Multi-environment QTL mapping of crown root traits in a maize RIL population

Journal Pre-proof Multi-environment QTL mapping of crown root traits in a maize RIL population

Pengcheng Li, Yingying Fan, Shuangyi Yin, Yunyun Wang, Houmiao Wang, Yang Xu, Zefeng Yang, Chenwu Xu PII:

S2214-5141(20)30036-2

DOI:

https://doi.org/10.1016/j.cj.2019.12.006

Reference:

CJ 448

To appear in:

The Crop Journal

Received date:

23 September 2019

Revised date:

17 November 2019

Accepted date:

20 February 2020

Please cite this article as: P. Li, Y. Fan, S. Yin, et al., Multi-environment QTL mapping of crown root traits in a maize RIL population, The Crop Journal (2020), https://doi.org/ 10.1016/j.cj.2019.12.006

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© 2020 Published by Elsevier.

Journal Pre-proof

Multi-environment QTL mapping of crown root traits in a maize RIL population Pengcheng Lia,b, Yingying Fana, Shuangyi Yina, Yunyun Wanga, Houmiao Wanga,b, Yang Xua,b, Zefeng Yanga,b,c,*, Chenwu Xu a,b,c,* a

Jiangsu Key Laboratory of Crop Genetics and Physiology/Key Laboratory of Plant Functional Genomics of the Ministry of

Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Agricultural College of Yangzhou University, Yangzhou 225009, Jiangsu, China b

Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009,

Jiangsu, China c

Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou

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University, Jiangsu, China

Abstract: Crown root traits, including crown root angle (CRA), diameter (CRD), and number (CRN), are major

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determining factors of root system architecture, which influences crop production. In maize, the genetic mechanisms determining crown root traits in the field are largely unknown. CRA, CRD, and CRN were

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evaluated in a recombinant inbred line population in three field trials. High phenotypic variation was observed for crown root traits, and all measured traits showed significant genotype–environment interactions.

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Single-environment (SEA) and multi-environment (MEA) quantitative trait locus (QTL) analyses were conducted for CRA, CRD, and CRN. Of 46 QTL detected by SEA, most explained less than 10% of the

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phenotypic variation, indicating that a large number of minor-effect QTL contributed to the genetic component of these traits. MEA detected 25 QTL associated with CRA, CRD, and CRN, and 2 and 1 QTL were identified

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with significant QTL-by-environment interaction effects for CRA and CRD, respectively. A total of 26.1% (12/46) of the QTL identified by SEA were also detected by MEA, with many being detected in more than one environment. These findings contribute to our understanding of the phenotypic and genotypic patterns of crown

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root traits in different environments. The identified environment-specific QTL and stable QTL may be used to improve root traits in maize breeding.

Keywords: Crown root traits; Maize; Quantitative trait locus; Multi-environment QTL mapping; Genotype–environment interaction

Abbreviations: CRA, crown root angle; CRD, crown root diameter; CRN, crown root number; CIM, composite interval mapping; GEI, genotype–environment interaction; MAS, marker-assisted selection; MEA, multi-environment QTL analysis; RIL, recombinant inbred line; SNP, single-nucleotide polymorphism; RSA, root system architecture; SEA, single-environment QTL analysis.

*

Corresponding authors: Zefeng Yang, E-mail address: [email protected], Chenwu Xu, E-mail address: [email protected], Tel.:

+86-514-87979358. Received: 2019-9-23; Revised: 2019-11-17; Accepted: 2020-02-20. 1

Journal Pre-proof 1. Introduction Water and nutrient deficiences are primary limitations to plant productivity, severely reducing yields globally and threatening global food security [1]. In the U.S. alone, approximately 67% of crop losses over the last 50 years have resulted from drought [2]. Roots mediate the uptake of water and nutrients and anchor plants. An optimal root system architecture (RSA) supports soil resource use and yield; for example, more roots distributed in deeper soil layers can help crops use the water and nitrate found there [3–6]. Plant breeders are starting to focus on roots in their efforts to produce crops with better yields; thus, roots are the key to a second green revolution [7]. The major challenge for root breeding is poor understanding of the genetic basis of root development. Maize RSA refers to the shape and spatial arrangement of root tissue in the soil [8,9] and comprises several

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traits and parameters including root number, length, angle, and diameter. Crown roots form the backbone of the maize root system [10]. An optimal crown root number (CRN) in maize promotes deep soil exploration and

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resource acquisition under drought conditions and suboptimal availability of mobile nutrients [5]. Thus, CRN is a trait that can potentially be used for the genetic improvement of nitrogen acquisition from low-nitrogen soils

