Lab 3 Example

1

Download and look at data • Download “polygonum.stru”’ • Look at “polygonum.stru” using a text editor – Column 1 refers to individual ID (516 total individuals) – Column 2 refers to population (16 total populations) – Column 3 refers to habitat (2 different habitats) – Remaining columns refer to alleles for 7 different loci – NA values are coded as -9

2

Pairwise Fst • Read data into R using read.structure() and save it to object called “polygonum” – Use str() to look at “polygonum” object • Make some summary plots • Test for Hardy-Weinberg Equilibrium • Are any loci in HWE?

3

Compute hierarchical F-stats • Compute hierarchical f-stats • What levels contains most of the variation? • Is Fst equal across loci? Are these values significant?

1

4

More hierarchical F-stats

What happens if we add a level for habitat? • Convert “polygonum” to genind object called “for.hier” using genind2hierfstat() – Add habitat column back to “for.hier” • Use varcomp.glob() to compute hierarchical f-stats (Hint: use help function!) • Levels should correspond to habitat and population • Interpret results with a partner: At what hierarchical level does it appear that most variation within populations is found? Among individuals within populations, among populations within habitats, or among habitats?

2

Lab 3 Example - GitHub

Download “polygonum.stru”'. • Look at “polygonum.stru” using a text editor. – Column 1 refers to individual ID (516 total individuals). – Column 2 refers to ...

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