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Photoperiod-sensitive plants such as soybean (Glycine max) often face threats from herbivorous insects throughout their whole growth period and especially during flowering; however, little is known about the relationship between plant flowering and insect resistance. Here, we used gene editing, multiple omics, genetic diversity and evolutionary analyses to confirm that the calcium-dependent protein kinase GmCDPK38 plays a dual role in coordinating flowering time regulation and insect resistance of soybean. Haplotype 2 (Hap2)-containing soybeans flowered later and were more resistant to the common cutworm (Spodoptera litura Fabricius) than those of Hap3. gmcdpk38 mutants with Hap3 knocked out exhibited similar flowering and resistance phenotypes as Hap2. Knocking out GmCDPK38 altered numerous flowering- and resistance-related phosphorylated proteins, genes, and metabolites. For example, the S-adenosylmethionine synthase GmSAMS1 was post-translationally upregulated in the gmcdpk38 mutants. GmCDPK38 has abundant genetic diversity in wild soybeans and was likely selected during soybean domestication. We found that Hap2 was mostly distributed at low latitudes and had a higher frequency in cultivars than in wild soybeans, while Hap3 was widely selected at high latitudes. Overall, our results elucidated that the two distinct traits (flowering time and insect resistance) are mediated by GmCDPK38.

Linkage mapping and GWAS identified 67 QTLs related to soybean glycinin, β-conglycinin and relevant traits. Polymorphisms of the candidate gene Gy1 promoter were associated with the glycinin content in soybean. The major components of storage proteins in soybean seeds are glycinin and β-conglycinin, which play important roles in determining protein nutrition and soy food processing properties. Increasing the protein content while improving the ratio of glycinin to β-conglycinin is substantially important for soybean protein improvement. To investigate the genetic mechanism of storage protein subunits, 184 recombinant inbred lines (RILs) derived from a cross of Kefeng No. 1 and Nannong 1138-2 and 211 diverse soybean cultivars were used to detect loci related to glycinin (11S), β-conglycinin (7S), the sum of glycinin and β-conglycinin (SGC), and the ratio of glycinin to β-conglycinin (RGC). Sixty-seven QTLs and 11 hot genomic regions were identified as affecting the four traits. One genetic region (q10-1) on chromosome 10 was associated with multiple traits by both linkage and association analysis. Eight genes in 11 hot genomic regions might be related to soybean protein subunit. The candidate gene analysis showed that polymorphisms in Gy1 promoters were significantly correlated with the 11S content. The QTLs and candidate genes identified in the present study allow for further understanding the genetic basis of 11S and 7S regulation and provide useful information for marker-assisted selection (MAS) in soybean quality improvement.

A soybean E3 ubiquitin ligase, GmRNF1a, may affect pod dehiscence and seed development through MADS family genes. These results would be useful for the study of soybean pod and seed development. Pod dehiscence is one of the critical causes of yield loss in cultivated soybeans, and it is of great significance to understand the molecular mechanisms underlying pod dehiscence in soybeans. In this study, we identified a new RING family member of the E3 ubiquitin ligase, GmRNF1a, which was observed to interact with the MADS-box protein GmAGL1 to regulate siliques dehiscence. Tissue-specific gene expression analysis revealed that GmRNF1a was mainly expressed in flowers and pods in soybean. The subcellular localization assay showed the nuclear and cytoplasmic localization of GmRNF1a. In addition, it was found that GmRNF1a exhibits higher promoter activity in soybean hairy roots as well as in Arabidopsis leaves, flowers, and siliques. Heterologous expression of GmRNF1a in Arabidopsis showed that the transgenic Arabidopsis siliques had a faster maturation rate and cracked earlier than the wild-type plants. The functional and nucleotide diversity analysis suggests that GmRNF1a might play an important role in pod maturation and dehiscence and has been strongly selected for during soybean domestication.