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[11]. Crown root angle (CRA) influences vertical and horizontal root distributions in the soil and is associated with rooting depth [12]. A deeper root system helps crops extract water and nitrogen from deeper soil layers to

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increase yield [5,6,13]. Because of the higher phosphorus availability in topsoil, a shallow basal root growth angle is important for phosphorus acquisition in common bean [14]. Several overlapping quantitative trait loci

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(QTL) region in sorghum showed correlations between CRA and yield [15]. Root diameter, correlated with anatomical phenes such as cortical thickness and stele diameter, was correlated with root penetration capacity

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and biomechanical properties [16]. Thicker roots more readily penetrate hard soils to use deeper soil resources [17]. Root biomechanical properties influence root bending strength and adventitious root angle, plant anchorage, and lodging resistance [18]. Large diameters and steep growth angles work together to increase root

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penetration into hard soils [16]. Identifying QTL or genes associated with CRN, CRA, and CRD will be needed for future root breeding aimed at improving crop performance under diverse soil conditions. Genes/QTL for crown root traits have been identified in biparental populations in cereal crops. Cai et al. [19] identified five QTL for CRN at three maize developmental stages, including one consensus QTL on chromosome bin 10.4. Ku et al. [20] identified two coincident major QTL for total brace root tier number using a set of recombinant inbred lines (RILs) and an immortalized F2 population derived from these RILs, and the largest additive effect was 16.4%–17.9%. For root angle, QTL mapping has been conducted in maize, rice, and sorghum. In two maizeteosinte F2 populations, 10 root-angle QTL were identified, and the QTL on chromosome 7 was found consistently in all populations [21]. Six major QTL for root angle (DRO1, DRO2, DRO3, DRO4, DRO5, and qSOR1) were confirmed in rice [22], and DRO1 was the first cloned gene associated with root angle that increased drought avoidance in rice [6]. Four root-angle QTL have been identified in sorghum, and an individual QTL explained 29.78% of phenotypic variation [15]. In rice, Courtois et al. [23] summarized 675 root QTL detected in 12 populations for 29 root traits and found 123 QTL for root thickness; however, no QTL associated with root diameter have been cloned. A QTL controlling root thickness and root length has been placed in an 11.5-kb region by fine mapping [24]. Overexpressing OsNAC5 and OsNAC9 2

Journal Pre-proof increased root diameter, drought tolerance, and grain yield under field conditions [25,26]. Few genetic studies of crown root traits in maize, especially in the field, have been reported. Plant root architecture shows high plasticity in response to environmental factors such as drought and nutrient availability [27]. Water deficit strongly suppresses the development of crown roots, and this response is widely conserved across grass species [28]. CRA becomes steeper to allow roots to capture nitrogen in deep soil layers under low-nitrogen conditions [12]. To improve root traits across different environments, it is necessary to characterize genotype-by-environment interaction (GEI). Previous studies [29,30] extended QTL mapping to the detection of QTL-by-environment interaction (QEI) of root traits. However, little information is available on multi-environment QTL mapping of crown root traits in maize, especially in the field. To investigate the genetic basis of crown root traits in maize, we characterized CRA, CRD, and CRN in a

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maize RIL population in three field trials and performed single- and multi-environment QTL mapping using a high-density linkage map. Our objectives were to evaluate phenotypic variation in crown root traits in several

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field environments and to identify QTL and QEI for crown root traits.

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2. Materials and methods

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2.1. Plant materials, field experiments, and root system evaluation A set of 208 RILs derived from two maize inbred lines, T877 and DH1M, were used as described by Yin et al.

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[31]. Field trials were conducted in three environments: Sanya (N18°23′, E109°44′) in 2015 (15SY) and 2016 (16SY), and Yangzhou (N32°22′, E119°16′) in 2017 (17YZ). In each environment, the population was evaluated in a completely randomized block design of one-row plots with two replications. Each row was 3 m long and

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0.5 m wide and contained 11 plants. Shovelomics [32, 33] was performed at maturity. Six plants with uniform growth were randomly selected from one plot, and roots of each plant were excavated with shovels by removal

