Skip to main content
NCBI home page
Annals of Botany logoLink to Annals of Botany
. 2012 Jan 9;109(4):747–759. doi: 10.1093/aob/mcr323

Quantitative trait loci for seed yield and yield-related traits, and their responses to reduced phosphorus supply in Brassica napus

Guangda Ding 1,2, Zunkang Zhao 1,2, Yuan Liao 1,2, Yifan Hu 1,2, Lei Shi 1,2, Yan Long 1, Fangsen Xu 1,2,*
PMCID: PMC3286287  PMID: 22234558

Abstract

Background and Aims

One of the key targets of breeding programmes in rapeseed (Brassica napus) is to develop high-yield varieties. However, the lack of available phosphorus (P) in soils seriously limits rapeseed production. The aim of this study was to dissect the genetic control of seed yield and yield-related traits in B. napus grown with contrasting P supplies.

Methods

Two-year field trials were conducted at one site with normal and low P treatments using a population of 124 recombinant inbred lines derived from a cross between ‘B104-2’ and ‘Eyou Changjia’. Seed yield, seed weight, seed number, pod number, plant height, branch number and P efficiency coefficient (PEC) were investigated. Quantitative trait locus (QTL) analysis was performed by composite interval mapping.

Key Results

The phenotypic values of most of the tested traits were reduced under the low P conditions. In total, 74 putative QTLs were identified, contributing 7·3–25·4 % of the phenotypic variation. Of these QTLs, 16 (21·6 %) were detected in two seasons and in the mean value of two seasons, and eight QTLs for two traits were conserved across P levels. Low-P-specific QTLs were clustered on chromosomes A1, A6 and A8. By comparative mapping between Arabidopsis and B. napus, 161 orthologues of 146 genes involved in Arabidopsis P homeostasis and/or yield-related trait control were associated with 45 QTLs corresponding to 23 chromosomal regions. Four gene-based markers developed from genes involved in Arabidopsis P homeostasis were mapped to QTL intervals.

Conclusions

Different genetic determinants were involved in controlling seed yield and yield-related traits in B. napus under normal and low P conditions. The QTLs detected under reduced P supply may provide useful information for improving the seed yield of B. napus in soils with low P availability in marker-assisted selection.

Keywords: Brassica napus, phosphorus deficiency, phosphorus use efficiency, recombinant inbred line, seed yield, quantitative trait locus, comparative mapping

INTRODUCTION

Brassica napus (genome AACC, 2n = 38), which is commonly used as food oil for human and animal feed, is the second most important oilseed crop in the world after soybean. One of the key targets of breeding programmes in B. napus is to develop high-yield varieties. However, yield is the most complex trait in crops. It is directly determined by three yield-component traits (seed weight, pod number and seed number per pod) and is also indirectly influenced by other yield-related traits, such as plant height, branch number, and resistance to biotic and abiotic stresses. Each of these traits is complex and is quantitatively controlled by several genes. Hence, it is difficult to accurately evaluate and select for high-yield traits in conventional breeding programmes, owing to the influence of the interactions between the environment and the genotype in all growth and development processes (Quarrie et al., 2006).

The application of molecular marker techniques for quantitative trait locus (QTL) analysis has proved to be a powerful genetic approach to dissect complex traits (Paran and Zamir, 2003). Several research groups have associated QTLs with yield and yield-related traits in B. napus, including plant height (Mei et al., 2009), yield and yield components (Radoev et al., 2008; Fan et al., 2010), and yield and other complex traits (Quijada et al., 2006; Udall et al., 2006; Chen et al., 2007; Li et al., 2007; Basunanda et al., 2010). Eighty-five QTLs for seed yield along with 785 QTLs for eight yield-associated traits were identified in ten natural environments and two related populations of rapeseed by Shi et al. (2009). More recently, Zhang et al. (2011) performed QTL mapping for silique traits in a double haploid population across three seasons and two locations and detected a number of QTLs with stable effects across environments. However, the genetic basis and possible candidate genes for these traits in rapeseed are not well elucidated, and QTL analysis of seed yield and yield-related traits under abiotic stress has not been well investigated so far in B. napus.

Phosphorus (P) is an essential macronutrient in plants (Marschner, 1995). Although the total amount of P in soils may be high, P is diluted and less available for plants in the rhizosphere because of its high absorption and low mobility in soils. A lack of available P in soils seriously limits crop production (Vance et al., 2003; Raghothama and Karthikeyan, 2005). P deficiency can be alleviated by the application of inorganic P fertilizers, but the high P-absorption capacity of the soils results in a very low P recovery rate in plants. High inputs and low recovery rates of P fertilizers not only increase financial costs for farmers but also accelerate the exhaustion of non-renewable phosphate resources. Therefore, the development of cultivars with enhanced P-use efficiency would be a more economical and sustainable way for the management of P nutrition in crop production. Rapeseed, which is sensitive to P stress and needs massive amounts of P for high seed yield and oil content, has suffered from constant P deficiency worldwide (Yan et al., 2006; Tiessen, 2008; Cordell et al., 2009). Thus, breeding B. napus cultivars with enhanced P-use efficiency and improved seed yield is imperative.

Unveiling the molecular mechanism behind the P starvation responses of plants will be an important first step to solve this issue. QTLs for P-use efficiency in different Brassica species have been identified by several research groups (Wu et al., 2008; Zhao et al., 2008; Hammond et al., 2009; Liu et al., 2009; Ding et al., 2010; Yang et al., 2010, 2011). However, QTLs for seed yield under P stress conditions have not been characterized in Brassica species so far. In the model Brassicaceae Arabidopsis thaliana, genes involved in P homeostasis as well as genes controlling yield and yield-related traits have been well characterized. Hence, making full use of the gene information in A. thaliana will be of great assistance for the identification of putative candidate genes for target traits in other Brassica species such as B. napus. The existence of a common ancestor for B. napus and Arabidopsis enables comparative genome mapping techniques between these two species (Parkin et al., 2005; Schranz et al., 2006). By in silico mapping between Arabidopsis and B. napus, genes involved in different biological processes in Arabidopsis can be mapped to the target QTL intervals in B. napus such as QTLs for flowering time (Long et al., 2007), shoot mineral concentrations (Liu et al., 2009), seed yield and yield-related traits (Shi et al., 2009), seed mineral concentrations (Ding et al., 2010), and P-efficiency traits (Yang et al., 2011). Such information facilitates the transfer of information from what we know about Arabidopsis to B. napus.

In this study, QTLs associated with seven yield-related traits were identified using an F10 recombinant inbred line (RIL) population across two seasons and two P treatments. Genes involved in P homeostasis and yield and yield-related trait control were mapped to the QTL regions. To the best of our knowledge, this is the first report on QTL analysis of yield and yield-related traits under low P conditions. The QTLs detected under reduced P supply may provide useful information for improving the seed yield of B. napus in soils with low P availability by using marker-assisted selection (MAS).

MATERIALS AND METHODS

Plant materials

The plant materials were detailed in Yang et al. (2010). ‘B104-2’ and ‘Eyou Changjia’ were selected from 194 rapeseed (Brassica napus) cultivars. These cultivars differ largely from each other in phosphorus efficiency coefficient (PEC), which was defined as the ratio of the shoot dry weight or seed yield under low P availability to that under adequate P availability (Duan et al., 2009). Our previous studies showed that ‘Eyou Changjia’ had a larger root system, acquired more P and yielded a higher biomass than ‘B104-2’ under low P conditions in various culture systems such as pot culture, root-soil compartments and hydroponic culture (Duan et al., 2009; Hu et al., 2010; Yang et al., 2010). A total of 124 F2-derived F10 RILs were obtained from a cross between these two cultivars and were used for QTL detection.

