Design and Analysis of Genetic-based Association Studies

This Advanced Course aims to give researchers involved in disease studies a firm grounding in the use of the latest statistical methods and software for analysis of genetic association studies. The course will cover both theoretical and practical aspects of the design and analysis of such studies. Each topic will include a lecture followed by a practical session in which state-of-the-art statistical software will be applied to relevant datasets. The practical sessions will illustrate the ideas presented in the lectures. All the software used will be freely available so skills learnt can be applied to the datasets after the course.
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Introduction to genetic association studies
Overview and history of genetic association studies leading up to and including genome-wide association studies.

Basic association analysis
Single marker association tests including Frequentist and Bayesian tests for association. Calculation of Odds Ratios and Relative risks. Logistic regression. Gene-environment and gene-gene interaction.

Quality control and population structure
Data quality control procedures to avoid the generation of spurious false positives in association studies. The confounding effects of population structure on association studies and methods for protecting against these effects. PCA and mixed model approaches.

Haplotype estimation
Methods for inferring haplotypes from genotype data. The use of haplotypes in genetic association studies for detection of disease variants.

Genotype imputation
Methods for genotype imputation using publicly available reference panels. Pre-phasing based imputation. Frequentist and Bayesian methods of testing association at imputed SNPs and indels. Quality control for imputed SNPs. Meta-analysis using imputed data.

Analysis of rare variants
Methods for analysing rare variants from re-sequencing, genotyping and imputation studies via “collapsing approaches”.

Family-based association studies
Testing for association using family-based study designs. Comparison with designs that use unrelated individuals.

Practical Sessions
Lectures are followed by practical sessions using realistic datasets so that students learn how to apply the theory. Students will learn to use the following computer programs during the course: IMPUTE2, SHAPEIT2, SNPTEST2, QCTOOL, META, GENEBPM, and GRANVIL.

Course instructors
Heather Cordell (Institute of Genetic Medicine, Newcastle University, UK)
Andrew Morris (Wellcome Trust Centre for Human Genetics, University of Oxford, UK)
Jonathan Marchini (Department of Statistics, University of Oxford, UK)

23 Sep - 27 Sep 2013
United Kingdom
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