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of a soil cylinder 40 cm in diameter and 25 cm deep. The excavated root crowns were shaken briefly to remove most of the soil adhering to the crown, and the root layer could be clearly distinguished. Three roots on the first layer of the nodal root situated flush with the soil surface were selected for measurement of CRA, CRN, and CRD [33]. CRA was measured with a protractor as degrees from horizontal: horizontal roots were assigned a CRA of 0 and vertical roots a CRA of 90 (Fig. S1). Selected crown roots were cut off at 1 cm from the top of the root and a vernier caliper was used to measure CRD. 2.2. Genotyping and construction of genetic linkage maps A high-density linkage map has been constructed for the T877 × DH1M population by single-nucleotide polymorphism (SNP) genotyping using the Maize56K SNP Array [31] and contains 56,110 SNPs covering the entire maize genome. Briefly, SNPs polymorphic between two parental lines were allocated to 3227 bin markers, which were then ordered using the ripple function in the qtl package [34]. Genetic distances between bin markers were calculated using the Kosambi function [35]. The numbers of bin markers per chromosome varied from 111 to 503, and the total length of the linkage map was 2450 cM, with a mean genetic distance between adjacent markers of 0.76 cM. 2.3. Data analyses 3

Journal Pre-proof Phenotypic data analyses, including descriptive statistics (mean, range, standard deviation, skewness, and kurtosis) and Pearson correlations, were calculated using IBM SPSS Statistics 21.0. The “lme4” package in R was used to estimate genotypic (σ2G), G-E interaction (σ2GE) and error variances (σ2e). The broad-sense heritability (h2) of each measured trait was calculated following Hallauer et al. [36]. Single-environment QTL analysis (SEA) was performed with qtl package in R [34] by composite interval mapping (CIM) with a 1-cM step length and a 10-cM window size. The phenotype in environments 15SY, 16SY, and 17YZ, and the simple mean values across environments, were used for the SEA. Colocalized QTL separated in SEA by a distance of less than 10 cM were defined as QTL. Multi-environment QTL mapping (MEA) was performed with the MET (multi-environment trial) functionality in QTL IciMapping 4.0 [37, 38]. In view of the complexity of root traits, a suggestive LOD threshold value of 2.5 was used to avoid ignoring minor-effect loci, following previous reports [19, 39]. If a QTL identified by SEA shared the same marker with one identified by MEA-QTL, it was assigned as having been detected by

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both SEA and MEA. Boxplots, correlation diagrams, LOD curves, and QTL profiles in three environments were

character) + trait (capital letter) + chromosome + QTL number.

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3. Results

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drawn using corrplot and the ggplot2 package in R. The QTL nomenclature is as follows: environment (subscript

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3.1 Crown root phenotypic assessment

The values of the three crown root traits (CRA, CRN, and CRD) differed significantly between the two parental

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lines except for CRN in 2015SY (Fig. 1). Compared to DH1M, T877 showed larger CRA in 15SY and 16SY but slightly smaller CRA in 17 YZ. DH1M showed larger CRD and CRN than T877 in 15 SY and 17YZ, whereas

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a smaller CRA than T877.

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smaller CRD and CRN in 16SY (Fig. 1; Table 1). On average, DH1M showed a larger CRD, a greater CRN, and

Fig. 1 – Phenotypic differences between the two parental maize lines DH1M and T877 in different environments. Traits differing (* P < 0.05) between the two lines are marked with asterisks.

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Journal Pre-proof Table 1 – Descriptive statistics and ANOVA for crown root traits in three environments. Parent Trait

DH1M CRA

CRD

CRN

RIL population T877

Mean ± SD

Range

Skew.

Kurt.

CV (%)

2015SY

41.2

47.8

48.58 ± 4.92

25.00–61.75

−0.347

2.347

10.13

2016SY

59.3

63.1

56.03 ± 3.27

46.46–65.63

−0.123

−0.059

5.83

2017YZ

46.4

44.2

50.76 ± 8.14

32.50–66.25

−0.911

−0.751

16.03

Mean

49.2

51.6

51.64 ± 3.81

41.30–63.69

−0.13

0.171

7.38

2015SY

4.1

3.5

3.87 ± 0.41

2.73–5.25

0.159

0.395

10.66

2016SY

3.4

4.1

3.74 ± 0.36

2.71–4.85

0.223

0.645

2017YZ

4.9

4.4

4.74 ± 0.65

2.30–6.47

−0.987

−0.666

Mean

4.1

3.9

4.04 ± 0.33

2.96–4.85

−0.06

2015SY

11.6

9.7

11.56 ± 1.55

8.00–15.67

0.211

−0.395

13.37

2016SY

11.1

12.3

12.87 ± 1.80

8.00–15.67

0.337

e 0.428

13.99

2017YZ

13.0

14.0

16.07 ± 2.33

9.50–25.5

−0.925

−0.717

14.55

Mean

12.0

11.9

13.15 ± 1.59

9.00–18.39

0.115

0.419

12.09

A significant effect is indicated by “**” (P < 0.01).

