Linkage disequilibrium and population structure in a core collection of Brassica napus (L.).

Estimation of genetic diversity in rapeseed is important for sustainable breeding program to provide an option for the development of new breeding lines. The objective of this study was to elucidate the patterns of genetic diversity within and among different structural groups, and measure the extent of linkage disequilibrium (LD) of 383 globally distributed rapeseed germplasm using 8,502 single nucleotide polymorphism (SNP) markers. We divided the germplasm collection into five subpopulations (P1 to P5) according to geographic and growth habit-related patterns. All subpopulations showed moderate genetic diversity (average H = 0.22 and I = 0.34). The pairwise Fst comparison revealed a great degree of divergence (Fst > 0.24) between most of the combinations. The rutabaga type showed highest divergence with spring and winter types. Higher divergence was also found between winter and spring types. Admixture model based structure analysis, principal component and neighbor-joining tree analysis placed all subpopulations into three distinct clusters. Admixed genotype constituted 29.24% of total genotypes, while remaining 70.76% belongs to identified clusters. Overall, mean linkage disequilibrium was 0.03 and it decayed to its half maximum within < 45 kb distance for whole genome. The LD decay was slower in C genome (< 93 kb); relative to the A genome (< 21 kb) which was confirmed by availability of larger haplotype blocks in C genome than A genome. The findings regarding LD pattern and population structure will help to utilize the collection as an important resource for association mapping efforts to identify genes useful in crop improvement as well as for selection of parents for hybrid breeding.

Introduction

Rapeseed (Brassica napus L., AACC, 2n = 4x = 38), is a recent allopolyploid of polyphyletic origin that evolved from hybridization events between two parental ancestors of B. oleracea (Mediterranean cabbage, CC, 2n = 2x = 18) and B. rapa (Asian cabbage, AA, 2n = 2x = 20) [1]. Rapeseed genotypes having < 2% erucic acid in seed and < 30 μM glucosinolates in seed meal is known as canola, which is the second largest oilseed crops produced in the world after soybean [2]. Canola oil is mostly used in frying and baking, margarine, salad dressings, and many other products. Because of its fatty acid profile and the lowest amount of saturated fat among all other oils, it is commonly consumed all over the world and is considered a very healthy oil [3]. Canola oil is also rich with alpha-linolenic acid (ALA), which is associated to a lower risk of cardiovascular disease [3]. Additionally, canola is utilized as a livestock meal and is the second largest protein meal in the world after soybean [4]. Rapeseed oil has various industrial usages. The rapeseed oil, being simple alkyl esters is the best alternative to diesel fuel. It is more energy-economic and environment friendly than diesel fuel [5]. The high erucic acid content in rapeseed oil also made it suitable for using as lubricants [6] and surfactants [7]. Rapeseed expresses three growth habits, winter, spring, and semi-winter. The spring canola is planted in the early spring and harvested in the late spring of the same growing season [8]. The winter type canola is seeded in the fall, vernalized over the winter to induce flower and harvested in the summer [8]. The semi‐winter type is needed for a shorter period of vernalization to induce flower [9].

Rutabaga (Brassica napus ssp. napobrassica L.) is a cool-weather root crop, grown as table vegetable and fodder for animals [10]. Likewise rapeseed, rutabaga was also derived from natural or spontaneous hybridization between B. rapa and B. oleracea [11]. European immigrants brought rutabaga to North America [12] from its center of origin Sweden or Finland [10, 13]. Likewise most cruciferous vegetables, rutabaga bears anti-cancer properties [14] and showed considerable variability for morphology, biotic and abiotic stress resistance, seed yield and quality [10, 15].

In the United States of America (USA), the canola production increased 13.5 folds from five years average of 1991–1995 (0.11 m tons) to five years average of 2015–2019 (1.49 m tons) [16]. At the same time, canola oil consumption has increased rapidly in last few years. Statistics shows, though canola production increased, but it is not enough to meet the demand. That’s why, every year USA imports huge amount of canola oil (2.50 m tons in 2019) from other countries [2]. In USA, canola production is restricted to north-central region and North Dakota (ND) is the leading canola growing state, where 83% of US canola is grown. The North Dakota State University (NDSU) canola-breeding program could play a vital role in canola economy by developing high yielding varieties, shortening the breeding cycle and expanding canola growing acreage.

NDSU canola breeding has already developed few varieties and handsome amount of breeding populations. However, in recent years, the low genetic diversity of the parental stock is hampering the sustainability of the program. This happened because of same sets of parents has already been crossed in different combinations. The recent origin of B. napus as a species and its very recent domestication (400 years ago), as well as selection on few phenotypes (e.g. low erucic and glucosinolate acids, seed yield) also accelerated the low diversity which threatens sustainable improvement of the crop [17]. The narrow genetic diversity might also limit the prospects for hybrid breeding where complementing genepools are needed for the optimal exploitation of heterosis [18]. Therefore, we want to expand the genetic base of NDSU stock by incorporating diversified germplasms to existing collection. To shorten the breeding cycle and maximize genetic gain, we want to use cutting-edge breeding techniques such genome wide association mapping (GWAS) and marker-assisted selection. The knowledge of population structure, genetic relatedness, and patterns of linkage disequilibrium (LD) are also prime requirements for genome-wide association study (GWAS) and genome selection directed breeding strategies [19, 20]. Therefore, it is crucial to study, preserve, and even introduce genetic diversity into rapeseed since the diversity ensures the variability for biotic and abiotic stress resistance, and various agronomical and morphological traits.

We could assess the diversity of a germplasm collection by observing the phenotypic variations or genomic variations among the individuals. Before the advent of marker technology and next generation sequencing technique (NGS), crop diversity was usually assessed based on phenotypic performance. However, phenotyping is time consuming and labor intensive. Moreover, plant growth stages and environmental factors severely affect the phenotyping, results in erroneous prediction [21]. To overcome phenotyping limitations, researchers use DNA-based molecular markers for assessing the genetic diversity. Utilization of molecular markers accelerates the pre-breeding activities, as field phenotyping and pedigree information are not required [22]. Multiple genetic diversity and population structure studies, based on molecular markers [23-27], whole genome resequencing [28], transcriptome and organellar sequencing [29] have already provided information regarding genetic diversity in various B. napus collections around the world. However, the genetic diversity of the core collection maintained by the NDSU canola-breeding program has not been revealed yet. That is why; we carried out this research to explore the genetic diversity, population structure level and relatedness among the genotypes and to investigate the linkage disequilibrium (LD) and haplotype block pattern.

Materials and methods

Plant materials

A core collection of 383 rapeseed germplasm accessions was used for this study. The core is composed of 67 advanced breeding lines developed by NDSU canola breeding program, 252 germplasm accessions collected from North Central Regional Plant Introduction Station (NCRPIS), Ames, Iowa, USA and 64 varieties collected from different countries. The breeding lines are F7 generation genotypes, obtained by crossing different parents in different combinations. Initially, we collected 500 accessions from NCRPIS and phenotyped them under field conditions. No flowering occurred in case of winter type. Among them, we choose 252 relatively homogeneous genotypes for the core collection. Finally, the core collection was composed of 155 spring, 151 winter, 60 semi-winter, and 17 rutabaga types (S1 Table). The core collection is being and will be maintained through selfing. We grouped the core collection into five subpopulations (P1 to P5) according to their type and origin. Hereafter, we referred the European winter type as subpopulation-1 (P1), Asian semi-winter type as subpopulation-2 (P2), spring type NDSU genotypes (advanced breeding lines) as subpopulation-3 (P3), spring type from different countries other than NDSU breeding lines as subpopulation-4 (P4), and rutabaga type as subpopulation-5 (P5).

Genotyping and sequencing

DNA was extracted from young leaf tissue, collected from 30 days old plants. We collected three leaf samples per genotype in tubes and flash frozen in liquid nitrogen. Each sample was composed of leaves from three different plant of same genotype. Then we lyophilized leaf tissue and ground it in tubes with stainless beads using a plate shaker. Qiagen DNeasy Kit (Qiagen, CA, USA) was used for DNA extraction (3 samples per genotype) following the manufacturer’s protocol. DNA concentration was measured using a NanoDrop 2000/2000c Spectrophotometer (Thermofisher Scientific). The sample that contains good concentration of DNA was kept and other two discarded. Then we prepared the GBS library using ApekI enzyme [30]. Finally, Sequencing of the library was done at the University of Texas Southwestern Medical Center, Dallas, Texas, USA using Illumina HiSeq 2500 sequencer.

SNP calling

SNP calling was done by TASSEL 5 GBSv2 pipeline [31] was used for SNP calling using a 120-base kmer length and minimum kmer count of ten. For alignment of the reads the rapeseed reference genome [32] (available at: ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/686/985/GCF_000686985.2_Bra_napus_v2.0/) was used. The alignment was done using Bowtie 2 (version 2.3.0) alignment tool [33]. After passing all the required steps, TASSEL 5 GBSv2 pipeline yielded 497,336 unfiltered SNPs. To obtain high quality SNPs, we filtered the raw SNPs using VCFtools [34]. Filtering criteria: minor allele frequency (MAF) ≥ 0.05, missing values (max-missing) ≤ 50%, depth (minDP) ≥ 5, min-alleles = 2 and max-alleles = 2 was maintained to have bi-allelic SNPs. This filtering yielded 53,616 SNPs. To make SNP unlinked, we thinned out SNPs present within 1,000 bp distance. The SNPs that were located outside chromosomes (i.e., position unknown), were removed. As canola is a self-pollinating crop, the SNPs that were heterozygous in more than 25% of total genotypes, were also removed using TASSEL [35]. Finally, we selected 8,502 SNP markers for this study.

