PPT-Imputation Sarah Medland

Boulder 2015 What is imputation Marchini amp Howie 2010 3 main reasons for imputation Metaanalysis Fine Mapping Combining data from different chips Other less common uses sporadic missing data imputation . 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 . 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. 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. . & Nathan Gillespie. Types of Heterogeneity. Terminology depends on research question. Moderation, confounding, . GxE. Systematic differences. Measured or Manifest moderator/confounder. Discrete traits. 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. SGDP Summer School . July . 2010. Twin Model. Hypothesised. Sources of. Variation. Predicted Var/Cov. from Model. Structural Equation Modelling . (SEM). Path Tracing. Rules. Matrix . Algebra. Model. with thanks to Lucia . Colodro. Conde & Baptiste . Couvy. Duchesne. What are Polygenic risk scores (PRS)?. PRS are a . quantitative measure of the cumulative genetic risk . or vulnerability that an individual possesses for a trait.. Colodro. Conde. sarah. /2020/. thursday. What are Polygenic risk scores (PRS)?. PRS are a . quantitative measure of the cumulative genetic risk . or vulnerability that an individual possesses for a trait..