Identification and genomic characterization of major effect bacterial blight resistance locus (BB-13) in Upland cotton (Gossypium hirsutum L.)

doi:10.21203/rs.3.rs-1601401/v1

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Identification and genomic characterization of major effect bacterial blight resistance locus (BB-13) in Upland cotton (Gossypium hirsutum L.)

https://doi.org/10.21203/rs.3.rs-1601401/v1

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Cotton bacterial leaf blight (CBB), caused by Xanthomonas citri subsp. malvacearum (Xcm) has been periodically a damaging disease in the U.S.A. Identification and deployment of genetic resistance in the cotton cultivars is the most economical and efficient means of reducing the crop losses due to CBB. In the current study, a combined genome-wide association study (GWAS) and linkage-mapping approach was used to map the CBB resistance gene in Upland cotton. An elite diversity panel of 380 accessions, genotyped with the Cotton 63K single nucleotide polymorphism (SNP) array and phenotyped with race-18 of CBB was used in the GWAS. The GWAS localized the CBB resistance to a 2.01 Mb region in the long arm of chromosome D02. Mapping of this CBB resistance was further resolved using linkage mapping in an F₆ recombinant inbred line (RIL) population derived from Acala Maxxa × Arkot 8102. The CBB resistance in Arkot 8102 showed monogenic inheritance. The CBB resistance locus (BB-13) was mapped within the 0.95 cM interval near the telomeric region in the long arm of chromosome D02. Flanking SNP markers, i25755Gh (p = 19.29) and i46775Gh (p = 19.29) of the BB-13 locus from the linkage analysis showed the highest significant marker-trait associations (MTAs) in the GWAS study. Using these SNPs, we targeted the BB-13 locus to a 371 Kb genomic region on chromosome D02. Candidate gene analysis identified thirty putative gene sequences in the targeted region. Nine of the thirty putative genes were involved in disease resistance in plants.

cotton

bacterial leaf blight

disease resistance

SNP markers

Xanthomonas citri

GWAS

mapping

Identification and genomic characterization of major resistance locus against cotton bacterial blight (CBB) using GWAS and linkage mapping to enable genomics-based development of durable CBB resistance and gene discovery in cotton.

Cotton lint is the primary source of natural fiber and its seeds are a source of plant oil and animal feed. Cotton lint is indispensable for global textile and apparel manufacturing which are valued at $748 and $786 billion, respectively (Voora et al. 2020). Cotton belongs to the genus Gossypium that comprises over 50 recognized species (Wendel and Grover 2015). Among the cultivated cotton species, two are diploids (Gossypium arboreum and G. herbaceum) and two are tetraploids (G. hirsutum and G. barbadense). Due to its superior productivity and adaptability, G. hirsutum is the most widely grown cotton species in the world, accounting for 96 percent of total production (Meyer 2021). Annual business revenue stimulated by cotton in the U.S. economy exceeds $140 billion, making cotton America’s number one value-added crop (National Cotton Council 2019).

G. hirsutum, also called Upland cotton, is affected by biotic and abiotic stresses throughout the U.S. cotton belt. Among the biotic stresses, cotton bacterial blight (CBB) caused by Xanthomonas citri subsp. malvacearum (Xcm) was first documented in 1891 (Atkinson 1892). Typical CBB symptoms appear as water-soaked lesions on the abaxial surface of cotyledonary and true leaves. Symptoms may also appear on petals and stems. Disease lesions may be surrounded by a yellow halo and turn into black/brown lesions over time. Infection on the bracts along with the secondary boll rotting fungi leads to boll rot. Apart from affecting the yielding ability of the plants through photosynthetic inhibition, severe infection also affects the fiber quality (Bird and Blank 1951). CBB has the potential to cause significant yield losses. For instance, in New Mexico, Xcm infected roughly 40,000 acres in 1949, resulting in 35 to 50% yield losses (Bird and Blank 1951).

Bacterial pathogens, such as Xcm, use Type III secreted effectors (T3Es) to cause disease in susceptible plant hosts (Feng and Zhou 2012). Plant genomes encode immune receptors known as Nucleotide Binding Site Leucine Rich Repeat receptors (NBS-LRRs) that recognize specific effectors, directly or indirectly, and mount a defense response (De Young and Innes 2006). Often, NBS-LRR mediated defense responses include a hypersensitive response (HR), in which locally infected cells undergo rapid death that minimizes pathogen growth and spread (Doyle et al. 2013). Resistance triggered through NBS-LRRs and effectors is known as gene-for-gene resistance (Flor 1971).

In 2011, an outbreak of CBB started in Northeastern Arkansas and spread throughout the Central Delta (Phillips et al. 2017). In 2012, Mississippi State University reported that Xcm had spread to 23 counties due to the cultivation of vulnerable cultivars. In 2015 and 2016, CBB was problematic in Georgia, Mississippi, and Texas, among other states (Roberts 2016; Phillips et al. 2017). There are 22 known Xcm races in cotton (Delannoy et al. 2005), with Race-18 historically being the most prevalent in the United States. Initially, it was thought that the outbreak might be due to mutations that allowed the pathogen to overcome existing resistance or introduction of a new race of the pathogen. However, genomic sequencing and pathogenicity assays suggested that the recent outbreak was not due to genetic evolution in Xcm strains to circumvent existing resistance effectors (Phillips et al. 2017; Wheeler et al. 2021). Notably, the bacteria can survive in seeds, even acid-delinted seed (Alexander 2009) and recent studies revealed that the percentage of susceptible Upland cotton cultivars has increased dramatically since 2009 (Phillips et al. 2017), which has likely contributed to the re-emergence of this disease. Hence, there is an urgent need to rapidly deploy cultivars with resistance to CBB in cotton.

