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. 2025 Jun 20;15(8):jkaf133. doi: 10.1093/g3journal/jkaf133

Measuring genetic diversity of Texas bluegrass and allele sharing with Kentucky bluegrass interspecific hybrids using genome-wide markers

Nicholas A Boerman 1, Matthew D Robbins 2,✉,2, Ambika Chandra 3, Samantha Brentano 4, Tom Brentano 5, B Shaun Bushman 6
Editor: T Jamann
PMCID: PMC12415329  PMID: 40540539

Abstract

Kentucky bluegrass is an important cool-season turfgrass species. However, heat and drought tolerance is an issue. Interspecific hybridization with a related species, Texas bluegrass, is an approach used to improve heat and drought tolerance. We report herein a contig assembly and annotation of Texas bluegrass that was completed and used to measure the population structure of Texas bluegrass, interspecific lines between Texas and Kentucky bluegrass, and the percent allele sharing between advanced interspecific lines and cultivars to Texas bluegrass. The contig assembly was comprised of 367 contigs and spanned 6.6 Gb with 198,746 predicted gene models and an assembly and transcriptome completeness of over 97% as indicated by BUSCO orthologous gene alignment. It was used to call 14,504 high-quality SNPs. A principal component analysis showed separation of populations across the first 3 PCs, explaining 21.5, 11.1, and 5.4%, respectively, of the variation across the populations. Advanced interspecific lines and cultivars diverged from Texas bluegrass while sharing 62–74% of their alleles with Texas bluegrass. Interspecific Texas × Kentucky bluegrasses could be important for improving heat and drought tolerance among bluegrasses.

Keywords: Texas bluegrass, Kentucky bluegrass, turfgrass, genome assembly

Introduction

Kentucky bluegrass (Poa pratensis L.) is a sod forming perennial cool-season grass having perfect flowers and facultative apomixis (Hitchcock 1951). Kentucky bluegrass is also an important species in cool-season turfgrass systems; however, heat and drought stress are a major concern (Bonos and Murphy 1999; Jiang and Huang 2000). One approach used to reduce the effects of heat and drought stress has been to make interspecific hybrids by crossing Kentucky bluegrass as the pollen parent with Texas bluegrass (Poa arachnifera L.) as the seed parent (Vinall and Hein 1937). Texas bluegrass is a cool-season grass native to the rangelands of the Southern Great Plains, therefore having excellent heat and drought tolerance (Hitchcock 1951). It is a dioecious, sexually reproducing species, whose seed has copious amounts of cotton-like fibers on its lemma that prevent economical seed production of the species per se (Kindiger 2014 ). Texas bluegrass is an octaploid that has exhibited segmental allo-polyploidy according to analysis with AFLP markers (Renganayaki et al. 2005).

The first interspecific hybrid between Texas bluegrass and Kentucky bluegrass to be commercialized was released in 1998. ‘Reveille’ interspecific bluegrass had similar turf quality to ‘Baron’ Kentucky bluegrass but had greater heat and drought tolerance than other Kentucky bluegrasses in a nursery study at Dallas, TX (Read et al. 1999). Su et al. (2007) observed that the hybrid cultivar ‘Thermal Blue’ had lower electrolyte leakage and soil surface temperatures, greater photosynthesis rate, and greater dry matter production than ‘Apollo’ Kentucky bluegrass and ‘Dynasty’ tall fescue (Festuca arundinacea) under heat stress. However, when evaluating drought stress, the differences between ‘Thermal Blue’, ‘Apollo’, and ‘Dynasty’ were negligible, (Su et al. 2007). In a different study, a Texas bluegrass collection, ‘Midnight’ Kentucky bluegrass, and 2 interspecific hybrid BC4 lines between Texas bluegrass and ‘Midnight’ exhibited greater drought tolerance than 29 other BC4 lines, 1 BC3 line, and 2 Kentucky bluegrass lines (Abraham et al. 2004). Richardson et al. (2009) observed that several interspecific hybrids had “good” drought tolerance, but the best line overall was a pure Kentucky bluegrass cultivar. These studies indicate that Texas bluegrass can provide favorable alleles for heat tolerance, but the environment, genetic background, and method for quantifying heat and drought tolerance play a major role in their performance.

