The genetic basis of plant architecture in 10 maize ......2017/08/24 · 47 architecture in maize breeding. 48 Key words: Maize, Genetic basis, Plant architecture, Quantitative trait

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The genetic basis of plant architecture in 10 maize 1

recombinant inbred line populations 2

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Qingchun Pan1, Yuancheng Xu1, Kun Li1, Yong Peng1, Wei Zhan1, Wenqiang Li1, Lin 4

Li1* and Jianbing Yan1* 5

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1National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural 7

University, Wuhan 430070, China 8

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*Author for correspondence: Jianbing Yan and Lin Li 10

Tel: +86-027-87280110 Fax: +86-27-87384670 11

Email: [email protected] and [email protected] 12

Running title: Genetic basis of maize plant architecture 13

One-sentence summary: A large scale QTL mapping on 10 plant architecture traits 14

across 10 RIL populations reveals complex genetic basis of plant architecture in maize. 15

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Author contributions: J.Y. and L.L. designed and supervised this study. Q.P. performed 17

the data analysis. Q.P., L.L., and J.Y. prepared the manuscript. Y.X. conducted the QTL 18

fine mapping. W. L. took care of the field experiment. Y.P., K.L, and W.Z contributed to 19

RNA extraction and library construction of RNA-sequencing. 20

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Funding information: This research was supported by the National Natural Science 22

Foundation of China (31501315; 91635303), the National Key Research and 23

Development Program of China (2016YFD0101003; 2016YFD0100404), and Huazhong 24

Agricultural University Scientific & Technological Self-innovation Foundation 25

Plant Physiology Preview. Published on August 24, 2017, as DOI:10.1104/pp.17.00709

Copyright 2017 by the American Society of Plant Biologists

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ABSTRACT 26

Plant architecture is a key factor affecting planting density and grain yield in maize. 27

However, the genetic mechanisms underlying plant architecture in diverse genetic 28

backgrounds have not been fully addressed. Here, we performed a large-scale 29

phenotyping of 10 plant architecture-related traits and dissected the genetic loci 30

controlling these traits in 10 recombinant inbred line (RIL) populations derived from 14 31

diverse genetic backgrounds. Nearly 800 quantitative trait loci (QTLs) with major and 32

minor effects were identified as contributing to the phenotypic variation of plant 33

architecture-related traits. Ninety-two percent of these QTLs were detected in only one 34

population, confirming the diverse genetic backgrounds of the mapping populations and 35

the prevalence of rare alleles in maize. The number and effect of QTLs are positively 36

associated with the phenotypic variation in the population, which in turn correlates 37

positively with parental phenotypic and genetic variations. A large proportion (61.5%) of 38

QTLs was associated with at least two traits, suggestive of frequent occurrence of 39

pleiotropic loci or closely linked loci. Key developmental genes, which have previously 40

been shown to affect plant architecture in mutant studies, were found to co-localize with 41

many QTLs. Five QTLs were further validated using the segregating populations 42

developed from residual heterozygous lines present in the RIL populations. Additionally, 43

one new plant height QTL, qPH3, has been fine-mapped to a 600-Kb genomic region 44

where three candidate genes are located. These results provide insights into the genetic 45

mechanisms controlling plant architecture and will benefit the selection of ideal plant 46

architecture in maize breeding. 47

Key words: Maize, Genetic basis, Plant architecture, Quantitative trait locus (QTL), 48 Pleiotropy, Parental effects 49

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INTRODUCTION 51

Maize is the most widely grown grain crop worldwide and has become one of the most 52

important crops for food, animal feed, and bio-energy production. Maize grain yield in 53

the US has increased eight-fold in the past 80 years, of which half was the result of 54

breeder selection (Duvick, 2005). Although high grain yield per hectare is a primary 55

breeding goal, these increases in grain yield are predominantly due to higher plant density 56

(Duvick, 2005). In US, the average plant density of maize has increased from 30,000 57

plants per hectare in the 1930s to > 80,000 plants per hectare currently, which has been 58

accompanied by a change in maize plant aboveground architecture, especially maize leaf 59

angle, which has become significantly more upright (Duvick, 2005). Ideal plant 60

architecture in high plant density maize production can optimize canopy architecture, 61

improve photosynthetic efficiency, and prevent lodging, thus resulting in overall high 62

grain yield. 63

Here, plant architecture primarily refers to the aboveground parts of maize. It is a 64

function of numerous specific traits, such as plant height (PH), ear height (EH), tassel 65

branch number (TBN), tassel main axis length (TMAL), leaf length (LL), leaf width 66

(LW), middle leaf angle (MLA), leaf number above ear (LNAE), leaf number blow ear 67

(LNBE), and ear leaf number (LN) etc. As breeder selection for plant density leads to 68

increased leaf angle, leaf size, tassel size and angle, these traits have been optimized, 69

allowing light penetrate into the aboveground canopy with considerable yield advantages 70

(Pepper et al., 1977; Fischer et al., 1987; Begna et al., 1999). All of the aboveground 71

parts of maize plants are derived from the shoot apical meristem and its derivative 72

inflorescence meristem. The maintenance and differentiation of the shoot apical meristem 73

have been reported to determine the morphology of plant aboveground parts (Thompson 74

et al., 2015). Developmental origination of all plant architecture traits explains their high 75

correlations with each other. Previous mutant studies on SAM indicate that multiple 76

genes especially homeobox genes, their interaction as well as phytohormone genes, are 77

involved in the regulation of meristem identity and differentiation (Barton, 2013). 78

Therefore, plant architecture exhibits quantitative variation with complex genetic 79

mechanisms. Although clarifying the genetic basis of these traits is still challenging, the 80



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genetic dissection of plant architecture will improve breeding for yield at high plant 81

density and thus lead to continued maize productivity improvement. 82

Hundreds of QTLs related to plant architecture traits have been identified 83

(http://www.maizegdb.org/data_center/qtl-loci-summary). Tian and colleagues performed 84

a genome-wide association study of leaf architecture in the maize nested association 85

mapping population (NAM), and demonstrated that the genetic basis of the leaf traits is 86

dominated by small effects with little epistasis, environmental interaction or pleiotropy 87

(Tian et al., 2011). Peiffer et al dissected the genetic basis of maize height in maize NAM 88

populations and revealed that maize height is under strong genetic control and has a 89

highly polygenic genetic basis (Peiffer et al., 2014). Li et al employed a teosinte 90

introgression population, conducted a comprehensive genetic dissection on leaf number 91

and its genetic relationship to flowering time, and demonstrated that these traits are under 92

relatively independent genetic control (Li et al., 2016). Genes such as teosinte branched1, 93

teosinte glume architecture1, dwarf plant3, dwarf plant8, dwarf plant9, nana plant1, and 94

brachytic plant2 have been cloned and their regulation of flowering time and plant height 95

was explained at the molecular level (Winkler and Helentjaris, 1995; Doebley et al., 1997; 96

Multani et al., 2003; Wang et al., 2005; Lawit et al., 2010; Hartwig et al., 2011). 97

However, functional alleles of many of the genes associated with plant architecture have 98

been already fixed in maize elite germplasm, limiting their use in maize improvement. 99

Most of previous genetic studies focused on single or a few plant architecture traits, and 100

lacked power to dissect plant architecture traits as a whole. Bouchet et al. assessed the 101

genetic basis of 24 correlated maize traits and identified major pleioptropic effects and/or 102

linkage for plant architecture traits in one population with 336 lines, which, however, 103

only represents a small proportion of genetic diversity in maize (Bouchet et al., 2017). 104

Despite this progress, the genetics of plant architecture related traits in maize have not 105

been fully investigated in multiple diverse genetic backgrounds. 106

To this end, we constructed 10 RIL maize populations called it as random-open-107

parent association mapping (ROAM) population (Xiao et al., 2016; 2017), conducted 108

large-scale phenotyping on 10 plant architecture traits, and performed a comprehensive 109

genetic dissection of plant architecture in maize. Approximately 800 QTLs with major 110



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and minor effects were identified across 10 diverse genetic backgrounds in maize. Both 111

pleiotropic and linked QTLs were detected, which to a large extent explains the high 112

correlations between plant architecture traits. The phenotypic differences between the 113

two parents for each of the 10 maize RIL populations were shown to be associated with 114

the phenotypic diversity and the number of QTLs detected within populations, indicating 115

that mendalian effects contribute predominantly to phenotypic variation in the progeny. 116

Additionally, RHL family analyses validated five QTLs and fine-mapped one plant 117

height QTL to the 600 Kb genomic region, confirming the robustness of our analysis. All 118

of these results further our understanding of plant architecture variation and provide 119

selection loci for further progress towards the ideal maize plant architecture. 120

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Results and Discussion 123

Large-scale phenotyping reveals complex relationships among plant 124

architecture-related traits in maize 125

The ROAM population, consisting of 10 RIL populations derived from 14 elite 126

maize inbreds, for a total of 1,887 RIL lines was developed (Pan et al., 2016; Xiao et al., 127