Data analysis

To investigate the population structure, the core collection was differentiated into clusters using STRUCTURE v2.3.4 [36] software. For this purpose, we used admixture model with various combinations of burn-in lengths (5,000 to 100,000) and Monte Carlo Markov Chain (MCMC) lengths (5,000 to 100,000). Each combination was replicated 10 times per K (K1-K10). As we grouped the collection into five subpopulations according to their type and origin, we ran each replication considering genotype assigned to specific subpopulation as well as no subpopulation i.e. genotype unassigned to any specific subpopulation. These were done to determine the parameters needed to reach convergence. We used DeltaK approach [37] to determine the ideal number of subpopulations, which was performed by Structure Harvester [38]. We also used median (MedMedK and MaxMedK) or mean (MedMeaK and MaxMeaK) estimators of the “best” K to group the subpopulations into optimum clusters [39, 40]. Ten replicates of Q matrix were assembled using CLUMPP [41] to get individual Q matrix. Structure output was visualized using the Structure Plot v2 software [42]. Principal component analysis (PCA) was conducted by covariance standardized approach in TASSEL [35]. We constructed phylogenetic tree using MEGAX program with 1,000 bootstraps [43] using neighbor-joining (NJ) algorithm. Resulting tree was displayed using FigTree V1.4.4 [44].

We calculated analysis of molecular variance (AMOVA) to partition the genetic variance among subpopulations in Arlequin3.5. To show the divergence, we calculated average pairwise between subpopulations F values using Arlequin3.5 [45]. Tajima’s D value of each group was calculated using MEGAX software [43]. GenAlex v6.5 [46] was used to estimate percentage of polymorphic loci, number of effective alleles, Shannon’s information index, expected heterozygosity and unbiased expected heterozygosity of each marker and subpopulation. To visualize SNP density, we developed a distribution plot of SNP using R package CMplot (available at: https://github.com/YinLiLin/R-CMplot). The polymorphism information content (PIC) of markers was calculated using software Cervus [47]. To show relatedness among individuals, we calculated kinship (IBS) matrix using software Numericware i [48] on a 1 to 2 scale. The kinship heatmap and histogram were visualized using R package ComplexHeatmap [49]. The correlation between level of relatedness (IBS coefficients) and Shannon’s information index (I) and diversity (H) was calculated in R v3.5.2 [50].

Linkage disequilibrium (LD) pattern of whole collection and different subpopulations were analyzed using PopLDdecay [51]. The mean linked LD was calculated by dividing total r value with total number of corresponding loci pair. In this case, r > 0.2 was considered only. Same procedure was followed to calculate mean unlinked LD where r ≤ 0.2 was considered. Haplotype block analysis was done using PLINK [52] with a window size of 5 Mb. Confidence interval (CI) method [53] was used to identify haplotype blocks with high LD. Haplotype blocks (>19 kb), observed in one subpopulation but not in the other, were considered to be subpopulation-specific block. Haplotype blocks (>19 kb) shared by more than one subpopulation, were considered to be common to corresponding subpopulations.

Results

SNP profile

We used 8,502 SNPs, covering 19 chromosomes for this study. The marker density was one per 99.5 kb. Highest number (685 SNPs, 8.06%) markers was situated on chromosome A3 and lowest (236 SNPs, 2.78%) was on chromosome A4. In terms of density, it was highest on chromosome A7 (71.1 kb) and was lowest on chromosome C9 (134.5 kb) (Table 1, Fig 1).

Table 1

Chromosome-wise distribution of SNP markers.

Chromosome	No. of SNPs	% SNPs	Start position a	End position b	Length (Mb)	Density c (Kb)
A1	440	5.18	149163	35806075	35.7	81.0
A2	392	4.61	13430	34692905	34.7	88.5
A3	685	8.06	2769	49103583	49.1	71.7
A4	236	2.78	32805	23517671	23.5	99.5
A5	413	4.86	18668	31435105	31.4	76.1
A6	448	5.27	120409	36005103	35.9	80.1
A7	384	4.52	85869	27388322	27.3	71.1
A8	281	3.31	231427	27734410	27.5	97.9
A9	541	6.36	81404	45841268	45.8	84.6
A10	305	3.59	133853	22085737	22.0	72.0
C1	445	5.23	86671	50660872	50.6	113.7
C2	589	6.93	92431	68260222	68.2	115.7
C3	651	7.66	3839	80365889	80.36	123.4
C4	634	7.46	138930	70507417	70.4	111.0
C5	366	4.30	26760	44124497	44.1	120.5
C6	414	4.87	275190	45479327	45.2	109.2
C7	518	6.09	271113	62304827	62.0	119.8
C8	383	4.50	57934	46317429	46.3	120.8
C9	377	4.43	920885	51627086	50.7	134.5
Mean	447.47					99.5

a Position of the 1st marker on a particular chromosome corresponding to reference genome

b Position of the last marker on a particular chromosome corresponding to reference genome

c Density was calculated by dividing the length with the marker number.

Fig 1

Chromosome-wise SNP density map.

Frequency of SNPs varies according to color gradient.

The transition SNPs (4,956 SNPs) was more frequent than transversions (3,546 SNPs) with a ratio of 1.40. The ratio of transitions to transversions SNPs was higher in A genome (1.41) than that of in C genome (1.38). In both genome, G/C transversions were lowest (4.33% and 4.29%), but A/G and C/T transitions occurred in almost similar frequencies (Table 2). The inbreeding coefficient within individuals (F), inbreeding coefficient within subpopulations (F), observed heterozygosity (Ho) and fixation index (F) of all the markers ranged from -0.45 to 1.00, 0 to 0.73, 0 to 0.57 and 0.40 to 1.00, respectively. The mean Shannon’s information index (I) of all markers 0.37 with a range from 0.10 to 0.69. The expected heterozygosity (He) was from 0.05 to 0.50 with a mean value of 0.27. The polymorphic information content (PIC) of all markers was less than 0.50 with a mean value of 0.22 (range: 0.05 to 0.37) (S2 Table). Subpopulation-wise marker diversity parameters are presented in S3 Table.

Table 2

Transition and transversion SNPs across the genome.

Genome	SNP type	Model	No. of sites	Frequencies (%)	Total (percentage)
A	Transitions	A/G	1195	14.06	2416 (28.3%)
		C/T	1221	14.36
	Transversions	A/T	457	5.38	1709 (20.1%)
		A/C	424	4.99
		G/T	460	5.41
		G/C	368	4.33
C	Transitions	A/G	1273	14.97	2540 (29.9%)
		C/T	1267	14.90
	Transversions	A/T	496	5.83	1837(21.6%)
		A/C	482	5.67
		G/T	494	5.81
		G/C	365	4.29

Population structure

We did structure analysis seven times with accessions unassigned and seven times with accession assigned to their type and countries of origin. Delta K approach indicated 3 to 9 clusters (Fig 2A and 2B), while four alternative statistics (MedMedK, MedMeaK, MaxMedK, and MaxMeaK) determined following Puechmaille [39] and Li and Liu [40] indicated 3 clusters (Table 3, Fig 2C). For each run, Delta K approach showed differences in cluster number for both conditions: genotypes unassigned or assigned to their respective type and countries of origin. However, opposite scenario was found for MedMedK, MedMedK, MedMedK, and MaxMeaK statistic i.e., for each run it indicated three clusters. These outputs confirmed that Puechmaille [39] and Li and Liu [40] method was more consistent than Evanno [37] method (Table 3). Structure analysis revealed that 70.76% of genotypes belong to any of the three clusters at similarity coefficient of 0.7 and 29.24% of genotypes are admixed (Table 4, Fig 2D). Spring type accessions fall under cluster-1, whereas winter type European accessions fall under cluster-3. Cluster-2 consists of all rutabaga types and different type rapeseed accessions (Table 4). We performed principal component analysis (PCA) to show the genetic similarity among subpopulations and genotypes. The first two axes explained 21% (PCA1 13.5% and PCA2 7.22%) of the total observed variation (S4 Table). The PCA revealed that rutabaga (P5) and other types having Asian origin make one group, whereas spring type (P3, P4) and European winter type (P1) make two distinct groups (Fig 3). In addition to that, we also constructed unrooted phylogenetic tree based on neighbor joining (NJ) criteria (Fig 4). The output of neighbor-joining (NJ) tree analysis was in line with that of PCA.

Fig 2

Bayesian clustering of whole collection using 8,502 SNP markers in STRUCTURE v.

2.3.4. Graphical representation of optimal number of clusters (K) determined by Evanno’s method [37] with genotypes unassigned (A) and assigned (B) to their respective countries, as well as by Puechmaille [39] and Li and Liu [40] method (C). Estimated population structure of 383 rapeseed genotypes on K = 3 (D) using Puechmaille [39] and Li and Liu [40] method.

