Genetic analysis of QTLs controlling allelopathic characteristics in sorghum.

Mature sorghum herbage is known to contain several water-soluble secondary metabolites (allelochemicals). In this study, we investigated quantitative trait loci (QTLs) associated with allelochemical characteristics in sorghum using linkage mapping and linkage disequilibrium (LD)-based association mapping. A sorghum diversity research set (SDRS) of 107 accessions was used in LD mapping whereas, F2:3 lines derived from a cross between Japanese and African landraces were used in linkage mapping. The QTLs were further confirmed by positional (targeted) association mapping with Q+K model. The inhibitory effect of water-soluble extracts (WSE) was tested on germination and root length of lettuce seedlings in four concentrations (25%, 50%, 75% and 100%). A Significant range of variations was observed among genotypes in both types of mapping populations (P < 0.05). A total of 181 simple sequence repeats (SSRs) derived from antecedently reported map have been used for genotyping of SDRS. A genetic linkage map of 151 sorghum SSR markers was also developed on 134 F2 individuals. The total map length was 1359.3 cM, with an average distance of 8.2 cM between adjacent markers. LD mapping identified three QTLs for inhibition effect on germination and seven QTLs for root length of lettuce seedlings. Whereas, a total of six QTLs for inhibition of germination and ten QTLs for root length were detected in linkage mapping approach. The percent phenotypic variation explained by individual QTL ranged from 6.9% to 27.3% in SDRS and 9.9% to 35.6% in F2:3 lines. Regional association analysis identified four QTLs, three of them are common in other methods too. No QTL was identified in the region where major gene for sorgoleone (SOR1) has been cloned previously on chromosome 5.

Introduction

Every living organism interacts with others and form an ecological system on the earth. Such interactions between organisms at several levels have been long known and studied. Allelopathy is a phenomenon observed in large number of plants that emit specific chemicals interacting on other organisms, including animals and microorganisms, in either inhibitory or excitatory way. The use of traditional methods to control weeds are often very costly, labor intensive and dependent on weather conditions. Likewise, the extensive utilization of chemicals for controlling weeds may cause serious environmental hazards. Recently, in wheat (Triticum aestivum L.), sorghum allelopathic characteristics has been exploited as a substitute for chemical herbicides to reduce environmental pollution. Mature sorghum herbage contains a number of water-soluble chemical compounds (allelochemicals) that has the tendency in controlling weeds. Even the residues of sorghum minimize the incidence and growth of a number of weed species such as purslane and smooth crabgrass [1], green foxtail, velvetleaf and smooth pigweed [2, 3]. Incorporation of herbage of mature sorghum plants into the soil at 2 to 6 ton/ha and a spray of water extracts reduces the occurance of weeds and increase the yield of irrigated wheat as reported in Cheema et al. [4]. The interplanting of sorghum is observed to reduce the growth and biomass of important weeds of different crops [2]. Sorghum residues release many allelochemicals such as sorgoleone, cyanogenic glycosides-dhurrin and many other products due to the breakdown of large chemical compounds. All these chemicals plays an important role in weed suppression and thus, can be affectively utilized as natural control of major weeds in crops. Sorghum has a great potential for the use as an allelopathic crop to manage parasitic weeds, particularly Striga and Orobanche species that pose a significant risk to agriculture because of difficulty to deal with them.

Sorghum is producing phytotoxins such as the potent benzoquinone sorgoleone (2-hydroxy-5-methoxy-3- [(8/Z, 11/Z)-8/, 11/, 14/-pentadecatriene]-p-benzoquinone) and its analogs, generally depicting the principal constituent of Sorghum bicolor root exudates. Until most recently, a single gene SOR1 has been cloned which is associated with the production of sorgoleone in sorghum [5].

Sorgoleone is one of the most investigated allelochemicals [5-10]. It was first discovered by researchers studying secondary metabolites affecting germination of parasitic weed Striga asicatica (witchweed) [11]. The biosynthesis of sorgoleone has been elucidated using retrobiosynthetic NMR analysis [12, 13]. Sorgoleone is absorbed through the hypocotyl and cotyledonary tissues of growing seedlings, consequently impeding the process of photosynthesis. The mode of action of sorgoleone is similar to soil-applied herbicides because this phytotoxic exudate is also discharged into the soil instantaneously through the root nodules. Most importantly, the effect of sorgoleone into the soil may sustain for longer period than that of applied herbicide.

Recently, DNA marker-assisted selection has been implied to identify QTLs responsible for the production of allelochemicals in various crops including rice and rapeseed. In sorghum, a lot of work has been done to explore the biosynthetic pathway for the production of sorgoleone in root hair cells [14]. Identification of genes controlling the production of allelochemicals would help in improving cereals for enhancing the release of these chemicals.

The study of genetic mechanism involved in the allelopathic effect of crop plants is a new challenge for biological weed control but very little work has been done till present. Most of the research performed is centered on biochemical analysis and there is extremely limited knowledge on genetic aspect of such characteristics. Especially in sorghum there is no information about the chromosomal regions that trigger the release of allelochemicals. Due to lack of such important information, we have designed our experiment to map the chromosomal regions associated with allelopathic effect of sorghum. We have concluded two new and original hypotheses, (1) Sorgoleone may not be the only phenolic compound responsible for the allelochemicals characteristics in sorghum and other parts of sorghum also produce such chemicals which inhibit the growth of other plants. (2) There might be several QTLs/genes controlling this complex mechanism in sorghum. This study will serve as a pioneer work in the genetic study of sorghum allelopathic effects and will help in marker-assisted selection for the improvement of allelopathic characteristics in cereals. Our findings will give a base for better understanding of regulatory mechanism of crop allelopathy.

Materials and methods

Plant materials

For association mapping, we used our previously developed sorghum diversity research set (SDRS) of 107 accessions (landraces) derived from Asian and African countries [15]. The accessions are characterised geographically into three different groups consisting of (i) East Asia (25 accessions) and Southeast Asia (2 accessions), (ii) South Asia (26 accessions) and Southwest Asia (2 accessions) and (iii) Africa (52 accessions). All plant materials were grown in a glasshouse under natural day-length condition (12L/12D) in 2010 sorghum growing season at agricultural research farm of the University of Tsukuba in Japan (36° N, 139° E). Seeds were treated with fungicide before sowing. Six seeds per accession were sown in round plastic pots (20 cm diameter × 25 cm tall). Regular irrigation, fertilizer and nutrients were applied during the plant growth and insecticide was also used after germination. The plants at six-leaf stage were harvested for bioassay about 45 days after germination.

In linkage mapping, we developed a population from a cross between two sorghum accessions, non-allelopathic Tokibi from Japan (ID; 104JP) and allelopathic Phatsai from Morocco (ID; 33MA) selected from SDRS [15]. The cross was made in year 2008. F1 seeds were sown in 2009 and interbred to produce F2 seeds. During the 2010 growing season, all F2 plants were grown at the distance of 30 cm apart in a field at University of Tsukuba. A total of 134 F2 plants were secured and used for linkage mapping. F2 plants were self-pollinated to produce F3 family lines and subsequently F3 seeds were harvested from each F2 plant. Thirty F3 seeds per F2 plant were sown in pots and leaves were harvested at six-leaf stage about 45 days after germination.

DNA extraction

For DNA extraction the protocol of Shehzad et al. 2009b [16] was used. Briefly, 40 days old seedlings were selected genomic DNA was isolated from leaf tissues using a modified cetyltrimethylammonium bromide (CTAB) method. The extraction buffer contained 2% (mg/L) CTAB, 50 mM Tris·HCl (pH 8.0), 10 mM EDTA, 0.7 M NaCl, 0.1% SDS, 0.1 mg/ml proteinase K, 2% insoluble polyvinylpyrrolidone (PVP), and 2% 2-mercaptoethanol. Further purification was performed by extraction in chloroform:isoamyl alcohol (24:1 v/v). Then the DNA was precipitated in 2-isopropanol and the precipitate was finally washed in 70% then 90% ethanol. The DNA pellet was dissolved in 50 μl of 1/10 TE buffer containing RNase “A” enzyme, then incubated at 42 °C for an hour. After quantification with V-630Bio (JASCO) spectrophotometer the final DNA concentration of 10 ng/μl was maintained as working dilution.

