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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Plant Sci.</journal-id>
<journal-title>Frontiers in Plant Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Plant Sci.</abbrev-journal-title>
<issn pub-type="epub">1664-462X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fpls.2023.1239290</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Plant Science</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Development of genomic resources for Rhodes grass (<italic>Chloris gayana</italic>), draft genome and annotated variant discovery</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Maybery-Reupert</surname>
<given-names>Kellie</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2336953"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Isenegger</surname>
<given-names>Daniel</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1481674"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Hayden</surname>
<given-names>Matthew</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/476173"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Cogan</surname>
<given-names>Noel</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/799992"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Agriculture Victoria Research, AgriBio, The Centre for AgriBioscience</institution>, <addr-line>Bundoora, VIC</addr-line>, <country>Australia</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>School of Applied Systems Biology, La Trobe University</institution>, <addr-line>Bundoora, VIC</addr-line>, <country>Australia</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Miloslava Fojtova, Masaryk University, Czechia</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Chris S. Jones, International Livestock Research Institute, Ethiopia; Matej Lexa, Masaryk University, Czechia; Jan Skal&#xe1;k, Central European Institute of Technology (CEITEC), Czechia</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Kellie Maybery-Reupert, <email xlink:href="mailto:kellie.maybery-reupert@agriculture.vic.gov.au">kellie.maybery-reupert@agriculture.vic.gov.au</email>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>04</day>
<month>09</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>14</volume>
<elocation-id>1239290</elocation-id>
<history>
<date date-type="received">
<day>13</day>
<month>06</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>18</day>
<month>08</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2023 Maybery-Reupert, Isenegger, Hayden and Cogan</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Maybery-Reupert, Isenegger, Hayden and Cogan</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>Genomic resources for grasses, especially warm-season grasses are limited despite their commercial and environmental importance. Here, we report the first annotated draft whole genome sequence for diploid Rhodes grass (<italic>Chloris gayana</italic>), a tropical C4 species. Generated using long read nanopore sequencing and assembled using the Flye software package, the assembled genome is 603 Mbp in size and comprises 5,233 fragments that were annotated using the GenSas pipeline. The annotated genome has 46,087 predicted genes corresponding to 92.0% of the expected genomic content present via BUSCO analysis. Gene ontology terms and repetitive elements are identified and discussed. An additional 94 individual plant genotypes originating from three diploid and two tetraploid Rhodes grass cultivars were short-read whole genome resequenced (WGR) to generate a single nucleotide polymorphism (SNP) resource for the species that can be used to elucidate inter- and intra-cultivar relationships across both ploidy levels. A total of 75,777 high quality SNPs were used to generate a phylogenetic tree, highlighting the diversity present within the cultivars which agreed with the known breeding history. Differentiation was observed between diploid and tetraploid cultivars. The WGR data were also used to provide insights into the nature and evolution of the tetraploid status of the species, with results largely agreeing with the published literature that the tetraploids are autotetraploid.</p>
</abstract>
<kwd-group>
<kwd>Rhodes grass</kwd>
<kwd>genome sequence</kwd>
<kwd>genetic diversity</kwd>
<kwd>tetraploid</kwd>
<kwd>orphan crop</kwd>
</kwd-group>
<counts>
<fig-count count="3"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="66"/>
<page-count count="10"/>
<word-count count="5606"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Plant Genetics, Epigenetics and Chromosome Biology</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Rhodes grass (<italic>Chloris gayana</italic>) is a tropical C4 species native to most of Africa that is grown across the continent as hay and permanent pasture, as well as to control erosion due to its deep rooting (<xref ref-type="bibr" rid="B48">Skerman and Riveros, 1990</xref>). It is a member of the genus <italic>Chloris</italic> that includes 56 species and sub-species, most of which are weedy types (<xref ref-type="bibr" rid="B2">Anderson, 1974</xref>). Rhodes grass is most closely related to <italic>Chloris virgata</italic>, which is more commonly known as feather fingergrass or occasionally Rhodes grass or feathertop Rhodes grass (<xref ref-type="bibr" rid="B18">Edwards, 2012</xref>; <xref ref-type="bibr" rid="B43">Rojas-Sandoval, 2016</xref>). Exported in the early 1900s, <italic>C. gayana</italic> is now cultivated in predominantly tropical and sub-tropical areas across the globe (<xref ref-type="bibr" rid="B32">Loch et&#xa0;al., 2004</xref>). Demonstrated by its extensive distribution in its native Africa and its establishment in non-indigenous regions, Rhodes grass has wide environmental adaptation potential, proliferating in a wide range of soil types and with cultivars capable of growing at temperatures between 8-35&#xb0;C (<xref ref-type="bibr" rid="B32">Loch et&#xa0;al., 2004</xref>). Rhodes grass is a halophytic grass, capable of tolerating higher levels of soil salinity than many other C4 grasses due to its ability to partition salt ions such that photosynthesis is unaffected (<xref ref-type="bibr" rid="B8">Bogdan, 1969</xref>; <xref ref-type="bibr" rid="B36">Oi et&#xa0;al., 2022</xref>). Its tolerance of salt has seen the species used to restore highly degraded soils and to provide an additional source of fodder on otherwise underutilised land (<xref ref-type="bibr" rid="B1">Abate et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B47">Singh et&#xa0;al., 2022</xref>). Global warming-mediated temperature changes in traditionally temperate regions has increased interest in Rhodes and other C4 tropical grasses as they are considered potentially important to the future of a range of livestock industries; despite the majority of current research focusing on C3 forages (<xref ref-type="bibr" rid="B30">Leegood, 2013</xref>).</p>