Table 3

Clustering of core collection based on Evanno et al. (2005) [37] and Puechmaille et al. (2016) [39] methods using different combinations of burn-in lengths and Markov Chain Monte Carlo (MCMC) lengths.

Run #	Burn-in lengths	MCMC lengths	Number of clusters (K)	Number of Reps	Number of populationsα		Number of populationsβ
					ΔK (Unassigned) a	ΔK (Assigned) b	MedMedK	MedMeaK	MaxMedK	MaxMeaK
1	5000	5000	10	10	3	6	3	3	3	4
2	10000	10000	10	10	8	8	3	3	3	3
3	20000	20000	10	10	8	3	3	3	3	3
4	20000	50000	10	10	8	3	3	3	3	3
5	50000	50000	10	10	9	6	3	3	3	3
6	50000	100000	10	10	9	3	3	3	3	3
7	100000	100000	10	10	3	7	3	3	3	4

α The ad hoc ΔK method [31]

aAccessions unassigned to any subpopulation

bAccessions assigned to subpopulation based on type and origin

βThe median (MedMedK and MaxMedK) or mean (MedMeaK and MaxMeaK) [33] estimators used to determine the number of cluster (K).

Table 4

Proportion of admixed and non-admixed accessions per subpopulation based on membership coefficients.

Cluster (K)	Core collection subpopulation based on type and origina					Total Number
	P1: Winter (151)	P2: Semi-winter (60)	P3: Spring_mixed origin (88)	P4: Spring_NDSU (67)	P5: Rutabaga (17)
K1	3	12	58	39	0	112
K2	15	12	8	0	17	52
K3	99	6	1	1	0	107
Admixture b	34	30	21	27	0	112
In-Cluster	77.48%	50%	76.13%	59.71%	100%	70.76%
Admixture	22.52%	50%	23.87%	40.29%	0%	19.24%

a Number of genotypes having q ≥ 0.7 were assigned to specific cluster.

b Genotypes having q < 0.7 were considered as admixed genotype.

Fig 3

Principal component analysis of SNP diversity based on genetic distance.

Colors represent subpopulations.

Fig 4

Phylogenetic tree (unrooted) based on neighbor-joining (NJ) algorithm using information from 8,502 SNP markers based on 1000 bootstraps.

Each branch is color-coded according to genotype belongs to subpopulation P1 to P5. Genotypes were grouped into three clusters by dividing the tree using black solid lines according to structure output.

Population diversity

Polymorphic loci percentage was greater than 75% in all subpopulations. P1 bears highest (99%) polymorphic loci, whereas it was lowest in P5 (75%). The diversity (H) was lowest in P4 and P5 (0.19) and was highest in P2 (0.25) with an average of 0.22. The Shannon’s information index (I) ranged from 0.31 (P4 and P5) to 0.40 (P2) with an average of 0.34. The Tajima’s D value ranged from -0.70 (P4) to 0.53 (P1) with an average of 0.13 (Table 5).

Table 5

Subpopulation-wise diversity parameters.

Subpopulations	Polymorphic loci (%)	Na a	Ne b	I c	H d	Uh e	Tajima’s D*
P1	99.12	1.99	1.32	0.35	0.21	0.21	0.53
P2	94.32	1.94	1.40	0.40	0.25	0.25	0.30
P3	96.98	1.97	1.35	0.36	0.22	0.23	0.30
P4	80.67	1.81	1.30	0.31	0.19	0.19	-0.70
P5	75.25	1.75	1.31	0.31	0.19	0.20	0.23
Mean	89.27	1.89	1.34	0.34	0.22	0.22	0.13

a No. of different alleles

b No. of effective alleles

c Shannon’s information index

d Diversity

e Unbiased diversity. SE (standard error) was zero in all cases. Indices calculated using 8191 SNPs with GenAlex v. 6.5.

* was calculated with 1000 permutations.

Population genetic differentiation

The analysis of molecular variance (AMOVA) revealed that variance among subpopulations covered 24% of total variation and rest of its was covered by among individual variance (Table 6) with a F and Nm value of 0.24 and 1.28, respectively.

Table 6

Summary of AMOVA.

Sources of variation	d.f.	Sum of squares	Variance components	% of variation	F st	N m
Among subpopulations	4	130814.6	228.5***	23.5	0.24	1.28
Within subpopulations	761	565699.6	743.4	76.5
Total	765	696514.1	971.9

*** indicates p < 0.001 for 1023 permutations.

We found significant (p < 0.01) between subpopulation F in all combinations. Except combinations P3 and P4 (0.11), P1 and P2 (0.19), we found F > 0.20 for all combinations. The pairwise F > 0.30 was observed between P1 and P5, P3 and P5, P4 and P5 (Table 7).

Table 7

Genetic differentiation among subpopulations.

Subpopulation pairwise Fst
	P1	P2	P3	P4	P5
P1	0
P2	0.19**	0
P3	0.25**	0.24**	0
P4	0.21**	0.24**	0.11**	0
P5	0.34**	0.24**	0.34**	0.39**	0

Diagonal values are pairwise F comparisons, performing 1000 permutations using Arlequin v. 3.5.

**indicates p < 0.01.

Kinship analysis showed that the IBS coefficients of the collection ranged from 1.21 to 1.94 with an average coancestry 1.47 between any two canola genotypes (Fig 5, S5 Table). Under P2 subpopulation, almost 50% of total genotypic pairs shows IBS coefficients less than 1.50. In case of other subpopulation, portion of genotypic pairs having IBS coefficient less than 1.50, was very low (Table 8, S1 Fig).

Fig 5

Heatmap of kinship matrix of entire collection.

Table 8

Summary of subpopulation-wise kinship (IBS) matrix.

Subpopulations	Whole collection	P1	P2	P3	P4	P5
IBS coefficients range	1.21–1.94	1.40–1.94	1.27–1.93	1.29–1.93	1.46–1.94	1.35–1.92
Mean of IBS coefficients	1.47	1.58	1.49	1.55	1.62	1.60
Pairs having ≤ 1.50 IBS coefficients (%)	63.9	9.6	50.7	21.7	1.1	18.0
Pairs having ˃ 1.50 IBS coefficients (%)	36.1	90.4	49.3	78.3	98.9	82.0

We also performed correlation analysis between mean pairwise relatedness (IBS coefficients) among individuals within subpopulation and Shannon’s information index (I), diversity (H). The I and H were significantly and negatively correlated with relatedness (r = -0.97, -0.98, and p < 0.01), respectively.

Linkage disequilibrium pattern

Subpopulation, genome, and chromosome-wise linkage disequilibrium (LD) pattern was investigated. LD = r values showed inverse relationship with distance i.e., mean LD was high (r > 0.22) at short distance bin (0–2 kb) and decreases with bin distance increment (S6 Table). In the entire collection considering both A and C genome, the mean linked LD and mean unlinked LD was 0.44 and 0.02 respectively; and loci pair under linked LD and unlinked LD was 1.81% and 98.20%, respectively. Subpopulation-wise mean linked LD ranged from r = 0.41 (P2) to r = 0.48 (P1). Subpopulation P5 harbored highest (8.76%) loci pair in linked LD and it was lowest in P1 (1.52%). The mean linked LD, mean LD and loci pair under linked LD was always higher in all cases in case of C genome than that of A genome (Table 9). We also compared the LD decay rate based on distance at which LD decayed to its half maximum (half-life), which is the point at which the observed r between sites decays to less than half the maximum r value. In the whole collection, LD decayed to its half maximum within < 45 kb distance for whole genome, < 21 kb for A genome, and < 93 kb for C genome. In all subpopulations, the distance for LD decay to its half maximum was always higher for C genome than A genome. LD decay rate also varied according to chromosome (S2 and S3 Figs). LD decay was lowest in chromosome C1 (348 kb) and C2 (244 kb), but was highest in chromosome A5 (13 kb) and A1 (16 kb) (S7 Table). LD decayed to its half–maximum within < 29 kb for P1, <45 kb for P2 & P3, <101 kb for P4, and <120 kb for P5. In all subpopulations, LD persisted also longest in all chromosomes of C genome than that of A genome (Fig 6, S7 Table).

Table 9

Linkage disequilibrium in the studied collection.

Subpopulation	Mean linked LD a	Mean unlinked LD b	Mean LD c	Loci pairs in linked LD (%)	Loci pairs in unlinked LD (%)
AC_Genome
Whole collection	0.44	0.02	0.03	1.81	98.2
P1	0.48	0.01	0.02	1.52	98.5
P2	0.41	0.02	0.03	2.65	97.4
P3	0.45	0.02	0.03	1.94	98.1
P4	0.45	0.02	0.04	3.98	96.0
P5	0.43	0.03	0.07	8.76	91.2
A_Genome
Whole collection	0.33	0.02	0.02	1.34	98.7
P1	0.38	0.01	0.02	1.12	98.9
P2	0.32	0.02	0.03	2.02	98.0
P3	0.36	0.02	0.02	1.41	98.6
P4	0.40	0.02	0.03	3.45	96.6
P5	0.38	0.04	0.06	7.06	92.9
C_Genome
Whole collection	0.50	0.02	0.03	2.21	97.8
P1	0.52	0.01	0.02	1.83	98.2
P2	0.46	0.02	0.04	3.27	96.7
P3	0.50	0.02	0.03	2.35	97.7
P4	0.48	0.02	0.04	4.41	95.6
P5	0.46	0.03	0.08	10.57	89.4

a Mean linked LD was calculated by dividing total r (r > 0.2 was considered) value with total number of corresponding loci pair.

b Mean unlinked LD was calculated by dividing total r (r ≤ 0.2 was considered) value with total number of corresponding loci pair.

c Mean LD was calculated by dividing total value with total number of corresponding loci pair.

