario with selection against LnVarNBA, the range was 0.6-1.1% per year. It is still possible to increase litter size and birthweight further, however, a balance between mean and variability of reproduction traits is required, which can be obtained only by a very well designed breeding programme.

Selection for environmental variance of litter size in rabbits involves genes in pathways controlling animal resilience

Background

Environmental variance (V_E) is partially under genetic control, which means that the V_E of individuals that share the same environment can differ because they have different genotypes. Previously, a divergent selection experiment for V_E of litter size (LS) during 13 generations in rabbit yielded a successful response and revealed differences in resilience between the divergent lines. The aim of the current study was to identify signatures of selection in these divergent lines to better understand the molecular mechanisms and pathways that control V_E of LS and animal resilience. Three methods (F_ST, ROH and varLD) were used to identify signatures of selection in a set of 473 genotypes from these rabbit lines (377) and a base population (96). A whole-genome sequencing (WGS) analysis was performed on 54 animals to detect genes with functional mutations.

Results

By combining signatures of selection and WGS data, we detected 373 genes with functional mutations in their transcription units, among which 111 had functions related to the immune system, stress response, reproduction and embryo development, and/or carbohydrate and lipid metabolism. The genes TTC23L, FBXL20, GHDC, ENSOCUG00000031631, SLC18A1, CD300LG, MC2R, and ENSOCUG00000006264 were particularly relevant, since each one carried a functional mutation that was fixed in one of the rabbit lines and absent in the other line. In the 3ʹUTR region of the MC2R and ENSOCUG00000006264 genes, we detected a novel insertion/deletion (INDEL) variant.

Conclusions

Our findings provide further evidence in favour of V_E as a measure of animal resilience. Signatures of selection were identified for V_E of LS in genes that have a functional mutation in their transcription units and are mostly implicated in the immune response and stress response pathways. However, the real implications of these genes for V_E and animal resilience will need to be assessed through functional analyses.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00653-y.

Evaluation of direct and maternal responses in reproduction traits based on different selection strategies for postnatal piglet survival in a selection experiment

BACKGROUND

Postnatal piglet survival is important both in economic and animal welfare terms. It is influenced by the piglet's own direct genetic effects and by maternal genetic effects of the dam, associated with milk production and mothering abilities. These genetic effects might be correlated, affected by other non-genetic factors and unfavourably associated with other reproduction traits such as litter size, which makes the development of optimal breeding strategies a challenge. To identify the optimum selection strategy for piglet survival, a selection experiment was carried out to compare responses in survival and reproduction traits to selection on only direct, only maternal, or both genetic effects of postnatal survival. The data of the experiment were recorded from outdoor reared pigs, with first- and second-generation sires selected based on their estimated breeding values for maternal and direct effects of postnatal survival of indoor reared offspring, respectively, with the opportunity to identify potential genotype-by-environment interaction.

RESULTS

A Bayesian multivariate threshold-linear model that was fitted to data on 22,483 piglets resulted in significant (Pr(h2 > 0) = 1.00) estimates of maternal and direct heritabilities between 0.12 and 0.18 for survival traits and between 0.29 and 0.36 for birth weight, respectively. Selection for direct genetic effects resulted in direct and maternal responses in postnatal survival of 1.11% ± 0.17 and - 0.49% ± 0.10, respectively, while selection for maternal genetic effects led to greater direct and maternal responses, of 5.20% ± 0.34 and 1.29% ± 0.20, respectively, in part due to unintentional within-litter selection. Selection for both direct and maternal effects revealed a significant lower direct response (- 1.04% ± 0.12) in comparison to its expected response from single-effect selection, caused by interactions between direct and maternal effects.

