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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 discusses the different ways by which mutation can introduce variation in a genome and how genome-wide polymorphisms can be analysed to identify selective agents in the wild. Substitutions, deletions, insertions, and inversions may lead to single nucleotide polymorphisms and simple sequence repeats, such as microsatellites. Mobile genetic elements are another important source of variation in many species. The chapter explains how the rate of nonsynonymous to synonymous substitutions can be used to study adaptation in loci subject to accelerated evolution and provides an overview of population genomic studies, demonstrating adaptation in a variety of ecological contexts. It discusses the principles of sequence-based population genomics, including the use of Tajima's D statistic to identify selective sweeps in a genome; cis- versus trans-regulatory change; and the evolution of promoter structure. Studies have suggested that trans-acting regulation is the greatest source of variability in gene expression, especially for genes with TATA-box driven promoters. A number of fish studies are discussed that illustrate surprising patterns of parallel and convergent evolution on the molecular level. Finally, the chapter provides an overview of epigenetic variation, including an explanation of the various epigenetic mechanisms.

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.

In a segregating population, quantitative trait loci (QTL) mapping can identify QTLs with a causal effect on a phenotype. A common feature of these methods is that QTL mapping and phenotype network reconstruction are conducted separately. As both tasks have to benefit from each other, this chapter presents an approach which jointly infers a causal phenotype network and causal QTLs. The joint network of causal phenotype relationships and causal QTLs is modeled as a Bayesian network. In addition, a prior distribution on phenotype network structures is adjusted by biological knowledge, thus extending the former framework, QTLnet, into QTLnet-prior. This integrative approach can incorporate several sources of biological knowledge such as protein-protein interactions, gene ontology annotations, and transcription factor and DNA binding information. A Metropolis-Hastings scheme is described that iterates between accepting a network structure and accepting k weights corresponding to the k types of biological knowledge.

Most phenotypic traits are the product of many genes as well as environmental effects, and the resulting phenotypic variation is quantitative rather than qualitative. The extent to which traits are under genetic control is termed heritability, and can be estimated by analyzing the phenotypic similarity of related individuals. Quantitative genetic approaches can be used to estimate population differentiation. Selection on quantitative traits produces changes in phenotypes as a function of the heritability, the intensity of selection, and the amount of phenotypic variation within a population. Human activities, such as size-limited harvesting and habitat degradation, can impose selection on natural populations and result in changes in phenotypes, and genetic drift in small populations can erode quantitative genetic variation. Genome-wide association studies can identify genes and markers associated with quantitative trait variation that can then be used to predict phenotypes from polygenic scores.

Studies of the ecology of mutualisms far outpace those of the genetics and evolution of mutualisms. This chapter develops a conceptual framework for the study of mutualism evolution and reviews the empirical literature. An overarching theme is how mutualistic evolution may differ from evolution driven by abiotic factors. It is argued that because mutualists can coevolve, while the abiotic environment cannot, adaptive peaks in a mutualism are relatively “mobile.” Thus, the genetic step sizes in the evolution of mutualisms may be larger than those involved in abiotic adaptation, an idea that receives some support from QTL studies of plant–pollinator mutualisms. It is also suggested that a moving adaptive peak may lead to selection being stronger and more directional in a mutualism, and that the early genetic steps in mutualism evolution might be large if adaptive peaks on the fitness landscape are far apart and separated by deep valleys. There are insufficient data at present to test either of these ideas. Long-term coevolution may “fine-tune” mutualisms, particularly in specialized, intimate associations, and in support of this view, genomic studies of bacterial endosymbionts of insects find evidence of substantial gene loss. To motivate further research on the many unanswered questions in mutualism evolution, detailed descriptions are provided of three mutualisms that are exemplar model systems: legumes and nitrogen-fixing bacteria, the attine ant–fungus mutualism, and squid and bioluminescent bacteria. Finally, several future directions are examined in hopes of stimulating further research on the genetics and evolution of mutualisms.

Most phenotypic traits are affected by a multitude of genes, which may interact in complex ways. This means that the single locus model explored in chapters 3 and 4 is not always able to capture the full complexity of genetic evolution. In many cases, multiple genes are involved and so this chapter formalizes the analysis of multilocus evolution. Concepts such as linkage disequilibrium and epistasis are introduced, both of which are necessary to properly understand multilocus evolution. The currently highly active field emerging as a result of a crossover between quantitative genetics and genomics is further explored, including methods such as quantitative trait locus (QTL) analysis and genome wide association study (GWAS) that allow phenotypic variation to be associated with likely causative genes and that have made important advances in our understanding of the genetic underpinnings of disease.

The joint action of genetic drift and mutation results in the divergence of trait means over time. This chapter examines the expected amount of divergence, which forms the basis for a number of tests on whether an observed pattern is either too large relative to drift (suggesting directional selection) or two small (suggesting stabilizing selection). It then applies these results to examine tests for selection over a very diverse range of data sets, ranging from a stratophenetic series of fossils to divergence in gene expression over time. It also examines a number of trait-augmented marked-based tests (such as using the QTLs or GWAS hits for a trait) for departures from neutrality.