genetic dissection of leaf development inbrassica rapa · genetic dissection of leaf development...

Genetic Dissection of Leaf Development in Brassica rapaUsing a Genetical Genomics Approach1[W]

Dong Xiao, Huange Wang, Ram Kumar Basnet, Jianjun Zhao, Ke Lin, Xilin Hou*, and Guusje Bonnema*

State Key Laboratory of Crop Genetics and Germplasm Enhancement, Horticultural College, NanjingAgricultural University, Nanjing, Jiangsu 210095, China (D.X., X.H.); Wageningen UR Plant Breeding (D.X.,R.K.B., K.L., G.B.) and Biometris (H.W.), Wageningen University and Research Centre, Wageningen 6700AJ,The Netherlands; Horticultural College, Hebei Agricultural University, Baoding 071001, China (J.Z.); andInstitute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China (G.B.)

ORCID ID: 0000-0002-2298-6849 (G.B.).

The paleohexaploid crop Brassica rapa harbors an enormous reservoir of morphological variation, encompassing leafy vegetables,vegetable and fodder turnips (Brassica rapa, ssp. campestris), and oil crops, with different crops having very different leafmorphologies. In the triplicated B. rapa genome, many genes have multiple paralogs that may be regulated differentially andcontribute to phenotypic variation. Using a genetical genomics approach, phenotypic data from a segregating doubled haploidpopulation derived from a cross between cultivar Yellow sarson (oil type) and cultivar Pak choi (vegetable type) were used toidentify loci controlling leaf development. Twenty-five colocalized phenotypic quantitative trait loci (QTLs) contributing to naturalvariation for leaf morphological traits, leaf number, plant architecture, and flowering time were identified. Genetic analysis showed thatfour colocalized phenotypic QTLs colocalized with flowering time and leaf trait candidate genes, with their cis-expression QTLs and cis-or trans-expression QTLs for homologs of genes playing a role in leaf development in Arabidopsis (Arabidopsis thaliana). The leaf geneBRASSICA RAPA KIP-RELATED PROTEIN2_A03 colocalized with QTLs for leaf shape and plant height; BRASSICA RAPAERECTA_A09 colocalized with QTLs for leaf color and leaf shape; BRASSICA RAPA LONGIFOLIA1_A10 colocalized with QTLs forleaf size, leaf color, plant branching, and flowering time; while the major flowering time gene, BRASSICA RAPA FLOWERING LOCUSC_A02, colocalized with QTLs explaining variation in flowering time, plant architectural traits, and leaf size. Colocalization of theseQTLs points to pleiotropic regulation of leaf development and plant architectural traits in B. rapa.

Six Brassica species are cultivated worldwide: threediploid Brassica species, Brassica rapa (A genome; n = 10),Brassica nigra (B genome; n = 8), and Brassica oleracea (C ge-nome; n = 9), and three amphidiploids, Brassica juncea(AB; n = 18), Brassica napus (AC; n = 19), and Brassicacarinata (BC; n = 17), derived by spontaneous hybridiza-tion among the three diploid species (Nagaharu, 1935). Alldiploid Brassica species have undergone a whole-genometriplication since their divergence from Arabidopsis(Arabidopsis thaliana; Lysak et al., 2005; Town et al.,

2006). The B. rapa genome sequence became available in2011, which permitted a detailed analysis of gene fate aftermultiple genome duplications. The 41,174 protein-codinggenes in the B. rapa genome are fewer than simple tripli-cation of the 25,498 (125 Mb) genes in the Arabidopsisgenome due to gene loss (fractionation) after triploidiza-tion (Wang et al., 2011). Gene loss was certainly not ran-dom. For example, circadian clock genes and floweringgenes were preferentially retained in the evolution inB. rapa (Lou et al., 2012; Tang et al., 2012; Xiao et al., 2013).

B. rapa displays extreme morphological diversity, in-cluding leafy vegetables, turnips, and oil types, which islikely the result of both genetic and epigenetic variation,selected by plant breeders (Zhao et al., 2005; Bonnemaet al., 2011). Variations in leaf shape, color, size, angle,and numbers affect productivity but also attractiveness,as many B. rapa morphotypes are consumed as vegeta-bles. This variation in leaves is enormous, with, amongothers, smooth dark green leaves with enlarged whitemidribs of cv Pak choi, the numerous small dark greensmooth round leaves of Wutacai, the knotted leaf surfaceof the large light green leaves with wide midribs fromChinese cabbage (B. rapa ssp. pekinensis), the many elon-gated slender leaves or the highly serrated leaves ofMizuna (B. rapa ssp. nipposinica), and the oval or serratedleaves of different turnip types. Dissection of genetic andepigenetic variation will increase our understanding ofthis phenotypic variation and provide molecular tools tobreed for B. rapa vegetable crops with novel leaf char-acteristics. In Arabidopsis, studies have documented

1 This work was supported by the Program Strategic Alliancesprogram (grant no. 08–PSA–BD–02), the National Program on KeyBasic Research Projects of China (973 Program, grant no.2012CB113900), the National High Technology Research and Devel-opment Program of China (863 Program, grant no. 2012AA100101),the National Natural Science Foundation of China (grant no.31171976), and the Hebei Science Fund for Distinguished YoungScholars (grant no. C2013204118).

* Address correspondence to [email protected] and [email protected].

The author responsible for distribution of materials integral to thefindings presented in this article in accordance with the policy de-scribed in the Instructions for Authors (www.plantphysiol.org) is:Guusje Bonnema ([email protected]).

D.X. and J.Z. performed the experiments; D.X., H.W., R.K.B., andK.L. performed the microarray and genetic network analysis; D.X.,X.H., and G.B. designed and wrote the manuscript. All authors readand approved the final manuscript.

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that early leaf development relies on the control of leafinitiation and formation on the flanking regions of theshoot apical meristem (Kim and Cho, 2006; Cha et al.,2007). Leaf size and shape in later development in-volves the coordination between cell proliferation andpolar cell division and expansion (e.g. adaxial-abaxialpolarity, proximal-distal polarity, symmetry, and flatmorphology; Kim andCho, 2006; Tsukaya, 2006; Barkoulaset al., 2007; Micol, 2009; Gonzalez et al., 2010). In addition,the plant hormones belonging to strigolactone, auxin,cytokinin, and GA influence leaf development andmorphogenesis (Bertoni, 2010; Beveridge and Kyozuka,2010; Mauriat et al., 2011).

B. rapa has a triplicated genome, and as a result, severalgenes have multiple paralogs. Duplicated genes are ofmajor importance for evolutionary novelty, since they cancontribute to functional innovation by mutation of theircoding sequences, expression divergence, and rewiringregulatory networks through variation in interactionsamong different orthologs (Gaeta et al., 2007; Liu andAdams, 2007; De Smet and Van de Peer, 2012). Severalstudies have correlated variation in gene expression or thefate of duplicated genes to phenotypic diversity, such asflowering time (Ft) variation, leaf shape, size, and num-bers, pest resistance, and stress tolerance in eukaryotes(Gaeta et al., 2007; Hovav et al., 2008; Feng et al., 2009;Whittle and Krochko, 2009; Combes et al., 2012; Costaet al., 2012; Xiao et al., 2013).

Pleiotropy implies a single gene affecting multiple traits,while polygenic control implies that one trait is controlledby multiple genes. The genetic regulation of leaf architec-ture has been unraveled through quantitative trait locus(QTL) analyses in Brassica species (Lou et al., 2007; Kuboet al., 2010; Li et al., 2012; Yu et al., 2013). Lan and Paterson(2001) located significant QTLs explaining 45% of thephenotypic variation in lamina length (LL), three of whichcolocalized with QTLs that affected leaf width (LW) inB. oleracea. In another study, Lou et al. (2007) detected10 QTLs for leaf traits using three different mapping popu-lations. By synteny analysis of QTL regions of B. rapa withthe Arabidopsis genome, Li et al. (2009, 2013) identifiedthe leaf lobe depth and leaf hairiness genes, BRASSICARAPA GIBBERELLIN 20-OXIDASE 3 (BrGA20OX3) ANDBRASSICA RAPA GLABRA1, and Zhang et al. (2009)successfully cloned a BRASSICA RAPA TRANSPARENTTESTA GLABRA1 gene controlling leaf hairiness and seedcoat color. Genetical genomics or expression quantitativetrait locus (eQTL) mapping entails a QTL analysis of high-throughput transcript expression patterns in a genotypedpopulation. Genetical genomics can be used as a tool toidentify candidate genes for phenotypic traits, as thecause of variation in many traits is differences in theexpression of specific genes (Alonso-Blanco et al., 2009).

In this study, aiming to understand the underlyinggenetic architecture of leaf development, a genetical ge-nomics approach (Jansen and Nap, 2001) was used in adoubled haploid (DH) population from a cross betweencv Yellow sarson and a cv Pak choi accession. A largenumber of genetic markers based on Arabidopsis leafdevelopment genes (hereafter, leaf genes) were mapped

in silico, and a subset was genetically mapped in the DHpopulation, which allowed us to address whether leafgenes are preferentially retained in the B. rapa genome.Using variation in the expression of transcripts and 17phenotypic traits combined with linkage mapping, weconstructed coregulatory networks. Bayesian networkreconstruction was used to identify the best potentialregulator for the genetic regulation of these complex traits(Spirtes et al., 2000). Using colocalization analysis ofphenotypic QTLs (pQTLs) and eQTLs, we identified thecis-regulated genes BRASSICA RAPA FLOWERINGLOCUS C_A02 (BrFLC2_A02), BRASSICA RAPA KIP-RELATED PROTEIN2_A03 (BrKRP2_A03), BRASSICARAPA ERECTA_A09 (BrER_A09), and BRASSICA RAPALONGIFOLIA1_A10 (BrLNG1_A10) that colocalized withcolocalized phenotypic QTLs (copQTLs) and several trans-eQTLs, suggesting pleiotropic regulation of leaf develop-ment in B. rapa.

RESULTS

Genetic Map of DH68, Enriched with LeafCandidate Genes

A total of 176 B. rapa genes were identified in theChinese cabbage cv Chiifu-401 genome sequence homol-ogous to 91 Arabidopsis genes with roles in leaf devel-opment (Supplemental Table S1). Among them, 33% (30of 91) Arabidopsis orthologous genes were presented bythree or more paralogs in B. rapa, 34.1% (31 of 91) by twoB. rapa paralogs, 24.2% (22 of 91) by one B. rapa homolog,and for 8.8% (eight of 91) of the Arabidopsis genes, nohomologs in B. rapa were identified. For 80 leaf candidategenes, 145 PCR primer pairs were designed and screenedfor polymorphisms between the parents. The 79 poly-morphic PCR products were profiled over the DH68mapping population, which resulted in genetic mappositions for 60 leaf genes distributed over all 10 linkagegroups, corresponding to 42 Arabidopsis orthologs(Supplemental Fig. S1A; Supplemental Table S2). Thegenetic map (whole length, 1,328 centimorgan [cM])contains 509 markers (average distance = 2.6 cM;Supplemental Fig. S1B).