CONCLUSIONS

Selection successfully improved post- and perinatal survival and birth weight, which indicates that they are genetically determined and that genotype-by-environment interactions between outdoor (experimental data) and indoor (selection data) housed pigs were not important for these traits. A substantially increased overall (direct plus maternal) response was obtained using selection for maternal versus direct or both direct and maternal effects, suggesting that the maternal genetic effects are the main limiting factor for improving piglet survival on which selection pressure should be emphasized.

Genetic parameters for uniformity of harvest weight in Pacific white shrimp (Litopenaeus vannamei)

Background

Uniformity of body weight is a trait of great economic importance in the production of white shrimp (Litopenaeus vannamei). A necessary condition to improve this trait through selective breeding is the existence of genetic variability for the environmental variance of body weight. Although several studies have reported such variability in other aquaculture species, to our knowledge, no estimates are available for shrimp. Our aim in this study was to estimate the genetic variance for weight uniformity in a farmed population of shrimp to determine the potential of including this trait in the selection program. We also estimated the genetic correlation of weight uniformity between two environments (selection nucleus and commercial population).

Methods

The database contained phenotypic records for body weight on 51,346 individuals from the selection nucleus and 38,297 individuals from the commercial population. A double hierarchical generalized linear model was used to analyse weight uniformity in the two environments. Fixed effects included sex and year for the nucleus data and sex and year-pond combination for the commercial data. Environmental and additive genetic effects were included as random effects.

Results

The estimated genetic variance for weight uniformity was greater than 0 (0.06 ± 0.01) in both the nucleus and commercial populations and the genetic coefficient of variation for the residual variance was 0.25 ± 0.01. The genetic correlation between weight and weight uniformity was close to zero in both environments. The estimate of the genetic correlation of weight uniformity between the two environments (selection nucleus and commercial population) was 0.64 ± 0.06.

Conclusions

The existence of genetic variance for weight uniformity suggests that genetic improvement of this trait is possible. Selection for weight uniformity should not decrease weight, given the near zero genetic correlation between these two traits. The strong genetic correlation of weight uniformity between the two environments indicates that response to selection for uniformity in the nucleus will be at least partially transmitted to the commercial population if this trait is included in the breeding goal.

Genetic parameters for canalization analysis of morphological traits in the Pura Raza Español horse

Measurements from 13 different morphological traits of importance in the Pura Raza Español (PRE) horse were used to estimate genetic and environmental parameters following a heteroscedastic model in which data were assigned to stallions. Data sets used ranged from 20,610 (height at withers) to 48,486 measurements (length of shoulder), and the number of animals analysed in the pedigrees varied from 17,662 (height at withers) to 23,962 (dorsal-sternal diameter). Results of heritabilities of the traits varied from 0.09 (width of chest and upper neck line) to 0.30 (muscular development). Further, genetic correlations between traits and their environmental variability were estimated, obtaining values from -0.56 (muscular development) to 0.69 (height at withers). Also, predicted breeding values for the mean and for the environmental variability were obtained for all horses in the pedigrees, providing individual information about not only the expected phenotypic value of their offspring but also the expected heterogeneity among them. Results proved the possibility of improving morphological traits and reducing the heterogeneity of offspring at a time by the selection of animals and levels of systematic effects.

Detection of unrecorded environmental challenges in high-frequency recorded traits, and genetic determinism of resilience to challenge, with an application on feed intake in lambs

BACKGROUND

Resilient animals can remain productive under different environmental conditions. Rearing in increasingly heterogeneous environmental conditions increases the need of selecting resilient animals. Detection of environmental challenges that affect an entire population can provide a unique opportunity to select animals that are more resilient to these events. The objective of this study was two-fold: (1) to present a simple and practical data-driven approach to estimate the probability that, at a given date, an unrecorded environmental challenge occurred; and (2) to evaluate the genetic determinism of resilience to such events.

METHODS

Our method consists of inferring the existence of highly variable days (indicator of environmental challenges) via mixture models applied to frequently recorded phenotypic measures and then using the inferred probabilities of the occurrence of an environmental challenge in a reaction norm model to evaluate the genetic determinism of resilience to these events. These probabilities are estimated for each day (or other time frame). We illustrate the method by using an ovine dataset with daily feed intake (DFI) records.