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0.106

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5

ANOVA (F-test)

h2 (%)

Env.

Environment 57.3

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9.61

13.72

1032.32

**

Genotype 10.23

**

Interaction 7.95**

46.8

649.83**

4.54**

3.24**

63.9

817.09**

7.81**

3.07**

8.17

Journal Pre-proof In the RIL population, lines in 17YZ showed larger CRD, greater CRN, and smaller CRA than those in other environments, and wide phenotypic variation for the three crown root traits in all environments was observed (Table 1). Coefficients of variation (CV) ranged from 7.38% to 12.09%, with the greatest phenotypic variation occurring in 17YZ, with a CV that ranged from 13.72% to 16.03%. The crown root traits showed approximately normal distributions in all environments (Fig. 2; Table S1). The effects of genotype, environment and GEI were significant at P < 0.01 for all traits (Table 1), indicating that crown roots were highly sensitive to environment. The h2 values were moderate, varying from 46.8% to 63.9% (Table 1). Only CRD was positively correlated with

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CRN (r = 0.50, P < 0.001) (Fig. 2).

Fig. 2 – Pearson correlations among crown root angle, diameter, and number. Red, green, and blue areas represent phenotypic distributions in the 15SY, 16SY, and 17YZ environments, respectively. ***: significant at P < 0.001.

3.2. Single-environment QTL analysis of maize crown root traits SEA identified 46 putative QTL on all chromosomes, including 12, 17, and 17 QTL associated with CRA, CRD, and CRN, respectively (Table 2; Fig. 3). These QTL explained 1.01%–10.60% of the phenotypic variation. Of the identified QTL, 29 and 17 carried favorable alleles from T877 and DH1M, respectively. For CRA, respectively 3, 4, 2, and 3 QTL were detected in 15SY, 16SY, 17YZ, and the averaged value over the environments. The proportion of phenotypic variation explained by these individual QTL ranged from 2.03% to 10.60%, and QTL-15CRA1 was the largest QTL for CRA. Respectively 4, 4, 4, and 5 QTL were identified for 6

Journal Pre-proof CRD 15SY, 16SY, 17YZ, and the averaged value over the environments and each explained 1.38%–8.61% of the phenotypic variation. For CRN, respectively 5, 4, 3, and 5 QTL were detected in 15SY, 16SY, 17YZ, and the average of the three environments. The proportions of phenotypic variation explained by these individual QTL ranged from 1.01% to 8.27% (Table 2; Fig. 3-C). Several QTL were detected in multiple environments: QTL-16CRA1 was colocalized with

17CRA1

on chromosome 1, and QTL-15CRA3 was also identified in the

averaged value over the environments (avCRA3). Three colocalized QTL, 16CRD4, 15CRD6, and 15CRD9, were detected for CRD, and were detected the averaged value over the environments (avCRD4, avCRD6, and avCRD9, respectively). QTL-15CRN3 was colocalized with

17CRN3

on chromosome 3, and QTL-17CRN6 was also

identified the averaged value over the environments (avCRN6). CRN was significantly correlated with CRD. Pairs of QTL for CRN and CRD were co-localized on chromosomes 8 (201.0–205.0 cM) and 10 (158.0–162.8

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cM) (Table 2).

Fig. 3 – Single-environment QTL analysis of crown root traits in the RIL population. (A) LOD curves under different environmental conditions. (B) Additive effects of individual QTL. A positive value indicates that T877 and a negative value, that DH1M carried the allele responsible for an increase in the trait. (C) Phenotypic variation explained by individual QTL.

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Journal Pre-proof Table 2 – QTL for root traits detected in three environments.

15CRD10 15CRN1 15CRN3 15CRN7 15CRN8 15CRN10

2016SY

16CRA1 16CRA3 16CRA6 16CRA8 16CRD4 16CRD5 16CRD8 16CRD9 16CRN1 16CRN5 16CRN9 16CRN10

2017YZ

17CRA1 17CRA6 17CRD2 17CRD4 17CRD6 17CRD10 17CRN3 17CRN4 17CRN6

MEAN

avCRA3 avCRA7 avCRA9 avCRD3 avCRD4 avCRD6 avCRD8 avCRD9 avCRN1 avCRN2 avCRN5 avCRN6 avCRN10