2016). These 14 elite maize inbreds were selected from a large association mapping panel 128

and exhibited extensive genetic diversity (Yang et al., 2011; Supplemental Fig. S1). We 129

phenotyped the ROAM population at 5-12 locations for 10 aboveground traits, including 130

plant height (PH), ear height (EH), tassel branch number (TBN), tassel main axis length 131

(TMAL), ear leaf length (LL), ear leaf width (LW), middle leaf angle (MLA), leaf 132

number above ear (LNAE), leaf number below ear (LNBE), and ear leaf number (LN) 133

were measured manually (Figure 1A; Material and Method) 134

The best linear unbiased prediction (BLUP) analyses revealed extensive 135

phenotypic variation (Figure 1B; Supplemental Table S1). All 10 traits exhibited normal 136

distributions with a 2- to 10-fold difference between the smallest and the biggest lines in 137

the RIL populations (Figure 1B; Supplemental Table S1; Fig S2). Tassel branch number 138

had the most dramatic variation with a 10-fold change (the smallest and biggest tassel 139

numbers are 2 and 20, respectively) (Figure 1B; Supplemental Table S1). Tassel size is a 140

key factor associated with maize grain yield (Duvick, 2005). Smaller tassel is frequently 141

associated with higher yield, however, hybrid production would be affected if the tassel is 142

too small (Hunter et al., 1969). The dramatic tassel branch number variation in ROAM 143

population might allow a large flexible selection of optimal tassel size. And leaf angle 144

had the second most extensive variation of ~4-fold change, while the other eight traits 145

had less than 3-fold phenotypic variations (Fig 1B; Supplemental Table S1). Meanwhile, 146

the broad sense heritability (H2) of all 10 plant architecture-related traits ranged from 147

0.90 to 0.95 (Table 1), which is significantly higher than that (0.76-0.87) of grain yield-148

related traits (Xiao et al., 2016). The extensive phenotypic variation and high heritability 149

suggest that these 10 RIL populations have a diverse genetic background and are suitable 150

for dissecting the genetic factors underlying plant architecture-related traits in maize. 151



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Plant architecture exhibits quantitative variation, which is sensitive to 152

environment. Even for a specific genotype, plant individuals may show extensive 153

phenotypic variations due to both genetic variation such as new mutations and 154

environmental changes (Zhang, 2008; Fraser and Schadt, 2010). Although plant 155



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architecture traits had pretty high heritability, a fraction of phenotypic variation were 156

derived from environmental effects. The environmental effects of phenotypic variations 157

for all 10 plant architecture traits vary from 2.2% to 13.3% (Supplemental Table S2). 158

Additionally, significant effect of genotype by environment was observed (Supplemental 159

Table S2). 160

Analyses showed that the 10 plant architecture-related traits are significantly 161

correlated with each other, with each trait correlating with one to six other traits (Figure 162

1C; Supplemental Table S3). We detected a total of 45 correlations among the 10 traits, 163

the majority (43) of which are positive associations, except the two correlations between 164

LL and LW or LNBE (Figure 1C; Supplemental Table S3). We performed a phenotypic 165

clustering analysis based on the phenotypic variation across the 10 RIL populations 166

(Figure 1D). A phentoypic tree of all 10 plant architecture traits was constructed based on 167

the clustering analysis (see Materials and Methods) and demonstrated highly correlations 168

as well as an extensive phenotypic variation among plant architecture traits. This 169

classified the 10 plant architecture traits into three unrooted groups (Figure 1D). The 170

biggest cluster includes eight measured traits, PH, EH, LL, LW, LN, LNAE, LNBE, and 171

TMAL. All aboveground parts of maize plants are derived from shoot apical meristem 172

(SAM) and its derivative inflorescence meristem (Barton, 2010). Recent studies have 173

uncovered phenotypic correlations between meristem morphology and adult plant traits, 174

revealing links between the undifferentiated and differentiated plant organs (Thompson et 175

al., 2015). Additionally, Baute and colleagues revealed that the whole SAM traits are 176

correlated with mature plant morphology related traits (Baute et al., 2015). Similar 177

origination of all aerial morphology traits may be the developmental factor that classifies 178

these eight traits into one highly-correlated phenotypic cluster. 179

Of particular interest, MLA and TBN are singletons in the clustering analysis, 180

suggesting that these two traits may have a distinct genetic contribution to plant 181

architecture compared to traits in the biggest cluster. Interestingly, these two traits also 182

have the largest phenotypic variation in the mapping population. As plant density has 183

increased, leaves have become more upright and TBN has been dramatically reduced to 184

optimize light penetration into the plant canopy, suggesting that TBN and MLA are the 185



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important selection targets during maize improvement (Fischer et al., 1987; Russell, 1991; 186

Andrade et al., 1993). Most of the parents of these 10 RIL populations are elite inbreds 187

from different breeding programs. TBN and MLA, which have the most phenotypic 188

variation, reflect the genetic diversity in our mapping populations. The extensive and 189

continuous phenotypic variation of these two traits and their divergence from the major 190

phenotypic cluster implicate that there were many different loci with rare alleles each 191

contributing a small amount of variation for TBN and MLA in maize. 192

Genetic dissection of plant architecture uncovered hundreds of QTLs 193

with major and minor effects that contribute to phenotypic variation 194

To dissect the genetic mechanisms underlying plant architecture in maize, single 195

linkage mapping (SLM), joint linkage mapping (JLM), and genome-wide association 196

study (GWAS) were employed to map QTLs underlying phenotypic variation. While 197

SLM identified QTLs in each RIL population, JLM and GWAS identified QTLs or SNPs 198

by a joint analysis of all 10 populations using an integrated high-density map. Hundreds 199

of consistent QTLs were identified through all three genetic algorithms (Table 1; Figure 2; 200

Supplemental Fig. S3-S12). 201

Through the SLM method, a large number of QTLs ranging from 64 to 83 were 202

identified for each plant architecture trait. In total, 752 QTLs were uncovered for the 10 203

traits across 10 RIL populations, indicative of the complexity of maize plant development 204

(Table 1). Of these 752 QTLs, 23.2% have an effect of more than 10% of phenotypic 205

variation (Supplemental Table S4; Supplemental Fig. S13). The proportion of QTLs with 206

an additive effect greater than 10% ranged from 16% to 31% (Supplemental Fig. S14), 207

suggesting that a few major genetic factors with large effects along with many genetic 208

factors with marginal effects contribute to the phenotypic variation of maize plant 209

architecture. QTL comparison among different RIL populations shows that a large 210

proportion (ranging from 57 to 76) of QTLs could only be identified in one population, 211

while a very small (from 3 to 12) proportion of QTLs can be simultaneously uncovered in 212

at least 2 populations. This observation confirmed that our ROAM has an extensive 213

genetic diversity. For example, out of the 83 QTLs associated with plant height across the 214



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10 RIL populations, only 7 could be detected in at least 2 populations. Interestingly, the 215

number of QTLs detected in only a single population was significantly more than those 216

detected in multiple populations (χ2 test, P = 1.1E-26), but there was no difference for 217

QTL effects (χ2 test, P = 0.15). These results suggest that a large number of rare genetic 218

factors play an important role in the phenotypic variation in maize. 219

Using the JLM method, we mapped 50-96 QTLs with R2 ranging from 0.31% to 4% 220

for each trait. Concordant with the result of SJM, majority QTLs detected by JLM 221

method are unique to a single population (Table 1; Supplemental Table S5). Compared 222



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with SLM, the resolution of JLM reached up to 1.2Mb, which was significantly smaller 223

than the resolution of 3.4Mb achieved by SLM (P < 2.0E-16) (Supplemental Fig. S15). 224

Through GWAS, we mapped 9-43 significant SNPs associated with plant architecture 225

traits (Table 1). The effect of most loci was small than 1% (Supplemental Table S6). In 226

total, these significant loci could explain 30.13%-53.49% of the phenotype variation 227

(Supplemental Fig. S16). 228

Different genetic algorithms identified different number of loci underlying plant 229

architecture variation (Supplemental Table S4; Supplemental Table S5; Supplemental 230

Table S6). Unlike NAM populations, the ROAM population were derived from random 231

crosses of 14 diverse genetic backgrounds, which make the whole population lack 232

common variants. Therefore, GWAS was likely to detect less significant loci with smaller 233

genetic effect conferring plant architecture variation. However, comparisons of the QTLs 234

detected by the three methods still showed that a large proportion (ranging from 15.8% to 235

30.6% for each trait) of QTLs could be detected by all three methods (Supplemental Fig. 236

S17). Additionally, a majority (60.0-77.2% for SLM vs. JLM, 26.6-52.2% for SLM vs. 237

