The module will provide an introduction to statistical and computational methods of 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:

Introduction to genetic and gene expression data exploration and visualisation
	Genetic Association Studies
	Association studies (GWAS, eQTL)
	Gene expression variation (differential expression analysis of RNA-Seq data)
	Population genetics: genetic drift, Hardy-Weinberg equilibrium, coalescent model, linkage disequilibrium
	Population demographics and species tree estimation using the multispecies coalescent: Bayesian computation and Markov Chain Monte Carlo (MCMC) methods applied to comparative genomics
	Mechanistic modelling of population dynamics using differential equations
	Cancer genomics: clonal decomposition and phylogenetic tree reconstruction (tracing histories of cancer evolution through analysis of somatic mutations)
	Mechanistic modelling of population dynamics
Learning objectives:

After taking this module you should be able to:

Use computational and statistical techniques to analyse genetic and genomic data, particularly to understand population genetic structure and the role of genetics in disease causation.
	Interpret the results of statistical/computational analyses.
	Understand the rationale underlying standard computational statistics procedures, and the situations in which different procedures are applicable.
	Develop your own scripts in R to read, process and analyse a variety of datasets.
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, Markov chains 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: https://www.huber.embl.de/msmb/)
Further reading:

Handbook of Statistical Genetics. Balding et al. (eds). 3rd Edition. Wiley.2007

Applied Statistical Genetics. R. Foulkes, Andrea S. 2009. XXIII