<p>As a member of the PACMAD clade that includes all C4 grasses, Rhodes grass falls under the Cynodonteae tribe within the Chloridoideae subfamily (<xref ref-type="bibr" rid="B18">Edwards, 2012</xref>). Only four of at least 850 recognised Cynodonteae species have whole or draft genomic sequences available (<italic>Oropetium thomaeum</italic>, 245Mb genome; <italic>Eleusine coracana</italic>, 1453Mb genome; <italic>Eleusine indica</italic>, 584Mb genome; and <italic>Cynodon transvaalensis</italic>, 454Mb genome) (<xref ref-type="bibr" rid="B56">VanBuren et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B23">Hittalmani et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B49">Soreng et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B66">Zhang et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B15">Cui et&#xa0;al., 2021</xref>). While no molecular dating of the divergence of <italic>C. gayana</italic> from other grass species is available, it belongs to the subtribe <italic>Eleusininae</italic> which is known to have originated approximately 22mya and <italic>C. gayana</italic>&#x2019;s closest known relative, <italic>C. virgata</italic>, is estimated to have diverged from a different <italic>Chloris</italic> species approximately 3.6mya (<xref ref-type="bibr" rid="B18">Edwards, 2012</xref>; <xref ref-type="bibr" rid="B59">Wang et&#xa0;al., 2022</xref>). Of the four Cynodonteae species with available draft genome sequences, those most closely related to the <italic>Chloris</italic> genus, <italic>E. coracana</italic> and <italic>E. indica</italic>, are approximately 15 million years diverged (<xref ref-type="bibr" rid="B59">Wang et&#xa0;al., 2022</xref>). The evolutionary distance from Rhodes grass to these other species makes comparative genomics challenging. Furthermore <italic>E. coracana</italic> and <italic>E. indica</italic> are both allotetraploids, which limits their application for a diploid genome assembly, and all four Cynodonteae species with genome sequence are assembled only to a scaffold level. <italic>Setaria italica</italic> is the most closely related species to <italic>C. gayana</italic> that is both a diploid and has a chromosome-level genome assembly available (<xref ref-type="bibr" rid="B7">Bennetzen et&#xa0;al., 2012</xref>).</p>
<p>Rhodes grass has both diploid (2n=20) and tetraploid varieties (2n=40), with some reports of triploid (2n = 30) varieties existing (<xref ref-type="bibr" rid="B2">Anderson, 1974</xref>; <xref ref-type="bibr" rid="B32">Loch et&#xa0;al., 2004</xref>). The tetraploid varieties are currently proposed to be autotetraploids or segmental allotetraploids based on findings from a single study (<xref ref-type="bibr" rid="B33">Nakagawa et&#xa0;al., 1987</xref>). However, the exact genetic composition of the tetraploid varieties is unknown and to date no suggestions of speciation events have been proposed (<xref ref-type="bibr" rid="B32">Loch et&#xa0;al., 2004</xref>).</p>
<p>Within the current known range of grass genome sizes (the smallest being <italic>Orpetium thomaeum</italic> at 245Mb and one of the largest being <italic>Triticum aestivum</italic> L. at ~17Gb), the estimated genome size of Rhodes grass is relatively compact (1C = ~293Mb) (<xref ref-type="bibr" rid="B6">Bennett and Smith, 1976</xref>; <xref ref-type="bibr" rid="B56">VanBuren et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B46">Shi and Ling, 2018</xref>). As an outbreeding species, it is highly heterozygous with substantial intra- and inter-cultivar variability (<xref ref-type="bibr" rid="B32">Loch et&#xa0;al., 2004</xref>; <xref ref-type="bibr" rid="B54">Ubi et&#xa0;al., 2004</xref>). This diversity gives rise to a range of beneficial traits that can be exploited by breeders. However, modern breeding tools such as genomic selection and genome editing have not yet been applied as limited research has been conducted on the species. Hence, Rhodes grass can be considered an orphan crop as it is one of the most important perennial C4 forage grasses, yet its available genomic and genetic resources are disproportionately few (<xref ref-type="bibr" rid="B21">Gondo et&#xa0;al., 2007</xref>). Currently available resources include a genetic linkage map (<xref ref-type="bibr" rid="B54">Ubi et&#xa0;al., 2004</xref>), unassembled short Illumina sequences available on GenBank from a DNA barcoding study (<xref ref-type="bibr" rid="B20">Gill et&#xa0;al., 2019</xref>) and short sequences from unpublished research. Previous research has utilised conventional anonymous molecular marker techniques such as RAPD, AFLP, ISSR and SRAP markers to examine diploid and tetraploid Rhodes grass diversity (<xref ref-type="bibr" rid="B39">P&#xe9;rez et&#xa0;al., 1999</xref>; <xref ref-type="bibr" rid="B53">Ubi et&#xa0;al., 2000</xref>; <xref ref-type="bibr" rid="B52">Ubi et&#xa0;al., 2001</xref>; <xref ref-type="bibr" rid="B55">Ubi et&#xa0;al., 2003</xref>; <xref ref-type="bibr" rid="B42">Ribotta et&#xa0;al., 2019</xref>). The main conclusions of all these studies were similar; in Rhodes grass, there is a large degree of both intra- and inter-cultivar variation. The most recent study on Rhodes grass population structure and genetic diversity assessed 104 accessions using SNP and SilicoDArT markers generated with DArTseq (<xref ref-type="bibr" rid="B34">Negawo et&#xa0;al., 2021</xref>). This study examined Rhodes grass accessions in the context of their geographical origin but found no clear relationship between country of origin and genetic similarity. Consistent with the previous molecular marker studies, Rhodes grass was found to have considerable genetic diversity between accessions (<xref ref-type="bibr" rid="B53">Ubi et&#xa0;al., 2000</xref>; <xref ref-type="bibr" rid="B52">Ubi et&#xa0;al., 2001</xref>; <xref ref-type="bibr" rid="B55">Ubi et&#xa0;al., 2003</xref>). In their study, <xref ref-type="bibr" rid="B34">Negawo et&#xa0;al. (2021)</xref> attempted to align their molecular markers to reference genomes of closely related species but only a small proportion (1-5.5% of SNP markers) could be mapped to the available <italic>Chloridoideae</italic> genomes. They noted the number of mappable markers was significantly higher when a reference genome for that species was used, highlighting the importance of whole genome sequence resources. As their study focussed on describing a geographically diverse collection of Rhodes grass, it did not sample individual accessions deeply or provide information on ploidy status.</p>