The 176 B. rapa genes were mapped in silico using thereference genome. The comparison of the genetic mappositions of leaf development genes in DH68 with their insilico predicted map positions indicated that the order ofthese gene markers in the genetic map was almost iden-tical to that of the physical map. Only four had inconsis-tent order between both maps but mapped to the samelinkage groups in all cases (Supplemental Fig. S2). Therelationship between the physical and genetic distancesin this euchromatic sequenced part of the genome was1 cM = approximately 210 kb.

Phenotypic Variation

To identify the genetic loci responsible for the variationin leaf development, we conducted experiments in 2008

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and 2010 (Table I). The YS-143 parent plant had light leafcolor and lobed leaves, with on average 5.5 leaf lobes(LB), while PC-175 had entire dark green leaves withwhite midribs (Fig. 1; Supplemental Fig. S3). Data from2008 and 2010 were analyzed separately, as the experi-mental design differed. For 2008 data, ANOVA showedthat, for most traits, there were no significant DH line 3block interactions at P , 0.001 (the only exceptions werethe traits LB and blade length [BL]; Supplemental Table S3).Every trait value was reported as the average fromthree blocks in this study. A total of 17 phenotypic traits,including plant architecture, leaf traits, and Ft, wereevaluated in this study, and most traits (except for leafwing depth [LD]) were normally distributed and showedtransgressive segregation, indicating that the leaf traitsare controlled by multiple genes in the mapping popu-lation (Supplemental Fig. S4, A–S). Six traits that werephenotyped in both 2008 and 2010 were analyzed sta-tistically, and significant effects of genotype, year, andgenotype3 year were identified (Supplemental Table S4).In 2008, LL ranged within the DH population from

66 to 330 mm (5-fold), while LW ranged from 57 to 170mm (3-fold; Supplemental Table S5). The LB numberranged from 1.5 to 13.7. In addition, the variation of leafsize in time was further characterized in 2010 by mea-suring leaf size parameters (BL, LL, LW, and leaf index[LI]) every 3 to 5 d during development until the DHlines flowered. As shown in Supplemental Figure S4, T toW, both parents and the DH population showed a similartemporal pattern of growth as measured by LL, LW, andBL. LL increased until stage VIII (50 d), after whichgrowth ceased, while for LW, at stage VI the growth ofYS-143 leaves ceased and for PC-175 leaves growthceased at stage VIII; thus, they became wider than YS-143leaves (Supplemental Fig. S4, T and U).

Phenotype QTL Analysis

To detect the relationship between genotype and phe-notypic traits, pQTL analysis was performed. A totalof 167 pQTLs were identified for 17 traits including the

Table I. Description of B. rapa morphological traits measured in this study

SPAD, Special Products Analysis Division.

Trait Name Trait Code Trait Description Stagea Unit Replication

LN LN_2008 Counted the LN 42 d after sowing No. Three blocksLN_2010 Counted the LN When first open flower No. One block

LC LC_2008 Chlorophyll level of the leaves,measured with SPAD meter

42 d (fifth leaf) SPAD Three blocks

LC_2010 Chlorophyll level of the leaves,measured with SPAD meter

29 d (second leaf) SPAD One block

Pmh Pmh_2008 Assumed plants do not formextra branches anymore

Measured when seed pods are mature cm Three blocks

PB PB_2008 Assumed plants do not formextra branches anymore

Measured when seed pods are mature No. Three blocks

Ft Ft_2008 Ft When first open flower Score Three blocksLL LL_2008 Length from base of petiole

to tip of lamina49 d (third leaf) mm Three blocks

LL_2010 Length from base of petioleto tip of lamina

I to X (third leaf) mm One block

LW LW_2008 Width of leaves at the widest point 49 d (third leaf) mm Three blocksLW_2010 Width of leaves at the widest point I to X (third leaf) mm One block

BL BL_2008 Distance from the tip to the first lobe 49 d (third leaf) mm Three blocksBL_2010 Distance from the tip to the first lobe II to X (third leaf) mm One block

PL PL_2008 Calculated by subtracting LL from BL 49 d (third leaf) mm Three blocksLA LA_2008 Total surface of the leaf area, petiole

not included49 d (third leaf) mm2 Three blocks

LP LP_2008 Total length of the perimeter 49 d (third leaf) mm Three blocksLI LI_2008 Index of the leaf, calculated by

dividing LL by LW49 d (third leaf) Ratio Three blocks

LI_2010 Index of the leaf, calculated bydividing LL by LW

I to X (third leaf) Ratio One block

LB LB_2008 No. of LB 49 d (third leaf) No. Three blocksLBb LBb_2008 No. of LBb 49 d (third leaf) No. Three blocksLBs LBs_2008 No. of LBs 49 d (third leaf) No. Three blocksLD LD_2008 Depth of the leaf wings, classified

in three classes49 d (third leaf) Scale Three blocks

Lcu Lcu_2008 Curling of the leaves, classifiedin seven classes

49 d (third leaf) Scale Three blocks

aNumber of days was counted from sowing to day of measure. Dynamic measure includes 10 stages: I (25 d); II (29 d); III (32 d); IV (36 d); V (39 d);VI (43 d); VII (46 d); VIII (50 d); IX (53 d), and X (57 d).

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temporal phases for LL, LW, BL, and LI in 2010, rang-ing from 36 pQTLs for LI located on seven linkagegroups to only one pQTL for LD and leaf curling (Lcu)on A02 (Fig. 2A; Supplemental Table S6). The 167pQTLs were distributed over all linkage groups, and25 or more pQTLs were located on each of the linkagegroups A01, A02, A03, A09, and A10. Individual pQTLsexplained between 6.2% and 38.9% of the phenotypicvariation, with six pQTLs explaining less than 10% andfive pQTLs explaining more than 30% (SupplementalTable S6). In Table II, the total phenotypic variationexplained by the QTL for each trait is summarizedunder different growth conditions and stages, and thistotal explained variation ranged from 12.3% (LL instage V in 2010) to 95.2% (leaf color [LC] in 2008) ofthe phenotypic variance. The distribution of pQTLsshows that different loci affected leaf development inthe various years and development stages. For example,two pQTLs were detected for LL in 2008 (A01, 89.4 cM,marker BrGRF5P1b; A02, 141.1 cM, marker BrMAF4P1d).Also in 2010, LL was phenotyped 10 times during plantdevelopment, resulting in the identification of 25 pQTLs,with a total of one to four pQTLs detected at each devel-opmental stage. pQTLs at A01 were detected both in2008 and 2010 (stages I–III), while the A02 pQTL wasonly detected in 2008 (Table II; Supplemental Table S6).

Identification of copQTL and Colocalization of copQTLwith Candidate Genes

We analyzed whether pQTLs for multiple phenotypictraits in different years and growth stages colocalized, andbased on that we defined copQTLs (Table III). In total, 25copQTL regions that integrated 144 initial pQTLs were

identified, and 34 Ft and leaf trait genes were inferred onseven chromosomes. These copQTLs were identified forall traits except Lcu (Table III). In total, 20 out of these 34candidate genes were genetically mapped in DH68, whilethe genetic map positions of the other 14 genes wereestimated based on their physical positions relative tomarkers with both physical position in the reference ge-nome and genetic map position in DH68 (SupplementalTable S7). copQTL23 combined QTLs for eight traits, leafsize (petiole length [PL], LI, LL, LW, and BL), LC, plantbranching (PB), and Ft, integrating 22 initial pQTLs(Supplemental Table S8), and colocalized with BrLNG1 at73.9 cM on A10, explaining 9.7% to 18.4% of the pheno-typic variation for each trait. copQTL6 (21.1–36.9 cM)combined QTLs for seven traits, leaf size (LL, LW, BL, andLI), plant architecture traits (leaf number [LN] and PB),and Ft, integrating 15 initial pQTLs and four peak pQTLsoverlapping (conflated 95% confidence interval) withBrFLC2, BrGA20OX3, BRASSICA RAPA ASYMMETRICLEAVES ENHANCER3 (BrAE3), and BrLNG1 (4.9–44.7cM), explaining 10.7% to 38.9% phenotypic variationfor each trait. Four copQTL combined four traits each:copQTL2 for leaf size (LL, PL, and LI) and plant matureheight (Pmh) mapped to BRASSICA RAPA GROWTH-REGULATING FACTOR5 (BrGRF5) at 89.4 cM at A01;copQTL12 for leaf size (LL, LW, and BL) and LNmappedto BrFLC5 at 9.2 cM at A03 and indicated the nearest leafcandidate gene BrAE3; copQTL14 for leaf size (LL, LW,BL, and LI) mapped to BRASSICA RAPA ASYMMETRICLEAVES1 (BrAS1) at 55.2 cM on A03; and copQTL15 forleaf size (LW and LI), small leaf lobes (LBs), and Pmhmapped to BrKRP2 at 139.5 cM on A03. Similarly, fivecopQTLs for three traits colocalized with BRASSICARAPA HASTY1 (BrHST1) on A01, BrGA20OX3 on A02,BRASSICA RAPA LEAFY PETIOLE on A03, BRASSICA

Figure 1. Leaf morphology of parental genotypescv Yellow sarson (YS-143) and cv Pak choi(PC-175) of B. rapa. The different traits related toleaf morphology measured in this study, and pa-rental lines at the 10th stage, are shown. A list ofall traits with their descriptions is presented inTable I. In Supplemental Figure S3, both parentgenotypes and their DH progeny are shown at thefirst stage (25 d after sowing) and the 10th stage(57 d after sowing).


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Figure 2. Profile of pQTLs and eQTLs mapped in DH68 of B. rapa. A, Distribution of pQTLs for leaf morphology-related traitsmeasured in 2008 and 2010. The color on the left indicates the two experiments conducted in 2008 and 2010, whereas thecolor on the x axis on the top indicates the 10 different linkage groups (A01–A10). The color of the pQTL profile represents thedifferent levels of LOD score. B, Distribution of eQTLs for leaf trait-related candidate genes showing their cis-/trans-regulation.The y axis indicates the physical positions of genes/probes, and the x axis indicates the genetic positions of markers in the 10linkage groups. The color bars on the left and on the top indicate the 10 linkage groups. The color of each eQTL reflects thedifferent levels of LOD score. Gene names highlighted in the figure indicate colocation with pQTLs, or high-LOD eQTLs, orgenes associated with traits in network analyses. C, Location of markers of candidate genes for leaf and Ft traits on the geneticmap of the DH68 population of a cross from cv Yellow sarson and cv Pak choi. Marker names in black indicate the genesmapped in DH68, and those in red indicate genes mapped in silico based on the reference genome (Chinese cabbage cv Chiifu-401). The shape and color of the boxes under candidate gene markers indicate cis- or trans-regulation and different functionalpathways, respectively. Triangles indicate cis-regulation, squares indicate trans-regulation, and diamonds indicate cis- andtrans-regulated genes. For detailed functional pathway descriptions, see Figure 4. The data on the regulation of the Ft genes cis-BRASSICA RAPA MADS AFFECTING FLOWERING4 (BrMAF4) and trans-BRASSICA RAPA PHYTOCHROME A are accordingto our previous study (Xiao et al., 2013).





