DOI: 10.5061/dryad.1ns1rn8rf
title: Data from: Secondary contact and genomic admixture between rhesus and long-tailed macaques in the Indochina Peninsula

description: Understanding the process and consequences of hybridization is one of the
 major challenges in evolutionary biology. A growing body of literature has
 reported evidence of ancient hybridization events or natural hybrid zones
 in primates, including humans; however, we still have relatively limited
 knowledge about the pattern and history of admixture because there have
 been little studies that simultaneously achieved genome-scale analysis and
 a geographically extensive sampling of wild populations. Our study applied
 double-digest restriction site-associated DNA sequencing to samples from
 the six localities in and around the provisional hybrid zone of rhesus and
 long-tailed macaques and evaluated population structure, phylogenetic
 relationships, demographic history, and geographic clines of morphology
 and allele frequencies. A latitudinal gradient of genetic components was
 observed, highlighting the transition from rhesus (north) to long-tailed
 macaque distribution (south) as well as the presence of one northern
 population of long-tailed macaques exhibiting unique genetic structure.
 Interspecific gene flow was estimated to have recently occurred after an
 isolation period, and the migration rate from rhesus to long-tailed
 macaques was slightly greater than in the opposite direction. Although
 some rhesus macaque-biased alleles have widely introgressed into
 long-tailed macaque-populations, the inflection points of allele
 frequencies have been observed as concentrated around the traditionally
 recognized interspecific boundary where morphology discontinuously
 changed; this pattern was more pronounced in the X-chromosome than in
 autosomes. Thus, due to geographic separation before secondary contact,
 reproductive isolation could have evolved, contributing to the maintenance
 of an interspecific boundary and species-specific morphological
 characteristics.
description: Data structure:<br> .<br> ├── README.txt  # This
 file<br> │<br> │<br> ├── fastsimcoal2 (compressed in zip
 format)  # The data for fastsimcoal2<br> │   ├── setting  #
 Estimation files (.est) and template files (.tpl)<br> │   │   ├──
 default  # Main analysis<br> │   │   ├── n_her  # Rhesus population
 size is fixed at 239704 (479408 genomes)<br> │   │   ├── n_xue  #
 Rhesus population size is fixed at 71000 (142000 genomes)<br>
 │   │   ├── p_2fold  # SFS projection size is two-hold (40, 50) <br>
 │   │   └── p_half  # SFS projection size if half (10, 13)<br>
 │   │<br> │   └── sfs  # Multidimensional SFS provided by
 easySFS.py<br> │       ├── default_bootstrap  # Main analysis. 100
 bootstrap samples and the original.<br> │       ├── p_2fold  # SFS
 projection size is two-hold (40, 50) <br> │       ├── p_half  # SFS
 projection size if half (10, 13) <br> │       ├── sub1  # Subset 1.
 Populations close to interspecific boundary (RH-BSS, RH-WTPMH, and LT-WHM)
 are excluded.<br> │       ├── sub2  # Subset 2. Populations close to
 and disproportionately-far-away from interspecific boundary (RH-BSS,
 RH-WTPMH, LT-WHM, and LT-Sumatra) are excluded.<br> │       └── sub3
  # Subset 3. Captive populations (RH-China and LT-Sumatra) are
 excluded.<br> │<br> │<br> ├── fineRADstructure
 (compressed in zip format)  # The haplotype data used in fineRADstructure
 (the oupput of Stacks populations command with --radpainter
 option)<br> │   ├── autosome.haps.radpainter  # All the
 samples<br> │   └── autosome_wild.haps.radpainter  # Wild samples
 (RH-China and LT-Sumatra are excluded)<br> │<br> │<br>
 ├── geocline (compressed in zip format)  # The data and R script used in
 geographic cline analysis<br> │   ├── genome.csv  # Location
 information for SNPs data<br> │   ├── geocline_functions.R  # R
 script of supportive functions<br> │   ├── mtd.csv  # mtDNA data
 (after Figure 2 of Bunlungsup et al., 2017)<br> │   ├── rtl.csv  #
 Relative tail length data (after Table 1 of Hamada et al., 2015)<br>
 │   └── ych.csv  # Y-chromosome data (after Figure 2 of Bunlungsup et al.,
 2017)<br> │<br> │<br> ├── nj (compressed in zip format)
  # The input and output of PAUP<br> │   ├── nj_a.dist  # Uncorrected
 P-distance of autosomal SNPs calculated by PAUP. This is used for
 Neighbor-net analysis in SplitsTree4.<br> │   ├── nj_a.nexus  #
 Nexus of autosome used in PAUP NJ analysis<br> │   ├──
 nj_a.support.tre  # NJ tree with bootstrap support value of
 autosome<br> │   ├── nj_x.nexus  # Nexus of X-chromosome used in
 PAUP NJ analysis<br> │   ├── nj_x.support.tre  # NJ tree with
 bootstrap support value of X-chromosome<br> │   ├── nj_y.nexus  #
 Nexus of Y-chromosome used in PAUP NJ analysis<br> │   └──
 nj_y.support.tre  # NJ tree with bootstrap support value of
 Ychromosome<br> │<br> │<br> ├── sex_pop_list.txt  #
 Tab-delimited text file of ID, sex, and population<br> │<br>
 │<br> └── vcf_filtered (compressed in zip format)  # Filtered vcf
 files<br> │   ├── autosome.vcf  # This is used in population
 structure analyses. This is also converted to nexus format for
 phylogenetic analyses using vcf2phylip.py.<br> │   ├──
 autosome_sfs.vcf  # This is used for making SFS (the input for
 easySFS.py).<br> │   ├── x_chromosome.vcf  # This is used in
 population structure analyses. This is also converted to nexus format for
 phylogenetic analyses using vcf2phylip.py.<br> │   └──
 y_chromosome.vcf  # This is converted to nexus format for phylogenetic
 analyses using vcf2phylip.py.<br> │<br> │<br> └──
 vcf_raw (compressed in zip format)  # Raw vcf files (the output of Stacks
 populations command with --vcf option)<br>     ├──
 autosome_raw.vcf<br>     ├── x_chromosome_raw.vcf<br>     └──
 y_chromosome_raw.vcf<br>