GWAS, and 6.5-14.4% for JLM vs. GWAS) of QTLs could be detected by two genetic 238

algorithms, suggestive of robustness of QTLs identified in our study. 239

The density of genetic markers and the size of recombination blocks could allow 240

us to map QTLs into 2 Mb genomic regions (Pan et al., 2016). We divided maize genome 241

into 2Mb-slidling window bins for the comparison of QTLs of different plant architecture 242

traits across 10 RIL populations. Combining all three statistical methods, a total of 434 243

genomic recombinant bins with peaks significantly associated with phenotypic variation 244

were identified. Co-localization of these genomic bins with well-known mutant genes 245

indicates that 150 out of 416 plant architecture well-known genes were located within the 246

confidence intervals of plant architecture QTLs for JLM, and 250 well-known genes were 247

coincident with detectable QTLs by SLM (Figure 2). For example, brachytic2 (Br2) is 248

coincident with a QTL on chr.1 conferring plant height variation in ZONG/YU87-1 249

population which was also validated by QTL cloning (Multani et al., 2003; Xing et al., 250

2015; Figure 3A). And liguleless 1 (lg1) is co-localized with a QTL on chr.2 that controls 251

leaf angle (Becraft et al., 1990; Figure 3B). Maize plants without ligule have upright 252



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leaves, for which leaf angle is nearly zero (Sylvester et al., 1990). Natural genomic 253

variation in lg1 between inbreds is likely to quantitatively change leaf angle. Therefore, 254

the identification of natural favorable alleles of lg1 would aid the target selection of leaf 255

angle. Notably, based on the co-localization of well-known genes with QTLs by SLM, 256

39.9% of well-known plant architecture related maize genes are not located in the QTL 257

regions identified here as associated with plant architecture variation. The 258

noncoincidence of well-known genes with detectable QTLs suggests either that most of 259

these genes may have been fixed during the domestication and selection of maize, or that 260

the alleles present in the parents of the mapping population contribute little to the 261

variation and thus were not detected, or that the power to detect these loci is still 262

insufficient. While a proportion (27.3%) of QTLs underlying plant architecture variation 263

detected by SLM are not co-localized with any known functional mutant genes, implying 264

that the genetic basis of plant architecture is complex and that many QTLs identified in 265

present study would provide useful selection targets for the ideal plant architecture 266

breeding in maize. New alleles at the loci lacking variation in the breeding germplasm 267

may be introduced from the unadapted germplasm. 268

To detect any epistatic interactions between the plant architecture QTLs, 2-way 269

ANOVA was employed and 100 epistatic pairs between QTLs were identified at the P 270

value of 0.05. However, after Bonferroni correction, no significant epistatic interaction 271

was retained. This is consistent with previous studies (Buckler et al., 2009; Tian et al., 272



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2010), which also showed that the variation of plant architecture was rarely the result of 273

epistatic effect. 274

Mendalian effects play an important role in the phenotypic variation of 275

progeny for plant architecture-related traits 276

Phenotypic variation between parental lines is a major consideration when 277

developing a segregating population for QTL mapping on the assumption that large 278

phenotypic variation can lead to the identification of large-effect QTLs and large number 279

of QTLs. The inclusion of multiple traits and multiple populations in our study allowed 280

us to directly test this assumption. Surprisingly, analysis indicated that neither the number 281

nor the effect of QTLs is significantly correlated with parental phenotypic variation. 282

However, they both showed significantly positive association with phenotypic variation 283

in the populations, which itself is weakly correlated to parental phenotypic variation 284

(Figure 4A-4E, Supplemental Fig. S18), suggesting the presence of parental mendalian 285

effects. Such mendelian effects indicate that properly selection of parents is not only the 286

basis of QTL mapping but also affect the efficiency of crop improvement. Interestingly, 287

male parents provided significantly more additive alleles than female parents (55.8% vs. 288

44.2%, P = 6.1E-3, ANOVA, Figure 4F). However, the two parental alleles contributed 289

equally to the additive phenotypic variations (P = 0.77; Fig 4G). Taken together, these 290

results indicate that parental mendalian factors may play a critical role in the phenotypic 291

variation of the progeny. 292

Transcriptomic analyses of tens of thousands of expressed genes in shoot apical 293

stem of 105 RILs and their parents (B73 and Mo17) uncovered majority of maize genes 294

exhibited mendalian variation where the expression of genes in progeny is largely 295

determined by parental variation, confirming the prevalence of mendalian effects (Li et 296

al., 2013b). Here, we analyzed 10 plant architecture-related traits across 10 RIL 297

populations, and observed that parental phenotypic variations are significantly associated 298

with progeny phenotypic variation, the number of QTLs, and the effects of QTLs. 299

Notably, although the number of QTLs with positive effects varies between male and 300

female parents for plant architecture-related traits, the total positive contribution of alleles 301



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from two parents is equal. Consistent with previous studies, our results confirm that the 302

parental mendalian effects are widespread for many agronomic traits. Most of our elite 303

parents are derived from the conventional breeding program, indicating that parental 304

mendalian effects may originate from the process of breeding selection. The phenomena 305

of parental mendalian effects and the QTLs associated with plant architecture traits may 306

help breeders to select elite inbreds for the selection breeding cycle and accelerate the 307

breeding process. 308

A substantial number of pleiotropic loci or linked QTLs conferring 309

plant architecture in maize are likely to be key developmental genes 310

QTL mapping of different plant architecture traits showed that QTLs controlling 311

different traits often overlap with each other. For any two plant architecture-related traits, 312

the number of QTLs controlling both traits ranged from 17 to 43 (Figure 5A). As the 313

phenotypic distance between the two traits increased, the number of common QTLs for 314

both traits reduced (Figure 5B,C), which is consistent with the idea that phenotypic 315



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correlation among traits is due to the same or linked genetic factors controlling these 316

traits. We identified 55 QTLs that are associated with at least three different traits (Figure 317

5D). Notably, there are 11 QTLs that are significantly associated with at least 5 traits 318

(Supplemental Fig. S19). Of all 434 2Mb-sliding genomic bins with detectable QTLs, 319

61.5% are only associated with a single plant morphology trait, while 38.5% are related 320

to at least 2 traits. 321

Consistent with the phenotypic clustering tree of all 10 plant morphology traits, a 322

large number (275) of pleiotropic loci have been detected for any pair of morphology 323

traits within the biggest phenotypic cluster, significantly more (P = 9.0E-3) than that for 324

phenotypic pairs between TBN, MLA, and the eight traits within the biggest cluster 325

(Figure 5E). TBN and MLA, which are two singletons in the phenotypic clustering tree, 326



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showed the lowest genetic overlap as compared with any phenotypic pairs of all 10 plant 327

morphology traits. Based on the significance P values of pleiotropic QTL matrix of any 328

pair of plant architecture traits, genetic distances of any two plant architecture traits were 329

obtained. Further, we constructed a phylogenetic tree of plant architecture traits, which is 330

largely consistent with phenotypic clustering tree (Supplemental Fig. S20). Both 331

phylogenetic and phenotypic clustering trees intuitively represent the divergence and 332

relationships among plant architecture traits in maize. 333

Co-localization analysis between pleiotropic loci and well-known genes, of which 334

most have been cloned through mutant studies (Schnable et al., 2011), suggested that 335

many mutant genes (166) are not located in any QTL regions, such as tassel branch1, 336

rough sheath1, rough sheath2, and tassel seed1 (Doebley et al., 1995; Schneeberger et al., 337

1995; Timmermans et al., 1999; Theodoris et al., 2003; Acosta et al., 2009). This 338

indicates that there is little phenotypically relevant genetic diversity for these genes 339

among our parents, or perhaps in the maize breeding germplasm. However, we did 340

observe that a large number of 250 well-known genes, especially the key developmental 341

genes, are coincident with QTLs by SLM and 119 are in the genomic regions with 342

pleiotropic effects for plant morphology traits, which is significantly higher (χ2 test, P < 343

0.01) than that of expectation (Supplemental Table S7). 344

This biggest phenotypic cluster contains mainly shoot apical meristem-derived or 345

inflorescence meristem-derived traits, which may be controlled by developmental genetic 346

mechanisms (Sheridan, 1988). Previous studies have identified and cloned many 347

developmental genes controlling the SAM identity and differentiation (Barton, 2010; 348

Schnable et al., 2011). Interestingly, these well-known cloned developmental genes (416) 349

are frequently co-located with plant architecture QTL regions, especially the pleiotropic 350

or linked genomic regions (Supplemental Table S7). Zea floricaula leafy2 (Zfl2), which is 351

a homolog of the Arabidopsis mutant gene LEAFY, which generates more rosette leaves, 352

functions by promoting the transition from inflorescence to floral meristem and controls 353

quantitative aspects of inflorescence phyllotaxy in maize (Weigel et al., 1992; Bomblies 354

et al., 2003). As expected, Zfl2 is coincident with a pleiotropic genomic region on chr2, 355

which controls LNBE, LN, and LW at the same time. This coincidence makes Zfl2 a 356



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strong candidate for this pleiotropic locus for future study (Figure 6A). A pleiotropic 357

locus on chr8, controlling PH and LL, is coincident with Zmhox1a, which is a homeobox 358

gene controlling tissue differentiation and development (Bellmann et al., 1992) (Figure 359

