PPT-Imputation for GWAS

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 untyped variants that are present in an external highdensity reference panel of phased . longitudinal health . records. Irene Petersen and Cathy Welch. Primary Care & Population Health. Today. Issues with missing data and multiple imputation of longitudinal records. Twofold algorithm . María. . García. , Chandra Erdman, and Ben Klemens. Outline. Background on the Survey of Income and Program Participation (SIPP). Methods for missing data imputation. - . Randomized Hot deck. - SRMI . Presenter: . Ka. -Kit Lam. 1. Outline. Big Picture and Motivation. IMPUTE. IMPUTE2. Experiments. Conclusion and Discussion. Supplementary : . GWAS. Estimate on mutation rate . 2. Big Picture and Motivation. with large proportions of missing data. :how much is too much? . Texas A&M HSC . Jin. is designed by . Dr. Huber. Korean Female Colon Cancer. Risk. Factors. Range. Event . Non-event. HR. 95% CI. 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. Cattram Nguyen, Katherine Lee, John . Carlin. Biometrics by the Harbour, 30 Nov, 2015. Motivating example: Longitudinal Study of Australian Children (LSAC). 5107 infants (0-1 year) recruited in 2004. 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. Sarah Medland. 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. WHI using reference haplotypes from the 1000 Genomes Project. Presented by Qing Duan. Dr. Yun Li group. UNC at Chapel Hill. 09-13-2012. Outline. Imputation. Study samples: WHI African Americans and Hispanics samples. 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 . Hardy-Weinberg equilibrium. Meta-analysis. SNP Imputation. Review what we have learned about the genetics of common disease from GWAS. Where do we go from here? What do we go with GWAS results.. functional characterization of GWAS loci. Inês Barroso. Joint Head of Human Genetics. Metabolic Disease Group Leader. Wellcome. Trust Sanger Institute. 1. Objectives. Why perform meta-analysis?. How? . What are the issues to consider?. What can you gain?. (CHARGE-S). Eric Boerwinkle. Washington DC. April 7, 2010. Overall Objective. “This . proposed research will leverage existing population, laboratory and computational resources to identify susceptibility .