Thus far, phenotypic selection has been predominantly used when breeding bluegrasses due to a lack of genomic resources, and ecotypic selection due to the challenging breeding systems of both species. When making interspecific hybrids, a dioecious Texas bluegrass female plant is pollinated by apomictic Kentucky bluegrass. The progeny are then grown, and those having perfect flowers are selected. The selected plants are self-pollinated and the progeny grown to evaluate uniformity that is used as an indication for the degree of apomixis, which is ideally 90–98%. The lines having greater than 90% uniformity are then tested in multiple environments for seed yield and turfgrass performance, with the best lines eventually released. These lines are often re-used as parents in new crosses with other Kentucky bluegrass male parents, or interspecific hybrids, in a modified backcross breeding scheme. When selecting plants having a Kentucky bluegrass phenotype and using modified backcrossing, the percent of Texas bluegrass genome recovered is not known.

The objective of this study is to develop and utilize a contig-level assembly of Texas bluegrass for calling single nucleotide polymorphisms (SNPs) in Texas bluegrass and interspecific germplasm. Those SNPs will then be used herein to evaluate the diversity of Texas bluegrass and interspecific hybrid populations, and the percent allele sharing with a Texas bluegrass plant among several commercially available interspecific hybrid cultivars, advanced F1, and presumed BC1 breeding lines.

Materials and methods

Plant materials

A single plant from the D4 population (22D4_37) of Texas bluegrass was used for PacBio sequencing. The D4 population is a collection from a natural stand of Texas bluegrass near Vici, OK. To evaluate the population structure of Texas bluegrasses, between 4 and 6 plants from 32 populations were sampled. Single plants from 8 interspecific F1 or BC1 hybrid cultivars between Texas bluegrass and Kentucky bluegrass were sampled: ‘Bandera’, ‘Reveille’ (Read et al. 1999), ‘SPF 30', ‘Southern Blue’ (Meeks et al. 2015), ‘Sunbelt Blue’, ‘Thermal Blue’, ‘Fahrenheit 90', and ‘Solar’. Eighteen advanced F1 and BC1 breeding lines from the USDA-ARS Woodward, OK location (OKBG code), 9 breeding lines from Novel Ag Inc. (NAI code), and 15 F1 and BC1 crosses from the Woodward USDA-ARS breeding program having a Texas bluegrass phenotype (s16-x-x or s22-x-x) were included.

Tissue preparation and sequencing

Young leaf tissue for PacBio HiFi sequencing was collected into two 50 mL centrifuge tubes and flash frozen using liquid nitrogen. High-molecular weight DNA was extracted using a phenol CTAB extraction, and sequencing was conducted using 10 PacBio HiFi SMRT cells at the USDA-ARS GBRU in Stoneville, MS. For genotyping, leaf tissue from young leaves of plants grown in the greenhouse was sampled, lyophilized, and 15 mg of dried tissue measured into 1.5 mL microtiter tubes. Zymo Research Directzol DNA extraction kits were used for DNA extraction. Agarose electrophoresis and spectrophotometry were used for quality control, and a double-digest genotyping-by-sequencing (GBS) library was prepared according to Poland et al. (2012). Three libraries each containing 100 samples were sequenced on an Illumina NextSeq 500 having high-throughput 75 cycle flowcells by the Utah State University Center for Integrated Biosystems in Logan, UT.