Field trials and phenotyping

Two field trials were conducted in paddy soil in Daye, Hubei Province, China, in the 2006–2007 and 2007–2008 crop seasons. The average Olsen-P in the 0–25-cm soil profile sampled before fertilization was 12·5 mg kg−1. In both trials, two P treatments were used: low P treatment, with an application of 30 kg P2O5 ha−1; and normal P treatment, with an application of 90 kg P2O5 ha−1. The amount of N, K and B fertilizers applied for each treatment was calculated according to the following nutrient rates: 180 kg N ha−1, 83 kg K2O ha−1 and 15 kg borax ha−1. K as potassium chloride and P as ordinary superphosphate were applied before sowing, and N as urea was split into 120 kg before sowing, 30 kg at the seedling stage and 30 kg at the bolting stage. The seeds of the 124 RILs, together with two parental lines, were sown at the end of September, and the plants were harvested at the beginning of the following May. Planting was conducted in a randomized complete-block design with three replicates. All the plant materials were grown in a 2·5-m-long row, with 40 cm between rows. Each RIL was planted in two rows consisting of 18 individuals in one replicate, with 25 cm between adjacent plants. The seeds were sown by hand, and the field management followed standard agricultural practice. In each replicate, six representative individuals from the middle of each row were harvested at physiological maturity.

Seed yield and five yield-related traits were investigated, and the PEC for each RIL was then calculated as the ratio of mean values of seed yield in three replicates under low P condition to that under normal P condition. Procedures for measurement of each trait in these two field trials were similar to those described in Shi et al. (2009). Briefly, seed yield (SY) was recorded as the average seed dry weight of the harvested individuals. Seed weight (SW) was measured based on 1000 fully developed seeds from each replicate samples. Seed number (SN) was counted as the average number of well-filled seeds from 100 well-developed pods, which were sampled from the primary branch in the middle of the harvested individuals. Pod number (PN) was the number of normally developed pods on each harvested individual. Plant height (PH) was measured from the base of the stem to the tip of the main shoot of each harvested individual. Branch number (BN) was counted as the number of effective primary branches.

SSR loci amplification and mapping of polymorphic loci

A new set of simple sequence repeat (SSR) markers developed by Li et al. (2011) were used for PCR in a 10-μL reaction volume. PCR amplification was performed as previously described (Cheng et al., 2009). Polymorphisms of PCR products were detected by electrophoresis on denaturing polyacrylamide gels. After electrophoresis, the gels were silver stained (Sanguinetti et al., 1994).

Genotyping data generated in this study were integrated with the marker loci into the framework linkage map (Yang et al., 2010) using JoinMap software version 4·0 (Van Ooijen, 2006). A new genetic map was constructed according to the approach described previously (Yang et al., 2010). Recombination frequencies were converted into map distances in centiMorgans (cM) based on Kosambi's mapping function (Kosambi, 1944). After integration of new loci, the genetic map was aligned to the A. thaliana genome using the comparative mapping approach described by Long et al. (2007). Based on the distribution of 24 conserved chromosomal blocks described for a hypothetical ancestral karyotype of the A. thaliana and Brassica lineages by Schranz et al. (2006), several syntenic blocks or insertion fragments (islands) were identified between the Arabidopsis genome and the BE-RIL linkage map. Genes of Arabidopsis with known functions relating to seed yield, yield-related traits and P homeostasis on each identified syntenic block were identified by using the TAIR website (http://www.arabidopsis.org/). These genes were aligned to the BE-RIL linkage map according to their closest anchored markers in the same syntenic block.

Statistical analysis

Statistical analysis for all traits was conducted using SAS 8·1 (SAS Institute, Cary, NC, USA). Histograms and normality tests (Pearson's chi-square test) were used to describe the variation of the phenotypic traits. Pearson's phenotypic correlation coefficients among seven traits across all environments were calculated to examine their phenotypic association using SAS PROC CORR. Analysis of variance was conducted using the SAS general linear model (GLM) procedure. Environments including year and P level were treated as a fixed effect, while genotype was treated as a random effect. The broad-sense heritability (h2) for each trait was calculated at both P levels as follows: h2 = σg2/(σg2 + σge2/n + σe2/nr), where σg2 is the genotypic variance, σge2 is the interaction variance of genotype with environment, σe2 was the error variance, n was the number of environments and r was the number of replicates.

QTL analysis and association with functional genes in Arabidopsis

QTL detection was conducted by composite interval mapping (CIM) (Zeng, 1994) using Model 6 of WinQTL cartographer 2·5 software (Wang et al., 2011). Because of missing values in the RIL population, the least squares means (lsmeans) of the genotypic effects and the mean value of two seasons were used for QTL detection. The number of control markers, window size and walking speed were set to 5, 10 cM and 2 cM, respectively. The backward regression algorithm was used to obtain cofactors. The minimum acceptable size range that defines a QTL peak was set to 5 cM. A 1000-permutation test of shuffling the phenotypes means with the genotypes was performed to estimate a genome-wide LOD score threshold for a QTL at a significance level of P = 0·05 (Doerge and Churchill, 1996). The estimated additive effect and the percentage of phenotypic variation explained by each putative QTL were obtained using the software with the CIM model. QTL confidence intervals were determined by 2-LOD intervals surrounding the QTL peak. When QTLs for the same trait across two years with the same P treatment had overlapping confidence intervals, they were assumed to be identical. Genes were identified by comparative mapping between the B. napus linkage groups and the A. thaliana genome in each syntenic block of A. thaliana and then associated with each putative QTL. If the position of an aligned gene(s) was located in the confidence interval of a QTL, the orthologous gene(s) was considered to be associated with the target QTL.

RESULTS

Construction of an improved genetic map

A set of 786 novel SSR markers developed by Li et al. (2011) was screened for polymorphism on the parental genotypes ‘B104-2’ and ‘Eyou Changjia’ for polymorphism. In total, 132 loci were integrated into our previously constructed map (Ding et al., 2011). The present map has 840 loci, including 62 amplified fragment length polymorphisms (AFLPs), 257 sequence-related amplified polymorphisms (SRAPs), 472 SSRs and 49 gene-based markers (GBMs) which were developed from Arabidopsis functional genes involved in P homeostasis. The total map length was 1913·6 cM, with an average distance of 2·3 cM between two loci (Supplementary Data Table S1). Based on a χ2 test for goodness-of-fit to the expected 1 : 1 Mendelian segregation ratio, a total of 248 markers (29·5 %) showed distorted segregation, with 165 markers from ‘B104-2’ and 83 markers from ‘Eyou Changjia’ (P < 0·05) (Supplementary Data Table S1). The present map is very effective for QTL identification in the C genome of the BE-RIL population.