<p>Due to its small genome size and genome plasticity (<xref ref-type="bibr" rid="B33">Nakagawa et&#xa0;al., 1987</xref>), <italic>Chloris gayana</italic> has potential as a model C4 grass species with the development of relevant underpinning genomic resources. Here, we present the first <italic>de novo</italic> draft reference genome assembly in the <italic>Chloris</italic> genus, as well as a large SNP resource developed via whole genome resequencing of a range of current commercial diploid and tetraploid cultivars of Rhodes grass. The SNP resource enables identification of within and between cultivar relationships, while the WGR data itself can be aligned to the diploid reference genome to provide insights into tetraploid Rhodes grass origins. These genomic resources will facilitate future breeding efforts and genome editing studies, as well as providing further insight in the grass phylogeny.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<label>2</label>
<title>Materials and methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Plant materials and genomic DNA extraction</title>
<p>Rhodes grass cv. Tolgar, line 108 (T108) is a diploid tissue culture responsive genotype selected for its high <italic>in vitro</italic> regeneration response and transformability, and ability to be maintained clonally by micropropagation. Young leaves of T108 from tissue culture grown, clonally propagated plants were used for DNA extraction. Approximately 200mg of leaf tissue was frozen in liquid nitrogen, ground to a fine powder, lysed in 500&#xb5;L CTAB extraction buffer (100mM Tris-HCl pH 8.0, 1.4M NaCl, 20mM EDTA, 2% w/v CTAB, 2% w/v PVP-40) with 2&#xb5;L (100mg/mL) RNAse and incubated for 20 minutes at 65&#xb0;C. After extraction with 500&#xb5;L chloroform/isoamyl (24:1) and centrifugation for 10 minutes at 16,000g, the supernatant was removed, combined with 0.7X volume of isopropanol and stood for 10 minutes at room temperature, followed by centrifugation for 10 minutes at 16,000g. The liquid phase was decanted, before 200&#xb5;L of 70% (v/v) ethanol was added to the DNA pellet followed by centrifugation for 5 minutes at 13,000g. Following decanting of the ethanol, the DNA pellet was allowed to air dry before resuspension in 50&#xb5;L nuclease-free water and storage at -20&#xb0;C. DNA quality was assessed by Genomic DNA ScreenTape (Agilent 2200 TapeStation) according to the manufacturer&#x2019;s instructions (Agilent Technologies, Santa Clara, CA, United States).</p>
<p>DNA was extracted from young leaf tissue of 94 Rhodes grass plants from five cultivars (Finecut, Tolgar, Endura, Mariner and Toro; <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;1</bold>
</xref>) from a combination of glasshouse and tissue culture grown plants using the DNeasy 96 Plant Kit (Qiagen, Hilden, Germany) as per the manufacturer&#x2019;s instructions. The DNA was quantified using a Qubit 3.0 Fluorometer (Invitrogen&#x2122;, Thermo Fisher Brand, CA, United States) and stored at -20&#xb0;C.</p>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Library preparation and genome sequencing</title>
<p>Enrichment of high molecular weight DNA from the CTAB-extracted samples was performed using the Size Selection Protocol from the Circulomics Short Read Eliminator Kit (PacBio, Menlo Park, CA, United States). DNA sample integrity was assessed using Genomic DNA ScreenTape (Agilent 2200 TapeStation) according to the manufacturer&#x2019;s instructions (Agilent Technologies, Santa Clara, CA, United States). A genomic DNA library was constructed using the Ligation Sequencing Kit (Oxford Nanopore Technologies, United Kingdom; Genomic DNA by Ligation (SQK-LSK11)) as per the manufacturer&#x2019;s instructions with a minor modification; when preparing the library for loading onto the flow cell, 51&#xb5;L DNA library and 24&#xb5;L loading buffer were used. The prepared DNA library was loaded onto a PromethION R9.4 flow cell and sequenced as per the manufacturer&#x2019;s instructions (Oxford Nanopore Technologies, Oxon, United Kingdom).</p>
<p>DNeasy extracted samples were prepared for short-read sequencing using a Nextflex Rapid XP DNA-Seq Kit (Perkin Elmer, Waltham, MA, United States) as per manufacturer&#x2019;s instructions with a minor modification at the Adapter Ligation step, where in-house adaptors were used instead of NextFlex barcodes. DNA quantity and length was determined using Genomic DNA ScreenTape (Agilent 2200 TapeStation) performed according to the manufacturer&#x2019;s instructions (Agilent Technologies, Santa Clara, CA, United States). The samples were sequenced using an Illumina NovaSeq 6000 according to the manufacturer&#x2019;s instructions (Illumina, San Diego CA, USA).</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Genome assembly and annotation</title>
<p>The Oxford Nanopore Technologies (ONT) reads were assembled using the Flye (v-2.8.2, <ext-link ext-link-type="uri" xlink:href="https://github.com/fenderglass/Flye">https://github.com/fenderglass/Flye</ext-link>) software package with the following parameters: estimated genome size set to 300Mb, minimum overlap of 10kb, the 40x longest reads for contig assembly and all reads for error correction (<xref ref-type="bibr" rid="B29">Kolmogorov et&#xa0;al., 2019</xref>). The assembled Rhodes grass genome was annotated using the GenSAS pipeline (<xref ref-type="bibr" rid="B25">Humann et&#xa0;al., 2019</xref>), which consists of multiple tools. The tools used were RepeatModeler (<italic>de novo</italic> repeat identification and repeat masking), followed by a BLASTn alignment to the <italic>Setaria italica</italic> genome (<xref ref-type="bibr" rid="B7">Bennetzen et&#xa0;al., 2012</xref>) (default settings for transcript alignments), and Augustus for gene prediction using <italic>Zea mays</italic> as a reference. Finally, Evidence Modeler was used to create a consensus gene set from the Augustus and BLASTn outputs, followed by PASA refinement using the <italic>Setaria italica</italic> genome (GenBank assembly accession: GCA_000263155.2) as a reference, and functional annotation with BLASTp (<xref ref-type="bibr" rid="B7">Bennetzen et&#xa0;al., 2012</xref>).</p>
<p>Strudel (<xref ref-type="bibr" rid="B5">Bayer et&#xa0;al., 2011</xref>) was used for a preliminary assembly of pseudomolecules using the <italic>S. italica</italic> genome as a reference for genomic locations. To prepare data for analysis in Strudel, regions of similarity between the coding sequence of Rhodes grass contigs and the <italic>S. italica</italic> genome were identified using BLAST. Contig locations for the subsequent list of aligned Rhodes grass genes were determined and filtered by removing contigs that aligned to more than one <italic>S. italica</italic> chromosome. These filtered contig lists were then concatenated with N&#x2019;s to make pseudomolecules and used for alignment in Strudel.</p>
