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This introductory chapter outlines ten big questions which are central to current evolutionary quantitative genetics. It also lists five reasons for addressing these questions in wild populations experiencing natural environments. The application of quantitative genetics analyses to wild populations is a field that has expanded rapidly in recent years, motivated by these questions. The chapters of this book showcase this recent work, and illustrate how quantitative genetic analyses applied to the study of wild populations have improved our understanding of life-history evolution and evolutionary ecology.

Organisms use behavioural, physiological, and life-historical strategies to cope with changes in temperature over space and time. These strategies can be represented as thermal reaction norms, which describe relationships between environmental temperature and organismal phenotypes. Although much research has focused on describing variation in thermal reactions norms, far less emphasis has been placed on developing a mathematical theory to understand empirical patterns. Evolutionary theory provides tools for understanding variation in thermal reaction norms among populations and species. Optimality, quantitative genetic, and allelic models generate quantitative predictions about the evolution of thermal reaction norms. These predictions can be tested by experimental evolution and comparative analysis. Throughout the book, these theoretical and empirical approaches are integrated to gain new insights about evolutionary thermal biology.

This chapter considers a general description of natural selection: the Price equation. Developed by George Price in the late 1960s, the Price equation can be applied to the change of any quantity under any selective regime. It is thus not limited to considering simple haploid single-locus traits, unlike the replicator dynamics, and indeed it is not even limited to considering evolutionary selection. The Price equation provides an instantaneous description of selection in action. The simplicity of the equation makes it a useful conceptual tool for understanding selective processes such as natural selection. The chapter first describes the general Price equation before discussing its use to understand genetic selection. It then shows how the Price equation can be used to derive two classical results from population and quantitative genetics: Fisher's “fundamental theorem of natural selection” and the breeder's equation.

Chapters 1 (and 2) provide a common basis and a vocabulary for appreciating the contours of behavioral and psychiatric genetics. There are three dialogues between a behavioral geneticist and an appeals court judge who wishes to find out more about behavioral genetics for eventual use in her court cases. The distinction between classical quantitative genetics and modern molecular genetics is introduced. In the former, no specific genes are identified, though heritability estimates are provided. The contentious concept of "heritability" is analyzed in detail and its limitations delineated in the first dialogue. A simple mathematical argument is sketched to show how twin studies yield heritabilities and environmental influences for both traits and disorders, and the critical assumptions of such studies are summarized. The chapter closes with an analysis of the shared and nonshared environment issue and a sketch of what is known as an ACE diagram.

This chapter considers some of the ways in which the conditions for existence can serve as a unifying concept in evolutionary biology. It examines some of the areas in which recognition of the principle of the conditions for existence can highlight interconnections that are not often made, both within evolutionary biology and between evolutionary biology and other fields. These fields include quantitative genetics, the levels of selection, evo-devo, the ecological niche, physiology, and conservation biology.

This chapter starts out with a review of what is known about the potential for evolutionary change, the strength and pattern of selection on phenotypic traits, and rates of evolution and adaptation. This is then used to discuss and evaluate quantitative genetics models for evolution on an adaptive landscape. It is argued that the predicted dynamics is too fast to adequately represent patterns of macroevolution. Instead macroevolutionary dynamics must reflect long-term changes in the adaptive landscape itself. Models and theories about the evolution of the adaptive landscape are then reviewed and critically evaluated.

This chapter first reviews the stochastic processes of mutation, drift, and migration that underlie genetic and non-genetic variation, which are essential for evolutionary change, and then moves on to systematic changes reflected in the rules of Mendelian inheritance. It looks at models based on one locus and two alleles to examine the population consequences of simple inheritance. These models are used to reveal the four ‘laws of adaptation’. Here, the map of evolution has a clear and unambiguous genetic origin. There is a direct link between the frequency of alleles in populations, genetics, and the traits the chapter studies. Those simple models are expanded to quantitative traits, nongenetic inheritance, and the mechanics of populations. It is shown that no matter how thorough our understanding of their mechanistic basis, genetics and development gain evolutionary context through their function.

Emerging interest on the part of behavioural ecologists into the causes and consequences of individually repeatable behaviour substantially crosses over methodology and theory well developed in the field of quantitative genetics. Unfortunately, how behavioural ecological concepts translate to quantitative genetic parameters has been under-recognized by researchers in both groups. In this chapter, this overlap is discussed, behavioural ecology terms like ‘animal personality’ and ‘behavioural syndrome’ explicitly defined as quantitative genetic parameters, and adaptive explanations for between-individual behavioural variation are also examined. In addition, this chapter talks about what is known about patterns of behavioural heritabilities, additive genetic correlations between behaviours, and how behavioural correlations might constrain evolutionary responses. Finally, this chapter describes ways in which theory and empirical research in behavioural ecology might inform attempts among the broader quantitative genetics community to understand how and why variation is distributed.

