On models and muddles of heritability

Genetic versus environmental influences on callous-unemotional traits in preadolescence: The role of parenting and parental psychopathology

Children with callous-unemotional (CU) traits are at risk for severe conduct problems. While CU traits are moderately heritable, parenting also predicts risk. However, few studies have investigated whether parenting factors (e.g., acceptance, conflict, parental psychopathology) moderate the etiology of CU traits, while accounting for gene-environment correlations. To address this knowledge gap, we used data from 772 twin pairs from the Adolescent Brain and Cognitive Development Study to test bivariate models that explored overlapping etiological influences on CU traits and child reports of their parenting environment. We also used gene-by-environment interaction models to test whether parenting moderated genetic versus environmental influences. There were no overlapping etiological influences on CU traits and parental acceptance, but modest genetic and non-shared environmental overlap between CU traits and family conflict. Parental acceptance and psychopathology moderated non-shared environmental influences, with stronger non-shared environmental influences on CU traits among children who experienced lower parental acceptance and greater parental psychopathology. Family conflict only moderated environmental influences when models did not covary for conduct problems. Parental acceptance and parental psychopathology may be specific environmental protective and risk factors for CU traits, whereas family conflict may represent a general environmental risk factor for both CU traits and conduct problems.

Cross-cultural differences in visuo-spatial processing and the culture-fairness of visuo-spatial intelligence tests: an integrative review and a model for matrices tasks

Visuo-spatial reasoning tests, such as Raven's matrices, Cattell's culture-fair test, or various subtests of the Wechsler scales, are frequently used to estimate intelligence scores in the context of inter-racial comparisons. This has led to several high-profile works claiming that certain ethnic groups have lower intelligence than others, presumably due to genetic inferiority. This logic is predicated on the assumption that such visuo-spatial tests, because they are non-verbal, must be culture-fair: that their solution process does not significantly draw on factors that vary from one culture to the next. This assumption of culture-fairness is dubious at best and has been questioned by many authors. In this article, I review the substantial body of psychological and ethnographic literature which has demonstrated that the perception, manipulation and conceptualization of visuo-spatial information differs significantly across cultures, in a way that is relevant to intelligence tests. I then outline a model of how these inter-cultural differences can affect seven major steps of the solution process for Raven's matrices, with a brief discussion of other visuo-spatial reasoning tests. Overall, a number of cultural assumptions appear to be deeply ingrained in all visuo-spatial reasoning tests, to the extent that it disqualifies the view of such tests as intrinsically culture-fair and makes it impossible to draw clear-cut conclusions from average score differences between ethnic groups.

Estimating the heritability of psychological measures in the Human Connectome Project dataset

The Human Connectome Project (HCP) is a large structural and functional MRI dataset with a rich array of behavioral and genotypic measures, as well as a biologically verified family structure. This makes it a valuable resource for investigating questions about individual differences, including questions about heritability. While its MRI data have been analyzed extensively in this regard, to our knowledge a comprehensive estimation of the heritability of the behavioral dataset has never been conducted. Using a set of behavioral measures of personality, emotion and cognition, we show that it is possible to re-identify the same individual across two testing times (fingerprinting), and to identify identical twins significantly above chance. Standard heritability estimates of 37 behavioral measures were derived from twin correlations, and machine-learning models (univariate linear model, Ridge classifier and Random Forest model) were trained to classify monozygotic twins and dizygotic twins. Correlations between the standard heritability metric and each set of model weights ranged from 0.36 to 0.7, and questionnaire-based and task-based measures did not differ significantly in their heritability. We further explored the heritability of a smaller number of latent factors extracted from the 37 measures and repeated the heritability estimation; in this case, the correlations between the standard heritability and each set of model weights were lower, ranging from 0.05 to 0.43. One specific discrepancy arose for the general intelligence factor, which all models assigned high importance, but the standard heritability calculation did not. We present a thorough investigation of the heritabilities of the behavioral measures in the HCP as a resource for other investigators, and illustrate the utility of machine-learning methods for qualitative characterization of the differential heritability across diverse measures.

