publication . Article . 2009

Single versus multiple imputation for genotypic data

Fridley Brooke L; McDonnell Shannon K; Rabe Kari G; Tang Rui; Biernacka Joanna M; Sinnwell Jason P; Rider David N; Goode Ellen L;
Open Access English
  • Published: 01 Dec 2009 Journal: BMC Proceedings, volume 3, issue Suppl 7, pages S7-S7 (eissn: 1753-6561, Copyright policy)
  • Publisher: BioMed Central
<p>Abstract</p> <p>Due to the growing need to combine data across multiple studies and to impute untyped markers based on a reference sample, several analytical tools for imputation and analysis of missing genotypes have been developed. Current imputation methods rely on single imputation, which ignores the variation in estimation due to imputation. An alternative to single imputation is multiple imputation. In this paper, we assess the variation in imputation by completing both single and multiple imputations of genotypic data using MACH, a commonly used hidden Markov model imputation method. Using data from the North American Rheumatoid Arthritis Consortium ge...
arXiv: Quantitative Biology::GenomicsStatistics::ApplicationsStatistics::Methodology
free text keywords: Proceedings, R, Science, Q, General Biochemistry, Genetics and Molecular Biology, General Medicine, Imputation (statistics), Medicine, business.industry, business, Data mining, computer.software_genre, computer, Reference sample, Bioinformatics, Hidden Markov model, Linkage disequilibrium
Related Organizations
Funded by
NIH| Genetic Analysis of Common Diseases: An Evaluation
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01GM031575-22
NIH| Genetic Variation in the NF-kappaB Pathway and Ovarian Cancer Etiology
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01CA122443-06
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