Soybean provides essential protein and amino acids for humans and animals, while sulfur-containing amino acids (SAA), including methionine (Met) and cysteine (Cys), are very limited. In this study, we constructed a high-density bin-map with 3420 bin markers using 676 857 SNPs of a recombinant-inbred line (RIL) population derived from a cross between Kefeng no. 1 and Nannong 1138-2. Quantitative trait loci (QTL) mapping was performed for Cys, Met, SAA, and the protein content using this high-density bin-map. Twenty-five QTLs linked to these four traits were identified, and four genomic regions located on chromosomes (Chr) 07, 08, 15, and 20 were overlapped by multiple QTLs. Among them, bin 115-124 located on Chr 15 was associated with all four traits and was a novel locus with a high LOD value. These findings will provide a basis for nutritional quality improvement using marker-assisted selection breeding and clarify the genetic mechanisms of SAA and protein in soybean.

Soybean provides abundant dietary proteins for humans and livestock. However, the low content of sulfur-containing amino acids [SAA, including methionine (Met) and cysteine (Cys)] in soybean protein limits the quality and prevents it from becoming an ideal food and feed ingredient. Thus, the exploration of novel high-SAA soybean germplasm and identification of related genes is necessary. In this study, a genome-wide association study (GWAS) was conducted for Cys, Met and SAA in 165 soybean materials genotyped with a high-density SNP array. A total of 138 significant SNPs associated with three traits were identified. Moreover, one SNP on chromosome 7 was identified in three environments. Glyma.07g175700 and Glyma.07g176000 at the LD of AX-94036794 were considered candidate genes. Quantitative real-time PCR showed that different expression levels of these genes were observed in high-SAA and low-SAA material, which suggested that these two genes may be involved in SAA synthesis. These results will help to increase our understanding of the genetic mechanisms of SAA and improve nutritional quality through molecular marker-assisted selection breeding in soybeans.

A total of 41 SNPs were identified as significantly associated with five yield-related traits in wild soybean populations across multiple environments, and the candidate gene GsCID1 was found to be associated with seed weight. These results may facilitate improvements in cultivated soybean. Crop-related wild species contain new sources of genetic diversity for crop improvement. Wild soybean (Glycine soja Sieb. and Zucc.) is the progenitor of cultivated soybean [Glycine max (L.) Merr.] and can be used as an essential genetic resource for yield improvements. In this research, using genome-wide association study (GWAS) in 96 out of 113 wild soybean accessions with 114,090 single nucleotide polymorphisms (SNPs) (with minor allele frequencies ≤ 0.05), SNPs associated with five yield-related traits were identified across multiple environments. In total, 41 SNPs were significantly associated with the traits in two or more environments (significance threshold P ≤ 8.76 × 10–6), with 29, 7, 3, and 2 SNPs detected for 100-seed weight (SW), maturity time (MT), seed yield per plant (SY) and flowering time (FT), respectively. BLAST search against the Glycine soja W05 reference genome was performed, 20 candidate genes were identified based on these 41 significant SNPs. One candidate gene, GsCID1 (Glysoja.04g010563), harbored two significant SNPs—AX-93713187, with a non-synonymous mutation, and AX-93713188, with a synonymous mutation. GsCID1 was highly expressed during seed development based on public information resources. The polymorphisms in this gene were associated with SW. We developed a derived cleaved amplified polymorphic sequence (dCAPS) marker for GsCID1 that was highly associated with SW and was validated as a functional marker. In summary, the revealed SNPs/genes are useful for understanding the genetic architecture of yield-related traits in wild soybean, which could be used as a potential exotic resource to improve cultivated soybean yields.

