Suchergebnisse

AbstractTwins, data and emails. Some of the words that first come to mind when I think of Nick. Lots of twins. With lots of data. And short single-finger-typed emails. And great wine. Well, it works, there is no doubt. That's how I ended up in Australia, working on asthma genetics.

Asthma and hay fever are likely to share hundreds if not thousands of genetic risk variants. Despite this, the extent to which the power to identify shared risk variants could be improved by considering information from both diseases when designing or analyzing genetic studies has not been studied in detail. Simulations were performed to quantify the power to detect an association between case-control status and a bi-allelic risk variant shared between asthma and hay fever across a range of disease and genetic models, as well as different ascertainment and analytical strategies. For a fixed sample size, when designing a new genome-wide association study (GWAS), selecting for genotyping cases with both asthma and hay fever (A+H+), and controls with neither disease (A-H-) was the study design that provided the greatest power to identify a shared risk variant. On the other hand, when analyzing an existing GWAS, power was greatest across a wide range of scenarios, when cases were defined as individuals who suffered from either disease (A+ or H+) and controls as those who suffered from neither (A-H-). Bivariate analysis of asthma and hay fever provided comparable but slightly decreased power. In conclusion, new GWAS can be designed and existing GWAS reanalyzed more efficiently to identify risk variants for allergic disease by using ascertainment or analytical strategies that consider both asthma and hay fever information.