Copyright Ó 2009 by the Genetics Society of America DOI: 10.1534/genetics.109.101642
Unraveling the Complex Trait of Crop Yield With Quantitative Trait Loci Mapping in Brassica napus Jiaqin Shi,* Ruiyuan Li,* Dan Qiu,* Congcong Jiang,* Yan Long,* Colin Morgan,† Ian Bancroft,† Jianyi Zhao‡ and Jinling Meng*,1 *National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China, † John Innes Centre, Norwich Research Park, Colney, Norwich, NR4 7UH, United Kingdom and ‡Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China Manuscript received February 10, 2009 Accepted for publication April 21, 2009 ABSTRACT Yield is the most important and complex trait for the genetic improvement of crops. Although much research into the genetic basis of yield and yield-associated traits has been reported, in each such experiment the genetic architecture and determinants of yield have remained ambiguous. One of the most intractable problems is the interaction between genes and the environment. We identified 85 quantitative trait loci (QTL) for seed yield along with 785 QTL for eight yield-associated traits, from 10 natural environments and two related populations of rapeseed. A trait-by-trait meta-analysis revealed 401 consensus QTL, of which 82.5% were clustered and integrated into 111 pleiotropic unique QTL by metaanalysis, 47 of which were relevant for seed yield. The complexity of the genetic architecture of yield was demonstrated, illustrating the pleiotropy, synthesis, variability, and plasticity of yield QTL. The idea of estimating indicator QTL for yield QTL and identifying potential candidate genes for yield provides an advance in methodology for complex traits.
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IELD is the most important and complex trait in crops. It reflects the interaction of the environment with all growth and development processes that occur throughout the life cycle (Quarrie et al. 2006). Crop yield is directly and multiply determined by yieldcomponent traits (such as seed weight and seed number). Yield-related traits (such as biomass, harvest index, plant architecture, adaptation, resistance to biotic and abiotic constraints) may also indirectly affect yield by affecting the yield-component traits or by other, unknown mechanisms. Increasing evidence suggests that ‘‘fine-mapped’’ quantitative trait loci (QTL) or genes identified as affecting crop yield involve diverse pathways, such as seed number (Ashikari et al. 2005; Tian et al. 2006b; Burstin et al. 2007; Xie et al. 2008; Xing et al. 2008; Xue et al. 2008), seed weight (Ishimaru 2003; Song et al. 2005; Shomura et al. 2008; Wang et al. 2008; Xie et al. 2006, 2008; Xing et al. 2008; Xue et al. 2008), flowering time (Cockram et al. 2007; Song et al. 2007; Xie et al. 2008; Xue et al. 2008), plant height (Salamini 2003; Ashikari et al. 2005; Xie et al. 2008; Xue et al. 2008), branching (Clark et al. 2006; Burstin et al. 2007; Xing et al. 2008), biomass
Supporting information is available online at http://www.genetics.org/ cgi/content/full/genetics.109.101642/DC1. 1 Corresponding author: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China. E-mail:
[email protected] Genetics 182: 851–861 ( July 2009)
yield (Quarrie et al. 2006; Burstin et al. 2007), resistance and tolerance to biotic and abiotic stresses (Khush 2001; Brown 2002; Yuan et al. 2002; Waller et al. 2005; Zhang 2007; Warrington et al. 2008), and root architecture (Hochholdinger et al. 2008). Many experiments have explored the genetic basis of yield and yield-associated traits (yield components and yield-related traits) in crops. Summaries of identified QTL have been published for wheat (MacCaferri et al. 2008), barley (Von Korff et al. 2008), rice, and maize (http://www.gramene.org/). The results show several common patterns. First, QTL for yield and yield-associated traits tend to be clustered in the genome, which suggests that the QTL of the yield-associated traits have pleiotropic effects on yield. Second, this kind of pleiotropy has not been well analyzed genetically. The QTL for yield (complicated factor), therefore, have not been associated with any yield-associated traits (relatively simple factors, such as plant height). Therefore, they are unlikely to predict accurately potential candidate genes for yield. Third, only a few loci (rarely .10) have been found for each of these traits. Thus, the genetic architecture of yield has remained ambiguous. Fourth, trials were carried out in a few environments and how the mode of expression of QTL for these complex traits might respond in different environments is unclear. In this study, the genetic architecture of crop yield was analyzed through the QTL mapping of seed yield and eight yield-associated traits in two related populations of
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rapeseed (Brassica napus) that were grown in 10 natural environments. The complexity of the genetic architecture of seed yield was demonstrated by QTL metaanalysis. The idea of estimating indicator QTL (QTL of yield-associated traits, which are defined as the potential genetic determinants of the colocalized QTL for yield) for yield QTL in conjunction with the identification of candidate genes is described. MATERIALS AND METHODS Plant material, field experiments, and trait measurements: A population of doubled haploids (DHs) of 202 lines was developed by microspore culture from the F1 cross between Tapidor (a European winter cultivar) and Ningyou7 (a Chinese semi-winter cultivar) and named TNDH (Qiu et al. 2006). A reconstructed F2 (RC-F2) population was made from 101 crosses per round, between pairs of DH lines randomly chosen from the 202 lines of the TNDH population. In 2004 and 2005, three and four rounds of crossing by hand emasculation and pollination resulted in 303 and 404 crosses, respectively. The two populations and both parents were grown in five locations in China over 1, 3, or 4 years, for 10- and 3-year– location combinations for the DH and RC-F2 populations, respectively. Year–location combinations were treated as microenvironments. Microenvironment–population combinations were treated as experiments. These microenvironments were split into two contrasting macroenvironments (winter and semi-winter) and three agroecological areas (the Loess Plateau, the middle valley of the Yangtze River, and the lower valley of the Yangtze River) (supporting information, Table S1). Details of the climatic conditions during the growing season for each of the 10 microenvironments are described in Figure S1. The planting followed a randomized complete-block design with three replicates. Each plot was 3.0 m2 with 30 plants in all microenvironments (except S5, which was 4.0 m2 with 40 plants), with 40 cm between rows and 25 cm between individual plants. The seeds were sown by hand and the field management followed standard agricultural practice. In each plot, 9 representative individuals from the middle of each row in the S3, S4, N3, and N4 microenvironments, or 12 in the S5, S6, and N6 microenvironments, or all individuals in the S7, N7, and E7 microenvironments were harvested by hand from ground level at maturity. Seed yield and eight yield-associated traits were investigated. Flowering time was measured as the interval between the date of sowing and the date when the first flowers emerged on 50% of the plants in a plot. Maturity time was measured as the interval between the date of sowing and the date when pods on most of the plants in a plot were yellow. Plant height was the height of each harvested individual in a plot, measured from the base of the stem to the tip of the main shoot. Branch number was the number of branches arising from the main shoot of each harvested individual in a plot. Pod number was the number of well-filled, normally developed pods on each harvested individual in a plot. Seed number per pod was the average number of well-filled seeds from 100 well-developed pods, sampled from the primary branch in the middle of the harvested individuals in a plot. Seed weight was the average dry weight of 1000 well-filled seeds from three replicate samples, from the mixed seeds of the harvested individuals in a plot. Biomass yield per plant was measured as the average total above-ground (except the seeds) dry weight of the harvested individuals in a plot. Seed yield per plant was measured as the average dry weight of seeds of the harvested individuals in a plot.