RAPA FERREDOXIN-NADP(1)-OXIDOREDUCTASE1on A09, and BRASSICA RAPA SHOOTMERISTEMLESS,BRASSICA RAPA HUA ENHANCER1 and BRASSICARAPA PINHEAD (BrPNH) on another locus on A09. Inaddition, eight copQTLs for two traits and six copQTLsfor one trait colocalized within a total of 22 leaf and Ftcandidate genes (Table III). Interestingly, some sets ofcopQTLs colocalized with sets of paralogs: copQTL3 forleaf size (LI) and copQTL17 for leaf size (BL and LI)colocalized with two different paralogs of BRASSICARAPA SQUAMOSA PROMOTER BINDING PROTEIN-LIKE5 (BrSPL5) on A01 and A05, respectively; whilecopQTL4 for leaf size (LL, LW, and BL) and copQTL18 forLcu colocalized with two paralogs of BrHST1 on A01 andA05, respectively.

eQTL Analysis

To identify the molecular mechanisms underlying thepQTL/copQTL, the expression profiles of leaves from5-week-old DH68 lines were determined by microarrayand quantitative real-time PCR (RT-qPCR). Of all 96,557probes on the microarray, 96 probes represented 41Arabidopsis leaf development genes corresponding to64 B. rapa paralogs (Supplemental Table S9). The 96probes represented 36 B. rapa genes with eQTLs, whilethe transcripts of the other 138 genes from the selected184 B. rapa genes were quantified using RT-qPCR.For seven genes, no PCR product was obtained (dueto bad primer quality). Of three genes (BRASSICA

RAPA CYCLIN D1;1_A02 [BrCYCLIN D1;1_A02],BrKRP2_A03, and BRASSICA RAPA DEFORMED ROOTAND LEAVES1_A08 [BrDRL1_A08]) with significantcis-eQTLs detected by microarray, their expressionwas validated by RT-qPCR, illustrating the reproduc-ibility of the microarray results (Supplemental Fig. S5;Supplemental Table S10). Based on both microarrayand RT-qPCR transcriptional analyses, a total of 95 B.rapa genes (53.7%, 177) had variation in expression inthe DH68 population (fold exchange ranged from 0.3to 15.3); 47 genes (26.6%, 177) were expressed in bothparents and the DH68 population, but their expressionin the DH68 population was not variable. Thirty-fivegenes (19.8%, 177) were not expressed in parents andthe DH68 progeny (Supplemental Table S1).

Variation in gene transcript abundance in the segre-gating population was treated as a quantitative traitand subjected to eQTL analysis against 509 markers ofthe genetic map. Twelve Ft candidate genes associatedwith Ft as a positive control were added in the analysis(Xiao et al., 2013). A total of 118 probes/genes repre-senting 110 B. rapa genes orthologous to 72 Arabidopsisgenes revealed a total of 173 eQTLs (marker probe as-sociations) against all 509 genetic markers, with at leastone to five eQTLs per probe (log of the odds [LOD]$ 3;Fig. 2B; Supplemental Table S10). The results showedthat 39 (33.1%) probes/genes had cis-eQTLs, 59 (50%)probes/genes had trans-eQTLs, while 20 (17%) probes/genes had cis- and trans-eQTLs (Supplemental Table S10).An almost linear genome-wide relation along the di-agonal of the graph was observed, with the effect forthe cis-eQTL (average LOD = 7.5, median of 6.3) beingstronger than for the trans-eQTL (average LOD = 5.1,median of 4.8; Fig. 2B).

Identification of Traits and Gene Modules

The 17 phenotypic traits measured in 2008 wereevaluated on the same DH plants that were used fortranscriptional analysis. We next grouped the patternsof trait variation and expression variation of individualgenes by Spearman correlation and found seven clus-ters with strong correlation (Supplemental Fig. S6;Supplemental Table S11). For ease of description, wenumbered these clusters from block A through blockG. Three out of these seven clusters included bothphenotypic traits and expression levels of genes (Fig. 3).In cluster A, an LL leaf gene, cis-BrLNG1_A10, and sixFt genes are positively correlated with leaf size traits(LW, leaf area [LA], leaf perimeter [LP], and BL) andplant architectural traits (PB, LN, and Pmh). In clusterB, the trait LBs is positively correlated with the ex-pression of 11 leaf genes. In cluster C, three Ft genesand four leaf genes were positively correlated with Ft.Notably, phenotypic and expression traits in cluster Cwere significantly negatively correlated with those incluster A. The other clusters (D–G) only consisted ofgene expression levels with significant positive corre-lation within clusters (Supplemental Fig. S6).

Table II. Total percentage of phenotypic variance explained by theadditive effects of all detected pQTLs for each trait in B. rapa

Trait

Involved

2008 No.

of QTLsa2010 No.

of Stagesb,d2010 No.

of QTLsc,dTotal Variation

Explainede

%LN 4 1 4 53.0–84.2LC 6 1 5 88.4–95.2LL 2 10 1–4 12.3–59.1LW 3 9 2–5 21.8–70.4BL 3 7 1–5 19.8–75.7PL 2 – – 19.7LA 2 – – 23.5LP 3 – – 31.3LI 2 10 2–5 20.4–79.0LB 3 – – 30.5LBb 5 – – 68.6LBs 3 – – 38.4LD 1 – – 16Lcu 1 – – 12.3Pmh 3 – – 48.8PB 4 – – 56.6Ft 4 – – 80.2

aIn 2008, each trait was only measured once. The number of pQTLs islisted. bIn 2010, several traits were measured at a number of stages(Table I). cThe range (minimum to maximum) of pQTLs is listed in2010. d2 means that these traits were not measured in this experi-ment. eRange of total variation explained per trait per year and/or mea-surement at different stages (for details, see Supplemental Table S6).


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Table III. Details of copQTLs of the 17 phenotypic traits, with genetic positions of B. rapa genes for leaf traits and Ft that colocate with thesecopQTLs

Traits Involved copQTLa,b LG IntervalcNo. of

pQTLs

Nearest

Candidate

Gene

Peak pQTL

Distance

between

Peak LOD

and Candidate

Gened

GroundeAssociated

Functional

Pathway

cM cM cMLW copQTL1 A01 67.9–86.6 3 BrKAN2 83.6 2.6 Mapped Adaxial-abaxial

polarityLL, PL, LI, Pmh copQTL2 A01 89.4–92.0 9 BrGRF5 89.4 0 Mapped Leaf shapeLI copQTL3 A01 97.4–105.9 7 BrSPL5 97.4 0 Mapped FtLL, LW, BL copQTL4 A01 113.7–123.9 5 BrHST1 122.7 0 Mapped Adaxial-abaxial

polarityLI copQTL5* A02 4.9–9 2 BrBFT 9 0 Mapped FtLN, Ft, PB, LL,

LW, BL, LIcopQTL6* A02 21.1–36.9 15 BrFLC2 24.1 4.7 Mapped Ft

LL, LW, BL copQTL7* A02 14.28–30.82 6 BrGA20OX3 28.8 – Inferred Leaf shapeLL, LW copQTL8* A02 24.2–48.2 4 BrLNG1 35 – Inferred LLLBb, LD copQTL9 A02 32–51.6 2 BRASSICA RAPA

PISTILLATA48.2 0 Mapped Ft

BrKAN1 48.2 – Inferred Adaxial-abaxialpolarity

BrREV 48.2 – Inferred Adaxial-abaxialpolarity

LL, LA copQTL10 A02 79.2–85.6 4 BRASSICA RAPAPINFORMED1

82.6 1.6 Mapped Leaf shape

LL, BL copQTL11 A02 139.6–148.2 2 BRASSICA RAPAMADS AFFECTINGFLOWERING4

141 1.4 Mapped Ft

BrKAN2 141 – Inferred Adaxial-abaxialpolarity

LN, LL, LW, BL copQTL12 A03 2–14.3 6 BrFLC5 9.2 0 Mapped FtBrAE3 9.2 – Inferred Others

LN, LW, Ft copQTL13 A03 14.3–29.2 3 BRASSICA RAPALEAFY PETIOLE

22.4 – Inferred Others

LL, LW, BL, LI copQTL14 A03 49.6–67.5 16 BrAS1 55.2 10.4 Mapped SymmetryLBs, Pmh, LW, LI copQTL15 A03 110.3–137.3 5 BrKRP2 114.2 25.3 Mapped LLLW, LP copQTL16 A04 39.9–63.4 2 BRASSICA RAPA

PHYTOCHROME A58.5 0 Mapped Ft

BL, LI copQTL17 A05 111.3–117.8 3 BrSPL5 117.8 0 Mapped FtLC copQTL18 A05 125.9–175.9 2 BrHST1 158 0 Mapped Adaxial-abaxial

polarityLN, Pmh, PB copQTL19 A09 37.2–46.4 3 BRASSICA RAPA

FERREDOXIN-NADP(1)-OXIDOREDUCTASE1

41.3 0 Mapped LW

BL, LI copQTL20 A09 56.2–65.4 2 BrRON1 61.6 – Inferred LLBrWUS 61.6 – Inferred Meristems

LW, BL, LI copQTL21 A09 62.9–70.1 15 BRASSICA RAPASHOOTMERISTEMLESS

70.1 – Inferred Meristems

BRASSICA RAPA HUAENHANCER1

70.1 – Inferred Others

BrPNH 70.1 – Inferred Adaxial-abaxialpolarity

LC, LB copQTL22 A09 114.7–126.5 2 BrER 121.8 2.1 Mapped OthersPL, LI, LC, LL,

LW, BL, PB, FtcopQTL23 A10 65.9–82.1 22 BrLNG1 73.9 0 Mapped LL

(Table continues on following page.)