6B). 360

Although TBN and MLA were divergent from the biggest phenotypic cluster, 361

there are a few pleiotropic loci that have been co-localized with well-known 362

developmental genes. The dissection of the genetic basis of floral branch systems in 363

maize and related grasses suggested a model in which ramosa1, ramosa2, barren stalks, 364

and tassel branch1 modulate plant inflorescence and plant architecture (Vollbrecht et al., 365

2005). Except for tassel branch1, the other three key genes were co-localized in the 366

pleiotropic loci, which are associated with TBN, TMAL, LL, LW, and EH. Notably, 367

liguleless1, which was previously reported to induce ligules and auricles during maize 368

leaf organogenesis and further affect proximal-distal signaling and leaf growth, is 369

coincident with a pleiotropic locus affecting MLA, LL, LW, and TBN as well (Moreno et 370

al., 1997; Moon et al., 2013). These results indicate that developmentally-associated loci 371

contribute to more correlations between plant architecture related traits than expected by 372

chance (P = 1E-3; Monte Carlo 1000 random resample), suggesting that key 373

developmental genes may be the source of genetic overlap of plant architecture in maize. 374

(Supplemental Table S7). 375



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Validation of five QTLs using Residual Heterozygous Lines 376

Although fine-mapping using near isogenic lines (NILs) is an accepted standard 377

way to validate a locus that controls a phenotypic variation, the process of developing 378

NILs is time- and labor-consuming. RHLs (Residual Heterozygous Lines) which harbor 379

heterozygous regions where the phenotypic QTL of interest is located may be available 380

from an RIL population (Yamanaka et al., 2004; McMullen et al., 2009). Use of RHLs to 381

validate and fine-map QTLs has proven to be an efficient method (Yamanaka et al., 2004; 382

Watanabe et al., 2011). To validate the QTL mapping results of plant architecture QTLs 383

identified in this study, RHLs were obtained for five randomly selected QTLs of plant 384

height, TMAL and TBN. As expected, all five QTLs could be detected with genetic 385

effects consistent with whole genome QTL scanning (Figure 7A-E). This implies the 386

robustness of our genetic dissection and provides candidate fine-mapping regions for the 387

future improvement of plant morphology in maize. 388

We selected to focus on the fine-mapping of qPH3, which is a major QTL with an 389

additive effect of ~8 cm and which explains 17% of the plant height variation in DE3 × 390

BY815 RIL population (Figure 8A). Primary QTL mapping has anchored qPH3 into a 391

genomic region ranging from 162.2 Mb to 166.7 Mb on chromosome 3 (Figure 8B). We 392

obtained the segregating RHLs and developed nine competitive allele specific PCR 393

(KASP) markers in the target region of qPH3. Phenotypic student’s T-test of the progeny 394

of these segregating RHLs narrowed down the target region to between KASP markers 395

K4 and K8, which spans a 1Mb (from 165.72 Mb to 166.59 Mb) genomic region 396

according to B73 reference genome (Figure 8C). RHL subfamilies with segregating 397

genomic substitution were planted, subjected to phenotyping and genotyping with KASP 398

marker M1 to M5. The progeny phenotypic test indicates that the causal gene of qPH3 is 399

located in ~600 Kb genomic region from M1 to M5 (Figure 8D). 400

The 600-Kb genomic region has 16 annotated genes according to the maize B73 401

reference genome (Schnable et al., 2009). By integrating the genome-wide association 402

mapping results of plant height in a Chinese association panel consisting of 505 diverse 403

inbred lines (Yang et al., 2011), eight SNPs derived from 4 genes in the 1 Mb target 404

genomic region were identified to be significantly associated with plant height in maize 405



19

(Figure 9A). Subsequent RNA-Seq analysis on the leaves of the segregating lines of 406

RHLs identified 146 differentially expressed genes on genome-wide. GO enrichment 407

showed that these differentially expressed genes were enriched in the category of 408

developmental growth, response to biotic stimulus etc. Of these differentially expressed 409

genes between progeny lines with BY815 alleles and the ones with DE3 alleles, only two 410

genes (GRMZM2G058250 and GRMZM2G177227) are located in the target genomic 411

region, providing targets of the cloning of qPH3 (Figure 9B). For a causal gene of the 412

QTL of interest, the parents of the mapping population should have different haplotypes. 413

We analyzed the haplotype variation at all 16 genes in the target genomic region, 13 of 414

which have alternative haplotypes between parents of five RIL populations (K22×CI7, 415

K22×DAN340, K22×BY815, BY815×KUI3, and KUI3×B77). However, qPH3 has not 416

been detected in all the five RIL populations. Re-sequencing of these 16 candidate genes 417



20

in the parents also revealed that three genes had dramatic genomic variations, which 418

could result in non-synonymous mutations, early translation termination, etc., between 419

parents of all five RIL populations (Supplementary Table S8; Supplementary Table S9). 420

However, these three genes (GRMZM2G058250, GRMZM2G177227, and 421

GRMZM2G177220) within the target region, two of which were differentially expressed, 422

have identical haplotypes for the parents of these five RIL populations, but different 423

haplotypes between DE3 and BY815. qPH3 has been identified in DE3×BY815 RIL 424

population, implying that these three genes are likely to be the causal candidates of qPH3 425

(Figure 9C). Of particular interest, GRMZM2G177220 has a nucleotide mutation between 426

DE3 and BY815, which could cause an early stop codon. This nucleotide mutation does 427

not exist in the parents of the other five RIL populations, but solely in between DE3 and 428

BY815 (Figure 9D). 429



21

Taking the results from association mapping, transcriptome analysis, and 430

haplotype diversity based on candidate gene re-sequencing together, GRMZM2G058250, 431

GRMZM2G177227, and GRMZM2G177220 are considered to be the candidate genes 432

underlying qPH3. These three genes encode plastid-specific ribosomal protein 4, exocyst 433

subunit exo70 family protein F1, and a transcription factor with Myb-like domain and 434



22

Homeodomain, respectively. Of particular interest, GRMZM2G177227 has been shown to 435

be associated with plant height variation in a US association mapping panel (Wallace et 436

al., 2014). The Arabidopsis homologous gene GRMZM2G177220 contains Myb-like, 437

Homeodomain-like and signal transduction response domains, and its mutants showed 438

reduced sensitivity to cytokinin inhibition on root elongation and lateral root formation, 439

suggestive of a potential role in the regulation of plant height (Riechmann et al., 2000). 440

The identification of these candidate genes provides critical information for future 441

cloning of qPH3. 442

Materials and Methods 443

Populations and phenotypes 444

In previous studies, we developed a random-open-parent association mapping (ROAM) 445

population, consisting of 10 RIL populations (B73×BY804, BY815×KUI3, 446

DAN340×K22, DE3×BY815, K22×BY815, K22×CI7, KUI3×B77, YU87-1×BK, 447

ZHENG58×SK, and ZONG3×YU87-1), derived from 14 diverse elite inbred lines (Pan et 448

al., 2016; Xiao et al., 2016). Populations of B73×BY804, KUI3×B77, K22×CI7, 449

DAN340×K22, ZHENG58×SK, YU87-1×BK, and ZONG3×YU87-1 were planted in 13 450

locations including Hubei (E114。17; N30°35), Sichuan (E104°04; N30°40), Guangxi 451

(E108°19; N22°48), Chongqing (E106°33; N29°35), Henan (E113°40; N34°46), 452

Yunnan (E102°42; N25°04), and Hainan (E109°31; N18°14) province in 2011 and 453

Hubei (E114。17; N30°35), Chongqing (E106°33; N29°35), Henan (E113°40; N34°46), 454

Yunnan (E102°42; N25°04), and Hainan (E109°31; N18°14) province in 2012. The 455

other three populations, DE3×BY815, K22×BY815, and BY815×KUI3, were planted in 456

five locations in 2012: Hubei, Chongqing, Henan, Yunnan, and Hainan province. In each 457

location, RILs from a RIL family were randomly placed, with 12 plants of one RIL 458

planted in a row. Five plants in the middle of the row were selected for the phenotyping 459

of each RIL. 460

All populations were subject to phenotyping in 2011 and 2012. Totally, 10 plant 461

architecture related traits including plant height (PH, cm), ear height (EH, cm), tassel 462

branch number (TBN, No), tassel main axis length (TMAL, cm), leaf number above ear 463



23

(LNAE, No), leaf number below ear (LNBE, No), leaf number (LN, No), leaf length of 464

ear (LL, cm), leaf width of ear (LW, cm), middle leaf angle (MLA, ∠°) were manually 465

measured (Supplemental Fig. S2). For the measurement of LNAE and LNBE, we 466

considered the topmost ear as the divided point regardless of how many ears in the main 467

stalk. We counted the leaf number above and below the topmost ear as LNAE and LNBE, 468

respectively. To accurately measure the phenotypic variations, we transformed the raw 469

phenotypic data from all 13 and 5 locations of two years into best linear unbiased 470

prediction (BLUP) values for all 10 plant architecture traits using R packages “lme4” of 471