Construction of the linkage map and alignment with the genome of Arabidopsis: A total of 786 markers were mapped to the new linkage map generated with the TNDH population using JoinMap 3.0 (http://www.kyazma.nl/index.php/ mc.JoinMap). This covered 19 chromosomes identified as A1–A10 and C1–C9, with an average distance of 2.7 cM between markers (Table S2 A). The threshold for goodness of fit was set to #5.0 with logarithm of the odds ratio (LOD) scores .1.0 and a recombination frequency ,0.4. The order of the markers on the linkage map agreed well with our published maps (Qiu et al. 2006; Long et al. 2007). The genotype of each RC-F2 line was deduced from the corresponding genotype of their parents. Two hundred and seventy-seven markers with known sequence information were employed as anchored markers for the map alignment between B. napus and Arabidopsis thaliana, on the basis of comparative mapping reported previously (Parkin et al. 2005). Details of the alignment are descried in Long et al. (2007). Several syntenic blocks or insertion fragments (islands) were identified between the Arabidopsis genome and the TNDH linkage map (Table S2 B). A total of 425 genes of Arabidopsis with known functions relating to flowering time, plant height, branch number, and other traits investigated in this study were collected from the TAIR website (http://www.arabidopsis.org/) and published articles (Table S2 C). These genes were located on each syntenic block according to their physical positions in the genome of Arabidopsis. The positions of putative genes were aligned to the TNDH linkage map according to their closest anchored markers in the same syntenic block (Table S2 D). Statistical analysis, QTL mapping, and meta-analysis: Environment was treated as a fixed effect, while genotype (DH or RC-F2 lines) was treated as a random effect. The broad-sense heritability was calculated as: h2 ¼ b2g/(b2g 1 b2ge/n 1 b2e/nr), where b2g is the genotypic variance, b2ge is the interaction variance of genotype with environment, b2e was the error variance, n was the number of environments, and r was the number of replicates. The genetic correlation was calculated as: rG ¼ covGxy /(b2Gx 3 b2Gy )1/2, where covGxy , b2Gx , and b2Gy were the genotypic covariance and variance of the pairwise traits, respectively. The significance of each genetic correlation was determined using a t-test of the correlation coefficients (Kong 2005). Estimates of components of variance and covariance were obtained using the SAS general linear model (GLM) procedure (Sas Institute 2000). QTL were detected by composite interval mapping (Zeng 1994) using WinQTL cartographer 2.5 software (http:// statgen.ncsu.edu/qtlcart/WQTLCart.htm). The number of control markers, window size, and walking speed were set to 5, 10 cM, and 1 cM, respectively. The default genetic distance (5 cM) was used to define a QTL in an experiment. The experimentwise LOD threshold was determined by permutation analysis (Churchill and Doerge 1994) with 1000 repetitions. LOD scores corresponding to P ¼ 0.05 (2.5–3.2 for DH and 3.8–4.4 for the RC-F2 population) were used for identifying significant QTL. To avoid missing QTL with very small effects, a lower LOD score corresponding to P ¼ 0.50 (2.1–2.3 for the DH population and 2.9–3.1 for the RC-F2 population) was adopted for the presence of suggestive QTL. The overlapping suggestive QTL and all significant QTL were admitted (Long et al. 2007) and named as identified QTL. Since QTL of the same or related traits, which were detected in different experiments and mapped to the same region of a chromosome, might be several estimates of the position of a single QTL, algorithms for QTL meta-analysis were used to estimate the number and positions of the meta-QTL underlying the analyzed QTL (Goffinet and Gerber 2000). This approach provides a modified Akaike criterion that can be
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TABLE 1 Phenotypic variation of the two parents for seed yield and eight yield-associated traits in two macroenvironments
Trait
Branch no.
Biomass yield (kg/ha)
Flowering time (days)
Maturity time (days)
Plant height (cm)
Pod no.
Seed no.
Seed weight (g/1000)
Seed yield (kg/ha)
Tapidor 9.5 6 1.5 Ningyou7 7.8 6 1.1 P-valuea 0.020
4950 4101 –
In four winter-type microenvironments 198 6 3 251 6 2 146 6 17 503 6 97 189 6 7 242 6 6 124 6 16 361 6 78 0.013 0.010 0.001 0.008
17.8 6 6.2 2.60 6 0.40 2188 6 373 15.7 6 5.1 3.09 6 0.61 1715 6 318 0.038 0.030 0.000
Tapidor 4.8 6 1.3 Ningyou7 8.5 6 2.5 P-value 0.036
2939 3617 –
In six semi-winter-type microenvironments 174 6 6 224 6 5 117 6 14 220 6 23 161 6 10 216 6 6 141 6 15 394 6 63 0.002 0.001 0.002 0.002
11.1 6 3.2 2.66 6 0.12 972 6 261 13.3 6 2.9 3.51 6 0.29 1714 6 494 0.002 0.000 0.005
Tapidor 7.1 6 3.3 3945 6 1422 Ningyou7 8.1 6 0.5 3859 6 342 P-value 0.774 0.929 a
In two macroenvironments (winter/semi-winter) 186 6 17 237 6 19 131 6 21 362 6 200 14.5 6 4.7 2.63 6 0.04 1580 6 860 175 6 20 229 6 18 132 6 12 377 6 23 14.5 6 1.7 3.30 6 0.30 1714 6 1 0.023 0.029 0.97 0.94 0.985 0.036 0.466
P-values obtained by t-test.
used to determine the number of meta-QTL that best fitted the results on a given linkage group. It also groups the QTL that were detected in the different experiments into classes that correspond to the same QTL and provides a consensus estimate of QTL positions. Computations were conducted using the BioMercator 2.1 software (Arcade et al. 2004). At present, the method used in this software cannot distinguish between models with more than four meta-QTL on the same linkage group. If the estimated number of meta-QTL is more than four, Biomercator 2.1 declares that the most probable model is one with a number of meta-QTL that is equal to the number of analyzed QTL. The delete function of the software was used to select specific segments of a linkage group separated by regions with no QTL, and to apply QTL metaanalysis to these segments separately. The software also provides a method for calculating 95% confidence intervals for the meta-QTL, 3:92 ffi; C:I: ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pi¼k 2 ð1=S Þ i¼1 i where S2i is the variance of position of the QTLi, and k is the total number of QTL integrated into the meta-QTL. A two-round strategy of QTL meta-analysis was adopted. The QTL identified in different experiments were first integrated into consensus QTL, trait by trait (Figure 1A). Two types of consensus QTL were defined: major QTL (those occurring at least once with R 2 $20% or at least twice with R 2 $ 10%), and minor QTL (the remainder with relatively small effect) (Price 2006; MacCaferri et al. 2008). In the second round of QTL meta-analysis, the consensus QTL for the different traits were integrated into unique QTL (Figure 1B). For each unique QTL in which a consensus QTL for seed yield was integrated, one or more consensus QTL of other yield-associated traits were chosen as indicator QTL, which were defined as potential genetic determinants of the colocalized QTL for seed yield. The criteria for selecting indicator QTL were the magnitude of the LOD scores, the number of experiments in which they were detected, the overlapping of the confidence interval, and the number of common environments within which the two QTL (i.e., the QTL for yield-associated traits and those for seed yield) were identified.
RESULTS
Phenotypic variation and correlation among traits and across environments: The two parents (Tapidor and Ningyou7) differed significantly for most trait– environment combinations (Table S3). Ningyou7, a semi-winter cultivar, had larger seeds and flowered and matured earlier than the winter cultivar Tapidor, in all microenvironments. However, for the other six traits, the performance of the two parents in the two macroenvironments (winter and semi-winter) was reversed, such that the means in the two macroenvironments were similar (Table 1). This reflects the adaptability of the two cultivars to the different macroenvironments (e.g., the climatic factors, Figure S1). Normal or near-normal distribution (data not shown) with wide transgressive segregation was observed in both populations (DH and RC-F2), for all traits in all microenvironments (Table S3), which indicated a quantitative genetic control. Most pairwise genetic correlations between the two populations within the same microenvironment differed little in degree (Table S4 A–J). In contrast, pairwise genetic correlations in different microenvironments differed considerably (mostly in degree, a few in direction), which suggested that genetic correlation depended strongly on the environment. Genetic correlations among traits were also calculated across environments for both populations (Table 2). In general, seed yield was correlated with all investigated traits in both populations: negatively for flowering and maturity times, and positively for the other six traits. As expected, all pairwise combinations among the three yieldcomponent traits (pod number, seed number, and seed weight) were significantly negatively correlated, which suggested competition among the sinks for assimilates. Significant correlations were also observed for most
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J. Shi et al. TABLE 2 Genetic correlations and broad-sense heritability in DH (below diagonal) and RC-F2 (above diagonal) populations
Trait
Branch no.
Biomass Flowering Maturity Plant yield time time height
Branch no. 0.41*** Biomass yield 0.30*** Flowering time 0.24** 0.24** Maturity time 0.35*** 0.27*** Plant height 0.30*** 0.64*** Pod no. 0.51*** 0.58*** Seed no. 0.01 0.03 Seed weight 0.05 0.07 Seed yield 0.46*** 0.71*** Mean r2 0.10 0.18 Broad-sense 0.71 0.67 heritability
Pod no.
0.25*** 0.29*** 0.36*** 0.36*** 0.05 0.04 0.70*** 0.62*** 0.63*** 0.19* 0.00 0.71*** 0.08 0.05 0.22** 0.07 0.34*** 0.06 0.09 0.34*** 0.05 0.17* 0.05 0.35*** 0.25*** 0.06 0.04 0.22** 0.36*** 0.25*** 0.37*** 0.60*** 0.11 0.10 0.10 0.16 0.93 0.86 0.78 0.65
Seed no.
