DOI: 10.5061/dryad.tht76hf3b
title: Data from: geo-genomic predictors of genetree heterogeneity explain phylogeographic and introgression history: a case study in an Amazonian bird (Thamnophilus aethiops)

description: Can knowledge about genome architecture inform biogeographic and
 phylogenetic inference? Selection, drift, recombination, and gene flow
 interact to produce a genomic landscape of divergence wherein patterns of
 differentiation and genealogy vary nonrandomly across the genomes of
 diverging populations. For instance, genealogical patterns that arise due
 to gene flow should be more likely to occur on smaller chromosomes, which
 experience high recombination, whereas those tracking histories of
 geographic isolation (reduced gene flow caused by a barrier) and
 divergence should be more likely to occur on larger and sex chromosomes.
 In Amazonia, populations of many bird species diverge and introgress
 across rivers, resulting in reticulated genomic signals. Herein, we used
 reduced representation genomic data to disentangle the biogeographic
 history of four populations of an Amazonian antbird, Thamnophilus
 aethiops, whose biogeographic history was associated with the dynamic
 evolution of the Madeira River Basin. Specifically, we evaluate whether a
 large river capture event ca. 200 kya, gave rise to reticulated
 genealogies in the genome by making spatially explicit predictions about
 isolation and gene flow based on knowledge about genomic processes. We
 first estimated chromosome-level phylogenies and recovered two primary
 topologies across the genome. The first topology (T1) was most consistent
 with predictions about population divergence, and was recovered for the Z
 chromosome. The second (T2), was consistent with predictions about gene
 flow upon secondary contact. To evaluate support for these topologies, we
 trained a convolutional neural network to classify our data into
 alternative diversification models and estimate demographic parameters.
 The best-fit model was concordant with T1 and included gene flow between
 non-sister taxa. Finally, we modeled levels of divergence and
 introgression as functions of chromosome length, and found that smaller
 chromosomes experienced higher gene flow. Given that (1) gene-trees
 supporting T2 were more likely to occur on smaller chromosomes and (2) we
 found lower levels of introgression on larger chromosomes (and especially
 the Z-chromosome), we argue that T1 represents the history of population
 divergence across rivers and T2 the history of secondary contact due to
 barrier loss. Our results suggest that a significant portion of genomic
 heterogeneity arises due to extrinsic biogeographic processes such as
 river capture interacting with intrinsic processes associated with genome
 architecture. Future biogeographic studies would benefit from accounting
 for genomic processes, as different parts of the genome reveal
 contrasting, albeit complementary histories, all of which are relevant for
 disentangling the intricate biogeographic mechanisms of biotic
 diversification.
description: # Geo-genomic predictors of genetree heterogeneity explain phylogeographic
 and introgression history: a case study in an Amazonian bird (Thamnophilus
 aethiops) This dataset contains R scripts for replicating analyses with
 chromosome length, along with output from TWISST Key to directories: 1.
 treeslider: directory containing chromosome trees from astral (.tre
 files), output from treeslider analyses containing sliding window
 topologies (Thamno_aethio_50000bp_window_min5snps.tree_table.csv), and the
 key to scaffold names in the .csv results file (Scaffold_names.csv).
 "NA" values in "Scaffold_names.csv" are given for
 non-chromosome assembly scaffolds as these were not used in any analyses.
 Columns for these files can be defined as follows: * X - The scaffold
 "number" used in the analysis, corresponds to
 "scaffold" column in
 "Thamno_aethio_50000bp_window_min5snps.tree_table.csv" *
 scaffold_name - The name of the scaffold according to the reference genome
 used in the ipyrad assembly * scaffold_length - The total length of each
 scaffold (used downstream for chromosome lengths) * tree.file - The name
 of the chromosome phylogeny .tre file in the same directory * number - the
 chromosome number ("NA" values given for unmapped scaffolds) *
 start - The start position on a given chromosome of each window * end -
 The end position on a given chromosome of each window * sites - The total
 number of sites with genotype data in a given window * snps - The total
 number of polymorphic sites in a given window * samples - The total number
 of samples with sequence data for a given window * missing - THe total
 number of missing genotypes in a given window * tree - The inferred
 genetree at a given window. These are not given if the window did not fit
 our filters (see paper methods for details). 2. pixy_results: spreadsheets
 with genome wide summary statistics from pixy. Here results from pixy are
 divided into genome wide estimates of nucleotide diversity (files with
 "pi" in the filename), relative divergence (files with
 "fst" in the filename), and absolute divergence (files with
 "dxy" in the filename). Each pixy run was replicated for for
 windows 50 kilobases in length (files with "50kb" in the
 filename) and 250 kilobases (files with "250kb" in the
 filenames). They were also replicated at different sequence depths of 6
 (files with "dp6" in the filename) and 10 (files with
 "dp10" in the filename. Columns in each file are defined as
 follows (from documentation at this link:
 https://pixy.readthedocs.io/en/latest/output.html): <br> `pop` - The
 ID of the population from the population file (pi file only) `pop1` - The
 ID of the first population from the population file (dxy and fst files).