RESULTS

Using the proposed method, we estimated the probability of the occurrence of an unrecorded environmental challenge, which proved to be informative and useful for inclusion as a covariate in a reaction norm animal model. We estimated the breeding values for sensitivity of the genetic potential for DFI of animals to environmental challenges. The level and slope of the reaction norm were negatively correlated (- 0.46 ± 0.21).

CONCLUSIONS

Our method is promising and appears to be viable to identify unrecorded events of environmental challenges, which is useful when selecting resilient animals and only productive data are available. It can be generalized to a wide variety of phenotypic records from different species and used with large datasets. The negative correlation between level and slope indicates that a hypothetical selection for increased DFI may not be optimal depending on the presence or absence of stress. We observed a reranking of individuals along the environmental gradient and low genetic correlations between extreme environmental conditions. These results confirm the existence of a G [Formula: see text] E interaction and show that the best animals in one environmental condition are not the best in another one.

The evolution of group differences in changing environments

The selection pressures that have shaped the evolution of complex traits in humans remain largely unknown, and in some contexts highly contentious, perhaps above all where they concern mean trait differences among groups. To date, the discussion has focused on whether such group differences have any genetic basis, and if so, whether they are without fitness consequences and arose via random genetic drift, or whether they were driven by selection for different trait optima in different environments. Here, we highlight a plausible alternative: that many complex traits evolve under stabilizing selection in the face of shifting environmental effects. Under this scenario, there will be rapid evolution at the loci that contribute to trait variation, even when the trait optimum remains the same. These considerations underscore the strong assumptions about environmental effects that are required in ascribing trait differences among groups to genetic differences.

Phenotypic noise and the cost of complexity

Experimental studies demonstrate the existence of phenotypic diversity despite constant genotype and environment. Theoretical models based on a single phenotypic character predict that during an adaptation event, phenotypic noise should be positively selected far from the fitness optimum because it increases the fitness of the genotype, and then be selected against when the population reaches the optimum. It is suggested that because of this fitness gain, phenotypic noise should promote adaptive evolution. However, it is unclear how the selective advantage of phenotypic noise is linked to the rate of evolution, and whether any advantage would hold for more realistic, multidimensional phenotypes. Indeed, complex organisms suffer a cost of complexity, where beneficial mutations become rarer as the number of phenotypic characters increases. Using a quantitative genetics approach, we first show that for a one-dimensional phenotype, phenotypic noise promotes adaptive evolution on plateaus of positive fitness, independently from the direct selective advantage on fitness. Second, we show that for multidimensional phenotypes, phenotypic noise evolves to a low-dimensional configuration, with elevated noise in the direction of the fitness optimum. Such a dimensionality reduction of the phenotypic noise promotes adaptive evolution and numerical simulations show that it reduces the cost of complexity.

Genetic variants and underlying mechanisms influencing variance heterogeneity in maize

Traditional genetic studies focus on identifying genetic variants associated with the mean difference in a quantitative trait. Because genetic variants also influence phenotypic variation via heterogeneity, we conducted a variance-heterogeneity genome-wide association study to examine the contribution of variance heterogeneity to oil-related quantitative traits. We identified 79 unique variance-controlling single nucleotide polymorphisms (vSNPs) from the sequences of 77 candidate variance-heterogeneity genes for 21 oil-related traits using the Levene test (P < 1.0 × 10^-5 ). About 30% of the candidate genes encode enzymes that work in lipid metabolic pathways, most of which define clear expression variance quantitative trait loci. Of the vSNPs specifically associated with the genetic variance heterogeneity of oil concentration, 89% can be explained by additional linked mean-effects genetic variants. Furthermore, we demonstrated that gene × gene interactions play important roles in the formation of variance heterogeneity for fatty acid compositional traits. The interaction pattern was validated for one gene pair (GRMZM2G035341 and GRMZM2G152328) using yeast two-hybrid and bimolecular fluorescent complementation analyses. Our findings have implications for uncovering the genetic basis of hidden additive genetic effects and epistatic interaction effects, and we indicate opportunities to stabilize efficient breeding and selection of high-oil maize (Zea mays L.).

