PPT-Efficient Algorithms for Imputation of Missing SNP Genotype Data

Mihajlovi ć ambiz2005gmailcom V Milutinovi ć vmetfrs Definitions Imputation Given a relevant data set with missing values Discover hidden knowledge between the known values. I.Wasito. . Faculty of Computer Science. University of Indonesia. . F. aculty of Computer Science (Fasilkom), University of indonesia. . at a glance. Initiated . as the . C. enter . of Computer Science (. 6 December 2012. Introduction. I. mputation describes the process of predicting genotypes that have not been directly typed in a sample of individuals:. m. issing genotypes at typed variants;. genotypes at un-typed variants that are present in an external high-density “reference panel” of phased . Trivellore Raghunathan. Chair and Professor of Biostatistics, School of Public Health. Research Professor, Institute for Social Research. University of Michigan. Presented at the National Conference on Health Statistics, August 16-18, 2010 . Aleksandar. R. . Mihajlovic. Technische. . Uni. versität München. mihajlovic@mytum.de. +49 176 673 41387. +381 63 183 0081. 1. Overview . Explain input data based imputation algorithm categorization scheme. Katherine Lee. Murdoch Children’s Research Institute &. University of Melbourne. Missing data in epidemiology & clinical research. Widespread problem, especially in long-term follow-up studies. Matt Spangler. University of Nebraska-Lincoln. Imputation. Imputation creates data that were not actually collected . I. mputation allows us to retain observations that would otherwise be left out of an analysis. Warren W. . Kretzschmar. DPhil Genomic Medicine and Statistics. Wellcome. Trust Centre for Human . Genetics, Oxford. , UK . Supervisor: Jonathan . Marchini. C. ommonest . psychiatric disorder and the second ranking cause of morbidity world-. Walter Leite. College of Education. University of Florida. Burak. Aydin. Recep. . Tayyip. . Erdo. ğ. an. University. Turkey. Sungur. . Gurel. Siirt. . University. Turkey. Duygu. Cetin-Berber. Statistical Modeling of Adolescent Fertility.  . Dudley . L. Poston, Jr.. Texas A&M . University. &. Eugenia . Conde. Rutgers University. Missing Data. Missing data are a pervasive challenge in. Trivellore Raghunathan. Chair and Professor of Biostatistics, School of Public Health. Research Professor, Institute for Social Research. University of Michigan. Presented at the National Conference on Health Statistics, August 16-18, 2010 . Boulder 2015. What is imputation? . (. Marchini. & . Howie. 2010). . 3 main reasons for imputation. Meta-analysis. Fine Mapping. Combining data from different . chips. Other less common uses. sporadic missing data imputation . Markov Models of Haplotype Diversity. Justin Kennedy. Dissertation . Defense for . the Degree of Doctorate in Philosophy. Computer Science & Engineering Department. University of Connecticut. 1. Outline. Sources of Missing Data. People refuse to answer a question. Responses are indistinct or ambiguous. Numeric data are obviously wrong. Broken objects cannot be measured. Equipment failure or malfunction. Tor . Neilands. UCSF . Center for AIDS Prevention . Studies. Part 2. January 18, 2013. 1. Contents. 1. Summary of Part 1. 2. EM Algorithm . 3. Multiple Imputation (MI) for normal data. 4. Multiple Imputation (MI) for mixed data.