LOD 6.7 3.8 4.3 4.1 3.1 3.0 5.8 3.1 3.5 5.4 3.4 2.7

PVE 10.6 5.74 6.49 6.74 2.47 2.74 6.57 5.64 1.01 3.73 3.08 3.35

Effect −2.622 1.794 2.178 0.219 0.132 0.091 −0.200 −0.657 0.332 −0.439 0.525 −0.682

1 3 6 8 4 5 8 9 1 5 9 10

1.08 3.07 6.04 8.04 4.03 5.04 8.08 9.01 1.05 5.04 9.05 10.05

203.6 170.5 146.7 96.4 42.2 75.5 201.0 16.0 55.8 106.1 126.3 115.9

AX-86286113 AX-86311821 AX-86317136 AX-86301957 AX-86328809 AX-86292228 AX-86261174 AX-86276318 AX-86301428 AX-86315231 AX-86321499 AX-86259633

AX-86265979 AX-86280218 AX-116873190 AX-86320012 AX-116875075 AX-86292344 AX-86297233 AX-86254284 AX-86265130 AX-86249000 AX-86277049 AX-86323007

4.1 4.8 4.6 3.3 6.0 3.5 3.8 4.1 6.8 3.0 6.5 4.4

3.46 5.28 6.07 4.83 6.83 4.88 1.38 2.35 5.93 4.86 2.36 3.09

1.034 1.655 −1.751 1.803 0.189 −0.178 −0.091 −0.122 0.847 −0.830 0.541 0.809

1 6 2 4 6 10 3 4 6

1.08 6.06 2.08 4.09 6.03 10.07 3.08 4.02 6.01

208.9 262.0 95.0 195.0 134.6 158.0 209.0 4.4 13.4

AX-86286150 AX-86301179 AX-86304513 AX-86247410 AX-86294208 AX-86239098 AX-86238861 AX-86269682 AX-86316338

AX-86325006 AX-86294626 AX-86279787 AX-86291600 AX-95674752 AX-86256958 AX-86312033 AX-86290184 AX-86316369

4.5 4.9 2.6 9.4 3.1 3.3 3.7 2.8 4.5

3.35 5.93 2.17 8.61 4.78 1.42 1.27 5.54 8.27

−2.976 3.778 −0.158 −0.336 0.265 0.085 −0.383 1.163 1.404

3 7 9 3 4 6 8 9 1 2 5 6 10

3.06 7.04 9.01 3.05 4.04 6.03 8.04 9.03 1.10 2.07 5.05 6.01 10.04

155.9 137.9 18.9 91.1 47.8 122.9 67.7 88.9 287.6 81.0 149.3 13.4 85.3

AX-86269297 AX-86318844 AX-86261249 AX-91343802 AX-86312388 AX-86250446 AX-86252774 AX-86297311 AX-86266184 AX-86328648 AX-86249312 AX-86316338 AX-86256358

AX-86326690 AX-86261395 AX-86278498 AX-86244885 AX-86312428 AX-86299607 AX-86281776 AX-86320914 AX-86308169 AX-116874766 AX-86272528 AX-86316369 AX-86259331

4.4 3.4 3.4 2.9 2.9 3.5 3.3 4.9 3.3 3.3 3.5 3.0 3.3

5.76 4.11 2.03 2.73 3.25 5.76 4.55 1.52 5.27 2.88 1.69 3.93 4.26

1.682 1.336 1.258 −0.107 0.117 0.146 0.144 0.052 −0.679 0.526 −0.538 0.795 0.616

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15CRD9

Right marker AX-86285600 AX-86311820 AX-86275016 AX-116876725 AX-86250764 AX-86320771 AX-86323079 AX-86241714 AX-86245516 AX-86328205 AX-86297233 AX-95677277

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15CRD6

Left marker AX-86307155 AX-86311757 AX-95673104 AX-86309311 AX-86250446 AX-86320769 AX-86256908 AX-86286382 AX-86245513 AX-86317794 AX-86305461 AX-95676136

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15CRD2

Position 73.8 156.4 178.0 26.7 122.0 87.0 148.0 262.4 219.0 38.9 205.0 162.8

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15CRA7

bin 1.06 3.06 7.05 2.06 6.03 9.03 10.06 1.09 3.08 7.02 8.08 10.07

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15CRA3

Chr. 1 3 7 2 6 9 10 1 3 7 8 10

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QTL 15CRA1

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Env. 2015SY

PVE: percentages of variance explained by each QTL. Effect: additive effect of a QTL. A positive value means that T877 and a negative value, that DH1M carried the allele responsible for an increase in the trait. 8