“lme” function. The heritability of 10 plant architecture traits was also calculated using R 472

package “lme4” of “lme” function. All analyses, including phenotypic clustering, 473

correlation, and ANOVA were based on the BLUP phenotype data values. 474

Genotyping and mapping 475

The Illumina MaizeSNP50 BeadChip of 56,110 single nucleotide polymorphisms (SNPs) 476

(Illumina Inc., San Diego, CA, USA) was employed to identify the recombination bins, 477

and ultra-high resolution genetic maps were constructed for all 10 RIL populations (Pan 478

et al., 2016). Additionally, 14 diverse elite inbred lines were selected from a diverse 479

association mapping panel (Yang et al., 2010; 2011) and were subject to RNA-Seq 480

experiments on kernels of 15 days after pollination (Fu et al., 2013), generating hundreds 481

of thousands of SNP markers. Recombination bins, and ultra-high density linkage maps 482

of the ROAM populations were used to dissect the genetic factors underlying maize plant 483

architecture: 484

1. Linkage mapping (SLM) was employed to detect QTLs in each RIL population. 485

Winqtlcart 2.5 software along with linkage map and BLUP phenotypic data were used for 486

QTL mapping (Wang et al., 2006). Mapping function “cim” was used. The 95% LOD 487

values for 10 traits across 10 RIL populations were obtained through 1000 permutations. 488

The LOD thresholds ranged from 2.89-3.53, with a mean value of 3.0. If the genomic 489

region had a detectable LOD value beyond 3, we defined it as one QTL, the QTL 490

confidence interval spanned the genomic regions corresponding to 1 LOD drop from the 491

peak. 492



24

2. Joint linkage mapping (JLM) was performed based on the combined 10 RIL 493

populations and the BLUP phenotypic data. We used the following formula to estimate 494

the relationship between ROAM genotype and phenotype. 495 y = X + Z + +

where y is the phenotype of ROAM populations with 1887 individuals; X is the 496

populations mean value of phenotype; Z is an N × P matrix for the genotype of the 497

haplotype; N is 1887; and P = 14 is the parent number; γ is a vector of genetic effects for 498

the SNP of interest. The likelihood ratio test (LRT) was employed to test the phenotypic 499

association significance of each haplotype bins. Permutation test was conducted 500 500

times to determine the threshold of LRT value, and 99.5 percentile of the LRT value was 501

defined as the cut off. The final threshold of LRT was 2.76 (Xiao et al., 2016). 502

3. Genome-wide association mapping (GWAS) based on ultra-high density 503

genotyping data across 10 RIL populations was performed. High-density SNP genotypes 504

of parents derived from RNA-Seq were projected to offspring based on the 505

recombination/haplotype bins of 1887 lines, which generated the ultra-high density 506

genotyping data for subsequent GWAS with plant architecture data. The main step of this 507

method was stepwise regression. Briefly, a subsample comprised of 80% lines randomly 508

selected from whole ROAM population without replacement was used for GWAS scans 509

and the process was repeated 100 times. Resample model inclusion probability (RMIP), 510

was used to evaluate the robustness of the SNPs included in the model. RMIP cutoff was 511

defined as 0.05. 512

Detailed description of all three methods can be found in a previous study (Xiao 513

et al., 2016). 514

Analysis of epistasis 515

Based on all significant QTLs or loci obtained as described above by SLM, JLM, and 516

GWAS we extracted loci with the peak value LOD score for each QTL. For the single 517

locus, we performed epistatic analysis for every pair of peak loci using two-way ANOVA 518

(Analysis of variance) in R environment. A P value less than 0.05 was adjusted by the 519

total number of tests for the detection of significant epistatic interaction. Combined with 520

the genotypic information of all significant single loci and two-locus interaction, we used 521



25

the “lm” of R language to estimate their contributions to the phenotypic variation (Yu et 522

al., 1997). 523

Estimation of correlations between parental variation and 524 progeny’s diversity 525

To understand the source of genetic diversity of plant architecture traits, we conducted a 526

comprehensive correlation analysis of parental phenotypic variation (PPV), parental 527

genomic variation (PGV), standard deviation of phenotypic variation in the progeny 528

(STV), QTL number (QN), and QTL effect (QPV) for all 10 plant architecture traits 529

across 10 maize RIL populations. PPVs of all 10 aboveground traits were the absolute 530

difference between two parents of a population. The formula used was: 531 PPV = abs (P1 − P2)

Where P1 is the one parent phenotypic value, P2 is the other parent phenotypic value, 532

where P1 and P2 were the male and female parents of the RIL population. PGVs of all 533

the pairs of parents were calculated based on genetic diversity estimated with high-534

density markers. We evaluated the genetic relationship between the pairs of parents based 535

on the high density markers generated by our lab previously (Yang et al., 2014). QN was 536

obtained based on the results of the SLM method (Supplemental Table S3). QPV was 537

calculated by integrating all significant QTLs of SLM together with “lm” function to 538

represent the proportion of phenotypic variation that a QTL could explain for a specific 539

trait. In order to make all different variables comparable, we adjusted each variable using 540

this formula, 541 = (X − mean( ))/mean( )

to compare the relationship for PPV, PGV, STV, QN and QPV. Where X is the original 542

value of PPV, PGV, STV, QN and QPV. Y is the adjusted value for comparison. Mean is 543

the average of X. Further, for the 10 RIL populations, the QTL number and phenotypic 544

effect to which male and female parent contributed positively were also summarized 545

based on the SLM results. All statistical analyses of this section were performed using R 546

software (https://www.r-project.org/). 547

The dissection of QTL overlap of plant architecture traits 548

In order to detect pleiotropic QTLs, two methods were used. One, using SLM we 549

compared the QTL confidence intervals to calculate the number of overlapping QTLs for 550



26

different traits. Two, we split the genome into sliding 2 Mb bins, if a specific genomic 551

bin had at least two SLM QTL peaks for different traits, it was defined as a pleiotropic 552

locus. 553

To test the pleiotropic effect between different traits, we used the following 554

formula based on SLM QTL information to estimate the relationships (Li et al., 2016): 555 =

Where n is the number of QTLs, which is calculated as the total physical genome 556

length divided by the average QTL interval. In this study, the average QTL confidence 557

interval was 2Mb, so n was equal to 1031; m is the number of overlapping QTLs between 558

two traits of Methods 2 in this section; l is the QTL number for the trait with more 559

detectable QTLs, s is the QTL number for the trait with less detectable QTLs by SLM. 560

To systematically dissect the genetic overlap among different plant architecture 561

traits, a phylogenetic tree of plant architecture traits were constructed based on the 562

converted distance of P values described above. Brifely, we used the R packages “ape” 563

and “phangorn” to do the phylogenetic tree analysis. “nj” function was used to re-564

estimate the genetic relationships by Neighbour-Joining method. 565

Construction of phenotypic clustering tree of all maize plant 566

architecture traits 567

For all 1887 progeny families of 10 phenotypic traits, we first used the following formula 568

to adjust the different traits’ phenotypic range to make different traits comparable: 569 = (X − mean(X))/mean(X)

Where X is original phenotypic value of 10 traits. Y is the transformed value, which was 570

used for further analysis. The correlation analyses of 10 plant architecture traits were 571

implemented based on the transformed Y values. Second, we used the “dist” function of 572

“euclidean” method in R environment with transformed phenotypic values to calculate 573

their distance. The “euclidean” distance was calculated between all pairs of traits. The 574

formula used is: 575



27

= ( − )

Ymn is the Euclidean distance value between traits m and n, where m and n are traits 576

selected from the 10 measured traits. X is the transformed phenotypic value. Based on 577

phenotypic distance of all pairs of 10 traits, we used the “hclust” function to construct the 578

hierarchical cluster, which we called a phenotypic clustering tree relationship. Further, in 579

order to dissect the genetic basis that underlies the phenotypic clustering tree relationship 580

of all 10 plant architecture traits, we analyzed the relationships between phenotypic 581

distances of all 10 plant architecture traits, the number of pleiotropic QTL (previous 582

Methods section), and the effect of pleiotropic QTL (the proportion of phenotypic 583

variation that pleiotropic QTL could explain). The effect of pleiotropic QTL was 584

calculated as pleiotropic QTL divided by total QTL effect value. Based on phenotypic 585

distance, pleiotropic QTL, and pleiotropic QTL effect, we used the “lm” function of R to 586

estimate the relationship using the following formula: 587 = +

Y is the phenotypic distances. X is the pleiotropic QTLs or pleiotropic QTL effect. a and 588

b are the evaluation parameters. For the regression formula, regression coefficient and p 589

value were also calculated. 590

QTL validation using residual heterozygous lines. 591

Five QTLs were arbitrarily selected to verify mapping results. They were: qPH3 on 592

chr3:161959511-166378535 affecting PH in DE3×BY815 populations, qTMAL3 on 593

chr3:172477014-176570244 affecting TMAL in DE3×BY815 populations, qPH1 on 594

chr1:98297572-116455713 affecting PH in SK×ZHENG58 populations, qPH7 on chr7: 595