Genome assembly, annotation, and SNP calling

An approach similar to Robbins et al. (2023) was used for PacBio HiFi assembly and quality testing. Briefly, the PacBio HiFi reads were assembled using hifiasm v. 0.19-r534 (Cheng et al. 2021) with the “-hom-cov” parameter set at 60 to produce a HiFi-only assembly. The poales_odb10 dataset in BUSCO v. 5.4.7_cv1 (Manni et al. 2021) was used to assess gene coverage of the assembly. Since the purpose of the assembly was for mapping GBS markers, contigs were filtered to those that contained at least 7 BUSCO orthologs to target contigs with genic regions and reduce repetitive and duplicated contigs. The LTR Assembly Index (LAI; Ou et al. 2018) was then calculated with LTRharvest (Ellinghaus et al. 2008) through GenomeTools v1.6.3 (Gremme et al. 2013), LTR_FINDER_parallel v1.1 (Ou and Jiang 2019), and LTR_ retriever v2.9.6 (Ou and Jiang 2018). Assembly metrics, which were calculated using QUAST v. 5.2.0 (Mikheenko et al. 2018) with the --large option, the LAI, and BUSCO scores are reported for the filtered assembly in Table 1.

Table 1.

Descriptive metrics of Texas bluegrass reference assembly and annotation.

Assembly metrics Contig count 367
Largest contig 141,900,499
Total length 6,632,030,032
GC (%) 45.93
N50 31,639,897
L50 60
LAI 20.40
Assembly BUSCO Complete 4,828 (98.6%)
Complete single copy 32 (0.7%)
Complete duplicated 4,796 (98.0%)
Fragmented 5 (0.1%)
Missing 63 (1.3%)
Total BUSCOs 4,896
Annotation Gene count 198,746
Percent of assembly covered by genes 11.1%
Single exon gene count 27,535
Mean gene length (bp) 3,717
Mean exons per gene 6.0
Mean introns per gene 4.5
Genes with functional annotation 185,802 (93.5%)
Annotation BUSCO Complete 4,752 (97.1%)
Complete single copy 142 (2.9%)
Complete duplicated 4,610 (94.2%)
Fragmented 12 (0.2%)
Missing 132 (2.7%)
Total BUSCOs 4,896
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To perform ab initio prediction of gene models in the assembly, Helixer v0.3.4 (Holst et al. 2023) was employed with default parameters using the land plant model. Predicted genes were analyzed using CD-HIT v4.8.1 (Fu et al. 2012; Li and Godzik 2006) with the sequence identity threshold of 1 to identify duplicates and TEsorter v1.4.6 (Zhang et al. 2022) using the REXdb Viridiplantae v3.0 database (Neumann et al. 2019) to identify transposable elements. Duplicates and TEs were removed from the .gff file with AGAT v1.0.0 (Dainat et al. 2022). Gene models were functionally annotated using EnTAP v1.0.0 (Hart et al. 2020) configured to use the NCBI RefSeq (O'Leary et al. 2016) plant (release 220), NCBI BLAST (Sayers et al. 2025) nr (downloaded 2023 October 23), UniProtKB/Swiss-Prot (The UniProt Consortium 2017) (release 2023_04), and eggNOG (Huerta-Cepas et al. 2019) v5.0.2 databases employing TransDecoder v5.7.1 for frame selection, DIAMOND v2.1.8 (Buchfink et al. 2021) for similarity searching, and eggNOG-mapper v2.1.12 (Cantalapiedra et al. 2021) and InterProScan v5.64-96.0 (Jones et al. 2014) for gene family and ontology analysis. Only transcripts that contained open reading frames (ORFs), identified by TransDecoder, were kept using AGAT to filter, and custom scripts to add the functional annotations to the .gff file. The GffRead (Pertea and Pertea 2020) utility from Cufflinks was used to create protein and nucleotide sequences from the gene models. Transcriptome completeness was evaluated on the nucleotide sequences from the gene models using BUSCO in transcriptome mode as above.

An approach similar to Bushman et al. (2024) was used for genotyping. Briefly, SNPs were called using the GBS pipeline in Tassel v. 5.0 (Bradbury et al. 2007). Genotypes were mapped against the 22D4_37 reference contigs using Bowtie2 v. 2.3.4.1 (Langmead and Salzberg 2012). Custom scripts were used to filter the genotype sites generated in Tassel, where SNPs with a minor allele frequency below 0.05, SNPs having homozygous base calls below 7 per site, and SNPs with more than 20% missing data were removed. Missing data were imputed in Tassel using the numerical imputation by k-nearest-neighbors method, default k-value of 5, and Euclidean distance measure for computing the nearest neighbor. Allele values were then converted to a 0,1,2 format using custom scripts in R.