Fig 6

Linkage disequilibrium (LD) differences and decay pattern among subpopulations.

We also performed haplotype block (HBs) analysis to investigate LD variation patterns across whole genome. A total 200 blocks covering 18 Mb out of the 976 Mb anchored B. napus reference genome [32], were identified. A and C genome contained 67 and 133 haplotype blocks, respectively. The total length of A and C genome specific HBs were 1.8 Mb and 16 Mb, respectively. The total length of HBs varied greatly from chromosome to chromosome. Total HBs length varies from 24 kb on A1 to 901 kb on A9 in A genome and in C genome it varies between 40 kb on C9 to 3,610 kb on C2. The haplotype block (HBs) number and size in C genome chromosome was always higher than that of A genome chromosome (Table 10). We analyzed subpopulation specific and common HBs. We found C genome chromosome bears more subpopulation specific HBs than A genome chromosome (Table 11). We also found some HBs were shared by different subpopulations, but we did not find any HBs blocks that was shared by all five subpopulations (Table 12). The shared HBs were usually located on C genome chromosome. Rutabaga type shared different HBs with other types also.

Table 10

Subpopulation-wise number and length of haplotype blocks (HBs) along chromosomes.

	Entire panel		P1		P2		P3		P4		P5
Chr.	Noa	Sizeb	Noa	Sizeb	Noa	Sizeb	Noa	Sizeb	Noa	Size b	Noa	Sizeb
A1	5	24	6	733	2	5	6	557	5	2080	0	0
A2	8	80	7	96	1	11	8	654	3	15	0	0
A3	6	46	5	29	5	44	8	51	10	927	1	8
A4	5	46	1	27	2	17	0	0	1	6	0	0
A5	7	57	2	13	3	503	5	138	5	427	1	1
A6	9	308	6	564	4	29	5	52	5	51	0	0
A7	8	70	7	72	2	15	7	412	9	1092	1	13
A8	4	304	1	2	3	21	2	234	6	2654	0	0
A9	10	901	7	62	14	1493	6	64	9	1393	1	21
A10	5	29	6	50	2	21	1	6	5	45	1	1
C1	23	3099	14	3314	14	4638	19	2947	20	4295	1	14
C2	26	3611	19	3141	9	4503	22	3756	16	5237	7	3594
C3	14	969	13	930	13	1480	16	1603	9	1070	2	192
C4	16	3440	15	5351	15	5330	14	4502	13	6063	4	3989
C5	9	423	8	410	10	516	7	1148	7	2679	1	2
C6	18	1204	9	972	7	1191	13	1206	15	3357	3	954
C7	17	2394	17	2583	7	70	16	2479	10	1414	1	13
C8	4	893	5	865	10	1289	6	941	7	1143	1	73
C9	6	41	7	49	4	438	7	223	3	11	1	14
AC Genome	200	17938	155	19264	127	21615	168	20975	158	33956	26	8888
A Genome	67	1865	48	1648	38	2160	48	2168	58	8688	5	43
C Genome	133	16073	107	17616	89	19455	120	18807	100	25267	21	8845

a Number of haplotype blocks on each chromosome.

b Total length of haplotype blocks for each chromosome in kb.

Table 11

Subpopulation specific number and length of haplotype blocks (HBs) along chromosomes.

	P1 specific		P2 specific		P3 specific		P4 specific		P5 specific
Chr.	No1	Size2	No1	Size2	No1	Size2	No1	Size2	No1	Size2
A1	1	683.06	0	0.00	0	0.00	2	1491.24	0	0.00
A2	1	39.03	0	0.00	2	598.97	0	0.00	0	0.00
A3	0	0.00	0	0.00	0	0.00	2	878.65	0	0.00
A4	1	27.02	0	0.00	0	0.00	0	0.00	0	0.00
A5	0	0.00	2	491.27	1	109.37	2	408.85	0	0.00
A6	1	522.43	0	0.00	1	23.04	0	0.00	0	0.00
A7	1	28.42	0	0.00	3	392.36	5	1054.82	0	0.00
A8	0	0.00	0	0.00	0	0.00	3	2411.28	0	0.00
A9	0	0.00	4	1409.29	1	36.87	3	1339.60	1	20.50
A10	0	0.00	0	0.00	0	0.00	1	22.15	0	0.00
C1	4	1723.88	4	3236.73	1	800.29	7	2334.07	0	0.00
C2	6	1537.92	7	4464.18	6	2741.63	5	3464.31	5	2796.14
C3	2	616.65	4	1024.20	4	724.95	2	378.76	0	0.00
C4	4	1764.07	6	2844.27	2	237.73	7	5148.84	1	3439.16
C5	1	29.99	2	121.08	1	771.31	2	2315.20	0	0.00
C6	3	795.86	1	691.72	4	782.76	4	2310.66	1	715.45
C7	3	2085.43	0	0.00	4	767.17	2	511.88	0	0.00
C8	0	0.00	1	390.00	1	885.21	4	1121.11	1	73.33
C9	1	27.36	2	262.00	1	32.61	0	0.00	0	0.00

1 Number of specific haplotype blocks longer than 19 kb on each chromosome

2 Total length (kb) of specific haplotype blocks longer than 19 kb on each chromosome.

Table 12

Shared haplotype blocks (HBs) among subpopulation along chromosomes.

Chr.	Shared HBs (size and corresponding subpopulation) a
A1	19.991 (P1, P4), 515.231 (P3, P4)
A2	0
A3	0
A4	0
A5	0
A6	0
A7	0
A8	232.016 (P3, P4)
A9	19.986 (P1, P2, P4)
A10	0
C1	20.457 (P1,P2, P3, P4), 38.038 (P1, P3), 134.065 (P2, P3, P4), 241.815 (P2, P3), 260.121 (P2, P3, P4), 336.839 (P1, P3), 374.884 (P3, P4), 438.341 (P1, P4), 652.629 (P3, P4), 718.372 (P1, P2)
C2	20.408 (P1, P2, P4), 28.252 (P1, P4), 164.414 (P3, P4), 729.678 (P1, P3, P4), 781.808 (P1, P4, P5)
C3	39.715 (P1, P4), 191.098 (P2, P5), 202.569 (P1, P2, P3), 611.558 (P3, P4)
C4	98.616 (P3, P5), 149.89 (P1, P2, P3), 378.898 (P1, P3), 436.853 (P1, P2, P3), 447 (P1, P3, P5), 601.227 (P2, P3), 867.133 (P1, P3, P4), 1265.92 (P1, P2, P3)
C5	337.986 (P1, P2, P3, P4)
C6	96.217 (P4, P5), 136.404 (P3, P4), 142.519 (P2, P5), 237.159 (P3, P4), 308.114 (P2, P4)
C7	390.085 (P1, P3), 828.502 (P3, P4)
C8	255.517 (P1, P2), 592.433 (P1, P2)
C9	171.465 (P2, P3)

a Length (kb) of common NBs longer than 19 kb on each chromosome with their corresponding subpopulation shown in parenthesis.

Discussion

Genotyping-by-sequencing [30] is one approach to obtain high frequency SNPs. The strategy has been used for population genetic studies, association mapping, and proven to be a powerful tool to dissect multiple genes/QTL in many plant species [54-56]. We obtained 497,336 unfiltered SNPs markers of which 8,502 high quality SNP markers were used for genetic diversity analysis of 383 genotypes. Delourme et al. (2013) [23] conducted genetic diversity analysis in B. napus using 7,367 SNP markers of 374 genotypes. However, different marker technologies such as Single Sequence Repeat (SSR), Sequence Related Amplified Polymorphism (SRAP) markers have been used by other researchers for genetic diversity analysis in B. napus. Chen et al. (2020) [57] used 30 SSR markers, Wu et al. (2014) [58] utilized 45 SSR markers, Ahmad et al. (2014) [59] used 20 SRAP markers for genetic diversity and population structure analysis of B. napus. Earlier, our group conducted a genetic diversity study of flax using 373 germplasm accessions with 6200 SNP markers [60].

The SNP markers were distributed throughout 19 chromosomes of B. napus and the marker density was one per 99.5 kb. This is comparable density to earlier study conducted by Delourme et al. (2013) [23]. Therefore, this marker density provides a sufficient resolution to estimate genome-wide diversity as well as the extent of LD within the genome. This marker density will also help in association mapping studies to identify a causal locus/loci or linked loci that can be further used either in MAS or to pinpoint the causative locus [61] especially for oligogenic traits. However, for polygenic traits such as seed yield, it is better to incorporate more markers for genome wide association studies. The core collection utilized in this study represents mostly adapted lines from various breeding programs. Therefore, sources of variation, markers of interest identified in the collection can be directly used in breeding programs.