Screening SSR markers and genotyping

In our previous study the SDRS was genotyped using 98 SSR markers [16]. A new set of SSRs were selected from Yonemaru et al. [17], which reported more than 5000 newly developed SSR markers from sorghum whole-genome shotgun sequences. In this report, we selected 672 random SSR loci from whole genome of sorghum. After screening with 8 diverse landraces, we selected the best 83 markers (S1 Table) with clear banding pattern. In total, a genotypic data of 181 SSRs were used for association study in SDRS. The parent of F2 lines were also screened with SSRs and finally 152 polymorphic loci were selected for genotyping covering the whole genome of sorghum. Among them, 124 SSRs were from Yonemaru et al. [17] and 28 from other published maps. The PCR reaction and electrophoresis were performed according to the protocols given in [16]. After screening with molecular markers, genotype “A” was assigned to female parent (Tokibi) allele, “B” for the male parent (Phatsai) allele and “H” for heterozygote.

Bioassay of allelopathic effect using water-soluble extract (WSE)

The bioassay method reported in Ebana et al. 2001 applied to assess allelopathic effect among sorghum accessions. In this method, water-soluble extracts were prepared form all accessions by grinding freeze dried leaf samples (6-leaf stages) to powder in a mortar and stirred in cold and sterilized distilled water at the rate of 10 ml/g of fresh sample. The mixture was chilled in refrigerator for 2 hours and after stirring, centrifuged at 15, 00 rpm for 15 minutes. The supernatant was collected and diluted with distilled water to prepare solutions of four different concentrations (v/v): 25, 50, 75 and 100%. Lettuce as susceptible to allelochemicals was used as a test plant. Fifty seeds of the lettuce variety Great Lakes 366 were placed on a filter paper (Advantec No. 2) in a 9 cm diameter sterilized plastic petri dishes. Three milliliters of each concentration of crude extract and sterilized distilled water as control were applied to lettuce seeds. Lettuce seeds with no treatment were also kept as control with each treatment of WSE. The petri dishes were sealed and incubated at 25 °C in dark for three days in randomized complete block design (RCBD). Soon before the start of germination, the outer filter paper was removed from Petri dishes. The Petri dishes were kept moist consistently by applying distilled water. Data was recorded on germination and root length of 10 randomly selected seeds in 5 replications for each extract and 2 replications for each accession. A total of 250 lettuce seeds were used in data recording for every treatment (concentration) in each replication. Correlation among these traits studied on lettuce is given in Table 1.

Table 1

Correlation among eight parameters studied on lettuce treated with WSE derived from SDRS (lower half) anf F3 (upper half) lines.

Traits	Germ25%	Germ50%	Germ75%	Germ100%	RL25%	RL50%	RL75%	RL100%
Germ25%	1	0.012NS	0.076NS	0.018NS	0.033NS	0.064NS	0.071NS	0.091NS
Germ50%	0.135NS	1	0.521***	0.076NS	0.230*	0.223*	0.103NS	0.378***
Germ75%	0.097NS	0.435***	1	0.428***	0.224*	0.190NS	0.189NS	0.359***
Germ100%	0.018NS	0.075NS	0.474***	1	0.366**	0.444***	0.563***	0.394***
RL25%	0.033NS	0.230*	0.224*	0.366**	1	0.814***	0.704***	0.354***
RL50%	0.064NS	0.223*	0.190NS	0.444***	0.814***	1	0.684***	0.754***
RL75%	0.071NS	0.110NS	0.225*	0.477***	0.704***	0.843***	1	0.518***
RL100%	0.083NS	0.228*	0.348**	0.505***	0.633***	0.751***	0.806***	1

Germ: Germination; RL: Root Length; SDRS: Sorghum Diversity Research Set.

*** Signficant at P < 0.001,

** Significant at P < 0.01,

* Significant at P < 0.05, NS Non significance.

Statistical analysis

Phenotypic values of each accession and F3 lines were represented by the mean values of % germination and root length of lettuce seedlings treated with water-soluble extracts. For one-way analysis of variance (ANOVA) and non-parametric correlation, a statistical program, JMP v.10 [18] was used. Broad-sense heritability (h2) of the traits was also performed using the following formula; where, h2 is broad sense heritability

A linkage map of SSR markers was constructed using Mapmaker Exp/3.0b software [19]. The mapping function of Kosambi used to convert recombination frequencies were into map distances in cM.

QTL mapping of F3 lines

The statistical package WinQTLCart 2.0 [20] was used for QTL analysis using the algorithm of composite interval mapping (CIM). For each trait, LOD score threshold was identified using 1000 permutations at P-value = 0.05. We maintained a strict threshold of LOD ≥ 2.5 to identify the putative QTLs linked to the traits. The Model 6 of WinQTLCart 2.0 in CIM was used to estimate likelihood of each QTL and its corresponding effects were estimated every 1 cM. A forward–backward stepwise regression was applied to establish the number of significant marker cofactors for background control. A window size of 30 cM was utilized, and consequently, cofactors within 30 cM on either side of the QTL test site were not included in the QTL model. The adjacent QTLs identified for the same trait with non-overlapping intervals on same chromosome were considered as different QTLs. The total phenotypic contribution rate (R2) was calculated as the percent of variation explained by each QTL. A standard nomenclature was applied each QTL with italicized insignia consisting of “qtl”, one or two digits representing the chromosome number, a hyphen followed by an extra digit if more than one QTL was found on the same chromosome for the same trait, and ending with a trait symbol.

Population structure and association mapping

The statistical software STRUCTURE version 2.2 [21] was used to perform population structure. After the initial 105 cycles of burn-in period, Markov-chain Monte Carlo (MCMC) sampling was repeated 106 times. Bayesian clustering was applied in population structure analysis with the admixture model. The number of subpopulations (J) ranged from 1 to 10 and each run was repeated three times. The optimal number of J was determined on the basis of estimated logarithmic posterior probability of the Bayesian clustering and ad hoc statistic Delta (Δ) J. Here, J = 3 was selected because it had the largest value of posterior probability and ΔJ among other values (S1 Fig).

A population structure (Q) matrix, whose (i,j)-th element was expressed as q, was incorporated into the association mapping model where structure effect was examined. Similarly, a kinship matrix, K, was calculated as the allele-sharing rates of the 181 SSR loci as suggested by Zhao et al. [22]. To measure the LD between SSR markers, standard disequilibrium coefficients, D and r were used. Where D is the standardized disequilibrium coefficient and r represents the correlation between alleles at two loci. For association analysis, the statistical software TASSEL (Trait Analysis by Association, Evolution, and Linkage) ver. 5.0.8 [23] was used and the P-values representing the significance of LD was measured (S2 Fig). The mixed linear model (MLM) was applied to identify QTLs significantly associated with allelopathic characteristics in sorghum. After comparing multiple models of associations, the model that utilized both population structure and kinship (Q+K) was selected. This model identifies P-values based on nominal test of individual makers and then corrected for multiple testing. The P-values obtained were converted into–Log10 (P). The false discovery rate (FDR) of P values was calculated as described by Benjamini & Hochberg [24]. The “BH” method implemented in R package was used for this purpose. In this method, the P-values are first sorted in decreasing order and then they are ranked starting from 1 given to smallest until the last value. The FDR-corrected P-value is calculated as P-value*(total number of hypotheses tested) / (rank of the P-value). The threshold of significance was set as 2.0 value of–Log10 (FDR-corrected P).

In the current study, we used regional association mapping to validate 11 major QTLs of germination and root length studied on lettuce using four concentrations of WSE that were identified by the preliminary linkage mapping. As genome-wide association studies results in spurious associations in particular with low number of markers. To control high false positive rates and for validation and of the target major QTLs we applied regional association analysis. The previously selected model of association study i.e. Q+K implemented in MLM method of TASSEL 5.0.8 software was used. The significant marker-trait associations were declared for P ≤ 0.01.