Genetic Regulatory Network

To further visualize the correlation between traits andgenes, we constructed a genetic regulatory networkbased on LOD values using the Spearman correlation(Fig. 4; Supplemental Table S12). LOD values of 17phenotypes (2008) and 118 probes/genes that are clas-sified into nine functional pathways were used to con-struct the coexpression network. The results show that10 out of 17 traits were associated with 22 (35 links)candidate genes/probes belonging to five functionalpathways (adaxial-abaxial polarity, four; Ft, eight; LL,five; leaf shape, four; other, one). Two traits for leaf size(LA and BL) were not associated with the expressionprofiles of genes. Many traits related to flowering, plantarchitecture, and leaf shape (Ft, LN, PB, leaf big lobes[LBb], and LD) were positively associated with the Ftgenes investigated. A subset of these traits (Ft, LN,and LBb) was also positively associated with fouradaxial-abaxial polarity genes (BRASSICA RAPAKANADI2 [BrKAN2_A01], BrPNH_A09, BRASSICARAPA PHAVOLUTA_A09, and BRASSICA RAPAYABBY2_A09). Three traits defining plant architecturaltraits (PB) and leaf size (LW and LI) were associatedwith four LL genes (BrCycB2;4_A07, BRASSICA RAPAAUXIN-REGULATED GENE INVOLVED IN ORGANSIZE_A07 [BrARGOS_A07], BRASSICA RAPA ARGOS-LIKE_A03 [BrARL_A03], and BrCYCB1_A01), whiletraits related to leaf shape (LB) and Pmh were mainlynegatively associated with genes with function in leafshape in Arabidopsis. The genetic network showedcorrelation of the 22 candidate genes to phenotypictraits, which suggests a role for these genes in leafdevelopment, plant architecture, and Ft.

Bayesian Network Analysis to Identify GenesRegulating Phenotypes

The relationships among the 17 traits were estimatedby the PC (for Peter and Clark) algorithm (Spirtes et al.,2000; Supplemental Fig. S7). This clearly showed three

groups, one including plant architectural traits (PB, LN,and Pmh), LC, and Ft, one consisting of traits related toleaf size (LL, PL, BL, LP, LA, LW, and LI), and one re-lating to leaf shape (LB, LBs, LBb, Lcu, and LD), whichwe call subnetworks 1, 2, and 3. Based on copQTLs andcandidate genes, Spearman correlation of trait variation,and the genetic regulatory network described above (see“Materials and Methods”), we prioritized 62 candidategenes/probes associated with the 17 traits. We appliedthe PC algorithm with a greater significance level (0.05)to further improve the identification of sets of candidategenes that regulate the phenotypes (Fig. 5), with asignificance level at 0.01 (Supplemental Fig. S8). Sub-network 1 included phenotypic traits related to plantarchitecture, LC, and Ft plus 43 genes (Fig. 5A), result-ing in the association of 20 genes (six functional path-ways) with the five traits. For example, LC associatedwith four different genes: BrER_A09 and BrGRF2_A03,both with roles in leaf chlorophyll in tomato (Solanumlycopersicum) and Arabidopsis (Liu et al., 2012; Seo et al.,2012), while BrKRP2_A09 and BrKAN2_A05 have neverbeen implicated in LC variation so far. Subnetwork 2includes seven traits that define leaf size and 22 genes(Fig. 5B). In this subnetwork, 15 genes (four functionalpathways) were associated with the seven traits. Forexample, LW was associated with three LL pathwaygenes, BrARGOS_A09, BrARL_A03, and BrKRP2_A03.2.Among of them, BrARGOS_A09 and BrARL_A03 in-fluence cell size and are up-regulated by brassinoste-roids (Hu et al., 2006; Feng et al., 2011), while anotherreport describes that auxin acts via the ARGOS proteinto regulate leaf size (Hay et al., 2004). BrKRP2_A03.2was involved in the regulation of cell proliferation andpostmitotic cell expansion to control organ size in Arabi-dopsis (Kawade et al., 2010). The other six traits in thissubnetwork are associated with genes from four func-tional pathways, including five genes involved in LL,three genes involved with adaxial-abaxial polarity, onesymmetry gene BRASSICA RAPA GENERAL TRAN-SCRIPTION FACTOR GROUP E6_A03 (BrGTE6_A03),and two “other” pathway genes (BrAE3_A02 and

Table III. (Continued from previous page.)

Traits Involved copQTLa,b LG IntervalcNo. of

pQTLs

Nearest

Candidate

Gene

Peak pQTL

Distance

between

Peak LOD

and Candidate

Gened

GroundeAssociated

Functional

Pathway

LI copQTL24 A10 63.4–96.9 2 BRASSICA RAPACONSTANS-LIKE1

84.9 0 Mapped Ft

BrKAN1 84.9 – Inferred Adaxial-abaxialpolarity

LI copQTL25 A10 84.9–111.1 2 BrAE3 96.9 – Inferred OthersBRASSICA RAPAHYPONASTIC LEAVES1

96.9 6.9 Mapped Others

aIncluded initial pQTLs (Supplemental Table S6). bAsterisks mean that these copQTLs largely overlap and may represent a single QTL. cA 1.0 LOD 95%confidence interval. d2 means that the distance between the candidate gene and the LOD peak cannot be calculated, as the position of thecandidate gene is not mapped in DH68, but inferred based on their physical positions and those of neighboring genes with genetic map positions(Supplemental Table S7). eGenes were either mapped in DH68 or their inferred positions were based on their physical positions and those ofneighboring genes with genetic map positions (Supplemental Table S7).


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BRASSICA RAPA CORONA_A07). Subnetwork 3 in-cludes five leaf shape traits and 24 candidate genes(Fig. 5C). Fifteen genes (eight functional pathways)were associated with the five traits. For example, leafshape (LBb) was associated with the adaxial-abaxialpolarity gene BRASSICA RAPA YABBY2_A09, theLW gene BRASSICA RAPA ANGUSITFOLIA3_A09(BrAN3_A09), the Ft gene BrFLC5_A03, and BRASSICARAPA RNA-DEPENDENT RNA POLY MERASE6_A01(BrRDR6_A01) involved in yet another functional path-way (e.g. leaf adaxial polarity and Lcu; Yuan et al., 2010;Kojima et al., 2011). Genes identified through Bayesian

network analysis are promising candidates regulatingvariation in leaf development of B. rapa.

Identification of Putative Genes Regulating Trait Variation

We combined the results of copQTLs, correlationnetworks, and genes predicted by Bayesian networks tosearch for candidate genes for leaf variation. We de-scribe six loci where copQTLs, high LOD cis-eQTLs, andtrans-eQTLs colocate; for four of these loci (BrFLC2_A02,BrKRP2_A03, BrER_A09, and BrLNG1_A10), the candi-date gene at the peak of the QTL has a high LOD cis-

Figure 3. Portion of the heat map showing the correlations among phenotyped traits and the expression of B. rapa leaf can-didate genes. Spearman correlation was calculated among 118 genes and 17 phenotypes to show coexpression patterns ofgenes using hierarchical clustering. Regions with absolute Spearman correlation of r . 0.3 and P , 0.05 are shown. Blueindicates negative correlation, and red indicates positive correlation. The traits and genes, and their coexpression, are high-lighted in the boxes showing the coexpressed genes based on the grouping structure in hierarchical clustering. The details ofwhole clusters and their correlation values are presented in Supplemental Figure S6 and Supplemental Table S11.







eQTL (Table IV). copQTL6, copQTL15, copQTL22, andcopQTL23 mapped to the candidate genes BrFLC2,BrKRP2, BrER, and BrLNG1, respectively. BrFLC2, aMADS domain transcription factor that has a major rolein repressing Ft, was cis-regulated at 21.1 to 36.9 cM onA02 (LOD = 24.2). It is important to note that thecopQTL6 for Ft, leaf size (LL, LW, BL, and LI), and plantarchitectural traits (LN and PB) and the cis-eQTLfor BrFLC2 colocalize with trans-eQTLs for BRASSICARAPA WUSCHEL_A06 (BrWUS_A06) and BrKAN2_A09.The BrKRP2 cis-eQTL is located on chromosome A03 at110.3 to 137.3 cM, with a peak LOD = 25.7, colocalizingwith copQTL15 for leaf size (LW and LI), leaf shape(LBs), and plant architectural traits (Pmh). The BrER cis-

eQTL is located on chromosome A09 at 99.4 to 133.7 cM(LOD = 12.2), colocalizing with copQTL22-involved LCand leaf shape (LB) traits. At these two loci (BrKRP2and BrER), additional cis- and trans-eQTLs colocalized,as summarized in Table IV. The BrLNG1 cis-eQTL onchromosome A10 at 65.9 to 84.9 cM (LOD = 15.1)colocalizes with copQTL23 for leaf size (PL, LI, LL, LW,and BL), PB, LC, and Ft and both BRASSICA RAPACONSTANS_A10 (BrCO_A10) and trans-BrKAN2_A01eQTLs. Additionally, copQTL14 (which contains 16 ini-tial pQTLs for leaf size) maps on A03 at 49.6 to 67.5 cM,colocalizing with four cis-eQTLs for BrLNG2, BRASSICARAPA SPLAYED (BrSYD), BrARL, and BRASSICARAPA ENHANCER OF AG-4 2 (BrHUA2) and five trans-

Figure 4. Network visualizing the correlation between leaf morphological traits and B. rapa candidate genes for leaf devel-opment. To show the functional relevance of the genes and leaf morphological traits in a coexpression network, Spearmancorrelation was calculated based on LOD scores including 12 leaf traits and the expression of 92 candidate genes, and onlysignificant absolute correlation at r . 0.3 and P , 0.05 are shown here. Nodes represent genes or leaf traits, and edgesrepresent correlations. The genes are arranged in nine functional pathways as shown by different node colors. The colors of theedges represent correlation between genes and/or traits: green for edges between genes and phenotypic traits, magenta foredges between phenotypic traits, gray and faint red for edges between interpathway genes, and dark blue for edges betweenintrapathway genes. The positive correlations are shown by the solid lines, and the negative correlations are shown by thedotted lines. Shapes of the nodes indicate the cis-/trans-regulation of genes (triangles, cis; squares, trans; diamonds, cis andtrans), and green round nodes indicate leaf traits.


Xiao et al.



eQTLs for BrAE3_A02, BrHUA2_A02, BrARGOS_A07,BrDRL1_A09, and BrARGOS_A09. A leaf symmetryfunctional pathway gene, BrAS1, was mappedwithin theregion. The BRASSICARAPAROTUNDIFOLIA1 (BrROT1)cis-eQTL mapped to A05 at 17.9 to 66.1 cM (LOD =7.1), colocalizing with trans-eQTLs for BrRDR6_A01,BrFLC5_A03, and BrAN3_A09 and partly with two leafshape pQTLs for LBb and LBs.