TwinMARM: two-stage multiscale adaptive regression methods for twin neuroimaging data

Twin imaging studies have been valuable for understanding the relative contribution of the environment and genes on brain structures and their functions. Conventional analyses of twin imaging data include three sequential steps: spatially smoothing imaging data, independently fitting a structural equation model at each voxel, and finally correcting for multiple comparisons. However, conventional analyses are limited due to the same amount of smoothing throughout the whole image, the arbitrary choice of smoothing extent, and the decreased power in detecting environmental and genetic effects introduced by smoothing raw images. The goal of this paper is to develop a two-stage multiscale adaptive regression method (TwinMARM) for spatial and adaptive analysis of twin neuroimaging and behavioral data. The first stage is to establish the relationship between twin imaging data and a set of covariates of interest, such as age and gender. The second stage is to disentangle the environmental and genetic influences on brain structures and their functions. In each stage, TwinMARM employs hierarchically nested spheres with increasing radii at each location and then captures spatial dependence among imaging observations via consecutively connected spheres across all voxels. Simulation studies show that our TwinMARM significantly outperforms conventional analyses of twin imaging data. Finally, we use our method to detect statistically significant effects of genetic and environmental variations on white matter structures in a neonatal twin study.

Measuring selection in contemporary human populations

Are humans currently evolving? This question can be answered using data on lifetime reproductive success, multiple traits and genetic variation and covariation in those traits. Such data are available in existing long-term, multigeneration studies - both clinical and epidemiological - but they have not yet been widely used to address contemporary human evolution. Here we review methods to predict evolutionary change and attempts to measure selection and inheritance in humans. We also assemble examples of long-term studies in which additional measurements of evolution could be made. The evidence strongly suggests that we are evolving and that our nature is dynamic, not static.

For analytic bioethics

This paper argues that bioethics requires analysis, which is not explicitly ethical in character. The first part of the paper argues the general point, that ethical problems can arise not only on occasions when moral values make conflicting recommendations, but also in understanding the facts. I suggest that this is particularly so where the facts are provided by the biomedical sciences, since it is often not clear how to relate their conceptual framework to that in which we frame our value judgements. In the second part, I illustrate the argument by criticizing the moral conclusions drawn by the authors of a recent widely publicized study of paediatric obesity. There is a failure to translate properly these results into the language we use for moral evaluation. The case therefore illustrates exactly the analytic gap which I suggest bioethics might do more to fill.

The equal environments assumption of classical twin studies may not hold

The classical twin method - comprising comparisons of monozygotic (MZ) and dizygotic (DZ) twins - in the domain of cognitive abilities and attainments has led to wide acceptance of results suggesting a large amount of additive genetic variance, with far-reaching implications both for the nature of future studies on the causes of cognitive variance and for intervention policies, as in education. However, this interpretation is only valid if the method observes a number of conditions, which have to hold. Here, we show that the most crucial of these, namely, the equal environments assumption (EEA), may not hold. Consequently, differences in twin correlations might be at least partly explained by treatment effects from parents, teachers, peers, and so on. In addition, well-known interactions at various levels confound the model of simple additive effects on which the classical twin method is predicated and results are interpreted. For example, at a socio-cognitive level, DZ twins may respond to treatments differently from MZ twins. This interaction may further explain MZ-DZ correlation differences. There is abundant evidence for such interactive effects in published twin data. We suggest that there is a need for a more thorough examination of these problems.

Are genetically informed designs genetically informative? Comment on McGue, Elkins, Walden, and Iacono (2005) and quantitative behavioral genetics

M. McGue, I. Elkins, B. Walden, and W. G. Iacono presented the findings from a twin study examining the relative contributions of genetic and environmental factors to the developmental trajectories of parent-adolescent relationships. From a behavioral genetics perspective, this study is well conceptualized, is well implemented, and raises some interesting developmental questions. Yet, the classic twin methodology and heritability estimates obfuscate the dynamic gene-ecology transactions that underlie these social developmental trajectories. There is a growing divide between the findings of quantitative behavioral genetics, with its foundational estimate of a statistical genetic influence, and developmental molecular genetics. This comment provides a brief overview of this divide and its implications for the findings of McGue et al. as well as quantitative behavioral genetics more broadly.

The heritability of mental traits in humans: A proposal for a more coherent discussion

It is proposed to simplify the debate about the heritability of mental traits in humans by concentrating on two crucial issues: Population specificity of heritability estimates and the relation of these estimates to the malleability of the trait to which they refer. It is demonstrated that due to vast differences between the environmental variations of populations there would have to be a heritability estimate for every imaginable population, and that a heritability estimate allows no predictions whatsoever about the malleability of the trait in question. It is therefore concluded that this field of research should be abandoned.