Journal
BIOINFORMATICS
Volume 35, Issue 13, Pages 2243-2250Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bty946
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Funding
- UK Medical Research Council [MC_PC_14115, MR/R025126/1]
- MRC [MR/R025126/1, MC_PC_14115] Funding Source: UKRI
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Motivation In clinical trials, individuals are matched using demographic criteria, paired and then randomly assigned to treatment and control groups to determine a drug's efficacy. A chief cause for the irreproducibility of results across pilot to Phase-III trials is population stratification bias caused by the uneven distribution of ancestries in the treatment and control groups. Results Pair Matcher (PaM) addresses stratification bias by optimizing pairing assignments a priori and/or a posteriori to the trial using both genetic and demographic criteria. Using simulated and real datasets, we show that PaM identifies ideal and near-ideal pairs that are more genetically homogeneous than those identified based on competing methods, including the commonly used principal component analysis (PCA). Homogenizing the treatment (or case) and control groups can be expected to improve the accuracy and reproducibility of the trial or genetic study. PaM's ancestral inferences also allow characterizing responders and developing a precision medicine approach to treatment. Availability and implementation PaM is freely available via Rhttps://github.com/eelhaik/PAM and a web-interface at http://elhaik-matcher.sheffield.ac.uk/ElhaikLab/.
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