126535776-130704819 affecting PH in K22×BY815 populations and qTBN3 on 596

chr3:173803352-178006685 affecting TBN in K22×BY815 populations. Based on the 597

high-density linkage map, we identified the Residual Heterozygous Lines (RHLs) from 598

the database (Liu et al., 2016) and planted them in Wuhan, China in 2015. Competitive 599

allele specific PCR (KASP) markers were designed and employed for the genotyping of 600



28

RHLs. We identified five lines homozygous for each parental allele in the region of QTL 601

of interest. Accurate phenotyping was performed in each RHL family. Phenotypic 602

difference between the lines homozygous for different alleles at the target QTL regions 603

were calculated and compared using ANOVA analysis. QTLs were preliminarily 604

validated if phenotypic variation between two different homozygous genotypes of the 605

target QTL region was significant (P <= 0.05). Primer information is provided in 606

Supplemental Table S10. 607

Fine-mapping of qPH3 and candidate gene mining using omics 608

A major QTL affecting PH, qPH3, with R2 > 15% was identified on chromosome 3 in the 609

DE3/BY815 population. RHLs were selected to fine-map this QTL. First, we developed 610

new markers to validate the QTL's existence as described above. Then, more KASP 611

markers were developed, additional RHLs with recombination crossovers in the region 612

were identified, and tested in 2015 and 2016. This narrowed down the target genomic 613

region to 600 Kb. RNA-Seq was conducted on the seedling leaves of RHLs with and 614

without 600 Kb genomic substitutions. RNA-Seq reads were mapped on maize reference 615

genome AGPv2 (www.maizesequence.org, Schnable et al., 2009) using Tophat 2.0 (Kim 616

et al., 2013) with default parameters and the reads per kilobase of exon model per million 617

mapped reads (FPKM) of all detectable genes in RHLs with and without 600 Kb target 618

genomic substitutions were calculated using Cufflinks (Trapnell et al., 2010). 619

Differentially expressed genes were identified between RHLs with and without 600 Kb 620

target genomic substitutions. Additionally, association mapping of the target genomic 621

region of qPH3 was performed using 1.03 million SNPs genotyped in a diverse Chinese 622

association mapping panel with 505 inbred lines (Yang et al., 2010; 2011; Li et al., 2013a; 623

Yang et al., 2014). Furthermore, the haplotypes of all candidate genes in the 600 Kb 624

genomic region in all 14 parents were analyzed and compared. Additionally, we designed 625

primers for the amplifications of genic CDS regions, 5’ 2Kb upstream and 3’ 1Kb 626

downstream regions of all 16 candidate genes in all 14 parents of ROAM population. The 627

PCR products were subjected to Sanger Sequencing for genomic variant identification 628

followed by the haplotype analysis for the determination of causal gene mining. Taking 629

account of the results from fine-mapping, RNA-Seq analysis, candidate association 630



29

mapping, and haplotype comparison together, we proposed the candidate genes. Primer 631

information of fine maping is provided in Supplemental Table S11. 632

633

Acknowledgements 634

We thank all the students and colleagues from J.Y.’s present and former laboratories at 635

China Agricultural University and X.Y. and J.L.’s laboratories at China Agricultural 636

University for help with phenotype survey. 637

Competing interests 638

The authors declare no competing interests. 639

640

Figure Legend 641

Figure 1. Extensive phenotypic variation of plant architecture in maize. (A) Diagram 642 of measured ten plant architecture traits in our study. PH (plant height), EH (ear height), 643 TBN (tassel branch number), TMAL (tassel main axis length), LNAE (leaf number above 644 ear), LNBE (leaf number below ear), LN (total leaf number), LL (ear leaf length), LW 645 (ear leaf width), MLA (middle leaf angle). (B) The phenotypic variation of five plant 646 architecture traits. (C) Relationships among ten architecture traits. Red lines designate 647 positive correlations between two traits, blue designate negative correlations. The 648 correlations with P value less than 1.0E-15 are shown. The circle size shows the number 649 of correlated traits. (D) Phenotypic tree of all 10 plant architecture traits. The scalar 650 represents Euclidean distance, which was calculated based on the standardized 651 phenotypic data across 10 RIL populations. 652

Figure 2. Genome-wide landscape of genetic factors underlying plant architecture 653 variation in maize. Genome-wide association mapping by the joint linkage method 654 (JLM) was employed to detect the genetic factors underlying maize plant architecture 655 variation. X axis shows the genomic position (Mb), and y axis shows the likelyhood ratio 656 (LTR). The gray section represents an LTR value significantly less than the cutoff of 2.76, 657 while the green and orange bars represents significant LTR values greater than the cutoff. 658 Genes in blue were coincident with QTLs. Dotted blue lines under each QTL mapping 659 result panel show significantly epistatic interactions between QTLs underlying plant 660 architecture variation in maize. 661

Figure 3. Co-localization of two well-known genes with QTLs underlying plant 662 architecture traits. (A) brachytic2 (Br2) is coincident with a QTL on chr.1 conferring 663



30

plant height variation in ZONG/YU87-1 population. (B) liguleless 1 (lg1) is co-localized 664 with a QTL on chr.2 that controls leaf angle in SK/ZHENG58 population. 665

Figure 4. Relationships between parental diversity and the phenotypic variation in 666 the progeny. (A) Parental phenotypic variation (PPV) is significantly associated with 667 standard deviation of progeny phenotypic variation (STV). (B) Parental genetic variance 668 (PGV) is significantly associated with STV. (C) STV is significantly associated with 669 QTL number (QN). (D) STV is significantly associated with QTL phenotypic variance 670 rate (QPV). (E) The model of relationships between STV, PGV, PPV, QN, and QPV. (F) 671 The proportion of QTLs with positive effects that were derived from male and female 672 alleles. (G) The distribution of QTL positive effects that were derived from male and 673 female alleles. 674

Figure 5. Genomic regions with pleiotropic effects contribute largely to the genetic 675 relationships within plant architecture traits in maize. (A) Summary of overlapped 676 QTLs between 10 plant architecture traits. Lower left shows the –log 10 value of p value, 677 showing the extent to which genetic overlap was between plant architecture traits. Upper 678 right illustrates the overlapped QTL number for different pairs of architecture traits. (B) 679 Relationship between proportion of pleiotropic QTLs and phenotypic correlation between 680 all pairs of architecture traits. PQR is the abbreviation of pleiotropic QTL rate. (C) 681 Relationship between proportion of phenotypic variation explained by pleiotropic QTL 682 and phenotypic correlation. PPR is the proportion of phenotypic variation explained by 683 pleiotropic QTL. (D) Pleiotropic QTL among different clusters. The cluster are defined as 684 described in Figure 1D. (F) Co-localization of well-known genes with pleiotropic QTLs. 685 Distribution of well-known genes in different genomic regions. 686

Figure 6. Co-localization of two well-known developmental genes with the 687 pleiotropic loci conffering plant architecture in maize. (A) Zea floricaula leafy2 (Zfl2) 688 is coincident with a pleiotropic genomic region on chr2, which controls LNBE, LN, and 689 LW. (B) Zmhox1a (hox1) is coincident with a pleiotropic genomic region on chr8, which 690 controls PH and LL. 691

Figure 7. Map-based validation using RHLs for five QTLs. (A-E). RHL family 692 validation of qPH3, qTMAL3, qPH1, qPH7, and qTBN3. For each trait, the upper panel 693 shows the phenotypic variation between the two parents, while the bottom panel shows 694 the phenotypic variation between two different homozygous genotypes of the target QTL 695 region. 696

Figure 8. Fine-mapping of qPH3 using RHLs in DE3/BY815 RIL population. (A) 697 Primary QTL mapping of qPH3 in DE3/BY815 population. (B) RHL screening of qPH3. 698 ANOVA test based on the progeny genotypic and phenotypic variation in the RHL 699 families in Wuhan (C) and Hainan (D). 700

Figure 9. Identification of candidate genes for qPH3. (A) Haplotype association 701 mapping of qPH3. The color of the points indicates minor allele frequency, yellow for 702 0.05-0.1, purple for 0.1-0.2, red for 0.2-0.3, blue for 0.3-0.4, dark blue for 0.4-0.5. (B) 703 Expression-level variation for all candidate genes in the qPH3 region between RHLs with 704 and without 600 Kb target genomic substitutions. (C) Haplotype comparison of all 705



31

candidate genes in qPH3 region in different elite inbred lines. Only DE3/BY815 RIL 706 population, of which the two parents had haplotype divergence for three genes, had qPH3 707 detected. (D) GRMZM2G177220 has a nucleotide mutation only detected in between 708 DE3 and BY815, which could cause an early stop codon. 709

710



32

Table 1. Summary of heritability and QTLs or SNPs identified by SLM, JLM and 711 GWAS methods for 10 plant architecture traits 712

PH EH TBN TMAL LNAE LNBE LN LL LW MLA H2 a 0.95 0.94 0.94 0.93 0.92 0.90 0.90 0.94 0.91 0.92

SLMb 83 85 73 76 64 80 79 69 77 66 JLMc 86 96 79 50 61 69 90 84 65 83

GWASd 38 43 30 28 39 39 44 42 36 10 a is the heritability value, b is the QTL number of single segregation populations, c is QTL number of 713 joint linkage populations, d is the significant SNP number. 714

