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Theoretical and Applied Genetics https://doi.org/10.1007/s00122-019-03392-3

ORIGINAL ARTICLE

Genetic analysis and fine mapping of phosphorus efficiency locus 1 (PE1) in soybean

Yongqing Yang1 · Ya Tong1 · Xinxin Li1 · Ying He1 · Ruineng Xu1 · Dong Liu1 · Qing Yang1 · Huiyong Lv1 · Hong Liao1

Received: 6 May 2019 / Accepted: 6 July 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019

AbstractKey message PE1 is a valuable locus synergistically regulating both P acquisition and utilization efficiency.Abstract Phosphorus (P) is required for crop production, particularly for soybean, due to its high protein and oil contents, and large demands for P in biological nitrogen fixation. Therefore, enhancing P efficiency is a practical and important target for soybean-breeding programs; however, the genetic mechanisms are still unclear. Previously, we identified a phosphorus efficiency locus 1 (PE1) in soybean through field evaluation. Here, fine mapping of PE1 was conducted in field using an expanded RIL population containing 168 F9:11 families derived from the same parents, BX10 and BD2. A high-resolution genetic map covering a 67.39 cM genetic region in a ~ 20.1 M physical region was constructed using 117 bin markers from chromosome 11, and scaffolds-21 and -32. The results revealed that PE1 was comprised of 15 QTLs for 11 of 18 tested traits, all of which could be detected under both P-deficient and P-sufficient conditions. Through map-based cloning, PE1 was further delimited to a 200-kb region located on scaffold-32, where 16 candidate genes were predicted for the PE1 locus. A set of NILs were also evaluated in P-deficient field conditions, with the results that P content and grain yield were 27.9% and 74.8% higher, respectively, and 25.4% more P was allocated to reproductive tissues in #pe1 than in #PE1 plants. The results herein suggest that PE1 is a valuable locus underlying both P absorption and utilization efficiency, which may be directly applicable in breeding programs seeking to develop elite P-efficient soybean varieties.

Keywords P efficiency · P content · Map-based cloning · Scaffold

Introduction

As one of three essential mineral macronutrients, phospho-rus (P) plays critical roles in plant growth and development as a key component of fundamental biomolecules participat-ing in multiple cellular activities (Zeng et al. 2016). How-ever, due to low mobility and high rate of fixation in soils, P is often a limiting factor of crop productivity, especially on acid soils, where PO4

3− is easily fixed by Fe2+, Al3+,

and Mn2+. In order to realize and maintain high yields, large amounts of P chemical fertilizers, often in excess of plant requirements, are often applied in agricultural systems (Dobre et al. 2014).

Most of the P fertilizer excess is either fixed by soil parti-cles (Schröder et al. 2011) or quickly leached out into water supplies, with the results that farmers must pay for exces-sive fertilizer and much of this excess causes environmental pollution, such as water eutrophication (Nemery and Gar-nier 2016; Cordell et al. 2009). To date, the environmental impacts of P fertilization have been growing more severe due to a continual increase in total soil P content; even as many crops remain physiologically deficient in P status (Vincent et  al. 2012). Therefore, existing evidence strongly sug-gests that exploring genetic resources for P efficiency, and thereby increasing yield or biomass through more efficient P acquisition and utilization, will allow farmers to produce high-yielding crops while reducing P fertilizer application rates, which will reduce the impacts of P fertilizers on the environment (Lynch 2011).

Communicated by Albrecht E. Melchinger.

Electronic supplementary material The online version of this article (https ://doi.org/10.1007/s0012 2-019-03392 -3) contains supplementary material, which is available to authorized users.

* Hong Liao hliao@fafu.edu.cn

1 Root Biology Center, College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, China

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In modern breeding programs, traits of interests can be rapidly selected from germplasm resources through marker-assisted selection (MAS), on the condition that the mapping of specific and useful genes or QTLs have been successfully performed (Tian et al. 2012). MAS has contributed to many successes at breeding elite crops with high P efficiency, such as with rice, wheat, and soybean (Chin et al. 2011; Sun et al. 2012; Chen et al. 2017). Such efforts to enhance P efficiency and related traits in numerous staple crops (rice, maize, wheat rapeseed, etc.) have typically yielded over a hundred QTLs, a few of which contribute significantly toward over-all P efficiency (Chen et al. 2017). For instance, in rice, P efficiency and yield are associated with a major QTL, phos-phorus uptake1 (Pup1), which controls P uptake (Chin et al. 2011). Furthermore, the gene underlying PUP1 is known to be Pstol1, a protein kinase that improves P efficiency via modification of root system architecture (Gamuyao et al. 2012).

Generally, considering the objectives guiding most efforts to identify QTLs in MAS, it is advantageous to iden-tify QTLs in field conditions, and then to select as a target locus a QTL cluster accounting for variations in multiple traits. For example, in wheat, QTLs for P efficiency have been detected by observing 11 seedling and 13 maturity traits under field conditions, which yielded 11 relatively high-frequency QTLs and four important QTL clusters as potential targets for MAS in wheat breeding programs (Yuan et al. 2017). In addition, P efficiency is often tied to QTLs of related traits, such as root architecture. Gu et al. (2016) reported 22–26% increases in maize P uptake efficiency in P-deficient fields for nine advanced backcross-derived lines identified by MAS carrying Cl-bin3.04a or Cl-bin3.04b, which facilitate root development. In other crops, such as rapeseed (Yang et al. 2011) and barley (Gong et al. 2016), QTLs for P efficiency have also been identified, all of which act in accordance with the conclusion that P efficiency is strongly associated with yield. In many situations around the world, identifying QTLs for P efficiency and following up with targeted breeding efforts will be essential components of projects aimed at improving crop yield.