Allele sharing

A chi-square value was calculated for each marker, where the marker loci of the advanced and released interspecific hybrids were the observed values and 22D4_37 was the expected value. When an allele was shared between the interspecific plant and 22D4_37 at any given marker locus, the chi-square value was 0. This was calculated individually for each marker and plant, and the number of chi-square values equaling 0 was counted for each plant. The percent allele sharing was calculated by dividing the sum of markers with a chi-square value of 0 by the total number of markers. After filtering out markers that had a 0 allele code for 22D4_37, a total of 14,462 markers were used in the analysis.

Population structure

Marker data in numeric 0,1,2 format were converted to a genid object using the df2genid function and ploidy = 2 option from the adegenet package (Jombart 2008; Jombart and Ahmed 2011) in R (R Core Team 2024). The marker data were treated as bivalent products due to the allo-polyploid genome structure of P. arachnifera. The ideal number of clusters was identified using the find.clusters function in adegenet with a max of 20 clusters, 200 PCA, and scale = false parameters. A PCA was conducted using the glPca function in adegenet, and the ggplot function from the ggplot2 package (Wickham 2016 ) was used to plot PC1, PC2, and PC3.

Results and discussion

Genome assembly and annotation

The PacBio reads were assembled into 367 contigs, the longest of which was 141,900,499 bp. Total assembly length was 6,632,030,032 bp, and it had a percent GC of 45.93%. More than half of the nucleotides were found in contigs that were longer than 31 Mbp and were found in the 60 longest contigs when ordered by length (Table 1). Completeness of the assembly was assessed using the poales BUSCO orthologs (Manni et al. 2021), with 4,828 orthologs identified for a completeness of 98.6%. The LAI of 20.40, classified as Gold (Ou et al. 2018), also indicates that the assembly is complete. The assembly was sufficiently complete to call SNPs for genome-wide association studies and genomic selection. Duplicated BUSCOs were high at 4,791 (97.96%), which is expected since Texas bluegrass is an allo-octaploid.

The use of Helixer as an ab initio method to predict gene models from the assembly identified 296,838 genes across all 367 contigs. After removal of duplicates (28,779), TEs (4,963), and gene models without ORFs (64,350), 198,746 filtered genes remained (Table 1). Ninety-four percent of the 198,746 genes were functionally annotated. These genes covered 11% of the total assembly length with a mean gene length of 3,717 bp, a mean of 6.0 exons, and a mean of 4.5 introns. Similar to the assembly, BUSCO scores of the gene transcripts were above 97%, with 94% duplicated.

Allele sharing

The percent alleles shared with 22D4_37 among advanced lines and released cultivars ranged from a low of 62.7% for NAI 17-48 to a high of 73.6% for ‘Sunbelt Blue’ (Table 2). All BC1 lines were below 70% allele sharing with 22D4_37, except for 3 off-types from OKBG_09; which shared 72.5%, 73.9%, and 83.5% of their alleles with 22D4_37 (Table 2). Among the full sib BC1 lines NAI 17-38, NAI 17-43, NAI 17-48, and NAI 17-49, there were slight differences in allele sharing with 22D4_37, ranging from 0.1 to 0.3%. Whereas their F1 parent NAI 13-132 did not differ from its full sib NAI 13-14 in percent alleles shared with 22D4_37. Each of the progeny lines differed from each other by approximately 15 markers, or 0.1%. Therefore, phenotypic selection was effective in selecting plants that were genetically distinct from the seed parent at multiple loci. However, enrichment for Kentucky bluegrass alleles did not occur despite selection for a Kentucky bluegrass phenotype. The Woodward bluegrass BC1 lines OKBG_02, OKBG_05, and OKBG_07 were full sibs and differed in allele sharing by a minimum of 1.2% between OKBG_05 and OKBG_07 and a maximum of 5.3% between OKBG_02 and OKBG_05. The seed parent of those lines was the F1 OKBG_01, which had 2.7% higher allele sharing with 22D4_37 than the next closest OKBG_05 progeny. The difference in percent allele sharing between these lines was greater than the difference in allele sharing between Novel AG Inc. lines. This greater allele sharing difference among selected Woodward lines could be due to the difference in genetic backgrounds, selection criteria, or the heat and drought stress in Woodward that may allow for better discrimination between lines for selection. Further evaluation of these lines would be required to understand the cause of this difference.