715

716



33

Supplemental Data 717

Supplemental Figure S1. Genetic diversity of 14 parents of our ROAM population. 718 (A) PCA analysis of 14 parents of our ROAM population. (B) Phylogenetic tree of 719 inbreds in the association mapping panel. (C) cluster analysis of our 14 parents and 26 720 NAM parents. Green shows these 14 parents in our study, while blue shows the 26 721 NAM parents. 722

Supplemental Figure S2. Phenotypic variation of 10 plant architecture traits across 723 10 RIL populations in maize. 724

Supplemental Figure S3. Overview of QTL results for PH in maize. Top panel (Manhattan 725 plot): the colored dots show the significance of genome-wide blocks estimated by the joint 726 linkage mapping (JLM) method. Middle panel: triangles indicate significant single nucleotide 727 polymorphisms (SNPs) identified by genome-wide association study (GWAS), where the blue 728 upward triangles indicate that the allele increases PH relative to the other allele, whereas the 729 green downward triangles indicate the opposite effect. Bottom panel: colored rectangles indicate 730 separate linkage mapping (SLM) QTL regions across the 10 recombination inbred line (RIL) 731 populations. The color density of the rectangles indicates the magnitude of the logarithm of the 732 odds (LOD) values. LRT, likelihood ratio test. BPP, bootstrap posterior probability. 733

Supplemental Figure S4. Overview of QTL results for EH in maize. Legend is similar to 734 Fig S2. 735

Supplemental Figure S5. Overview of QTL results for TMAL in maize. Legend is similar 736 to Fig S2. 737

Supplemental Figure S6. Overview of QTL results for TBN in maize. Legend is similar to 738 Fig S2. 739

Supplemental Figure S7. Overview of QTL results for LNAE in maize. Legend is similar to 740 Fig S2. 741

Supplemental Figure S8. Overview of QTL results for LNBE in maize. Legend is similar to 742 Fig S2. 743

Supplemental Figure S9. Overview of QTL results for LN in maize. Legend is similar to 744 Fig S2. 745

Supplemental Figure S10. Overview of QTL results for LL in maize. Legend is similar to 746 Fig S2. 747

Supplemental Figure S11. Overview of QTL results for LW in maize. Legend is similar to 748 Fig S2. 749

Supplemental Figure S12. Overview of QTL results for MLA in maize. Legend is similar 750 to Fig S2. 751

Supplemental Figure S13. Distribution of QTL effects (R2) for all 10 plant architecture 752 traits by SLM method. 753

Supplemental Figure S14. Number of QTLs with R2 greater than 10% for all 10 plant 754 architecture traits in maize. 755



34

Supplemental Figure S15. Comparison of mapping resolution for SLM, JLM and GWAS 756 methods. 757

Supplemental Figure S16. Phenotypic variance explained by all significant GWAS SNPs. 758

Supplemental Figure S17. Number of consistent QTLs identified by SLM, JLM and 759 GWAS methods. Consistent QTLs were defined as the ones that were mapped simultaneously by 760 all three different statistic methods. 761

Supplemental Figure S18. Relationships between parents’ phenotypic variation (PPV), 762 parental genetic variation (PGV), progeny phenotypic average value variation (ATV), 763 progeny phenotypic standard deviation variation (STV), number of QTLs, and QTL 764 effects (QPV). 765

Supplemental Figure S19. Distribution of pleiotropic QTL. Upper panel is the number 766 of pleiotropic QTL for different traits. Lower is the distribution of all QTLs. Black lines 767 are the locations of pleiotropic QTLs that are associated with at least 3 morphology traits. 768

Supplemental Figure S20. Phylogenetic tree of different plant architecture traits 769 based on the QTL mapping results. 770

Supplemental Table S1. Phenotypic variation of 10 plant architecture traits across 771 10 RIL populations in maize. NA means not available. 772

Supplemental Table S2. Effects of environment and genotype by environment (G X 773 E) in the phenotypic variation of plant architecture traits in maize. 774

Supplemental Table S3. Correlations between 10 plant architecture traits of ROAM 775 population in maize. Right upper shows the significance P value of Pearson 776 correlation, while left lower shows correlation coefficient. 777

Supplemental Table S4. Detailed QTL information identified by single populations 778 linkage analyses for all 10 plant archiecture traits. a is genetic position of peak 779 block for each SLM QTL. b is physical position of QTL support interval at 99% 780 confidence. c is genetic position of QTL support interval at 99% confidence. 781

Supplemental Table S5. Detailed QTL information identified by joint populations 782 linkage analyses for all 10 plant archiecture traits. a, LRT is likelihood ratio test. b 783 is physical position of QTL support interval at 99% confidence. 784

Supplemental Table S6. Significant SNPs identified by genome-wide association 785 analyses for all 10 plant architecture traits. a is resample-based model inclusion 786 probability. 787

Supplemental Table S7. Coincidence between QTLs and well-known functional 788 genes. 789

Supplemental Table S8. Detailed polymorphism information of three candidate 790 genes GRMZM2G058250, GRMZM2G177227, and GRMZM2G177220 for the fine 791 mapping of QTL-qPH3. 792

Supplemental Table S9 Detailed polymorphism information of other candidate 793 genes in 600Kb region for the fine mapping of QTL-qPH3. 794



35

Supplemental Table S10. Detailed maker information for the validation of five 795 QTLs in Residual Heterozygous Lines. 796

Supplemental Table S11. Detailed marker information for the fine mapping of 797 QTL-qPH3. 798

799

800

801



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Bellmann R, Werr W (1992) Zmhox1a, the product of a novel maize homeobox gene, interacts with the Shrunken 26 bp feedback controlelement. EMBO J 11: 3367-3374


Bomblies K, Wang RL, Ambrose BA, Schmidt RJ, Meeley RB, Doebley J (2003) Duplicate FLORICAULA/LEAFY homologs zfl1 and zfl2control inflorescence architecture and flower patterning in maize. Development 130: 2385-2395


Bouchet S, Bertin P, T Presterl T, Jamin P, Coubriche D, Gouesnard B, Laborde J, Charcosset A (2017) Association mapping forphenology and plant architecture in maize shows higher power for developmental traits compared with growth influenced traits.Heredity 118: 249-259


Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, Ersoz E, Flint-Garcia S, Garcia A, Glaubitz JC, et al (2009) Thegenetic architecture of maize flowering time. Science 325: 714-718


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Duvick DN (2005) Genetic progress in yield of United States maize (Zea mays L.). Maydica 50: 193-202 www.plantphysiol.orgon October 10, 2020 - Published by Downloaded from

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http://www.ncbi.nlm.nih.gov/pubmed?term=Barton MK %282010%29 Twenty years on%3A the inner workings of the shoot apical meristem%2C a developmental dynamo%2E Dev Biol 341%3A 95%2D113&dopt=abstract

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http://www.ncbi.nlm.nih.gov/pubmed?term=Baute J%2C Herman D%2C Coppens F%2C De Block J%2C Slabbinck B%2C Dell%27Acqua M%2C P� ME%2C Maere S%2C Nelissen H%2C Inz� D %282015%29 Correlation analysis of the transcriptome of growing leaves with mature leaf parameters in a maize RIL population%2E Genome Biol 16%3A 168&dopt=abstract

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http://scholar.google.com/scholar?as_q=Correlation analysis of the transcriptome of growing leaves with mature leaf parameters in a maize RIL population.&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Baute&as_ylo=2015&as_allsubj=all&hl=en&c2coff=1

http://www.ncbi.nlm.nih.gov/pubmed?term=Becraft PW%2C Bongard%2DPierce DK%2C Sylvester AW%2C Poethig RS%2C Freeling M %281990%29 The liguleless%2D1 gene acts tissue specifically in maize leaf development%2E Dev Biol 141%3A 220%2D232&dopt=abstract

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http://www.ncbi.nlm.nih.gov/pubmed?term=Begna HS%2C Hamilton RI%2C Dwyer LM%2C Stewart DW%2C Smith DL %281999%29 Effects of population density on the vegetative growth of leafy reduced%2Dstature maize in short%2Dseason areas J%2E Agron Crop Sci 182%3A 49%2D55&dopt=abstract

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http://scholar.google.com/scholar?as_q=Duplicate FLORICAULA/LEAFY homologs zfl1 and zfl2 control inflorescence architecture and flower patterning in maize&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Bomblies&as_ylo=2003&as_allsubj=all&hl=en&c2coff=1

http://www.ncbi.nlm.nih.gov/pubmed?term=Bouchet S%2C Bertin P%2C T Presterl T%2C Jamin P%2C Coubriche D%2C Gouesnard B%2C Laborde J%2C Charcosset A %282017%29 Association mapping for phenology and plant architecture in maize shows higher power for developmental traits compared with growth influenced traits%2E Heredity 118%3A 249%2D259&dopt=abstract