Identification and accurate genomic location of CBB resistant genes is a prerequisite for leveraging genomics and molecular biological resources for developing durable resistance to CBB in cotton. To date, more than twenty sources of resistance and two polygene complexes have been identified that confer resistance to CBB (Green and Brinkerhoff 1956; Innes 1969; Knight 1963; Wallace and El-Zik 1989). However, the exact locations of resistance genes in the cotton genome have yet to be determined. The CBB resistance in G. hirsutum cv. S295, effective against all known races of Xcm (Girardot et al. 1986; Wallace and El‐Zik 1989), was mapped as a major gene and QTL on chromosome 14/(D02) using RFLP, SSR, AFLP and SNP markers, all using bi-parental mapping populations (Wright et al. 1998; Rungis et al. 2002; Xiao et al. 2009; Yang 2013). Additionally, a recent genome-wide association study (GWAS) of CBB resistance suggested that there are as many as eleven QTLs mapped on A01, A05, D02, D08, and D10 (Elassbli et al. 2021). The current study aims to identify the resistance haplotypes in the diversity panel and precisely map the CBB resistance gene(s) using GWAS and linkage mapping in Upland cotton.

Plant materials

In the current study, three hundred and eighty G. hirsutum accessions from the diversity panel assembled by Tyagi et al. (2014) were used for a genome-wide association study (GWAS) of CBB resistance. The diversity panel consisted of cotton accessions developed from the 1900s to 2005. The GWAS identified marker-trait associations (MTAs) were fine-mapped using the linkage mapping approach. For linkage mapping purposes, two accessions from the diversity panel Acala Maxxa (PI 540885) and Arkot 8102 (PI 595852) (Bourland et al. 1997) were crossed to develop a recombinant inbred line mapping population. The female parent Acala Maxxa was susceptible (water-soaked lesions) whereas the male parent Arkot 8102 (G. hirsutum cv Miscot T8-27 × G. hirsutum cv Stoneville 825) was resistant (hypersensitive response) to race-18 of Xcm (Fig. 1) (Bourland and Bridge 1988). One hundred and ten F₆ lines were developed using the single seed descent method by manual self-pollination of F₂ and subsequent generation plants. Parental accessions Acala Maxxa and Arkot 8102 were evaluated for their response to races 1 (strain BLX718), 2 (strain BLX719), 3 (strain BLX720), 12 (strain BLX721), and 18 (strains BLX910 and AP16) of Xcm.

Phenotyping

The diversity panel and the RILs were evaluated for response to Xcm race-18 (strain BLX910) infections. The Xcm race-18 was obtained from leaf samples from Dr. Terry Wheeler of Texas A&M University, Lubbock, TX. Isolate from a single bacterial colony was grown on NYGA medium (with peptone, yeast, glycerol, and 1% agar) with streptomycin at 30°C for 48 hours (Daniels et al. 1984). Cells were suspended to an optical density of 0.1 (OD600) in 10 mM MgCl₂ (~ 5×10⁷ cfu/ml). Cotton seeds were planted in 3.5-inch pots with two replicates per pot and grown in the greenhouse (28°C, 50% humidity, 16 hours light, 8 hours dark). Seven days after planting, the cotyledons of seedlings were infiltrated with ~ 0.1 ml of bacterial inoculum with a needleless syringe and 3–5 replicates were inoculated for each cotton line. The infiltration process was carried out in an inoculation room that is maintained at 50% humidity. Three hours after inoculation, seedlings were moved to a growth chamber set at 30℃ temperature, 80% humidity and 14-hour day length. Seven days post inoculation, infiltrated leaves were assessed for response to the pathogen. A susceptible response was characterized by water-soaked lesions, while a resistance response was characterized by the hypersensitive necrotic response. Pearson's Chi-square statistic was utilized to verify the Mendelian monogenic segregation ratio for the CBB resistance in the RIL population. The resistant male parent Arkot 8102 was also infiltrated with Xcm races 1, 2, 3, 12, and 18 to determine its response to various races of Xcm, following the same procedure described above.

DNA isolation and genotyping

Young leaf tissues from three- to four-week-old accessions of the diversity panel and F₆ RILs were collected in 2 ml centrifuge tubes and frozen in liquid nitrogen. The leaf tissues were ground using the YG5 3422011 macerator (BTLab systems, St. Louis, MO) and the DNA was isolated following a modified small-scale DNA extraction protocol using the DNeasy Plant Mini-Kit (Qiagen USA, Germantown, MD). The Nanodrop™ spectrophotometer ND1000 was used to estimate the quality and quantity of DNA in the samples. High-quality DNA (1.8 ≤ 260/280 ≤ 2.0 and 260/230 > 1.5) was utilized for genotyping using the Cotton SNP 63K array (Hulse-Kemp et al. 2015) following the standard Illumina protocol at Texas A&M Institute for Genome Sciences and Society. The amplified and fragmented DNA samples are purified before hybridizing with the bead array. After the single bead extension followed by staining, the samples were imaged in Illumina iScan (Illumina Inc., San Diego, CA, USA) array scanner. The image files were analyzed in GenomeStudio 2.0 using the curated cluster file for obtaining information on the 63,058 SNP calls.

Genome-wide Association Studies (GWAS)

The physical location of 63,058 SNP markers in the array was obtained from the CottonGen database (https://www.cottongen.org/find/snp_markers) (Yu et al. 2021) utilizing G. hirsutum cv. TM1 CRI v1 as the reference genome (Yang et al. 2019). Out of 63,058 SNP markers, 40,649 markers had specific chromosomal positions. Further, grouping data from four Upland cotton linkage maps (Zhang et al. 2019; Zhu et al. 2021) were used to assign the 1,983 SNP markers that had a homoeologous location on A and D sub genomes to specific chromosomes. Raw genotypic data of 380 G. hirsutum accessions having 42,518 SNP markers were filtered to remove poor quality SNPs using the following criteria, a) monomorphic markers in the panel were removed; b) minor allele frequency was set to 0.05; c) SNP markers having more than 30% missing data were removed, and d) SNPs markers having more than 30% heterozygosity were removed. Finally, a total of 18,124 high-quality polymorphic SNP markers were used for all the downstream analyses.