Phenotypic analysis of seven tested traits

The mean values, ranges and broad-sense heritability (h2) estimates of seven traits of the two parental lines and the 124-RILs population in four environments are presented in Table 1. A wide range of variation was observed for all the traits between the two parents and among the RILs in both years at both P supply levels. Significant differences were observed between the two parents harvested at maturity. The phenotypic values of ‘Eyou Changjia’ were higher than those of ‘B104-2’ at the low P level in both field trials, except seed weight. Compared with the normal P condition, both parental lines showed smaller values under the low P condition for all the tested traits, as well as the RILs. For PEC, a higher value was observed for ‘Eyou Changjia’ than for ‘B104-2’ in both years (Table 1). Moderate to high h2 was observed in the BE-RIL population for the six traits, ranging from 0·47 for branch number at the low P level to 0·86 for seed weight at the normal P level. In general, a higher h2 was observed in the normal P condition than in the low P condition (Table 1). Analysis of variance showed that genotype and P level had significant effects on the six traits (Table 2). The frequency distributions of all the traits showed continuous phenotypic variation, and significant transgressive segregation was observed in both directions, suggesting that multiple genes were involved (Figs 1 and 2).

Table 1.

Means, ranges and broad-sense heritability (h2) estimates of seven traits in the parental lines and the RIL population grown with different P supply levels

Parental line
 RILs
Trait Treatment Year ‘B104-2’ ‘Eyou Changjia’ Mean Range h2
SY Low P 2007 3·03  ±  0·12cd* 4·70  ±  0·15bc 3·03 0·82–6·25 0·49
2008 2·42  ±  0·10d 5·30  ±  0·24b 4·48 0·60–10·75
Normal P 2007 16·00  ±  0·19a 16·38  ±  0·85a 12·49 5·78–20·41 0·59
2008 15·06  ±  1·01a 14·72  ±  1·07a 12·08 4·16–21·51
SW Low P 2007 2·82  ±  0·06b 2·16  ±  0·04d 2·62 2·13–3·27 0·79
2008 2·89  ±  0·07b 2·31  ±  0·06cd 2·59 1·94–3·33
Normal P 2007 2·91  ±  0·05b 2·32  ±  0·05cd 2·79 1·80–3·51 0·86
2008 3·09  ±  0·06a 2·39  ±  0·04c 2·66 2·00–3·43
PN Low P 2007 80·40  ±  3·47d 98·94  ±  5·30cd 72·67 35·90–154·33 0·51
2008 92·25  ±  7·38d 123·72  ±  7·32c 99·79 30·50–220·33
Normal P 2007 259·07  ±  9·72b 253·24  ±  8·13b 254·3 176·38–373·83 0·62
2008 256·40  ±  1·37b 286·41  ±  16·67a 218·86 99·50–325·00
SN Low P 2007 11·74  ±  0·32c 22·06  ±  0·75ab 16·49 4·71–27·86 0·56
2008 9·18  ±  0·71c 19·92  ±  0·86b 17·05 4·33–27·71
Normal P 2007 21·26  ±  0·68b 24·55  ±  0·95a 21·29 6·37–35·47 0·67
2008 19·00  ±  0·98b 21·59  ±  1·82ab 20·72 10·64–36·36
BN Low P 2007 2·11  ±  0·07d 4·54  ±  0·34c 2·97 0·67–5·94 0·47
2008 3·22  ±  0·36d 5·30  ±  .074c 3·22 0·00–6·60
Normal P 2007 5·08  ±  0·05bc 6·31  ±  0·49ab 6·07 4·67–9·38 0·62
2008 6·07  ±  0·23ab 7·11  ±  0·33a 5·8 3·78–9·89
PH Low P 2007 86·69  ±  8·55e 111·67  ±  7·85d 104·69 68·00–149·08 0·56
2008 119·78  ±  1·31cd 134·25  ±  2·38bc 117·3 78·83–150·00
Normal P 2007 153·28  ±  2·96a 159·57  ±  5·64a 156·41 136·19–189·56 0·71
2008 146·59  ±  2·74ab 148·96  ±  2·57ab 142·58 113·17–175·83
PEC 2007 0·19 0·29 0·25 0·09–0·70 n.a.
2008 0·16 0·36 0·37 0·06–0·90
Open in a new tab

SY, seed yield (g per plant); SW, seed weight (g per 1000); PN, pod number; SN, seed number; BN, branch number; PH, plant height (cm); PEC, phosphorus efficiency coefficient; n.a., not analysed.

* Mean ± s.d., n = 3. Different letters indicate significant difference at P = 0·05.

Table 2.

Significance of three-way ANOVA for the six traits among RILs in low P and normal P treatments in two-year field trials

Source d.f. SY SW SN PN BN PH
Genotype (G) 123 * *** *** *** * *
Year (Y) 1 * *** ns ns ns ns
P level 1 *** *** *** *** *** ***
G × P 123 *** *** ** ns ns ns
G × Y 123 ** * ns *** ** ***
Y × P 1 *** *** ns *** *** ***
Open in a new tab

n.s., not significant; *P < 0·05, **P < 0·01, ***P < 0·001.

Fig. 1.

Fig. 1.

Open in a new tab

Frequency distributions of seed yield and yield-associated traits in the RIL population grown under normal P conditions (left) and low P conditions (right) in 2-year field trials. Solid arrows indicate ‘B104-2’, and dashed arrows indicate ‘Eyou Changjia’.

Fig. 2.

Fig. 2.

Open in a new tab

Frequency distributions of the PEC of the ‘B104-2’ × ‘Eyou Changjia’ RIL population in 2007 and 2008. B and E indicate the position of the parents, ‘B104-2’ and ‘Eyou Changjia’, respectively.

Genotypic correlation among seven tested traits

Significant positive correlations between seed yield and the other six traits were observed at both P levels, except for seed yield under normal P treatment and PEC (Table 3). Weak positive or significant negative correlations were observed in all pairwise combinations among three yield-component traits (seed number, seed weight and pod number) at both levels of P supply. Significant positive correlations were observed between PEC and other traits in the low P condition, except for seed weight. However, correlations between PEC and the other five traits were weak, or even negative, for the normal P treatment, except for seed weight, which showed a weak but significant positive correlation. This result indicates that PEC and other traits could share some genetic determinants or some mechanisms under low P conditions. There were significant positive correlations for the same traits between the two P conditions, except for pod number (Table 3).

Table 3.

Phenotypic correlation coefficients among seven tested traits in the 124 RILs of Brassica napus in the mean value of two seasons

SY SW SN PN BN PH PEC
SY 0·18* 0·30** 0·38*** 0·20* 0·44*** 0·30** –0·17
SW 0·31*** 0·68*** –0·09 –0·27** –0·26** –0·14 0·20*
SN 0·52*** 0·09 0·33*** –0·35*** –0·07 –0·09 –0·15
PN 0·42*** 0·03 –0·10 0·03 0·35*** 0·44*** –0·11
BN 0·46*** 0·15 –0·03 0·38*** 0·33*** 0·19 0·04
PH 0·33*** 0·04 –0·13 0·31*** 0·42*** 0·35*** –0·09
PEC 0·66*** 0·18 0·38*** 0·39*** 0·40*** 0·40***
Open in a new tab

*P < 0·05, **P < 0·01, ***P < 0·001. Values on the diagonal indicate the correlations between the two P conditions for each trait. Values in the lower left triangle and the upper right triangle indicate the correlations among seven traits under low P and normal P conditions, respectively.

Putative QTLs for seven tested traits

A total of 74 putative QTLs were found to be associated with the seven tested traits at both P levels, explaining 7·3–25·4 % of the phenotypic variation. Among them, six QTLs were identified for PEC, 14 for pod number, 12 for branch number, nine for plant height, 11 for seed yield, 11 for seed weight and 11 for seed number (Table 4). These QTLs were distributed across 16 linkage groups. However, nearly two-thirds of the QTLs (47 of 74) were mapped to the A genome (Fig. 3).