Role of Paralogs of Leaf Genes in Leaf Development

In order to understand the role of duplicated leafdevelopment genes in B. rapa, we investigated whether

paralogs of the same Arabidopsis genes colocalizedwith similar phenotypic QTLs. From the 118 leaf de-velopment genes with eQTLs, 67 (56.8%) had two ormore paralogs corresponding to 28 Arabidopsis orthol-ogous genes (Supplemental Table S13). From this set ofgenes, 15 subsets of paralogs colocalized with pQTLs forthe same phenotypic QTLs. For example, the cis-BrGA20OX3_A03 eQTL colocalized with pQTLs for leafsize (LL, LW, and BL), plant architecture (Ft and LN),and leaf shape (LBb), integrating 15 initial pQTLs onA03. The trans-BrGA20OX3_A02 had two eQTL re-gions: one on A01 where no pQTLs mapped, and theother on A03, colocalizing with the mentioned pQTLsand cis-BrGA20OX3_A03. The trans-BrGA20OX3_A10

Figure 5. Three networks for enriched sets of prioritized potential candidate genes for leaf development based on the Bayesiannetwork (PC algorithm with significance level of 0.05) in B. rapa. A, Subnetwork 1 was constructed based on plant architecturetraits, LC, and Ft. B, Subnetwork 2 was constructed based on leaf size traits. C, Subnetwork 3 was constructed based on leafshape traits. For explanations of shape and color descriptions of the nodes, see Figure 4. Red lines are the associations betweenphenotypic traits and genes; blue lines indicate associations between genes, with at least one gene associated with a phenotype;magenta lines are associations between phenotypic traits. Edge line width indicates the correlation value between corre-sponding nodes representing the strength of correlation. Solid lines indicate positive correlations, and dotted lines indicatenegative correlations.






colocalized with a single pQTL for leaf shape (LBs) onA03. Four paralogs of BrKAN2, distributed on A01, A02,A05, and A09, each colocalized with major pQTLs forleaf size traits (LL and LW). Three paralogs of BrAE3,distributed on A02, A03, and A10, colocalized withQTLs for LI. The fact that paralogs colocalized withpQTLs for the same traits suggests that they maintainedsimilar functions after genome triplication, while theother 13 subsets of paralogs may have diverged.

DISCUSSION

B. rapa is a species that displays extreme morpholog-ical variation, from heading Chinese cabbages, non-heading leafy vegetables, turnips, to oil crops. All thesemorphotypes differ in leaf number, size, shape, andcolor, and very little is known of the molecular nature ofthis variation.

Whole-genome duplication coupled with the re-tention of duplicated genes was proposed to in-crease morphological complexity in eukaryotic species(Freeling and Thomas, 2006). Comparedwith Arabidopsis,B. rapa underwent a genome triplication, and as a result,many genes have paralogs in syntenic blocks, and in-vestigation toward their roles in the regulation of leafdevelopment is important. In this study, 61 out of 91leaf genes from Arabidopsis have two or more paralogsin the B. rapa genome, while 22 have retained a singlecopy. Similar to this observation, genes related toflowering were maintained in higher numbers thanexpected when gene loss was random (Xiao et al., 2013;Supplemental Table S1). According to the “gene balancehypothesis” (Birchler and Veitia, 2010), dosage-sensitivegenes duplicated by polyploidy are those with productsthat function in multisubunit complexes. Our workpresents 15 sets of duplicated genes/paralogs with

Table IV. Identification of putative target loci by colocalization of pQTLs and eQTLs in B. rapa

Peak eQTL Marker

(Chromosome: cM)

Candidate Gene

RegulatedLOD

Percentage

Variance

Attributable

to eQTLs

cMa Colocated pQTL Trait Pb

BrFLC2 (A02: 24.1) Cis-BrFLC2_A02 24.2 71.4 21.1–36.9 copQTL6 0.011Cis-BrTCP11_A02 11.6 45.6Trans-BrFT_A07.1 8.2 35.1Trans-BrFT_A07.2 3.4 16.6Trans-BrSOC1_A05 5.0 23.5Trans-BrSOC1_A03 4.9 23.2Trans-BrCO_A10 5.5 25.3Trans-BrWUS_A06 3.0 15.8Trans-BrKAN2_A09 3.0 17.9

BrAS1 (A03: 65.6) Cis-BrLNG2_A03 4.5 21.4 49.6–67.5 copQTL14 0.002Cis-BrSYD_A03 3.2 15.7Cis-BrARL_A03 5.0 23.7Cis-BrHUA2_A03 4.2 20.1Trans-BrAE3_A02 4.5 21.6Trans-BrHUA2_A02 6.6 29.8Trans-BrARGOS_A07 3.0 15.1Trans-BrDRL1_A09 3.4 16.9Trans-BrARGOS_A09 3.0 15.1

BrKRP2 (A03: 132.6) Cis-BrKRP2_A03 25.7 74.8 108.1–139.5 copQTL15 0.008Cis-BrGTE6_A03 7.8 32.6Trans-BrCDC2_A01.2 3.4 16.2Trans-BrPHB_A04 4.3 19.8Trans-BrCDC2_A06.2 3.2 16.0Trans-BrCycD3;2_A07 3.7 18.1Trans-BrGA20OX3_A10 4.0 19.5

BrMS-034 (A05: 41.1) Cis-BrROT1_A05 7.1 31.7 17.9–66.1 2008-LBb, 2008-LBs ,0.001Trans-BrRDR6_A01 3.2 15.8Trans-BrFLC5_A03 4.2 19.9Trans-BrAN3_A09 6.6 30.2

BrER (A09: 123.9) Cis-BrER_A09 12.2 47.9 99.4–133.7 copQTL22 0.002Cis-BrARF3_A09 4.1 19.6Trans-BrKAN2_A05 3.7 18.4Trans-BrFT_A07.1 3.2 15.7Trans-BrKRP2_A09 9.7 37.6

BrLNG1 (A10: 73.9) Cis-BrLNG1_A10 15.1 55.5 65.9–84.9 copQTL23 0.002Cis-BrCO_A10 4.5 21.2Trans-BrKAN2_A01 3.0 14.9

aTotal length of the overlapped above the LOD . 3.0 threshold region in cM. bLargest overlap P value between pQTL and eQTL traits.


Xiao et al.




eQTLs colocalizing with pQTLs for the same pheno-typic traits, while 13 colocalized with pQTLs for differ-ent traits (Supplemental Table S13). For the remaining39 genes, either one or more paralogs did not colocatewith pQTLs. A previous study has demonstrated thatduplicated genes may lead to new functions of theparalogs (Blanc and Wolfe, 2004).The utilization of candidate gene markers and ge-

nomics platforms (pQTL and eQTL) in combination withsegregating populations results in data to discover thecomplex genetic mechanism of B. rapa leaf architecturevariation. The DH lines, derived from a cross between anoil type, cv Yellow sarson 143, and a leafy vegetable, cvPak choi 175, were evaluated for 15 plant morphologicaltraits, leaf number, and Ft in 2008 and 2010, and in 2010at consecutive leaf developmental stages. Significantdifferences were observed between pQTLs for each trait,their additive effect, and LOD size in different years andgrowth stages. This indicates genotype3 environment3developmental stage effects on leaf development. Severalother studies also showed the impact of diverse envi-ronments on QTL detection for leaf development inBrassica species and maize (Zea mays; Lou et al., 2007; Liet al., 2009; Ku et al., 2012; Raman et al., 2013). Temporalanalysis of leaf growth showed both different and com-mon pQTLs at different growth stages, which accumu-lated to a large number of loci affecting leaf development(Fig. 2A; Supplemental Tables S6 and S10). As shown inthis work, the LL increased until measurement stageVIII, after which growth ceased (Supplemental Fig. S4T).For instance, for LL, pQTLs from the first three stages(I–III) in copQTL2 were mapped to BrGRF5 at 89.4 cMon A01. pQTLs for seven stages (I–VII) in copQTL6were mapped to BrFLC2 on A02 (21.1–36.9 cM). pQTLsfrom four later stages (VII–X) in copQTL23 mapped toBrLNG1 at 73.9 on A10 (Table III; Supplemental TablesS6 and S8). Hence, a systematic temporal dissection isnecessary to decipher the complex genetic bases ofquantitative variation in leaf growth. The temporal pat-terns of pQTLs for LW and BL suggest two phases in leafdevelopment, with early- and late-acting pQTLs (Fig. 2;Supplemental Fig. S4, U and V). In this paper, we do notaddress heteroblasty, which refers to changes in leafshape and size (allometry) along stems (Feng et al., 2009;Costa et al., 2012). Leaf shape and size do vary withincreasing leaf number; however, we chose for this studythe third leaf, which in shape resembles the next leaves,while the first and second leaves are often asymmetricand smaller.A total of 34 B. rapa candidate genes colocalized with

copQTLs, indicating that these genes may affect multipleleaf architecture traits (Table III). copQTL4 for leaf size(LL, LW, and BL) was detected on A01, and copQTL18for LC was detected on A05, both colocalizing with dif-ferent paralogs of BrHST1. Mutants of this gene affectseveral processes, including leaf polarity, reduction inleaf size, sepals, and petals, uprolling of the leaf blade,reduction in leaf number, and cell growth in Arabidopsisdevelopment (Serrano-Cartagena et al., 2000; Bollmanet al., 2003). copQTL15 colocalizes with BrKRP2 on A03

and includes five initial pQTLs for leaf size (LW and LI),leaf shape (LBs), and a plant architectural trait (Pmh).Interestingly, at this same position, a QTL for leaf size(LW and LI) was also identified in the reciprocal DH38population (Lou et al., 2007). In this same study, a QTLfor LW in an F2:F3 population (rapid cycling 144 3Chinese cabbage 156) was detected on the bottom ofA05, corresponding to the copQTL17 colocalizing withthe BrSPL5 gene in our study. In the study of Lou et al.(2007) using the reciprocal DH38 population, anothercopQTL for LW, LA, and LI was detected on A09, whichcorresponds with copQTL21 for leaf size (LW, BL, andLI) in our research (Fig. 2A; Table III).