Marker localization and homology with known genes

We physically localized the SSR markers that were strongly linked with QTLs in this study by BLAST searches of sequences in http://www.phytozome.net/sorghum (accessed on 15 Nov 2019), http://www.plantgdb.org/SbGDB/ (accessed on 15 Nov 2019), and http://www.gramene.org/ (accessed on 15 Nov 2019). The Genome Data Viewer of NCBI (https://www.ncbi.nlm.nih.gov/genome/gdv/, accessed on 15 Nov 2019) was used to identify loci previously identified as linked to known genes in genome-based sequence information. After identification of maximum matching sequences, Genome Data Viewer was further utilized for searching the database. The sorghum genome database available at http://www.phytozome.net/sorghum was utilized to search the primer sequences when Map Viewer was unable to identify the position. Protein sequences predicted from genes were also used to search using BlastP of NCBI, and finally the homologous sorghum genes were identified by using the sorghum genome in http://www.plantgdb.org/SbGDB/.

Results

Assessment of phenotypes

Water-soluble extract (WSE) from each accession of SDRS was tested on lettuce seeds in four concentrations (25%, 50%, 75% and 100%). The allelopathic effect of each accession was calculated based on inhibition of germination and root length of lettuce seeds treated with WSE extracted from it at six-leaf stage. The control (no treatment of WSE) was also used for comparison. Among four concentrations of WSE, wide ranges of variations were observed in 100% (no dilution) of WSE derived from 107 accessions of SDRS as shown in Fig 1a. The frequency distribution of the allelopathic effect was continuous for both traits ranging from 17.5 to 100% for seed germination whereas, 14.1 to 78.8% of the control. Correlation among traits studied in SDRS and F2:3 lines shows germination of lettuce seeds treated with 25% WSE had non-significant relationship for all other traits (Table 1). Whereas, root length of lettuce treated with 100% WSE had highly significant correlations with all other traits except germination at 25% WSE in both types of populations. The same way of phenotyping was also performed in F2 derived F3 lines of the cross Tokibi x Phatsai. A wide range of variations were recorded for germination and root length of lettuce seeds treated with 100% WSE derived from F3 lines (Fig 1b). In this case also the frequency distribution was continuous ranging from 5 to 100% and 1 to 79% for root length of lettuce plants treated with WSE relative to control. Germination and root length of lettuce seeds treated with Tokibi were recorded as 95 ± 2.1% and 75 ± 1.4% respectively of the control while for Phatsai, 18 ± 2.1% germination and 14.5 ± 1.4% root length of the control were recorded. Analysis of variance and heritability of traits studied on F2:3 lines showed highly significant variations as well as high heritability for germination (100% WSE) and root length (25%, 50%, 75% and 100%) as shown in Table 2. Germination at 25%, 50% and 100% WSE had non-significant variations and also low heritability values. Based on phenotypic evaluations, lettuce treated with 75% and 100% showed the maximum strength to divide the plant material significantly. So these two concentrations were selected as the most affective ones.

Fig 1

(a) Distribution of germination and root length of lettuce treated with water soluble extract (WSE) of 107 sorghum accessions (SDRS) seedlings used in 100% concentration. (b) Distribution of germination and root length of lettuce treated with water soluble extract (WSE) of 134 F2 derived F3 family lines. The red curve is the normal density curve. The top part is Quantile Box Plot (the Outlier Box Plot) and the disconnected points are potential outliers. A red bracket defines the shortest half of the data (the densest region). The results of the first, second, and third run of the outlier identification are displayed in each individual plot from left to right. P1/P2 represents Parent 1 (Tokibi) and Parent 2 (Phatsai), the arrow indicates its trait value where it falls. The term relative represents % germination or root length of the values recorded for control.

Table 2

Analysis of variance (ANOVA), distribution, heritabilty (h2) and genetic advance (R) of seven yield and yield components studied in 134 F2:3 lines.

Traits	df	Mean squares	Vga	Veb	Vpc	F ratio	P > F	Mean	Std Dev	Min	Max	h2
Germ25%	133	85.2	7.1	82.1	28.5	1.7	0.049	97.7	6.3	50.0	100.0	0.24
Error	134	21.5
Germ50%	133	69.2	4.8	59.7	64.4	1.2	0.224	96.2	7.7	45.0	100.0	0.07
Error	134	59.7
Germ75%	133	260.0	84.2	91.5	175.7	2.8	<0.0001	91.2	11.4	45.0	100.0	0.47
Error	134	91.5
Germ100%	133	1026.6	467.1	92.5	559.6	11.1	<0.0001	77.2	22.9	17.5	100.0	0.83
Error	134	92.5
RL25%	133	392.0	164.5	63.0	227.5	6.2	<0.0001	1.3	0.2	0.8	2.1	0.72
Error	134	63.0
RL50%	133	541.4	242.8	27.3	298.6	9.7	<0.0001	1.0	0.3	0.5	1.8	0.81
Error	134	55.8
RL75%	133	470.4	221.6	27.3	248.8	17.2	<0.0001	0.8	0.2	0.3	1.4	0.89
Error	134	27.3
RL100%	133	437.3	185.7	65.9	251.6	6.6	<0.0001	0.6	0.3	0.3	1.4	0.74
Error	134	65.9

Genotypic mean variance is classified into genetic varaince (Vg), error varaince (Ve) and total phenotypic varaince (Vp).

Construction of linkage map and family-based QTL mapping

The linkage map was constructed from the selected 175 polymorphic markers. A total of 151 SSR loci (S1 Table) were mapped over 10 sorghum chromosomes excepting unlinked 24 SSRs (Fig 2). In this study, 8.2 cM was the average distance between markers whereas, the longest distance was 32.6 cM, and the shortest was 0.5 cM. All of the previously mapped 30 SSRs used among other markers in this study were mapped on the same chromosomes as previously reported (Fig 2).

Fig 2

Association analysis of 181 SSR markers and eight traits studied on germination and root length of lattice plants using water soluble extracts from 107 sorghum accessions (SDRS).

CIM identified five QTLs responsible for the inhibition of germination of lettuce seeds in F2:3. Among them, two QTLs were detected for 25% WSE located on Chr 1 (qtl1Germ) and Chr 8 (qtl8Germ) (Table 3, Fig 2). The LOD scores were 11.2 and 3.4, whereas R were 30.1% and 10.4%, respectively. A single QTL, qtl2Germ was found significantly associated with inhibitory effects on germination by 50% WSE with LOD value of 3.5 and R of 13.2%. The same QTL (qtl2Germ) with flanking markers Xtxp197 (183.0 cM) and Xtxp100 (187.4 cM) was also found strongly associated with the trait treated with 75% WSE with highest LOD score explaining 35.6% of total phenotypic variance. Another QTL with name qtl5Germ was also related to this trait studied on 75% WSE with 6.1 of LOD score and 16.0% of total phenotypic variance (R). Similarly, a single QTL (qtl3Germ) significantly inhibited germination in 100% WSE, indicating LOD score of 4.3 and R of 12.4%.

Table 3

QTLs identified by linkage analysis using composite interval mapping (CIM) model for the effects of WSE from 134 F3 lines on germination and root length growth of lettuce plant.