<p>Gene functions were identified in GenSAS using DIAMOND (protein dataset: NCBI refseq plant), after which the UniProt Retrieve/ID Mapping tool was used to identify GO annotation terms for the gene set and the quantity of genes relating to each function was determined and plotted using WEGO (<xref ref-type="bibr" rid="B62">Ye et&#xa0;al., 2006</xref>; <xref ref-type="bibr" rid="B24">Huang et&#xa0;al., 2011</xref>; <xref ref-type="bibr" rid="B63">Ye et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B51">The UniProt Consortium et&#xa0;al., 2022</xref>).</p>
</sec>
<sec id="s2_4">
<label>2.4</label>
<title>Assessment of cultivar diversity</title>
<p>The Illumina paired-end short read sequences were used to identify SNPs and assess sample diversity. The short reads were aligned to the draft Rhodes grass genome sequence assembly using BWA-MEM (<xref ref-type="bibr" rid="B31">Li and Durbin, 2009</xref>). Initial SNP discovery was performed using two software packages: GATK and SAMtools mpileup (<xref ref-type="bibr" rid="B57">Van der Auwera and O&#x2019;Connor, 2020</xref>; <xref ref-type="bibr" rid="B17">Danecek et&#xa0;al., 2021</xref>). Identified SNP, common between the GATK and SAMtools mpileup pipelines were identified using BCFtools isec (<xref ref-type="bibr" rid="B17">Danecek et&#xa0;al., 2021</xref>). These SNP were split into diploid- and tetraploid-only sets, and each was filtered with VCFtools (minor allele frequency 0.1, maximum missing sites 0.5 and minimum sequence read depth of 4) (<xref ref-type="bibr" rid="B16">Danecek et&#xa0;al., 2011</xref>). SNP loci were then manually removed from the dataset if the genotype calls for more than 75% of the samples at a given SNP locus were genotyped as heterozygous. Finally, a SNP locus was discarded if it had both an absent homozygous allele class and the proportion of heterozygous calls in the population was greater than 60%. The filtered diploid and tetraploid lists were then compared using BCFtools isec to obtain a high confidence subset of SNP loci that overlapped between the datasets to be used as the tetraploid SNP set for building the phylogenetic tree in the subsequent steps (<xref ref-type="bibr" rid="B17">Danecek et&#xa0;al., 2021</xref>).</p>
<p>The tetraploid and diploid data were combined and transformed using a neighbour joining algorithm within R Statistical Software using the vcfR, NostalgiR, tidyverse and StAMPP packages (<xref ref-type="bibr" rid="B38">Pembleton et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B35">Nguyen, 2015</xref>; <xref ref-type="bibr" rid="B28">Knaus and Gr&#xfc;nwald, 2017</xref>; <xref ref-type="bibr" rid="B61">Wickham et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B41">R Core Team, 2022</xref>). To enable the diploid and tetraploid samples to be compared, all samples were treated as if they were diploid and hence the allelic dosage of the tetraploid samples in the heterozygous state was not estimated. A phylogenetic tree based on these relationships was constructed in R Statistical Software using the ggplot2, ape and ggtree packages (<xref ref-type="bibr" rid="B60">Wickham, 2016</xref>; <xref ref-type="bibr" rid="B37">Paradis and Schliep, 2019</xref>; <xref ref-type="bibr" rid="B41">R Core Team, 2022</xref>; <xref ref-type="bibr" rid="B65">Yu, 2022</xref>). SnpEff was used to annotate the SNP variants and provide predictions of the effect of different SNPs (<xref ref-type="bibr" rid="B10">Cingolani et&#xa0;al., 2012</xref>). As per the SnpEff documentation, a database entry for Rhodes grass was built and the final SNP set (comprising both diploid and tetraploid individuals and containing 75,777 SNPs) was annotated.</p>
<p>The data presented in this study are publicly available and all genome sequence data can be found in NCBI under the BioProject ID PRJNA974075.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>Draft genome sequence assembly</title>
<p>The total sequence data generated by ONT long-read sequencing was 46.9Gb, an expected 156x coverage of the estimated haploid genome, with a raw read length N50 of 19.3kb. The total size of the assembled genome was 603Mb, comprising 5233 fragments with a N50 of 310kb (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>). The largest fragment was 8.8Mb. The assembled genome had a mean coverage of 76x and included 45.29% GC content and about 52% interspersed repetitive elements (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). The predominant repeat types were retroelements and LTR elements which accounted for 31.49% and 29.44% of the repeats, respectively (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). A total of 46,087 predicted genes were identified. An assessment of the completeness of the Flye genome assembly performed using BUSCO analysis (BUSCO 5.0.0, dataset poales_odb10) found 91.97% of the expected gene content to be present and complete, of which 71.59% were single copy and 20.38% were duplicated. The remainder comprised 2.06% of fragmented and 5.96% missing BUSCOs (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>).</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Summary statistics of the Rhodes grass whole genome sequence assembly and annotation.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Details</th>
<th valign="top" align="left">Value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Estimated genome size</td>
<td valign="top" align="left">2C= ~586Mb</td>
</tr>
<tr>
<td valign="top" align="left">Assembled genome size</td>
<td valign="top" align="left">603,261,687 bp</td>
</tr>
<tr>
<td valign="top" align="left">Number of fragments</td>
<td valign="top" align="left">5,233</td>
</tr>
<tr>
<td valign="top" align="left">Fragment N50</td>
<td valign="top" align="left">310,098 bp</td>
</tr>
<tr>
<td valign="top" align="left">Largest fragment</td>
<td valign="top" align="left">8,824,165 bp</td>
</tr>
<tr>
<td valign="top" align="left">Mean coverage</td>
<td valign="top" align="left">76x</td>
</tr>
<tr>
<td valign="top" align="left">Overall complete BUSCOs</td>
<td valign="top" align="left">91.97%</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Complete and single-copy BUSCOs</td>
<td valign="top" align="left">71.59%</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Complete and duplicated BUSCOs</td>
<td valign="top" align="left">20.38%</td>
</tr>
<tr>
<td valign="top" align="left">Fragmented BUSCOs</td>
<td valign="top" align="left">2.06%</td>
</tr>
<tr>
<td valign="top" align="left">Missing BUSCOs</td>
<td valign="top" align="left">5.96%</td>
</tr>
<tr>
<td valign="top" align="left">GC Content</td>
<td valign="top" align="left">45.29%</td>
</tr>
<tr>
<td valign="top" align="left">Number of predicted genes</td>
<td valign="top" align="left">46,087</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Repetitive sequence and their proportion in Rhodes grass genome.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Repeat type</th>
<th valign="top" align="center">Number of elements</th>
<th valign="top" align="center">Length occupied (bp)</th>
