P. Bijma

Multilevel Selection 1: Quantitative Genetics of Inheritance and Response to Selection

Interaction among individuals is universal, both in animals and in plants, and substantially affects evolution of natural populations and responses to artificial selection in agriculture. Although quantitative genetics has successfully been applied to many traits, it does not provide a general theory accounting for interaction among individuals and selection acting on multiple levels. Consequently, current quantitative genetic theory fails to explain why some traits do not respond to selection among individuals, but respond greatly to selection among groups. Understanding the full impacts of heritable interactions on the outcomes of selection requires a quantitative genetic framework including all levels of selection and relatedness. Here we present such a framework and provide expressions for the response to selection. Results show that interaction among individuals may create substantial heritable variation, which is hidden to classical analyses. Selection acting on higher levels of organization captures this hidden variation and therefore always yields positive response, whereas individual selection may yield response in the opposite direction. Our work provides testable predictions of response to multilevel selection and reduces to classical theory in the absence of interaction. Statistical methodology provided elsewhere enables empirical application of our work to both natural and domestic populations.

Multilevel selection 2: Estimating the genetic parameters determining inheritance and response to selection.

Interactions among individuals are universal, both in animals and in plants and in natural as well as domestic populations. Understanding the consequences of these interactions for the evolution of populations by either natural or artificial selection requires knowledge of the heritable components underlying them. Here we present statistical methodology to estimate the genetic parameters determining response to multilevel selection of traits affected by interactions among individuals in general populations. We apply these methods to obtain estimates of genetic parameters for survival days in a population of layer chickens with high mortality due to pecking behavior. We find that heritable variation is threefold greater than that obtained from classical analyses, meaning that two-thirds of the full heritable variation is hidden to classical analysis due to social interactions. As a consequence, predicted responses to multilevel selection applied to this population are threefold greater than classical predictions. This work, combined with the quantitative genetic theory for response to multilevel selection presented in an accompanying article in this issue, enables the design of selection programs to effectively reduce competitive interactions in livestock and plants and the prediction of the effects of social interactions on evolution in natural populations undergoing multilevel selection.

The joint effects of kin, multilevel selection and indirect genetic effects on response to genetic selection

Kin and levels‐of‐selection models are common approaches for modelling social evolution. Indirect genetic effect (IGE) models represent a different approach, specifying social effects on trait values rather than fitness. We investigate the joint effect of relatedness, multilevel selection and IGEs on response to selection. We present a measure for the degree of multilevel selection, which is the natural partner of relatedness in expressions for response. Response depends on both relatedness and the degree of multilevel selection, rather than only one or the other factor. Moreover, response is symmetric in relatedness and the degree of multilevel selection, indicating that both factors have exactly the same effect. Without IGEs, the key parameter is the product of relatedness and the degree of multilevel selection. With IGEs, however, multilevel selection without relatedness can explain evolution of social traits. Thus, next to relatedness and multilevel selection, IGEs are a key element in the genetical theory of social evolution.

The Contribution of Social Effects to Heritable Variation in Finishing Traits of Domestic Pigs (Sus scrofa)

Social interactions among individuals are ubiquitous both in animals and in plants, and in natural as well as domestic populations. These interactions affect both the direction and the magnitude of responses to selection and are a key factor in evolutionary success of species and in the design of breeding schemes in agriculture. At present, however, very little is known of the contribution of social effects to heritable variance in trait values. Here we present estimates of the direct and social genetic variance in growth rate, feed intake, back fat thickness, and muscle depth in a population of 14,032 domestic pigs with known pedigree. Results show that social effects contribute the vast majority of heritable variance in growth rate and feed intake in this population. Total heritable variance expressed relative to phenotypic variance was 71% for growth rate and 70% for feed intake. These values clearly exceed the usual range of heritability for those traits. Back fat thickness and muscle depth showed no heritable variance due to social effects. Our results suggest that genetic improvement in agriculture can be substantially advanced by redirecting breeding schemes, so as to capture heritable variance due to social effects.