Table 4.

Significant QTLs for seed yield and yield-associated traits in the RIL population under different phosphorus supply conditions over two years and in the mean value of the two years

2007
 2008
 Mean
Trait Chromosome QTL* Add.† PVE (%)‡ CI (peak)§ PVE (%) CI (peak) PVE (%) CI (peak)
PEC A1 PEC-A1 + 21·0 106·4–112·9 (110·7) 9·2 108·0–112·9 (108·8) 10·3 106·8–112·9 (108·8)
A6 PEC-A6 – 14·3 24·3–27·5 (26·1) 13·4 24·3–27·3 (26·1)
A8 PEC-A8 – 10·5 55·8–66·5 (63·5) 10·6 58·6–64·6 (61·5) 11·0 58·0–67·0 (61·5)
C3 PEC-C3a + 10·3 11·9–13·2 (12·4)
C3 PEC-C3b – 9·7 12·9–17·9 (13·6)
C3 PEC-C3c + 13·9 62·5–64·7 (62·5)
QTLs identified under normal phosphorus level
SY A3 SYNP-A3 – 16·1 80·2–88·4 (81·3) 8·6 78·9–82·9 (81·3)
A4 SYNP-A4 + 8·8 0·0–2·8 (0·0) 16·2 0·0–5·1 (0·0) 12·4 0·0–2·6 (0·0)
A5 SYNP-A5 + 14·5 88·9–98·1 (92·6)
C1 SYNP-C1 + 9·7 2·0–15·9 (6·0)
C5 SYNP-C5 – 21·0 49·2–59·7 (57·8) 11·1 56·6–60·5 (57·9) 25·4 49·5–59·6 (57·9)
C8 SYNP-C8 + 12·4 69·9–73·9 (70·9)
SW A3 SWNP-A3 - 10·3 90·3–92·5 (91·3)
A4 SWNP-A4 + 7·8 39·5–42·8 (40·5) 7·3 40·3–46·8 (40·5)
A10 SWNP-A10 – 10·3 52·1–60·8 (57·8) 8·1 52·3–58·6 (57·8) 14·4 52·7–60·6 (57·8)
C6a SWNP-C6 + 10·7 7·7–15·3 (11·9)
C6b SWNP-C6 + 11·3 22·9–34·0 (28·1)
SN A3 SNNP-A3 + 13·9 105·1–110·6 (110·1) 16·1 100·6–110·6 (110·1)
A9 SNNP-A9 – 9·3 42·1–45·2 (43·1) 12·9 44·3–47·6 (46·4) 10·5 37·1–45·8 (43·1)
C3 SNNP-C3 + 17·3 53·4–61·2 (53·5)
C5 SNNP-C5 – 12·9 50·8–65·9 (57·9) 11·5 49·9–60·4 (57·9)
PN A5 PNNP-A5 – 9·3 31·4–43·2 (33·3) 8·9 31·4–43·3(33·3)
A9 PNNP-A9a + 18·4 27·2–32·8 (30·6)
A9 PNNP-A9b + 10·7 86·5–96·9 (92·0)
C1 PNNP-C1 – 13·6 45·6–57·5 (52·4) 9·2 51·7–55·0 (52·4)
C3 PNNP-C3 – 9·5 14·2–18·1 (14·8)
C6 PNNP-C6 – 12·0 49·7–53·3 (51·3) 10·6 48·4–51·3 (50·4) 13·2 48·4–53·3 (51·3)
C8 PNNP-C8 + 8·1 54·3–60·4 (59·8)
BN A8 BNNP-A8 + 7·8 17·0–22·7 (18·6) 9·2 13·7–23·0 (21·2) 9·0 12·9–23·0 (18·6)
A9 BNNP-A9a – 11·0 0·3–9·8 (5·8)
A9 BNNP-A9b + 8·5 25·5–35·9 (32·6)
C3 BNNP-C3a – 19·8 29·0–31·5 (30·9) 15·3 29·0–31·7 (30·9)
C3 BNNP-C3b – 8·3 36·8–38·4 (37·3)
C6 BNNP-C6a – 16·1 10·6–15·3 (13·5)
C6 BNNP-C6b – 16·5 22·1–30·1 (26·1)
C7 BNNP-C7 + 7·7 51·5–66·5 (55·1)
PH A3 PHNP-A3a + 19·9 28·5–39·0 (37·6)
A3 PHNP-A3b + 10·2 40·3–44·9 (42·7) 11·1 39·8–44·0 (42·7)
A8 PHNP-A8 + 11·7 14·7–27·0 (15·7)
C3 PHNP-C3 – 10·8 14·0–20·2 (14·8)
C5 PHNP-C5 – 10·9 47·7–59·3 (57·8) 10·8 48·6–55·8 (53·2) 10·0 49·2–64·5 (57·8)
C9 PHNP-C9 – 10·9 0·0–2·7 (0·0)
QTLs identified under low phosphorus level
SY A1 SYLP-A1a - 11·2 97·4–101·2 (99·4)
A1 SYLP-A1b + 13·7 108·8–112·9 (110·7) 11·0 110·7–114·9 (112·9) 11·4 110·7–112·9 (112·7)
A5 SYLP-A5 – 10·4 66·8–76·6 (68·8)
A6 SYLP-A6 – 11·0 30·2–33·3 (31·8) 12·9 29·5–37·7 (30·1) 10·7 28·5–37·7 (30·1)
A8 SYLP-A8 – 14·2 58·9–66·1 (61·5) 10·0 58·6–66·6 (61·5)
SW A3 SWLP-A3a + 8·8 5·0–11·6 (7·8)
A3 SWLP-A3b – 8·9 80·8–85·0 (82·5)
A3 SWLP-A3c – 15·5 89·7–93·0 (92·6) 11·1 88·5–93·5 (92·6)
A4 SWLP-A4 + 9·7 39·1–45·5 (40·5)
A10 SWLP-A10a + 8·2 34·5–41·1 (36·4)
A10 SWLP-A10b – 14·8 51·0–60·7 (57·8) 9·0 52·1–58·6 (57·8) 16·3 52·4–60·6 (57·8)
SN A2 SNLP-A2 – 11·2 65·4–67·9 (67·2)
A3 SNLP-A3 – 9·4 81·8–84·7 (82·9) 11·4 81·8–84·2 (82·9)
A6 SNLP-A6 + 11·2 87·2–102. (88·6) 12·4 90·2–96·9 (91·6) 13·0 88·0–100·0 (93·6)
A8 SNLP-A8 + 9·4 27·0–37·6 (30·3)
A10 SNLP-A10 + 8·5 35·7–37·7 (36·4) 12·8 35·7–37·5 (36·4)
C1 SNLP-C1 + 11·9 51·4–55·0 (52·4)
C5 SNLP-C5 + 14·7 42·6–49·0 (45·7)
PN A1 PNLP-A1 + 23·9 104·1–114·9 (112·9) 11·2 110·7–114·9 (112·9) 13·7 111·0–114·9 (112·9)
A2 PNLP-A2 – 10·4 60·0–67·3 (63·2)
A3 PNLP-A3 – 9·2 10·5–15·7 (13·7)
A6 PNLP-A6 – 13·1 25·4–34·8 (27·3) 13·3 25·4–34·8 (27·3)
A8 PNLP-A8 – 11·5 62·6–66·7 (64·6)
C8 PNLP-C8 + 9·0 72·2–74·2 (73·9)
C9 PNLP-C9 + 12·0 4·2–19·9 (12·2)
BN A3 BNLP-A3a – 9·2 76·0–82·5 (81·3) 8·7 78·6–82·1 (81·3)
A3 BNLP-A3b – 10·4 87·6–92·6 (92·1)
A6 BNLP-A6 – 9·4 30·2–33·1 (31·8)
A8 BNLP-A8 – 12·8 62·9–66·2 (64·6) 10·0 50·9–64·6 (61·5) 9·7 61·5–67·0 (64·6)
PH A3 PHLP-A3 + 11·1 39·2–57·4 (43·5) 11·3 42·7–46·9 (44·7) 13·9 42·7–57·0 (50·0)
A8 PHLP-A8 – 9·1 62·9–68·9 (64·6) 18·7 57·5–66·2 (64·6)
C6 PHLP-C6 + 15·9 23·4–29·6 (26·1) 11·4 23·2–29·0 (24·1)
Open in a new tab