<th valign="top" align="center">Percentage of sequence</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Retroelements</td>
<td valign="top" align="left">111,680</td>
<td valign="top" align="left">189,986,408</td>
<td valign="top" align="left">31.49</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;SINEs</td>
<td valign="top" align="left">608</td>
<td valign="top" align="left">148,775</td>
<td valign="top" align="left">0.02</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;LINEs</td>
<td valign="top" align="left">22,725</td>
<td valign="top" align="left">12,220,339</td>
<td valign="top" align="left">2.03</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;LTR Elements</td>
<td valign="top" align="left">88,347</td>
<td valign="top" align="left">177,617,294</td>
<td valign="top" align="left">29.44</td>
</tr>
<tr>
<td valign="top" align="left">DNA transposons</td>
<td valign="top" align="left">36,947</td>
<td valign="top" align="left">23,533,254</td>
<td valign="top" align="left">3.90</td>
</tr>
<tr>
<td valign="top" align="left">Rolling-circles</td>
<td valign="top" align="left">3,006</td>
<td valign="top" align="left">1,221,082</td>
<td valign="top" align="left">0.20</td>
</tr>
<tr>
<td valign="top" align="left">Unclassified</td>
<td valign="top" align="left">262,656</td>
<td valign="top" align="left">99,192,100</td>
<td valign="top" align="left">16.44</td>
</tr>
<tr>
<td valign="top" align="left">Total interspersed repeats</td>
<td valign="top" align="left"/>
<td valign="top" align="left">312,711,762</td>
<td valign="top" align="left">51.84</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>For the complete BUSCO genes, DIAMOND analysis returned 828,107 hits due to multiple matches for each gene. Following the use of UniProt Retrieve/ID Mapping tool to remove duplicate protein entries, a total of 143,045 gene matches remained, of which 117,625 were assigned at least one GO term (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>). The assigned gene functions (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>) split almost evenly into biological processes (31.21%), cellular components (31.52%) and molecular function (37.27%).</p>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>Summary of the main predicted Rhodes grass gene functions from WEGO.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left"/>
<th valign="middle" align="center"/>
<th valign="middle" align="center">Number of genes</th>
<th valign="middle" align="center">Percent (%)</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left"/>
<td valign="middle" align="left">Total</td>
<td valign="top" align="center">143,045</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="middle" align="left"/>
<td valign="middle" align="left">Annotated genes</td>
<td valign="top" align="center">117,625</td>
<td valign="top" align="center">82.23</td>
</tr>
<tr>
<td valign="top" rowspan="4" align="left">GO terms</td>
<td valign="middle" align="left">Biological process</td>
<td valign="top" align="center">76,326</td>
<td valign="top" align="center">31.21</td>
</tr>
<tr>
<td valign="middle" align="left">Cellular component</td>
<td valign="top" align="center">77,091</td>
<td valign="top" align="center">31.52</td>
</tr>
<tr>
<td valign="middle" align="left">Molecular function</td>
<td valign="top" align="center">91,169</td>
<td valign="top" align="center">37.27</td>
</tr>
<tr>
<td valign="middle" align="left">Total</td>
<td valign="top" align="center">244,586</td>
<td valign="top" align="center"/>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>The predicted function of Rhodes grass genes as determined by DIAMOND functional analysis and plotted using WEGO.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-14-1239290-g001.tif"/>
</fig>
<p>Alignment of the assembled Rhodes grass contigs to the published <italic>Setaria italica</italic> genome allowed the identification of an ordered set of contigs that could be considered an evaluation of genome assembly completeness. Of the 5233 assembled contigs, 1362 (~153Mb, approx. half of the haploid genome size) uniquely aligned to the nine <italic>S. italica</italic> chromosomes. The remaining 3871 Rhodes grass contigs were discarded as they were unaligned or had alignments to multiple <italic>S. italica</italic> chromosomes. The uniquely aligned Rhodes grass contigs were ordered based on <italic>S. italica</italic> alignment and arranged into 9 pseudochromosomes (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Preliminary Rhodes grass pseudomolecules aligned to <italic>Setaria italica</italic> genome assembly, visualised using Strudel (<xref ref-type="bibr" rid="B5">Bayer et&#xa0;al., 2011</xref>). Chromosome numbers are presented for the <italic>Setaria italica</italic> genome (left, in order 1-9 from the top to bottom of the image) and that numbering convention has been preserved for the <italic>Chloris gayana</italic> hypothetical pseudomolecules (right).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-14-1239290-g002.tif"/>
</fig>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Whole genome resequencing for variant discovery</title>
<p>Ninety-four individual plants representing five commercial diploid and tetraploid Rhodes grass cultivars were used to generate short-read sequence data. Alignment of the resulting 1,454,838,833 reads to the draft Rhodes grass genome assembly found that the vast majority mapped with high confidence. The alignment quality did not differ between the diploid (98.02% of reads mapped, 83.87% properly paired) and tetraploid (97.63% mapped, 83.55% properly paired) datasets. The average depth of sequencing per individual was 2.34x. A total of 38,307,269 and 38,686,098 SNP were identified with GATK and mpileup, respectively, of which 26,019,620 SNP overlapped between these two genotype calling pipelines and were subjected to subsequent filtering steps. Following filtering, 75,777 diploid and 380,133 tetraploid SNP remained, of which 26,544 were common between the two sets. Attrition at each filtering step is shown in <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;2</bold>
</xref>.</p>