Trait	WSEa	QTL	Chromosome	Marker/Interval	Position (cM)	Physical Position (Start-End) of Markers	F3
							Effects		R2 (%)	LOD
							Add	Dom
Germination	25%	qtl1Germ	1	SB0229-SB0258	47.2–59.2	(157142..157290)-(11943960..11944153)	1.4	-0.7	30.1	11.2
		qtl8Germ	8	Xtxp354-SB4578	40.6–47.3	(48122788..48122851)-(51583239–51583426)	7.1	-7.5	10.4	3.4
	50%	qtl2Germ	2	Xtxp197-Xtxp100	183.0–187.4	(1449013..1449076)-(69473471..69473534)	-5.9	-6.7	13.2	3.5
	75%	qtl2Germ	2	Xtxp197-Xtxp100	183.0–187.4	(1449013..1449076)-(69473471..69473534)	-7.4	-0.5	35.6	12.4
		qtl5Germ	5	SB3151-Xtxp23	22.1–44.8	(9589292..9589470)-(54447105..54447168)	-7.8	-0.6	16.0	6.1
	100%	qtl3Germ	3	SB1783-SB1799	44.9–56.4	(7055966..7056239)-(6841185..6841450)	7.3	-7.1	12.4	4.3
Root Length	25%	qtl4RL	4	SB2607-SB2544	34.7–49.9	(17481687..17481840)-(7368441..7368710)	-2.6	-2.0	9.9	2.9
	50%	qtl2RL	2	SB1209-SB1287	31.9–44.2	(57080999..57081201)-(61259880..61260075)	-7.0	-8.7	22.1	5.3
		qtl6RL	6	SB3583-SB3630	43.2–66.1	(49670815..49670986)-(52813876..52814028)	9.2	-7.5	12.4	2.7
		qtl7RL	7	SB3996-SB4145	21.9–33.2	(7788962..7789259)-(58412340..58412533)	0.4	-8.7	12.2	2.5
		qtl8RL	8	SB4956-Xtxp273	21.2–34.7	(51161240..51161383)-(157079..157142)	-6.0	-6.4	17.0	3.1
		qtl10RL	10	Xtxp270-SB5329	32.6–35.5	(11037040..11037103)-(41184605..41184836)	-2.1	-6.1	10.5	2.8
	75%	qtl4RL	4	SB2836-Xtxp21	172.2–177.4	(62144483..62144661)-(67894269..67894331)	-7.1	-8.0	16.1	4.6
		qtl10RL	10	Xtxp270-SB5329	32.6–35.5	(11037040..11037103)-(41184605..41184836)	-0.9	-7.6	34.5	9.8
	100%	qtl1RL	1	SB0863-SB0852	205.6–208.3	(71693225..71693516)-(70830608..70830842)	-7.5	-2.4	11.5	2.4
		qtl10RL	10	Xtxp270-SB5329	32.6–35.5	(11037040..11037103)-(41184605..41184836)	-8.5	-8.4	14.3	3.7

a; Water soluble extract of sorghum accessions used in four different concentrations of 25%, 50%, 75% and 100%.

A single QTL, qtl4RL was identified to inhibit root length of lettuce seedlings treated with 25% WSE derived from F2:3 lines with LOD score of 2.9 and controlling 9.9% of total phenotypic variation (Table 3, Fig 2). In case of 50% WSE used, maximum number of QTLs (five) were identified as strongly associated with root inhibitory effect with LOD score values ranging from 2.7 to 5.3. R ranged from 10.5% to 22.1%. These include qtl2RL, qtl6RL, qtl7RL, qtl8RL and qtl10RL located on Chrs 2, 6, 7, 8 and 10, respectively.

For 75% WSE, two QTLs were identified for root length inhibition, on Chr 4 (qtl4RL) and Chr 10 (qtl10RL), with LOD scores of 4.6 and 9.8, respectively and R2 values of 16.1% and 34.5%, respectively. Similarly for 100% WSE, two QTLs were detected as significantly controlling inhibitory action of WSE on root length of lettuce. These include qtl1RL with LOD score of 2.4 and R value of 11.5% on Chr 1 and another QTL qtl10RL with LOD score of 3.7 and R value of 14.3%. Among these QTLs, qtl10RL with flanking markers Xtxp270 and SB5329 at position 32.6–35.5 cM was commonly associated with the root length inhibition of lettuce treated with 50%, 75% and 100% WSE.

Population structure and genome-wide association study

In this study, 181 SSR markers were used to genotype 107 accessions of SDRS covering the whole genome of sorghum. The number of subpopulations (J) was tested from two to ten, each repeated three times. The posterior probability and ad hoc statistic values of J = 3 were the largest among other subpopulations (S1 Fig). Therefore, we chose J = 3 and obtained estimates of q for the proportion of i’s genome that originated from population j. The first subpopulation (J = 1) was composed of 33 accessions: 27 from Africa and 6 from Asia. The second subpopulation (J = 2) was the largest group that comprised 39 accessions from East and South Asian countries. The third subpopulation (J = 3) had 35 sorghum accessions derived from African and Asian origin. A low to medium range of LD among SSR loci were observed in this study. To control false positives, both population structure (Q) and kinship (K) were implemented in MLM model of association analysis that is generally called a complete model or Q+K model. Using this model, two significant loci were associated with germination of lettuce seeds treated with 25% of WSE at P ≤ 0.001 (Table 4, Fig 3). One of them was located on chromosome (Chr) 1 (Xtxp335) and was named qtl1Germ while the other was located on Chr 8 (qtl8Germ) linked to Xtxp47. The–Log10 (P) of these QTLs were 2.2 and 2.1 while explained phenotypic variance (R) were 17.1 and 9.2, respectively. In treatment of 75% WSE, a single locus (Xtxp100) was strongly associated with germination located on Chr 2. This QTL (qtl2Germ) had the maximum–Log10 (P) value of 4.2 and explaining 14.8% of the phenotypic variance.

Table 4

QTLs identified by association mapping using Q+K model for the effects of WSE from SDRS on germination and root length growth of lettuce plant.

Trait	WSEa	QTL	Chromosome	Marker	Start Position (bp)	End Position (bp)	-Log10(P)	% R2
Germination	25%	qtl1Germ	1	Xtxp335	55750529	55750592	2.2	17.1
		qtl8Germ	8	Xtxp47	2912606	2912668	2.1	9.2
	75%	qtl2Germ	2	Xtxp100	69473471	69473534	4.2	14.8
Root Length	50%	qtl4RL	4	SB2836	62144483	62144661	2.7	21.7
		qtl7RL	7	SB4003	8643296	8643486	2.0	6.9
		qtl8RL	8	Xtxp273	157079	157142	2.1	13.0
		qtl10RL	10	Xtxp270	11037040	11037103	2.0	23.6
	75%	qtl10RL	10	Xtxp270	11037040	11037103	3.6	27.3
	100%	qtl1RL	1	SB0863	71693225	71693516	2.2	22.6
		qtl10RL	10	Xtxp270	11037040	11037103	2.1	22.5

a; Water soluble extract of sorghum accessions used in four different concentrations of 25%, 50%, 75% and 100%.

b; -Log10 of P-values determined for Q+K model with 2.0 as threshold value for strong association.

Fig 3

Linkage map generated using 151 SSR markers on 134 F2 population.

QTL analysis was performed on eight allelopathy related traits studied on germination and root length of lettuce plants exposed to water soluble extracts (WSE) from F3 family lines. WSE was used in four different concentrations i.e. 25% (★), 50% (◆), 75% (⚫) and 100% (❖).

The maximum QTLs i.e. four were resolved for the inhibitory effect on root length treated with 50% of WSE on chromosomes 4 (qtl4RL), 7 (qtl7RL), 8 (qtl8RL) and 10 (qtl10RL). The values of–Log10 (P) were recorded as 2.7 (R = 21.7), 2.0 (R = 6.9), 2.1 (R = 13.0) and 2.0 (R = 23.6), respectively. A single locus Xtxp270 was found significantly associated with root length inhibition by 75% WSE with–Log10 (P) value of 3.6 and explaining 22.5% of total phenotypic variance. Similarly two loci (SB0863 and Xtxp270) showed highly significance at P ≤ 0.001 with the root length inhibition trait (RL) by 100% WSE. These QTLs were located on chromosomes 1 (qtl1RL) and 10 (qtl10RL) with–Log10 (P) values of 2.2 (R = 22.6) and 2.1 (R = 22.5), respectively (Table 4, Fig 3). No QTL was detected for germination of lettuce seeds treated with 50 and 100% WSE.

Regional association mapping

The corresponding genomic regions of 13 major unique QTLs were identified by the alignment between the primer sequences of tightly linked SSR markers. Considering the population structure and family relatedness within the population, the association analysis was conducted with a mixed linear model (MLM) by TASSEL ver. 5.0.8 using the 107 landraces (SDRS) and 21 QTL-linked SSR loci in the target regions. Notably, 14 out of 21 loci showed lower–Log10 of P-values than the threshold of 2.5 whereas, seven loci had significant values. At strict threshold of 3.0, only four loci were highly significantly associated with allelopathic characteristics of SDRS (Fig 4). Among them, two SSRs loci, Xtxp100 (Chr 2) and SB3630 (Chr 6) were associated with germination at 75% WSE treatment having -Log10 (P) of 4.2 (R = 14.8) and 3.6 (R = 12.3), respectively. The locus SB0863 (Chr 1) showed strong association (-Log10 (P) = 3.6, R = 11.8) for root length at treatment of 75% WSE. Another QTL, Xtxp27 (Chr 10) also had strong association for root length of lettuce treated with WSE of sorghum accessions at 75% (-Log10 P-value = 3.6, R = 12.5) and 100% (Log10 P-value = 4.6, R = 21.2) as shown in Fig 4.

