PPT-Quality control for GWAS

Author : mitsue-stanley | Published Date : 2017-08-18

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

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Quality control for GWAS: Transcript

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 . Jess Paulus, ScD. January 30, 2013. . Today’s topics. Case-control studies. . Population based. Hospital based. Nested studies. Selection bias. Introduction to population stratification. John . Derber. NCEP/EMC. Reference slide at end. Quality Control - General. Quality control in data assimilation is used to ensure that the analysis is not degraded by the inclusion of certain observations. 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. IGSS 2015 Tutorial. Ben . Domingue. Gameplan. Overview. What are polygenic scores?. How are they used?. How are they created?. You create them.. Top hits. Homebrewed Genome-wide. PRSice. . Caveats, etc.. 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. James H. Nichols, PhD, DABCC, FACB. Chairholder. EP23 Document Development Committee. Professor of Clinical Pathology, Microbiology and Immunology. Vanderbilt University School of Medicine. Medical Director, Clinical Chemistry. Mechanisms. Vlad . Hosu. Universität Konstanz, 21.06.2017. With. . thanks. . to. Matthias Hirth . from. University . of. Würzburg. Agenda. Honesty in Online Labor Markets. Exemplary . Quality Control Mechanisms. 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. 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. WW-ADNI Meeting, Washington DC. July 17, 2015. Andy Saykin, . Indiana University. asaykin@iupui.edu. For the Genetics Core/Working Groups. Original ADNI-2 Specific Aims. Progress Report & Impact. 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 . Adeetya's Kitchen & Furniture in Pune offers a selection of top-quality kitchen trolleys to maximize storage space and improve the functionality of any kitchen. https://adeetyas.com/high-quality-kitchen-trolleys-in-pune.php

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