<p>The subset of SNP common between the diploid and tetraploid samples was used to assess the relationships between individuals from the five Rhode grass cultivars. A phylogenetic tree of the diploid and tetraploid samples broadly split into three branches, with one branch encompassing the tetraploids and the other two including all diploid samples (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>). The tetraploid branch included two subgroups, one comprised of nine Mariner samples and the other of another four subgroups, of which two were solely Mariner and Toro samples containing four and ten individuals, respectively. The other two subgroups were combinations of tetraploid genotypes, altogether consisting of the remaining six Mariner and seven Toro samples. Separated by this tetraploid branch were the two clusters of diploid individuals, where the smaller diploid branch was not notably clustered by cultivar but rather consisted of four Tolgar, three Finecut and one Endura sample. The larger branch consisted of two subgroups, one of which contained four Endura and six Tolgar samples, each of which clustered according to cultivar. The remaining samples split into two groups, one containing cultivar-clustered Finecut (seven samples) and Tolgar (nine samples) individuals. The remaining branch included samples from all diploid cultivars (four Endura, nine Finecut and one Tolgar), with clustering between cultivars. Phylogenetic trees constructed for each of the diploid and tetraploid samples using the ploidy-specific SNP are shown in <xref ref-type="supplementary-material" rid="SF1">
<bold>Supplementary Figures&#xa0;1</bold>
</xref>, <xref ref-type="supplementary-material" rid="SF2">
<bold>2</bold>
</xref>.</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Rhodes grass phylogenetic tree based on a neighbour-joining distance matrix of filtered SNP relationships between diploid and tetraploid individuals.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-14-1239290-g003.tif"/>
</fig>
<p>The largest grouping (43.52%) of SNP annotated by SnpEff were found in intergenic regions, followed by SNPs either 5kb upstream (21.33%) or downstream (20.06%) of a gene (<xref ref-type="table" rid="T4">
<bold>Table&#xa0;4</bold>
</xref>). Synonymous (5.94%), missense (5.37%) and intron (3.52%) variants were the next largest SNP categories, with the remaining categories altogether comprising less than 1% of variant types. A summary of SNP annotations is provided as a <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary file</bold>
</xref>.</p>
<table-wrap id="T4" position="float">
<label>Table&#xa0;4</label>
<caption>
<p>The type of effects predicted to be caused by SNPs in the Rhodes grass genome as determined by SnpEff (<xref ref-type="bibr" rid="B10">Cingolani et&#xa0;al., 2012</xref>).</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Type</th>
<th valign="top" align="center">Count</th>
<th valign="top" align="center">Percent (%)</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Downstream gene variant</td>
<td valign="top" align="center">26,016</td>
<td valign="top" align="center">20.057</td>
</tr>
<tr>
<td valign="top" align="left">Initiator codon variant</td>
<td valign="top" align="center">1</td>
<td valign="top" align="center">0.001</td>
</tr>
<tr>
<td valign="top" align="left">Intergenic region</td>
<td valign="top" align="center">56,451</td>
<td valign="top" align="center">43.520</td>
</tr>
<tr>
<td valign="top" align="left">Intron variant</td>
<td valign="top" align="center">4,561</td>
<td valign="top" align="center">3.516</td>
</tr>
<tr>
<td valign="top" align="left">Missense variant</td>
<td valign="top" align="center">6,959</td>
<td valign="top" align="center">5.365</td>
</tr>
<tr>
<td valign="top" align="left">Splice acceptor variant</td>
<td valign="top" align="center">10</td>
<td valign="top" align="center">0.008</td>
</tr>
<tr>
<td valign="top" align="left">Splice donor variant</td>
<td valign="top" align="center">16</td>
<td valign="top" align="center">0.012</td>
</tr>
<tr>
<td valign="top" align="left">Splice region variant</td>
<td valign="top" align="center">224</td>
<td valign="top" align="center">0.173</td>
</tr>
<tr>
<td valign="top" align="left">Start lost</td>
<td valign="top" align="center">21</td>
<td valign="top" align="center">0.016</td>
</tr>
<tr>
<td valign="top" align="left">Stop gained</td>
<td valign="top" align="center">67</td>
<td valign="top" align="center">0.052</td>
</tr>
<tr>
<td valign="top" align="left">Stop lost</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">0.007</td>
</tr>
<tr>
<td valign="top" align="left">Stop retained variant</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">0.002</td>
</tr>
<tr>
<td valign="top" align="left">Synonymous variant</td>
<td valign="top" align="center">7,705</td>
<td valign="top" align="center">5.940</td>
</tr>
<tr>
<td valign="top" align="left">Upstream gene variant</td>
<td valign="top" align="center">27,669</td>
<td valign="top" align="center">21.331</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>An annotated draft genome sequence assembly of Rhodes grass was constructed using long-read sequencing technology and the tissue culture responsive line T108 isolated from the diploid cultivar Tolgar. At 603 Mb, the total length of the draft assembly is about the estimated diploid genome size for Rhodes grass (2C= ~586Mb) (<xref ref-type="bibr" rid="B6">Bennett and Smith, 1976</xref>; <xref ref-type="bibr" rid="B56">VanBuren et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B46">Shi and Ling, 2018</xref>). Due to the Flye assemblers (v-2.8.2, <ext-link ext-link-type="uri" xlink:href="https://github.com/fenderglass/Flye">https://github.com/fenderglass/Flye</ext-link>) attempts to collapse haplotigs, the assembled genome was expected to be smaller than the actual generated assembly (603Mbp), with this larger than expected assembly potentially providing an indication of the genetic diversity of the genome. While the error rate of the ONT pore 9.4 is higher than other technologies, it is unlikely it would be sufficiently significant to cause the large assembly size of the genome. An additional hypothesis regarding the assembled data size could be seed contamination or mislabelling such that the plant sequenced to generate whole genome sequence (Tolgar 108) is tetraploid and not diploid. However, as this plant genotype was resequenced and analysed in the broader cohort of plants and subsequently shown to align more with diploid individuals (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>), this also may be unlikely. Comprising 5,233 contigs with a N50 of 310kb and 46,087 predicted genes (including 91.97% of expected BUSCO genes, of which 71.59% are single copy), the quality and completeness of the draft genome assembly is comparable to that of other draft orphan crop genomes; e.g. <italic>Digitaria exilis</italic>- 3329 contigs, BUSCO: 98.1% (<xref ref-type="bibr" rid="B58">Wang et&#xa0;al., 2021</xref>). As generated by EvidenceModeler from the consensus of the Augustus gene predictions and the <italic>S. italica</italic> BLASTn alignment, the predicted gene number for Rhodes grass (46,078) was in a similar range of gene number compared to closely related species <italic>E. coracana</italic> (57,180 predicted genes (<xref ref-type="bibr" rid="B22">Hatakeyama et&#xa0;al., 2018</xref>)), <italic>S. italica</italic> (24,000-29,000 predicted genes (<xref ref-type="bibr" rid="B7">Bennetzen et&#xa0;al., 2012</xref>)) and <italic>L. perenne</italic> (38,868 predicted genes (<xref ref-type="bibr" rid="B19">Frei et&#xa0;al., 2021</xref>)). The accuracy of the Rhodes grass predicted gene set could be improved by creating transcriptome resources for training prediction models, however this was outside the scope of this study.</p>
