This essay examines the origin of genotype-environment interaction, or G×E. "Origin" and not "the origin" because the thesis is that there were actually two distinct concepts of G×E at this beginning: a biometric concept, or \[G \times E_B\], and a developmental concept, or \[G \times E_D \]. R. A. Fisher, one of the founders of population genetics and the creator of the statistical analysis of variance, introduced the biometric concept as he attempted to resolve one of the main problems (...) in the biometric tradition of biology - partitioning the relative contributions of nature and nurture responsible for variation in a population. Lancelot Hogben, an experimental embryologist and also a statistician, introduced the developmental concept as he attempted to resolve one of the main problems in the developmental tradition of biology - determining the role that developmental relationships between genotype and environment played in the generation of variation. To argue for this thesis, I outline Fisher and Hogben's separate routes to their respective concepts of G × E; then these separate interpretations of G × E are drawn on to explicate a debate between Fisher and Hogben over the importance of G × E, the first installment of a persistent controversy. Finally, Fisher's \[G \times E_B\] and Hogben's \[G \times E_D \] are traced beyond their own work into mid-2Oth century population and developmental genetics, and then into the infamous IQ Controversy of the 1970s. (shrink)

Quantitative genetics (QG) analyses variation in traits of humans, other animals, or plants in ways that take account of the genealogical relatedness of the individuals whose traits are observed. “Classical” QG, where the analysis of variation does not involve data on measurable genetic or environmental entities or factors, is reformulated in this article using models that are free of hypothetical, idealized versions of such factors, while still allowing for defined degrees of relatedness among kinds of individuals or “varieties.” The gene (...) - free formulation encompasses situations encountered in human QG as well as in agricultural QG. This formulation is used to describe three standard assumptions involved in classical QG and provide plausible alternatives. Several concerns about the partitioning of trait variation into components and its interpretation, most of which have a long history of debate, are discussed in light of the gene-free formulation and alternative assumptions. That discussion is at a theoretical level, not dependent on empirical data in any particular situation. Additional lines of work to put the gene-free formulation and alternative assumptions into practice and to assess their empirical consequences are noted, but lie beyond the scope of this article. The three standard QG assumptions examined are: (1) partitioning of trait variation into components requires models of hypothetical, idealized genes with simple Mendelian inheritance and direct contributions to the trait; (2) all other things being equal, similarity in traits for relatives is proportional to the fraction shared by the relatives of all the genes that vary in the population (e.g., fraternal or dizygotic twins share half of the variable genes that identical or monozygotic twins share); (3) in analyses of human data, genotype-environment interaction variance (in the classical QG sense) can be discounted. The concerns about the partitioning of trait variation discussed include: the distinction between traits and underlying measurable factors; the possible heterogeneity in factors underlying the development of a trait; the kinds of data needed to estimate key empirical parameters; and interpretations based on contributions of hypothetical genes; as well as, in human studies, the labeling of residual variance as a non-shared environmental effect; and the importance of estimating interaction variance. (shrink)

How micro- and macroevolutionary evolutionary processes produce phenotypic change is without question one of the most intriguing and perplexing issues facing evolutionary biologists. We believe that roadblocks to progress lie A) in the underestimation of the role of the environment, and in particular, that of the interaction of genotypes with environmental factors, and B) in the continuing lack of incorporation of development into the evolutionary synthesis. We propose the integration of genetic, environmental and developmental perspectives on the evolution of (...) the phenotype in the form of the concept of the developmental reaction norm (DRN) The DRN represents the set of multivariate ontogenies that can be produced by a single genotype when it is exposed to environmental variation. It encompasses: 1) the processes that alter the phenotype throughout the ontogenetic trajectory, 2) the recognition that different aspects of the phenotype are (and must be) correlated and 3) the ability of a genotype to produce phenotypes in different environments. This perspective necessitates the explicit study of character expression during development, the evaluation of associations between pairs or groups of characters (e.g., multivariate allometries), and the exploration of reaction norms and phenotypic plasticity. We explicitly extend the concept of the DRN to encompass adjustments made in response to changes in the internal environment as well. Thus, ‘typical’ developmental sequences (e.g., cell fate determination) and plastic responses are simply manifestations of different scales of ‘environmental’ effects along a continuum. We present: (1) a brief conceptual review of three fundamental aspects of the generation and evolution of phenotypes: the changes in the trajectories describing growth and differentiation (ontogeny), the multivariate relationships among characters (allometry), and the effect of the environment (plasticity); (2) a discussion of how these components are merged in the concept of the developmental reaction norm; and (3) a reaction norm perspective of major determinants of phenotypes: epigenesis, selection and constraint. (shrink)

