Decomposition of multivariate phenotypic means in multigroup genetic covariance structure analysis

Observed differences in phenotypic means between groups such as parents and their offspring or male and female twins can be decomposed into genetic and environmental components. The decomposition is based on the assumption that the difference in phenotypic means is due to a difference in the location of the normal genetic and environmental distributions underlying the phenotypic individual differences. Differences between the groups in variance can be accommodated insofar as they are due to differences in unique variance or can be modeled using a scale parameter. The decomposition may be carried out in the standard analysis of genetic covariance structure using, for instance, LISREL. Illustrations are given using simulated data and twin data relating to blood pressure. Other possible applications are mentioned. KEY WORDS: group differences in phenotypic means; genetic means; environmental means; genetic and environmental covariance structure; twin data; parent-offspring data

Simultaneous genetic analysis of means and covariance structure: Pearson-Lawley selection rules

The object of this paper is to indicate that the Pearson-Lawley selection rules form a plausible general theory for the simultaneous genetic analysis of means and covariance structure. Models are presented based on phenotypic selection and latent selection. Previously presented quantitative genetic models to decompose means and covariance structure simultaneously are reconsidered as instances of latent selection. The selection rules are very useful in the context of behavior genetic modeling because they lead to testable models and a conceptual framework for explaining variation between and within groups by the same genetic and environmental factors. © 1994 Plenum Publishing Corporation

Phenotypic Complexity, Measurement Bias, and Poor Phenotypic Resolution Contribute to the Missing Heritability Problem in Genetic Association Studies

Background The variance explained by genetic variants as identified in (genome-wide) genetic association studies is typically small compared to family-based heritability estimates. Explanations of this ‘missing heritability’ have been mainly genetic, such as genetic heterogeneity and complex (epi-)genetic mechanisms. Methodology We used comprehensive simulation studies to show that three phenotypic measurement issues also provide viable explanations of the missing heritability: phenotypic complexity, measurement bias, and phenotypic resolution. We identify the circumstances in which the use of phenotypic sum-scores and the presence of measurement bias lower the power to detect genetic variants. In addition, we show how the differential resolution of psychometric instruments (i.e., whether the instrument includes items that resolve individual differences in the normal range or in the clinical range of a phenotype) affects the power to detect genetic variants. Conclusion We conclude that careful phenotypic data modelling can improve the genetic signal, and thus the statistical power to identify genetic variants by 20-99

Model modification

model modification, maximum likelihood estimation, factor analysis, structural equation models, step-wise regression,

Exploring the formal and informal roles of regulatory intermediaries in transnational multi-stakeholder regulation

Research on regulation and regulatory processes has traditionally focused on two prominent roles: rule -making and rule -taking. Recently, the mediating role of third party actors, intermediaries, has started to be explored – notably by Abbott and colleagues in a dedicated special issue of the Annals of the American Academy of Political and Social Science. The present special issue extends this line of research by elaborating the distinction between formal and informal modes of regulatory intermediation, in the specific context of transnational multi- stakeholder regulat ion. In this introduction, we identify two key dimensions of inte rmediation (in)formalism: officialization and formalization. This allows us to develop a typology of intermediation in multi-stakeholder regulatory processes: formal, interpretive, alternative and emergent. Leveraging examples from the papers for this special issue, we discuss how these four types of intermediation co-exist and evolve over time. Finally, we elaborate on the implications of our typology for regulatory processes and outcomes