Through the integration of gene expression profilingand the colocalization of copQTL and eQTL, four can-didate genes with cis-eQTLs (BrFLC2_A02, BrKRP2_A03,BrER_A09, and BrLNG1_A10) were identified that reg-ulate multiple traits (Table III). In a recent publication,QTL colocalizing with the FLC gene contributed to nat-ural variation in Ft and reduced stem branching genes(RSB) in Arabidopsis (Huang et al., 2013). Their geneticanalyses showed that the reduced stem branching QTLsRSB6 and RSB7, corresponding to Ft genes FLC andFRIGIDA, regulate stem branching. They also showedthat gene FLOWERING LOCUS T (FT), which corre-sponds to another reduced stem branching QTL, RSB8,caused pleiotropic effects not only on Ft but, in thespecific background of active FRIGIDA and FLC alleles,also on the stem branching trait. In our study, wedetected a similar phenomenon, as illustrated bycopQTL6 for leaf size (LL, LW, BL, and LI), plant ar-chitecture traits (LN and PB), and Ft (Table III). BrFLC2not only regulates Ft but is associated with variation inplant architectural traits (PB, LN, and Pmh) based oncorrelation analysis (Fig. 4), Bayesian network analysis(Fig. 5A), and colocalization analysis (Fig. 2A; Table III),which point to a pleiotropic regulation of these traits.copQTL15 (LBs, Pmh, LW, and LI) colocalized with acis-eQTL for BrKRP2_A03, another cis-eQTL forBrGTE6_A03, and five trans-eQTLs for BRASSICA RAPACELL DIVISION CONTROL2_A01.2 (BrCDC2_A01.2),BrCDC2_A06.2, BRASSICA RAPA PHABULOSA_A04(BrPHB_A04), BrCycD3;2_A07, and BrGA20OX3_A10(Table IV). KRP2 and GTE6 play roles in organ size andleaf development in Arabidopsis (Chua et al., 2005;Kawade et al., 2010). The PHB transcription factor causesthe transformation of abaxial to adaxial leaf fates(Bao et al., 2004). The other trans-regulated gene,BrGA20OX3_A10, is involved in controlling leaf lobes inB. rapa (Li et al., 2009). CDC2 has a role in the cell cycle(Hemerly et al., 1993), and CycD3;2 has a function inthe regulation of cell numbers during apical growth.CycD3;2 was mapped within an important meta-QTLinterval involved in leaf angle, leaf orientation value, LL,and LW in maize (Ku et al., 2012). In this study,copQTL22 for LC and leaf lobes colocalized with acis-eQTL for BrER_A09, which also colocalized with acis-eQTL for BRASSICA RAPA ADP-RIBOSYLATIONFACTOR3_A09 (BrARF3_A09) and trans-eQTLs forBrKAN2_A05, BrFT_A07.1, and BrKRP2_A09. ER encodes











a Leu-rich repeat receptor-like kinase, with pleiotropiceffects on many traits, including morphological differ-ences, leaf chlorophyll, and tolerance to drought and saltstresses in transgenic tomato plants (Keurentjes et al.,2007; Seo et al., 2012; Villagarcia et al., 2012). In Arabi-dopsis, Kelley et al. (2012) provided evidence that KANand ARF3 proteins formed a functional complex active inleaf development.

copQTL23 on A10 (PL, LI, LC, LL, LW, BL, PB, andFt) at the BrLNG1 locus colocalized with a cis-eQTL forBrLNG1, a trans-eQTL for BrKAN2, and a cis-eQTL forthe Ft gene BrCO. Furthermore, both Bayesian networkand colocalization analyses showed that BrLNG1_A10associated with the trait PL (Figs. 2 and 5B; Table III).LNG1 mutants affect leaf polar cell elongation inArabidopsis (Lee et al., 2006). The previously charac-terized leaf-adaxialized kan1, kan2 double mutantproduces finger-shaped protrusions on the abaxialsurface (Pekker et al., 2005). All the above-mentionedcopQTL combining traits related to leaf shape, size,flowering time, and plant architecture with eQTL (cis-and trans-regulated) suggest pleiotropic regulationof leaf development and plant architecture traits.To further investigate the set of candidate genes, wesequenced the coding regions and around 1,000-bppromoter regions from a rapid cycling line (RC-144), aJapanese turnip DH line (VT-117), and a cv Pak choiline (PC-001) and compared the sequence with theChiifu reference sequence (cv Chiifu-401). We identi-fied several amino acid changes in coding regions andseveral changes in the promoter regions that couldaffect gene function (Supplemental Fig. S9). Furtherstudies are needed to pinpoint which mutations affectgene function/expression and, thus, phenotypic traitvariation.

In conclusion, we combined data analysis of candidategene expression and phenotypic QTLs for leaf traits, Ft,and plant architecture to increase our understanding ofthe molecular basis of leaf development. This led to theidentification of several candidate genes for these phe-notypic traits, with focus on the roles of BrKRP2_A03,BrER_A09, BrLNG1_A10, and BrFLC2_A02 that pleio-tropically regulate leaf development, Ft, and plant ar-chitecture in B. rapa.

MATERIALS AND METHODS

Plant Materials and Growth Conditions

Brassica rapa ‘Yellow sarson’ YS-143, an early-flowering Indian oilseed plant asfemale parent, was crossed with B. rapa ‘Pak choi’ PC-175, a late-flowering Chi-nese leafy vegetable type as male parent, to produce the DH68 population. Seedsof the DH lines and parents were germinated in petri dishes at 18°C in the darkfor 36 h in the growth chamber to accelerate and synchronize germination, thentransferred to trays with soil and transplanted on day 18 after sowing in pots.Single plants were cultivated in plastic pots (diameter, 17 cm) under 16-h days,24°C/8-h nights, 10°C in a glasshouse in 2010. The 2008 conditions are describedin Xiao et al. (2013).

These DH lines (92) were evaluated for several phenotypic traits under dif-ferent conditions when grown from October 2008 to January 2009 and fromFebruary to May 2010. The 92 genotypes were planted in three blocks in 2008

and in one block in 2010, where genotypes were randomized. A total of 17 traitswere recorded in the 2008 experiment, while six phenotypic traits were recordedin 2010 (LN, LC, LL, LW, BL, and LI). In 2010, four traits related to leaf size (LL,LW, BL, and LI) were evaluated at nine or 10 time points spaced in intervals of 3to 5 d (Table I). Diagrams illustrating the scoring scales of LD and Lcu are shownin Supplemental Figure S10, A and B. LB are classified in two categories,LBb and LBs. The distinction between these two classes is not always clear(Supplemental Fig. S10C). The leaf characteristics of fully expanded third leavesand the phenotypes of the two parents are illustrated in Figure 1 andSupplemental Figure S3.

In 2008, RNA was extracted from the 92 DH lines for eQTL analysis. Thethird and fourth leaves of three biological replicate were collected 5 weeksafter transplanting in the morning (10 AM to 12 noon); thereafter, each replicatewas ground individually, and equal amounts of powder were mixed and usedfor transcript profiling.

Development of Genetic Markers for Genes Involved inLeaf Traits

We identified 91 candidate genes belonging to nine functional pathways inthe literature that are implicated in leaf development control in Arabidopsis(Arabidopsis thaliana; Supplemental Table S1). Using the Arabidopsis locusname, homologous genes were annotated in the B. rapa reference genome(Chinese cabbage cv Chiifu-401; http://brassicadb.org/brad/). All genes oncontigs matching the same Arabidopsis coding sequence generated byBLASTP best hit were considered as B. rapa paralogs (cutoff E-value of e25).Gene structures were predicted by sequence comparison with the Arabidopsiscoding sequence using DNASTAR Lasergene 9.0 (Lasergene). The PCRprimers for genetic markers were designed by Primer 3 (http://frodo.wi.mit.edu/primer3/), with expected sizes of amplified fragments being approxi-mately 200 to 300 bp. The markers based on the candidate genes were namedas follows: Br, Arabidopsis gene name, genome locus, ordered primer code.

Linkage Map Construction and pQTL Analysis

Genotyping for polymorphic candidate gene markers was conductedaccording to the manufacturer’s protocol for the 96-well LightScanner System(ID Technology; Montgomery et al., 2007; Xiao et al., 2013). The primers usedfor this study are listed in Supplemental Table S2.

Sixty leaf candidate genes, 125 Ft candidate genes, amplified fragment lengthpolymorphism, simple sequence repeat, and insertion/deletion markers wereused to construct the genetic map using Joinmap 4.0 (Kyazma; http://www.kyazma.nl/) using a regression approach and the Kosambi map function. SinglepQTL analysis was undertaken with interval mapping and restricted and fullmultiple QTL model mapping using MAPQTL 5.0 (Van Ooijen, 2004). Initiallypeak markers from a map region with LOD score . 2 were used as cofactors,and a final list of cofactors was selected using the automatic cofactor selectionprocedure, which uses a backward elimination approach to select a final set ofcofactors. The restricted and full multiple QTL model mapping processes wererepeated with different sets of cofactors until the QTL profile was stable. Forestablishing a genome-wide significance threshold for the QTL analyses, a per-mutation test was done with 1,000 iterations; however, a fixed LOD threshold of3.0 was used as the final threshold to declare a QTL, because for most of thetraits, this was the 95th percentile of the permutation LOD scores. QTLs with aLOD score between 2 and 3 were considered as putative QTLs because, for mostof the traits, a LOD score of 2 was the significance threshold for a single specificlinkage group. Finally, 1 2 LOD support intervals were determined for theassigned QTL.

RNA Isolation and Microarray Design

By using the Trizol reagent (Invitrogen), total RNA was extracted from ap-proximately 300 mg of frozen leaf material (a mixture of three biological replicatesper DH line). The first strand of complementary DNAwas synthesized from 1 mgof total RNA using the I Amplification Grade kit (Invitrogen) according to themanufacturer’s instructions. Agilent 105K Brassica species oligoarrays (AgilentTechnologies), which contain 96,557 features, were used for two-color microarrayexperiments and implemented in the R package designGG (http://www.rug.nl/research/bioinformatics/). All microarray experiments were performed accord-ing to the manufacturer’s manual (Agilent Technologies).


Xiao et al.







http://brassicadb.org/brad/

http://frodo.wi.mit.edu/primer3/

http://frodo.wi.mit.edu/primer3/


http://www.kyazma.nl/

http://www.kyazma.nl/

http://www.rug.nl/research/bioinformatics/

http://www.rug.nl/research/bioinformatics/


eQTL Mapping Analysis

The eQTL analysis was performed using the basic single marker regressionprocedure present in R/QTL. The expression of genes represented as 60-merprobes on the array was measured using two-color array technology, and for themapping, we used the ratio between two genotypes: Yi = a + bGi + error, whereYi = probe intensity and Gi = genetic effect. In this model, the genetic effect wasannotated for the expression ratio as described (Fu and Jansen, 2006); b is theeffect of the different allele (1 for A . B, 0 for A = B, and 21 for A , B). Thismodel was evaluated at each marker to get an estimate of the allelic effect on theexpression probes. This results in a P value, which was transformed into a LODscore. The eQTL with LOD . 3.0 was considered significant. In our study, weused the gene expression and marker information from the 92 DH lines to detectthe eQTL for the annotated leaf candidate genes. In addition, the expression ofseveral B. rapa leaf candidate genes that were not represented on the microarraywas also profiled using RT-qPCR. The RT-qPCR experiments were performedaccording to our previous paper (Xiao et al., 2013). The primer sequences used inthis study are listed in Supplemental Table S14. cis-eQTL (local eQTL) was de-fined when the (derived) genetic position of the gene and its eQTL were locatedwithin 50 cM. The remaining eQTLs were defined as trans-eQTL (distant eQTL).

