The Centre held 2 short courses in September 2015. Dates for 2016 will be announced in January / February 2016.

Course 1: Introduction to Genetic Epidemiology in the GWAS era

Genetic epidemiology holds great potential for personalised medicine and improved biological knowledge of disease processes.  This course provides an introduction to the design, analysis and interpretation of genetic studies of disease, with a focus on state of the art analysis of genomewide association scans.  The course is 4 days long.  On days 1 and 2, basic genetics will be introduced and relevant statistical methods for linkage and association analysis will be described.  Days 3 and 4 will cover the design and analysis of genomewide association scans, including emerging applications to risk prediction and biological pathway analysis.  Throughout the course participants will gain practical experience of analysing genetic data in population and family studies.  By the end of the course participants will have an understanding of the fundamental concepts of genetic epidemiology, will have a working knowledge of the terminology and current status of the field, and will be able to perform many basic analyses of genetic data.  This course is followed by the companion course “High throughput sequencing in disease studies”.

Who should apply?
The course consists of alternating lectures and computer practical sessions. Comprehensive course notes will be provided at the start of the course.  Participants are expected to be epidemiologists, clinicians, applied statisticians or biologists with an interest in becoming familiar with, or working in, genetic epidemiology.  Basic knowledge of statistics is required, including familiarity with hypothesis testing and estimation. Experience with a statistical computing package such as R is required, all course attendees should complete the free online Statistical Computing with R introductory course prior to the start of the course.

Course Content
Introduction to Genetic Epidemiology
•    Basic genetics, genetic markers, sequencing technology
•    Basic population genetics
•    Segregation and linkage analysis
•    Association analysis in populations and families

Genomewide association scans (GWAS)
•    Rationale and design of GWAS
•    Association analysis, quality control and population stratification
•    Multiplicity, replication and meta-analysis
•    Emerging trends including pathway analysis and risk prediction

Course 2: High throughput sequencing in disease studies

Rapidly developing technologies now allow genomes to be sequenced more quickly and cheaply than ever before. This course will cover state of the art methods and applications of next generation sequencing. Participants will be introduced to tools for analysing high throughput sequence data, including methods for measuring copy number variants and allelespecific expression, and conducting disease association analysis with sequence data. There will be considerable opportunities to gain practical experience with new data types such as whole genome sequence, RNA- and ChIP-seq data. By the end of the course, participants will have a broad knowledge of current methods and applications and will be well equipped to analyse their own data. This course follows the companion course “Introduction to Genetic Epidemiology in the GWAS era”.

Who should apply?

Participants should have a working knowledge of genetics, epidemiology or bioinformatics, and have an interest in acquiring up to date knowledge about high throughput sequencing. While no preliminary experience with sequence data analysis is required, the class will be based on a unix/linux computing environment and some familiarity with command line would be beneficial. Experience with a statistical computing package such as R is required, all course attendees should complete the free online Statistical Computing with R introductory course prior to the start of the course.

Course Content

Analysis of High-Throughput Sequencing data

  • Introduction to Linux, R and bioinformatics
  • Sequencing technologies
  • Data formats, quality control and alignment
  • Assembly and annotation of genomes
  • SNP, indel and structural variant calling
  • 1000 Genomes data and accessing data from the short read archive
  • RNA-seq and ChIP-seq analysis
  • Applications of sequence data (phylogenetics assessing population structure, association studies and detection of genic selection)


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