Gene-environment interaction: New insights into perceived parenting and social anxiety among adolescents

Background.

Social anxiety symptoms (SAS) are among the most common mental health problems during adolescence, and it has been shown that parenting influences the adolescent’s level of social anxiety. In addition, it is now widely assumed that most mental health problems, including social anxiety, originate from a complex interplay between genes and environment. However, to date, gene–environment (G × E) interactions studies in the field of social anxiety remain limited. In this study, we have examined how 274 genes involved in different neurotransmission pathways interact with five aspects of perceived parenting as environmental exposure (i.e., support, proactive control, psychological control, punitive control, and harsh punitive control) to affect SAS during adolescence.

Methods.

We have applied an analytical technique that allows studying genetic information at the gene level, by aggregating data from multiple single-nucleotide-polymorphisms within the same gene and by taking into account the linkage disequilibrium structure of the gene. All participants were part of the STRATEGIES cohort of 948 Flemish adolescents (mean age = 13.7), a population-based study on the development of problem behaviors in adolescence. Relevant genes were preselected based on prior findings and neurotransmitter-related functional protein networks.

Results.

The results suggest that genes involved in glutamate (SLC1A1), glutathione neurotransmission (GSTZ1), and oxidative stress (CALCRL), in association with harsh punitive parenting, may contribute to social anxiety in adolescence. Isolated polymorphisms in these genes have been related to anxiety and related disorders in earlier work.Conclusions: Taken together, these findings provide new insights into possible biological pathways and environmental risk factors involved in the etiology of social anxiety symptoms’ development.

Conclusions.

Taken together, these findings provide new insights into possible biological pathways and environmental risk factors involved in the etiology of social anxiety symptoms’ development.

Patterns of environmental variance across environments and traits in domestic cattle

The variance in phenotypic trait values is a product of environmental and genetic variation. The sensitivity of traits to environmental variation has a genetic component and is likely to be under selection. However, there are few studies investigating the evolution of this sensitivity, in part due to the challenges of estimating the environmental variance. The livestock literature provides a wealth of studies that accurately partition components of phenotypic variance, including the environmental variance, in well-defined environments. These studies involve breeds that have been under strong selection on mean phenotype in optimal environments for many generations, and therefore represent an opportunity to study the potential evolution of trait sensitivity to environmental conditions. Here, we use literature on domestic cattle to examine the evolution of micro-environmental variance (CVR-the coefficient of residual variance) by testing for differences in expression of CVR in animals from the same breed reared in different environments. Traits that have been under strong selection did not follow a null expectation of an increase in CVR in heterogenous environments (e.g., grazing), a pattern that may reflect evolution of increased uniformity in heterogeneous environments. When comparing CVR across environments of different levels of optimality, here measured by trait mean, we found a reduction in CVR in the more optimal environments for both life history and growth traits. Selection aimed at increasing trait means in livestock breeds typically occurs in the more optimal environments, and we therefore suspect that the decreased CVR is a consequence of evolution of the expression of micro-environmental variance in this environment. Our results highlight the heterogeneity in micro-environmental variance across environments and point to possible connections to the intensity of selection on trait means.