The linkage disequilibrium (LD) decay plot was drawn in RStudio (Team RC 2021) using the LD statistics from the TASSEL software version 5.0 (Bradbury et al. 2007). The LD decay calculations were performed as described by Remington et al. (2001). Squared allelic frequency correlations (r²) of all the SNPs and the LD decay curve analyzed using a nonlinear regression model were plotted against the physical distance in mega bases (Mb). For phylogenetic analysis, the genotypic data was subsetted to remove all SNPs with heterozygous calls. Missing SNP calls were imputed with Begale software version 5.4 (Browning et al. 2018; Browning et al. 2021). Nie’s distance matrix (Nei 1972) and Neighbor joining (NJ) tree (Saitou and Nei 1987) were generated using R-package poppr version 2.9.3 (Kamvar et al. 2015). The constructed NJ tree was saved into a nexus file using R-package ape version 5 (Paradis and Schliep 2019) and the circular dendrogram was generated using ITOL online tool (Letunic and Bork 2021). The population structure and population membership matrix (Q-matrix) were analyzed in the STRUCTURE software version 2.3.4 (Pritchard et al. 2000). The number of populations range (K) was set from 2 to 15 and each K range was replicated ten times. The length of the Burnin period and the number of Markov chain Monte Carlo (MCMC) reps after Burnin were set to 10,000. The STRUCTURE HARVESTER (http://taylor0.biology.ucla.edu/structureHarvester/) was utilized for determining the optimum number of populations (Earl and vonHoldt 2011). The VanRaden method of kinship calculations (VanRaden 2008) was utilized for obtaining K-matrix using the rMVP package (Yin et al. 2021).

The mixed linear model (MLM, Q + K) accounting for structure-based Q-matrix and kinship-based K-matrix were utilized for the GWAS analysis (Buckler et al. 2006). The GWAS analysis and SNP density plotting were performed using the rMVP package (Yin et al. 2021) in RStudio. The Bonferroni correction (-Log₁₀P-value = 5.55) threshold was utilized for identifying significant MTAs (Hochberg 1988). The proportion of phenotypic variance explained (PVE%) by the SNPs was calculated using the simple linear regression model method as followed by Shim et al. (2015). The R package qqman (Turner 2018) was utilized to draw the regional Manhattan plot of significant MTAs. Haplotype analysis and LD heat map visualization were performed using HAPLOVIEW software version 4.1 (Barrett et al. 2005).

Linkage map construction

The genotypic data of the RIL population having 63,058 candidate SNPs were filtered to remove poor quality SNPs using the same criteria that were described for marker analyses in the diversity panel along with the removal of SNPs that were monomorphic and missing in the parents. For the mapping purposes, the phenotype of the RILs with susceptible response was coded as “a”, the resistant phenotype was coded as “b” and missing/unknown phenotype as “u”. For the genotypic data, the homozygous SNP alleles of Acala Maxxa were coded as “a” and homozygous SNP alleles of Arkot 8102 as “b”, heterozygous alleles as “h” and unknown/missing data as “u”. Linkage mapping of CBB resistance was performed using JoinMap 5.0 software (Van Ooijen 2018) using the logarithm of the odds threshold (LOD) of 9.0, maximum likelihood algorithm and the Kosambi mapping function. Segregation distortion was calculated using the Chi-square test at the probability value of 0.05. The segregation distortion regions (SDR) were identified when a minimum of three adjacent SNP markers showed segregation distortion. The pairwise recombination matrix and LOD score plot were performed using the R/QTL package (Broman et al. 2003) in RStudio. The physical location of the markers was obtained from CottonGen Blast + online tool (https://www.cottongen.org/blast) utilizing G. hirsutum cv. TM1 CRI v1 reference genome (Yang et al. 2019). Compliance of linkage groups and physical map groups were performed using ClicO FS online tool (http://103.47.253.210:3000/) (Cheong et al. 2015). The linkage map of chromosome arm with CBB resistance was reconstructed in RECORD software (Van et al. 2005). Iteration-based “Recombination counting and ordering” algorithm and Kosambi mapping function were used for the reproducible and collinear ordering of the markers with 1000 steps. The linkage map and its collinearity with the sequence-based physical map were drawn using MapChart version 2.32 software (Voorrips 2002).

Candidate gene analysis

For targeted genomic mapping of CBB resistant genes and to identify possible candidate genes, the genome was scanned using closely linked SNP markers in the reference genome G. hirsutum cv. TM1 CRI v1 (Yang et al. 2019). Enrichment and domain analysis of identified candidate genes was performed using the cotton functional genomics database (https://cottonfgd.org/analyze/) (Zhu et al. 2017). To enable the comparison of the physical position of the resistance gene from the previous studies to the current study, the BLAST search for the marker utilized in the previous studies was performed. The marker sequences from these studies were obtained from the CottonGen database (Yu et al. 2021). The BLAST search was performed using G. hirsutum cv. TM1 CRI v1 (Yang et al. 2019) reference genome. The genomic positions of the major genes/QTLs controlling CBB resistance reported in the previous studies and the current study were visualized using MapChart version 2.32 software.

Phenotyping

To explore sources of resistance to CBB present among cotton germplasm, the cotton diversity panel (Tyagi et al. 2014) was challenged with Xcm (race 18). Bacteria were infiltrated into cotyledons and resistance was observed as dead or necrotic tissue surrounding the site of inoculation indicating the hypersensitive response, whereas susceptibility was characterized by water-soaked lesions (Fig. 1). Out of 253 accessions screened, 61 (24.11%) accessions showed a resistant CBB response and 192 (75.89%) accessions showed a susceptible CBB response (Supplementary Table 1, Fig. 2). Among the resistant varieties, Arkot 8102 was selected for in depth characterization. A consistent hypersensitive resistance response was observed in Arkot 8102 infiltrated with various races of Xcm (races 1, 2, 3, 12, and 18), suggesting Arkot 8102 carries broad-spectrum resistance gene(s) to CBB.