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3.3. Multi-environment QTL analysis of maize crown root traits In the MEA, LOD, LODA, and LODAE were defined as LOD scores for detecting respectively QTL with both average effect of the putative QTL across the environments and QEI effects, QTL with only average effects, and QTL with only QEI effects. Profiles of LOD, LODA and LODAE along the maize genome are shown in Fig. 4. A total of 25 QTL affecting crown root traits were identified in the three environments (Table 3). These QTL explained 2.24%–10.53% of the phenotypic variation. Eight QTL were detected for CRA and explained 2.24%–10.53% of the phenotypic variation. Two CRA-QTL (MECRA1 and

MECRA7)

with significant QEI were

detected on chromosomes 1 and 7, respectively, and the QEI explained respectively 4.15% and 4.80% of phenotypic variation. Four QTL,

MECRA3-2, MECRA4, MECRA8-1,

and

MECRA8-2

showed strong QEI with

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LODAE values greater than their LODA values, indicating that the QEI effect dominated the phenotypic variation. Nine putative QTL for CRD explained 2.54%–6.25% of phenotypic variation, and only

MECRD4-1,

on

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chromosome 4, showed a significant QEI. Four QTL, MECRD2, MECRD3, MECRD5, and MECRD10, also showed strong QEI. Eight QTL affecting CRN were identified: four on chromosome 1 and one each on chromosomes 4,

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5, 6, and 10, and explained 2.50%–6.52% of phenotypic variation. No QEI were detected for CRN-QTL, and only one QTL-MECRN1-1 showed strong QEI. Twelve QTL detected by QEI mapping were also detected by

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SEA, including 4 QTL each for CRA, CRD, and CRN (Fig. 5; Table S2).

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Journal Pre-proof Table 3 – Multi-environment QTL mapping of crown root traits in RIL population. Trait CRA

QTL

Chr.