Soybean (Glycine max (L.) Merr.), with high seed protein and oil contents, is one of the most widely consumed food and feed crops in the world (Clemente and Cahoon 2009). Global production of soybean grain is estimated to be over 350 million metric tons annually (USDA 2018). Equal sig-nificance is the fact that soybean is also an important green manure crop, due to a high capability to convert atmospheric nitrogen into available forms through biological nitrogen fixation (BNF) (Coale et al. 1985; Kumudini et al. 2008; Yang et al. 2017).

Among the 17 essential mineral elements, P is particularly important for soybean growth and development, not only as a structural compound of nucleic acids, phospholipids and ATP,

but also as a key metabolite involved in energy transfer, protein activation, and the regulation of BNF and metabolic processes (Marschner 1995; King et al. 2013; Chen et al. 2017). There-fore, improving P efficiency has become an important target in breeding programs seeking to improve soybean produc-tion with varieties that both yield well and have a high BNF capacity. Even so, research on the physiology of P efficiency in soybean remains limited. To date, although several QTLs for P efficiency and related traits have been reported for soy-bean (https ://www.soyba se.org/), the diversity of available P efficiency traits remains narrow, because most of the available P-efficient lines have been derived from three recombinant inbred line (RIL) populations. For example, 34 additive QTLs were detected at the seed stage using RILs derived from Nan-nong 94-156 (high P efficiency) and Bogao (P stress sensitive) (Zhang et al. 2009), which has also led to proposed roles in P efficiency for several candidate genes underlying some of these QTLs (Zhang et al. 2016). In other works, seven QTLs have been detected and mapped on two linkage groups in a RIL population derived from Kefeng No. 1 and Nanong 1138-2 ( Li et al. 2005). Unfortunately, all the QTLs identified in both of the RIL populations listed above were detected under labo-ratory conditions, and the traits were measured in seedlings (Zhang et al. 2009, 2016; Li et al. 2005). Nevertheless, these QTLs identified in hydroponics or bonsai environments might yet prove valuable in efforts to breed soybean for agriculture field settings.

In addition to the two RIL populations developed by others described above, we have also constructed a RIL population from the soybean accessions BD2 and BX10 that contrast in both P efficiency and root architecture (Liang et al. 2010). A QTL cluster on the B1 linkage group near the SSR marker Satt519, and other QTLs associating with root architecture traits have all been connected with P efficiency under both low P and high P field conditions. Hereafter, we labeled the QTL cluster near Satt519 as Phosphorus Efficiency Locus 1 (PE1). In order to more thoroughly explore this documented genetic resource for breeding soybean varieties with high P efficiency, we expanded the RIL population from the same parents (BD2 andBX10) and developed a set of near-isogenic lines (NILs) for the PE1 locus. The objectives of this study were to: (1) further validate and confirm the effect of PE1 on soybean P efficiency in field; (2) identify an elite allele of PE1 to be utilized in soybean-breeding projects; (3) clone the candidate genes for PE1 in map-based protocols.

Materials and methods

Plant materials

Two soybean (Glycine max (L.) Merr) accessions contrasting in root architecture and P efficiency, BX10 and BD2, were

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crossed to develop recombinant inbred lines (RILs) using the single seed descent (SSD) method (Jinks et al. 1976). An expanded population with 168 F9:11 families was derived and used to construct a linkage map and detect QTLs for P efficiency (PE) and root architecture (RA) traits, as well as grain weight (GW)-related traits in field trials.

For map-based cloning of PE1, heterozygous plants at the PE1 locus were selected from F5 plants to develop sub-F2 and sub-F2:3 populations using linked markers. Recombined plants were then identified from the sub-population. A set of near-isogenic line (NILs) for the PE1 locus isolated from a sub-F3:4 line was also developed to characterize the function of PE1 in a P-deficient field.

Field trials

Two field trials were conducted from June to October in 2016 to 2018 at the South China Agriculture University experimental farm (E114.28°, N23.18°) in Boluo County, Guangdong Province, South China, where the soils are acidic and P deficient. The two trials were managed on the two near fields for fine mapping of PE1 according to methods outlined by Liang et al. (2010) as briefly described here. The low P trail was performed on the field not being fertilized with P for many years and run under natural P deficiency conditions without any fertilization (low P), while the high P trail was carried on the other field being continu-ously fertilized with P and also fertilized with 160 kg P/ha as base fertilizer (high P). No other fertilizers applied during soybean growth. The basic characteristics of the top 20 cm of soil for the two field are listed in Table S1. Besides soil available P (25.33 mg/kg for high P and 11.24 mg/kg for low P), all the other tested soil fertility indices did not significantly differ between two fields. For each trail, there were three replicates as the main plots, and each genotype of the parents and RIL population was planted in a split plot design with plots arranged in randomized complete blocks within each block of split plots. Six seeds of each genotypes were sown per plot in a single row 1.2 m in length. Rows were spaced 0.6 m apart, and there were total three replicates for each plot in whole experimental.

