ArticleBMC genetics2018
Combining controls can improve power in two-stage association studies.
Article in BMC genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
backgroundHigh dimensional case control studies are ubiquitous in the biological sciences, particularly genomics. To maximise power while constraining cost and to minimise type-1 error rates, researchers typically seek to replicate findings in a second experiment on independent cohorts before proceeding with further analyses. This can be an expensive procedure, particularly when control samples are difficult to recruit or ascertain; for example in inter-disease comparisons, or studies on degenerative diseases.
resultsThis paper presents a method in which control (or case) samples from the discovery cohort are re-used in a replication study. The theoretical implications of this method are discussed and simulated genome-wide association study (GWAS) tests are used to compare performance against the standard approach in a range of circumstances. Using similar methods, a procedure is proposed for 'partial replication' using a new independent cohort consisting of only controls. This methods can be used to provide some validation of findings when a full replication procedure is not possible. The new method has differing sensitivity to confounding in study cohorts compared to the standard procedure, which must be considered in its application. Type-1 error rates in these scenarios are analytically and empirically derived, and an online tool for comparing power and error rates is provided.
conclusionsIn several common study designs, a shared-control method allows a substantial improvement in power while retaining type-1 error rate control. Although careful consideration must be made of all necessary assumptions, this method can enable more efficient use of data in GWAS and other applications.
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