Seed weight
Seed yield
0.16* 0.08 0.46*** 0.07 0.23** 0.73*** 0.19* 0.19* 0.33*** 0.32*** 0.19* 0.29*** 0.08 0.15* 0.44*** 0.31*** 0.31*** 0.66*** 0.35*** 0.36*** 0.33*** 0.25*** 0.46*** 0.24** 0.06 0.04 0.21 0.81 0.90 0.60
Broad-sense Mean r2 heritability 0.10 0.20 0.08 0.09 0.13 0.16 0.07 0.05 0.22
0.61 0.69 0.89 0.78 0.71 0.61 0.76 0.82 0.55
Mean r2 is the average determination coefficient of the trait with the other eight traits. *P # 0.05; **P # 0.01; ***P # 0.001, respectively.
pairwise combinations among the five yield-related traits, and for half of the pairwise combinations between the five yield-related and three yield-component traits. Seed yield, among all the investigated traits, notably showed the highest correlation with the other eight traits, and on average, showed the highest coefficients of determination (mean r2 ¼ 21 and 22% for DH and RCF2 populations, respectively). The broad-sense heritability of the DH population was slightly higher than that of the RC-F2 population for all investigated traits, except biomass yield, and varied from 60% and 55% (seed yield) to 93% and 89% (flowering time), respectively (Table 2). Genomewide detection, meta-analysis, and expression mode of QTL from multiple environments: An analysis of variance indicated that, despite strong genotypic and environmental effects for all investigated traits, there were significant genotype 3 environment interactions (Table S5). Consequently, each environment and population was analyzed separately for QTL. A total of 1022 QTL (614 significant QTL and 408 suggestive QTL) were detected for the nine traits in both populations in the 10 microenvironments. After deleting 152 nonoverlapping suggestive QTL (Table S6 A), a total of 870 identified QTL were identified for the nine traits, of which nearly 10% (85) were for seed yield (Table 3). The identified QTL can explain 2.2–33.4% of the phenotypic variance, although the majority (84%) showed only moderate effects, with R2 , 10% (Table S6 A). Most of the Tapidor alleles in the identified QTL increased flowering and maturity times but decreased seed weight, which accorded well with the performance of the two parents for the traits (Table 3). Where the confidence intervals of the identified QTL for each trait in different experiments overlapped (Table S6 B), meta-analysis integrated these identified QTL into single consensus QTL trait by trait (Figure 1A). As a result, the 694 overlapping identified QTL
were integrated into 225 reproducible consensus QTL with reduced confidence intervals, from an average of 6.5 to 3.7 cM (Table 3). The proportion of the reproducible consensus QTL was highest (80.7% ¼ 46/ 57) for flowering time and lowest (36.4% ¼ 20/55) for seed yield, a result that reflected the heritability of these traits. Nearly half of the 401 consensus QTL for the nine traits, particularly those for seed yield, were specifically detected in 1 of the 10 microenvironments, and no consensus QTL, except 2 for flowering time, appeared consistently in .8 microenvironments (Figure 2A). It is interesting that the proportions of the consensus QTL for the three traits (flowering time, maturity time, and seed weight) specific to the macroenvironments were all lower than for the other six traits. This was in accordance with the variation in parental performance for these traits in the two macroenvironments. Two-thirds of the consensus QTL were detected in either the winter or semi-winter macroenvironment, and only one-third of the consensus QTL appeared in both. This strongly suggested that more than half of the QTL were expressed principally in response to the specific environment in which the population was grown. For example, the three major QTL clustered on the C7 linkage group (qSY.C7-2, qSY.C7-3, and qSY.C7-4) were all specific to the semi-winter macroenvironment. They were strongly expressed in Hangzhou (in the E7 microenvironment; LOD scores .10; R2 . 20%) and two of them were weakly expressed in Wuhan (in the S4 and S7 microenvironments, respectively; LOD scores ,3; R2 ,5%) (Figure 2B and Table S6 B). Wuhan and Hangzhou are in the agroecological area of the middle and lower valley, respectively, of the Yangtze River, both of which are semi-winter macroenvironments in China (Table S1). The favorable alleles come from the semi-winter parent Ningyou7 (which was bred near Hangzhou), which indicated that these major QTL played a key role
b
a
Maturity time
57 (4.4) 46 3.8 6 11 23 47
6.1
6.9
87 (8.7) 63 (6.3) 24 (2.4) 341/53 7.2–3.8 3.0–16.2 70
Plant height
Consensus QTL 52 (3.8) 44 (4.4) 36 27 3.6 3.5 1 0 20 20 28 36 43 38
6.8
Identified QTL 201 (15.2) 126 (9.5) 143 (11) 84 (6.5) 58 (4.2) 42 (3.0) 1641/37 941/32 2.7–2.3 0.90–0.95 2.2–20.8 2.3–15.5 190 110
Flowering time
35 (3.5) 14 3.6 0 22 29 29
5.9
50 (5.0) 35 (3.5) 15 (1.5) 291/21 45–38 3.4–13.7 29
Pod no.
35 (3.5) 21 3.8 0 17 25 28
5.7
69 (6.9) 49 (4.9) 20 (2.0) 381/31 1.81–1.32 3.2–15.5 55
Seed no.
Plus (1) and minus () represent the allele increasing the phenotype value from Tapidor and Ningyou7, respectively. The number of the pleiotropic unique QTL obtained after meta-analysis of 329 consensus QTL.
20/5 3 4.6 0 19 19 19
7.0 44 (4.4) 19 3.8 0 25 33 34
7.7
70 (7.0) 53 (5.3) 17 (1.7) 331/37 0.73–0.61 3.7–18.0 45
Total (mean per experiment) At ‘‘significant’’ level At ‘‘suggestive’’ level Additive-effect direction (1/)a Additive-effect range R 2 (%) range Integrated into reproducible consensus QTL Average confidence interval (cM)
Total (mean per experiment) Reproducible QTL Average confidence interval (cM) Major QTL Microenvironment specific QTL Macroenvironment specific QTL Integrated into pleiotropic unique QTL
23 (5.8) 21 (5.3) 2 (0.5) 111/12 322–342 3.1–11.4 6
Branch no.
Trait
Biomass yield
59 (5.0) 39 3.3 4 22 35 45
6.5
159 (13.3) 109 (9.1) 50 (4.2) 501/109 0.21–0.17 2.4–20.5 139
Seed weight
Overview of identified QTL and consensus QTL for seed yield and eight yield-associated traits
TABLE 3
55 (4.2) 20 4.3 4 35 44 47
6.9
85 (6.5) 57 (4.4) 28 (2.1) 371/48 326–238 3.1–33.4 50
Seed yield
401 (4.2) 225 3.7 15 191 272 329 (111b)
6.5
870 (9.2) 614 (6.5) 256 (2.7) 4901/380 – 2.2–33.4 694
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Figure 1.—Demonstration of the process and result of two rounds of QTL meta-analysis for QTL mapped on 30- to 60-cM segments of A2 linkage group. (A) Thirteen identified QTL for seed yield detected from five experiments were integrated into four consensus QTL (which are shown with gray bars on the left) with shorter confidence intervals after the first round of metaanalysis. (B) The four consensus QTL for seed yield along with other consensus QTL for the four traits, flowering time, maturity time, plant height, and seed number discriminated with different color, were integrated into four unique QTL with further reduced confidence intervals after the second round of meta-analysis.