We have identified higher frequency of transition SNPs over transversion SNPs that is an agreement with Bus et al. (2012) [62], Clarke et al. (2013) [63], and Huang et al. (2013) [64] in B. napus. Higher number of transition SNPs over transversion is also reported in other crop species such as Hevea brasiliensis [65], Camellia sinensis [66], Camelina sativa [67], and Linum usitatissimum [60].

To assess the suitability of marker for linkage analysis and diversity, we calculated PIC and expected heterozygosity (He) of markers [68]. In our research, the PIC value is ranged from 0.05 to 0.35 indicating that the markers are modestly informative. The similar lower PIC value (0.1 to 0.35) was reported by Delourme et al. (2013) [23] in B. napus. The lower PIC value is a result of bi-allelic nature of SNP markers and probable low mutation rate [69]. In our study, the He value of each marker was always greater than corresponding PIC value indicating an average lower allele frequency in the population [68].

Population diversity and structure

We have identified a moderate diversity (average H = 0.22) within the subpopulations. B. napus is capable of self-pollination, and little cross-pollination may be occurred by insect. Being a mostly self-pollinated crop a low to moderate subpopulation diversity in B. napus is expected. Low to moderate diversity was also found in previous studies [70-72]. Along with the reproduction system, one needs to look at evolution and domestication history for explaining low to moderate levels of diversity in B. napus. This allopolyploid species originated at Mediterranean coast because of a natural cross between B. rapa and B. oleracea which occurred approximately 0.12–1.37 million years ago [73, 74]. The domestication of B. napus occurred very recently, around 400 years ago with the first rapeseed being most likely a semi-winter type due to the mild climate in the region [75, 76]. Later on, European growers developed the winter and spring type Brassicas through selection for cold hardiness or early flowering to expand its cultivation in further North in the last century [77]. Therefore, the low to moderate diversity in winter and spring B. napus can be mostly explained by a recent history of the species, followed by infrequent exchange of genetic material with other Brassicas [23], as well as by the traditional breeding practices selecting for only few phenotypes. In our study, the more diversity in semi-winter type (P2, H = 0.25) than winter (P1, H = 0.21) and spring (P4, H = 0.19) type is supported by its domestication history. The N value was greater than one, which indicates that there was enough gene flow among semi-winter, winter, and spring types. These findings also support the evolution of winter and spring types from semi-winter type. In this research, Tajima’s D value was calculated to identify the extent of availability rare and unique alleles [78]. Recently, the NDSU canola-breeding program developed the P4 advanced breeding lines through crossing different genetic resources including winter, spring, and semi winter types and subsequent selection. This current expansion of P4 was supported by its negative Tajima’s D value, which harbors more rare alleles [79]. The subpopulation P1, P2, P3, and P5 showed positive Tajima’s D value indicating an excess of intermediate frequency alleles, which may be caused by balancing selection, population bottleneck, or population subdivision. Previously, negative Tajima’s D values were found in spring and winter type B. napus accessions [80]. The negative correlation between diversity indices (H and I) and relatedness (average IBS coefficients) indicates that subpopulation differentiation was also due to selfing and genetic drift. Flax [60] and Arapaima gigas species [81] also showed same scenario.

To exploit diversity and transgressive segregation, parents from divergent group should be crossed. Pairwise F statistic, a parameter describing population structure differentiation [82], was estimated among five subpopulations. In the present study all pairwise F values comprising both low and high values, were statistically significant. Similar results were also found in other studies [80, 83–85]. Lower pairwise F (0.11) was identified between spring type originated in USA (P4) and spring type originated in other countries (P3). This is reasonably justified as both subpopulations comprise of spring type genotypes and germplasm exchanged occurred between USA and other countries. It also indicating that we will not get higher genetic diversity in population if we use only spring types in the crossing program. But this combination is good for accumulating specific elite trait if the targeted trait is found in members of one and missing from the members of another group. We found spring type (P3 and P4) genotypes are greatly divergent (F > 0.20) from winter and semi-winter type (P1 and P2) genotypes. Utilization of genotypes from these groups in crossing program will broaden the genetic base of developed population results in high heterosis. This potentiality has already been proved as hybrids between the Chinese semi-winter and European (including Canada) spring type exhibited high heterosis for seed yield [86]. The P5 (rutabaga type) showed the higher F with other subpopulations such as the highest F was observed between P5 and P4 (NDSU spring type) followed by P3 (other spring type), P1 (winter type) and P2 (semi-winter type). This outcome clearly shows that rutabaga is genetically distinct from spring and winter type canola that is confirmed by previous studies [75, 87, 88]. This distinctness of rutabaga can be exploited through heterosis breeding. Several previous studies have already showed rutabaga as a potential gene pool for the improvement of spring canola [89, 90]. NDSU canola breeding program also utilized winter and rutabaga types in the breeding program for increasing genetic diversity and for improvement of spring canola. AMOVA showed that variation among individual within subpopulation captured greater portion of total variation, than that by among subpopulation. This finding is also supported by earlier researches [56, 72, 91, 92]. This finding supports that within subpopulation genotype from P2, P3, and P1 could be crossed as they showed high diversity (H > 0.20) for cultivar development.

Principal component analysis and distance-based population structure analysis such as NJ tree yielded three subgroups in the core collection. Here, we ran structure analysis many times to obtain convergence before the best number of clusters was determined. It was done because previous studies [39, 93] reported that STRUCTURE program did not depict the main clusters within a collection. Based on Evanno’s ΔK method [37] and MedMedK, MedMeaK, MaxMedK and MaxMeaK statistics [39], structure analysis divided the core collection into three distinct clusters. Cluster-1 contains spring type NDSU advanced breeding lines (P4) and spring type (P3) other than those. This finding is supported by low genetic differentiation (F = 0.11) between P3 and P4 due to sharing of parents by advanced breeding lines from P3. Cluster-3 is solely dominated by European winter type (P1) genotypes. This is also supported by high F between winter and other types which may be due to geographic barriers between Europe and America, Asia. Cluster-2 contained all rutabaga types as well as other type Asian genotypes which indicates that all types share considerable amount of SNP markers attributing to this cluster. These findings also indicate that there is gene flow among different types, which is also supported by N > 1. Structure analysis revealed that all clusters contained both non-admixed as well as admixed (share alleles attributed to different subpopulation) genotypes. For broadening genetic diversity of population, non-admixed genotypes should be crossed. However, for improving or introgression of specific traits, admixed genotypes could also be crossed which will reduce the population size required for phenotypic screening. However, structure analysis may overestimate the differentiation among individuals, as the individuals may not share alleles from same ancestors [94]. Since a breeder would like to combine historically never combined favorable alleles, IBS values directs which individuals should be crossed. Low IBS is the best. However, self-pollinated crops exhibit higher kinship values than cross-pollinated crops, as homozygosity increases probability of being identical by state [95]. We found approximately 64% of pairwise coancestry ranged from 1.21 to 1.50. Crossing among genotypes from subpopulation P2 will demonstrate more diversity, than that of other subpopulations, as most genotypic combinations of P2 shows low IBS coefficients than others do. This finding is in line with the evolutionary history of B. napus where semi-winter type is the base population containing more divergence. Gradually this diversity is narrowed down in P3 (spring type, mixed origin) and P1 (winter type), because genotypic pairs belong to P3 and P1 having high IBS values evolved from semi-winter type [77]. Subpopulation P4 exhibited highest number of pairs having IBS > 1.5, which is obvious as these genotypes are advanced breeding lines developed from crossing of same set of parents in different combinations. Genotypic pairs of P5 (rutabaga type) also showed high coancestry may be due to the duplicates which is supported by low genetic differentiation of Nordic rutabaga accessions [27]. We could discard the duplicates during the crossing program.

Linkage disequilibrium

Linkage disequilibrium can be defined as the correlation among polymorphisms in a given population [96]. The strength of association mapping relies on the degree of LD between the genotyped marker and the functional variant. Linkage disequilibrium analysis provides insight into the history of both natural and artificial selection (breeding) and can give valuable guidance to breeders seeking to diversify crop gene pools [17]. SNPs in strong LD are organized into haplotype blocks, which can extend even up to few Mb based on the species and the population used. Genetic variation across the genome is defined by these haplotype blocks. Haplotypes, which are subpopulation-specific, are defined by various demographic parameters like population structure, domestication, and selection in combination with mutation and recombination events. Conserved haplotype structure can then be used for the identification and characterization of functionally important genomic regions during evolution and/or selection [97]. In addition, the extent of LD needs to be quantified across the genome at high resolution (down to approximately one Kbp) [98]. The information is important for choosing crossing schemes, association studies and germplasm preservation strategies [99-102].

