2022 | 24.–28. Januar, Online

Introduction to the statistical analysis of genome-wide association studies


Audience: Geneticists facing the need to analyse from small to large-scale human genotyping data in relation to their effect on common human traits and diseases. Scientists and students in training aiming to undertake SNP-based association analyses, genome-wide association studies and their meta-analyses. Researchers willing to understand better the statistical approaches and analytical procedures for the genetic association studies.

Applicants’ background: Applicants should understand basic genetic principles such as modes of inheritance, DNA and gene structure, SNPs and other genetic variants, principles of crossing over and recombination, concepts of heritability and penetrance. Additionally, knowledge of basic statistical tests and some command line scripting skills would be an advantage.

Course content: This course will enable you to analyse large-scale genetic data using standard analytical approaches and freely available software tools. The course will cover statistical background for association studies; primer on scripting in the most frequently used computational environments, design and analysis of such studies, interpretation of the results. Each topic will be covered by a lecture, followed by a practical exercise, which will include use of the state-of-art software tools and example datasets. Practical exercises will be tailored to illustrate the ideas discussed during lectures and will be accompanied by discussion of the results.

Topics covered:

  • Introduction to statistics for geneticists
  • Introduction to Linux and R
  • Genome-wise association studies (GWAS)
  • Quality Control (QC) for GWAS
  • Association analysis
  • Population structure
  • Imputation of GWAS
  • Meta-analysis of GWAS
  • Analysis of rare variants
  • Genetic risk scores (polygenic risk scores)
  • Mendelian Randomization

Website der Veranstaltung: https://www.surrey.ac.uk/cpd-and-short-courses/introduction-statistical-analysis-genome-wide-association-studies