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http://www.ncbi.nlm.nih.gov/pubmed?term=Buckler ES%2C Holland JB%2C Bradbury PJ%2C Acharya CB%2C Brown PJ%2C Browne C%2C Ersoz E%2C Flint%2DGarcia S%2C Garcia A%2C Glaubitz JC%2C et al %282009%29 The genetic architecture of maize flowering time%2E Science 325%3A 714%2D718&dopt=abstract

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http://www.ncbi.nlm.nih.gov/pubmed?term=Fischer K%2C Edmeades G%2C Johnson E %281987%29 Recurrent selection for reduced tassel branch number and reduced leaf area density above the ear in tropical maize populations%2E Crop Sci 27%3A 1150%2D1156&dopt=abstract

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http://www.ncbi.nlm.nih.gov/pubmed?term=Fraser HB%2C Schadt EE %282010%29 The quantitative genetics of phenotypic robustness%2E PLoS One 5%3A e8635&dopt=abstract

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http://scholar.google.com/scholar?as_q=The quantitative genetics of phenotypic robustness.&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Fraser&as_ylo=2010&as_allsubj=all&hl=en&c2coff=1

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Tian F, Bradbury PJ, Brown PJ, Hung H, Sun Q, Flint-Garcia S, Rocheford TR, McMullen MD, Holland JB, Buckler ES (2011) Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat Genet 43: 159-164


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Thompson AM, Yu J, Timmermans MC, Schnable P, Crants JC, Scanlon MJ, Muehlbauer GJ (2015) Diversity of maize shoot apicalmeristem architecture and its relationship to plant morphology. G3 5: 819-827


Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L (2010) Transcript assemblyand quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotech 28: 511-515


Vollbrecht E, Springer PS, Goh L, Buckler ES. Martienssen R (2005) Architecture of floral branch systems in maize and related grasses.Nature 436: 1119-1126


Wallace JG, Bradbury PJ, Zhang N, Gibon Y, Stitt M, Buckler ES (2014) Association mapping across numerous traits reveals patterns offunctional variation in maize. PLoS Genet 10: e1004845


Wang H, Nussbaum-Wagler T, Li B, Zhao Q, Vigouroux Y, Faller M Bomblies K, Lukens L, Doebley JF (2005) The origin of the nakedgrains of maize. Nature 436: 714-719


Wang S, Basten CJ. Zeng Z (2006) Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh,NC


Watanabe S, Xia Z, Hideshima R, Tsubokura Y, Sato S, Yamanaka N, Takahashi R, Anai T, Tabata S, Kitamura K, et al (2011) A map-based cloning strategy employing a residual heterozygous line reveals that the GIGANTEA gene is involved in soybean maturity andflowering. Genetics 188: 395-407


Weigel D, Alvarez J, Smyth DR, Yanofsky MF, Meyerowitz EM (1992) LEAFY controls floral meristem identity in Arabidopsis. Cell 69: 843-859


Winkler RG, Helentjaris T (1995) The maize Dwarf3 gene encodes a cytochrome P450-mediated early step in Gibberellin biosynthesis.Plant Cell 7: 1307-1317


Xiao YJ, Tong H, Yang XH, Xu S, Pan Q, Qiao F, Raihan MS, Luo Y, Liu H, Zhang X, et al (2016) Genome-wide dissection of the maizeear genetic architecture using multiple populations. New Phytol 210: 1095-1106

Pubmed: Author and Title www.plantphysiol.orgon October 10, 2020 - Published by Downloaded from Copyright © 2017 American Society of Plant Biologists. All rights reserved.

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http://www.ncbi.nlm.nih.gov/pubmed?term=Tian F%2C Bradbury PJ%2C Brown PJ%2C Hung H%2C Sun Q%2C Flint%2DGarcia S%2C Rocheford TR%2C McMullen MD%2C Holland JB%2C Buckler ES %282011%29 Genome%2Dwide association study of leaf architecture in the maize nested association mapping population%2E Nat Genet 43%3A 159%2D164&dopt=abstract

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http://scholar.google.com/scholar?as_q=Genome-wide association study of leaf architecture in the maize nested association mapping population&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

http://scholar.google.com/scholar?as_q=Genome-wide association study of leaf architecture in the maize nested association mapping population&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Tian&as_ylo=2011&as_allsubj=all&hl=en&c2coff=1

http://www.ncbi.nlm.nih.gov/pubmed?term=Timmermans MC%2C Hudson A%2C Becraft PW%2C Nelson T %281999%29 ROUGH SHEATH2%3A a Myb protein that represses knox homeobox genes in maize lateral organ primordia%2E Science 284%3A 151%2D153&dopt=abstract

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http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Timmermans&hl=en&c2coff=1

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http://www.ncbi.nlm.nih.gov/pubmed?term=Thompson AM%2C Yu J%2C Timmermans MC%2C Schnable P%2C Crants JC%2C Scanlon MJ%2C Muehlbauer GJ %282015%29 Diversity of maize shoot apical meristem architecture and its relationship to plant morphology%2E G3 5%3A 819%2D827&dopt=abstract

http://search.crossref.org/?page=1&rows=2&q=Thompson AM, Yu J, Timmermans MC, Schnable P, Crants JC, Scanlon MJ, Muehlbauer GJ (2015) Diversity of maize shoot apical meristem architecture and its relationship to plant morphology. G3 5: 819-827

http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Thompson&hl=en&c2coff=1

http://scholar.google.com/scholar?as_q=Diversity of maize shoot apical meristem architecture and its relationship to plant morphology&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

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http://www.ncbi.nlm.nih.gov/pubmed?term=Trapnell C%2C Williams BA%2C Pertea G%2C Mortazavi A%2C Kwan G%2C van Baren MJ%2C Salzberg SL%2C Wold BJ%2C Pachter L %282010%29 Transcript assembly and quantification by RNA%2DSeq reveals unannotated transcripts and isoform switching during cell differentiation%2E Nat Biotech 28%3A 511%2D515&dopt=abstract

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http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Trapnell&hl=en&c2coff=1

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http://scholar.google.com/scholar?as_q=Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Trapnell&as_ylo=2010&as_allsubj=all&hl=en&c2coff=1

http://www.ncbi.nlm.nih.gov/pubmed?term=Vollbrecht E%2C Springer PS%2C Goh L%2C Buckler ES%2E Martienssen R %282005%29 Architecture of floral branch systems in maize and related grasses%2E Nature 436%3A 1119%2D1126&dopt=abstract

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http://www.ncbi.nlm.nih.gov/pubmed?term=Wallace JG%2C Bradbury PJ%2C Zhang N%2C Gibon Y%2C Stitt M%2C Buckler ES %282014%29 Association mapping across numerous traits reveals patterns of functional variation in maize%2E PLoS Genet 10%3A e1004845&dopt=abstract

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http://www.ncbi.nlm.nih.gov/pubmed?term=Wang H%2C Nussbaum%2DWagler T%2C Li B%2C Zhao Q%2C Vigouroux Y%2C Faller M Bomblies K%2C Lukens L%2C Doebley JF %282005%29 The origin of the naked grains of maize%2E Nature 436%3A 714%2D719&dopt=abstract

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http://www.ncbi.nlm.nih.gov/pubmed?term=Watanabe S%2C Xia Z%2C Hideshima R%2C Tsubokura Y%2C Sato S%2C Yamanaka N%2C Takahashi R%2C Anai T%2C Tabata S%2C Kitamura K%2C et al %282011%29 A map%2Dbased cloning strategy employing a residual heterozygous line reveals that the GIGANTEA gene is involved in soybean maturity and flowering%2E Genetics 188%3A 395%2D407&dopt=abstract

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http://www.ncbi.nlm.nih.gov/pubmed?term=Weigel D%2C Alvarez J%2C Smyth DR%2C Yanofsky MF%2C Meyerowitz EM %281992%29 LEAFY controls floral meristem identity in Arabidopsis%2E Cell 69%3A 843%2D859&dopt=abstract

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http://www.ncbi.nlm.nih.gov/pubmed?term=Xiao YJ%2C Tong H%2C Yang XH%2C Xu S%2C Pan Q%2C Qiao F%2C Raihan MS%2C Luo Y%2C Liu H%2C Zhang X%2C et al %282016%29 Genome%2Dwide dissection of the maize ear genetic architecture using multiple populations%2E New Phytol 210%3A 1095%2D1106&dopt=abstract


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http://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Xiao&hl=en&c2coff=1

http://scholar.google.com/scholar?as_q=Genome-wide dissection of the maize ear genetic architecture using multiple populations&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

http://scholar.google.com/scholar?as_q=Genome-wide dissection of the maize ear genetic architecture using multiple populations&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Xiao&as_ylo=2016&as_allsubj=all&hl=en&c2coff=1

http://www.ncbi.nlm.nih.gov/pubmed?term=Xiao Y%2C Liu H%2C Wu L%2C Warburton M%2C Yan J %282017%29 Genome%2Dwide Association Studies in Maize%3A Praise and Stargaze%2E Mol Plant 10%3A 359%2D374&dopt=abstract

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