We used markers from across the genome to quantify the LD for the core collection. Low level of LD was evident for each individual subpopulation in A, C, and whole gnome. The low level of LD can be due to multiple factors. First, canola is a partially outcrossing species with an average of 21–30% of cross-pollination [103-105]. The outcrossing occurring in canola leads to more recombination and to a breakdown of haplotype blocks. Secondly, the ancestral history of canola is limited in comparison with other crops, such as rice, common bean, wheat, and corn, restricting the selection of desirable haplotypes during the evolution. In other words, there was no adaptation or domestication pressure on the species, which would lead towards positive selection. Third, the only selection pressure imposed on the species for a relatively short time was breeding. However, the breeding practices were biased towards selection of only few phenotypes. Additionally, the short period under selection pressure might have not been sufficient to select favorable haplotypes in the genome. Fourth, since canola cultivars with different growth habits are compatible there has been always gene flow present between them contributing to the low level of LD. The N >1 was observed in this study, which supports this gene flow. Fifth, the restriction enzyme used to develop the libraries for sequencing of the core collection helped in identification of SNPs largely residing in genic regions, which are prone to high recombination, contributing to the low level of LD. Finally, the low level of LD may be due to thinning of markers, as we did not use all markers (53,616) for LD analysis rather used 8,502 markers after thinning. That can be confirmed by analysis the LD using whole marker set in further analysis.

In this study, we have identified that the LD decay in B. napus varied across chromosomes of both A and C genomes. In addition, LD in C genome decayed much slower than A genome. C genome also contained larger haplotype blocks than A genome. This LD patterns are consistent with previous findings [17, 26, 106–108]. The slower LD decay and presence of long haplotype blocks in C genome indicates that high level of gene conservation could have resulted from limited natural recombination or could be exchanged of large chromosomal segment during evolution. In the whole genome, presence of subpopulation specific haplotype blocks suggests that these regions had been experienced selection pressure for specific geographic regions adaptation. In all subpopulations, presence of shorter haplotype blocks in A genome than C genome reveals that B. rapa progenitor of B. napus containing A genome, which has been used as oilseed crop and probably being used in hybridization process. Sharing haplotype blocks by different subpopulations especially in C genome also confirms its conserved nature. The low level of LD or haplotype blocks has implications for association mapping and a proper experimentation design is necessary for utilizing a reduced set of markers by tagging major haplotypes [109]. Though low LD of A genome requires more markers to pinpoint the location of various QTL, but once a marker is found to be significantly associated with a phenotype, there might be a higher probability of identifying the casual gene than that of C genome.

Conclusions

This study provides a new insight to select the best parents in crossing plan to maximize genetic gain in the population. The population structure analysis showed a clear geographic and growth habit related clustering. The rutabaga type showed the highest genetic divergence with spring and winter types accessions. Therefore, the breeding strategies to increase the genetic diversity may include generating population from rutabaga and spring crosses, or using rutabaga and winter crosses. The linkage disequilibrium analysis revealed the decay pattern and haplotype blocks in A and C genome. This output will help the breeder to formulate breeding strategies to develop improved cultivars using modern breeding tools by utilizing this collection and SNP markers.

List of the genotypes analyzed in this study.

(XLSX)

Click here for additional data file.

Marker diversity parameters.

(XLSX)

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Subpopulation-wise marker diversity parameters.

(XLSX)

Click here for additional data file.

(XLSX)

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Kinship matrix.

(XLSX)

Click here for additional data file.

Mean LD values according to distance.

(XLSX)

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Subpopulation-wise and chromosome-wise LD decay rate (Kb) within each subpopulation.

(XLSX)

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Histogram of IBS coefficients.

(TIFF)

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Chromosome-wise LD decay rate (Kb) in A genome considering whole collection.

(TIFF)

Click here for additional data file.

Chromosome-wise LD decay rate (Kb) in C genome considering whole collection.

(TIFF)

Click here for additional data file.

3 Jun 2021

PONE-D-21-09997

Linkage disequilibrium and population structure in a core collection of Brassica napus (L.)

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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 proposed to analyse the genetic diversity present in a collection of 383 accessions of Brassica napus representing genotypes present worldwilde and aims at characterizing the genepool present in US breeding programs. This seems quite interesting and the size of of the populations should be sufficient to draw interesting conclusions. However hypotheses and research questions are not clearly expressed at the end of the introduction, and too many extra data are provided and not used in the mansucript (for instance flowering time data). Moreover, many analyses are performed , but may be too much without really refering to a global strategy and the global aim of each batch of analyses.

Detailled analysis and questions are reported in the attached file

A rapid check at table S1 showed some problems considering geogagraphic origins of different accessions

for instance Lembkes, Krapphauser, fertodi => Germany instead of South Korea

Dramor (Poland) should be Darmor (France) I think

Bienvenu (USA) should be Bienvenu (France)

Jet-neuf (Canada) should be Jet-neuf (France)

.... I didn't check carefully all accessions but these mistakes may lead to inappropriate interpertations of subpopulations composition

Reviewer #2: The manuscript describes an attempt to study genetic diversity and population differentiation inherent in a germplasm assemblage of Brassica napus. Study is of interest for plant breeders engaged in the improvement of this important oilseed crop. Abstract and introduction are very poorly written. The method and material section must include some information about the procedure followed to maintain germplasm lines used for genotyping. In the absence of any information, one would suspect these to be the products of open pollination. Inherent heterozygosity can be a problem for genetic studies in any often cross-pollinated crop like B.napus. Filtering out of a large proportion of heterozygous SNPs may be the cause for the retention of only 8,502 SNPs out 497336 discovered initially. Structure analysis shows lot of admixing. Heterozygosity inherent in the germ plasm will affect all genetic inferences e.g. polymorphic loci, population variation, LD, genetic differentiation and even genetic diversity. It is always better to use homozygous lines. One may not like to use the term core collection for any germplasm assemblage. Authors may like to provide reference or provide details regarding method used to extract the core ( if it is actually a core collection) from a large germplasm base. In spite of these limitations, it was interesting to find clear geographic and growth habit related clustering from STRUCTURE analysis. The manuscript also suffers from narrative coherence.

Reviewer #3: Manuscript PONE-D-21-09997 describes genetic diversity, population structure and LD in Brassica napus - the second major oilseed crop grown worldwide. Here are some comments:

This study does not provide details on sampling (how many plants were sampled for DNA isolation, how SNPs were scored codominant/dominant; how heterozygosity was handled etc). I could not see genotypic data of 383 accessions. Have authors submitted genotypic data to any public database repository or provided in a supplementary Table?

L172-173: Table 1 and Fig 1 does not support this claim.

L191-192: It is worthwhile to provide statistics of transitions and transversions across different chromosomes

L340: GBS is one of the approaches (Illumina SNP, resequencing, sequencing and DArTseq)

L360: There are no wild accessions of B. napus.

L460: Favourable alleles?

Fig 3: How much genetic variation is explained by PC1 and PC2? What these lines indicate?

Fig4: I could not see bootstrap values here. Please label all accessions (name or serial number 1 to 383 and support with supplementary Table)

Figure 5 is not legible. You may move to supplementary information.

Minor comments

L52: Use either rapeseed or canola (it is already described in L49)

L72: There are several studies to support collinearity between Arabidopsis and Brassica (see publications of Parkin and Chris Pires groups, https://doi.org/10.1111/pce.12644 etc)

L527: There are several reports. You meant NSDU accessions?

**********

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31 Aug 2021

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Response: The study was funded by the U.S. Department of Agriculture - National Institute of Food and Agriculture (Hatch Project No. ND01581).

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https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-06922-2

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Response: We have rephrased the duplicated text in revised throughout the manuscript.

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: No

Reviewer #2: Partly

Reviewer #3: Yes

Response: We edited revised manuscript to solve these issues.

________________________________________

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

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: 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: Yes

Reviewer #2: No

Reviewer #3: No

Response: Data are available online

________________________________________

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: No

Reviewer #2: No

Reviewer #3: Yes

Response: We edited and rearranged text in revised manuscript to solve these issues.

5. Review Comments to the Author

Reviewer #1: The authors proposed to analyse the genetic diversity present in a collection of 383 accessions of Brassica napus representing genotypes present worldwilde and aims at characterizing the genepool present in US breeding programs. This seems quite interesting and the size of of the populations should be sufficient to draw interesting conclusions. However hypotheses and research questions are not clearly expressed at the end of the introduction, and too many extra data are provided and not used in the manuscript (for instance flowering time data). Moreover, many analyses are performed , but may be too much without really refering to a global strategy and the global aim of each batch of analyses.

Detailed analysis and questions are reported in the attached file

A rapid check at table S1 showed some problems considering geogagraphic origins of different accessions

for instance Lembkes, Krapphauser, fertodi => Germany instead of South Korea

Dramor (Poland) should be Darmor (France) I think

Bienvenu (USA) should be Bienvenu (France)

Jet-neuf (Canada) should be Jet-neuf (France)

.... I didn't check carefully all accessions but these mistakes may lead to inappropriate interpertations of subpopulations composition

Response: Thank you for pointing these out. In revised manuscript, we have tried to incorporate the suggestions of the reviewer to make the hypotheses and research questions more clear (at later part of introduction). We remove the flowering time data and clarified how the core collection was obtained (Line: 107-116 in manuscript). We think performed analyses strengthen each other as well as the objective.

In case of table S1, data was provided according to USDA (GRIN-Global) website (source: https://npgsweb.ars-grin.gov/gringlobal/search). We did a google search to know the origin of many cultivars and failed to get the information. USDA-GRIN recorded many genotypes on the basis of country of collection – and we used this recorded information in this manuscript. Therefore, to clear it out, we have written as “Country or origin/collected”.

