DOI: 10.5061/dryad.h44j0zphd
title: Spatial, climate and ploidy factors drive genomic diversity and resilience in the widespread grass Themeda triandra

description: This data set was used to assess the climate resilience of Themeda
 triandra, a foundational species and the most widespread plant in
 Australia, by assessing the relative contributions of spatial,
 environmental, and ploidy factors to contemporary genomic variation.
 Reduced-representation genome sequencing on 472 samples from 52
 locations was used to test how the distribution of genomic variation,
 including ploidy polymorphism, supports adaptation to hotter and drier
 climates.
description: For reduced-representation library preparation and sequencing,
 genomic DNA from each individual was isolated from approximately 25 mg of
 silica-dried leaf tissue using the Stratec Invisorb DNA Plant HTS 96 kit
 (Invitek, Berlin, Germany). Libraries for each individual were created
 similarly to Ahrens et al. (2017). Briefly, extracted DNA was digested
 with PstI for genome complexity reduction, and ligated with a uniquely
 barcoded sequencing adapter pair. We then amplified each sample
 individually by PCR. Amplicons between 350 and 600 bp were selected from
 an agarose gel. The final library pool was sequenced on three Illumina
 NextSeq400 lanes using a 75 bp paired-end protocol on a high output
 flowcell at the Biomolecular Resources Facility at the Australian National
 University, generating approximately 864 million read pairs. 
 We checked the quality of the raw short-read sequencing reads
 with FastQC v0.10.1 (Andrews, 2010), then demultiplexed the raw reads
 associated with each sample’s unique combinatorial barcode using AXE
 v0.2.6 (Murray & Borevitz, 2018). During this step we were unable
 to assign 19% of the reads. Each sequence was trimmed to 64 basepairs
 while removing the barcodes. Read quality was assessed with  trimmomatic v
 0.38 (Bolger, Lohse, & Usadel, 2014) using a sliding window of 4
 basepairs (the number of bases used to average quality) and a quality
 score of 15 (the average quality required among the sliding window), and
 if the average quality dropped below 15, the sequences were cut.
 Long-reads were indexed (Figure S2 for distribution of length and number
 of reads sequenced) using the BWA software and the
 <i>index</i> argument. Short-reads were aligned to the
 long-reads for more accurate SNP calling compared to a <i>de
 novo</i> pipeline. Short-reads were aligned using BWA-mem v
 0.7.17-r1198 (Li, 2013), as paired reads, with 82.5% of reads successfully
 mapped. Samtools v 1.9 (Li et al<i>.</i>, 2009) was used to
 transform the SAM files to BAM files for use within STACKS v 2.41
 (Catchen, Hohenlohe, Bassham, Amores, & Cresko, 2013). The
 argument <i>gstacks</i> and populations were used in that
 order on the BAM files to create a VCF file, minimum thresholds (minor
 allele frequency = 0.01; one random SNP per read was retained) were set
 here for further filtering in R (R core development team 2019).
 The minimum missing data threshold was set to 50% per locus and
 individual which resulted in an average of 30% missing data from the whole
 SNP dataframe. Minor allele frequency was set to 0.05 to avoid identifying
 patterns of population structure that may be due to locally shared
 alleles.
description: .lfmm file for snmf analysis - 012 file represents count of minor
 allele. missing values = 9 |||| the individual key file
 is: themea_52_012v2_indkey.key genpop file provided for
 input as genind object. missing values = 0000