Increasing seed oil content is one of the most important breeding goals for soybean due to a high global demand for edible vegetable oil. However, genetic improvement of seed oil content has been difficult in soybean because of the complexity of oil metabolism. Determining the major variants and molecular mechanisms conferring oil accumulation is critical for substantial oil enhancement in soybean and other oilseed crops. In this study, we evaluated the seed oil contents of 219 diverse soybean accessions across six different environments and dissected the underlying mechanism using a high-resolution genome-wide association study (GWAS). An environmentally stable quantitative trait locus (QTL), GqOil20, significantly associated with oil content was identified, accounting for 23.70% of the total phenotypic variance of seed oil across multiple environments. Haplotype and expression analyses indicate that an oleosin protein-encoding gene (GmOLEO1), colocated with a leading single nucleotide polymorphism (SNP) from the GWAS, was significantly correlated with seed oil content. GmOLEO1 is predominantly expressed during seed maturation, and GmOLEO1 is localized to accumulated oil bodies (OBs) in maturing seeds. Overexpression of GmOLEO1 significantly enriched smaller OBs and increased seed oil content by 10.6% compared with those of control seeds. A time-course transcriptomics analysis between transgenic and control soybeans indicated that GmOLEO1 positively enhanced oil accumulation by affecting triacylglycerol metabolism. Our results also showed that strong artificial selection had occurred in the promoter region of GmOLEO1, which resulted in its high expression in cultivated soybean relative to wild soybean, leading to increased seed oil accumulation. The GmOLEO1 locus may serve as a direct target for both genetic engineering and selection for soybean oil improvement., Author summary Soybean seed oil is an important quality trait targeted during domestication and breeding. However, the molecular mechanism of soybean oil regulation is largely unknown due to its complex genetic architecture and environmental sensitivity. In this paper, we integrated GWAS across multiple environments, haplotype analysis, genetic transformation, and diversity analysis to study the genetic architecture of oil content and the underlying mechanism in soybean. This combined analysis enabled us to identify an environmentally stable QTL (GqOil20) and functionally verified that GmOLEO1 positively regulates total seed oil accumulation in soybean seeds. In addition, we found that GmOLEO1 showed a higher level of expression in cultivated soybean seeds than in wild soybean seeds, possibly as the result of the positive selection of the promoter, resulting in seed oil accumulation. Moreover, we identified an elite GmOLEO1 haplotype that correlated strongly with high oil content in soybean, holding great potential for assisting oil improvement in soybean breeding. Our study provided a new genetic resource for oil content improvement in soybean and other oilseed crops.

Pod dehiscence (shattering) is the main cause of serious yield loss during the soybean mechanical harvesting process. A better understanding of the genetic architecture and molecular mechanisms of pod dehiscence is of great significance for soybean breeding. In this study, genome-wide association analysis (GWAS) with NJAU 355K SoySNP array was performed to detect single nucleotide polymorphisms (SNPs) associated with pod dehiscence in an association panel containing 211 accessions across five environments. A total of 163 SNPs were identified as significantly associated with pod dehiscence. Among these markers, 136 SNPs identified on chromosome 16 were located in the known QTL qPDH1. One, one, three, eleven, three, one, three, three and one SNPs were distributed on chromosome 1, 4, 6, 8, 9, 11, 17, 18, and 20, respectively. Favorable SNPs and six haplotypes were identified based on ten functional SNPs; among those Hap2 and Hap3 were considered as optimal haplotypes. In addition, based on GWAS results, the candidate gene Glyma09g06290 was identified. Quantitative real-time PCR (qRT-PCR) results and polymorphism analysis suggested that Glyma09g06290 might be involved in pod dehiscence. Furthermore, a derived cleaved amplified polymorphic sequences (dCAPS) marker for Glyma09g06290 was developed. Overall, the loci and genes identified in this study will be helpful in breeding soybean accessions resistant to pod dehiscence.