<p>Among commercial cultivations of Rhodes grass, cultivars descended from Katambora are common. Hierarchical clustering of 104 Rhodes grass accessions by <xref ref-type="bibr" rid="B34">Negawo et&#xa0;al. (2021)</xref> showed the presence of two main subpopulations, with Katambora clustering within the larger group. As Tolgar is a descendent of Katambora, the draft genome sequence generated is expected to be useful as a representative for diploid Rhodes grass and broadly applicable for a majority of Rhodes grass cultivars.</p>
<p>The reference genome for <italic>Setaria italica</italic>, that is assembled to chromosome level, was used to order the assembled draft Rhodes grass genome contigs, based on synteny and visualised using Strudel (<xref ref-type="bibr" rid="B5">Bayer et&#xa0;al., 2011</xref>; <xref ref-type="bibr" rid="B7">Bennetzen et&#xa0;al., 2012</xref>). <italic>Setaria italica</italic> is the most closely related species to Rhodes grass with an available whole genome sequence and has a similar number of chromosomes to diploid Rhodes grass (<italic>S. italica</italic> 2n = 18; diploid <italic>C. gayana</italic> 2n = 20). The <italic>S. italica</italic> subfamily Panicoideae is estimated to have diverged from the other PACMAD subfamilies about 20mya (<xref ref-type="bibr" rid="B11">Cotton et&#xa0;al., 2015</xref>). The comparative alignment and syntenic comparison provides an indication of genome assembly completeness, and a preliminary indication of how the Rhodes grass contigs may be ordered at a chromosome level. The contig ordering and alignment from this approach has been provided for reference only and has not been used for any further analysis. The availability of a Rhodes grass high-density genetic linkage or optical map anchored to chromosomes would allow for the contigs to be more accurately assembled into pseudochromosomes (<xref ref-type="bibr" rid="B64">You et&#xa0;al., 2018</xref>). However, to date molecular genetic studies for Rhodes grass have generally focussed only on assessing cultivar diversity (<xref ref-type="bibr" rid="B39">P&#xe9;rez et&#xa0;al., 1999</xref>; <xref ref-type="bibr" rid="B53">Ubi et&#xa0;al., 2000</xref>; <xref ref-type="bibr" rid="B52">Ubi et&#xa0;al., 2001</xref>; <xref ref-type="bibr" rid="B55">Ubi et&#xa0;al., 2003</xref>; <xref ref-type="bibr" rid="B42">Ribotta et&#xa0;al., 2019</xref>). To our knowledge, only one genetic linkage map has been constructed for Rhodes grass based on 25 restriction fragment length polymorphisms (RFLPs) and augmented with amplified fragment length polymorphisms (AFLP) markers (<xref ref-type="bibr" rid="B54">Ubi et&#xa0;al., 2004</xref>). The density of this linkage map is insufficient for anchoring and ordering the draft Rhodes grass genome assembly.</p>
<p>It is known from available breeding information (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;1</bold>
</xref>) that the diploid cultivars Finecut, Tolgar and Endura are all descendants of Katambora, a landrace derived from seed collected along the Northern Rhodesian bank of the Zambesi River in Zimbabwe (<xref ref-type="bibr" rid="B13">CSIRO, 1972b</xref>). Hence, the overlapped grouping of the Rhodes grass diploid cultivars was expected due to their shared ancestry and may also reflect the limited selection and outcrossing nature of its breeding system (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>). In general, the diploid samples clustered according to the cultivar from which they were derived, although there was some overlap and mixing most likely due to their shared ancestry from cv. Katambora. The tetraploid cultivars Toro and Mariner clustered into their own group, separate from the diploid samples, with some mixing between cultivars. Despite this, these cultivars have not been reported to share close genetic or geographic origin. Mariner and Toro are descended from cultivars Samford and Callide, respectively (<xref ref-type="bibr" rid="B40">Plant Breeders Rights, 2022</xref>; <xref ref-type="bibr" rid="B3">Barenbrug, 2022a</xref>; <xref ref-type="bibr" rid="B4">Barenbrug, 2022b</xref>). Callide originates from what is now Tanzania, while Samford was derived from ecotypes grown in Sierra Leone that arose from a prior introduction from Kenya (<xref ref-type="bibr" rid="B12">CSIRO, 1972a</xref>; <xref ref-type="bibr" rid="B14">CSIRO, 1972c</xref>). The tetraploids cluster away from the diploids in our phylogeny, as they are likely derived from a speciation event and therefore have more closely linked genetic ancestry. These three parental cultivars, Katambora, Callide, and Samford, were shown by <xref ref-type="bibr" rid="B34">Negawo et&#xa0;al. (2021)</xref> to cluster together when evaluated against other Rhodes grass cultivars, indicating a degree of similarity despite differences in geographic origin and ploidy. The pedigree of the cultivars likely contributes to the overlapping and integrated clades within the Rhodes grass phylogenetic tree. It is also possible that some overlap of individuals among cultivars results from inadvertent seed mixing or interbreeding in the field. The SNP lists used to construct the phylogeny were filtered with strict parameters, which likely resulted in the exclusion of some genuine SNPs and rare alleles. However, as these data are the first of their kind for Rhodes grass we have chosen to provide the most conservative highest quality SNP set as a foundation for future research, also considering the modest average sequence coverage and samples per cultivar that the study generated.</p>