Root sampling and measurements

Fifty days after planting (around R1–R4 stage), three rep-resentative plants were carefully extracted manually from each plot with roots largely intact. Dry weight of the whole plant (PDW), stem (STDW), leaf (LDW), shoot (SHDW), and root (RDW) were all measured, and the ratio of root to shoot (RRS) weight was calculated. After ash digestion, P content was also assessed for various tissues, including the whole plant (PPC), root (RPC), shoot (SHPC), stem (STPC), and leaf (LPC) using colorimetric techniques as described

by Murphy and Riley (1962). For measurements of root traits, roots were pretreated according to Yang et al. (2017) and then quantified using computer image analysis software (Win-RhizoPro, Régent Instruments, Québec, Canada) for measuring total root length (RTL), total root surface area (RSA), root average diameter (RAD), and total root volume (RTV). At maturity, three representative plants from each plot were individually harvested, and seed number (SN), hundred grain weight (HGW), and grain weight (GW) per plant were obtained as yield parameters.

High‑resolution genetic map construction

A high-resolution genetic map was constructed by whole genomic re-sequencing of the two parents and genotyping by sequencing (GBS). Briefly, the commercial extraction kit DNeasy Plant Mini Kit (QIAGEN, Germany) was used to extract DNA for library construction. Following extrac-tion, the GBS libraries were digested by the EcoRI and NIaIII enzymes according to Elshire’s protocol (Elshire et al. 2011), and then parental re-sequencing and progeny GBS libraries were sequenced on an Illumina sequencing platform. Clean reads derived from each plant were aligned against the reference genome Glycine max Wm82.a2.v1 with the settings ‘mem 4 -k 32 -M’ (Li and Durbin 2009), and the genotype of each RIL was determined using the GATK’s Unified Genotyper. Polymorphic markers consistent with the BX10 genotype or BD2 genotype were recorded as “a” or “b,” respectively, and ambiguous segregation patterns were recorded as missing data “−”. Polymorphic markers were designated as Ch “x”.“y” according to the chromosome num-ber and physical position, respectively. The whole process above was performed by Genedenovo Biotechnology Co., Ltd (Guangzhou, China). Qualified polymorphic markers on Chromosome 12 and unaligned scaffolds were then used to construct a genetic linkage map using Join Map 4.1. Linkage analysis, with marker distances calculated by the regression algorithm and Kosambi mapping function. MapChart 2.2 software (Voorrips 2002) was used to draw the linkage map.

QTL analysis

QTL analysis was performed in MapQTL 6.0 (Van Ooijen 2004). The position of each QTL was first checked by the interval mapping (IM) option, and then the markers with peak logarithm of odds (LOD) values were selected as cofac-tors using subsequent MQM (Multiple QTL Model) QTL detection. An LOD threshold score of 2.5 was considered as the minimum necessary in order to declare that any given QTLs were significant. Finalized QTL profiles were drawn with Map Chart 2.2 software (Voorrips 2002).

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Development of dCAPS markers

After the PE1 locus was fine mapped in the high-resolution genetic map, dCAPS markers were further developed to delimit physical locations of PE1 in the sub-F2 and sub-F2:3 population, which contained more than 1200 individual plants. SNP and Indel polymorphisms were first identified from the two parental genotypes by whole-genome re-sequencing. Single nucleotide mismatches were introduced adjacent to SNP positions as dCAPS forward or reverse primers so that restriction enzyme recognition sites were created in the amplified PCR products for one of the two alternative sequences (Li et al. 2012). To determine the genotype of the tested soybean lines, PCRs were carried out in a total volume of 25 μL as follows: template DNA, 50 ng, 10 × Ex Tag HS buffer, 2.5 μL, 0.2 mM dNTP, 2 μL, 1U Ex Taq HS DNA polymerase, 0.125 μL, 15 pmol of each forward and reverse primer, 0.5 μL, and water up to 25 μL. The procedure for PCR amplification was: 95 °C for 5 min and then 32 cycles each at 94 °C for 40 s, 50–60 °C for 30 s, and 72 °C for 30 s, followed by a final extension at 72 °C for 10 min. Amplified PCR products were digested using corresponding restriction endonucleases in a final volume of 20 μL according to the kit protocol. Then, digested products were electrophoresed in 3% agarose gel, and the results were imaged in a gel imaging system.

Results

Effects of P availability on soybean growth and P efficiency in RILs

Eighteen traits related to soybean growth and P efficiency, including three for grain yield, five for P content, five for biomass, and five for root architecture, were evaluated in RILs grown at two P levels in the field. Among the tested 18 traits, 15 increased by 40–58%, and RRS decreased by 11.3% with P fertilization, while only HGW and RAD remained unchanged (Fig. S1). These results demonstrate that P significantly influenced soybean growth and P effi-ciency in the field, which might facilitate further identifica-tion of the QTLs for P efficiency in soybean.

Construction of a high‑resolution genetic linkage map for P efficiency

Previously, genetic loci for synergistically regulating root architecture, P efficiency, and yield traits were mapped in field trials using a RIL population consisting of 106 lines (Liang et al. 2010). Among the identified QTLs, 18 for root architecture, P efficiency, and yield traits were very closely linked on the B1 linkage group. Therefore, we speculated

that these traits are synergistically controlled by the same genetic locus on the B1 linkage group, which is hereby named as Phosphorus Efficiency Locus 1 (PE1). Based on previous results, PE1 is known to be positioned between the SSR markers Satt298 and Sat_149 (Liang et al. 2010). In this study, a larger RIL population consisting of 168 lines was developed to construct a genetic map with high resolution between Satt638 and Satt444 so that the candidate region for PE1 could be narrowed down to a more manageable region.