Reviewer #2: The manuscript describes an attempt to study genetic diversity and population differentiation inherent in a germplasm assemblage of Brassica napus. Study is of interest for plant breeders engaged in the improvement of this important oilseed crop. Abstract and introduction are very poorly written. The method and material section must include some information about the procedure followed to maintain germplasm lines used for genotyping. In the absence of any information, one would suspect these to be the products of open pollination. Inherent heterozygosity can be a problem for genetic studies in any often cross-pollinated crop like B.napus. Filtering out of a large proportion of heterozygous SNPs may be the cause for the retention of only 8,502 SNPs out 497336 discovered initially. Structure analysis shows lot of admixing. Heterozygosity inherent in the germ plasm will affect all genetic inferences e.g. polymorphic loci, population variation, LD, genetic differentiation and even genetic diversity. It is always better to use homozygous lines. One may not like to use the term core collection for any germplasm assemblage. Authors may like to provide reference or provide details regarding method used to extract the core ( if it is actually a core collection) from a large germplasm base. In spite of these limitations, it was interesting to find clear geographic and growth habit related clustering from STRUCTURE analysis. The manuscript also suffers from narrative coherence.

Response: Thanks for your critical inputs. In revised manuscript, we have made the changes according to reviewer’s suggestions. We rewrite the introduction section to make it clear. To improve the narrative coherence, we edited almost all section in revised manuscript.

How core collection has been obtained and maintained, was described in materials and methods section (Line 107 to 116).

SNP number reduced, because of following strict filtering criteria (Line 134 to 139)

Reviewer #3: Manuscript PONE-D-21-09997 describes genetic diversity, population structure and LD in Brassica napus - the second major oilseed crop grown worldwide. Here are some comments:

This study does not provide details on sampling (how many plants were sampled for DNA isolation, how SNPs were scored codominant/dominant; how heterozygosity was handled etc). I could not see genotypic data of 383 accessions. Have authors submitted genotypic data to any public database repository or provided in a supplementary Table?

Response: Thank you for pointing these points. In revised manuscript, we have incorporated the information according to reviewer’s suggestion.

For DNA extraction, we used three leaf samples per genotype (Line 119)

For diversity analysis we do not need to score the codominant/dominant markers. To run structure, we scored the homozygous major, homozygous minor and heterozygous SNP as 1, 0 and 0.5 respectively using Tassel.

Data availability: GBS and SNP data are available at: PRJNA687906 (https://www.ncbi.nlm.nih.gov/biosample/17159566) and PRJEB42419 (https://www.ebi.ac.uk/eva/?eva-study=PRJEB42419), respectively.

Reviewer #3: L172-173: Table 1 and Fig 1 does not support this claim.

Response: While we appreciate the reviewer’s feedback, we respectfully disagree. The SNP density was highest on chromosome A7 as every 71.1 kb distance contains at least one SNP and was lowest on chromosome C9 as every 134.5 kb distance contains at least one SNP.

Reviewer #3: L191-192: It is worthwhile to provide statistics of transitions and transversions across different chromosomes

Response: As our primary objective is not to describe the SNPs in details, we think statistics of transitions and transversions across genome is enough to give an idea to the reader.

Reviewer #3: L340: GBS is one of the approaches (Illumina SNP, resequencing, sequencing and DArTseq)

Response: Yes.

Reviewer #3: L360: There are no wild accessions of B. napus.

Response: Yes, we agree the reviewer’s comment. We deleted this information from revised manuscript

Reviewer #3: L460: Favorable alleles?

Response: We changed positive alleles to favorable alleles.

Reviewer #3: Fig 3: How much genetic variation is explained by PC1 and PC2? What these lines indicate?

Response: PC1 and PC2 explained 13.50% and 7.22% of variation respectively. Lines indicate regression. (Line 226)

Reviewer #3: Fig4: I could not see bootstrap values here. Please label all accessions (name or serial number 1 to 383 and support with supplementary Table)

Response: bootstrap value added. But did not add label as it make the figure congested.

Reviewer #3: Figure 5 is not legible. You may move to supplementary information.

Response: We would like to keep it as it is for the better understanding of readers.

Minor comments

Reviewer #3: L52: Use either rapeseed or canola (it is already described in L49)

Response: In revised manuscript, we used rapeseed.

Reviewer #3: L72: There are several studies to support collinearity between Arabidopsis and Brassica (see publications of Parkin and Chris Pires groups, https://doi.org/10.1111/pce.12644 etc)

Response: Yes.

Reviewer #3: L527: There are several reports. You meant NSDU accessions?

Response: Yes, we mean NDSU accessions i.e. NDSU advanced breeding lines.

Submitted filename: Response to editor and reviewer.docx

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21 Oct 2021

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We look forward to receiving your revised manuscript.

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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 #2: All comments have been addressed

Reviewer #4: (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 #2: Yes

Reviewer #4: No

**********

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

Reviewer #2: Yes

Reviewer #4: N/A

**********

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 #2: Yes

Reviewer #4: 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 #2: Yes

Reviewer #4: 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 #2: (No Response)

Reviewer #4: (No Response)

**********

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Reviewer #2: No

Reviewer #4: Yes: Rudolph Fredua-Agyeman

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Submitted filename: Reviewer Comments.pdf

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29 Nov 2021

The manuscript describes studies the population structure and genetic diversity of 383 Brassica napus (canola) accessions using 8502 SNP. The authors inferred six groups based on STRUCTURE, principal component analysis and NJ analyses. The manuscript is well written. However, the STRUCTURE analysis was not comprehensive enough. In addition, the interpretation of the results was not consistent with the Figures 2 and 3. Therefore, I recommend rejection of the MS and re-consideration for publication after major revisions.

Major issues

1. Introduction

The literature cited by the authors are dated (2012 to 2014). More recent population studies on B. napus and other Brassica exist e.g., An et al. (2019) Nature Communications; Lu et al. (2019) Nature Communications and Yu et al (2021) BMC Genomics. The authors should include how their study will add to the current knowledge on genetic diversity of canola.

Response: We have added the references mentioned by the reviewer in revised manuscript. The study was done to exploit the diversity of NDSU canola core collection. The results will be used to enrich NDSU canola parental stock.

2. Materials and Methods

The authors run the STRUCTURE program using the admixture model at 10,000 burning period and 50,000 Monte Carlo Markov Chain. Yu et al (2021) showed that Puechmaille (2016) method (MedMedK, MedMeaK, MaxMedK and MaxMeaK statistics) was a better estimator of the number of clusters compared to the Evanno et al. (2005) method (DeltaK statistics) which was used by the authors. The authors should re-run STRUCTURE under different parameters to verify that the inference on population sub-populations is robust.

Response: We re-ran the structure analysis according to reviewer suggestion. We discussed it in methodology section (Line151 to line160).

3. Results

(a) A requirement for the STRUCTURE program is that markers be unlinked. Inclusion of multiple closely linked markers may have a large effect on calculation of population structure. Also, it is not known whether these SNPs are in non-coding region or are synonymous, to eliminate the bias introduced by selection when inferring population structure. The authors should comment on the appropriateness of using the selected SNP markers for their study.

Response: To break the linkage between markers we considered the markers which are located > 1000bp from each other (L145 to L146). We didn’t consider whether SNPs are synonymous or non- synonymous as dN/dS ratio is insensitive to selection pressure in intraspecific individuals or individual from same population (Kryazhimskiy and Plotkin, 2008).

(b) The authors did not interpret their STRUCTURE and PCA data correctly:

(i) Figure 2 shows that the maximum peak is at K=3. The second and smaller peak is at K=6. Therefore, the number of inferred clusters using STRUCTURE was likely 3 and not 6.

(ii) Figure 3-The Principal component analysis shows 3 clusters and not 6. Group 1-red (winter Europe),

Group 2 -green and blue (spring_mixed origin and spring_NDSU) and Group 3-violet and yellow (Rutabaga, winter and semi winter Asia)

NB: the miscellaneous group (black) which are scattered is in all 3 groups cannot be considered as a different subgroup (Authors own admission Lines 220-221).

Response: In revised manuscript, we corrected and discussed the figures according to reviewer suggestion (L218 to L237).

(c) The authors should indicate the NJ using rooted and unrooted trees. The authors should also indicate UPGMA rooted and unrooted trees.

Response: Unrooted NJ tree was used. We belief that Structure, PCA and NJ tree already addressed our targets.

(d) Please could the numbers on the trees be related back to the accession identifiers?

Response: Yes, We hope the provided new picture will clarify the issue.

(e) Some accessions had the same name or variation of it but different accession number. Some accessions were also advanced breeding lines. Can the authors comment on the genetic similarity of such accessions in the results section? Will these influence the calculation of population structure?

Response: Before formulating the core collection, we discarded the accession, which has same name with different PI number or same PI number with different name. So that we did not have any accession like that. The similarity among advanced breeding lines as well as among other genotypes has been discussed in discussion section.

Discussion

The authors need to be update after the suggested changes.

Response: We updated the discussion section.

Minor issues

1. Line 112-Did the authors use leaves from single plants or bulk plants?

Response: Bulk plants

2. Line 136-reword the core collection was divided into subgroups-Do you mean the number of sub-groups were inferred using STRUCTURE.