Genetic Regulation Network Analyses

To explore the modular association with the traits and gene expression data,we first computed a Spearman correlation among the 17 phenotypes (measureddata) and 118 genes/probes (expression data by log10 scale). The modules areshown using the heat-map tool (Spearman product r . 0.3, P , 0.05). Fur-thermore, to elucidate a coregulation network, the eQTL and pQTL LOD profilesacross the genetic map were calculated, and these top covariates (Spearmanproduct r . 0.3, P , 0.05) were used to construct the regulation network.

Additionally, we applied a Bayesian network to evaluate subnetworks ofthe expression and phenotypic QTL data traits from the DH68 population. TheBayesian network is a probabilistic graphical model of multiple variables thathas adequate statistical power (Spirtes et al., 2000; Li et al., 2005; SupplementalText S1). In order to reduce the computational load, a limited set of candidategenes are selected based on (1) copQTLs and candidate genes; (2) clustersidentified by Spearman correlation of trait variation; and (3) a genetic regu-latory network based on LOD scores. The selected candidate genes werecalculated into three conditionally independent phenotypic structures.

Visualization of the genetic regulation networks was plotted using Cytoscape2.8.2 (http://www.cytoscape.org/; Shannon et al., 2003). All calculations weredone in the open statistical software R 2.13.1 (Ihaka and Gentleman, 1996).

Colocalization of pQTLs, eQTLs, and Candidate Genes

To identify the colocalization of pQTLs, eQTLs, and candidate genes, wecomputed multiple test P value corrections with false discovery rate (Steibelet al., 2011). Given a particular eQTL region, delimited by a 5-cM interval toeach side of the peak, all overlapping pQTL regions were selected (West et al.,2007). The P of two intervals of this length (10 cM) overlapping in a 1,328-cM-long genome (the length of our linkage map) is

P ¼ 12�12

101328

�2

¼ 0:015

Sequence data from this article can be found in the Brassica Database(http://brassicadb.org/brad/).

Supplemental Data

The following materials are available in the online version of this article.

Supplemental Figure S1. Genetic map of the B. rapa DH68 population.

Supplemental Figure S2. Mapped leaf trait candidate genes as anchors be-tween the genetic map (YS-1433 PC-175; cM) and the physical map (Mb).

Supplemental Figure S3. Images of parents and their DH progeny areshown: first stage (25 d after sowing) and 10th stage (57 d after sowing).

Supplemental Figure S4. Frequency distribution for the morphologicaltraits studied in the B. rapa DH68 population (YS-143 3 PC-175).

Supplemental Figure S5. Validation by RT-qPCR analysis of transcriptswith cis-eQTL: BrCYCD1;1_A02, BrKRP2_A03, and BrDRL1_A08.

Supplemental Figure S6. Correlation heat map of gene expression datausing microarray and RT-qPCR data and phenotype traits in B. rapa.

Supplemental Figure S7. Bayesian network for 17 phenotypic leaf traits ofB. rapa.

Supplemental Figure S8. Bayesian network analysis identified (PC algo-rithm with significance level of 0.01).

Supplemental Figure S9. For the genes BrFLC2_A02 (Bra028599),BrKRP2_A03 (Bra012894), BrER_A09 (Bra007759), BrLNG1_A10(Bra008689), BrLNG1_A02 (Bra023526), BrKAN2_A09 (Bra023254),BrKAN2_A05 (Bra033844), BrHST1_A05 (Bra039468), BrARGOS_A07(Bra003394), BrARGOS_A09 (Bra007491), BrGRF5_A03 (Bra001532),and BrGRF5_A05 (Bra027384) colocalizing with QTLs for copQTL6,copQTL8, copQTL15, copQTL18, copQTL22, and copQTL23, the codingsequence and 1,000-bp upstream sequence (promoter region) were se-quenced to assess allelic variation.

Supplemental Figure S10. Diagram of LD, Lcu, LBb, and LBs of aB. rapa leaf.

Supplemental Table S1. List of selected genes involved in different func-tional pathways of leaf development in B. rapa.

Supplemental Table S2. Sequence informative markers for the leaf traitgenes that are genetically mapped by LightScanner on the 92 B. rapaDH68 lines.

Supplemental Table S3. Variation explained (%) by genotype, genotype 1block in ANOVA test for 17 traits measured in year 2008.

Supplemental Table S4. Variation explained (%) by different models ofANOVA for the common traits measured in 2008 and 2010 experiments.

Supplemental Table S5. Summary statistics of leaf morphological traits inthe B. rapa DH68 population and parental genotypes in 2008 and 2010.

Supplemental Table S6. pQTL results of leaf morphological traits inB. rapa.

Supplemental Table S7. List of all candidate genes for Ft and leaf traitswith their physical order and eQTL, either genetically mapped in DH68or with inferred map position based on physical position and geneticallymapped flanking markers.

Supplemental Table S8. List of copQTLs with initial pQTLs.

Supplemental Table S9. Annotation of leaf trait genes represented on themicroarray.

Supplemental Table S10. Detailed information for all pQTLs and eQTLsidentified in this study with their LOD score, functional pathways, andinformation on cis-/trans-regulation.

Supplemental Table S11. Coexpression correlation among all probes andphenotypic traits.

Supplemental Table S12. Coregulation of all the leaf traits and expressionof leaf trait-related candidate genes by calculating the correlation basedon LOD score profile.

Supplemental Table S13. eQTL location of duplicated genes for a set ofleaf trait candidate genes (two to four copies).

Supplemental Table S14. List of primers for leaf development candidategenes designed for RT-qPCR gene expression.

Supplemental Text S1. Structure learning method for undirectednetworks.

ACKNOWLEDGMENTS

We thank Fred van Eeuwijk for critical reading of the manuscript,Ningwen Zhang for useful discussion on the experimental design, andXiaoxue Sun for her advice concerning a number of figures.

Received August 22, 2013; accepted January 1, 2014; published January 6,2014.







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LITERATURE CITED

Alonso-Blanco C, Aarts MG, Bentsink L, Keurentjes JJ, Reymond M,Vreugdenhil D, Koornneef M (2009) What has natural variation taughtus about plant development, physiology, and adaptation? Plant Cell 21:1877–1896

Bao N, Lye KW, Barton MK (2004) MicroRNA binding sites in Arabidopsisclass III HD-ZIP mRNAs are required for methylation of the templatechromosome. Dev Cell 7: 653–662

Barkoulas M, Galinha C, Grigg SP, Tsiantis M (2007) From genes toshape: regulatory interactions in leaf development. Curr Opin Plant Biol10: 660–666

Bertoni G (2010) Cytokinin and compound leaf development. Plant Cell22: 3191

Beveridge CA, Kyozuka J (2010) New genes in the strigolactone-relatedshoot branching pathway. Curr Opin Plant Biol 13: 34–39

Birchler JA, Veitia RA (2010) The gene balance hypothesis: implications forgene regulation, quantitative traits and evolution. New Phytol 186:54–62

Blanc G, Wolfe KH (2004) Functional divergence of duplicated genesformed by polyploidy during Arabidopsis evolution. Plant Cell 16:1679–1691

Bollman KM, Aukerman MJ, Park MY, Hunter C, Berardini TZ, PoethigRS (2003) HASTY, the Arabidopsis ortholog of exportin 5/MSN5, regu-lates phase change and morphogenesis. Development 130: 1493–1504

Bonnema G, Carpio DPD, Zhao JJ (2011) Diversity analysis and moleculartaxonomy of Brassica vegetable crops. In C Kole, J Sadowski, eds, Ge-netics, Genomics and Breeding of Crop Plants. Science Publishers, En-field, NH, pp 81–124

Cha YK, Liu L, Kim JY (2007) Signaling network for stem cell maintenanceand functioning in Arabidopsis shoot apical meristem. J Plant Biol 50:274–281

Chua YL, Channelière S, Mott E, Gray JC (2005) The bromodomain proteinGTE6 controls leaf development in Arabidopsis by histone acetylation atASYMMETRIC LEAVES1. Genes Dev 19: 2245–2254

Combes MC, Cenci A, Baraille H, Bertrand B, Lashermes P (2012) Ho-meologous gene expression in response to growing temperature in arecent allopolyploid (Coffea arabica L.). J Hered 103: 36–46

Costa MMR, Yang S, Critchley J, Feng X, Wilson Y, Langlade N, CopseyL, Hudson A (2012) The genetic basis for natural variation in hetero-blasty in Antirrhinum. New Phytol 196: 1251–1259

De Smet R, Van de Peer Y (2012) Redundancy and rewiring of geneticnetworks following genome-wide duplication events. Curr Opin PlantBiol 15: 168–176

Feng G, Qin Z, Yan J, Zhang X, Hu Y (2011) Arabidopsis ORGAN SIZERELATED1 regulates organ growth and final organ size in orchestrationwith ARGOS and ARL. New Phytol 191: 635–646

Feng X, Wilson Y, Bowers J, Kennaway R, Bangham A, Hannah A, CoenE, Hudson A (2009) Evolution of allometry in Antirrhinum. Plant Cell 21:2999–3007

Freeling M, Thomas BC (2006) Gene-balanced duplications, like tetra-ploidy, provide predictable drive to increase morphological complexity.Genome Res 16: 805–814

Fu J, Jansen RC (2006) Optimal design and analysis of genetic studies ongene expression. Genetics 172: 1993–1999

Gaeta RT, Pires JC, Iniguez-Luy F, Leon E, Osborn TC (2007) Genomicchanges in resynthesized Brassica napus and their effect on gene ex-pression and phenotype. Plant Cell 19: 3403–3417

Gonzalez N, De Bodt S, Sulpice R, Jikumaru Y, Chae E, Dhondt S, VanDaele T, De Milde L, Weigel D, Kamiya Y, et al (2010) Increased leafsize: different means to an end. Plant Physiol 153: 1261–1279

Hay A, Barkoulas M, Tsiantis M (2004) PINning down the connections:transcription factors and hormones in leaf morphogenesis. Curr OpinPlant Biol 7: 575–581

Hemerly AS, Ferreira P, de Almeida Engler J, Van Montagu M, Engler G,Inzé D (1993) cdc2a expression in Arabidopsis is linked with competencefor cell division. Plant Cell 5: 1711–1723

Hovav R, Udall JA, Chaudhary B, Rapp R, Flagel L, Wendel JF (2008)Partitioned expression of duplicated genes during development andevolution of a single cell in a polyploid plant. Proc Natl Acad Sci USA105: 6191–6195