<p>Previous cytologic work has suggested that tetraploid Rhodes grass is likely to be an autotetraploid or segmental allotetraploid due to the observed presence of multiple multivalent formations during meiosis (<xref ref-type="bibr" rid="B33">Nakagawa et&#xa0;al., 1987</xref>). Autotetraploids have two duplicated copies of the genome originating from a single ancestor, while segmental allopolyploids originate from doubled diploid hybrids of closely related species with chromosome pairing generally favoured between common ancestors (<xref ref-type="bibr" rid="B50">Sybenga, 1996</xref>). Given there was little difference (0.39%) in the proportion of diploid and tetraploid sample reads that were mapped to the diploid reference genome, our results suggest the second sub-genome of the tetraploid cultivars is sufficiently similar to the diploid reference to correctly align. The consistency of alignment suggests that the tetraploids originated from a self-genome replication event consistent of an autotetraploid. If the tetraploids were segmental allotetraploids, as has been previously suggested, a considerable difference in the alignment statistics might be expected (<xref ref-type="bibr" rid="B33">Nakagawa et&#xa0;al., 1987</xref>; <xref ref-type="bibr" rid="B32">Loch et&#xa0;al., 2004</xref>). A much higher frequency of heterozygous SNP genotype calls would also be expected for the tetraploid samples when their sequence reads were mapped to the diploid genome assembly due to sequence divergence between some chromosomes of one genome and those of the other genome in the tetraploid samples. Consistent with previous cytological studies which showed that tetraploid Rhodes grass forms multivalents during meiosis, our results allow us to rule out the possibility that the tetraploids are allotetraploids (two distinct genomes brought together through hybridisation between two species followed by doubling of the chromosomes of the resulting hybrid). If allotetraploids, sequence reads from the second unrepresented sub-genome would be expected to misalign with the diploid Rhodes grass genome assembly, resulting in a high frequency of heterozygous SNP genotype calls which was not observed. Further, a proportion of the SNP from the tetraploid samples would be expected to possess a nucleotide variant not observed among SNP discovered in the diploid samples. Hence, our results suggest that tetraploid Rhodes grass is an autopolyploid.</p>
<p>This understanding of Rhodes grass as an autopolyploid can facilitate breeding and genome editing for improved forage qualities. As the chromosome sets in tetraploid Rhodes are homologous, polysomic inheritance can occur with recombination of alleles possible between sub genomes, allowing for inheritance of desirable traits (<xref ref-type="bibr" rid="B27">Klie et&#xa0;al., 2014</xref>). Genome editing polyploids can be complicated due to circumstances including high heterozygosity, presence of homoeologous alleles and repetitive DNA. Nevertheless, genome editing of several polyploid species has been achieved with various site-directed nuclease (SDN) technologies, such as Talens and CRISPR-Cas9 (<xref ref-type="bibr" rid="B44">Schaart et&#xa0;al., 2021</xref>). Previous work on editing autotetraploid crops has been facilitated by the presence of the whole genome sequence of the autotetraploid, the comparison of autotetraploid short read sequence to a diploid genome, or by characterising specific target regions then looking for genetic variation between targets (<xref ref-type="bibr" rid="B26">Johansen et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B9">Chen et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B45">Sevestre et&#xa0;al., 2020</xref>). By generating short read sequence and SNP data for Rhodes grass tetraploids, as well as a diploid whole genome sequence, our work could facilitate future breeding and genome editing of tetraploid Rhodes grass.</p>
<p>The draft genome sequence generated here enables transcriptome studies on the variation of important traits in Rhodes grass, like salt tolerance and resilience to varying growth temperatures (<xref ref-type="bibr" rid="B8">Bogdan, 1969</xref>; <xref ref-type="bibr" rid="B32">Loch et&#xa0;al., 2004</xref>; <xref ref-type="bibr" rid="B36">Oi et&#xa0;al., 2022</xref>). Additionally, the SNP annotation data gives an insight into variations in the sequence that may impact gene function between individuals and can better explain varying phenotypes between individuals. These data will be of significant value for the research community that is gaining increased interest in this species as it will enable a wide range of molecular breeding tools as well as an array of biotechnology approaches to now be possible.</p>
</sec>
<sec id="s5" sec-type="conclusion">
<label>5</label>
<title>Conclusion</title>
<p>The draft diploid Rhodes grass genome assembly presented here represents the second draft genome assembly among Cynodonteae forages and first within the <italic>Chloris</italic> genus. It provides an important resource to underpin the genetic improvement of important breeding traits such as forage digestibility. Our results provide supporting evidence that is consistent with tetraploid Rhodes grass having evolved through an autopolyploid process. The genomic resources generated can be used to facilitate genomic breeding approaches and genome editing, and provide the opportunity to use Rhodes grass as a model C4 forage species for functional genomic studies. Rhodes grass is a resilient warm season forage crop, which would benefit from genetic improvement of its forage quality since it has been identified as a potential future forage species for dairy and livestock industries in regions where global warming is expected to reduce temperate pasture quality and productivity.</p>
</sec>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Bioproject accession number: PRJNA974075.</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>The authors contributed to the paper as follows, study conception; NC, MH, KM-R, and DI: data collection; KM-R: data analysis and interpretation; KM-R: paper drafting; KM-R: paper revision and editing; KM-R, NC, DI, and MH. All authors contributed to the article and approved the submitted version.</p>
</sec>
</body>
<back>
<sec id="s8" sec-type="funding-information">
<title>Funding</title>
<p>The authors declare that this study received funding from Agriculture Victoria, Dairy Australia and The Gardiner Dairy Foundation through the DairyBio initiative and La Trobe University. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>We thank Dr. Michelle Malmberg for her assistance with SNP filtering. We also thank Saila Vishwanathan and Bec Baillie for assistance with DNA extraction and sequencing.</p>
</ack>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s10" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s11" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fpls.2023.1239290/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fpls.2023.1239290/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Image_1.jpeg" id="SF1" mimetype="image/jpeg">
<label>Supplementary Figure&#xa0;1</label>
<caption>
<p>Rhodes grass phylogenetic tree based on a neighbour-joining distance matrix of filtered SNP relationships between diploid individuals</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Image_2.jpeg" id="SF2" mimetype="image/jpeg">
<label>Supplementary Figure&#xa0;2</label>
<caption>
<p>Rhodes grass phylogenetic tree based on a neighbour-joining distance matrix of filtered SNP relationships between tetraploid individuals</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Table_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
<supplementary-material xlink:href="DataSheet_1.csv" id="SM2" mimetype="text/csv"/>
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