Causal analyses, statistical efficiency and phenotypic precision through Recall-by-Genotype study design
Corbin L., Tan V., Hughes D., Wade K., Paul D., Tansey K., Butcher F., Dudbridge F., Howson J., Jallow M., John C., Kingston N., Lindgren C., O’Donavan M., O’Rahilly S., Owen M., Palmer CNA., Pearson E., Scott R., Heel DV., Whittaker J., Frayling T., Tobin M., Wain L., Evans D., Karpe F., McCarthy M., Danesh J., Franks P., Timpson N.
Abstract Genome-wide association studies have been useful in identifying common genetic variants related to a variety of complex traits and diseases; however, they are often limited in their ability to inform about underlying biology. Whilst bioinformatics analyses, studies of cells, animal models and applied genetic epidemiology have provided some understanding of genetic associations or causal pathways, there is a need for new genetic studies that elucidate causal relationships and mechanisms in a cost-effective, precise and statistically efficient fashion. We discuss the motivation for and the characteristics of the Recall-by-Genotype (RbG) study design, an approach that enables genotype-directed deep-phenotyping and improvement in drawing causal inferences. Specifically, we present RbG designs using single and multiple variants and discuss the inferential properties, analytical approaches and applications of both. We consider the efficiency of the RbG approach, the likely value of RbG studies for the causal investigation of disease aetiology and the practicalities of incorporating genotypic data into population studies in the context of the RbG study design. Finally, we provide a catalogue of the UK-based resources for such studies, an online tool to aid the design of new RbG studies and discuss future developments of this approach.