Fig 4

Targeted association of 22 QTL-SSR markers and eight traits studied on germination and root length of lattice plants using water soluble extracts from 107 sorghum accessions (SDRS).

Physical co-localization with known genes

We examined the position of each QTL identified here with previously known genes by physically localizing the QTL markers on sorghum chromosomes. Several candidate genes were identified in the proximal QTL regions of this study. The locus SB0852, which flanks qtl1RL, is located on Chr 1 at 70817033 bp, is reported as the location of protein-coding gene SB01G047730 in a previous study. This gene is predicted to function in protein and nucleic acid binding processes. Another marker locus associated with qtl1Germ on Chr 1, Xtxp335 (55750572 bp) is located within a candidate gene SB01G032850, which is predicted to play a role in the light reactions of photosynthesis.

Similarly, Xtxp273 located at 19929776 bp identified by association analysis on Chr 8 (qtl8RL) contained within a candidate gene coding fascidin-like arabinogalactin protein 7 in Brassica rapa. A locus Xtxp354 identified in family-based linkage analysis flanking qtl8Germ located on Chr 8 at 48122951 bp and another locus Xtxp100 (Chr2: 269473512 bp) found significant in both association and linkage mapping encodes reverse transcriptase RVT-2 superfamily gene. This gene can be found in maize, switch grass, sorghum, grape wine and other plants. Marker locus SB0229 (Chr 1: 10155578) of the QTL qtl1 Germ found in family-based linkage analysis encodes helitron helicase-like that is recognized eukaryotic transposon predicted to amplify by roling-circle mechanism. Another locus SB4145 on Chr 7 (58359565 bp) flank with a QTL qtl7RL inhibiting root length of lettuce plant contained within a gene (Sb07g023510) that has 25 orthologs in different plant species. Similarly one flanking marker of qtl10RL (SB5329) on Chr 10 at 41202394 bp, is associated with a candidate gene Sb10g019340, which encodes a known protein with transferase activity. This gene has 18 orthologs in other plant species.

Discussion

Allelopathy is an ecological term, derived from two Greek words “Allelon” means mutual and “Pathos”, means harm [25]. Allelopathy plays important role in plant to plant interaction and its surroundings through the production of chemical compounds (allelochemicals) that are released into the environment and effecting the whole agroecosystem. Previously, sorghum allelopathy against weeds has been extensively studied for biological weed control. Sorghum residues release a number of allelochemicals such as sorgoleone, dhurrin and a number of secondary metabolites that bring about weed suppression. Sorghum has a great potential for allelopathic crop to control parasitic weeds, especially Striga and Orobanche species which pose a serious threat to agriculture as they are difficult to manage.

Several strategies have been adopted to control weeds including the use of sorghum extracts, use of sorghum residues as mulch and cover crops or soil incorporation, and also the use of sorghum in crop rotations [26, 27, 10]. Until recent years, a wide range of sorghum allelochemicals have been isolated and characterized from shoots, roots as well as root exudates. Among those chemcals, numerous phenolics, a cyanogenic glycoside (dhurrin), and a hydrophobic p-ben- zoquinone (sorgoleone) have been reported in several studies. In terms of mode of action and specificity, sorgoleone has been widely investigated which is continuously released by living root hairs. The study of genetic factors involved in sorghum allelopathy has not been given much attention except SOR1, a gene associated with the production of sorgoleone [5]. A differential expression analysis and real-time PCR revealed that this gene is expressed in the roots of sorghum but not in shoots. Here we report the genetic analysis of phytotoxins produced in sorghum parts other than sorgoleone in roots.

In this study two different approaches of QTL mapping, LD based mapping and linkage mapping, has been used for the first time to study the genetics of allelopathy in sorghum. LD mapping approach identified three QTLs associated with inhibiting germination and seven QTLs with root length inhibition effect. Whereas, linkage mapping detected total of 16 QTLs including six for inhibition effect of WSE on germination and ten for inhibition effect on root length of lettuce plants.

The association analysis was performed using SDRS assessed with 181 SSR molecular markers. The linkage map reported here, is composed of 151 mapped SSR markers covering all number of sorghum chromosomes. An additional 24 markers were also tested but could not be mapped due to segregation distortions. The SSRs reported here were developed from shotgun sequences of the whole sorghum genome [17]. Similarly, 30 previously mapped SSRs reported by Bhattramakki et al. [28] were also mapped and showed consistency in the pattern of recombination with other markers. All of the 30 loci were mapped to the same chromosomes as previously reported, which shows the accuracy of locating the markers in this new map. Most of the other new loci selected from Yonemaru et al. [17] were mapped to the same chromosomes as previously reported, with few exceptions (ESM1). SB1707 was mapped on Chr 1 but originally selected from genome sequence of Chr 3. Two other loci, SB3664 and SB2613 were mapped on Chr 4 in this study but were localized on Chr 6 and Chr 1, respectively in previous report [17]. Likewise, in Yonemaru et al. 2009 [17] the two markers SB4925 and SB4956 are reported on Chr 6 but mapped differently in our study. As these discrepancies are common due to sampling variation or the mapping of paralogous loci (i.e., loci arising from gene duplication). Most of the molecular markers in this study were mapped to the same chromosomes as reported earlier by linkage analysis or physical mapping. This supports the accuracy and reliability of the linkage map developed here. Some of the markers mapped in this population has also been used in another study by Shehzad and Okuno [29] and showed no discrepancies in mapping these loci on chromosomes.

We used four different concentrations of WSE, affecting the germination and root growth of lettuce plant at different levels. This is due to the contents of allelochemicals in lower to higher concentrations of WSE. Previously, Agarwal et al. [30], Iqbal et al. [31], Fateh et al. [32] and Shang & Xu [33] also reported an increase in phytotoxicity of allelochemicals with increasing concentration. After statistical analyses, large variations were observed in the traits studied on lettuce treated with 75% and 100% WSE concentrations in comparison with 25% and 50% concentrations. This shows 75% and 100% concentration of WSE is more appropriate to study allelopathic characteristics in sorghum.

The two QTL mapping approaches identified several similar and unique sets of QTLs as significantly associated with the allelopathic characteristics in sorghum. This agreement between the methods supports the efficiency and reliability of our findings. The genomic sequence of SOR1 was physically mapped on Chr 5 (216857–218904 bp). In this study, no QTL was located in the region of where SOR1 is localized, suggesting that water-insoluble sorgoleone has different chemical properties than the allelochemicals present in water soluble extract. The LD mapping found nine QTLs (three for inhibiting germination and six for root length of lettuce) and linkage study identified total of 17 QTLs (seven for inhibiting germination and 10 for root length of lettuce). These results show the presence of several allelochemicals and mutli-genic nature of allelopathic traits in sorghum. In other crops, such as rice and wheat, allelopathic characteristics associated with several chromosomal regions have been reported, suggesting the presence of several allelochemicals [34-38].

In total, LD-based association mapping identified ten QTLs, including three for germination and seven for root length inhibition. Among them, qtl10RL (Xtxp270) on Chr 10 was commonly associated with root inhibition at 50%, 75% and 100% WSE. While no QTL was identified for inhibiting germination by 50% and 100% concentrations of WSE and root length by WSE used in 25% concentration. The linkage mapping detected 16 QTLs in F2:3 lines including six for germination inhibition and ten QTLs for root length inhibition. Here, qtl10RL (Xtxp270–SB5329) was also found significantly controlling allelopathic effect on root length of lettuce by WSE used in 50%, 75% and 100% concentrations. We could not find any significant QTL for allelopathic effect of WSE used in 50% and 100%, whereas QTL for root length inhibition with 25% WSE. The QTLs detected in SDRS by association analysis were also mapped in similar positions in F2:3 family lines by linkage analysis along with other unique QTLs. This shows the accuracy for our methodologies and confirming the stability of QTLs mentioned in this report.