Calving date and its variability as a potential trait in the breeding objective to account for reproductive seasonality in alpacas

The low fertility and offspring survival indicators in alpacas can be partially due to their particularity seasonal reproduction that reduces the opportunities of the females to become pregnant within a season, with the survival of the offspring concerned by the availability of food and exposure to diseases that depends on the calving date. Optimizing the date of delivery and reducing its variability are shown as eligible criteria that could be used as selection criteria within the genetic improvement programmes in alpacas, the calving date being a much more appropriate trait to measure and optimize fertility unlike of age at first calving and the calving interval, this due to the reproductive seasonality in camelids. For this study, 6,533 birth date records were taken between 2001 and 2018 of Peruvian alpacas, to estimate the genetic parameters. Models assuming heterogeneity in the residuals were fitted besides classical homogeneous models to address, not only the possibility of forwarding or delaying the calving date, but also the trend to have parturitions in similar dates. The heritability and repeatability ranged from 0.07 to 0.20 for a homogeneity model and from 0.08 to 0.23 for a heterogeneity model, and suggest the possibility of advancing or delaying the calving date. It should be taken into account that the gestation length of camelids makes it difficult to adapt many reproductive traits, and trying to centre the calving date could delay it. It was concluded the feasibility to genetically select the calving date, also in the production of camels and dromedaries, which have the same reproductive characteristics as alpacas. This selection can be combined with other traits. The heterogeneity model was shown to provide a better fit.

Challenging the selection for consistency in the rank of endurance competitions

Background

Control of the environmental variability by genetic selection offers possibilities for new selection objectives for productive traits. This methodology aims at reducing heterogeneity in productive traits and has been applied to several traits and species for which animal homogeneity is profitable. In horse breeding programmes, rank in competitions is a common selection objective but has been challenging to model. In this study, the parameters of environmental variability for the rank of a horse were computed to analyse the capability of a horse to maintain the best ranking across competitions that consist of long-distance races in which the adapted physical condition of the horse is essential. The genetic component of the environmental variance for the rank in endurance competitions was evaluated, which resulted in proposing a new transformation of horse scores in competitions.

Results

Homogeneous and heterogeneous variance models were compared by assaying three random effects that affect both the rank and its variability, using endurance ride data consisting of 2863 records. The pedigree relationship matrix contained 5931 animals. The rank trait was transformed into a normalized variable to prevent false estimates of the genetic correlation by inappropriate artificial skewness. The models included the number of participants in the race, sex, and age as systematic effects. The rider, the rider-horse interaction, or an environmental permanent effect were tested as random effects, in addition to additive genetic and residual effects. The models were analysed using the GSEVM program. Estimates of heritability for rank ranged from 0.12 to 0.15. The heterogeneous variance model that fitted the rider was assessed as the best model based on the deviance information criterion. Estimates of genetic variance for rank variability ranged from 0.12 to 0.13. The genetic correlation between the rank and its environmental variability was low and did not differ from 0.

Conclusions

These results offer an opportunity to select animals for canalization by reducing the variability of race results and achieving the best positions, which could be a new selection objective by weighting estimated breeding values for rank and its variability in a selection index.

Context-dependent genetic architecture of Drosophila life span

Understanding the genetic basis of variation in life span is a major challenge that is difficult to address in human populations. Evolutionary theory predicts that alleles affecting natural variation in life span will have properties that enable them to persist in populations at intermediate frequencies, such as late-life-specific deleterious effects, antagonistic pleiotropic effects on early and late-age fitness components, and/or sex- and environment-specific or antagonistic effects. Here, we quantified variation in life span in males and females reared in 3 thermal environments for the sequenced, inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) and an advanced intercross outbred population derived from a subset of DGRP lines. Quantitative genetic analyses of life span and the micro-environmental variance of life span in the DGRP revealed significant genetic variance for both traits within each sex and environment, as well as significant genotype-by-sex interaction (GSI) and genotype-by-environment interaction (GEI). Genome-wide association (GWA) mapping in both populations implicates over 2,000 candidate genes with sex- and environment-specific or antagonistic pleiotropic allelic effects. Over 1,000 of these genes are associated with variation in life span in other D. melanogaster populations. We functionally assessed the effects of 15 candidate genes using RNA interference (RNAi): all affected life span and/or micro-environmental variance of life span in at least one sex and environment and exhibited sex-and environment-specific effects. Our results implicate novel candidate genes affecting life span and suggest that variation for life span may be maintained by variable allelic effects in heterogeneous environments.