In total, 72 bin makers on chromosome 11 were selected for genetic map construction based on their physical posi-tions (Fig. 1). Due to the incomplete genome draft status of Williams 82, hundreds of chromosome scaffolds were not mapped on the 20 chromosomes. Linkage analysis was per-formed between all bin markers on scaffolds and the 72 bin makers. As a result, seven bin markers on scaffolds_32 and 38 bin markers on scaffolds_21 were integrated to the final genetic map, which also located both scaffolds on chromo-some 11. Finally, a genetic map was constructed using the 117 bin markers. The complete genetic and physical dis-tance of the linkage map covered 67.39 cM and ~ 20.1 MB, respectively, with the respective average genetic and physi-cal distances between two adjacent markers being 0.58 cM and 0.17 MB. This clearly shows that the linkage map con-structed here was of sufficiently high quality and resolution for use in further fine mapping studies.

QTL analysis for P efficiency related traits

All tested traits were included in QTL analysis. When grouped as categories of traits, a total of 4, 2, 3, and 6 signifi-cant QTLs explaining 6.7–19.4% of the phenotypic variation observed among the 168 F9:11 soybean RILs were identified for biomass, P efficiency, yield, and root architecture-related traits (Table 1 and Fig. 1). Not surprisingly, all of the alleles increasing efficiency were derived from the P-efficient par-ent, BX10. The LOD values of these QTLs varied from 2.55 to 7.86, and the markers with the largest LOD on the linkage map were S_32.78737 and S_32.163339. Importantly, all 15 of the significant QTLs shared the same or overlapped confidence intervals, strongly indicating that all of these traits were regulated by the same genetic locus, i.e., PE1, and the candidate interval for PE1 falls between markers S_32.56629 and S_21.3315382.

In order to further determine the effects of PE1 across different P conditions, the RIL population was split into two genotype groups based on two closely linked markers S_32.56629 and S_21.3315382. The groups were named #RIL-PE1 and #RIL-pe1, signifying the respective groups in which the PE1 locus was present and absent, respectively. The relative coefficients of all tested traits were evaluated for both genotype groups, and variations between the two geno-type groups were also determined. The results showed that

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only 2 of 18 tested traits, SN and GW varied significantly between the two groups, and #RIL-pe1 had higher relative coefficients than #RIL-PE1 (Fig. 2 and Fig. S2). This sug-gests that the increasing P efficiency effect locus PE1 could sustain relatively higher yield under low-P conditions.

Map‑Based cloning of candidate genes for PE1

PE1 had an impact on all of the traits observed for map-ping P efficiency both in this study and a previous report (Liang et al. 2010). Due to the high LOD and PVE values

Sat_2720.00

Satt50921.90

Satt63828.30

Sat_1490.00Sat_1280.90

Satt5199.20

Satt29814.50

Satt_34824.70

Satt_36033.30

Satt58338.60

Satt33241.00

Satt44445.20

PE1

Satt4530.00

BE8015387.10

Satt6380.00Chr11.6324779Chr11.6254452Chr11.6324789

0.18

Chr11.63366300.19Chr11.61558390.46Chr11.61453620.47Chr11.67044943.93Chr11.67651795.07Chr11.67651195.30Chr11.68818995.46Chr11.68841315.48Chr11.68706635.63Chr11.7434069Chr11.74340339.36Chr11.74845869.63Chr11.760914210.38Chr11.7953565Chr11.7781759Chr11.7894302

12.09

Chr11.7781808Chr11.776128512.19Chr11.7829956Chr11.771681612.30Chr11.775884012.31Chr11.829625913.16Chr11.937562226.87Sat_14926.93Chr11.936582027.12Chr11.972132231.31Chr11.1019244836.65Chr11.1021309836.91S_32.5646942.58S_32.5662942.81S_32.16333943.19Chr11.1095038343.77S_21.331538246.72S_21.336001446.83S_21.331022446.90S_21.163837847.27S_21.2384173S_21.306016047.33S_21.302990947.34S_21.311824847.52S_21.316506347.53S_21.3162591S_21.316276347.54S_21.279585S_21.1306128S_21.562302

47.71

S_21.216012647.72S_21.288014S_21.1636567S_21.960571

47.73

S_21.193328447.75S_21.184390947.86S_21.96037247.89S_21.1306189S_21.63226347.98S_21.13349848.09S_21.11298548.24S_21.17856348.35Chr11.2437375948.54Chr11.2446903149.69Chr11.2471986951.14Chr11.2484297151.22Chr11.2476660851.53Chr11.2500174552.94Chr11.2526775953.34Chr11.2542263054.68Chr11.2527667455.11Chr11.2591039455.49Chr11.2616930258.29Chr11.2625748160.76Chr11.2626005460.87Chr11.2643418362.20Chr11.2649277462.67Satt44467.39

qLDW-H

P

qSHDW-H

P

qSTDW-H

P

qPDW-H

P

qLPC-H

P

qPPC-H

P

qGW-LP

qSN-H

P

qSN-LP

qRTL-H

P

qRTL-LP

qRTV-H

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qRTV-LP

qRSA-H

P

qRSA-LP

0 5

(Liang et al.2010)