Arkot 8102 was crossed with Acala Maxxa (susceptible) and advanced to the F₆ generation to form a recombinant inbred line (RIL) population. Screening of 104 RILs at the seedling stage with Xcm yielded 54 resistant (51.92%) and 50 susceptible (48.08%) lines. This 1:1 ratio of phenotypic segregation (Test statistic = 0.15, p = 0.69), suggests that CBB resistance in accession Arkot 8102 is controlled by a single locus with a major phenotypic effect.

Genome-wide Association Studies

Genotyping of 380 G. hirsutum accessions with the Cotton SNP 63K array resulted in a total of 18,124 high-quality polymorphic SNPs. The number of polymorphic SNPs in the sub genomes was uneven; the D-genome had 11,004 polymorphic SNPs, whereas in the current study, only 7,120 polymorphic SNPs were identified for the A-genome. The 18,124 SNPs covered a total length of 2,213.6 Mb (99.42% of G. hirsutum cv. TM1 CRI v1 reference assembly). These polymorphic SNPs were evenly distributed on all 26 chromosomes (Supplementary Fig. 1a), with an average SNP density of one SNP per 163.70 Kb. The number of polymorphic markers in the A-genome was comparatively lower than in the D-genome. The SNP density in the A-genome was lower (one SNP per 245.98 Kb) compared to the D-genome (one SNP per 81.42 Kb) (Supplementary Table 2). The estimation of linkage disequilibrium (LD) decay indicated the existence of large linkage blocks (average size 3.02 Mb) in the genome when compared to self-pollinated crops (Andrade et al. 2019; Panahabadi et al. 2021; Würchum et al. 2021) (Supplementary Fig. 1b). The existence of large LD blocks might be attributed to less reshuffling of genomic segments among the accessions.

Among the 380 accessions in this study, the three CBB response phenotypes (resistant, susceptible and unknown) were distributed across all the diverse groups identified by the NJ phylogeny tree (Fig. 2), indicating that population structure would not pose problems for the downstream analysis. MLM analysis with kinship and population structure as covariates identified fifty-eight markers above the Bonferroni threshold p-value (Table 1; Fig. 3; Fig. 4). All the fifty-eight associations were identified in a 2.01 Mb region of the long arm of chromosome D02 (543,166 to 2,553,694 bp). The SNP markers i25755Gh and i46775Gh had the highest -Log₁₀p-value of 19.29 and explained the highest PVE of 28.48% (Table 1, Supplementary Table 3).

Table 1

Significant marker trait associations for Cotton bacterial blight (CBB) resistance identified through genome wide association studies (GWAS) in the diversity panel consisting of 253 *G. hirsutum* accessions.
SNP	Chromosome	Position (base pairs)	Reference allele	Alternate allele	Minor allele frequency	-Log10 p-values	PVE %
i04845Gh	D02	543166	C	A	0.18	10.75	16.46
i31037Gh	D02	545043	T	G	0.20	8.44	12.92
i15279Gh	D02	559284	C	T	0.18	10.75	16.46
i15281Gh	D02	562823	C	A	0.18	10.75	16.46
i34589Gh	D02	624606	T	C	0.24	5.63	8.46
i33474Gh	D02	755697	T	C	0.20	13.45	20.44
i21644Gh	D02	796513	G	A	0.20	13.45	20.44
i04874Gh	D02	827194	A	G	0.20	14.58	22.05
i35403Gh	D02	870024	T	C	0.23	11.57	17.69
i22212Gh	D02	896017	T	C	0.20	14.58	22.05
i18845Gh	D02	949231	T	C	0.23	18.12	26.92
i04876Gh	D02	951167	C	A	0.23	18.12	26.92
i18846Gh	D02	951367	G	A	0.23	18.12	26.92
i69508Gl	D02	957010	A	G	0.23	14.66	22.17
i04878Gh	D02	970930	T	C	0.21	17.90	26.63
i04879Gh	D02	971612	G	A	0.21	17.90	26.63
i04880Gh	D02	973912	G	A	0.21	17.90	26.63
i15296Gh	D02	981029	T	C	0.21	17.90	26.63
i15297Gh	D02	981804	C	T	0.21	17.90	26.63
i15298Gh	D02	982155	C	T	0.21	17.90	26.63
i04882Gh	D02	983729	G	T	0.21	17.90	26.63
i04883Gh	D02	989410	T	C	0.21	17.90	26.63
i15307Gh	D02	992868	A	G	0.25	7.11	10.83
i18847Gh	D02	993950	T	C	0.24	7.04	10.72
i04886Gh	D02	1000201	C	T	0.21	15.67	23.59
i30611Gh	D02	1001262	A	G	0.21	17.90	26.63
i35404Gh	D02	1011058	A	C	0.25	6.53	9.91
i04890Gh	D02	1037321	G	A	0.21	17.90	26.63
i25755Gh	D02	1147586	T	C	0.21	19.29	28.48
i46775Gh	D02	1151037	T	C	0.21	19.29	28.48
i04909Gh	D02	1420750	T	C	0.21	15.62	23.52
i40135Gh	D02	1443517	A	G	0.23	14.77	22.33
i04915Gh	D02	1470075	A	G	0.23	5.77	8.70
i04916Gh	D02	1471015	T	C	0.23	6.53	9.91
i42410Gh	D02	1489885	A	G	0.23	5.77	8.70
i04917Gh	D02	1533931	A	C	0.23	16.82	25.17
i15329Gh	D02	1534113	G	A	0.29	11.29	17.28
i51275Gb	D02	1534368	A	G	0.29	11.29	17.28
i15330Gh	D02	1538965	G	A	0.22	16.82	25.17
i04918Gh	D02	1540924	C	A	0.22	16.82	25.17
i15331Gh	D02	1541147	A	C	0.22	13.13	19.98
i14029Gh	D02	1548603	A	G	0.22	16.82	25.17
i00270Gh	D02	1967963	T	C	0.21	7.49	11.44
i04929Gh	D02	1979246	T	C	0.23	7.62	11.63
i30271Gh	D02	2103773	A	G	0.23	8.34	12.77
i00697Gh	D02	2113080	C	T	0.23	9.87	15.12
i35889Gh	D02	2173210	T	C	0.21	9.70	14.86
i22184Gh	D02	2177973	C	A	0.23	8.34	12.77
i21826Gh	D02	2208558	T	C	0.23	8.34	12.77
i00293Gh	D02	2231591	G	A	0.21	9.70	14.86
i04940Gh	D02	2289925	C	T	0.20	5.88	8.86
i04941Gh	D02	2294468	A	G	0.21	8.21	12.56
i48135Gh	D02	2296995	A	C	0.21	8.21	12.56
i47804Gh	D02	2333681	A	G	0.22	6.71	10.19
i47803Gh	D02	2414378	A	G	0.22	6.40	9.71
i39157Gh	D02	2471169	A	G	0.21	7.42	11.32
i04944Gh	D02	2507620	G	A	0.25	7.42	11.32
i40777Gh	D02	2553694	A	G	0.26	7.62	11.65
_{PVE % = Phenotypic variance explained by the SNP markers expressed in percentage}.