In recent years, philosophers of science have found a renewed interest in mechanisms. The motivation is the thought that the elucidation of a mechanism generates a causal explanation for the phenomenon under investigation. For example, a question such as, How do rats form spatial memories of their environments?, is answered by elucidating the regular causal mechanisms responsible for the individual development of spatial memory in rats. But consider a slightly different question: How do some rats come to have better spatial (...) memory than other rats? This is a question about the causes of variation responsible for variation in spatial memory. The first question demands an answer about regularity; the second question demands an answer about variation. The account of causal-mechanical explanation on offer by philosophers of science captures regularity, but it neglects variation. In this essay, I attempt to modify the mechanical program so as to incorporate both regularity and variation. The task is to explicate the relationship between the regular causal mechanisms responsible for individual development and the causes of variation responsible for variation in populations. As it turns out, this is precisely the relationship that has divided the biometric research tradition and the developmental research tradition in the long-standing debates over genotype-environment interaction, or G×E. Ultimately, the product will be an account of causal-mechanical explanation that captures both regularity and variation, and which may be utilized to resolve an aspect of the debates over G×E. (shrink)

When Genotype × Environment (G × E) interactions are present, heritability estimates are not interpretable. Mealey cites abundant evidence for G × E interactions in the etiology of sociopathy, thereby completely undermining estimates of the heritability of sociopathy which form the foundation of her model. Without proper evidence for a genetic basis of sociopathy, Mealey's sociobiological model collapses under its own great weight.

The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the ‘‘blueprint’’ of an organism, organisms are ‘‘reverse engineered’’ to discover their func- tionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adapta- tions, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In (...) particular, the analogy with human-made machines falters when we move down to the level of molecular biology and genetics. Living organisms are far more messy and less transparent than human-made machines. Notoriously, evolution is an oppor- tunistic tinkerer, blindly stumbling on ‘‘designs’’ that no sensible engineer would come up with. Despite impressive technological innovation, the prospect of artificially designing new life forms from scratch has proven more difficult than the superficial analogy with ‘‘programming’’ the right ‘‘software’’ would sug- gest. The idea of applying straightforward engineering approaches to living systems and their genomes— isolating functional components, designing new parts from scratch, recombining and assembling them into novel life forms—pushes the analogy with human artifacts beyond its limits. In the absence of a one-to-one correspondence between genotype and phenotype, there is no straightforward way to imple- ment novel biological functions and design new life forms. Both the developmental complexity of gene expression and the multifarious interactions of genes and environments are serious obstacles for ‘‘engi- neering’’ a particular phenotype. The problem of reverse-engineering a desired phenotype to its genetic ‘‘instructions’’ is probably intractable for any but the most simple phenotypes. Recent developments in the field of bio-engineering and synthetic biology reflect these limitations. Instead of genetically engi- neering a desired trait from scratch, as the machine/engineering metaphor promises, researchers are making greater strides by co-opting natural selection to ‘‘search’’ for a suitable genotype, or by borrowing and recombining genetic material from extant life forms. (shrink)

The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the ‘‘blueprint’’ of an organism, organisms are ‘‘reverse engineered’’ to discover their functionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adaptations, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In particular, the (...) analogy with human-made machines falters when we move down to the level of molecular biology and genetics. Living organisms are far more messy and less transparent than human-made machines. Notoriously, evolution is an opportunistic tinkerer, blindly stumbling on ‘‘designs’’ that no sensible engineer would come up with. Despite impressive technological innovation, the prospect of artificially designing new life forms from scratch has proven more difficult than the superficial analogy with ‘‘programming’’ the right ‘‘software’’ would suggest. The idea of applying straightforward engineering approaches to living systems and their genomes— isolating functional components, designing new parts from scratch, recombining and assembling them into novel life forms—pushes the analogy with human artifacts beyond its limits. In the absence of a one-to-one correspondence between genotype and phenotype, there is no straightforward way to implement novel biological functions and design new life forms. Both the developmental complexity of gene expression and the multifarious interactions of genes and environments are serious obstacles for ‘‘engineering’’ a particular phenotype. The problem of reverse-engineering a desired phenotype to its genetic ‘‘instructions’’ is probably intractable for any but the most simple phenotypes. Recent developments in the field of bio-engineering and synthetic biology reflect these limitations. Instead of genetically engineering a desired trait from scratch, as the machine/engineering metaphor promises, researchers are making greater strides by co-opting natural selection to ‘‘search’’ for a suitable genotype, or by borrowing and recombining genetic material from extant life forms. (shrink)