B1 Chr11

PE1

Fig. 1 Fine mapping of PE1 using putatively related traits. The high-resolution genetic map was constructed according to the published location of PE1 by Liang et  al. (2010). The corresponding markers on both genetic maps were linked by the colored lines. SNP mark-ers between SSR markers Satt638 and Satt444 were selected based

on their physical locations. The segment and markers in brown repre-sented the putatively narrowed region for PE1. The bars and lines in pink, dark blue, green, and red on the right map represent biomass, P efficiency, root architecture, and yield-related traits, respectively. The dotted line represents the threshold value of a 2.5-LOD score

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observed for PE1 effects on SN in both P treatments, SN was further used to detect the candidate gene for PE1 through map-based cloning. Two mapping populations were derived from two individual F5 residual heterozygous plants (RHP), #32 and #64, which were heterozygous at the PE1 locus. Recombined plants were initially identified from sub-F2 and sub-F2:3 populations by observing changes in the pattern of the dCAPS markers dCAPS-Chr11-1, dCAPS-dS_32-2, dCAPS-dS_32-6 and dCAPS-dS_21-7, and the crossover points were further narrowed down by observing the patterns

among three additional markers dCAPS-dS_32-3, dCAPS-dS_32-4 and dCAPS-dS_32-5 (Table 2). The genotype of PE1 in these recombinant lines was determined by decipher-ing the segregation patterns among F3:4 progeny under P deficiency field conditions.

In the end, six recombinants carrying one or two crossover points between markers dCAPS-Chr11-1 and dCAPS-dS_21-7 were identified in sub-F2:3 population #32, and five were observed for sub-F2:3 population #64 (Fig. 3a). No recombination was found between the markers

Table 1 Putative QTLs detected for biomass, P content, and root architecture traits using 168 F9:11 soybean RILs grown in the field

The phenotypes measured include GW grain weight, PPC whole plant P contentLPC leaf P content, STDW stem dry weight, LDW leaf dry weight, SHDW shoot dry weight, PDW whole plant dry weight, RTL total root length, RSA total root surface areas, RTV total root volume, -LP or -HP represent that the QTL was determined under P deficient or P sufficient field conditions, respectivelya QTLs were named based on the tested trait and field conditionsb Marker or interval, markers or support intervals on the linkage map in which the LOD was the largestc PVE (%), percentage of phenotypic variance explained by the QTLd Added value > 0 or < 0 stand for increasing P efficiency effects of the QTLs derived from BD2 or BX10. The QTLs were designated as “qTrait-P level”, SN (seed number)

Related traits QTLa Position (cM) Marker and intervalb LOD PVE (%)c Addd

Yield qSN-LP 43.19 S_32.163339 6.97 17.4 − 10.96qSN-HP 43.19 S_32.163339 3.00 7.9 − 9.90qGW-LP 43.19 S_32.163339 7.86 19.4 − 1.39

P content qPPC-HP 42.813 S_32.78737 2.56 6.8 − 0.87qLPC-HP 42.813 S_32.78737 4.06 10.5 − 0.51

Biomass qSTDW-HP 42.813 S_32.78737 2.92 7.7 − 0.66qLDW-HP 42.813 S_32.78737 4.18 10.8 − 1.28qSHDW-HP 42.813 S_32.78737 3.24 8.5 − 2.13qPDW-HP 42.813 S_32.78737 3.29 8.6 − 2.23

Root architecture qRTL-HP 42.813 S_32.78737 3.25 8.6 − 103.08qRTL-LP 43.19 S_32.163339 2.55 6.7 − 52.11qRSA-HP 42.813 S_32.78737 3.83 10.0 − 24.42qRSA-LP 43.19 S_32.163339 2.93 7.7 − 14.89qRTV-HP 42.813 S_32.78737 2.76 7.3 − 0.52qRTV-LP 43.19 S_32.163339 2.73 7.2 − 0.34

Fig. 2 Evaluation of P utiliza-tion efficiency for polymorphic PE1 alleles. RILs containing different PE1 alleles were genotyped through PE1 linked markers and grouped as #RIL-PE1 and #RIL-pe1, respec-tively. P utilization efficiency was reflected in relative GW (a) and SN (b) under varied P conditions. Asterisks indicate significant differences between the two groups in the Stu-dent’s t test at the p = 0.05*, p = 0.01**, and p = 0.001*** levels

0.5

1.0

P=7.41E-3

**

0

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#RIL-pe1 #RIL-PE1

a

RelativeGW

2.0

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1.0

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P=3.91E-3**

#RIL-PE1#RIL-pe1

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RelativeSN

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dCAPS-dS_32-4 and dCAPS-dS_32-5, possibly due to a small physical distance of ~ 35 kb with low recombination rate. With this limitation, PE1 could still be delimited to an interval of ∼ 200 kb between the markers dCAPS-dS_32-3 and dCAPS-dS_32-6 (Fig. 3a, b). This region harbors 16 predicted genes (Glyma.U033500-Glyma.U03500), each of which has been annotated in Phytozome 12.0 (https ://phyto zome.jgi.doe.gov/pz/porta l.html) (Fig. 3c).