Linkage mapping

In addition to the GWAS in the diversity panel, we performed linkage mapping to further resolve the broad-spectrum resistance to bacterial leaf blight in accession Arkot 8102. SNP marker analyses of the genotypic data in the RILs showed that out of 63,058 SNP markers, 5,714 (9.06%) markers were polymorphic between parents Acala Maxxa and Arkot 8102. A total of 2,745 non-redundant SNPs were used for the construction of the linkage map. Mapping of these polymorphic markers at the LOD score 9.0 threshold yielded a total of 105 linkage groups. Ninety-two of these groups, consisting of 2,630 SNP markers, were merged into 26 linkage groups. Thirteen groups consisting of 104 nonredundant SNP markers and eleven problematic SNPs could not be assigned. The 26 linkage groups spanned 4,942.62 cM with a marker density of 1.88 cM (Supplementary Table 4). A-genome spanned 2,508.73 cM with 1,250 markers and D-genome spanned 2,433.90 cM with 1,380 SNP markers. Chromosome D08 had the highest marker density of 1.14 cM per marker while Chromosome D11 had the lowest marker density of 4.18 cM. A total of 289 SNP markers (10.99%) showed segregation distortion in the mapped markers. Higher distorted SNP markers were observed in the D-genome (166 SNPs) compared to the A-genome (123 SNPs). Ten SDRs were identified in the A-genome compared to thirteen SDRs in the D-genome (Supplementary Table 5). The pairwise recombination fractions and LOD score plot of the linkage groups showed the correct grouping of the markers. Further, there were no signals of relatedness of markers between the linkage groups (Supplementary Fig. 2). However, there were few linkage blocks (Yellow squares) detected in the linkage groups (Supplementary Fig. 2).

The comparison of linkage map with sequence-based physical map showed high collinearity between the physical map with a genetic map indicating correct ordering of the markers and there are no major chromosome rearrangements in the mapping populations used in comparison to the sequence-based physical map (Supplementary Fig. 3; Supplementary Table 6). The linkage groups covered a total of 2.02 Gb (91.10%) of the physical map (Supplementary Table 7). In general, the marker coverage of the individual chromosomes was high. All the chromosomes showed a high marker coverage of greater than 90% up to 99.41% except A09, A10, A12, D05, D11, and D13. Chromosomes A09 and D05 were poorly covered with 22.18% and 60.31%, respectively (Supplementary Table 7).

Linkage mapping showed CBB phenotype segregated with the group-19 of the JoinMap grouping tree at LOD 9.0. Group-19 contained 93 markers (inclusive of redundant markers), Current study assigned group-19 as the long arm of chromosome D02 based on physical position and anchor marker (BNL2882) information (Shan et al. 2016). The removal of redundant markers and iteration-based ordering of the markers resulted in the refined ordering of SNPs on the long arm of D02. The resultant genetic map of the D02 long arm contained 31 SNP markers with a total map length of 8.43 cM and a marker density of 0.76 cM (Fig. 5). Six redundant markers were also included in the i04900Gh flanking locus distal to telomere (i15323Gh, i04897Gh, i04898Gh, i14028Gh, i04904Gh, i04905Gh, i04907Gh) of CBB resistance (Fig. 5). The CBB resistance gene (BB-13) was mapped at 0.47 cM to SNP i04890Gh proximal to telomere and 0.46 cM to i04900Gh SNP marker distal to telomere (Fig. 5). All the SNPs genetically mapped on the long arm of chromosome D02 were collinear with their genomic positions in the physical map (Fig. 5). These mapped SNP markers corresponded to a genomic region of 1,442,843 bp between 133,045 bp to 1,575,888 bp on chromosome D02. This genomic region accounted for two percent of the total length of chromosome D02 (71,136,581 bp) and contained thirty putative gene sequences (Supplementary Table 8).

