Why children differ in motivation to learn: Insights from over 13,000 twins from 6 countries.

Little is known about why people differ in their levels of academic motivation. This study explored the etiology of individual differences in enjoyment and self-perceived ability for several school subjects in nearly 13,000 twins aged 9 to 16 from 6 countries. The results showed a striking consistency across ages, school subjects, and cultures. Contrary to common belief, enjoyment of learning and children’s perceptions of their competence were no less heritable than cognitive ability. Genetic factors explained approximately 40% of the variance and all of the observed twins’ similarity in academic motivation. Shared environmental factors, such as home or classroom, did not contribute to the twin’s similarityin academic motivation.Environmental influences stemmedentirely from individual specific experiences

Brain magnetic resonance imaging in tyrosinemia

WOS: 000232782700012PubMed ID: 16334844A 3.5-year-old girl with tyrosinemia is reported. A computed tomography scan of the abdomen revealed multiple hepatic nodules. Brain magnetic resonance imaging revealed bilateral high-signal changes confined to the globus pallidus on T2-weighted images. Globus pallidus lesions likely represented neuropathologic changes such as astocytosis, delayed myelination, and status spongiosus (myelin splitting and vacuolation)

Precision and bias of a mixture distribution model to analyse twin data when zygosity is unknown: Simulations and application to IQ phenotypes on a large sample of twin pairs

The classification of twin pairs based on zygosity into monozygotic (MZ) or dizygotic (DZ) twins is the basis of most twin analyses. When zygosity information is unavailable, a normal finite mixture distribution ( mixture distribution) model can be used to estimate components of variation for continuous traits. The main assumption of this model is that the observed phenotypes on a twin pair are bivariately normally distributed. Any deviation from normality, in particular kurtosis, could produce biased estimates. Using computer simulations and analyses of a wide range of phenotypes from the U. K. Twins' Early Developments Study (TEDS), where zygosity is known, properties of the mixture distribution model were assessed. Simulation results showed that, if normality assumptions were satisfied and the sample size was large (e. g., 2,000 pairs), then the variance component estimates from the mixture distribution model were unbiased and the standard deviation of the difference between heritability estimates from known and unknown zygosity in the range of 0.02-0.20. Unexpectedly, the estimates of heritability of 10 variables from TEDS using the mixture distribution model were consistently larger than those from the conventional (known zygosity) model. This discrepancy was due to violation of the bivariate normality assumption. A leptokurtic distribution of pair difference was observed for all traits (except non-verbal ability scores of MZ twins), even when the univariate distribution of the trait was close to normality. From an independent sample of Australian twins, the heritability estimates for IQ variables were also larger for the mixture distribution model in six out of eight traits, consistent with the observed kurtosis of pair difference. While the known zygosity model is quite robust to the violation of the bivariate normality assumption, this novel finding of widespread kurtosis of the pair difference may suggest that this assumption for analysis of quantitative trait in twin studies may be incorrect and needs revisiting. A possible explanation of widespread kurtosis within zygosity groups is heterogeneity of variance, which could be caused by genetic or environmental factors. For the mixture distribution model, violation of the bivariate normality assumption will produce biased estimates