PPT-Data QC / cleaning in Genome-Wide Association Studies (GWAS)

2023 Statistical Genetics workshop Presenter Daniel Howrigan Data group leader Neale Lab MGH Broad Institute Slides adapted from previous workshop presenters Lucia Colodro Conde QIMR Katrina . 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 . 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. Jeff Barrett. What are the goals of disease genetics?. For a given disease, we would like to:. Predict if someone will get sick, how bad it will be, and what treatment will work.. Test hypotheses about relationships to other diseases and traits.. Lecture 1: Introduction. Linkage studies. Traditional approach to identifying genes for human traits and diseases was through linkage.. For . Mendelian. diseases (e.g. Huntington’s disease) there is a clear co-segregation of genetic markers with disease within pedigrees.. BIOST 2055. 04/01/2015. Human Genome and Single Nucleotide Polymorphisms (SNPs). 23 chromosome pairs. 3 billion bases. A single nucleotide change between pairs of chromosomes. E.g. . Haplotype1. : AAGG. 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”. Aaron Johnson. Vitaly Shmatikov. Background. Main goal: . discovering genetic basis for disease. Requires analyzing large volumes of genetic information from multiple individuals. Voluntary and mandated sharing of genetic datasets between hospitals, biomedical research orgs, other data holders. Bas E. Dutilh. March 4. th. 2013. Protein function. Phenotypic. . function. E.g. . apoptosis. GO: . Biological. . process. Cellular. . function. E.g. . ribosome. GO: . Cellular. component. Molecular. Africa. Dr Kirk Rockett. 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. Genomics Lesson 12_1. Ross . Hardison. 4/19/15. 1. Genetic association. The occurrence together in a population, more often than can be readily explained by chance, of two or more traits of which at least one is known to be genetic.. Kesheng Wang, PhD. Department of Biostatistics and Epidemiology. College of Public Health. East Tennessee State University. 2. Outline. Introduction . Alcohol dependence (AD). Genetic study. . Subjects and Methods. 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?. Q2. Is there such a thing as addictive personality? . Q3. What does SNP stand for? . Q4. In terms of identifying addiction risk genes, have we had any success? . . Do drugs “hijack the brain” robbing the user of their free will? . 2017. ggibson.gt@gmail.com. http://www.cig.gatech.edu. Outline. General . overview of association . studies. Sample Results. Three steps to GWAS:. Primary scan. Replication. Fine mapping. Individual Site Score.