On the Performance of Multiple Imputation Based on Chained Equations in Tackling Missing Data of the African α(3.7) -Globin Deletion in a Malaria Association Study.


Sepúlveda, N; Manjurano, A; Drakeley, C; Clark, TG; (2014) On the Performance of Multiple Imputation Based on Chained Equations in Tackling Missing Data of the African α(3.7) -Globin Deletion in a Malaria Association Study. Annals of human genetics, 78 (4). pp. 277-89. ISSN 0003-4800 DOI: https://doi.org/10.1111/ahg.12065

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Abstract

Multiple imputation based on chained equations (MICE) is an alternative missing genotype method that can use genetic and nongenetic auxiliary data to inform the imputation process. Previously, MICE was successfully tested on strongly linked genetic data. We have now tested it on data of the HBA2 gene which, by the experimental design used in a malaria association study in Tanzania, shows a high missing data percentage and is weakly linked with the remaining genetic markers in the data set. We constructed different imputation models and studied their performance under different missing data conditions. Overall, MICE failed to accurately predict the true genotypes. However, using the best imputation model for the data, we obtained unbiased estimates for the genetic effects, and association signals of the HBA2 gene on malaria positivity. When the whole data set was analyzed with the same imputation model, the association signal increased from 0.80 to 2.70 before and after imputation, respectively. Conversely, postimputation estimates for the genetic effects remained the same in relation to the complete case analysis but showed increased precision. We argue that these postimputation estimates are reasonably unbiased, as a result of a good study design based on matching key socio-environmental factors.

Item Type: Article
Faculty and Department: Faculty of Infectious and Tropical Diseases > Dept of Immunology and Infection
Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology
Faculty of Infectious and Tropical Diseases > Dept of Pathogen Molecular Biology
Research Centre: Malaria Centre
PubMed ID: 24942080
Web of Science ID: 337794000004
URI: http://researchonline.lshtm.ac.uk/id/eprint/1785935

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