This course explores some of the important statistical methods available for the advanced analysis of population genetic patterns in plants and animals. 

The course covers a wide range of standard and new statistical procedures for the population analysis of single-locus codominant markers (e.g. microsatellites and SNPs), multiple-locus dominant markers (e.g. AFLPs), and haploid markers (e.g. mtDNA and cpDNA sequences). The course is taught in intensive workshop format and employs a combination of mini-lectures, tutorials and computer based practical sessions designed to ensure that students fully understand the procedures involved.  For many of the analyses, students will learn to calculate the various statistics by hand, before attempting the self-paced, computer-based tutorial examples, pursuing further understanding and interpretation of the results. Lectures, with examples drawn from a wide range of organisms, will illustrate both statistical analyses and biological interpretations of real data sets.

Topics to be covered will include: 

• Genetic distance metrics and distance matrices, as used in population analysis.
• Procedures for exploring population genetic patterns, including: F-statistics; Analysis of Molecular Variance (AMOVA); Mantel tests and Principal Coordinates procedures.
• Spatial-autocorrelation methods for micro-spatial genetic structure analysis.
• Assignment tests for determining the genetic origins of individuals.
• Principles for good experimental design and sampling strategies.  

On satisfying the requirements of this course, students will have the knowledge and skills to:

1. Score haploid, dominant and codominant genetic data for subsequent statistical analysis.
2. Demonstrate proficiency in performing relevant population genetic analysis by hand and calculator.
3. Use the software package GenAlEx to perform the analysis of population genetic datasets.
4. Analyse and interpret in written and oral form the outcomes of statistical analysis of a real data set.

Assessment will be based on:

• Written tutorial reports that demonstrate capacity to perform statistical analysis by hand, calculator and computer software  (60%; LO 1, 2, 3)
• Written report and oral presentation that demonstrate capacity to analyse and interpret real data sets (40%; LO 1, 2, 3, 4)

Bachelor of Science or equivalent with a major in the field of biological science with relevant experience or academic achievement. 

First year Biology, first year Genetics and first year Statistics are recommended. 

First year Chemistry is desirable.

Completion of BIOL6004 and BIOL6006