Hu Y, Poh HM, Chua NH (2006) The Arabidopsis ARGOS-LIKE gene reg-ulates cell expansion during organ growth. Plant J 47: 1–9

Huang X, Ding J, Effgen S, Turck F, Koornneef M (2013) Multiple loci andgenetic interactions involving flowering time genes regulate stembranching among natural variants of Arabidopsis. New Phytol 199:843–857

Ihaka R, Gentleman R (1996) R: a language for data analysis and graphics.J Comput Graph Statist 5: 299–314

Jansen RC, Nap JP (2001) Genetical genomics: the added value from seg-regation. Trends Genet 17: 388–391

Kawade K, Horiguchi G, Tsukaya H (2010) Non-cell-autonomously coor-dinated organ size regulation in leaf development. Development 137:4221–4227

Kelley DR, Arreola A, Gallagher TL, Gasser CS (2012) ETTIN (ARF3)physically interacts with KANADI proteins to form a functional com-plex essential for integument development and polarity determinationin Arabidopsis. Development 139: 1105–1109

Keurentjes JJB, Fu J, Terpstra IR, Garcia JM, van den Ackerveken G,Snoek LB, Peeters AJM, Vreugdenhil D, Koornneef M, Jansen RC(2007) Regulatory network construction in Arabidopsis by using genome-wide gene expression quantitative trait loci. Proc Natl Acad Sci USA 104:1708–1713

Kim GT, Cho KH (2006) Recent advances in the genetic regulation of theshape of simple leaves. Physiol Plant 126: 494–502

Kojima S, Iwasaki M, Takahashi H, Imai T, Matsumura Y, Fleury D, VanLijsebettens M, Machida Y, Machida C (2011) Asymmetric leaves2 andElongator, a histone acetyltransferase complex, mediate the establish-ment of polarity in leaves of Arabidopsis thaliana. Plant Cell Physiol 52:1259–1273

Ku LX, Zhang J, Guo SL, Liu HY, Zhao RF, Chen YH (2012) Integratedmultiple population analysis of leaf architecture traits in maize (Zeamays L.). J Exp Bot 63: 261–274

Kubo N, Saito M, Tsukazaki H, Kondo T, Matsumoto S, Hirai M (2010)Detection of quantitative trait loci controlling morphological traits inBrassica rapa L. Breed Sci 60: 164–171

Lan TH, Paterson AH (2001) Comparative mapping of QTL determiningthe plant size of Brassica oleracea. Theor Appl Genet 103: 383–397

Lee YK, Kim GT, Kim IJ, Park J, Kwak SS, Choi G, Chung WI (2006)LONGIFOLIA1 and LONGIFOLIA2, two homologous genes, regulatelongitudinal cell elongation in Arabidopsis. Development 133: 4305–4314

Li F, Kitashiba H, Inaba K, Nishio T (2009) A Brassica rapa linkage map ofEST-based SNP markers for identification of candidate genes controllingflowering time and leaf morphological traits. DNA Res 16: 311–323

Li F, Zou Z, Yong HY, Kitashiba H, Nishio T (2013) Nucleotide sequencevariation of GLABRA1 contributing to phenotypic variation of leafhairiness in Brassicaceae vegetables. Theor Appl Genet 126: 1227–1236

Li H, Lu L, Manly KF, Chesler EJ, Bao L, Wang J, Zhou M, Williams RW,Cui Y (2005) Inferring gene transcriptional modulatory relations: a ge-netical genomics approach. Hum Mol Genet 14: 1119–1125

Li Y, Liu Z, Wang Y, Yang N, Xin X, Yang S, Feng H (2012) Identification ofquantitative trait loci for yellow inner leaves in Chinese cabbage(Brassica rapa L. ssp. pekinensis) based on SSR and SRAP markers. SciHortic (Amsterdam) 133: 10–17

Liu J, Hua W, Yang HL, Zhan GM, Li RJ, Deng LB, Wang XF, Liu GH,Wang HZ (2012) The BnGRF2 gene (GRF2-like gene from Brassica napus)enhances seed oil production through regulating cell number and plantphotosynthesis. J Exp Bot 63: 3727–3740

Liu Z, Adams KL (2007) Expression partitioning between genes duplicatedby polyploidy under abiotic stress and during organ development. CurrBiol 17: 1669–1674

Lou P, Wu J, Cheng F, Cressman LG, Wang X, McClung CR (2012) Pref-erential retention of circadian clock genes during diploidization fol-lowing whole genome triplication in Brassica rapa. Plant Cell 24: 2415–2426

Lou P, Zhao J, Kim JS, Shen S, Del Carpio DP, Song X, Jin M,Vreugdenhil D, Wang X, Koornneef M, et al (2007) Quantitative traitloci for flowering time and morphological traits in multiple populationsof Brassica rapa. J Exp Bot 58: 4005–4016

Lysak MA, Koch MA, Pecinka A, Schubert I (2005) Chromosome tripli-cation found across the tribe Brassiceae. Genome Res 15: 516–525

Mauriat M, Sandberg LG, Moritz T (2011) Proper gibberellin localizationin vascular tissue is required to control auxin-dependent leaf develop-ment and bud outgrowth in hybrid aspen. Plant J 67: 805–816

Micol JL (2009) Leaf development: time to turn over a new leaf? Curr OpinPlant Biol 12: 9–16


Xiao et al.



Montgomery J, Wittwer CT, Palais R, Zhou L (2007) Simultaneous muta-tion scanning and genotyping by high-resolution DNA melting analysis.Nat Protoc 2: 59–66

Nagaharu U (1935) Genomic analysis in Brassica with special reference tothe experimental formation of B. napus and peculiar mode of fertiliza-tion. J Japanese Bot 7: 389–452

Pekker I, Alvarez JP, Eshed Y (2005) Auxin response factors mediateArabidopsis organ asymmetry via modulation of KANADI activity. PlantCell 17: 2899–2910

Raman H, Raman R, Eckermann P, Coombes N, Manoli S, Zou X,Edwards D, Meng J, Prangnell R, Stiller J, et al (2013) Genetic andphysical mapping of flowering time loci in canola (Brassica napus L.).Theor Appl Genet 126: 119–132

Seo YS, Choi JY, Kim SJ, Kim EY, Shin JS, Kim WT (2012) Constitutiveexpression of CaRma1H1, a hot pepper ER-localized RING E3 ubiquitinligase, increases tolerance to drought and salt stresses in transgenic to-mato plants. Plant Cell Rep 31: 1659–1665

Serrano-Cartagena J, Candela H, Robles P, Ponce MR, Pérez-Pérez JM,Piqueras P, Micol JL (2000) Genetic analysis of incurvata mutants re-veals three independent genetic operations at work in Arabidopsis leafmorphogenesis. Genetics 156: 1363–1377

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N,Schwikowski B, Ideker T (2003) Cytoscape: a software environment forintegrated models of biomolecular interaction networks. Genome Res13: 2498–2504

Spirtes P, Glymour C, Scheines R (2000) Causation, Prediction, andSearch: Adaptive Computation and Machine Learning. CambridgeUniversity Press, Cambridge, UK

Steibel JP, Bates RO, Rosa GJM, Tempelman RJ, Rilington VD,Ragavendran A, Raney NE, Ramos AM, Cardoso FF, Edwards DB, et al(2011) Genome-wide linkage analysis of global gene expression in loinmuscle tissue identifies candidate genes in pigs. PLoS ONE 6: e16766

Tang H, Woodhouse MR, Cheng F, Schnable JC, Pedersen BS, Conant G,Wang X, Freeling M, Pires JC (2012) Altered patterns of fractionationand exon deletions in Brassica rapa support a two-step model of paleo-hexaploidy. Genetics 190: 1563–1574

Town CD, Cheung F, Maiti R, Crabtree J, Haas BJ, Wortman JR, Hine EE,Althoff R, Arbogast TS, Tallon LJ, et al (2006) Comparative genomics

of Brassica oleracea and Arabidopsis thaliana reveal gene loss, fragmenta-tion, and dispersal after polyploidy. Plant Cell 18: 1348–1359

Tsukaya H (2006) Mechanism of leaf-shape determination. Annu Rev PlantBiol 57: 477–496

Van Ooijen JW (2004) MapQTL 5, software for the mapping of quantitativetrait loci in experimental populations. Wageningen, The Netherlandshttp://www.kyazma.nl/index.php/mc.MapQTL/ (September 2013)

Villagarcia H, Morin AC, Shpak ED, Khodakovskaya MV (2012) Modi-fication of tomato growth by expression of truncated ERECTA proteinfrom Arabidopsis thaliana. J Exp Bot 63: 6493–6504

Wang X, Wang H, Wang J, Sun R, Wu J, Liu S, Bai Y, Mun JH, Bancroft I,Cheng F, et al (2011) The genome of the mesopolyploid crop speciesBrassica rapa. Nat Genet 43: 1035–1039

West MAL, Kim K, Kliebenstein DJ, van Leeuwen H, Michelmore RW,Doerge RW, St Clair DA (2007) Global eQTL mapping reveals thecomplex genetic architecture of transcript-level variation in Arabidopsis.Genetics 175: 1441–1450

Whittle CA, Krochko JE (2009) Transcript profiling provides evidence offunctional divergence and expression networks among ribosomal pro-tein gene paralogs in Brassica napus. Plant Cell 21: 2203–2219

Xiao D, Zhao JJ, Hou XL, Basnet RK, Carpio DP, Zhang NW, Bucher J, LinK, Cheng F, Wang XW, et al (2013) The Brassica rapa FLC homologueFLC2 is a key regulator of flowering time, identified through tran-scriptional co-expression networks. J Exp Bot 64: 4503–4516

Yu X, Wang H, Zhong W, Bai J, Liu P, He Y (2013) QTL mapping of leafyheads by genome resequencing in the RIL population of Brassica rapa.PLoS ONE 8: e76059

Yuan Z, Luo D, Li G, Yao X, Wang H, Zeng M, Huang H, Cui X (2010)Characterization of the AE7 gene in Arabidopsis suggests that normal cellproliferation is essential for leaf polarity establishment. Plant J 64:331–342

Zhang J, Lu Y, Yuan Y, Zhang X, Geng J, Chen Y, Cloutier S, McVettyPBE, Li G (2009) Map-based cloning and characterization of a genecontrolling hairiness and seed coat color traits in Brassica rapa. Plant MolBiol 69: 553–563

Zhao J, Wang X, Deng B, Lou P, Wu J, Sun R, Xu Z, Vromans J, KoornneefM, Bonnema G (2005) Genetic relationships within Brassica rapa as in-ferred from AFLP fingerprints. Theor Appl Genet 110: 1301–1314





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