Causal Analyses, Statistical Efficiency And Phenotypic Precision Through Recall-By-Genotype Study Design
Laura J. Corbin,
Vanessa Y Tan,
David A Hughes,
Kaitlin H. Wade,
Dirk S. Paul,
Katherine E. Tansey,
Joanna M Howson,
Momodou W Jallow,
Cecilia M. Lindgren,
Michael J. Owen,
Colin N.A. Palmer,
Ewan R Pearson,
Robert A Scott,
David A van Heel,
Martin D Tobin,
Louise V. Wain,
David M. Evans,
Mark I. McCarthy,
Paul W. Franks,
Nicholas J. Timpson
Posted 12 Apr 2017
bioRxiv DOI: 10.1101/124586
Posted 12 Apr 2017
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 and 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 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 innovative approach that enables genotype-directed deep-phenotyping and improvement in drawing causal inferences. Specifically, we present two simple RbG designs — using a single variant and multiple variants (RbGsv and RbGmv, respectively) — and discuss the inferential properties, analytical approaches and applications of both to understanding causal relationships. 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. 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.
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