Estimating Relatedness Between Individuals in General Populations With a Focus on Their Use in Conservation Programs

Relatedness estimators are widely used in genetic studies, but effects of population structure on performance of estimators, criteria to evaluate estimators, and benefits of using such estimators in conservation programs have to date received little attention. In this article we present new estimators, based on the relationship between coancestry and molecular similarity between individuals, and compare them with existing estimators using Monte Carlo simulation of populations, either panmictic or structured. Estimators were evaluated using statistical criteria and a diversity criterion that minimized relatedness. Results show that ranking of estimators depends on the population structure. An existing estimator based on two-gene and four-gene coefficients of identity performs best in panmictic populations, whereas a new estimator based on coancestry performs best in structured populations. The number of marker alleles and loci did not affect ranking of estimators. Statistical criteria were insufficient to evaluate estimators for their use in conservation programs. The regression coefficient of pedigree relatedness on estimated relatedness (β2) was substantially lower than unity for all estimators, causing overestimation of the diversity conserved. A simple correction to achieve β2 = 1 improves both existing and new estimators. Using relatedness estimates with correction considerably increased diversity in structured populations, but did not do so or even decreased diversity in panmictic populations.

Best Linear Unbiased Prediction of Genomic Breeding Values Using a Trait-Specific Marker-Derived Relationship Matrix

Background With the availability of high density whole-genome single nucleotide polymorphism chips, genomic selection has become a promising method to estimate genetic merit with potentially high accuracy for animal, plant and aquaculture species of economic importance. With markers covering the entire genome, genetic merit of genotyped individuals can be predicted directly within the framework of mixed model equations, by using a matrix of relationships among individuals that is derived from the markers. Here we extend that approach by deriving a marker-based relationship matrix specifically for the trait of interest. Methodology/Principal Findings In the framework of mixed model equations, a new best linear unbiased prediction (BLUP) method including a trait-specific relationship matrix (TA) was presented and termed TABLUP. The TA matrix was constructed on the basis of marker genotypes and their weights in relation to the trait of interest. A simulation study with 1,000 individuals as the training population and five successive generations as candidate population was carried out to validate the proposed method. The proposed TABLUP method outperformed the ridge regression BLUP (RRBLUP) and BLUP with realized relationship matrix (GBLUP). It performed slightly worse than BayesB with an accuracy of 0.79 in the standard scenario. Conclusions/Significance The proposed TABLUP method is an improvement of the RRBLUP and GBLUP method. It might be equivalent to the BayesB method but it has additional benefits like the calculation of accuracies for individual breeding values. The results also showed that the TA-matrix performs better in predicting ability than the classical numerator relationship matrix and the realized relationship matrix which are derived solely from pedigree or markers without regard to the trait. This is because the TA-matrix not only accounts for the Mendelian sampling term, but also puts the greater emphasis on those markers that explain more of the genetic variance in the trait.

Intrinsic Aerobic Capacity Sets a Divide for Aging and Longevity

Rationale: Low aerobic exercise capacity is a powerful predictor of premature morbidity and mortality for healthy adults as well as those with cardiovascular disease. For aged populations, poor performance on treadmill or extended walking tests indicates closer proximity to future health declines. Together, these findings suggest a fundamental connection between aerobic capacity and longevity. Objectives: Through artificial selective breeding, we developed an animal model system to prospectively test the association between aerobic exercise capacity and survivability (aerobic hypothesis). Methods and Results: Laboratory rats of widely diverse genetic backgrounds (N:NIH stock) were selectively bred for low or high intrinsic (inborn) treadmill running capacity. Cohorts of male and female rats from generations 14, 15, and 17 of selection were followed for survivability and assessed for age-related declines in cardiovascular fitness including maximal oxygen uptake (VO2max), myocardial function, endurance performance, and change in body mass. Median lifespan for low exercise capacity rats was 28% to 45% shorter than high capacity rats (hazard ratio, 0.06; P<0.001). VO2max, measured across adulthood was a reliable predictor of lifespan (P<0.001). During progression from adult to old age, left ventricular myocardial and cardiomyocyte morphology, contractility, and intracellular Ca2+ handling in both systole and diastole, as well as mean blood pressure, were more compromised in rats bred for low aerobic capacity. Physical activity levels, energy expenditure (VO2), and lean body mass were all better sustained with age in rats bred for high aerobic capacity. Conclusions: These data obtained from a contrasting heterogeneous model system provide strong evidence that genetic segregation for aerobic exercise capacity can be linked with longevity and are useful for deeper mechanistic exploration of aging.