Haplotype analysis and candidate gene identification

The combined mapping through GWAS and linkage analysis was used to improve the resolution of the mapping and genomic targeting of the CBB resistance locus. The fifty-eight MTAs detected through GWAS were spread out in the 2.01 Mb region (543,166 to 2,553,694 bp) (Table 1). The CBB resistance locus (BB-13) in linkage analysis was mapped to a region between 1,037,321 bp to 1,408,478 bp (Fig. 5). From the combined analyses the overlapping target genomic region for the CBB resistance was 371 Kb on D02 (Fig. 4). In this targeted genomic region, there were seven haplotypes in the linkage block (Fig. 4b). The haplotype “ACCGCCTTAAAG'' was found in fifty-three of the resistant lines. The haplotypes “GCCATTCCGGGT” and “GATGTTTTAAAG” were found in four resistant lines each. The fifty-three accessions having the same haplotype suggest a major resistant gene common to these accessions. The accessions with the haplotype “GCCATTCCGGGT” and “GATGTTTTAAAG” likely contain additional or different gene(s) for CBB resistance. Cotton reference genome scan in the orthologous genomic region between SNP markers i04890Gh and i04907Gh (1,037,321 to 1,408,478 bp) closely linked to CBB resistance locus (BB-13) identified a total of thirty putative genes (Supplementary Table 8). Out of these, nine putative genes were reported to be involved in disease resistance mechanisms in plants (Table 2). The enrichment analysis of the identified thirty putative genes indicated that the cysteine-type peptidase activity and proteolysis activity are enriched (Supplementary Table 9). Further, the top two significant MTAs in the study i25755Gh (1,147,586 bp; p = 19.29) and i46775Gh (1,151,037 bp; p = 19.29) are the flanking markers (out of four markers that are proximal towards telomere) of the CBB resistance locus BB-13 in the linkage study. The nearest putative genes to marker i25755Gh & i46775Gh are viz., NAC DOMAIN-CONTAINING PROTEIN 14 (1,004,477 to 1,037,718 bp), DELLA PROTEIN GAI (1,053,601 to 1,054,329 bp), CYSTEINE-TYPE PEPTIDASE genes ULP1B (1,121,200 to 1,123,019 bp) and ESD4 (1,134,403 to 1,135,511 bp).

Table 2
Putative candidate genes for CBB resistance in the targeted genomic region identified using combined genome wide association studies (GWAS) and linkage analyses in the Upland cotton.
Gene ID	Gene Name	Description	Start	End	Domains	Reference
Gh_D02G012100	RGA4	Putative disease resistance protein RGA4	980685	983807	NB-ARC & LRR	Song et al. 1995; Zhou et al. 1995; Zhou et al 2001; Feuillet et al. 2003
Gh_D02G012200	At4g27190	Disease resistance protein At4g27190	987105	994162	NB-ARC & LRR	Song et al. 1995; Zhou et al. 1995; Zhou et al 2001; Feuillet et al. 2003
Gh_D02G012500	NAC014	NAC domain-containing protein 14	1004477	1037718	NAM	Kaneda et al. 2009; Park et al. 2017
Gh_D02G012800	GAI	DELLA protein GAI	1053601	1054329	GRAS domain family	De Vleesschauwer et al. 2016; Hou et al. 2010; Wild et al. 2012
Gh_D02G013400	ULP1B	Putative ubiquitin-like-specific protease 1B	1121200	1123019	Ulp1 protease family & C-terminal catalytic domain	Üstün and Börnke 2014; Gilroy et al. 2007; Shabab et al. 2008; Song et al. 2009
Gh_D02G013500	ESD4	Ubiquitin-like-specific protease ESD4	1134403	1135511	Ulp1 protease family & C-terminal catalytic domain	Üstün and Börnke 2014; Gilroy et al. 2007; Shabab et al. 2008; Song et al. 2009
Gh_D02G013900	GAI	DELLA protein GAI	1205161	1208450	GRAS domain family	De Vleesschauwer et al. 2016; Hou et al. 2010; Wild et al. 2012
Gh_D02G014100	RPL38	60S ribosomal protein L38	1233364	1235198	Ribosomal L38e protein family	Nagaraj et al. 2015
Gh_D02G014400	Ulp-1	Sentrin-specific protease	1308195	1309121	Ulp1 protease family & C-terminal catalytic domain	Üstün and Börnke 2014; Gilroy et al. 2007; Shabab et al. 2008; Song et al. 2009
Gh_D02G014600	ESD4	Ubiquitin-like-specific protease ESD4	1322758	1324872	Ulp1 protease family & C-terminal catalytic domain	Üstün and Börnke 2014; Gilroy et al. 2007; Shabab et al. 2008; Song et al. 2009
Gh_D02G015100	CRT3	Calreticulin-3	1367646	1373635	Calreticulin protein family	Li et al. 2009; Matsukawa et al. 2013

The BB-13 locus may not be completely syntenic between the resistant varieties and the reference genome variety (TM1 CR1 v1) (Yang et al. 2019). Considering the larger genomic region (2.01 Mb) identified by GWAS only analyses, there were nineteen putative R-genes, of which four were NBS-LRR genes, six LRR repeat proteins and nine were WALL ASSOCIATED KINASES. The closest R-genes identified adjacent to the overlapping region were At4g27190 (987,105 to 994,162 bp) and RGA4 (980,685 to 983,807 bp) located 46 to 55 Kb to flanking SNPs i25755Gh and i46775Gh (Table 2).

In the present study, we report on the identification of a major CBB resistance locus for Xcm race 18 and its targeted genomic mapping on D02 using a combined association and linkage mapping approach.

Genotyping

In the present study, genotyping of 380 accessions using the Cotton 63K SNP array showed 18,124 (28.74%) SNPs were polymorphic with a density of 163.70 Kb per SNP (Supplementary Table 2). These results are within the range of the polymorphism percent of 16.90 to 53.76 and marker density of 29.2 Kb to 237.32 Kb per SNP observed in Upland cotton association/diversity panels in cotton (Hinze et al. 2017; Yuan et al. 2018; Abdelraheem et al. 2019; Song et al. 2019; Song et al. 2021). The high-quality polymorphic SNPs in the study covered 2,213.6 Mb (99.42% of G. hirsutum cv. TM1 CRI v1 reference assembly) which is higher than coverage reported in the cotton association panels (1934.65 Mb; Yuan et al. 2018). The variation in SNP polymorphism and their distribution with respect to current study and other studies may be due to the diversity of genotypes used. Variation in diversity is attributed to accumulated point mutations (SNPs) and meiotic recombination within the geographically isolated accessions (Bhandari et al. 2017; Chang et al. 2022).