Response: Rewording done in revised manuscript.

Reference

Kryazhimskiy, S., and J.B. Plotkin. 2008. The population genetics of dN/dS. PLoS Genet. 4(12): e1000304.

Submitted filename: Response to reviewer.docx

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13 Jan 2022

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'.

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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: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

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Academic Editor

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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 #2: All comments have been addressed

Reviewer #5: (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 #2: Yes

Reviewer #5: Yes

**********

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

Reviewer #2: Yes

Reviewer #5: No

**********

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 #2: Yes

Reviewer #5: 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 #2: Yes

Reviewer #5: No

**********

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 #2: The authors have revised the manuscript as per the suggestions by the reviewers. The revised manuscript can be accepted for publication.

Reviewer #5: Review of Rahman et al.

Rahman et al. analyzed the genotypes of 383 germplasms of Brassica napus, a part of which consists of NDSU core collection using the genotype by sequencing method. They identified population structures and evaluated the level of linkage disequilibrium. They also observed that the C genome have much higher level of LD than the A genome.

This manuscript has been revised twice, but this is the first time for me to review the paper. I would agree that the manuscript would provide valuable information for this research area and the data presented here are mostly well analyzed, but the manuscript lacks coordination in writing and still has a lot of typos, some of which would be described later. I therefore cannot recommend the manuscript to be published in PLOS ONE in its current form.

Major comments

1. Although the introduction part is very detailed. Some important information seems missing. In particular, it is not clear how the authors defined the five subpopulations is not clear to me. There is no description for rutabaga type, which would be difficult to understand for non-specialist of B. napus.

2. I initially thought that 8502 high-quality SNPs out of 497,336 unfiltered SNPs are too small, but probably the number is the one after the thinning. Please describe the number of high-quality SNPs before the thinning.

3. There are several comparisons of statistics, but there are little statistical tests. For example, in the line 193, the authors stated that ts/tv ratio in A genome was higher than that in C genome, but the difference was not properly tested. So many of the analyses were more or less too descriptive.

4. In the STRUCTURE anslysis, I do not get what is the meaning of “accession assigned/unassigned”. STRUCTURE usually does not require any a priori assumption, in my understanding. Please add more description in the method section.

5. In the line 313, the meanings of “the mean linked LD”, “mean unlinked LD”, and “loci pair under linked LD”, are not clear to me. In the method section, the mean lined LD was calculated using SNPs with r2<0.2 (line 179), but it means they are unlinked. In addition, % is not added to 0.44, 0.02? It is somewhat confusing.

6. In the line 401, the authors mentioned that the higher transition to transversion ratio is due to natural selection, but I do not agree with it. The pattern is ubiquitously found in many organisms including humans, and in non-coding regions, and people usually think that this is due to higher mutation rate of transition than transversion.

7. The sentence in the line 427-428 seems contradicting to the previous sentence. Probably the words, “homogeneity of the diversity indices” is not appropriate.

8.In the line 471, NJ is certainly distance-based method, but PCA is not.

9.In the line 524, the authors mentioned that the level of LD in B. napus is low compared with the other crops, but there is no quantitative statement. I also wonder whether authors used the thinned data to calculate the mean LD. If ones remove the linked SNPs from the analysis, the level of LD obviously decreases.

Minor comments,

1. L143: row SNPs would be raw SNPs

2. L153: burn-in length

3. L405: Rephrase the words “moderate or low informative”.

4. L446: the word “type” is not necessary?

5. The assignment of Ks in Figure 4 is not clear. Are they determined using the presented tree?

**********

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Reviewer #2: No

Reviewer #5: No

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25 Jan 2022

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 #2: The authors have revised the manuscript as per the suggestions by the reviewers. The revised manuscript can be accepted for publication.

Response: Thank you very much for accepting the manuscript.

Reviewer #5: Review of Rahman et al.

Rahman et al. analyzed the genotypes of 383 germplasms of Brassica napus, a part of which consists of NDSU core collection using the genotype by sequencing method. They identified population structures and evaluated the level of linkage disequilibrium. They also observed that the C genome have much higher level of LD than the A genome.

This manuscript has been revised twice, but this is the first time for me to review the paper. I would agree that the manuscript would provide valuable information for this research area and the data presented here are mostly well analyzed, but the manuscript lacks coordination in writing and still has a lot of typos, some of which would be described later. I therefore cannot recommend the manuscript to be published in PLOS ONE in its current form.

Major comments

1. Although the introduction part is very detailed. Some important information seems missing. In particular, it is not clear how the authors defined the five subpopulations is not clear to me. There is no description for rutabaga type, which would be difficult to understand for non-specialist of B. napus.

Response: We added description of rutabaga type in introduction (line 68 to 74). We assumed the subpopulation of collection according to their type and origin. Details has been added in materials and method section (line 129 to 134).

2. I initially thought that 8502 high-quality SNPs out of 497,336 unfiltered SNPs are too small, but probably the number is the one after the thinning. Please describe the number of high-quality SNPs before the thinning.

Response: We added the number (line 151 to 164) in materials and method section

3. There are several comparisons of statistics, but there are little statistical tests. For example, in the line 193, the authors stated that ts/tv ratio in A genome was higher than that in C genome, but the difference was not properly tested. So many of the analyses were more or less too descriptive.

Response: ts/tv ratio is a simple descriptive statistics and ratio of two numbers. It does not need any statistical test. Other statistical test such as Tajima's D (line 277: 1000 permutations), AMOVA (line 291: 1023 permutations, p ˂ 0.001), Fst comparisons (line 296: 1000 permutations, p ˂ 0.01), NJ (line 251: 1000 bootstraps) has been performed accordingly. Structure analysis has been done in details.

4. In the STRUCTURE analysis, I do not get what is the meaning of “accession assigned/unassigned”. STRUCTURE usually does not require any a priori assumption, in my understanding. Please add more description in the method section.

Response: Yes, structure does not require any a priori assumption. But as we divided the collection into subpopulation according to their type and origin, that’s why we ran structure considering accession assigned to specific subpopulation and accession unassigned to specific subpopulation to determine the exact cluster number of the collection. Details added in material and method section (line 166 to 182).

5. In the line 313, the meanings of “the mean linked LD”, “mean unlinked LD”, and “loci pair under linked LD”, are not clear to me. In the method section, the mean lined LD was calculated using SNPs with r2<0.2 (line 179), but it means they are unlinked. In addition, % is not added to 0.44, 0.02? It is somewhat confusing.

Response: That was typo. We changed it accordingly (line 197 to 205 and line 335 to 354).

6. In the line 401, the authors mentioned that the higher transition to transversion ratio is due to natural selection, but I do not agree with it. The pattern is ubiquitously found in many organisms including humans, and in non-coding regions, and people usually think that this is due to higher mutation rate of transition than transversion.

Response: We agreed to reviewer comment and deleted the information (line 431-432).

7. The sentence in the line 427-428 seems contradicting to the previous sentence. Probably the words, “homogeneity of the diversity indices” is not appropriate.

Response: We agreed to reviewer comment and deleted the information (line 457 to 459).

8. In the line 471, NJ is certainly distance-based method, but PCA is not.

Response: We agreed to reviewer comment and changed accordingly (line 503 to 504).

9. In the line 524, the authors mentioned that the level of LD in B. napus is low compared with the other crops, but there is no quantitative statement. I also wonder whether authors used the thinned data to calculate the mean LD. If ones remove the linked SNPs from the analysis, the level of LD obviously decreases.

Response: We modified the information according to reviewer comments (line 558 to 561 and line 574 to 577)

Minor comments,

1. L143: row SNPs would be raw SNPs

2. L153: burn-in length

3. L405: Rephrase the words “moderate or low informative”.

4. L446: the word “type” is not necessary?

Response: We tried our best to correct typos and changed accordingly (line 151, line 161, line 411 and line 450)

5. The assignment of Ks in Figure 4 is not clear. Are they determined using the presented tree?

Response: Each branch is color-coded according to genotype belongs to subpopulation P1 to P5. Genotypes were grouped into three clusters by dividing the tree using black solid lines according to structure output (line 250 to 253).

Submitted filename: Response to reviewer.docx

Click here for additional data file.

15 Feb 2022

Linkage disequilibrium and population structure in a core collection of Brassica napus (L.)

PONE-D-21-09997R3

Dear Dr. Rahman ,

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.

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Kind regards,

Tzen-Yuh Chiang

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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 #5: All comments have been addressed

Reviewer #6: All comments have been addressed

**********

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 #5: Yes

Reviewer #6: Yes

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

Reviewer #5: Yes

Reviewer #6: 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 #5: Yes

Reviewer #6: No

**********

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 #5: Yes

Reviewer #6: 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 #5: This manuscript is a revised manuscript that I have reviewed before. The authors addressed all the issues I raised so the manuscript is ready for publication.

Reviewer #6: (No Response)

**********

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 #5: No

Reviewer #6: Yes: Reza Talebi

21 Feb 2022

PONE-D-21-09997R3

Linkage disequilibrium and population structure in a core collection of Brassica napus (L.)

Dear Dr. Rahman:

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. Tzen-Yuh Chiang

Academic Editor

PLOS ONE