Some of the QTLs identified here, appear to correspond to previously reported QTLs and genes for other related traits. In sorghum, dwarf3 (dw3) is one of four major dwarfing genes, has been cloned and sequenced by Multani et al. [39]. This gene is an ortholog of brachytic2 (br2) in maize and in sorghum it is mapped on Chr 7. In this study, we also identified a major QTL for root length inhibition effect (qtl7RL) on Chr 7 in both approaches of QTL analysis. In LD mapping, SB4003 was found significant with–Log10 (P-value) as 2.0 (Table 3) whereas, linkage mapping identified this QTL between SSR markers SB3996 (21.9 cM) and SB4145 (33.2 cM) (Table 4), which is the same location as dw3. Similarly, qtl6RL with flanking markers SB3583 (43.2 bp) and SB3630 (66.1 bp) falls in the location on Chr 6 where Ma gene of sorghum maturity is located. This is the major repressor of sorghum flowering under longer day-length, which encodes a protein (PRR37) modulating flowering time in sorghum [40]. The qtl10RL identified in this study (Figs 2, 3 and 4) is in similar genomic regions as the previously identified one of the stay-green QTL regions, StgG [41]. The relationship among allelopathic characteristics and these traits need to be tested and further to establish the nature of allelopathy and the effects of these QTLs.

Both approaches identified different QTLs for allelopathic effect on seed germination and root length of lettuce. This shows two different mechanisms are involved in controlling allelopathic effects at two stages. There might be different sets of allelochemicals affecting plants at different part and/or growth stage. A single common co-localized major QTL qtl10RL on Chr 10 between markers Xtxp270 (32.6 bp) and SB5329 (35.5 bp) was detected for inhibition of root length by WSE used in 50%, 75% and 100% concentrations in both approaches of QTL mapping. This shows the importance of this region involved in allelochemical characteristics in sorghum.

Regional association mapping helped in controlling spurious association that commonly occurs in genome-wide association analysis. Targeted or regional association mapping using MLM model could significantly control false positive rates thus resulting in more authentic detection of QTLs. In this study, regional association by 21 QTL loci identified total of four major QTLs out of which two were associated with germination and two with root length treated with WSE from sorghum landraces (Fig 4). Among them, a QTL on Chr 2 was identified for germination of lettuce treated with 75% WSE which is same as detected by linkage analysis of F2:3 population as well as genome wide association mapping. Another unique QTL for the same trait was identified on Chr 6 (-Log10 P = 3.6) that has not been shown in linkage and whole genome association mapping. We also detected two QTLs for root length of lettuce on Chr 1 (75% WSE) and Chr 10 (75%, 100 WSE), respectively (Fig 4). Both of these QTLs were co-localized by family based linkage and LD-based whole genome based association analysis.

Further study is required to finely dissect these target regions that will yield towards positional cloning of candidate gene(s). Chemical assessments are also essential for profiling allelochemicals that attribute allelochemical characteristics to sorghum. The results show that sorgoleone is not only phytotoxin existing in sorghum but several phenolic compounds are also involved in water-soluble extracts. Similarly SOR1 may not be the only gene involved in this phenomenon rather several QTLs/genes are responsible for allelopathy in sorghum. The major QTLs identified in this study could be used for fine mapping and further isolation of genes based on positional or map based cloning approach.

Structure analysis using burn-in length of 105 and MCMC cycles of 106 classified the population into three sub-populations (J), where J is varied from 1 to 9.

Circles represent independent runs for each value of J. Each run for J was repeated three times.

(PPTX)

Click here for additional data file.

Linkage disequilibrium (LD) plot generated by 181 SSR loci.

Each cell represents the relationship between two markers with color codes showing the level of significance.

(PPTX)

Click here for additional data file.

List of newly added 83 SSR markers used in genotyping of sorghum diversity research set (SDRS).

(XLSX)

Click here for additional data file.

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

13 Feb 2020

PONE-D-19-28312

Genetic analysis of QTLs controlling allelopathic characteristics in sorghum

PLOS ONE

Dear Dr. Shehzad,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

We would appreciate receiving your revised manuscript by Mar 29 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Craig Eliot Coleman, PhD

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

1. Please amend the subsection category “[FOR JOURNAL STAFF USE ONLY]” for your manuscript. Unfortunately, this is not a valid category. At this time, please choose one or more subsections that best represent the topic(s) of your study.

2. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

3. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

4. Please upload a new copy of Figures 1-4 as the detail is not clear. Please follow the link for more information: http://blogs.PLOS.org/everyone/2011/05/10/how-to-check-your-manuscript-image-quality-in-editorial-manager/

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors used two sorghum populations, i.e. SDRS and F2:3, to study the inhibitory effect of sorghum WSE on the seed germination and on the root length of lettuce seedlings. They used four concentrations of the WSE (25%, 50%, 75% and 100%). The authors identified several QTL in the F2:3 and in the association mapping populations. The work is designed in a proper way, however, I have the following comments;

• I couldn't find any figures attached to the manuscript that could have helped me to better understand the experimental design.

• Please, add how the heritability was calculated. Was the H2 for germ50% 0.07?

• Please, remove the last paragraph of the "Bioassay of allelopathic effect using water-soluble extract (WSE)" as it was explained again in the next paragraph "Statistical analysis".

• For QTL mapping, was threshold 2.5 chosen based on 1000 permutation or as an arbitrary threshold?

• I couldn't find any figure or data presenting the map of the F3 or the association panel. Please, provide this data.

• In such low density map, I would recommend using window size larger than 10 cM, e.g. 30 cM, to consider the following sentence correct "The adjacent QTLs identified for the same trait with non-overlapping intervals on same chromosome were considered as different QTLs". In addition, one can consider two adjacent qtl as different ones only when their effect is different, e.g. one has an effect from A and the other from B.

• Please change this title “Linkage disequilibrium (LD)-based association mapping” to “Population structure and association mapping” or add two subtitles “Population structure” and “Association mapping”

• “the P-values representing the significance of LD was measured”, how was P value calculated? What is threshold of 2.5 and strict threshold =3 ? Please explain. Have you used an arbitrary threshold or Bonferroni correction? I believe a P value of 2.5 is relatively low and will increase the false positives.

• Last paragraph of Linkage disequilibrium (LD)-based association mapping, “As genome-wide association studies results in spurious associations in particular with low number of markers.” Not clear what Authors want to say.

• In Marker localization and homology with known genes “The Map Viewer of NCBI website (http://www.ncbi.nlm.nih.gov/mapview/) was used to identify loci previously identified as linked to known genes in genome-based sequence information.” I don’t understand whether the author did that to know if their SSR were previously mapped, which could be done from previously published articles, or they did that to just know what is known. Please, clarify on that.

• In “Construction of linkage map……”, please, move the following sentence to discussion “These findings confirms the consistency and accuracy of the linkage map reported here.

• The discussion part, in general, needs to be restructured to avoid repeating sentences and contents. Below are some suggestions,

• when comparing the discrepancy in locating some SSR, e.g. SB3664 and SB2613, please give references after "in previous report".

• The paragraph that starts with "We used two approaches and two different types of populations ……………..", it reads like a review not discussion. Can be deleted or modified.

• "We used four different concentrations of WSE, ……………...." and then "This confirms...…....". It isn't clear what confirms what.

• It will be more coherent to gather all the discussion related to QTL and LD mapping in one paragraph, as now in several places I find the same conclusion, i.e. the suitability of the 2 approaches to map significant QTL ....."

• Why discussing SOR1? Please take the space needed to elaborate on why specifically the authors discussed this gene

• "A single common co-localized major QTL qtl10RL on Chr 10 between markers Xtxp270 (32.6 bp) and SB5329 (35.5 bp) was detected for inhibition of root length by WSE used in 50%, 75% and 100% concentrations in both approaches of QTL mapping". The content of this sentence was mentioned earlier in the discussion. Please, remove this sentence and all other repeated sentences.