Sewall Wright’s classic Adaptive Landscape has been a highly successful metaphor and scientific concept in evolutionary biology. It has influenced many different research subdisciplines in evolutionary biology and inspired generations of researchers, even though it has also sparked deep scientific and philosophical controversies. Among such subdisciplines are population genetics, evolutionary ecology, quantitative genetics, experimental evolution, conservation biology, speciation and macroevolutionary dynamics, mimicry, saltational evolution, behavioural ecology, molecular biology, protein networks, and theoretical studies on development.

Some people seem to have more joy juice than others, experiencing more positive emotions. Why is this so? An abundance of studies have shown that positivity runs in families, suggesting that positive emotions may be rooted in genetics or shared family circumstances. Over the last decade, behavior geneticists have tried to resolve why positivity is a family matter and shown family resemblance to be largely due to genes. By means of quantitative and molecular genetic techniques, behavior genetic studies have also enabled a growing understanding of the genetic and environmental processes underpinning variation, co-variation, stability, and change in positive emotions, and identified some of the specific genes involved. This chapter aims to provide an overview of methods, findings and implications from studies exploring genetic and environmental influences on a range of positivity indicators such as subjective well-being, life satisfaction, positive affect, and optimism. Thus, the focus is placed on positive emotionality broadly defined with the choice of indicators largely determined (or limited) by the research that has been published to date.

Heredity is a central concept in biology and one of the core principles needed for adaptive evolution. For most of the past 100 years, heredity has been defined and conceptualized in terms of transmission of genes. This is heuristically useful but imposes a certain structure on evolutionary theory and leaves out aspects of heredity that may be important to understand evolution. Emerging developmental perspectives on evolution suggests that alternative ways to represent heredity may prove useful. To this end, this chapter explains how evolutionary biologists treat heredity, conceptually and mathematically. It argues that treating heredity as an outcome of developmental processes not only makes it clearer how different mechanisms of inheritance contribute to evolution but also shows that inheritance cannot be treated as a static channel of transmission of information because it evolves as part of the process of adaptation.

Phenotypic traits may exert causal effects between them. For example, high yield in agricultural species may increase the liability to certain diseases and, conversely, the incidence of a disease may affect yield negatively. Likewise, the transcriptome may be a function of the reproductive status or developmental stage in plants and animals, which may depend on other physiological variables as well. Knowledge of phenotype networks describing such interrelationships can be used to predict the behavior of complex systems, e.g., biological pathways underlying complex traits such as diseases, growth, and reproduction. This chapter reviews the application of structural equation models and related techniques to study causal relationships among phenotypic traits in quantitative genetics. It is discussed how genetic factors can confound the search for causal associations, as well as how pedigree and genomic information can be used to control for such confounding effects and to aid causal inference.

During the past fifty years, academics have tried to understand the factors that influence the tendency of people to engage in entrepreneurial activity. Recently, researchers have examined whether there is a genetic predisposition to entrepreneurship. By dint of our genetic makeup, are some of us more likely than others to come up with new business ideas, start companies, and engage in the other activities that entrepreneurs undertake? This chapter provides a framework to explain how human biology affects the tendency of people to become entrepreneurs. It discusses how researchers have learned about genetic effects on entrepreneurial activity and how they have identified physiological pathways through which biological factors influence business behavior. It discusses the two primary ways in which researchers seek to identify genetic effects on entrepreneurial activity: quantitative and molecular genetics studies. It also examines how researchers use cognitive neuroscience and studies of hormones to investigate the physiological mechanisms through which biological factors operate. Finally, it discusses the implications of a biological perspective for entrepreneurship research.

This chapter takes a deeper look into the genetics of both the host and the parasite. First, it explores the genetic architecture of host resistance, as well as several methods that are used to elucidate genetic architecture – such as QTL-analysis, gene sequencing, comparative genetic studies, and quantitative genetics. The analyses suggest that host resistance is often based upon a limited number of genes with major effect. The genetics of parasite virulence, on the other hand, is illustrated by bacterial pathogens. The chapter indicates that pathogenicity islands are where importance virulence genes are often located. These pathogenicity islands have their own life-cycle, during which they are transferred to a new host, become adapted, and eventually might be excised and transferred again to a further line. The chapter furthermore explores variation in gene expression, which is also a major source of differences in host–parasite interactions.

This chapter focuses on common empirical methods for studying the genetics of adaptation: quantitative genetics, quantitative trait locus (QTL) linkage mapping, association mapping, genome scans, gene expression, and candidate genes. It addresses various aspects of adaptation, speciation, and eco-evolutionary dynamics. The key questions include examining how much additive genetic variation exists in fitness-related traits, to what extent nonadditive genetic variation (dominance and epistasis) influences phenotypic variation, how many loci are involved in adaptation and how large their effects are, to what extent the adaptation of independent populations to similar environments involves parallel/convergent genetic changes, whether adaptation to changing environments is driven mainly by new mutations or by standing genetic variation, and to what extent the ecological effects of individuals transmitted among generations are.