Biparental RIL populations are routinely used in cotton for trait mapping (Ramesh et al. 2019; Wang et al. 2019; Zhu et al. 2021). In the current study, the observed marker polymorphism was 9.06% and the marker density was 1.88 cM per marker in the RIL mapping population (Supplementary Table 4). The polymorphic SNPs in the linkage studies of intra G. hirsutum crosses using SNP array genotyping varied from 4.9 to 17.8% (Hulse-Kemp et al. 2015; Li et al. 2016; Tan et al. 2018; Ulloa et al. 2017; Zhang et al. 2019; Zhu et al. 2021). Marker density of constructed linkage maps ranges from 0.3 to 16.8 cM per marker (Semizer et al. 2015; Liu et al. 2021). Variation in SNP polymorphism is attributed to the diversity of the parents selected to develop the mapping population. Segregation distortion is a common phenomenon that leads to altered genetic distance and marker order (Xian et al. 2006). In the present study segregation distortion of 10.99% was observed (Supplementary Tables 4 & 5), which is lower than the reported high-density linkage maps in cotton (13.29–73.8%) (Li et al. 2016; Wang et al. 2019). Segregation distortion is attributed to preferential fertilization, genetic drift and chromosomal translocation (Kumar et al. 2019; Zhang et al. 2019). Marker grouping and ordering represent two major facets of linkage map development (Cheema and Dicks 2009). To cross verify any discrepancies in the grouping of markers a plot of recombination fractions and LOD score between all the pairs of markers was developed (Supplementary Fig. 2), these analyses indicate the absence of linkage between the SNPs belonging to different linkage groups inferring correct grouping of markers. The CIRCOS plot (Supplementary Fig. 3) indicated the strong congruence between the linkage groups and sequence-based physical groups, suggesting there were no major translocations segregating in the mapping population. Comparison of genetic and physical maps using G. hirsutum cv. TM1 CRI v1 reference assembly indicated the uneven distribution of recombination across the chromosomes (Supplementary Fig. 3). Uneven distribution of recombination may be due to the low crossing over and low gene density near the centromeres (Endo and Gill 1996; Wang et al. 2013).

Mapping CBB resistance genes

In the current study we mapped a major gene for resistance to CBB race 18 on chromosome D02. From the combined GWAS and linkage mapping CBB resistant locus (BB-13) is mapped within a 0.95 cM region flanked by markers i04890Gh and i04907Gh. This corresponded to a genomic region of 371 Kb region on D02 long arm (1,037,321 to 1,408,478 bp). Multitude of markers and diverse genetic populations were used for mapping CBB resistance in cotton (Wright et al. 1998; Rungis et al. 2002; Xiao et al. 2009; Yang 2013). More than twenty major genes for resistance to CBB were identified in cotton so far (Knight and Clouston 1939; Bird and Blank 1951; Green and Brinkerhoff 1956; Knight 1963; Bird 1982; Bourland 2018; Zhang et al. 2020). Resistance to CBB as a quantitative trait was also reported by Wright et al. (1998) and Elassbli et al. (2021) (Supplementary Fig. 4). Chromosome D02 was reported to harbor five QTLs (Wright et al. 1998; Elassbli et al. 2021) and one major locus, B-12 (Wright et al. 1998; Rungis et al. 2002; Xiao et al. 2009; Yang 2013) for CBB resistance. Using an interspecific mapping population (from S295 × Pima S-7), B-12 was mapped 11.4 cM distal to RFLP marker pAR043 (69,740,119 bp) in the short arm of D02 (Wright et al. 1998) and 0.9 cM distally to an SSR marker CIR246 (1,034,217 bp) on the long arm of chromosome D02 (Yang et al. 2013) (Supplementary Fig. 4). Intraspecific Upland cotton biparental mapping populations were also used to map the resistance to CBB on D02 (Rungis et al. 2002, Xiao et al. 2009). In these studies, the CBB resistance was named as the B12 locus and it was mapped between SNP markers NG0207069 (638,901 bp) and NG0207155 (988,671 bp) (Xiao et al. 2009) (Supplementary Fig. 4). Further, the CBB resistance to race 18 was mapped as a major QTL between the RFLP markers pAR129 (5,458,991 bp) and pAR043 (69,740,119 bp) and as a major recessive QTL between RFLP markers pAR545 (9,516,281 bp) and pAR043 (69,740,119 bp) (Wright et al. 1998). Three major QTLs viz., qBB-D02-1 (23,843,536 to 28,751,532 bp), qBB-D02-2 (38637020 to 45029632 bp) and qBB-D02-3 (51219204 to 64169928 bp) conferring resistance to race 18 were identified recently Elassbli et al. (2021) in an association mapping study (Supplementary Fig. 4). Although multiple studies confirmed that chromosome D02 carries resistant gene(s) to CBB, the precise number and location of the CBB resistance genes is unknown. This is evident from reporting of multiple map locations for B-12 gene and the identification of multiple QTLs on chromosome D02 (Supplementary Fig. 4 and Supplementary Table 10). In the absence of race differentials to precisely identify the unique CBB resistant genes, a comprehensive and systematic mapping of CBB resistance genes is needed. In the current study, the CBB resistance locus in Arkot 8102 was mapped and targeted to a small genomic region of 371 Kb (1,037,321 to 1,408,478 bp) on chromosome D02 using the combined resolving power of a high-density linkage map and GWAS. Considering the unique map position, we propose that CBB resistance in Arkot 8102 is due to a new gene(s) on chromosome D02 and is named the BB-13 locus (Supplementary Fig. 4). The resistance in Arkot-8102 is derived from Tamcot-SP37 through Miscot-T8-27 (Bourland and Bridge 1988), Tamcot-SP37 is a selection from a complex cross involving Blightmaster, K4808, 39-11-20 and Paymaster-54 (Bourland and Bridge 1988).