Bin

Position

Left marker

Right marker

LOD

LODA

LODAE

PVE

PVEA

PVEAE

AE1

AE2

AE3

Average effect

1 3 3 4 6 7 8 8

1.08 3.06 3.07 4.07 6.06 7.04 8.04 8.04

204 155 170 127 264 176 80 96

AX-91336867 AX-86289729 AX-86299586 AX-86313681 AX-86323966 AX-86252382 AX-86319861 AX-86296723

AX-86307803 AX-86289736 AX-86245303 AX-86313683 AX-86303371 AX-86275016 AX-86253384 AX-86296725

2.5 4.4 3.4 2.7 2.6 5.4 3.5 2.7

0 2.9 1.5 0.6 1.9 0.9 1.2 0.3

2.5 1.5 1.9 2.1 0.8 4.5 2.3 2.4

4.16 9.90 5.75 3.36 10.53 6.73 4.69 2.24

0 6.48 3.34 1.25 4.12 1.93 2.84 0.62

4.15 3.42 2.41 2.11 6.41 4.80 1.86 1.62

0.214 0.250 −0.580 −0.548 −0.240 0.738 0.523 −0.334

0.536 −0.699 0.193 0.117 −0.685 −0.036 −0.321 0.481

−0.751 0.450 0.387 0.431 0.925 −0.701 −0.202 −0.148

0.011 0.690 0.492 −0.314 0.544 0.373 0.461 0.216

2.06 3.04 4.04 4.09 5.05 6.03 7.04 8.04 10.06

27 22 49 192 125 121 192 68 147

AX-86287613 AX-86302440 AX-86245897 AX-86247410 AX-86249177 AX-86294191 AX-86252382 AX-86319608 AX-86278175

AX-86287624 AX-86327032 AX-116875075 AX-86247412 AX-86249183 AX-86328043 AX-86275016 AX-86296337 AX-86299407

3.7 2.7 5.8 2.5 3.1 4.6 3.5 3.9 3.2

1.3 0.7 2.6 1.7 1.5 3.8 3.4 3.1 1.6

2.4 2.0 3.3 0.9 1.6 0.8 0.1 0.7 1.7

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MECRD10

2 3 4 4 5 6 7 8 10

3.71 2.54 6.25 4.77 2.86 4.54 3.88 3.68 3.29

1.58 0.77 2.99 1.99 1.75 4.51 3.66 3.66 1.87

2.13 1.76 3.26 2.78 1.12 0.03 0.21 0.02 1.43

0.067 −0.047 −0.093 0.032 0.047 −0.005 0.004 0.004 −0.055

−0.026 −0.009 0.044 0.046 −0.034 0.007 0.016 0.002 0.031

−0.041 0.057 0.049 −0.078 −0.013 −0.002 −0.020 −0.006 0.023

0.041 −0.028 0.063 −0.047 −0.043 0.069 −0.062 0.063 −0.044

MECRN1-1

1

1.05

55

AX-86306721

AX-86285216

3.0

1.2

1.8

2.93

1.28

1.65

−0.075

0.232

−0.157

0.147

MECRN1-2

1

1.09

262

AX-86286406

AX-86241440

2.5

1.4

1.1

2.50

1.51

0.99

−0.156

0.161

−0.005

−0.160

MECRN1-3

1

1.1

288

AX-86241515

AX-86266200

4.4

3.3

1.0

4.24

3.56

0.68

−0.034

−0.113

0.147

−0.249

MECRN1-4

1

1.11

321

AX-86286594

AX-86241659

3.1

1.6

1.5

3.95

1.68

2.27

0.043

0.216

−0.259

−0.169

MECRN4

4

4.02

5

AX-91345522

AX-86290192

2.6

1.8

0.8

2.98

1.87

1.11

−0.171

−0.005

0.176

0.185

MECRN5

5

5.04

108

AX-86278306

AX-86292715

3.3

2.3

1.0

3.28

2.47

0.81

0.080

−0.167

0.087

-0.206

MECRN6

6

6.01

13

AX-116874463

AX-86293612

5.3

4.7

0.5

6.52

5.11

1.41

−0.124

−0.095

0.219

0.296

10

10.07

163

AX-86256958

AX-86256959

2.9

2.1

0.8

2.88

2.22

0.66

−0.134

0.124

0.010

−0.194

MECRA1 MECRA3-1 MECRA3-2 MECRA4 MECRA6 MECRA7 MECRA8-1 MECRA8-2

CRD

MECRD2 MECRD3 MECRD4-1 MECRD4-2 MECRD5 MECRD6 MECRD7 MECRD8

CRN

MECRN10

rn

u o

J

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r P

e

f o

LODA indicates average effect of the putative QTL across the environments at the testing position. LODAE indicates a QEI effect. PVE, PVEA, and PVEAE are the respective percentages of variance explained by all effects, major effect, and QEI effect of the detected QTL. AE1, AE2, AE3, and average effect represent QEI effect in E1, E2, E3, and average additive effect, respectively.

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Fig. 4 – LOD curves based on multi-environment QTL analysis. LODA indicates average effect of the putative QTL across the

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environments at the testing position; LODAE indicates a QEI effect.

Fig. 5 – QTL detected by single-environment (SEA) and multi-environment QTL mapping (MEA). The first column to right of a chromosome bar shows QTL identified by MEA and the second column shows QTL identified by SEA. Red asterisk in the chromosome bar indicates QTL detected by both SEA and MEA. 11

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4. Discussion Maize embryonic roots, including primary and seminal roots, are essential for the establishment of seedlings after germination. In contrast, a postembryonic component, crown roots, initiated from consecutive underground nodes of the stem, make up most of the maize root system and are primarily responsible for soil resource acquisition later in development [40]. Because it is difficult and laborious to evaluate root traits directly in the field, most root genetic studies are performed in laboratory experiments, such as paper roll systems, hydroponics, and pot experiments at the seedling stage [39,41,42]. These systems allow rapid, accurate and high-throughput analysis of root traits at an early growth stage; however, they are not effective for evaluating natural root architecture, especially crown roots, in field experiments. Evaluations of RSA in

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field-grown plants may reveal the actual root growth pattern in an agriculturally relevant context [43]. Currently, new methods are being developed to overcome low-resolution and low-throughput approaches for RSA

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phenotyping in the field [44]. Shovelomics [32] has allowed researchers to score ten root traits of an adult maize plant in the field in a few minutes. Automatic imaging approaches, such as DIRT (Digital imaging of root traits)

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and REST (Root Estimator for Shovelomics Traits), have further increased throughput [45]. In the present study, Shovelomics was used to evaluate the CRA, CRD, and CRN in a maize RIL population in three field trials at

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maturity stage and allowed the observation of a wide range of phenotypic variation. Approximately 1.54-, 1.64-, and 2.04-fold differences were observed for CRA, CRD, and CRN (Table 1; Fig. 1). Trachsel et al. [12] reported

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that CRA did not vary between different developmental harvest stages, but that significant genetic variation was observed between genotypes. A 1.20-fold difference for CRD was observed between 26 maize genotypes in a

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temperature-controlled growth chamber [16], and in the IBM RIL population, the difference of CRD was more than 3.80-fold [41]. At three developmental stages, CRN showed 1.85- to 2.06-fold variation [19]. The relatively high phenotypic variation in the present study indicates that this population is suitable for studying the

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genetic basis of crown root traits. A significant correlation was observed between CRD and CRN. The two pairs of QTL for CRN and CRD on chromosomes 8 and 10 can be used as target loci to improve CRN and CRD simultaneously.