Prediction of Breeding Values and Selection Responses With Genetic Heterogeneity of Environmental Variance

There is empirical evidence that genotypes differ not only in mean, but also in environmental variance of the traits they affect. Genetic heterogeneity of environmental variance may indicate genetic differences in environmental sensitivity. The aim of this study was to develop a general framework for prediction of breeding values and selection responses in mean and environmental variance with genetic heterogeneity of environmental variance. Both means and environmental variances were treated as heritable traits. Breeding values and selection responses were predicted with little bias using linear, quadratic, and cubic regression on individual phenotype or using linear regression on the mean and within-family variance of a group of relatives. A measure of heritability was proposed for environmental variance to standardize results in the literature and to facilitate comparisons to “conventional” traits. Genetic heterogeneity of environmental variance can be considered as a trait with a low heritability. Although a large amount of information is necessary to accurately estimate breeding values for environmental variance, response in environmental variance can be substantial, even with mass selection. The methods developed allow use of the well-known selection index framework to evaluate breeding strategies and effects of natural selection that simultaneously change the mean and the variance.

Survival of Laying Hens: Genetic Parameters for Direct and Associative Effects in Three Purebred Layer Lines

Mortality due to cannibalism is a major problem in laying hens. Due to prohibition of beak-trimming in the European Union, this problem will increase in the near future. One solution to reduce mortality due to cannibalism is to use genetic selection. Mortality due to cannibalism, however, differs from conventional breeding traits, because it depends on social interactions among individuals. Selection strategies aiming to reduce cannibalism, therefore, should consider both the direct effect of an individual on its own survival and the social effect of the individual on the survival of its group members (the so-called associative effect). Traditional breeding, however, accounts for only the direct effect. Recently, methods have been proposed to estimate variance components and breeding values for both direct and associative effects. This paper presents estimated genetic parameters for direct and associative effects on survival days in 3 purebred laying lines. For the analysis, 16,780 hens with intact beaks were used. When considering only direct effects, heritabilities ranged from 2 through 10%. When considering both direct and associative effects, the total heritable variance, expressed as a proportion of phenotypic variance, ranged from 6 through 19%. These results show that heritable variation in survival days is substantially larger than suggested by conventional direct effects models. This means that prospects for reducing mortality by means of genetic selection are good and may lead to substantial reduction of 1 of the major welfare problems in egg production.

Genetic Improvement of Traits Affected by Interactions Among Individuals: Sib Selection Schemes

Livestock populations are usually kept in groups. As a consequence, social interactions among individuals affect productivity, health, and welfare. Current selection methods (individual selection), however, ignore those interactions and yield suboptimal or in some cases even negative responses. In principle, selection between groups instead of individuals offers a solution, but has rarely been adopted in practice for two reasons. First, the relationship between group selection theory and common animal breeding concepts, such as the accuracy of selection, is unclear. Second, application of group selection requires keeping selection candidates in groups, which is often undesirable in practice. This work has two objectives. First, we derive expressions for the accuracy of individual and group selection, which provides a measurement of quality for those methods. Second, we investigate the opportunity to improve traits affected by interactions by using information on relatives kept in family groups, while keeping selection candidates individually. The accuracy of selection based on relatives is shown to be an analogy of the classical expression for traits not affected by interactions. Our results show that selection based on relatives offers good opportunities for effective genetic improvement of traits affected by interactions.