Evaluation of PE1 effects in a P‑deficient field

In order to facilitate PE1 functional analysis, a high genera-tion RHP flanked by marker dCAPS-dS_32-4 was screened from a sub-F3:4 line of population #64. A set of NILs was developed from the progeny of this RHP, and phenotype variants were further characterized in the P-deficient field. Hereafter, lines with the same phenotype as the heterozygous parent are referred to as #PE1, and allelic lines are labeled as #pe1. The alleles of #PE1 and #pe1 were derived from BD2 and BX10, respectively. These NILs were evaluated in observations of seven root architecture traits (Fig. 4b–h), five shoot traits (Fig. 4i–m), and one yield trait (Fig. 4n). In these evaluations, two of the 13 tested traits (small nodule number and branch number, Fig. 4f, m) did not vary significantly between #PE1 and #pe1, while only one trait (root diameter, Fig. 4d) was significantly higher for #PE1 than for #pe1, and the other 10 traits were all significantly higher in #pe1 than in #PE1. These results suggest that #PE1 strongly influences soybean growth in P-deficient fields and that the effective locus for grain yield is regulated by a recessive gene in #pe1 that was derived from the P-efficient parent BX10.

The complete set of observations also included measure-ments of P concentration, content and allocation in vari-ous tissues. As expected, the P concentration in four tissues (root, shoot, leaf, and pod wall) was significantly higher in #PE1 than #pe1 (Fig. 5a). However, the P concentration in seeds was significantly higher in #pe1 than #PE1. Impor-tantly, #pe1 plants acquired 27.9% more P than the PE1 plants and allocated 74% of acquired P to pods, while only 48% of acquired P was allocated to pods in #PE1. These results demonstrate that the pe1 allele leads to higher effi-ciencies in both P acquisition and utilization than PE1.

Discussion

At present, producing higher yields with lower environmen-tal costs has become a main target in crop-breeding pro-jects around the world. With a high grain value and a great capacity to participate in BNF, soybean is a critical crop for global food security and soil health. Soybean plants demand a higher supply of P than other crops due to its high protein and oil contents in seeds, as well as, a high demand for P in BNF (Chen et al. 2017). As a result, low P availability is a limiting factor on soybean productivity and BNF capac-ity worldwide, even in regions with reasonably high soil P contents (Tiessen 2008; Sánchez-Calderón et al. 2010; Qin et al. 2012).

Phosphorus is known to dramatically influence many soybean traits, such as growth, root architecture, and grain yield (Singh et al. 1995). Thus, soybean plants contain-ing different P efficiency genes/QTLs might display dif-ferent phenotypes in many agronomic traits. In this study,

Table 2 The dCAPs primers used in this study

a S_32 represents Scaffold_32b The underlined bold font indicated the position of the introduced SNP variation

Primer name aSNP location Product size Enzyme Prime sequence (F/R)b

dCAPS-Chr11-1 Chr11.10213098 217 bp/189 bp + 28 bp HaeIII GGC AAC ATA GTT TAC TTA TAC ATC TCGGCCTA GAA CAA AGT CAA CCA CA

dCAPS-dS_32-2 S_32.56469 220 bp/193 bp + 27 bp StuI CAG CCA CAA GCT GCA TCA GCA AGC AGGCC CAG CAG CAA CAA AAA GAA TC

dCAPS-dS_32-3 S_32.56629 230 bp/201 bp + 29 bp AluI ACC AAA TGC ACA GCC ACA AGCAG CAG CAA CAA AAA GAA TCG TTT CTC AGC

dCAPS-dS_32-4 S_32.163339 195 bp/165 bp + 30 bp AatII ATG GAA CTG AGC TAG CAG ATG GAG TGA CGGTA GTG TAA AAT CCT TGT CC

dCAPS-dS_32-5 S_32.198773 286 bp/257 bp + 29 bp Swa I CAC CTT GAC AAT CAG AAT GAT AAA TAT TTAAA CAC AAT GAG TGA CAT AAC TAC ACC TT

dCAPS-dS_32-6 S_32.258051 189 bp/162 bp + 27 bp NcoI GGG AAG AGG GTT TGA ATT GGT TTT CGC CAGTC ATC GCC GCT TTC CAT CCA TTG CT

dCAPS-dS_21-7 S_21.3315382 231 bp/205 bp + 26 bp SalI CAA AGG TTG ATT TGG TTC AAC CTG AGTCG CCG GTG AAA GAT ACA ACG AC

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QTL analysis of P efficiency was conducted with 18 traits, including three for grain yield, five for P content, five for biomass, and five for root architecture. Because of exten-sive influence of P efficiency genes/QTLs and high degree of co-linearity among some analyzed traits, it was not surprising that 15 QTLs for 11 traits co-located in PE1 (Fig. 1). This result stands in contrast to previous stud-ies, in which dozens of QTLs were identified, but none of them co-locate in PE1 (Zhang et al. 2009; Zhang et al. 2016; Li et al. 2005, 2016). One possible explanations for these contrasting results is that different studies have used different genetic materials. All P efficiency QTLs were previously identified using only two RIL populations generated from Kefeng No.1 × Nannong 1138-2 and Nan-nong 94-156 × Bogao crosses. The genotypic variation in

those crosses likely did match the variation carried by the parents in the present study. Another possible explana-tion for why markers co-located in PE1 in this study, but not in previous studies is that past work was conducted in artificial growth conditions. All the plants were grown in hydroponics and/or pots, which are obviously different from the field conditions in this study. Alternatively, dif-ferences in results might be due to differences in sampling methods, including developmental stages from which tis-sues were harvested. Previous efforts sampled and meas-ured soybean seedlings, while this study sampled soybeans at both the R5 and maturity stages of development. Due to sampling older plants in field conditions, the current study has likely identified an important locus for P efficiency in soybean. Furthermore, among the 15 identified QTLs,

