Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden (P.W.F.).
Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, United Kingdom (P.W.F.).
Circ Genom Precis Med. 2018 Aug;11(8):e001947. doi: 10.1161/CIRCGEN.118.001947.
In genotype-based recall (GBR) studies, people (or their biological samples) who carry genotypes of special interest for a given hypothesis test are recalled from a larger cohort (or biobank) for more detailed investigations. There are several GBR study designs that offer a range of powerful options to elucidate (1) genotype-phenotype associations (by increasing the efficiency of genetic association studies, thereby allowing bespoke phenotyping in relatively small cohorts), (2) the effects of environmental exposures (within the Mendelian randomization framework), and (3) gene-treatment interactions (within the setting of GBR interventional trials). In this review, we overview the literature on GBR studies as applied to cardiometabolic health outcomes. We also review the GBR approaches used to date and outline new methods and study designs that might enhance the utility of GBR-focused studies. Specifically, we highlight how GBR methods have the potential to augment randomized controlled trials, providing an alternative application for the now increasingly accepted Mendelian randomization methods usually applied to large-scale population-based data sets. Further to this, we consider how functional and basic science approaches alongside GBR designs offer intellectually intriguing and potentially powerful ways to explore the implications of alterations to specific (and potentially druggable) biological pathways.
在基于基因型的召回（GBR）研究中，从更大的队列（或生物库）中召回携带特定假设检验相关基因型的个体（或其生物样本），以进行更详细的研究。有几种 GBR 研究设计提供了一系列强大的选择，可以阐明 (1) 基因型-表型关联（通过提高遗传关联研究的效率，从而允许在相对较小的队列中进行定制表型研究），(2) 环境暴露的影响（在孟德尔随机化框架内），以及 (3) 基因-治疗相互作用（在 GBR 干预性试验的背景下）。在这篇综述中，我们概述了应用于心脏代谢健康结果的 GBR 研究的文献。我们还回顾了迄今为止使用的 GBR 方法，并概述了可能增强以 GBR 为重点的研究实用性的新方法和研究设计。具体来说，我们强调了 GBR 方法如何增强随机对照试验，为现在越来越多地接受的孟德尔随机化方法提供了一种替代应用，这些方法通常应用于大规模基于人群的数据集。除此之外，我们还考虑了功能和基础科学方法以及 GBR 设计如何为探索特定（可能可药物）生物途径的改变所带来的影响提供了有趣且潜在强大的方法。