in regulating the adaptability of Ningyou7 to the corresponding semi-winter agroecological area. Dissection of pleiotropic unique QTL and identification of candidate genes responsible for seed yield: Most of the consensus QTL determined for each trait overlapped with those determined for other traits, as shown on linkage groups A1 and C6 (Figure 3 and Figure S2). For example, a consensus QTL for seed yield (qSY.A2-2), detected in several microenvironments, colocalized precisely with three consensus QTL for flowering time, maturity time, and plant height (qFT.A2-4, qPH.A2-2, and qMT.A2-2, respectively) in a small region of the A2 linkage group (Figure 4). These consensus QTL were therefore subjected to the second round of meta-analysis (Figure 1B), which resulted in the integration of 329 overlapping consensus QTL into 111 pleiotropic unique QTL (Table 3) with reduced average putative confidence intervals of 2.5 cM (Table S6 C). The 55 consensus QTL for seed yield were classified into two types: (i) eight nonoverlapping QTL and (ii) 47 overlapping QTL, which were integrated into pleiotropic unique QTL with shorter average confidence intervals of 2.5 cM (Table S6 C). Confidence intervals for the new QTL were reduced by 2.5 and 1.8 times, compared with the average confidence intervals of the
85 identified QTL and 55 consensus QTL for seed yield (6.2 cM and 4.5 cM, respectively; Table S6 B). The two rounds of meta-analysis resulted in a more precise estimate of the QTL for seed yield. One to four consensus QTL of the other eight yield-associated traits were chosen as indicator QTL, which potentially controlled seed yield for each of the 47 pleiotropic unique QTL (Figure S2 and Table S6 C). The total of 63 indicator QTL could be associated with any of the eight yieldassociated traits, but the highest proportion was associated with plant height and the lowest proportion with maturity time (Table 4). Indirect evidence that indicator QTL influenced seed yield came from the extent to which the confidence intervals of the indicator QTL overlapped those of consensus QTL for seed yield, from an average of 71.9% to 92.5% (total mean ¼ 80.9%) for different traits (Table 4). In addition, the average distance between peak positions of the indicator QTL and consensus QTL for seed yield was small: 1.1 cM on average. Furthermore, in some cases, the putative change in the value of seed yield estimated from the effect of indicator QTL was close to the additive effect of the corresponding QTL for seed yield. For example, the additive effect (a ¼ 172 kg/ha) of the QTL for seed yield, qSY.A1-4, was about
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Figure 2.—Expression response of 401 consensus QTL in natural environments. (A, top) Number of consensus QTL appeared in 1 to 10 (all) microenvironments. (A, bottom) Number of consensus QTL appeared in winter, semi-winter, or both macroenvironments. Most consensus QTL appeared in only one or two microenvironments, while a few were detected repeatedly in more than five. (B) Detailed view of QTL for seed yield detected in a 34.6-cM region of the C7 linkage group. All three consensus QTL were strongly expressed in the E7 microenvironment, two of which were weakly expressed in the S4 and S7 microenvironments, respectively.
half that (a ¼ 328 kg/ha) of its indicator QTL, qBY.A1-2 (a QTL for biomass yield), which was involved in the pleiotropic unique QTL, uq.A1-9 (Table S6 C). This ratio (172/328 ¼ 52.1%) was close to the general ratio (57.1%; J. Shi, unpublished data) of seed yield to biomass yield in the corresponding experiment N6R. More importantly, the additive effect direction of the QTL for seed yield is generally opposite () to those of the corresponding indicator QTL for the two traits (flowering and maturity times), but mostly the same (1) for the other six traits. This is in accordance with the signs of the genetic-correlation coefficients of seed yield with the above eight yield-associated traits (Table 2).
On the basis of comparative mapping with the genome of Arabidopsis, candidate genes were located in the confidence interval of the reproducible (for the higher stringency) consensus QTL. Some candidate genes underlay the indicator QTL that potentially control seed yield (Table S6 C) and thus are potential candidate genes for seed yield. For example, two genes for flowering time, ZTL and VIN3, were located in the confidence interval of the QTL for flowering time, qFT.A2-4, which had been chosen as the indicator QTL of the QTL for seed yield, qSY.A2-2, and were thus potential candidate genes for seed yield (Figure 4).
Figure 3.—Distribution of 401 consensus QTL for seed yield and eight yield-associated traits on 19 linkage groups. The abscissa represents the 19 linkage groups that were divided into 10-cM bins and the ordinate represents the number of QTL. The long dotted line in the middle shows the expected QTL number in each bin at the hypothesis of even distribution. The black triangles on the bottom axis indicate the first bin of each linkage group.
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Figure 4.—Dissection of the overlapping QTL cluster for seed yield and yield-associated traits in a 7.2-cM region on linkage group A2. Identified QTL are indicated as LOD curves. Confidence intervals (CI) for each identified QTL are shown as horizontal lines under the curves with the same color and style as the QTL. The CIs for consensus QTL, estimated after the first round of metaanalysis, are shown in the same way in which the CI for indicator QTL is shown as a line with arrowheads. After the second round of metaanalysis, the CI for the unique QTL of seed yield was 1.2 cM, having been reduced from an average of 4.3 cM for identified QTL and 2 cM for consensus QTL. Pseudochromosome fragments of Arabidopsis (fragment B in chromosome 1 and fragment E in chromosome 5), aligned by in silico comparative mapping, are shown in the lower part of the figure. The candidate genes, VIN3 and ZTL, underlying the unique QTL, estimated from the indicator QTL for flowering time, are shown at the bottom.
DISCUSSION
Although much research into the genetic basis of yield and yield-associated traits has been reported, the genetic architecture of yield has remained ambiguous because few QTL have been identified in each such experiment. Here, we report 870 identified QTL: 85 for seed yield and 785 for eight yield-associated traits. These QTL were detected on a single genetic map of B. napus in two related populations grown in 10 natural environments. Three key factors for successful screening for QTL with high resolution are a high-density genetic map to separate multiple QTL ( Jiang and Zeng 1995), a large population, and replicated experiments in
multiple environments to assess genotype 3 environment interactions in the expression of QTL. The analysis of large numbers of QTL, however, is problematic. Meta-analysis originally integrated QTL of the same and/or related traits from different populations on the basis of an integrated map (Arcade et al. 2004). Due to variability in order and genetic distance of the common markers among the different linkage maps, the accuracy of the integrated map and estimate of the number and position of meta-QTL were equivocal (Veyrieras et al. 2007). We adopted meta-analysis to combine QTL detected in multiple environments and related populations for multiple yield-associated traits, however, based
TABLE 4 The character and indirect evidence that indicator QTL influence seed yield
Trait
Branch Biomass Flowering Maturity no. yield time time
No./proportion (%) of consensus 5/11.4 4/20.0 QTL chosen as indicator QTL 1.8 1.1 Average distance between peak position of indicator QTL and seed yield QTL (cM) 80.1 78.3 Average proportion (%) of overlapped confidence interval between indicator QTL and seed yield QTL 41/1 41 Coincidence (1)/opposition () between additive-effect direction of indicator QTL and seed yield QTL
Plant height
Pod no.
Seed no.