• The author should carefully interpret the results of the "regional mapping" and the physical co-location of with known genes as the QTL interval is normally large and contain several genes, especially with this low dense map. I would rather call them candidate genes

Reviewer #2: Allelopathic characteristics of sorghum has been studied at large, however, the reports of QTL for allelopathic characteristics were not reported. In this line, the study “Genetic analysis of QTLs controlling allelopathic characteristics in sorghum” has a merit. Authors did very good job by conducting experiment in optimal way and results are also very interesting however authors need to focus on the results of the study. I propose to recommend for publication in PLOSONE with few suggestions to improve the manuscript.

Major revision

1. The phytotoxin sorgoleone has been largely associated for allelopathic characteristics of sorghum. Due importance for sorgoleone production is not given in this study. It is suggested to study the QTL for sorgoleone content and yield. Further, QTL for sorgoleone needs to be associated with other components of allelopathic characteristics sorghum.

2. Validation of expression stability of QTL is missing in this study. It is suggested validate the identified QTL multiple seasons and location would be logical.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Mohamed El-Soda

Reviewer #2: Yes: Nagaraja Reddy

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

2 Jun 2020

Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Response: Thank you very much for your time and giving your valuable comments and suggestions to uplift the standard of our manuscript. Our detail response to each comment of reviewers are given below.

Reviewer #1: The authors used two sorghum populations, i.e. SDRS and F2:3, to study the inhibitory effect of sorghum WSE on the seed germination and on the root length of lettuce seedlings. They used four concentrations of the WSE (25%, 50%, 75% and 100%). The authors identified several QTL in the F2:3 and in the association mapping populations. The work is designed in a proper way, however, I have the following comments;

Response: Thank you very much for your support and cooperation. We highly appreciate your valuable comments and suggestions for the improvement of our manuscript.

• I couldn't find any figures attached to the manuscript that could have helped me to better understand the experimental design.

Response: Sorry to know about that. The figure were attached but might have experienced some technical issue. I hope you will access in the revised version of manuscript.

• Please, add how the heritability was calculated. Was the H2 for germ50% 0.07?

Response: Thank you for the comment and sorry for the typo, h2 for germ50% is 0.70.

Here, h2 was calculated as broad sense i.e. h2= Genetic Variance/Phenotypic variance

Where Genetic variance = MS (Genotypes) – MS (Phenotype)/Total genotypes and

Phenotypic variance = Genetic variance + Error Variance

• Please, remove the last paragraph of the "Bioassay of allelopathic effect using water-soluble extract (WSE)" as it was explained again in the next paragraph "Statistical analysis".

Response: Thanks for your suggestion, the paragraph has been removed in revised version of manuscript.

• For QTL mapping, was threshold 2.5 chosen based on 1000 permutation or as an arbitrary threshold?

Response: In QTL (Linkage) mapping, 2.5 threshold was chosen based on 1000 permutation using QTLcartographer.

• I couldn't find any figure or data presenting the map of the F3 or the association panel. Please, provide this data.

Response: I really didn’t understand why but all figures were uploaded in previous version as well. I hope this time there will be no issue in viewing all these figures.

Fig.2 is about Association mapping of 181 SSRs and 107 sorghum accessions.

Fig.3 is about Linkage mapping of 151 SSRs on 134 F2 population.

Fig. 4 is showing Targeted association analysis of 22 QTL linked markes.

• In such low density map, I would recommend using window size larger than 10 cM, e.g. 30 cM, to consider the following sentence correct "The adjacent QTLs identified for the same trait with non-overlapping intervals on same chromosome were considered as different QTLs". In addition, one can consider two adjacent qtl as different ones only when their effect is different, e.g. one has an effect from A and the other from B.

Response: Thank you very much for you suggestion. It has been revised in new version of manuscript (Page # 8). No change reported in total identified QTLs reported in this study even if the window size of 30 cM is used.

• Please change this title “Linkage disequilibrium (LD)-based association mapping” to “Population structure and association mapping” or add two subtitles “Population structure” and “Association mapping”

Response: Thanks, changed this heading in revised version to “Population structure and association mapping”.

• “the P-values representing the significance of LD was measured”, how was P value calculated? What is threshold of 2.5 and strict threshold =3 ? Please explain. Have you used an arbitrary threshold or Bonferroni correction? I believe a P value of 2.5 is relatively low and will increase the false positives.

Response: In Tassel software, MLM model identifies P-values based on nominal test of individual makers and then corrected for multiple testing. I used Benjaminin-Hochberg FDR method for corrected P-values. In this study we set the threshold of significance as –Log10 (P-corrected value) of 2.5 which is P<0.003 and more stringent threshold of –log10(corrected P-value) = 3.0 (i.e. P<0.001). Most of studies reported –log10(P)= 2.0 as threshold for quantitative traits which is equal to P value of 0.01. Even in some case a threshold of –log10(P)=1.3 (=pvalue 0.05) has been reported. As we believe allelochemical traits are complex and controlled by several QTLs with minor effects, therefore a threshold of –log10(corrected P-value) = 2.5 is quite reasonable. Also as mentioned earlier, the Benajimin-Hochberg correction is a strong tool for controlling fasle positives.

https://tassel.bitbucket.io/docs/bradbury2007bioinformatics.pdf

• Last paragraph of Linkage disequilibrium (LD)-based association mapping, “As genome-wide association studies results in spurious associations in particular with low number of markers.” Not clear what Authors want to say.

Response: Actually as we know GWAS has major issue of false positives and especially if the number of markers used are low in number. To avoid this constraint, we used regional association mapping approach in which we just used the already identified QTL markers in this study and applied in association model. This further strengthened the reliability of our findings.

• In Marker localization and homology with known genes “The Map Viewer of NCBI website (http://www.ncbi.nlm.nih.gov/mapview/) was used to identify loci previously identified as linked to known genes in genome-based sequence information.” I don’t understand whether the author did that to know if their SSR were previously mapped, which could be done from previously published articles, or they did that to just know what is known. Please, clarify on that.

Response: Infact we wanted to localize our QTL markers with sorghum genome database and see if these loci have been previously linked to some known genes. We also tried to establish if our loci have any homology with other genes previously identified in sorghum for other related traits.

• In “Construction of linkage map……”, please, move the following sentence to discussion “These findings confirms the consistency and accuracy of the linkage map reported here.

Response: Thanks, the sentence has been removed.

• The discussion part, in general, needs to be restructured to avoid repeating sentences and contents. Below are some suggestions,

• when comparing the discrepancy in locating some SSR, e.g. SB3664 and SB2613, please give references after "in previous report".

Response: Thanks, done.

• The paragraph that starts with "We used two approaches and two different types of populations ……………..", it reads like a review not discussion. Can be deleted or modified.

Response: Thank you very much. The paragraph has been removed and did modification in next one.

• "We used four different concentrations of WSE, ……………...." and then "This confirms...…....". It isn't clear what confirms what.

Response: Thanks, the sentence has been modified, “Agarwal et al. (2002), Iqbal et al. (2003), Fateh et al. (2012) and Shang & Xu (2012) also reported an increase in inhibitory effects with increasing concentration of allelochemicals.”

• It will be more coherent to gather all the discussion related to QTL and LD mapping in one paragraph, as now in several places I find the same conclusion, i.e. the suitability of the 2 approaches to map significant QTL ....."

Response: Thank you. We tried to make discussion more coherent and sequential in revised version.

• Why discussing SOR1? Please take the space needed to elaborate on why specifically the authors discussed this gene

Response: Actually until recent, Sorgoleone is considered the only major allelochemical which controls more than 95% of allelochemical characteristics in sorghum and most of genetic research has been oriented on this chemical compound only. SOR1 is the only gene cloned and characterized that is controlling the release of sorgoleone. In our study we identified several other chromosomal regions that controls the release of many other allelochemicals in sorghum and are present in other parts as well rather than only in roots where sorgoleone is release. In revised version we emphasized more on the importance of SOR1 in context to our study.