0 50 100 150 200 250 300 350 400

0 20 40 60 80 100 120 140

BX10 genotypeBD2 genotype

Heterozygous genotype

Variation of seed number (#/plant)b

n = 15

n = 20

n = 28

n = 12

n = 16

n = 15

n = 14

n = 23

n = 21

n = 27

n = 24

#64#64-1-23

#64-3-1

#64-22-53

#64-35-15

#64-52-61

#32

#32-41-21#32-31-37

#32-64-112#32-74-98

#32-132-210#32-201-24

a

c

200kb

Scaffold_32

Fig. 3 Localization of PE1 by map-based cloning. a The PE1 locus was delimited into a 200-kb region on scaffold-32 using a sub-F3:4 segregating population derived from two F5 residual heterozygous individual plants. Black, white and gray boxes indicate homozygosity for the allele from the BX10 and BD2 parents, and heterozygosity,

respectively. b Segregation of seed number in the sub-F3:4 inbred family. c The delimited 200-kb genomic region for PE1 contains 16 predicted genes in the reference genome of Williams 82 (color figure online)

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11 were detected under both P-deficient and P-sufficient field conditions (Fig. 1), which further implies that PE1 is a very important locus with critical functions under a variety of P conditions.

More than 90% of nutrients for plant growth are acquired from soils through roots (Marschner 1995). Therefore, root traits are likely the dominant traits in determining nutrient acquisition efficiency. Because of its low soil mobility and bioavailability, P tends to accumulate in topsoil, and thus shallow root architectures built to emphasize topsoil for-aging are often advantageous for P acquisition, especially under P deficiency conditions (Lynch 2011; Chen et al. 2017). An evaluation of the relationship between root archi-tecture and P efficiency using a “soybean core germplasm”

has also suggested that shallower root architectures facili-tate P acquisition efficiency (Zhao et al. 2004). However, the genetic basis underlying root architecture responses to P availability remain unclear.

In the present study, we explored the tentative genetic link between root architecture and P efficiency. As expected, QTLs for root architecture traits (RTL, RSA, and RTV) co-located with QTLs for P content traits (PPC and LPC) in PE1 under P-sufficient field conditions (Fig. 1). Further-more, all of the additive effects contributing to P efficiency were derived from BX10, the P-efficient parent. These results strongly suggest that PE1 synergistically regulates root architecture and P acquisition efficiency. At the same time, no QTLs for P content were identified in the P-deficient

#PE1 #pe10.00

0.15

0.30

0.45

0.60

#PE1 #pe1

Roo

t dia

met

ers

(mm

/pla

nt) ***

P=4.57E-04

0

15

30

45

60

#PE1 #pe1

Tota

l roo

t len

gth

(m/p

lant

)

0.0

1.5

3.0

4.5

6.0

#PE1 #pe1

Tota

l roo

t vol

ume

(cm

3 /pla

nt)

**P=1.70E-03

*P=1.06E-02

a b c d

0

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160

#PE1 #pe1

Big

nod

ule

num

ber (

#/pl

ant)

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#PE1 #pe1

Smal

l nod

ule

num

ber (

#/pl

ant)

0

150

300

450

600

#PE1 #pe1

Tota

l roo

t sur

face

(cm

2 /pla

nt)

**P=3.75E-03

*P=3.03E-02 ns

0

6

12

18

24

#PE1 #pe1

Shoo

t dry

wei

ght(g

/ pla

nt)

0

1

2

3

4

#PE1 #pe1

Roo

t dry

wei

ght (

g/pl

ant) ***

P=2.88E-06

0

20

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60

80

#PE1 #pe1

Plan

t he

ight

(cm

/pla

nt)

***P=1.44E-05

***P=6.63E-04

0

8

16

24

32

40

#PE1 #pe1

Pod

num

ber (

#/pl

ant)

**P=1.71E-03

0

20

40

60

80

#PE1 #pe1

Leav

es n

umbe

r (#/

plan

t)0

4

8

12

16

#PE1 #pe1

Nod

e nu

mbe

r (#/

plan

t)

**P=2.53E-06

0

1

2

3

4

#PE1 #pe1

Bra

nch

num

ber (

#/pl

ant)

ns ns

e f g h i

j lk m n

Fig. 4 Confirmation of PE1 identity and function by NIL analy-sis in P-deficient field conditions. Plants were evaluated P-deficient field conditions. The values of tested traits are given as mean ± SE

(n = 4 – 24 plants). Asterisks indicate significant differences between NILs in the Student’s t test at the p = 0.05*, p = 0.01** and p = 0.001*** levels, while “ns” represents no significant difference

Theoretical and Applied Genetics

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field, possibly due to environmental impacts or complex genetic background effects. Consequently, a set of NILs was evaluated in a P-deficient field, and the results further confirmed that PE1 contributes to shallower and larger root architectures (Fig. 4), which subsequently enhances P acqui-sition efficiency. In short, these results reveal a genetic basis for root architecture in relation to P acquisition, which prom-ises to be useful in efforts to breed elite varieties with high P acquisition efficiencies derived through root modifications.

