10.5061/DRYAD.DNCJSXKZ4
Thia, Joshua
0000-0001-9084-0959
University of Melbourne
Population structure and demographic analyses of Acanthocybium solandri
from the Indo-Pacific and Atlantic oceans
Dryad
dataset
2020
FOS: Biological sciences
evolutionary population genomics
Acanthocybium solandri
RADseq
ezRAD
Pool-Seq
Indo-Pacific
Atlantic
demographic inference
2022-06-29T00:00:00Z
2022-06-29T00:00:00Z
en
https://doi.org/10.1111/jbi.14135
41504098 bytes
6
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
This repository contains scripts, data and results for a populaton
genomics study of genetic structure and demography of wahoo, Acanthocybium
solandri, published in Journal of Biogeography: Haro-Bilbao et al. (2021)
Global connections with some genomic differentiation occur between
Indo-Pacific and Atlantic Ocean wahoo, a large circumtropical pelagic
fish. In this work, we generated population allele frequencies for wahoo
sampled at 11 locations around the globe using a pooled ezRAD approach.
Using thousands of genome-wide SNPs, we demonstrated a significant (but
subtle) genetic divide between wahoo from the Indo-Pacific and those from
the Atlantic. This genetic differentiation likely occurs against a
background of high gene glow throughout the evolutionary history of wahoo,
as we inferred from demographic analysis of select population pairs within
and between oceanic regions. Analyses contained in this repository are
for: (1) Filtering pooled ezRAD allele counts (assembled with dDocent and
imputed using poolne_estim); (2) Estimation of genetic differentiation
among globally sampled wahoo populations; (3) Estimation of site frequency
spectra from joint allele frequencies among select population pairs; (4)
Inference of demographic parameters (using δaδi); and (5) Generations of
demographic simulation summary statistics. Most of the analyses are
performed in R and can be run directly from within the repository
directory, this includes: allele filtering, estimation of genetic
differentiation, estimaiton of site frequency spectra, and generation of
demographic summary statistics. Demographic inference using δaδi requires
setup of a Unix environment: input data files and execution scripts are
provided, but their implementation needs to be customised.
Allele frequency data was obtained through a pooled ezRAD approach. De
novo assembly of RAD contigs and variant calling was performed using
the dDocent pipeline. Population allele frequencies were imputed
using poolne_estim. Additional quality filtering was performed in R.
Analysis of genetic differentiation was performed in R, which include:
estimates of FST and AMOVA (analysis of molecular variance). Generation
of site frequency spectra and summary of demographic analyses was
performed in R. Demographic inference was performed using δaδi, originally
on an HPC.
All R code can be run from within the respository directory using the R
project file, Wahoo_PROJ.Rproj. Demographic analyses using δaδi must be
run in a Unix environment. The scripts Wahoo_DADI_Demog_Models.py
and Wahoo_DADI_Generic_Execute.py can be used to set up a pipeline for
executing demographic simulations in a local system or on an HPC cluster.