Table 2.

Percent alleles shared with 22D4_37 for each advanced breeding line and cultivar.

Genotype % alleles shared w/ 22D4_37 Generation
NAI 17-48 62.7 BC1
NAI 17-49 62.8 BC1
OKBG_02 62.8 BC1
NAI 17-43 62.9 BC1
OKBG_08 62.9 BC1
NAI 17-38 63 BC1
NAI 13-132 63.2 F1
NAI 13-14 63.2 F1
Bandera 63.4 F1
Solar 63.6 F1
NAI 17-8 63.7 BC1
SPF 30 64.2 F1
Thermal Blue 65.7 F1
OKBG_09 65.8 BC1
OKBG_06 66.1 F1
OKBG_16 66.4 BC1
OKBG_07 66.9 BC1
NAI 20-171 67.2 F1
OKBG_03 67.3 BC1
Fahrenheit 90 67.4 F1
NAI 20-157 67.9 F1
OKBG_05 68.1 BC1
OKBG_14 68.2 BC1
OKBG_13 68.5 BC1
OKBG_10 68.8 BC1
OKBG_15 69.9 F1
Reveille 70.8 F1
OKBG_01 70.8 F1
OKBG_12 71 F1
Southern Blue 71.2 F1
OKBG_11 71.2 F1
OKBG_09_offtype-2 72.5 BC1
PC2Poland 72.6 F1
Sunbelt Blue 73.6 F1
OKBG_09_offtype-1 73.9 BC1
OKBG_09_offtype-4 83.5 BC1
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It is possible that Texas and Kentucky bluegrass share and have retained a common subgenome, or the same portion thereof, following divergence from a common ancestor. This scenario would increase the percent of allele sharing with Texas bluegrass as any of those alleles within a common subgenome would appear to be inherited from Texas bluegrass, even though the Kentucky bluegrass parent contributed them. While the cause of allele sharing is speculative, it does indicate that phenotypic selection for plants having a Kentucky bluegrass phenotype has not enriched the interspecific populations with alleles specific to Kentucky bluegrass.

Population structure

The first 2 principal components explained 21.5 and 11.1% of the variation and component 3 explained 5.4%. The Texas bluegrass collections were tightly clustered while F1 and BC1 interspecific lines and cultivars were more widely dispersed (Fig. 1). Thirteen F1 and BC1 interspecific hybrids clustered close to the Texas bluegrass entries. Differing 2C DNA content has been observed among Texas bluegrass populations ranging from 9.64 to 14.75 pg (Goldman 2015; Meeks and Chandra 2015), indicating that genome duplication or deletion events have occurred within the species. Stratification of the Texas bluegrass populations in this study was not observed, but that may be due to the plants sampled, or the duplication/deletion events observed in Goldman (2015) and Meeks and Chandra (2015) are recent and have not yet led to widespread stratification.

Fig. 1.

Fig. 1.

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Population structure explained by the first 2 PCs (upper panel), and the first and third PCs (lower panel) of Texas bluegrass populations from Oklahoma and Texas (TXOK), North-Central Texas (NCTX), F1 interspecific hybrids, and BC1 interspecific hybrids. Percent genetic variation explained by each PC is in parentheses on each axis.

