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 . HapMap. Peter Castaldi. January 29, 2013. Objectives. Introduce the concept of linkage disequilibrium (LD). Describe how the . HapMap. project provides publically available information on genetic variation and LD structure. World’s Oldest Human. Jeanne Calment . of Arles, France. 1875-1997. 122 . yrs. Longevity. is Heritable. From Gross L., 2006. Helen Reichert and siblings, as children and centenarians. Mollye. Marcus, 111 years old, and family. 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. 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. Presented by Karen . Xu. What you need to know. Basic genetic concepts behind GWAS. Genotyping technologies and common study designs . Statistical concepts for GWAS analysis. Replication, interpretation and follow-up of association results. 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. Jeff Barrett. Challenges to GWAS?. Data quality control . No common, single SNP main effects (all epistasis or rare variants or …). Sample size too small to detect effects. Computational . burden. Multiple . Matt . Hudson . Crop Sciences. NCSA. . HPCBio. IGB. University . of Illinois. Outline. How do we predict molecular or genetic functions using variants?. Predicting . when a coding SNP or SNV is “damaging”. for Colorectal . Cancer. Ulrike (. Riki. ) Peters. Fred Hutchinson Cancer Research Center. University of Washington. Overview. Significance and rationale. . Current efforts on rare and less frequent variants. 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 . 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 . Suxu Tan szt0038@auburn.edu GWAS analysis for resistance against enteric septicemia of catfish using the first - generation interspecific backcrosses Introduction - Catfish Y Catfish can survive i genetic variants. BMMB 551 Genomics. Ross . Hardison. 4/7/15. 1. Published. GWA Reports, 2005 . – 6/2012. Total . Number. of Publications. Calendar Quarter. Through . 6/30/12 postings. 1350. GWAS catalog interactive browser. (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 .