Salt stress is one of the major abiotic factors that affect the metabolism, growth and development of plants, and soybean [Glycine max (L.) Merr.] germination is sensitive to salt stress. Thus, to ensure the successful establishment and productivity of soybeans in saline soil, the genetic mechanisms of salt tolerance at the soybean germination stage need to be explored. In this study, a population of 184 recombinant inbred lines (RILs) was utilized to map quantitative trait loci (QTLs) related to salt tolerance. A major QTL related to salt tolerance at the soybean germination stage named qST-8 was closely linked with the marker Sat_162 and detected on chromosome 8. Interestingly, a genome-wide association study (GWAS) identified several single nucleotide polymorphisms (SNPs) significantly associated with salt tolerance in the same genetic region on chromosome 8. Resequencing, bioinformatics and gene expression analyses were implemented to identify the candidate gene Glyma.08g102000, which belongs to the cation diffusion facilitator (CDF) family and was named GmCDF1. Overexpression and RNA interference of GmCDF1 in soybean hairy roots resulted in increased sensitivity and tolerance to salt stress, respectively. This report provides the first demonstration that GmCDF1 negatively regulates salt tolerance by maintaining K+-Na+ homeostasis in soybean. In addition, GmCDF1 affected the expression of two ion homeostasis-associated genes, salt overly sensitive 1 (GmSOS1) and Na+/H+ exchanger 1 (GmNHX1), in transgenic hairy roots. Moreover, a haplotype analysis detected ten haplotypes of GmCDF1 in 31 soybean genotypes. A candidate-gene association analysis showed that two SNPs in GmCDF1 were significantly associated with salt tolerance and that Hap1 was more sensitive to salt stress than Hap2. The results demonstrated that the expression level of GmCDF1 was negatively correlated with salt tolerance in the 31 soybean accessions (r = -0.56, P < 0.01). Taken together, these results not only indicate that GmCDF1 plays a negative role in soybean salt tolerance but also help elucidate the molecular mechanisms of salt tolerance and accelerate the breeding of salt-tolerant soybean., Author summary Soil salinity can seriously threaten soybean growth and development and seed germination is a key phase in the soybean growth cycle. Thus, understanding the genetic mechanism of salt tolerance at the germination stage is very important for improving the salt tolerance of soybean at the germination stage. An analysis combining linkage mapping, GWAS and whole-genome sequencing of two soybean accessions revealed that the GmCDF1 was associated with soybean salt tolerance. The transformation of soybean hairy roots and an inductively coupled plasma optical emission spectrometry (ICP-OES) analysis confirmed that GmCDF1 negatively regulates salt tolerance by maintaining ion homeostasis in soybean. A haplotype analysis found ten haplotypes of GmCDF1 in 31 soybean accessions, and a candidate gene association analysis identified two probable causative GmCDF1 polymorphisms. Moreover, higher GmCDF1 expression resulted in salt sensitivity. Our results not only reveal the role of GmCDF1 in soybean adaptation to salt stress but also help elucidate the genetic molecular mechanisms of salt tolerance and facilitate the implementation of marker-assisted selection for salt tolerance in soybean breeding programs.

Salt tolerance in soybean [Glycine max (L.) Merr.] at the seed germination stage is a critical determinant of stable stand establishment in saline soil. This study examined one population of 184 recombinant inbred lines (RILs, F7:11) derived from a cross between Kefeng1 and Nannong1138-2 and one natural population consisting of 196 soybean landraces. A total of 11 quantitative trait loci (QTLs) and 22 simple sequence repeat (SSR) loci associated with three salt tolerance indices were detected by linkage and association mapping. The SSR marker Sat_162 was found to be closely linked to the co-localized QTLs at a site 792,811 bp from the gene Glyma08g12400.1, which was verified in response to salt stress at the germination stage. Five SSR markers, Satt201, BE475343, CSSR306, Satt664 and Satt567, were co-associated with two of the salt tolerance indices, and two SSR markers, Satt156 and Satt636, were co-associated with all three salt tolerance indices. Furthermore, elite alleles and their carrier materials were identified by analyzing alleles at the loci associated with these salt tolerance indices. These results may be beneficial for the future breeding of soybean salt tolerance at the germination stage using marker-assisted selection and molecular pyramiding breeding.

To evaluate the genetic diversity (GD) of wild and cultivated soybeans and determine the genetic relationships between them, in this study, 127 wild soybean accessions and 219 cultivated soybean accessions were genotyped using 74 simple sequence repeat (SSR) markers. The results of the study revealed that the GD of the wild soybeans exceeded that of the cultivated soybeans. In all, 924 alleles were detected in the 346 soybean accessions using 74 SSRs, with an average of 12.49 alleles per locus. In the 219 cultivated soybean accessions, 687 alleles were detected, with an average of 9.28 alleles per locus; in the 127 wild soybean accessions, 835 alleles were detected, with an average of 11.28 alleles per locus. We identified 237 wild-soybean-specific alleles and 89 cultivated-soybean-specific alleles in the 346 soybean accessions, and these alleles accounted for 35.28% of all the alleles in the sample. Principal coordinates analysis and phylogenetic analysis based on Nei's genetic distance indicated that all the accessions could be classified into two major clusters, corresponding to wild and cultivated soybeans. These results will increase our understanding of the genetic differences and relationships between wild and cultivated soybeans and provide information to develop future breeding strategies to improve soybean yield.

