Foreword

Notes on Three Decades of Methodology Workshops

Since 1987, a group of behavior geneticists have been teaching an annual methodology workshop on how to use state-of-the-art methods to analyze genetically informative data. In the early years, the focus was on analyzing twin and family data, using information of their known genetic relatedness to infer the role of genetic and environmental factors on phenotypic variation. With the rapid evolution of genotyping and sequencing technology and availability of measured genetic data, new methods to detect genetic variants associated with human traits were developed and became the focus of workshop teaching in alternate years. Over the years, many of the methodological advances in the field of statistical genetics have been direct outgrowths of the workshop, as evidence by the software and methodological publications authored by workshop faculty. We provide data and demographics of workshop attendees and evaluate the impact of the methodology workshops on scientific output in the field by evaluating the number of papers applying specific statistical genetic methodologies authored by individuals who have attended workshops.

Nick Martin and the 'Boulder Workshops'

The author provides a personal perspective on Nick Martin's contributions to behavioral genetics and his role in the workshops on statistical genetics held annually in Boulder. Highlighted are Prof. Martin's seminal work on multivariate behavioral genetics, his career-long commitment to the value of the study of twins, and his enthusiastic support of the didactic mission of the 'Boulder workshops'. These contributions and activities continue unabated as we celebrate Prof. Martin's 70th birthday.

Heritability of blood pressure increases during mental stress

We studied the influence of mental stress on the contributions of genes and environment to individual variation in systolic (SBP) and diastolic (DBP) blood pressure by structural equation modelling in 320 adolescent male and female twins. Blood pressure data were collected during rest and during a reaction time and a mental arithmetic task. Univariate analyses of SBP and DBP showed familial aggregation for blood pressure. A genetic explanation for this resemblance was most likely, although during rest conditions a model that attributed familial resemblance to shared environmental factors, also fitted the data. There was no evidence for sex differences in heritabilities. Multivariate analyses showed significant heterogeneity between sexes for the intercorrelations of the blood pressure data measured under different rest and task conditions. Multivariate genetic analyses were therefore carried out separately in males and females. For SBP and DBP in females and for SBP in males an increase in heritabilities was seen for blood pressure measured during stress, as compared to rest measurements. The influence of shared environ-mental factors decreased during stress. For DBP in males no significant contributions of shared environment were found. The multivariate analyses indicated that the same genetic and environmental influences are expressed during rest and stress conditions.

Insensitivity of the analysis of variance to heredity-environment interaction

It makes sense to attribute a definite percentage of variation in some measure of behavior to variation in heredity only if the effects of heredity and environment are truly additive. Additivity is often tested by examining the interaction effect in a two-way analysis of variance (ANOVA) or its equivalent multiple regression model. If this effect is not statistically significant at the α = 0.05 level, it is common practice in certain fields (e.g., human behavior genetics) to conclude that the two factors really are additive and then to use linear models, which assume additivity. Comparing several simple models of nonadditive, interactive relationships between heredity and environment, however, reveals that ANOVA often fails to detect nonadditivity because it has much less power in tests of interaction than in tests of main effects. Likewise, the sample sizes needed to detect real interactions are substantially greater than those needed to detect main effects. Data transformations that reduce interaction effects also change drastically the properties ofthe causal model and may conceal theoretically interesting and practically useful relationships. If the goal ofpartitioning variance among mutually exclusive causes and calculating “heritability” coefficients is abandoned, interactive relationships can be examined more seriously and can enhance our understanding of the ways living things develop.

The nature of nurture: Genetic influence on “environmental” measures

Evidence for genetic influence on environmental measures will emerge in quantitative genetic analyses if genetically influenced characteristics of individuals are assessed by these environmental measures. Recent twin and adoption studies indicate substantial genetic influence when measures of the environment are treated as phenotypes in genetic analyses. Genetic influence has been documented for measures as diverse as videotaped observations of parental behavior toward their children, ratings by parents and children of their family environment, and ratings of peer groups, social support, and life events. Evidence for genetic influence on environmental measures includes some of the most widely used measures of environment – the Home Observation for Measurement of the Environment, the Family Environment Scales, and the Social Readjustment Rating Scale of life events, for example. The goal of this article is to document and discuss these findings and to elicit commentary that might help to shape the course of research on this topic, which has far-reaching implications for the behavioral and brain sciences.

Genes, environment, and addictive behavior: etiology of individual differences and extreme cases

There is considerable interest in assessing the role of genetic factors in the aetiology of addiction. This paper reviews the possible contribution of a new quantitative technique, termed DF analysis after its originators DeFries & Fulker. DF analysis provides a way of examining whether aetiological factors relating to addiction as a discrete disorder (manifest as extreme scores on a diagnostic instrument) are similar to or different from those that relate to it as a continuum. For example, it is possible to estimate the contribution of familial factors to the development of a clinical condition defined in terms of an extreme score on an instrument by examining the extent of regression towards the unselected population mean of siblings' scores on that instrument. At the same time it is possible to estimate familial influences on the full range of values by examining the correlation between the scores of siblings. Insofar as the two methods produce different estimates, it is possible to infer different levels of involvement of familial factors for extreme cases than for the continuum as a whole. Information provided by such analyses could be critical when constructing models of addictive disorders and help direct more specific studies isolating particular mechanisms.

Reading disability, immune disorders and non-right-handedness: twin and family studies of their relations

Geschwind and colleagues discussed associations among learning disorders, immune disorders and non-right-handedness. In this study, we examined the associations between reading disability (RD) and both immune disorders (ID) and non-right-handedness (NRH) in family and twin samples (total N = 1731 cases) identified through an RD proband. We also conducted co-segregation analyses to ascertain the degree to which NRH, ID and RD were biologically related. There was little evidence for an overall association between RD and NRH. There was not convergent evidence across all four samples for an association between RD and ID, although we did find an association between RD and ID in two of four samples. Nor was there strong support for a subtype where RD and NRH, or RD and ID, co-segregate in families. These data suggest that the associations postulated by Geschwind and colleagues are not robust in RD samples, although we cannot completely rule out the possibility of an RD plus ID subtype.

Fitting genetic models to Carabelli trait data in South Australian twins

This study aimed to clarify genetic and environmental contributions to Carabelli trait variation on permanent first molar teeth in a large sample of South Australian twins. Estimates of polychoric correlations were obtained between pairs of monozygous (MZ) and dizygous (DZ) twins for Carabelli data and various gene-environment models fitted by a weighted least-squares approach. The favored model included additive genetic effects together with both a general environmental component and an environmental effect specific to each side. An estimate of heritability around 90% indicated a very strong genetic contribution to observed variation. The pattern of correlations for MZ and DZ data suggested that further studies involving other types of relatives would be worthwhile for detection of possible non-additive genetic effects of dominance or epistasis.