ArticleAnnals of human genetics2014
On the performance of multiple imputation based on chained equations in tackling missing data of the African α3.7 -globin deletion in a malaria association study.
Article in Annals of human genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Haplotype heterogeneity and low linkage disequilibrium reduce reliable prediction of genotypes for the ‑αWellcome open research · 2020Article
- Most associations of early-life environmental exposures and genetic risk factors poorly differentiate between eczema phenotypes: the Generation R Study.The British journal of dermatology · 2019Article
- Malaria Host Candidate Genes Validated by Association With Current, Recent, and Historical Measures of Transmission Intensity.The Journal of infectious diseases · 2017Article
- Sample size and power calculations for detecting changes in malaria transmission using antibody seroconversion rate.Malaria journal · 2015Article
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Authors and funding
4 authors at 4 institutions in 3 countries.
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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.
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