Seed weight
Total or mean
13/29.5 5/14.3 8/22.9 7/11.9
63/18.2
16/28.1
5/9.6
1.0
1.0
1.1
0.8
1.0
1.1
1.1
81.8
92.5
79.5
80.4
71.9
83.6
80.9
41/12
11/4 121/1 41/1
81
61/1 431/20
Unraveling the QTL for Yield in Crops
on a single linkage map. This increased the accuracy of the estimated positions of the meta-QTL and facilitated the dissection of the genetic architecture of yield. Nearly 80% of the 401 consensus QTL were from only one or two specific microenvironments, and no consensus QTL, except one for flowering time (qFT.C6-4), appeared in all microenvironments (Figure 2A). This QTL has not been detected in two spring-cropped microenvironments with the same population (Long et al. 2007). This suggests that few QTL are likely to be expressed universally. The high proportion of environmentspecific QTL indicates the large impact the natural environment has on the genes underlying seed yield and yield-associated traits. One hundred and twenty-nine of the 225 reproducible consensus QTL were found in the two macroenvironments (Table S6 B). These QTL are likely to be important targets for marker-assisted selection and the development of varieties with wide adaptability. Another 96 reproducible consensus QTL were only detected in either the winter or semi-winter macroenvironment. These QTL are also likely to be important targets for the development of varieties that have special adaptability. Thirteen of the 15 major QTL were repeatedly found 2–10 times (mean ¼ 7) in the different experiments (Table S6 B). Fine mapping these QTL and validating the potential candidate genes is a reliable and feasible strategy for QTL cloning. The clusters of major QTL for flowering time on linkage group C6 have been dissected successfully into several sub-QTL through substitution mapping ( J. Wang, unpublished data). More than half of the 386 minor QTL were also repeatedly identified 2–10 times. Cloning such QTL via fine mapping may be unlikely, but the application of bioinformatics (such as meta-analysis, comparative genomics, and haplotype association mapping) and/or transcriptomics to narrow QTL/QTG (quantitative trait genes) without fine mapping seems promising (Price 2006; Burgess-Herbert et al. 2008; Norton et al. 2008). Recently, four minor QTL for disease resistance in rice, each explaining ,5% of the phenotypic variation, have been isolated successfully by a strategy involving candidate genes that integrated expression profiling, bioinformatics, and functional complementation analysis (Hu et al. 2008). In other words, a minor QTL might be a major QTL if the variability of the parental alleles is sufficiently large and/or the environment allows the induction of differential expression of the two alleles. Therefore, the minor QTL that were repeatedly detected also provide an important genetic resource for marker-assisted selection, cloning of QTL, and the functional analysis of genes. Another important feature of the consensus QTL was the high proportion (82% ¼ 329/401) that colocalized at the genomic level. Forty-seven of the 55 QTL for seed yield colocalized with other QTL. Additionally, six of
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eight independent QTL for seed yield overlapped other QTL for traits not published in this article, i.e., glucosinolate content in seeds ( J. Feng, unpublished data) and resistance to Sclerotinia sclerotiorum (C. Jiang, unpublished data). These colocalizations indicate that the 55 QTL for seed yield are all dependent on the QTL of other yield-associated traits. This is not surprising, given that yield is determined by the cumulative effect of many processes/traits of growth and development, from sowing to harvest, when the conventional yieldassociated traits become dominant. Most functional genes in the genome, therefore, might contribute directly or indirectly to the trait of yield (Slafer 2003). When a locus controlling a trait for a component of yield (e.g., seed weight) or a yield-related trait (e.g., plant height) shows a phenotypic difference resulting from allelic variation between the parents at that locus, a detected QTL for yield might be the result of gene pleiotropy (Li et al. 1997). In other words, QTL for yield may result from pleiotropic QTL: genomic regions that affect multiple traits by containing multiple, tightly linked, trait-specific genes or genes that affect multiple traits (Hall et al. 2006). Most published fine-mapped QTL and the genes identified as affecting yield exhibit pleiotropic effects on at least one trait (Peng et al. 1999; Brown 2002; Yuan et al. 2002; Li et al. 2004; Ashikari et al. 2005; Waller et al. 2005; Clark et al. 2006; Tian et al. 2006a; Cockram et al. 2007) or multiple yieldassociated traits (Quarrie et al. 2006; Xie et al. 2006, 2008; Burstin et al. 2007; Song et al. 2007; Shomura et al. 2008; Xue et al. 2008). Even a short region with a confidence interval of 0.4 cM and a physical distance of 37.4 kb has revealed pleiotropic effects between seven QTL of different yield-associated traits and the QTL of grain yield in rice (Xie et al. 2008). Seed yield showed the highest correlation with the other eight traits (mean r2 ¼ 21 and 22% for DH and RCF2 populations, respectively), and a high percentage (85%) of QTL for seed yield colocalized with QTL for other yield-associated traits. These findings indicate that the QTL of yield-associated traits are potential contributors to the colocalized QTL for seed yield. On average, a QTL for seed yield involved 2.5 QTL of yield-associated traits, and at least one QTL for all eight yield-associated traits involved the QTL for seed yield, qSY.A1-4 (Table S6 C). To estimate which QTL of yieldassociated trait(s) were more likely to have pleiotropic effects on seed yield at a particular locus, the idea of indicator QTL was proposed to identify the probable genetic determinant of the colocalized QTL for seed yield. These indicator QTL are determined by their larger LOD scores, reproducibility, overlapping confidence intervals, and presence in common environment(s) with the QTL for seed yield. There is indirect evidence that supports the idea that pleiotropy is likely to be the genetic cause of the colocalization of indicator QTL and seed yield QTL: (1) the peak positions of the
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two kinds of QTL are very close to each other; (2) the proportion of the overlapping confidence intervals of the two kinds of QTL is very high; (3) the additive-effect directions of the seed yield QTL are generally opposite () to that of indicator QTL for two traits (flowering time and maturity time) while mostly the same (1) for the other six traits, which accorded well with the signs of the genetic-correlation coefficients of seed yield with the above eight yield-associated traits; (4) in many cases, the additive effect of the seed yield QTL was very near to the putative change in the value of seed yield that was estimated from the additive effect of the indicator QTL. Indicator QTL, from easily measured traits (such as flowering time and seed weight), usually had more stable expression, higher LOD scores, larger R 2 values, and identifiable candidate genes than the colocalized QTL for seed yield (Table S6 C). Indicator QTL will thus facilitate the cloning of these QTL for seed yield, assuming that pleiotropy and not tight linkage is the cause of the colocalization of the indicator QTL with the corresponding QTL for seed yield (MacCaferri et al. 2008). For example, the cloning of qSY.A2-2, a major QTL for seed yield, should be facilitated using its indicator QTL qFT.A2-4. This QTL is a major QTL for flowering time with a large genetic effect, stable expression in all six semi-winter microenvironments, and available candidate genes (Figure 4). Flowering time is also measured more easily and precisely than seed yield. The successful cloning of QTL for yield in rice (Xue et al. 2008) and wheat (Quarrie et al. 2006) was facilitated by indicator QTL: a QTL for flowering time and a QTL for biomass yield, respectively. Twenty-nine of 47 pleiotropic QTL for seed yield colocalized with more than two consensus QTL. This indicated that, in addition to pleiotropy, the effect of the QTL for seed yield could be a synthetic effect of several underlying tightly linked QTL of different yieldassociated traits. A previously mapped QTL may be dissected into several sub-QTL after subsequent fine mapping (Ashikari et al. 2005; Thomson et al. 2006; Christians and Senger 2007). Multiple indicator QTL were chosen for 14 pleiotropic QTL for seed yield, half of which involved QTL for seed yield in different environments. For example, qSY.A2-4 colocalized two indicator QTL (qPH.A2-4 and qSN.A2-1) in two (S4 and S6) and one (N6) microenvironments, respectively (Table S6 C). Multiple indicator QTL from different environments also revealed that environmental conditions contribute strongly to the variability and plasticity of QTL for yield. In conclusion, tens of QTL for seed yield and hundreds of QTL for yield-associated traits were identified from multiple environments and populations using a single genetic map in B. napus. The complexity of the genetic architecture of yield was demonstrated by meta-analysis, illustrating the pleiotropy, synthesis, variability, and plasticity of yield QTL. The assignment of
indicator QTL enabled the QTL for seed yield to be associated with specific yield-associated trait(s) in specific environment(s). In addition, in silico comparative mapping aligned many orthologs from the genome of Arabidopsis into particular regions of the reproducible QTL with tighter confidence intervals. Yield, the most complex trait in crops, was unraveled into tens of unique QTL indicated by one or multiple indicator QTL, with potential underlying candidate genes. The idea of estimating indicator QTL for yield QTL and identifying potential candidate genes for yield provides an advance in methodology for complex traits. The authors thank Eddie Arthur for his critical reading of the manuscript. The authors also thank Dianrong Li and Hao Wang (Hybrid Rapeseed Research Center of Shaanxi, Dali 715105, China) for the field work and collecting the phenotypic data. Financial support for this work was provided by the National Basic Research and Development Program (2006CB101600).
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Communicating editor: A. H. Paterson
Supporting Information http://www.genetics.org/cgi/content/full/genetics.109.101642/DC1
Unraveling the Complex Trait of Crop Yield With Quantitative Trait Loci Mapping in Brassica napus Jiaqin Shi, Ruiyuan Li, Dan Qiu, Congcong Jiang, Yan Long, Colin Morgan, Ian Bancroft, Jianyi Zhao, Jinling Meng
Copyright © 2009 by the Genetics Society of America DOI: 10.1534/genetics.109.101642
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J. Shi et al.
B 28
Monthly mean temperature (!) S3 S4 S5 S6 S7 E7 Semi-winter N3 N4 N6 N7 Winter
25 22 19 16 13 10 7 4 1 -2 Sep
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C
3 SI
Monthly maximum temperature (!)
34
S3 S4 S5 S6 S7 E7 Semi-winter N3 N4 N6 N7 Winter
31 28 25 22 19 16 13 10 7 4 Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
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Jun
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D 25
Monthly minimum temperature (!) S3 S4 S5 S6 S7 E7 Semi-winter N3 N4 N6 N7 Winter
20
15
10
5
0
-5
-10 Sep
Oct
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E300
5 SI
Monthly sunshine (hr) S3 S4 S5 S6 S7 E7 Semi-winter N3 N4 N6 N7 Winter
270 240 210 180 150 120 90 60 30 Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
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F 90
Monthly relative humidity (%)
80 S3 S4 S5 S6 S7 E7 Semi-winter N3 N4 N6 N7 Winter
70
60
50
40 Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
J. Shi et al.
G400
7 SI
Monthly rainfall (mm) S3 S4 S5 S6 S7 E7 Semi-winter N3 N4 N6 N7 Winter
350
300
250
200
150
100
50
0 Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
FIGURE S1.—The main climate factors in the rapeseed growing period in 10 microenvironments. (A) Showing the length of three main growing periods in ten microenvironments (S3-7,E7,N3-4,N6-7). ( B-G) The changes of climatic factors, calculated monthly, in different microenvironments and macroenvironments. Fine lines with different colors represent the different microenvironments in the south (red), north (blue) and east (pink) of China, and that with different styles represent different years. The two macroenvironments, winter and semi-winter, were shown with bolded lines.