* Nomenclature for QTLs: an italic trait abbreviation followed by a phosphorus-level designator (NP, normal P level; LP, low P level), a hyphen (-), chromosome (A1–A10 or C1–C9) on which the QTL is located and the serial letter (a, b, c, etc.) in the same linkage group.

† Additive effect. Positive additive effects are associated with increased effects from ‘B104-2’ alleles, and negative additive effects are associated with increased effects from ‘Eyou Changjia’ alleles.

‡ Percentage of phenotypic variation explained (PVE) by each identified QTL.

§ The 2-LOD confidence interval (CI) of QTL, given in cM. The peak position is denoted by the number in parentheses.

Fig. 3.

Fig. 3.

Fig. 3.

Open in a new tab

Chromosomal locations of putative QTLs for yield and yield-associated traits in the BE-RIL population. The length of the vertical black line to the right of the chromosomes indicates the 2-LOD support intervals. The peak positions of QTLs are shown by horizontal bars. The coloured bars to the left of the chromosomes represent different pseudochromosomes of A. thaliana that have been aligned to the linkage map of B. napus according to the 24 Arabidopsis genomic blocks identified by Schranz et al. (2006). GBMs located in the QTL intervals are shown to the right of the linkage group. Numbers at the bottom of each linkage group indicate the map length (cM).

Thirty-six QTLs were detected under normal P conditions for the six traits (Table 4). Among them, seven QTLs (SYNP-A4, SYNP-C5, SWNP-A10, SNNP-A9, PNNP-C6, BNNP-A8 and PHNP-C5) were identified in two seasons and in the mean value of two seasons. Eighteen QTLs showed negative effects, indicating that the positive alleles for higher phenotypic values were inherited from ‘Eyou Changjia’. QTLs for different traits were clustered on chromosomes A8, A9, C3, C5 and C6. On chromosome C5, QTLs for plant height (PHNP-C5), seed yield (SYNP-C5) and seed number (SNNP-C5) co-localized with each other, which accounted for 10·0–25·4 % of the total phenotypic variation, with the favourable alleles contributed by ‘Eyou Changjia’ (Table 4; Fig. 3).

Thirty-two QTLs were detected under low P condition for the six traits (Table 4). Seven of these QTLs (SYLP-A1b, SYLP-A6, SWLP-A10b, SNLP-A6, PNLP-A1, BNLP-A8 and PHLP-A3) were identified in two seasons and in the mean value of two seasons. These QTLs accounted for 9·0–23·9 % of the phenotypic variation. Higher values were conferred by the male parent (‘Eyou Changjia’) alleles at 18 loci and by the female parent (‘B104-2’) alleles at 14 loci. Two robust QTLs for seed yield (SYLP-A1b) and pod number (PNLP-A1) were clustered on chromosome A1, with favourable alleles conferred by ‘B104-2’. Another two low-P-specific QTL clusters were observed on A6 and on A8. QTLs on chromosome A6 for pod number (PNLP-A6), seed yield (SYLP-A6) and branch number (BNLP-A6) overlapped with each other, and QTLs for seed yield (SYLP-A8), branch number (BNLP-A8), pod number (PNLP-A8) and plant height (PHLP-A8) were clustered on chromosome A8. The favourable alleles in both QTL clusters were contributed by ‘Eyou Changjia’ (Table 4; Fig. 3).

Six putative QTLs for PEC were detected on chromosomes A1, A6, A8 and C3 (three QTLs), which accounted for 9·2–21·0 % of the total phenotypic variation. Two of them (PEC-A1 and PEC-A8) were identified in two seasons and in the mean value of two seasons. Four QTLs, PEC-A1, PEC-A6, PEC-A8 and PEC-C3b, overlapped with QTLs for other tested traits. A higher PEC was conferred by the ‘Eyou Changjia’ allele at three loci (PEC-A6, PEC-A8 and PEC-C3b) and by the ‘B104-2’ allele at another three loci (PEC-A1, PEC-C3a and PEC-C3c) (Table 4; Fig. 3).

Eight QTLs for the same traits were detected across P levels. Six QTLs detected under different P conditions for seed weight overlapped with each other on chromosomes A3, A4 and A10. Two QTLs (PHLP-A3 and PHNP-A3b) for plant height co-localized with each other on chromosome A3. Furthermore, QTLs for different traits that were detected at both P levels were clustered on chromosomes A3, C1, C6 and C8 (Fig. 3).

Association of QTLs with functional genes by in silico mapping

By in silico mapping between the A. thaliana genome and B. napus linkage groups, a total of 161 orthologues of 146 genes were mapped to the confidence intervals of 45 QTLs corresponding to 23 chromosomal regions. Of the 161 genes, 48 and 113 were involved in Arabidopsis P homeostasis and yield-related trait control, respectively (Supplementary Data Table S2). Furthermore, four GBMs developed from functional genes involved in P homeostasis in A. thaliana (Ding et al., 2011) were associated with putative QTLs (Fig. 3). BnIPS2-C3 (designed from the phosphate-starvation-response gene ATIPS2; Shin et al., 2006) was mapped to the confidence interval of PEC-C3b, which was detected during the 2006–2007 crop season with positive effect from ‘Eyou Changjia’. BnGPT1-A3 (developed from the phosphate transport gene GPT1 in A. thaliana; Niewiadomski et al., 2005) was distributed in the confidence interval of PNLP-A3. BnPAP25-A8, which was developed from Arabidopsis purple acid phosphatase 25 (PAP25), was mapped to the confidence interval of SNLP-A8. Another GBM, BnNPC4-A1, developed from NPC4, which encodes a functional phosphatidylcholine-hydrolysing phospholipase C and is greatly induced by phosphate deprivation (Nakamura et al., 2005), was distributed in the confidence intervals of the low-P-specific QTL cluster on chromosome A1.

DISCUSSION

Seed yield is one of the most important and complex traits in crops. In the present study, two-year field trials were conducted with two P treatments to identify QTLs for seed yield, seed weight, seed number, pod number, branch number, plant height and PEC, by using an F10 RIL population of B. napus derived from a cross between two cultivars, ‘B104-2’ and ‘Eyou Changjia’.

