Handedness in twins: Joint analysis of data from 35 samples

Simultaneous analysis of handedness data from 35 samples of twins (with a combined sample size of 21,127 twin pairs) found a small but significant additive genetic effect accounting for 25.47% of the variance (95% confidence interval [CI] 15.69-29.51%). No common environmental influences were detected (C = 0.00; 95% Cl 0.00-7.67%), with the majority of the variance, 74.53%, explained by factors unique to the individual (95% Cl 70.49-78.67%). No significant heterogeneity was observed within studies that used similar methods to assess handedness, or across studies that used different methods. At an individual level the majority of studies had insufficient power to reject a purely unique environmental model due to insufficient power to detect familial aggregation. This lack of power is seldom mentioned within studies, and has contributed to the misconception that twin studies of handedness are not informative.

Bias, precision and heritability of self-reported and clinically measured height in Australian twins

Many studies of quantitative and disease traits in human genetics rely upon self-reported measures. Such measures are based on questionnaires or interviews and are often cheaper and more readily available than alternatives. However, the precision and potential bias cannot usually be assessed. Here we report a detailed quantitative genetic analysis of stature. We characterise the degree of measurement error by utilising a large sample of Australian twin pairs (857 MZ, 815 DZ) with both clinical and self-reported measures of height. Self-report height measurements are shown to be more variable than clinical measures. This has led to lowered estimates of heritability in many previous studies of stature. In our twin sample the heritability estimate for clinical height exceeded 90%. Repeated measures analysis shows that 2-3 times as many self-report measures are required to recover heritability estimates similar to those obtained from clinical measures. Bivariate genetic repeated measures analysis of self-report and clinical height measures showed an additive genetic correlation > 0.98. We show that the accuracy of self-report height is upwardly biased in older individuals and in individuals of short stature. By comparing clinical and self-report measures we also showed that there was a genetic component to females systematically reporting their height incorrectly; this phenomenon appeared to not be present in males. The results from the measurement error analysis were subsequently used to assess the effects of error on the power to detect linkage in a genome scan. Moderate reduction in error (through the use of accurate clinical or multiple self-report measures) increased the effective sample size by 22%; elimination of measurement error led to increases in effective sample size of 41%.

Maximising the information gained from an experimental analysis of code inspection and static analysis for concurrent Java components

The results of empirical studies are limited to particular contexts, difficult to generalise and the studies themselves are expensive to perform. Despite these problems, empirical studies in software engineering can be made effective and they are important to both researchers and practitioners. The key to their effectiveness lies in the maximisation of the information that can be gained by examining existing studies, conducting power analyses for an accurate minimum sample size and benefiting from previous studies through replication. This approach was applied in a controlled experiment examining the combination of automated static analysis tools and code inspection in the context of verification and validation (V&V) of concurrent Java components. The combination of these V&V technologies was shown to be cost-effective despite the size of the study, which thus contributes to research in V&V technology evaluation.