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The module will provide an introduction to statistical and computational methods for analysing and interpreting genetics/genomics data. Emphasis is placed on statistical model application and interpretation. Students will learn how to implement various statistical methods, analyse and visualise genetic data through programming in R and command line tools.

The emphasis will be on students doing analyses in class, and on the interpretation of the results. Programming will form a major part of this course. Students will learn how to write their own scripts to perform advanced statistical analyses of genetic data. This is an advanced, fast-paced course with extensive programming assignments. We strongly advise students with no previous programming experience to undertake the R crash course. A short introduction to R at the start of the module will be given. If enrolling on the module with no previous programming knowledge, please be aware that programming skills can only be obtained through many hours of practice. Good performance in the module will be dependent upon additional private study to further develop your programming skills.

The topics to be covered include:

• Genetic and gene expression data exploration and visualisation
• Basics of association studies (GWAS, eQTL)
• Gene expression variation (differential expression analysis of RNA-Seq data)
• Population genetics: genetic drift, Hardy-Weinberg equilibrium, the coalescent model, inference of population sizes and selection
• Cancer genomics: clonal decomposition and phylogenetic tree reconstruction (tracing histories of cancer evolution through analysis of somatic mutations)

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Learning objectives:

After taking this module you should be able to:

1. Use computational and statistical techniques to analyse genetic and genomic data, particularly to understand the role of genetics in disease causation.
2. Develop your own scripts in R to read, process and analyse a variety of datasets.
3. Interpret the results of statistical/computational analyses.
4. Understand the rationale underlying standard computational statistics procedures, and the situations in which different procedures are applicable.
5. Understand the key concepts behind models employed in population genetics (e.g. Wright-Fisher, coalescent), convey these key points effectively and use them to interpret genomic data.
6. Understand the analysis steps required to perform a GWAS or eQTL study, including statistical modelling aspects and accounting for potential confounding effects; reason about and adapt these techniques to complex real-life scenarios.
7. Understand differential expression analysis as a method to identify changes in expression between groups; explain the analytical and statistical steps of the procedure; apply and reason about this technique in complex scenarios.
8. Understand cancer evolution principles, including after treatment, and the different models and the statistical principles/frameworks employed to study them; understand what the mutational data look like in different evolutionary scenarios, and how to interpret these data; reason about the applicability of such principles and statistical methods in complex scenarios.
9. Reason about diverse DNA and RNA sequencing and analytical techniques to solve complex real-life problems; interpret the results of such analyses.
10. Troubleshoot errors in code or scenarios where results are different from expectations.Ìý

Pre-requisites:

• Good numeracy skills
• Interest in developing programming skills
• In-depth knowledge of fundamental concepts in probability theory and statistics (e.g. continuous and discrete statistical distributions, probability theory, hypothesis testing, Bayes theorem etc.), Calculus, matrix algebra
• Good knowledge of fundamental biology and genetics concepts, including: DNA, gene, protein, chromosome, species, phylogenetic tree, Mendelian genetics, basics of human genetics, the central dogma of molecular biology, DNA/RNA sequencing

For students without a biological background, we recommend the following books:

• Principles of Genetics. 7th edition. D.P. Snustad and M.J. Simmons (2015) Wiley. Comprehensive introductory book to fundamentals of genetics.
• Human Molecular Genetics 4. T. Strachan & A. Read (2011) Garland Science. Very good and wide-ranging, covers most topics in considerable depth.
• Human Genes and Genomes: Science, Health, Society. L.E. Rosenberg & D.D. Rosenberg (2012) Academic Press. Not as detailed as HMG4 and with a stronger medical flavour but good overall.

For students who wish to refresh their statistical knowledge, we recommend the following books:

• Introduction to Statistics and Data Analysis. C. Heumann, M. Schomaker Shalabh. Springer. 2016
• Modern Statistics for Modern Biology. S. Holmes & W. Huber. Cambridge University Press. 2019 (online version here: )