Choosing between per-genotype,per-allele,and trend approaches for initial detection of gene–disease association

There are a number of approaches to detect candidate gene–disease associations including: (i) ‘per-genotype’, which looks for any difference across the genotype groups without making any assumptions about the direction of the effect or the genetic model; (ii) ‘per-allele’, which assumes an additive genetic model, i.e. an effect for each allele copy; and (iii) linear trend, which looks for an incremental effect across the genotype groups. We simulated a number of gene–disease associations, varying odds ratios, allele frequency, genetic model, and deviation from Hardy–Weinberg equilibrium (HWE) and tested the performance of each of the three methods to detect the associations, where performance was judged by looking at critical values, power, coverage, bias, and root mean square error. Results indicate that the per-allele method is very susceptible to false positives and false negatives when deviations from HWE occur. The linear trend test appears to have the best power under most simulated scenarios, but can sometimes be biased and have poor coverage. These results indicate that of these strategies a linear trend test may be best for initially testing an association, and the per-genotype approach may be best for estimating the magnitude of the association.     

Combining information from related meta-analyses of genetic association studies

Summary.  When synthesizing data from genetic association studies researchers frequently perform several related meta-analyses, perhaps on different polymorphisms of the same gene, or on different outcomes, or they might define subgroups of studies by factors such as ethnicity, gender or study design. Current practice is to perform a totally separate meta-analysis of each set of studies; however, when the meta-analyses investigate related questions, it is possible that the estimates in one meta-analysis could be improved by using information from another. The meta-analytic model for a genetic association study can be parameterized in terms of four meaningful parameters: the size of the genetic effect, the genetic model, the allele frequency in controls and the degree of departure from Hardy–Weinberg equilibrium in controls. Even when the size of the genetic effect differs across meta-analyses, it may be possible to assume that some of the other parameters are common. The models are applied to a meta-analysis of the same gene–disease relationship in three different ethnic groups.     

Cross‐border commuters: How to segment Them? A Framework of Analysis

The removal of barriers to labour mobility and the enhancement of cross‐border mobility are expected to reduce regional disparities by contributing to a more efficient allocation of labour. For some time now, however, researchers have considered borders and cross‐border regions to be marginal zones. Therefore, we contribute to the discussion by providing a qualitative framework for the segmentation of the cross‐border job market, capable of addressing some questions relevant to the management of cross‐border commuting regarding the nature of imbalances that characterize the cross‐border job market and the main factors underpinning the phenomenon. To do so, we focused specifically on the Euroregion “Regio Insubrica”, a cross‐boundary cooperation community between Italy and Switzerland. Nevertheless, the proposed framework may be easily applied to all the existing cross‐border regions across the world in order to help explain the imbalances that exist, and support decision‐makers regarding educational and labour market policies.