It has been estimated that nutrient acquisition com-prises ~ 70% of overall nutrient efficiency, though nutrient acquisition processes must be coordinated and regulated with nutrient utilization efficiency (Chen et al. 2017). Plus, P deficiency still exists for many crops even when the total P content of soils is relatively high and the fields receive P fertilizer (Vincent et al. 2012). Therefore, P utilization efficiency is also likely an important component of overall plant P efficiency.

Here, we evaluated P utilization efficiency of PE1 by relative P content values of soybean grown in P-sufficient or P-deficient conditions. Since the relative P content traits did not significantly vary between #RIL-PE1 and #RIL-pe1 plants (Fig. S2), the P acquisition ability of soybeans car-rying either of the two allele (PE1 or pe1) should not be

significantly altered by the external P supply. In contrast, the presence of the PE1 allele derived from BX10 led to signifi-cant increases in the relative values of SN and GW (Fig. 2). Taken together, these results suggest that PE1 might con-tribute to P utilization efficiency. In order to further confirm this speculation, P allocation as affected by the PE1 allele was determined in seven plant tissues (Fig. 5). Interestingly, under the pe1 genetic background, much more P was trans-ferred to reproductive tissues, which strongly demonstrates that pe1 harbors a higher P utilization efficiency than PE1. Taken together, PE1 was revealed to be a P efficiency locus that synergistically regulates both P acquisition and utiliza-tion efficiency.

Soybean is an ancient polyploid (paleopolyploid) crop in which genome duplication events occurred approximately 59 and 13 million years ago (Schmutz et al. 2010). The physical draft genome of soybean was assembled in 2010 (Schmutz et al. 2010), and has since been improved in recent years (Shen et al. 2018; Xie et al. 2019). However, due to a complex genome structure and limitations in sequenc-ing technology, hundreds of scaffolds, possibly containing some very useful genes, have not yet been mapped on soy-bean chromosomes (https ://phyto zome.jgi.doe.gov/pz/porta l.html), which might lead researchers to miss target genes in map-based cloning efforts. Here, in order to further explore the molecular mechanisms of P efficiency coded in PE1, map-based cloning using high-density markers was con-ducted in a search for a gene underlying the PE1 response. The high-resolution genetic map was constructed using SNP markers derived for GBS methods. Interestingly, no SNP markers located in the physical region of Chr11:10,950,383-24,373,759 were identified in a comparison of the parental sequences generated here with the reference genome Gly-cine max Wm82.a2.v1 (Fig. 1), which strongly suggested there might be one or more scaffolds missing in this unclear region. Therefore, all scaffolds were mapped using SNPs makers, with the result that scaffolds_21 and scaffolds_32 mapped to this region. The subsequent high-density genetic map containing these two scaffolds built in our study will facilitate work to clone the genes underlying the PE1 locus using map-based techniques.

In summary, the PE1 locus was fine mapped to a very narrow region in which specific alleles play important roles in both P acquisition and utilization efficiency. Co-locali-zation analysis demonstrated that PE1 associates with soy-bean yield, P efficiency, root architecture, and BNF capac-ity traits. On the whole, this research opens a new avenue for formulating strategies to breed soybean varieties with improved P efficiency. Closely linked markers within the PE1 locus might be useful for accelerating the breeding pro-cess through marker assistant selection.

2% 1% 5% 6% 12% 74% Root Nodule Stem Petiole Leaf Pod

4% 1%

14%

6%

27%

48%

#PE1

2%

1%

5% 6%

12%

74%

#pe1

0.0

1.5

3.0

4.5

6.0

N R S P L PW Se.

P co

ncen

tratio

n (m

g/g)

****

**

**

ns**

ns

a

b c d

**

#PE1 #pe1

0

40

80

120

160

#PE1 #pe1

P co

nten

t (m

g/pl

ant)

Fig. 5 P concentration (a), content (b), and allocation of P content (c, d) as affected by PE1 alleles in different tissues. N, nodules; R, root; S, stem; P, petiole; L, leaves; PW, pod wall; Se, seeds. Grey and white boxes represent the NILs containing the PE1 and #pe1 alleles, respec-tively. Plants were grown in P-deficient field conditions. Each bar represent the mean ± SE (n = 6). Asterisks indicate significant differ-ences between #PE1 and #pe1 in the Student’s t test at the p = 0.05*, p = 0.01**, and p = 0.001*** levels, while “ns” represents no signifi-cant difference. P allocation is shown in the pie charts as percentages (color figure online)

Theoretical and Applied Genetics

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Author contribution statement YY, XL and HL designed the experiments and analyzed the data; YT, YH, RX, DL, YQ and HLv carried out the experiments; YY wrote the paper, and HL critically revised the paper.

Acknowledgements We would like to acknowledge Dr. Thomas Walk of Golden Fidelity LLC for critical reading, and we also would like to thank the K + S Group for providing scholarships to YT and HLv.

Funding This work was jointly supported by China National Key Pro-gram for Research and Development (2017YFD0200204), National Natural Science Foundation of China (31830083).

Conflict of interest The authors declare that the research was con-ducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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