This genetic audit aimed to accurately establish the relationships and degree of relatedness among farmed abalone using a large number of genome-wide markers (single nucleotide polymorphisms - SNPs).
Discriminant Analysis of Principal Components (DAPC) using the R package “adegenet”. DAPC shows differences between groups (farms A, C, D, E and F) using k-means, a clustering algorithm which finds a given number (say, k) of groups maximizing the variation between groups, B(X) while minimizing variation within clusters. The optimal number of clusters is identified by running k-means sequentially with increasing values of k and comparing different clustering solutions using Bayesian Information Criterion (BIC).
Membership probabilities, inferred from DAPC analysis, of each abalone for the different groups (farms A, C, D, E and F) based on the retained discriminant functions.
Mutual k-nearest neighbour analysis for the different abalone groups (farms A, C, D, E and F) generated using the R package “NetView”, a network-based approach.
Strugnell JM, Silva CNS. Genetic diversity audit of farm held stocks of greenlip and blacklip abalone. Fisheries Research and Development Corporation, FRDC Report No 2016/142.