Crown roots of maize initiate at 10 days after germination, and successive whorls of nodal roots follow an approximately S-shaped growth curve, finally approaching the maximum root number at the end of the grain-filling stage [33, 46]. Comparisons of root traits of different whorls showed that CRN and CRD were most sensitive to node position and that CRA had the least variation [47]. Different supply levels of nitrogen in the soil had a significant impact on these crown traits [47]. These traits also showed significant variation under different water regimes, and CRA and CRD showed significant GEI effects [30]. Root systems are complex and dynamic and show high plasticity in response to environment; the heritability values were 57.3, 46.8, and 63.9 for CRA, CRD, and CRN, respectively (Table 1). The values were similar to those of other root traits in a field experiment [19]. A moderate heritability close to 0.5 suggests that some phenotypic variation of crown root traits was affected by the environment. Comparisons of crown root traits between the two parental lines in the three environments also showed that the phenotypes of the two lines differed with environment, and significant environment and GEI effects were observed for all the investigated traits. Thus, environmental variation 12

Journal Pre-proof strongly influences crown roots. The finding that most QTL explained less than 10% of phenotypic variation, indicates [19,30,41] that a large number of minor-effect QTL contribute to the genetic component of these crown root traits in maize. No QTL was identified in all three environments, whereas seven pairs of QTL were detected in multiple environments (Table 2, Fig. 5). Some QTL identified in this study lie near those identified in previous studies (Table S2). For example, QTL-avCRN10 on chromosome 10 (85.3 cM) was located in bin 10.04, a hotspot for root QTL. Three QTL for total CRN (qARN110-1, qARN210-1, and qARN310-1), covering all developmental stages, were located in a similar genomic region [19]. This region also contained QTL for total root length, root dry weight, and vertical root pulling resistance [19,48]. QEI in crops is widespread; to improve complex traits across environmental gradients, it is necessary to explicitly analyze GEI [49]. A total of 25 QTL affecting crown root

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traits were identified in this study by MEA, including two for CRA and one for CRD that had significant QEI effects. Complex traits such as root traits often show low heritability and large GEI [49]; SEA and MEA can

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assess both QTL stability and QEI effect. We found that 26.1% (12/46) of the QTL identified by SEA could also be detected by MEA, especially QTL detected in more than one environment (3/7). Furthermore, the QTL

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effects were similar [37].

Despite the importance of roots, direct selection for optimal RSA in the field is not routine in maize breeding

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programs [44]. However, over the past century, the evolution of maize root phenotypes has been consistent with increasing yield [47]. The CRA of the newest US maize was 7° shallower than the oldest material, and the CRN

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included at least 1.6 fewer nodal roots than older lines [47]. Although there is no easy method to select an optimal root system in the field, modifying RSA by marker-assisted selection (MAS) could contribute to

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improving of desirable agronomic traits, such as drought tolerance and nutrient efficiency [6,50,51]. The environment-specific QTL and stable QTL identified in the present study may be used to improve root traits in

Acknowledgments

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maize breeding.

This work was supported by the National Key Research and Development Program of China (2016YFD0100303), the National Natural Science Foundation of China (31972487, 31601810, and 31902101), the Natural Science Foundation of Jiangsu Province (BK20180920), the Innovative Research Team of Universities in Jiangsu Province, and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Author contributions PCL, ZFY and CWX conceived and designed the study. PCL, YYF, SYY, and YYW conducted experiments. PCL, YYF, and YX analyzed data. PCL, HMW, ZFY, and CWX wrote the manuscript. All authors read and approved the manuscript.

Conflict of interest Authors declare that they have no conflict of interest.

Supplementary material 13

Journal Pre-proof Supplementary figure and tables for this article can be found online at https://dx.doi.org/10.1016/j.cj 201x.xx.xxx. Fig. S1 – Measurement of crown root angle. Table S1 – Kolmogorov-Smirnov normality test for crown root traits in three environments. Table S2 – QTL identified in this study near those identified in previous studies.

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