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AbstractThis letter reflects on my collaborations with Nick Martin over the past 18 years. Working together we have applied twin-family and statistical genetics methods to examine the genetic architecture and identify genetic variants influencing a range of physical, psychological and social traits. The common thread across much of this work has been the empirical questions: Why are we the way we are and how can this knowledge help us when things go wrong?

AbstractModeling the data from extended twin pedigrees allows the estimation of increasing complex covariance relationships in which the effects of cultural transmission, nonrandom mating and genotype x environment covariation can be incorporated. However, the power to detect these effects in existing data sets has not yet been examined. The present study examined the effects that different family structures (i.e., the ratio of MZ to DZ families and the importance of cousins vs. avuncular relatives) have on statistical power. In addition, we examined the power to detect genetic and environmental effects within the context of two large data sets (VA30K and the OZVA60K). We found that power to detect additive genetic and cultural transmission effects were maximized by over sampling MZ families. In terms of ascertainment, there was little difference in power between samples that had focused on recruiting a third generation (the children of twins) versus those that had focused on recruiting the siblings of the twins. In addition, we examined the power to detect additive and dominant genetic effects, cultural transmission and assortative mating in the existing VA30K and OZVA60K samples, under two different models of mating: phenotypic assortment and social homogamy. There was nearly 100% power to detect assortative mating and cultural transmission, against a background of small additive and dominant genetic and familial environmental effects. In addition, the power to detect additive or dominant genetic effects quickly asymptoted, so that there was almost 100% power to detect effects explaining 20% or more of the total variance. These results demonstrate that the Cascade model has sufficient power to detect parameters of interest in existing datasets. Mx scripts are available from www.vipbg.vcu.edu/~sarahme/cascade.

As political scientists begin to incorporate biological influences as explanatory factors in political behavior, the need to present a methodological road map for utilizing biometric genetic theory and twin data is apparent. The classical twin design (CTD) remains the most popular design for initial examinations of the source of variance among social and political behaviors, and a vast majority of advanced variance components models as well as some molecular analyses are extensions of the CTD. Thus, it is appropriate to begin a series of works with the CTD and its most common variants. The CTD has strong roots in biometrical genetic theory and provides estimates of the correlations between observed traits of monozygotic and dizygotic twins in terms of underlying genetic and environmental influences. The majority of these analyses utilize SEMs of observed covariances for both twin types to assess the relative importance of these "latent" factors.

AbstractThe ratio of the lengths of the second to fourth digits of the hand (2D:4D) is a sexually dimorphic trait that has been proposed as a measure of prenatal testosterone exposure and a putative correlate of a variety of later behavioral and physiological outcomes including personality, fitness and sexual orientation. We present analyses of 2D:4D ratios collected from twins (1413 individuals) and their nontwin siblings (328 individuals) from 757 families. In this sample 2D:4D was measured from photocopies using digital calipers, and for a subset of participants, computer-aided measurement. Multivariate modeling of the left- and right-hand measurements revealed significant genetic and environmental covariation between hands. The two methods yielded very similar results, and the majority of variance was explained by factors shared by both measurement methods. Neither common environmental nor dominant genetic effects were found, and the covariation between siblings could be accounted for by additive genetic effects accounting for 80% and 71% of the variance for the left and right hands, respectively. There was no evidence of sex differences in the total variance, nor in the magnitude or source of genetic and environmental influences, suggesting that X-linked effects (such as the previously identified association with the Androgen receptor) are likely to be small. However, there were also nonshared environmental effects specific to each hand, which, in addition to measurement error, may in part explain why some studies within in the literature find effects for the 2D:4D ratio of one hand but not the other.

AbstractRecent studies in Asian populations have identified variants in theEDARandFGFR2genes that arose following the divergence of Asians and Europeans and are associated with thick straight hair. To date no genetic variants have been identified influencing hair texture in Europeans. In the current study we examined the heritability of hair curliness in three unselected samples of predominantly European ancestry (NS1= 2717;NS2= 3904;NS3= 5079). When rated using a three point scale (Straight/Wavy/Curly) males were ~5% more likely to report straight hair than females and there were suggestions in the data that curliness increased slightly with age. Across samples significant additive and dominant genetic influences were detected resulting in a broad sense heritability of 85–95%. Given the magnitude and the specificity of the EDAR effect on hair morphology in Asian populations we are hopeful that future association studies will detect similar genetic influences in European populations.

AbstractBiochemical traits such as plasma alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma glutamyltransferase (GGT) and uric acid are associated with obesity, and with risk of cardiovascular disease, metabolic syndrome and diabetes. Each is subject to genetic influences, but little is known about changes in genetic and environmental influences on these traits over time. We investigated the contribution of genetic and environmental influences to variation in these biochemical traits in adolescent twins and their nontwin siblings from 965 twin families. Twins were studied at ages 12, 14 and 16 years. Multivariate genetic models that included effects of age and sex were fitted to determine whether the same or different genetic or environmental factors influence each trait at different ages. Results showed that the genetic factors influencing AST, ALT, GGT and uric acid change over time during adolescence, and that the magnitude of these effects differs between males and females. The nonshared environment effects were generally time specific. There are developmental changes in genes affecting these traits during adolescence.