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Biomass-yield Branch-number Flowering-time2W+ Maturity-time Plant-height Pod-number Seed-number Seed-weight Seed-yield A03
B C
Seed-number 2B+ Seed-weight Seed-yield A04
1W2W+
1W1S+ 4B+
4S+
2B± 2S-
4S-
5B-
1S+ 1S+ 1S+ 2S+ 3S+ 2S+ 4B+ 1S+4B+2B+ 2S2B+ 2S+ 3S+ 1W+ 2S± 2S- 3B- 4B2S-
1S+ 3B1S+
Consensus-QTL
1S- 1S+
2S+
1S-
A
E
BC E
C
B
A
A A
2S+
2S6B- 3B- 4B1S1S-
2S-
D
B
2B±
4B-
ADED
B A
D C D
B
C
B
Unique-QTL 135.7cM
2S±
2S+ 4S-
C
1S+ 2S-
2B+
Consensus-QTL
1S+ 1S2S-
Unique-QTL 125.0cM C
C
1S+ 2B+
3S-
2S+
2S-
A A
1S+
1W+ 1W+ 2B+
Branch-number Flowering-time Maturity-time Plant-height Pod-number 1W+ Seed-number Seed-weight Seed-yield A05
1W+ 2B+ 5S+ 5S+ 1S+ 1S+1W+
1W+ 3B1S-
1S+ 1W+
1S+ 1S+ 1W1S- 2S-
Consensus-QTL 3S-
1S+
C
A
B C
C
B D ACBDBC
A
A
A
Unique-QTL 123.2cM
J. Shi et al.
1W+
Branch-number Flowering-time 1W+ Maturity-time Plant-height Pod-number Seed-weight Seed-yield
1S+
1W4S+
7B+
Consensus-QTL
1S+ 1W-
C
1W1S-
A
1W2S-
2W2S-
CC
1W1S-
A
1W+ 10B- 9B- 2W± 1W2B-
9B-
A
AB
E
3B2S±
A07
A
1W+
BA BBCBC
3B-
E
3B-
B
1S-
B
Unique-QTL 96.7cM B C
1S+
2S±
1W-
2B-
Consensus-QTL
2W-
1W-
D
D
CA E D E C E A
E
E
E
1W+
Maturity-time Plant-height Seed-number Seed-weight A08
4B+ 3B+ 2S+ 2S±
A06
Biomass-yield Branch-number Plant-height Pod-number Seed-number Seed-weight Seed-yield
9 SI
2S3B+
C
E B
B
4B+
B
2S2S- Consensus-QTL
2B+
B
B
A B
Unique-QTL 97.6cM
Unique-QTL 138.9cM E
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J. Shi et al.
Biomass-yield Branch-number Flowering-time Maturity-time Plant-height Pod-number Seed-number Seed-weight Seed-yield
1S-
1W+ 2S+ 2S- 2B- 3B1S1W-
2W-
1W+ 1W+
B
Biomass-yield Flowering-time Maturity-time Plant-height Pod-number Seed-weight Seed-yield
E
D
A
2B3S-
4B1S-
3B1S-
3B3B± 1W+
Consensus-QTL 1S-
2B+
D
2W+ 3B+ 1W+
B EB
3B+
5B+
3B+
1S+ 1S+ 1S1S-
E
B
D
6B+ 6B+ 3B+ 3W+ 3B+ 2B+
1S-
D
BC B
A
2W+ 5B+ 7B+ 2B+ 2B+ 1W+ Consensus-QTL 3B±
1S1W+
A10
C01
1S+ 1W-
3B+
A09
Branch-number Pod-number Seed-yield
2B-
C
2W-
ABE
1W-
DC
2W+ 2W+
1S-
A
A
B A
1W+
1W+
Consensus-QTL 1W+ 3B+
A
B
A
AAD
A
Unique-QTL 73.0cM
A
Unique-QTL 112.5cM
C B A
Unique-QTL 142.5cM
J. Shi et al.
Flowering-time Pod-number Seed-number Seed-weight 4SSeed-yield C02
Biomass-yield Branch-number Maturity-time Plant-height 3SPod-number Seed-number Seed-weight Seed-yield 3BC03
Biomass-yield Seed-weight Seed-yield C04
11 SI
2B+ 1S+
2S+
1W-
2B+ 5B-
Consensus-QTL 2B±
A
2B±
A
1S+
1S-
E
C
1W-
1S+
DD
F
2W-
Unique-QTL 123.0cM
1W+
1S-
1S+
1S-
3B+
1W-
5B+ 2W+
1S-
E
CE E
3B+
2B+
Consensus -QTL 2B+
A
A
C
A
C
A BE
1S+ 1W+ 1S+
2S±
C
CA
CD
1S-
1W+ 1W+ 1W-
1S+
A B
2S2W+
Consensus-QTL Unique-QTL 92.6cM
B
C
Unique-QTL 153.2cM
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J. Shi et al.
Flowering-time Pod-number Seed-weight
3B+
2B+ 2S+
2B-
C05
Biomass-yield 1SBranch-number Flowering-time 1WMaturity-time1WPlant-height 1WPod-number Seed-number 2S± Seed-weight Seed-yield C06
Unique-QTL 46.2cM
A
1W-
1W1S10B- 10B1S1S1W1S1S+ 4S+ 3B+ 1S+ 1S+
E
1S+ 10B1S2W1S+ 1S+
1S+ 2B2W+
Consensus-QTL 1W+
1S+
1W+
E
D A
Unique-QTL 102.6cM
D
2S+
Flowering-time Maturity-time Plant-height Seed-weight Seed-yield C07
Consensus-QTL
1W-
1S+ 1S+ 1S+
2S+ 1S+
1S+
2B+
Consensus-QTL 1S+
2S±
C
BA
BA
2S-
B
F
2S-
B
1S-
Unique-QTL 105.0cM B
J. Shi et al.
5BBranch-number Flowering-time 2B+ 7B+ 10B+ Maturity-time 2WSeed-number 1WSeed-weight 2BSeed-yield
C08
Biomass-yield Flowering-time Maturity-time Plant-height Pod-number Seed-number Seed-weight Seed-yield C09
2B-
2B-
13 SI
1W-
2B+ 1W+
2B+
Consensus-QTL
1W+
1S1S+
D
B
A
Unique-QTL 96.3cM
A
3B+ 2B+ 2B+ 1W+
1W+ 1W+ 6B+ 3B+ 1W+
1S+ 3W+
1S+ 4B-
1S+ 6B-
2B+
Consensus-QTL
6B-
2B+ 1S-
B
C D
A
Unique-QTL 108.2cM
FIGURE S2.—The distribution of consensus-QTL and unique-QTL on 19 linkage groups (LG) and the alignment between TNDH linkage map and pseudo-chromosomes of Arabidopsis. The LGs are shown as a thick black line with vertical lines to indicate the position of the molecular markers. Above the LG lines, the QTL are drawn with horizontal bars where their lengths show the confidence interval and the circle indicates the peak position. The letters above each consensus-QTL bars represent the number of microenvironments, abbreviations of macro-environments and the direction of additive effect. The three letters “S”, “W” and “B” represent semi-winter, winter and both macro-environments respectively. Major QTL and indicator-QTL were discriminated with wide and arrowhead bars respectively. The pseudo-chromosome of Arabidopsis was aligned under each LG line. The boxes of different colors represent the different pseudo-chromosomes fragment of Arabidopsis and the letters on the boxes was the name of the blocks (see Table S2b).