The interspecific hybrids were not as tightly grouped as the Texas bluegrasses entries while most of the F1 and BC1 Novel Ag Inc. lines grouped together above 20 in PC1 (Fig. 1). Given the tight clustering of Texas bluegrasses, the diversity of Kentucky bluegrass alleles likely explains the population structure among the interspecific lines and cultivars.

Conclusion

A contig assembly having 98% BUSCO completeness was developed and used for calling 14,504 high-quality SNPs. A subset of 14,462 SNPs was used to estimate percent allele sharing between advanced interspecific breeding lines and commercial cultivars to the Texas bluegrass plant used for the contig assembly. Over 60% of alleles mapping to the Texas bluegrass reference sequences were retained in the interspecific lines. Phenotypic selection was generally effective in selecting plants that were genetically distinct from their seed parent; however, no major enrichment of Kentucky bluegrass alleles occurred despite selecting plants having phenotypes similar to Kentucky bluegrass. On the one hand, a PCA showed Texas bluegrass to group very tightly relative to the F1 and BC1 interspecific germplasm sources, despite diverse geographic population sampling. On the other hand, all but 13 of the F1 and BC1 interspecific lines and cultivars diverged from the Texas cluster, indicating that the Kentucky bluegrass alleles are likely contributing population structure among these lines.

Acknowledgments

This research was supported in part by the United States Department of Agriculture Agricultural Research Service (USDA ARS). Kimberly Thorsted provided laboratory support.

Contributor Information

Nicholas A Boerman, Livestock, Forages, and Pasture Management Research Unit, USDA-ARS, 2000 18th St., Woodward, OK 73801, USA.

Matthew D Robbins, Forage and Range Research Laboratory, USDA-ARS, 695 North 1100 East, Logan, UT 84322, USA.

Ambika Chandra, Texas A&M Agrilife and Extension Center, Texas A&M University, 17360 Coit Rd., Dallas, TX 75252, USA.

Samantha Brentano, Novel Ag Inc., 17301 River Rd. Ne, St. Paul, OR 97137, USA.

Tom Brentano, Novel Ag Inc., 17301 River Rd. Ne, St. Paul, OR 97137, USA.

B Shaun Bushman, Forage and Range Research Laboratory, USDA-ARS, 695 North 1100 East, Logan, UT 84322, USA.

Data availability

The pseudohaploid contig-level assembly has been deposited at DDBJ/ENA/GenBank under the accession JBJNEG000000000 and is the version described in this paper. The raw PacBio HiFi reads have been deposited at NCBI GenBank under BioProject ID: PRJNA1190475, and BioSample accession: SAMN45046423. A description of the pipeline and the scripts used for de novo assembly are available on GitHub: https://github.com/MatthewRobbins-USDA/FRR_hfiasm_assemblies. The genome and annotation (.gff file) have also been deposited at CoGe as genome id68988 (https://genomevolution.org/coge/GenomeInfo.pl?gid=68988; last accessed 2025 May 7).

Funding

Funding was provided by the United States Department of Agriculture, Agricultural Research Service through base funds USDA ARS.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Dainat  J, Hereñú  D, Davis  E, Crouch  K, LucileSol, Agostinho  N. 2022. NBISweden/AGAT: AGAT-v1.0.0. Zenodo. doi: 10.5281/zenodo.7255559. [DOI]

Data Availability Statement

The pseudohaploid contig-level assembly has been deposited at DDBJ/ENA/GenBank under the accession JBJNEG000000000 and is the version described in this paper. The raw PacBio HiFi reads have been deposited at NCBI GenBank under BioProject ID: PRJNA1190475, and BioSample accession: SAMN45046423. A description of the pipeline and the scripts used for de novo assembly are available on GitHub: https://github.com/MatthewRobbins-USDA/FRR_hfiasm_assemblies. The genome and annotation (.gff file) have also been deposited at CoGe as genome id68988 (https://genomevolution.org/coge/GenomeInfo.pl?gid=68988; last accessed 2025 May 7).

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