Genetic variance for behavioural 'predictability' of stress response

Genetic factors underpinning phenotypic variation are required if natural selection is to result in adaptive evolution. However, evolutionary and behavioural ecologists typically focus on variation among individuals in their average trait values and seek to characterize genetic contributions to this. As a result, less attention has been paid to if and how genes could contribute towards within-individual variance or trait 'predictability'. In fact, phenotypic 'predictability' can vary among individuals, and emerging evidence from livestock genetics suggests this can be due to genetic factors. Here, we test this empirically using repeated measures of a behavioural stress response trait in a pedigreed population of wild-type guppies. We ask (a) whether individuals differ in behavioural predictability and (b) whether this variation is heritable and so evolvable under selection. Using statistical methodology from the field of quantitative genetics, we find support for both hypotheses and also show evidence of a genetic correlation structure between the behavioural trait mean and individual predictability. We show that investigating sources of variability in trait predictability is statistically tractable and can yield useful biological interpretation. We conclude that, if widespread, genetic variance for 'predictability' will have major implications for the evolutionary causes and consequences of phenotypic variation.

The Evolution of Variance Control

Genetically identical individuals can be phenotypically variable, even in constant environmental conditions. The ubiquity of this phenomenon, known as 'intra-genotypic variability', is increasingly evident and the relevant mechanistic underpinnings are beginning to be understood. In parallel, theory has delineated a number of formal expectations for contexts in which such a feature would be adaptive. Here, we review empirical evidence across biological systems and theoretical expectations, including nonlinear averaging and bet hedging. We synthesize existing results to illustrate the dependence of selection outcomes both on trait characteristics, features of environmental variability, and species' demographic context. We conclude by discussing ways to bridge the gap between empirical evidence of intra-genotypic variability, studies demonstrating its genetic component, and evidence that it is adaptive.

A fast score test for generalized mixture models

In biomedical studies, testing for homogeneity between two groups, where one group is modeled by mixture models, is often of great interest. This paper considers the semiparametric exponential family mixture model proposed by Hong et al. (2017) and studies the score test for homogeneity under this model. The score test is nonregular in the sense that nuisance parameters disappear under the null hypothesis. To address this difficulty, we propose a modification of the score test, so that the resulting test enjoys the Wilks phenomenon. In finite samples, we show that with fixed nuisance parameters the score test is locally most powerful. In large samples, we establish the asymptotic power functions under two types of local alternative hypotheses. Our simulation studies illustrate that the proposed score test is powerful and computationally fast. We apply the proposed score test to an UK ovarian cancer DNA methylation data for identification of differentially methylated CpG sites.