 `pop2` - The ID of the second population from the population file.
 `chromosome` - The chromosome/contig `window_pos_1` - The first position
 of the genomic window `window_pos_2` - The last position of the genomic
 window `avg_pi` - Average per site nucleotide diversity for the window.
 More specifically, pixy computes the *weighted average nucleotide
 diversity per site* for all sites in the window, where the weights are
 determined by the number of genotyped samples at each site. `avg_dxy` -
 Average per site nucleotide divergence for the window. `avg_wc_fst` -
 Average Weir and Cockerham’s FST for the window (per SNP, not per site).
 `no_snps` - Total number of variable sites (SNPs) in the window.
 `no_sites` - The total number of sites in the window that have at least
 one valid genotype. This statistic is included for the user, and not
 directly used in any calculations. `count_diffs` - The raw number of
 pairwise differences between all genotypes in the window. This is the
 numerator of avg_pi. `count_comparisons` - The raw number of non-missing
 pairwise comparisons between all genotypes in the window (i.e. cases where
 two genotypes were compared and both were valid). This is the denominator
 of avg_pi. `count_missing` - The raw number of missing pairwise
 comparisons between all genotypes in the window (i.e. cases where two
 genotypes were compared and at least one was missing). "NA"
 values represent windows where statistics could not be estimated 3.
 abba-baba: results from introgression analyses. <br>
 abbababa_100SNPs_ref-nro-sro_ein.csv: results excluding west inambari
 population. "nan" values are given when statistics could not be
 calculated due to insufficient data <br>
 abbababa_100SNPs_ref-nro-sro_win.csv: results excluding east inambari
 population. "nan" values are given when statistics could not be
 calculated due to insufficient data <br> Column names are defined as
 follows (but we suggest looking at documentation here
 https://github.com/gibert-Fab/ABBA-BABA): * windowID - A unique numeric
 identifier for each window in order by chromosome and across each
 chromosome * scaffold - The scaffold the window is on (e.g., 1 = chrom_1,
 10=chrom_10) * start - The window start position (inclusive) * end - The
 window end position * mid - The window midpoint position * sites - The
 number of genotype sites in the input file in each window * sitesUsed -
 The number of sites used to compute statistics (biallelic SNPs) * ABBA -
 Pseudo count of ABBA sites (including polymorphic sites) * BABA - Pseudo
 count of BABA sites (including polymorphic sites) * D - D statistic * fd -
 fd admixture estimation * fdM - Malinsky's modified statistic, fdM to
 accommodate admixture between either P1 and P3 or P2 and P3 * chromosome -
 Does the window lie on an autosome or sex chromosome? 4. TWISST: results
 from TWISST analyses: <br> 100SNPs_ein.weights...: Topology weight
 for three unrooted topologies for the relationships between an outgroup
 and three populations of T. aethiops performed in Twisst. Here, we
 excluded the western Inambari population to test for the relationship
 between Rondonia populations. Values represent the absolute weight
 estimated with twists. nan: windows dropped off the analyses by filtering
 requirements (see Materials and Methods). <br>
 100SNPs_ingroup.weights...: Topology weight for three unrooted topologies
 for the relationships between the four populations of T. aethiops
 performed in Twisst. Values represent the absolute weight estimated with
 twists. nan: Windows dropped off the analyses by filtering requirements
 (see Materials and Methods). <br> 100SNPs.data.tsv: Window
 coordinates used in Twisst analyses. Four R-scripts can be used to
 replicate several figures and statistical analyses from the manuscript: 1.
 chrom_architecture_new.R: This script will generate heatmaps in Figures 5,
 S6, and S7. 2. chrom_architecture.R: This script will generate Figures 4,
 S5, and S8 and replicate statistical analyses for correlations between
 genome summary stats and chromosome length. It requires the files from
 "treeslider" and "pixy_results" directories. 3.
 FDM_by_distance_from_center_by.R: This script will replicate Figure S9. 4.
 fdm_correlations.R: This script will replicate Figures S10 and S11 and
 explores many introgression~genome architecture models. There is some
 overlap with chrom_architecture.R with the goal of exploration of
 different models.