Candidate gene analysis

The overlapping region identified from the combined GWAS and linkage mapping approach (371 Kb; 1,037,321 to 1,408,478 bp) harbored thirty putative genes (Supplementary Table 8). Of these, nine are promising candidate genes for BB-13. Since the most significant GWAS MTA’s (i25755Gh; 1,147,586 bp and i46775Gh; 1,151,037 bp) was the flanking locus (proximal towards telomere) of BB-13, we propose that the resistance gene is localized either within the 371 Kb overlapping genomic region flanked by the SNP markers i25755Gh and i46775Gh markers or the genomic region immediately adjacent to these markers. The annotated putative genes within the identified region include NAC014 (NAC domain-containing protein 14), GAI (DELLA PROTEIN GAI), ULP1B (PUTATIVE UBIQUITIN-LIKE-SPECIFIC PROTEASE 1B) and ESD4 (UBIQUITIN-LIKE-SPECIFIC PROTEASE ESD4). As many as nineteen predicted R-genes were identified (Supplementary Table 8) in the 2.01 Mb region (543,166 to 2,553,694 bp) defined by the GWAS analysis. Apart from the candidate resistant genes in the overlapping region, a few of the important and unique candidate disease resistance genes in the GWAS defined genomic region are RGA3 (PUTATIVE DISEASE RESISTANCE PROTEIN RGA3; Position: 676,179 to718,723 bp), RGA4 (PUTATIVE DISEASE RESISTANCE PROTEIN RGA4; Position: 980,685 to 983,807 bp) and At4g27190 (DISEASE RESISTANCE PROTEIN At4g27190; Position: 987,105 to 994,162 bp), where RGA4 and At4g27190 found distal to the one of the flanking SNP marker i46775Gh (1,151,037 bp) of BB-13 (Supplementary Table 8). The RGA3, RGA4 and At4g27190 contain NB-ARC and LRR domains. Numerous studies indicate NBS-LRR genes with these domains are involved in hypersensitive response to microbial disease of crop plants (Song et al. 1995; Zhou et al 1995; Zhou et al 2001; Feuillet et al. 2003). Further, the D-genome is reported to be skewed with higher numbers of R-genes (Chen et al. 2020) and CBB resistant genes (Wright et al. 1998). Five percent (19 out of 384) of the predicted R-genes in the D-genome were found in the 2.01Mb GWAS defined genomic region. Nine genes that have potential to signal R-genes by acting as guard defenses were also found on D02 long arm (Table 2). Given the proximity of NBS-LRR genes to BB-13, and the potential for differences in synteny between the reference genome and the genomes of resistant BB-13 cotton varieties, we maintain that these NBS-LRR genes are strong candidates for BB-13. Further, NBS-LRR genes are known to occur in clusters and show variability between distinct varieties. Future work should focus on resolving the genomic sequence between the BB-13 flanking markers for resistant and susceptible cotton varieties.

Breeding for resistance to CBB was carried out in cotton breeding programs in the US (Knight and Clouston 1939; Knight 1944; Bird and Blank 1951; Green and Brinkerhoff 1956; Knight 1963; Innes et al. 1974; Bourland and Bridge 1988; Zhang et al. 2020). Twenty-two races have been identified for Xcm (Zhang et al. 2020). Race 18 is the predominant race affecting cotton production in the U.S. As the race differentials are not available in cotton, identification and development of easily assayable molecular markers for CBB resistance and systematic cataloging of the resistance genes are the essential steps to develop durable resistance to CBB in cotton. Thus, mapping and identification of closely linked SNP markers i25755Gh and i46775Gh for the CBB resistance gene in the current study will help develop CBB resistant cultivars using marker-assisted selection. Genomic targeting and identification of putative candidate genes would help map-based cloning of CBB resistant gene BB-13 in Upland cotton.

Author Contribution Statement:

SAG performed data analysis on the GWAS and bi-parental mapping population. AZP screened the NAM parents for resistance to CBB. TH performed additional screening of NAM parents and the bi-parental mapping population and contributed to writing of the manuscript. NS, KZ and HF developed the RIL population. FB developed and contributed Arkot lines of cotton used in the research. VK and SS genotyped the RILs and DIV panel. RB oversaw the phytopathology experiments, provided general feedback on the mapping and interpretation of data and contributed to writing of the manuscript. VK conceived of the project and oversaw all aspects of the work. SAG and VK co-wrote the paper.

Acknowledgments:

The authors acknowledge the funding support provided by Cotton Incorporated (CI) and North Carolina Cotton Producers’ Association for the current research and an assistantship to Navin Shrestha. We also thank Kelli Kochan of Texas A&M University for her help with genotyping the lines using the Cotton 63K SNP array. Thanks to Marisa Yoder and Alex Weil at the Danforth Plant Science Center for their help with bacterial infiltrations and cotton planting. We appreciate Josh Udall, James Frelichowski and Janna Love of the Crop Germplasm Research, USDA-ARS, College Station, TX for providing the Upland cotton accessions used in this study.

Conflict of interest statement:

The authors declare that they have no conflict of interest.

Funding support:

Funding support for this research provided by Cotton Incorporated (projects: projects: 19-969 and 19-952) and North Carolina Cotton Producers’ Association (project 19-796NC) to Vasu Kuraparthy, National Science Foundation 2014192836 and 1928344 to Anne Phillips and Rebecca Bart, respectively. Taylor Harris is supported by a NIGMS-funded Initiative for Maximizing Student Development grant to Washington University.

Data availability:

The plant material and datasets employed in this study are available from the corresponding author on reasonable request.

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Editorial decision: Major revisions
28 Jul, 2022
Reviewers agreed at journal
07 Jul, 2022
Reviews received at journal
09 May, 2022
Reviewers invited by journal
04 May, 2022
Editor assigned by journal
02 May, 2022
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Identification and genomic characterization of major effect bacterial blight resistance locus (BB-13) in Upland cotton (Gossypium hirsutum L.)

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Abstract

Figures

Key Message

Introduction

Materials And Methods

Plant materials

Phenotyping

DNA isolation and genotyping

Genome-wide Association Studies (GWAS)

Linkage map construction

Candidate gene analysis

Results

Phenotyping

Genome-wide Association Studies

Linkage mapping

Haplotype analysis and candidate gene identification

Discussion

Genotyping

Mapping CBB resistance genes

Candidate gene analysis

Declarations

References

Supplementary Files

Status:

Version 1