Body Weight Deviations as Indicator for Resilience in Layer Chickens

Resilience is the capacity of an animal to be minimally affected by disturbances or to rapidly return to the state pertained before exposure to a disturbance. Less resilient animals are expected to be more susceptible to environmental perturbations, such as diseases, and will consequently show more and/or greater fluctuations in production than more resilient animals. Natural antibodies (NAb) are antibodies recognizing antigens without previous exposure to these, and are hypothesized to be an indication of general disease resistance. The objective of this research was to investigate genetic parameters of resilience indicators based on standardized body weight (BW) deviations and to investigate its relation with immunity (i.e. NAb) and disease resistance. Keyhole limpet hemocyanin-binding NAb were measured in layer chickens, which were selectively bred for high and low keyhole limpet hemocyanin-binding NAb levels during six generations. In addition, BW data of these layers were collected on a four-weekly interval from 4 weeks of age until 32 weeks of age. Standardized deviations of BW from an individual were compared to lines’ average BW (i.e. across individuals), and these were used to calculate resilience indicators: natural logarithm-transformed variance [ln(variance)], skewness, and lag-one autocorrelation of deviations (i.e. all within an individual). Heritabilities of resilience indicators were between 0.09 and 0.11. Genetic correlations between the three resilience indicators were between -0.20 and 0.40 (with high SE), which might suggest that the resilience indicators capture different aspects of resilience. Genetic correlations between resilience indicators and NAb were close to zero, which suggests that the resilience indicators and NAb capture different aspects of immunity. This might indicate that, in this dataset, environmental perturbations are only to a small extent affected by disease incidence, possibly due to a lack of disease occurrence. However, a lower estimated breeding value for ln(variance) was predictive for lower lesion scores after an avian pathogenic Escherichia coli inoculation and vice versa. In conclusion, this study shows that there is genetic variation in resilience indicators based on BW deviations in layer chickens, which opens up possibilities to improve resilience by means of selective breeding.

Genetics and genomics of uniformity and resilience in livestock and aquaculture species: A review

Genetic control of residual variance offers opportunities to increase uniformity and resilience of livestock and aquaculture species. Improving uniformity and resilience of animals will improve health and welfare of animals and lead to more homogenous products. Our aims in this review were to summarize the current models and methods to study genetic control of residual variance, genetic parameters and genomic results for residual variance and discuss future research directions. Typically, the genetic coefficient of variation is high (median = 0.27; range 0-0.86) and the heritability of residual variance is low (median = 0.01; range 0-0.10). Higher heritabilities can be achieved when increasing the number of records per animal. Divergent selection experiments have supported the feasibility of selecting for high or low residual variance. Genomic studies have revealed associations in regions related to stress, including those from the heat shock protein family. Although the number of studies is growing, genetic control of residual variance is still poorly understood, but big data and genomics offer great opportunities.

'Fat's chances': Loci for phenotypic dispersion in plasma leptin in mouse models of diabetes mellitus

Background

Leptin, a critical mediator of feeding, metabolism and diabetes, is expressed on an incidental basis according to satiety. The genetic regulation of leptin should similarly be episodic.

Methodology

Data from three mouse cohorts hosted by the Jackson Laboratory– 402 (174F, 228M) F₂ Dilute Brown non-Agouti (DBA/2)×DU6i intercrosses, 142 Non Obese Diabetic (NOD/ShiLtJ×(NOD/ShiLtJ×129S1/SvImJ.H2^g7) N₂ backcross females, and 204 male Nonobese Nondiabetic (NON)×New Zealand Obese (NZO/HlLtJ) reciprocal backcrosses–were used to test for loci associated with absolute residuals in plasma leptin and arcsin-transformed percent fat (‘phenotypic dispersion’; PD_pLep and PD_AFP). Individual data from 1,780 mice from 43 inbred strains was also used to estimate genetic variances and covariances for dispersion in each trait.

Principal findings

Several loci for PD_pLep were detected, including possibly syntenic Chr 17 loci, but there was only a single position on Chr 6 for PD_AFP. Coding SNP in genes linked to the consensus Chr 17 PD_pLep locus occurred in immunological and cancer genes, genes linked to diabetes and energy regulation, post-transcriptional processors and vomeronasal variants. There was evidence of intersexual differences in the genetic architecture of PD_pLep. PD_pLep had moderate heritability (hs2=0.29) and PD_AFP low heritability (hs2=0.12); dispersion in these traits was highly genetically correlated r = 0.8).

Conclusions

Greater genetic variance for dispersion in plasma leptin, a physiological trait, may reflect its more ephemeral nature compared to body fat, an accrued progressive character. Genetic effects on incidental phenotypes such as leptin might be effectively characterized with randomization-detection methodologies in addition to classical approaches, helping identify incipient or borderline cases or providing new therapeutic targets.