14 SI
J. Shi et al.
TABLE S1 Field experiment design and traits investigated in multiple environments
Name of macro-
Name of agro-
environment
ecological area
Location and geographic feature
Rapeseed growing
Population
Code of
period
planted
experiment*
Investigated traits†
TNDH
S3D
BN,FT,MT,PH,PN,SN,SW,SY
TNDH
S4D
BN,FT,MT,PH,PN,SN,SW,SY
TNDH
S7D
FT,MT,SW,SY
TNDH
S5D
BN,FT,MT,PH,PN,SN,SW,SY
RC-F2
S5R
BN,FT,MT,PH,PN,SN,SW,SY
TNDH
S6D
BN,BY,FT,MT,PH,PN,SN,SW,SY
RC-F2
S6R
BN,BY,FT,MT,PH,PN,SN,SW,SY
TNDH
E7D
FT,MT,SW,SY
TNDH
N3D
BN,FT,MT,PH,PN,SN,SW,SY
Oct 2002 – May Wuhan, Hubei, E114°21´/ N30°37′/ 40m
2003 Oct 2003 – May
Wuhan, Hubei, E114°21´/ N30°37′/ 40m
2004 Oct 2006 – May
Wuhan, Hubei, E114°21′/ N30°37´/ 40m Middle valley of Yangtze River
2007 Oct 2004 – May
Jiangling, Hubei, E113°25´/N30°30´/ 40m
Semi-winter
2005 Oct 2004 – May
Jiangling, Hubei, E113°25´/N30°30´/ 40m
2005 Oct 2005 – May
Daye, Hubei, E114°48´/ N30°06´/ 100m
2006 Oct 2005 – May
Daye, Hubei, E114°48´/ N30°06´/ 100m Lower valley of Yangtze River Winter
Oct 2006 – May Hangzhou, Zhejiang, E120°12´/ N30°16´/ 40m
The Loess Plateau
2006
2007 Sep 2002 – Jun
Dali, Shaanxi, E109°56´/ N34°52´/ 800m
2003 Sep 2003 – Jun
Dali, Shaanxi, E109°56´/ N34°52´/ 800m
2004
TNDH
N4D
BN,FT,MT,PH,PN,SN,SW,SY
Dali, Shaanxi, E109°56´/ N34°52´/ 800m
Sep 2005 – Jun
TNDH
N6D
BN,BY,FT,MT,PH,PN,SN,SW,SY
J. Shi et al.
15 SI
2006 Sep 2005 – Jun Dali, Shaanxi, E109°56´/ N34°52´/ 800m
2006
RC-F2
N6R
BN,BY,FT,MT,PH,PN,SN,SW,SY
TNDH
N7D
FT,MT,SW,SY
Sep 2006 – Jun Dali, Shaanxi, E109°56´/ N34°52´/ 800m
2007
*The first letter represents the code of the agro-ecological area (middle valley of Yangtze River was in south China and coded as S; lower valley of Yangtze River was in east China and coded as E; the Loess Plateau was in north China and coded as N), the middle letter represents the year of harvest and the last letter represents the code of the population (D, DH population; R, RC-F2 population). †BN: branch number; BY: biomass yield; FT: flowering time; MT: maturity time; PH: plant height; PN: pod number; SN: seed number; SW: seed weight; SY: seed yield.
16 SI
J. Shi et al.
TABLES S2a-d Tables S2a-d are available as an Excel file at http://www.genetics.org/cgi/content/full/genetics.109.101642/DC1.
J. Shi et al.
17 SI
TABLE S3 Phenotypic variation of the two parents (Tapidor and Ningyou 7) and two populations (DH and RC-F2) for seed yield and eight yield-associated traits in ten microenvironments
Environment Macro
Micro
N3
N4
Winter
N6
N7
Semi-winter
S3
Branch
Biomass
Flowering
Maturity
Plant
Pod
Seed
Seed
Seed
Material
number
yield (kg/ha)
time (days)
time (days)
height (cm)
number
number
weight (g)
yield (kg/ha)
