Skip to Main content Skip to Navigation
Journal articles

Efficiency of multiple imputation to test for association in the presence of missing data.

Abstract : ABSTRACT : The presence of missing data in association studies is an important problem, particularly with high-density single-nucleotide polymorphism (SNP) maps, because the probability that at least one genotype is missing dramatically increases with the number of markers. A possible strategy is to simply ignore the missing data and only use the complete observations, and, consequently, to accept a significant decrease of the sample size. Using Genetic Analysis Workshop 15 simulated data on which we removed some genotypes to generate different levels of missing data, we show that this strategy might lead to an important loss in power to detect association, but may also result in false conclusions regarding the most likely susceptibility site if another marker is in linkage disequilibrium with the disease susceptibility site. We propose a multiple imputation approach to deal with missing data on case-parent trios and evaluated the performance of this approach on the same simulated data. We found that our multiple imputation approach has high power to detect association with the susceptibility site even with a large amount of missing data, and can identify the susceptibility sites among a set of sites in linkage disequilibrium.
Document type :
Journal articles
Complete list of metadatas

Cited literature [8 references]  Display  Hide  Download

https://www.hal.inserm.fr/inserm-00143688
Contributor : Christine Vaillant <>
Submitted on : Tuesday, January 22, 2008 - 4:05:55 PM
Last modification on : Wednesday, September 16, 2020 - 4:54:11 PM
Long-term archiving on: : Wednesday, April 7, 2010 - 2:08:13 AM

Identifiers

  • HAL Id : inserm-00143688, version 1
  • PUBMED : 18466521

Citation

Pascal Croiseau, Emmanuelle Génin, Claire Bardel. Efficiency of multiple imputation to test for association in the presence of missing data.. BMC Proceedings, BioMed Central, 2007, 1 Suppl 1 (Suppl 1), pp.S24. ⟨inserm-00143688⟩

Share

Metrics

Record views

290

Files downloads

304