Codes for the paper : "Sebastes spp."

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Laura Benestan

Montpellier, 2020

Submited to Evolutionary Applications, 2020

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Assignment tests

Use a bunch of assignment tests programs to perform efficient and optimal assignment test.

1. Install a R package to help you import your vcf data

First install radiator using devtools package.

if (!require("devtools")) install.packages("devtools")
devtools::install_github("thierrygosselin/radiator")
library(radiator)

2. Install a R package to perform assignment test

First install assigner package.

if (!require("devtools")) install.packages("devtools")
devtools::install_github("thierrygosselin/assigner")
library(assigner)

Import vcf data

To import your vcf, use the function tidy_genomic_data avaiable in radiator.

tidy_sebastes <- tidy_genomic_data(data="24603snps_416ind_mentella.recode.vcf", strata = "population_map_groups_mentella.txt", filename = NULL)

Perform assignment test

Using assigner, choose the following parameters:

• markers.sampling = "ranked" We want to detect the number of markers we need to accurately assign individuals then we need to raked the markers from the most to the less discriminant.

• assignment.analysis = gis_sim Gis_sim is a software designed specially to perform "Genetic Stock Identification" (GSI).

• thl = 0.2 This option is for markers.sampling = "ranked" only. Here 20 percent of the individuals, in each strata, are chosen randomly as holdout individuals in order to overestimate the sucess of the assignment.

• subsample = 30 This option is particularly relevant for datasets when your dataset is imbalanced. Your data set is imbalanced as your "populations" (here the groups you specified in the assignment test analysis) is not a 50/50 or 60/40 distribution. If you observe that assignment test success seems to be higher in the population where there is the highest number of samples, then you might need to balance your data set. Here we decide to subsample 30 individuals in each population as it was the minimum of individuals sampled per population.

In R you can run.

test1 <- assigner::assignment_ngs(
  data = tidy_sebastes,
  assignment.analysis = "gsi_sim",
  marker.number = c(1,2,5,10,100,200,1000,2000),
  markers.sampling = "ranked",
  thl = 0.2,
  subsample = 30,
  iteration.subsample = 3,
  parallel.core = 10)

Visualize the assignment test results

assigner outputs already provide you a nice way to visualize the assignment test results. To see the Figure you can obtain from assigner, run:

pdf("Assignment_new.pdf")
test1$assignment.plot
dev.off()

Assignment_test.png