PPT-Preparing data for GWAS analysis

Tommy Carstensen GWAS Quality Control QC Manhattan Plot 2 BB AA AB Bad QC gt Bad Data gt Bad Results Entebbe Uganda 3 Genotype data Entebbe Uganda 4 September 2013 SNP A SNP B. . from. . the. ENIGMA–DTI Working Group. . Neda Jahanshad. 1#. , . Peter . Kochunov. 2#. , Emma Sprooten. 3,4. , René C. Mandl. 5. , Thomas E. Nichols. 6,7. , Laura Almasy. 8. , John Blangero. 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.. Tommy . Carstensen. Wellcome. Trust Advanced Courses; Genomic Epidemiology in Africa. , . 21. st. – 26. th. June 2015. Africa . Centre for Health and Population Studies, University of KwaZulu-Natal, Durban, South Africa. 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”. Imputing big data from GWAS. What we’ll be covering today.... ...we will discuss genome-wide aspects of genetic epidemiological studies focusing on meta-analyses of imputed data. . . Discuss basic theory behind GWAS, imputation and large-scale meta-analyses.. 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 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. 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. �itC tC oin f tCinr snCtub uO d fdfn f tCinr tb �itContfti Crns( �itCtion fo t o irinCs &#x 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.. 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 .