Tapidor
11.0
/
201.0
253.0
155.0
573.0
12.0
2.78
1938
Ningyou7
9.0
/
195.0
249.0
134.0
395.0
11.0
3.27
1416
P t test
0.035
/
0.001
0.004
0.003
0.006
0.144
0.020
0.050
DH range
7.3-14.1
/
192-205
248-255
114-180
395-861
9.2-18.8
2.45-4.8
1470-4000
Tapidor
8.0
/
198.0
252.0
156.0
545.0
17.1
2.14
2000
Ningyou7
7.0
/
190.0
239.0
133.0
416.0
15.0
2.41
1513
P t test
0.144
/
0.001
0.000
0.002
0.017
0.019
0.039
0.059
DH range
5.1-10.9
/
187.3-202
236-250
103-155
272-823
9.1-22.6
1.81-4.24
520-3240
Tapidor
9.4
4950
198.0
254.0
126.7
393.0
24.3
2.87
2613
Ningyou7
7.3
4101
192.0
246.0
106.1
272.0
21.2
3.59
2120
P t test
0.031
0.053
0.000
0.000
0.004
0.021
0.010
0.006
0.057
DH range
4.8-11.4
2550-7300
190.3-203
246-255.3
96.2-137.4
268-655
12.1-29.3
2.69-4.36
1230-3540
RC-F2 range
5.2-12
3240-7720
189.3-200
245-253
109.5-155.6
223-610
15.4-31.3
2.58-4.52
1460-4390
Tapidor
/
/
194.0
245.0
/
/
/
/
2200
Ningyou7
/
/
179.0
235.0
/
/
/
/
1809
P t test
/
/
0.000
0.000
/
/
/
/
0.093
DH range
/
/
176.7-198
234.7-247.7
/
/
/
/
920-3350
Tapidor
3.00
/
173.00
222.00
97.00
200.00
7.10
2.57
563
18 SI
J. Shi et al.
S4
S5
S6
S7
E7
Ningyou7
8.00
/
165.00
213.00
120.00
360.00
10.00
3.43
1228
P t test
0.018
/
0.000
0.000
0.002
0.009
0.012
0.003
0.008
DH range
3.2-11.2
/
157-174
211-222
90.7-149.7
145-596
4.6-22.7
2.18-4.92
370-3140
Tapidor
5.0
/
173.0
224.0
124.0
243.0
10.6
2.60
1050
Ningyou7
12.0
/
154.0
213.0
156.0
477.0
12.6
3.20
2233
P t test
0.005
/
0.000
0.000
0.001
0.002
0.035
0.011
0.001
DH range
5-12.9
/
151.3-172.7
214-223
122.4-183.8
153-755
7.7-18
2.15-4.3
670-3650
Tapidor
6.1
/
183.2
228.3
126.9
200.0
14.8
2.70
1039
Ningyou7
8.0
/
177.8
224.3
144.3
334.0
16.9
3.75
1445
P t test
0.040
/
0.001
0.004
0.007
0.015
0.031
0.002
0.034
DH range
4.7-8.9
/
175.1-183.9
223.4-231.8
116.4-165.3
171-466
10.5-21.6
2.18-4.85
910-1820
RC-F2 range
4.8-11.1
/
173-184
223.3-231
113.8-176.3
133-510
12-24.7
2.36-4.21
680-2480
Tapidor
4.9
2939
167.7
216.3
119.6
236.0
12.0
2.88
881
Ningyou7
6.0
3617
158.0
212.3
141.8
404.0
13.7
3.94
1191
P t test
0.125
0.086
0.000
0.004
0.003
0.007
0.053
0.001
0.065
DH range
3.1-9.3
1570-4980
155.7-169
210.7-217
107.1-159.7
104-441
5.8-21.4
2.46-4.71
370-1840
RC-F2 range
3.4-7.9
2100-5790
156.7-167.3
210.5-216.3
118.6-164.8
160-658
9-21.6
2.82-4.41
720-2290
Tapidor
/
/
170.3
220.7
/
/
/
2.65
1364
Ningyou7
/
/
151.7
211.7
/
/
/
3.24
2292
P t test
/
/
0.000
0.000
/
/
/
0.011
0.021
DH range
/
/
149.3-175
209.7-222
/
/
/
2.27-4.48
430-2180
Tapidor
/
/
178.7
230.3
/
/
/
2.57
935
Ningyou7
/
/
157.3
222.0
/
/
/
3.50
1894
P t test
/
/
0.000
0.000
/
/
/
0.002
0.005
DH range
/
/
154-179
221-234
/
/
/
2.1-4.03
650-3520
J. Shi et al.
19 SI
TABLE S4a Genetic correlation coefficients among traits for DH (below diagonal) population in micro-environment N3
Trait
Branch number
Biomass yield
Flowering time
Maturing time
Plant height
Pod number
Seed number
Seed weight
Seed yield
Branch number Biomass yield
/
Flowering time
0.08
/
Maturing time
-0.05
/
0.65***
Plant height
0.07
/
0.52***
0.21**
Pod number
0.43***
/
-0.06
-0.03
0.02
Seed number
-0.10
/
0.22**
0.04
0.16*
-0.42***
Seed weight
0.07
/
-0.29***
-0.18*
0.01
-0.18*
-0.37***
0.35***
/
-0.16*
-0.06
0.22**
0.50***
0.46***
Seed yield
0.29***
TABLE S4b Genetic correlation coefficients among traits for DH (below diagonal) population in micro-environment N4
Trait
Branch number
Biomass yield
Flowering time
Maturing time
Plant height
Pod number
Seed number
Branch number Biomass yield
/
Flowering time
-0.52***
/
Maturing time
-0.60***
/
0.81***
0.01
/
0.42***
Pod number
0.01
/
0.22**
0.52***
0.35***
Seed number
0.32***
/
-0.41***
-0.36***
-0.20**
-0.43***
Seed weight
0.06
/
-0.03
0.30***
0.22**
-0.20**
Plant height
0.15*
-0.19*
Seed weight
Seed yield
20 SI
J. Shi et al.
Seed yield
0.36***
/
-0.28***
0.09
0.25***
0.45***
0.40***
0.40***
TABLE S4c Genetic correlation coefficients among traits for DH (below diagonal) and RC-F2 (up diagonal) populations in micro-environment N6 Trait
Branch number
Branch number Biomass yield
Biomass yield
Flowering time
Maturing time
0.27***
-0.45*** 0.27***
0.08
Plant height
Pod number
Seed number
Seed weight
Seed yield
-0.48***
0.18*
0.27***
0.33***
0.74***
0.57***
0.25***
0.05
0.45***
-0.03
0.29***
0.73***
0.73***
0.45***
0.18*
-0.29***
-0.16*
-0.22**
0.36***
0.06
-0.31***
0.10
-0.17*
0.50***
-0.09
0.27***
0.56***
-0.31***
-0.21**
0.68***
-0.26***
0.29***
Flowering time
-0.39***
0.38***
Maturing time
-0.42***
0.48***
0.73***
Plant height
0.09
0.65***
0.53***
0.38***
Pod number
0.44***
0.56***
0.18*
0.02
0.47***
Seed number
-0.01
-0.02
-0.21**
-0.19*
-0.05
-0.34***
Seed weight
0.03
0.23**
-0.23***
-0.05
0.00
-0.29***
-0.23**
0.43***
0.68***
-0.18*
-0.03
0.45***
0.58***
0.33***
Seed yield
0.31*** 0.22**
TABLE S4d Genetic correlation coefficients among traits for DH (below diagonal) population in micro-environment N7 Trait
Branch number
Biomass yield
Flowering time
Maturing time
Plant height
Pod number
Seed number
Seed weight
Branch number Biomass yield
/
Flowering time
/
/
Maturing time
/
/
0.61***
Plant height
/
/
/
/
Pod number
/
/
/
/
/
Seed number
/
/
/
/
/
/
Seed weight
/
/
/
/
/
/
/
Seed yield
/
/
-0.30***
-0.42***
/
/
/
/
Seed yield
J. Shi et al.
21 SI
TABLE S4e Genetic correlation coefficients among traits for DH (below diagonal) population in micro-environment S3 Trait
Branch number
Biomass yield
Flowering time
Maturing time
Plant height
Pod number
Seed number
Seed weight
Seed yield
Branch number Biomass yield
/
Flowering time
-0.23**
/
Maturing time
-0.31***
/
0.67***
Plant height
0.41***
/
0.01
0.21**
Pod number
0.78***
/
-0.20**
-0.24**
0.49***
Seed number
0.09
/
-0.08
-0.26***
0.03
0.01
Seed weight
0.07
/
-0.26***
0.20**
0.07
-0.03
-0.17*
0.65***
/
-0.40***
-0.35***
0.33***
0.68***
0.52***
Seed yield
0.24***
TABLE S4f Genetic correlation coefficients among traits for DH (below diagonal) population in micro-environment S4 Trait
Branch number
Biomass yield
Flowering time
Maturing time
Plant height
Pod number
Seed number
Seed weight
Branch number Biomass yield
/
Flowering time
-0.32***
/
Maturing time
-0.44***
/
0.79***
Plant height
0.50***
/
-0.29***
-0.26***
Pod number
0.58***
/
-0.68***
-0.73***
0.42***
Seed number
-0.35***
/
0.30***
0.34***
-0.03
-0.47***
Seed weight
0.28***
/
-0.33***
-0.27***
0.31***
-0.28***
-0.32***
Seed yield
0.57***
/
-0.71***
-0.67***
0.58***
0.71***
0.37***
0.31***
Seed yield
22 SI
J. Shi et al.
TABLE S4g Genetic correlation coefficients among traits for DH (below diagonal) and RC-F2 (up diagonal) populations in micro-environment S5 Trait
Branch number
Biomass yield
Flowering time
Maturing time
Plant height
Pod number
Seed number
Seed weight
Branch number
/
-0.03
0.03
0.41***
0.37***
-0.03
-0.10
0.35***
Biomass yield
/
/
/
/
/
/
/
/
0.51***
-0.10
0.34***
-0.06
-0.45***
0.04
-0.04
0.23**
-0.39***
0.04
-0.05
0.19*
0.22**
0.39***
-0.36***
-0.40***
0.60***
-0.30***
0.28***
Flowering time
-0.15*
/
Maturing time
-0.31***
/
Plant height
0.33***
/
0.01
0.06
Pod number
0.29***
/
0.40***
0.24**
0.16*
Seed number
0.07
/
-0.09
-0.34***
0.22**
-0.29***
Seed weight
-0.08
/
-0.45***
0.05
-0.06
-0.50***
-0.35***
0.42***
/
-0.18*
-0.22**
0.33***
0.43***
0.43***
Seed yield
0.59***
0.22**
Seed yield
0.05 -0.04
TABLE S4h Genetic correlation coefficients among traits for DH (below diagonal) and RC-F2 (up diagonal) populations in micro-environment S6 Trait
Branch number
Biomass yield
Flowering time
Maturing time
Plant height
Pod number
Seed number
Seed weight
Branch number
0.55***
-0.28***
-0.39***
0.30***
0.36***
0.27***
-0.25***
0.51***
Biomass yield
0.35***
-0.10
-0.29***
0.61***
0.62***
0.25***
0.02
0.72***
0.60***
0.10
-0.39***
-0.18*
0.16*
-0.42***
-0.16*
-0.35***
-0.28***
0.28***
-0.44***
0.27***
0.25***
0.00
0.42***
-0.01
-0.31***
0.73***
-0.54***
0.47***
Flowering time
0.00
Maturing time
-0.30***
0.09
0.60***
Plant height
0.29***
0.68***
0.23**
-0.01
Pod number
0.40***
0.51***
-0.30***
-0.44***
0.32***
Seed number
-0.10
-0.08
-0.15*
-0.18*
0.17*
-0.18*
Seed weight
-0.02
0.02
-0.20**
0.19*
-0.09
-0.24**
-0.54***
0.34***
0.64***
-0.36***
-0.48***
0.36***
0.69***
0.42***
Seed yield
0.08
Seed yield
0.20** 0.16*
J. Shi et al.
23 SI
TABLE S4i Genetic correlation coefficients among traits for DH (below diagonal)population in micro-environment S7 Trait
Branch number
Biomass yield
Flowering time
Maturing time
Plant height
Pod number
Seed number
Seed weight
Seed yield
Branch number Biomass yield
/
Flowering time
/
/
Maturing time
/
/
0.77***
Plant height
/
/
/
/
Pod number
/
/
/
/
/
Seed number
/
/
/
/
/
/
Seed weight
/
/
-0.15*
0.07
/
/
/
Seed yield
/
/
-0.54***
-0.45***
/
/
/
0.15*
TABLE S4j Genetic correlation coefficients among traits for DH (below diagonal) population in micro-environment E7
Trait
Branch number
Biomass yield
Flowering time
Maturing time
Plant height
Pod number
Seed number
Seed weight
Branch number Biomass yield
/
Flowering time
/
/
Maturing time
/
/
0.62***
Plant height
/
/
/
/
Pod number
/
/
/
/
/
Seed number
/
/
/
/
/
/
Seed weight
/
/
-0.37***
-0.24**
/
/
/
Seed yield
/
/
-0.26***
-0.35***
/
/
/
*, ** and *** represent the three significant levels of P=0.05, 0.01 and 0.001 respectively
0.25***
Seed yield
24 SI
J. Shi et al.
TABLE S5 ANOVA for seed yield and eight yield-associated traits in two (DH and RC-F2) populations Population Trait
DH
RC-F2
Source
DF
SS
MS
F value
P value
DF
SS
MS
F value
P value
Genotype
201
1169
5.8
9.7