Phenotypic investigation showed that, for the same genotype, there is a large genetic variation in the seven traits between the two parental lines and between the two P supply levels (Table 1). Considerable transgressive segregation was observed for most of the traits under both P conditions (Figs 2 and 3), indicating that yield and yield-related traits were quantitatively inherited traits controlled by multiple genes and could be improved genetically. In this study, broad-sense heritability (h2) was calculated at both P levels, and moderate h2 was observed in the BE-RIL population for yield and yield-related traits, except for seed weight, which had the highest heritability (0·86) (Table 1). Similar results have been reported by Shi et al. (2009), indicating that seed weight is genetically more tightly controlled and less influenced by environmental factors than other yield-related traits. For six of the traits (excluding PEC), compared with normal P levels, a 2·6–83·9 % decrease was observed for the mean values of the parental lines and the mean value of the RIL population under low P level during the two crop seasons, indicating that plant growth was seriously affected by P deprivation. This result was further confirmed by analysis of variation, which showed that the effects of genotype and P level were significant for all the tested traits (Table 2).

Quantitative traits show a range of sensitivity to environmental factors. Thus, some QTLs could only be detected in one year, either owing to the interaction of genes with the environment or to a power issue. In a recent study, Shi et al. (2009) found that nearly half of the QTLs were expressed principally in response to the specific environment and that few QTLs could be detected across all environments. In the current study, we identified 74 putative QTLs. However, only 16 QTLs (21·6 %) were consistently identified in both seasons and in the mean value of both seasons, and only eight QTLs were identified across P levels for the same traits. These eight included six QTLs for seed weight, which showed the highest heritability, and two QTLs for plant height (Table 4; Fig. 3). A three-way ANOVA showed that the interactions of genotype × P level, genotype × year and year × P level were significant among some traits (Table 2). This result indicates that different genetic determinants are involved depending on the P condition, and might be due to the differential regulation of the genes involved according to the environment or P supply. These findings also suggested that abiotic stress such as nutrient deficiency should be considered in setting up an efficient breeding programme for high seed yield in B. napus.

Genotypic correlations between traits are assumed to be due to either tight linkage of genes for different traits or to genes with pleiotropic effects (Xu, 1997). In this study, observations of correlations among traits were further supported by the genomic location and the effects of the QTLs detected. A total of 74 significant QTLs were detected on 16 linkage groups. Among these chromosomes, seven (A1, A3, A6, A8, C3, C5 and C6) were observed with QTLs for more than two traits that overlapped with each other (Fig. 3). Positive effects of these QTL clusters were from the same parent. Based on the common molecular markers of different genetic maps, we projected several QTLs for the same traits that we identified in other genetic populations of B. napus onto the BE-RIL genetic map using the map projection function of BioMercator 2·1 software (Arcade et al., 2004). In total, 22 QTLs were projected onto seven chromosomes of the BE-RIL genetic map (Supplementary Data Fig. S1). Interestingly, among these QTLs, some co-localization was observed with QTLs identified in normal P conditions (such as one QTL for seed yield on A4, one QTL for seed number on C3, and seven QTLs for seed weight and one QTL for branch number on C6), in both conditions (such as one QTL for seed weight on A3, and two QTLs for seed weight on A4) and with some QTLs identified specifically in low P conditions (such as one QTL for seed yield on A1, four QTLs for branch number on A3, one QTL for seed yield on A5, two QTLs for pod number on A6 and one QTL for plant height on C6). This result indicates that common genetic determinants could exist for some traits in different genetic backgrounds and environments.

Previous studies have shown that the efficient uptake of P by plants may be achieved through developed root morphology (Bates and Lynch, 2000; Gitte et al., 2003) and root architecture (Liao et al., 2001; He et al., 2003). Our previous results detected a number of genetic loci that control root morphology at the seedling stage in B. napus when grown at different P concentrations, with the same mapping population used here (Yang et al., 2010, 2011). Interestingly, QTLs detected on chromosome A3 for plant height, on A6 for seed yield, branch number, pod number and PEC, and on C3 for PEC, pod number and plant height co-localized with those involved in root morphology (Fig. 3). This may indicate that a developed root system contributes largely to traits that directly or indirectly increase seed yield at maturity.

QTL mapping is becoming increasingly important in modern breeding programmes by MAS, as well as for map-based gene discovery (Price, 2006). Among the six QTLs identified for PEC, three co-localized with QTLs for the other traits detected at the low P level, whereas only one co-localized with QTLs for the other traits detected at the normal P level. This suggests the existence of common genetic control for PEC and other yield-related traits under low P condition, and it indicates the potential of PEC as an indicator to evaluate P-use efficiency in B. napus. A QTL for PEC (PEC-C3b), detected on chromosome C3, also co-localized with a QTL that controls seed P concentration (detected by Ding et al., 2010) under low P condition and with QTLs (detected by Hammond et al., 2009) in Brassica oleracea, as determined by comparative genomic analyses between Arabidopsis and Brassica species. This result indicates that important genes that affect the P-use efficiency both at the seedling stage and at maturity are located on chromosome C3 of both B. napus and B. oleracea. A GBM (BnIPS2-C3) designed from the phosphate-starvation-response gene ATIPS2 (Ding et al., 2011) was mapped to the confidence interval of PEC-C3b (Fig. 3), and IPS2 could be a good candidate gene for the QTL.

The significance of QTL × environment interaction effects was tested using QTLNetwork 2·0 software based on a mixed linear model (Yang et al., 2008). The result indicated that only two QTLs (PHNP-C9 and BNNP-C6b) showed significant interaction with the environment (data not shown), possibly owing to a power issue with the study. However, the important role of the QTL × environment interaction in the expression of complex traits such as seed yield has previously been demonstrated by other researchers (Radoev et al., 2008; Basunanda et al., 2010).

The robustness of QTLs is important for further fine mapping and/or MAS. In this study, three low-P-specific QTL clusters were observed (Fig. 3). One QTL for PEC, seed yield and pod number was mapped to the base of chromosome A1, with positive effects from ‘B104-2’. This QTL cluster overlapped with a stable QTL for seed P concentration that was identified in our previous study (Ding et al., 2010), indicating the possibility of improving both P-use efficiency and seed yield simultaneously by manipulating these loci in MAS. Moreover, a GBM (BnNPC4-A1) developed from the phosphate-starvation-response gene NPC4 (Ding et al., 2011) was located in the QTL confidence interval (Fig. 3). Another two low-P-specific QTL clusters were observed on chromosomes A6 and A8, with favourable alleles contributed by ‘Eyou Changjia’. By in silico mapping between Arabidopsis and B. napus, several genes were associated with these QTL clusters (Supplementary Data Table S2). PEC was defined as the ratio of seed yield under low P availability to that under adequate P availability (Duan et al., 2009). Stable QTLs for PEC were detected in these three low-P-specific QTL clusters, suggesting the possibility of breeding B. napus cultivars with increased seed yield in P deprivation environments. In addition, robust QTLs identified at normal P level may be useful for a breeding scheme for rapeseed grown in a normal P environment.

High yield is a major breeding target in B. napus. The present investigation is the first comprehensive report on dissecting the genetic control of seed yield and yield-related traits under environmental P stress. A total of 74 putative QTLs were identified in two seasons and in the mean value of two seasons under two P conditions. Most of the stable QTLs were clustered on specific chromosomal regions. Thus, we believe that MAS with markers located in the intervals of QTL clusters would have good potential to increase the efficiency of breeding programmes that are seeking cultivars with high yield and simultaneously improve P-use efficiency. Moreover, a number of functional genes in Arabidopsis were associated with QTLs by in silico mapping between Arabidopsis and B. napus. This finding may increase our knowledge of the genes influencing target traits and reveal some interesting positional candidates. However, further fine mapping and analysis of near isogenic lines or mutants will be needed to confirm the involvement of potential candidate genes. Association mapping could also be a way to validate some candidate genes.