• "A single common co-localized major QTL qtl10RL on Chr 10 between markers Xtxp270 (32.6 bp) and SB5329 (35.5 bp) was detected for inhibition of root length by WSE used in 50%, 75% and 100% concentrations in both approaches of QTL mapping". The content of this sentence was mentioned earlier in the discussion. Please, remove this sentence and all other repeated sentences.

Response: Thank you very much. Infact this QTL is identified as major one by two approached we used and also in targeted association. That’s why we emphasized more on it and this region could be important in isolation and cloning genes for allelochemicals in sorghum. However, we tried to polish the discussion and removed the repeated sentences from the text.

• The author should carefully interpret the results of the "regional mapping" and the physical co-location of with known genes as the QTL interval is normally large and contain several genes, especially with this low dense map. I would rather call them candidate genes

Response: Thanks and modified in revised manuscript.

Reviewer #2: Allelopathic characteristics of sorghum has been studied at large, however, the reports of QTL for allelopathic characteristics were not reported. In this line, the study “Genetic analysis of QTLs controlling allelopathic characteristics in sorghum” has a merit. Authors did very good job by conducting experiment in optimal way and results are also very interesting however authors need to focus on the results of the study. I propose to recommend for publication in PLOSONE with few suggestions to improve the manuscript.

Response: Thank you very much for your time and support. We highly appreciate your valuable comments and suggestions for the improvement of our manuscript.

Major revision

1. The phytotoxin sorgoleone has been largely associated for allelopathic characteristics of sorghum. Due importance for sorgoleone production is not given in this study. It is suggested to study the QTL for sorgoleone content and yield. Further, QTL for sorgoleone needs to be associated with other components of allelopathic characteristics sorghum.

Response: Thank you very much. We have added some more details about sorgoleone in introduction.

Sorgoleone is the only main phytotoxin that has been extensively studied in sorghum and is controlled by a single gene “SOR1” that has been completely characterized. There is no QTL study for allelopathy in sorghum until recent or atleast to our knowledge. This study gives more insight to the production of phytotoxins in sorghum other than sorgoleone.

Actually, we have previously used same sets of SSR markers in an experiment on yield and yield components in Shehzad and Okuno (Euphytica 2015) but haven’t seen any major correlation between those QTLs and the ones reported in this study. We strongly agree with you about that QTL mapping for allelopathy should be associated with yield and hope to conduct such experiment in future.

2. Validation of expression stability of QTL is missing in this study. It is suggested validate the identified QTL multiple seasons and location would be logical.

Response: Sorghum diversity set used in this study was developed from genebank accessions and maintained in our lab since 2006. We have been growing these materials for many years in the field and maintaining its homogeneity. For Association study while using the SDRS, we selected seeds of same genotype harvested from different years and sown in pots. So there is less chance of skipping allelopathic affect due to environments.

Another approach we adapted here to validate our result was using Targeted Association Mapping. As in this study the number of genotypes and markers are not too much high so to avoid false positives we tried regional association mapping. In this method we only used QTL loci identified in this study and performed association analysis. We have found four common QTLs that were located in GWAS and Linkage mapping approach. This shows the credibility of these results and can further be utilized in gene cloning experiments.

In case of linkage analysis, the F2 were sown in field conditions and the field of University of Tsukuba is properly maintained and organized. Also there are less variations in weather patterns in Tsukuba each year. After harvesting F3 seeds, 30 seeds from each F2 line were planted in pot conditions in controlled greenhouse so there is less chance of any environmental fluctuations. Although release of secondary metabolites are mainly controlled by genes but still environment affects its release and to avoid those environmental factors we have grown the material in green house and used them at seedling stage. Although for mapping complex traits more advance generations upto F7 or F8 are best but several studies reported the use of F3 populations for QTL mapping has similar power as of F7 or F8 generations (for example, Shohei Takuno et al, PLoS One: October 9, 2012). Also as mentioned earlier, use of multiple replications and repeats in bioassays also minimized environmental error.

Finally, we are planning to further proceed with these findings and fine map major QTLs identified in this study. This will ultimately help us cloning genes responsible for allelochemical characteristics in sorghum.

Submitted filename: Review Comments to the Author.docx

Click here for additional data file.

11 Jun 2020

PONE-D-19-28312R1

Genetic analysis of QTLs controlling allelopathic characteristics in sorghum

PLOS ONE

Dear Dr. Shehzad,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jul 26 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Craig Eliot Coleman, PhD

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Authors have partially addressed the comments as they gave answers to the queries raised during the first round but only in their letter not in the main text. Please amend the manuscript based on the suggested changes. For example;

1. Please, add how heritability was calculated, and explain why you refer to SOR1 in the discussion part

2. Please, add to your methods how the threshold was chosen for QTL and association mapping. Please add R2 and how you calculated "FDR-corrected P value" for the association mapping, that might strengthen your point of using 2 as a threshold.

3. Please, check the language throughout the manuscript as many sentences are not correct. For example, "Each trait was analyzed with empirical experiment-wise threshold values for significance (P = 0.05) from estimating 1000 permutations"

4. In the QTL part, please discuss the QTL co-location as this will support your results.

5. Please rephrase the sentence you added to introduction, “Researchers studying secondary metabolites affecting germination of parasitic weed Striga asicatica (witchweed) first discovered it (Chang et al. 1986). Retrobiosynthetic NMR analysis was ................. ,

materials and methods, "We physically localized the SSR loci that were strongly linked with the traits by BLAST searches of sequences in http://www.phytozome.net/sorghum ……… ",

and to the discussion "also reported an increase in inhibitory effects with increasing concentration of allelochemicals. After statistical analyses, we detected 75% and 100% WSE as optimum --------", as they don’t read well.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Mohamed El-Soda

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

13 Jun 2020

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Thank you very much for the continuous support and help in revising our manuscript.

Reviewer #1: Authors have partially addressed the comments as they gave answers to the queries raised during the first round but only in their letter not in the main text. Please amend the manuscript based on the suggested changes. For example;

1. Please, add how heritability was calculated, and explain why you refer to SOR1 in the discussion part

Response: Thanks, added in revised manuscript. The method of calculating heritability is added in methods under to heading Statistical Analysis and referring SOR1 in discussion part. Also Table 4. is modified by adding R2 values in new column.

2. Please, add to your methods how the threshold was chosen for QTL and association mapping. Please add R2 and how you calculated "FDR-corrected P value" for the association mapping, that might strengthen your point of using 2 as a threshold.

Response: Thank you very much. We have added the method of FDR-corrected in material and methods and also R2 values for QTLs detected using GWAS and Targetted Association Analysis. The method of FDR corrected P value has been explained in material and methods under heading of Population Structure and association mapping.

3. Please, check the language throughout the manuscript as many sentences are not correct. For example, "Each trait was analyzed with empirical experiment-wise threshold values for significance (P = 0.05) from estimating 1000 permutations"

Response: Thank you very much. The manuscript has been gone through English editing once again by my colleague a native speaker and checked for linguistic errors.

4. In the QTL part, please discuss the QTL co-location as this will support your results.

Response: Thanks, we have already added the one found using several pipelines and added in the results and discussion part.

5. Please rephrase the sentence you added to introduction, “Researchers studying secondary metabolites affecting germination of parasitic weed Striga asicatica (witchweed) first discovered it (Chang et al. 1986). Retrobiosynthetic NMR analysis was ................. ,

materials and methods, "We physically localized the SSR loci that were strongly linked with the traits by BLAST searches of sequences in http://www.phytozome.net/sorghum ……… ",

and to the discussion "also reported an increase in inhibitory effects with increasing concentration of allelochemicals. After statistical analyses, we detected 75% and 100% WSE as optimum --------", as they don’t read well.

Response: Thanks, done.

Submitted filename: Review Comments to the Author2.docx

Click here for additional data file.

25 Jun 2020

Genetic analysis of QTLs controlling allelopathic characteristics in sorghum

PONE-D-19-28312R2

Dear Dr. Shehzad,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Craig Eliot Coleman, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

30 Jun 2020

PONE-D-19-28312R2

Genetic analysis of QTLs controlling allelopathic characteristics in sorghum

Dear Dr. Shehzad:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Craig Eliot Coleman

Academic Editor

PLOS ONE