Identifying genome-environment-associations in Zostera marina (eelgrass) using RADseq data

Project background

Acknowledgements

Workflow

1.1 Alignment & sorting

1.2 Variant calling

1.3 Filtering

2.1 Temperature data

2.2 RDA (1st method)

RDA allows us to do two things:

Determine how the temperature dataset (multiple metrics) is related to genetic variation (allele frequencies) in our dataset

Identify variants with strong association to the environmental data (i.e., outliers), which we then presume may be candidates for selection.

2.3 Gradient Forest Modeling (2nd method)

Run the gradient forest model on the allele frequency data for the outlier SNPs and the summarized temperature dataset.