Conclusions

An improved genetic linkage map was constructed in B. napus using a population of 124 RILs derived from a cross between ‘B104-2’ and ‘Eyou Changjia’. Seed yield, seed weight, seed number, pod number, plant height, branch number and PEC were investigated in two-year field trials with normal and low P treatments. Considerable variation was observed among the RILs, and the phenotypic values of the tested traits were decreased primarily under the low P condition, suggesting growth inhibition occurred in B. napus in a P-deprived environment. In total, 74 putative QTLs were identified. Of these, only eight QTLs for two traits were conserved across P levels, indicating different genetic determinants were primarily involved in controlling seed yield and yield-related traits in B. napus in normal and low P environments. Low-P-specific QTL clusters were observed on chromosomes A1, A6 and A8. These results could have great potential for improving the seed yield of B. napus in soils with low P availability by MAS.

SUPPLEMENTARY DATA

Supplementary data are available online at www.aob.oxfordjournals.org and consist of the following. Table S1: improved BE-RIL genetic linkage map and its syntenic segmental alignment with the Arabidopsis genome. Table S2: orthologous genes associated with yield and yield-associated QTLs detected in the ‘B104-2’ × ‘Eyou Changjia’ RIL population by in silico mapping between A. thaliana and B. napus. Fig. S1: QTL projection from another population onto the BE-RIL genetic map, via a map-projection using BioMercator 2·1 software based on common markers.

Supplementary Data
supp_109_4_747__index.html (1.3KB, html)

ACKNOWLEDGEMENTS

This work was supported by National Basic Research and Development Program (2011CB100301) and natural Science Funds for Distinguished Young Scholar in Hubei Province (2011CDA090), China. We thank the two anonymous reviewers for critical comments on an earlier version of this paper.

LITERATURE CITED

  1. Arcade A, Labourdette A, Falque M, et al. Biomercator: integrating genetic maps and QTL towards discovery of candidate genes. Bioinformatics. 2004;20:2324–2326. doi: 10.1093/bioinformatics/bth230. [DOI] [PubMed] [Google Scholar]
  2. Basunanda P, Radoev M, Ecke W, Friedt W, Becker HC, Snowdon RJ. Comparative mapping of quantitative trait loci involved in heterosis for seedling and yield traits in oilseed rape (Brassica napus L.) Theoretical and Applied Genetics. 2010;120:271–281. doi: 10.1007/s00122-009-1133-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bates TR, Lynch JP. The efficiency of Arabidopsis thaliana (Brassicaceae) root hairs in phosphorus acquisition. American Journal of Botany. 2000;87:964–970. [PubMed] [Google Scholar]
  4. Chen W, Zhang Y, Liu XP, Chen BY, Tu JX, Fu TD. Detection of QTL for six yield-related traits in oilseed rape (Brassica napus) using DH and immortalized F2 populations. Theoretical and Applied Genetics. 2007;115:849–858. doi: 10.1007/s00122-007-0613-2. [DOI] [PubMed] [Google Scholar]
  5. Cheng X, Xu J, Xia S, et al. Development and genetic mapping of microsatellite markers from genome survey sequences in Brassica napus. Theoretical and Applied Genetics. 2009;118:1121–1131. doi: 10.1007/s00122-009-0967-8. [DOI] [PubMed] [Google Scholar]
  6. Cordell D, Drangert JO, White S. The story of phosphorus: global food security and food for thought. Global Environmental Change. 2009;19:292–305. [Google Scholar]
  7. Ding GD, Yang M, Hu YF, et al. Quantitative trait loci affecting seed mineral concentrations in Brassica napus grown with contrasting phosphorus supplies. Annals of Botany. 2010;105:1221–1234. doi: 10.1093/aob/mcq050. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Ding GD, Liao Y, Yang M, Zhao ZK, Shi L, Xu FS. Development of gene-based markers from Arabidopsis thaliana functional genes involved in phosphorus homeostasis and mapping in Brassica napus. Euphytica. 2011;181:305–322. [Google Scholar]
  9. Doerge RW, Churchill GA. Permutation tests for multiple loci affecting a quantitative character. Genetics. 1996;142:285–294. doi: 10.1093/genetics/142.1.285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Duan HY, Shi L, Ye XS, Wang YH, Xu FS. Identification of phosphorous efficient germplasm in oilseed rape. Journal of Plant Nutrition. 2009;32:1148–1163. [Google Scholar]
  11. Fan C, Cai G, Qin J, et al. Mapping of quantitative trait loci and development of allele-specific markers for seed weight in Brassica napus. Theoretical and Applied Genetics. 2010;121:1289–1301. doi: 10.1007/s00122-010-1388-4. [DOI] [PubMed] [Google Scholar]
  12. Gitte K, Tara G, Nielsen NE. Variation in phosphorus uptake efficiency by genotypes of cowpea (Vigna unguiculata) due to differences in root and root hair length and induced rhizosphere processes. Plant and Soil. 2003;251:83–91. [Google Scholar]
  13. Hammond JP, Broadley MR, White PJ, et al. Shoot yield drives phosphorus use efficiency in Brassica oleracea and correlates with root architecture traits. Journal of Experimental Botany. 2009;60:1953–1968. doi: 10.1093/jxb/erp083. [DOI] [PubMed] [Google Scholar]
  14. He Y, Liao H, Yan XL. Localized supply of phosphorus induces root morphological and architectural changes of rice in split and stratified soil cultures. Plant and Soil. 2003;248:247–256. [Google Scholar]
  15. Hu YF, Ye XS, Shi L, Duan HY, Xu FS. Genotypic differences in root morphology and phosphorus uptake kinetics in Brassica napus under low phosphorus supply. Journal of Plant Nutrition. 2010;33:889–901. [Google Scholar]
  16. Kosambi DD. The estimation of map distance from recombination values. Annals of Eugenics. 1944;12:172–175. [Google Scholar]
  17. Li H, Chen X, Yang Y, et al. Development and genetic mapping of microsatellite markers from whole genome shotgun sequences in Brassica oleracea. Molecular Breeding. 2011;28:585–596. [Google Scholar]
  18. Li YY, Shen JX, Wang TH, et al. QTL analysis of yield-related traits and their association with functional markers in Brassica napus L. Australian Journal of Agricultural Research. 2007;58:759–766. [Google Scholar]
  19. Liao H, Rubio G, Yan X, Cao A, Brown KM, Lynch JP. Effect of phosphorus availability on basal root shallowness in common bean. Plant and Soil. 2001;232:69–79. [PubMed] [Google Scholar]
  20. Liu J, Yang JP, Li RY, et al. Analysis of genetic factors that control shoot mineral concentrations in rapeseed (Brassica napus) in different boron environments. Plant and Soil. 2009;320:255–266. [Google Scholar]
  21. Long Y, Shi J, Qiu D, et al. Flowering time quantitative trait loci analysis of oilseed Brassica in multiple environments and genomewide alignment with Arabidopsis. Genetics. 2007;177:2433–2444. doi: 10.1534/genetics.107.080705. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Marschner H. Mineral nutrition of higher plants. 2nd edn. London: Academic Press; 1995. [Google Scholar]
  23. Mei DS, Wang HZ, Hu Q, Li YD, Xu YS, Li YC. QTL analysis on plant height and flowering time in Brassica napus. Plant Breeding. 2009;128:458–465. [Google Scholar]
  24. Nakamura Y, Awai K, Masuda T, Yoshioka Y, Takamiya K, Ohta H. A novel phosphatidylcholine-hydrolyzing phospholipase C induced by phosphate starvation in Arabidopsis. The Journal of Biological Chemistry. 2005;280:7469–7476. doi: 10.1074/jbc.M408799200. [DOI] [PubMed] [Google Scholar]
  25. Niewiadomski P, Knappe S, Geimer S, et al. The Arabidopsis plastidic glucose 6-phosphate/phosphate translocator GPT1 is essential for pollen maturation and embryo sac development. The Plant Cell. 2005;17:760–775. doi: 10.1105/tpc.104.029124. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Paran I, Zamir D. titative traits in plants: beyond the QTL. Trends in Genetics. 2003;19:303–306. doi: 10.1016/S0168-9525(03)00117-3. [DOI] [PubMed] [Google Scholar]
  27. Parkin IAP, Gulden SM, Sharpe AG, et al. Segmental structure of the Brassica napus genome based on comparative analysis with Arabidopsis thaliana. Genetics. 2005;171:765–781. doi: 10.1534/genetics.105.042093. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Price AH. Believe it or not, QTLs are accurate! Trends in Plant Science. 2006;11:213–216. doi: 10.1016/j.tplants.2006.03.006. [DOI] [PubMed] [Google Scholar]
  29. Quarrie S, Pekic Quarrie S, Radosevic R, et al. Dissecting a wheat QTL for yield present in a range of environments: from the QTL to candidate genes. Journal of Experimental Botany. 2006;57:2627–2637. doi: 10.1093/jxb/erl026. [DOI] [PubMed] [Google Scholar]
  30. Quijada PA, Udall JA, Lambert B, Osborn TC. Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed (Brassica napus L.): 1. Identification of genomic regions from winter germplasm. Theoretical and Applied Genetics. 2006;113:549–561. doi: 10.1007/s00122-006-0323-1. [DOI] [PubMed] [Google Scholar]
  31. Radoev M, Becker HC, Ecke W. Genetic analysis of heterosis for yield and yield components in rapeseed (Brassica napus L.) by quantitative trait locus mapping. Genetics. 2008;179:1547–1558. doi: 10.1534/genetics.108.089680. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Raghothama K, Karthikeyan A. Phosphate acquisition. Plant and Soil. 2005;274:37–49. [Google Scholar]
  33. Sanguinetti CJ, Dias Neto E, Simpson AJ. Rapid silver staining and recovery of PCR products separated on polyacrylamide gels. Biotechniques. 1994;17:914–921. [PubMed] [Google Scholar]
  34. Schranz ME, Lysak MA, Mitchell-Olds T. The ABC's of comparative genomics in the Brassicaceae: building blocks of crucifer genomes. Trends in Plant Science. 2006;11:535–542. doi: 10.1016/j.tplants.2006.09.002. [DOI] [PubMed] [Google Scholar]
  35. Shi JQ, Li RY, Qiu D, et al. Unraveling the complex trait of crop yield with QTL mapping in Brassica napus. Genetics. 2009;182:851–861. doi: 10.1534/genetics.109.101642. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Shin H, Shin HS, Chen RJ, Harrison MJ. Loss of At4 function impacts phosphate distribution between the roots and the shoots during phosphate starvation. The Plant Journal. 2006;45:712–726. doi: 10.1111/j.1365-313X.2005.02629.x. [DOI] [PubMed] [Google Scholar]
  37. Tiessen H. Phosphorus in the global environment. In: White PJ, Hammond JP, editors. Ecophysiology of plant–phosphorus interactions. New York: Springer Science & Business Media; 2008. pp. 1–8. [Google Scholar]
  38. Udall JA, Quijada PA, Lambert B, Osborn TC. Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed (Brassica napus L.): 2. Identification of alleles from unadapted germplasm. Theoretical and Applied Genetics. 2006;113:597–609. doi: 10.1007/s00122-006-0324-0. [DOI] [PubMed] [Google Scholar]
  39. Van Ooijen JW. JoinMap®4·0: software for the calculation of genetic linkage maps in experimental populations. Wageningen: Kyazma BV; 2006. [Google Scholar]
  40. Vance CP, Uhde-Stone C, Allan DL. Phosphorus acquisition and use: critical adaptations by plants securing a nonrenewable resource. New Phytologist. 2003;157:423–447. doi: 10.1046/j.1469-8137.2003.00695.x. [DOI] [PubMed] [Google Scholar]
  41. Wang SC, Bastern J, Zeng ZB. Windows QTL Cartographer 2·5. Raleigh, NC: Department of Statistics, North Carolina State University; 2011. [Google Scholar]
  42. Wu J, Yuan YX, Zhang XW, et al. Mapping QTLs for mineral accumulation and shoot dry biomass under different Zn nutritional conditions in Chinese cabbage (Brassica rapa L. ssp. pekinensis ) Plant and Soil. 2008;310:25–40. [Google Scholar]
  43. Xu YB. Quantitative trait loci: separating, pyramiding, and cloning. Plant Breeding Reviews. 1997;15:85–139. [Google Scholar]
  44. Yan X, Wu P, Ling H, Xu G, Xu F, Zhang Q. Plant nutriomics in China: an overview. Annals of Botany. 2006;98:473–482. doi: 10.1093/aob/mcl116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Yang J, Hu CC, Hu H, et al. QTLNetwork: mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics. 2008;24:721–723. doi: 10.1093/bioinformatics/btm494. [DOI] [PubMed] [Google Scholar]
  46. Yang M, Ding GD, Shi L, Feng J, Xu FS, Meng JL. Quantitative trait loci for root morphology in response to low phosphorus stress in Brassica napus. Theoretical and Applied Genetics. 2010;121:181–193. doi: 10.1007/s00122-010-1301-1. [DOI] [PubMed] [Google Scholar]
  47. Yang M, Ding GD, Shi L, Xu FS, Meng JL. Detection of QTL for phosphorus efficiency at vegetative stage in Brassica napus. Plant and Soil. 2011;339:97–111. [Google Scholar]
  48. Zeng ZB. Precision mapping of quantitative trait loci. Genetics. 1994;136:1457–1468. doi: 10.1093/genetics/136.4.1457. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Zhang L, Yang G, Liu P, Hong D, Li S, He Q. Genetic and correlation analysis of silique-traits in Brassica napus L. by quantitative trait locus mapping. Theoretical and Applied Genetics. 2011;122:21–31. doi: 10.1007/s00122-010-1419-1. [DOI] [PubMed] [Google Scholar]
  50. Zhao J, Jamar DL, Lou P, et al. Quantitative trait loci analysis of phytate and phosphate concentrations in seeds and leaves of Brassica rapa. Plant, Cell and Environment. 2008;31:887–900. doi: 10.1111/j.1365-3040.2008.01791.x. [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supplementary Data
supp_109_4_747__index.html (1.3KB, html)
supp_mcr323_mcr323supp_figs.pdf (569.8KB, pdf)
supp_mcr323_mcr323supp_table1.pdf (1.1MB, pdf)
supp_mcr323_mcr323supp_table2.pdf (282.8KB, pdf)

Articles from Annals of Botany are provided here courtesy of Oxford University Press

RESOURCES