Compared to β-conglycinin, glycinin contains 3-4 times the methionine and cysteine (sulfur-containing amino acids), accounting for approximately 40 and 30%, respectively, of the total storage protein in soybean. Increasing the soybean storage protein content while improving the ratio of glycinin to β-conglycinin is of great significance for soybean breeding and soy food products. The objective of this study is to analyze the genetic mechanism regulating the glycinin and β-conglycinin contents of soybean by using a recombinant inbred line (RIL) population derived from a cross between Kefeng No. 1 and Nannong 1138-2. Two hundred and twenty-one markers were used to map quantitative trait loci (QTLs) for glycinin (11S) and β-conglycinin (7S) contents, the ratio of glycinin to β-conglycinin (RGC), and the sum of glycinin and β-conglycinin (SGC). A total of 35 QTLs, 3 pairs of epistatic QTLs, and 5 major regions encompassing multiple QTLs were detected. Genes encoding the subunits of β-conglycinin were localized to marker intervals sat_418-satt650 and sat_196-sat_303, which are linked to RGC and SGC; marker sat_318, associated with 11S, 7S, and SGC, was located near Glyma10g04280 (Gy4), which encodes a subunit of glycinin. These results, which take epistatic interactions into account, will improve our understanding of the genetic basis of 11S and 7S contents and will lay a foundation for marker-assisted selection (MAS) breeding of soybean and improving the quality of soybean products.

With 2 figures and 5 tables Abstract Low phosphorus (P) stress limits soybean production. A population of 152 recombinant inbred lines (RILs) derived from a cross between ‘Bogao’ (P sensitive variety) and ‘Nannong 94-156’ (P tolerant variety) and 248 markers were used to map quantitative trait loci (QTLs) for low-P tolerance. Two pot culture trials were conducted and low-P tolerance evaluated using flower and pod abscission rate under low P and normal P. Conditional QTLs and epistasis for tolerance to low P were also analysed. A conditional QTL (near Satt274) on linkage group D1b+W was identified which conferred low-P tolerance epistatic effects and coincided with previously discovered QTLs. An additive QTL, qFARLPG-07, for flower abscission rate under low P was detected with a LOD score of 7.79 and explained 32.3% of phenotypic variation. It was detected at the same interval of the corresponding QTL for other traits across years. This region coincided with two conditional QTLs (cqFARLPG-07 and cqPARLPG-07), from the P-tolerant parent ‘Nannong 94-156’ related to low-P tolerance. These results will provide a basis for further fine mapping and eventual cloning of the P-efficiency genes in soybean.

Phosphorus deficiency is a primary constraint to soybean productivity in acid and calcareous soils. Our aim was to map quantitative trait loci (QTL) controlling phosphorus deficiency tolerance using 152 recombinant inbred lines derived from a cross between the P stress tolerant variety Nannong94-156 and the P stress sensitive variety Bogao. Five traits were used as parameters to evaluate phosphorus deficiency tolerance at seedling stage under different phosphorus levels in experiments 2005 and 2006. As a result, thirty-four additive QTLs were detected on nine linkage groups, with corresponding contribution ratios of 6.6–19.3%. There were three clusters of QTL found in genomic regions S506-Satt534 (on linkage group B2-1), Sat_183-Satt274 (on linkage group D1b + W), and Sat_185-Satt012 (on linkage group G). The locus flanked by Sat_183-Satt274 on linkage group D1b + W was coincident with four previously discovered QTLs with phosphorus efficiency. Another interesting locus flanked by Sat_185-Satt012 on linkage group G was detected across years. The identified QTL will be useful to improve the stress resistance of soybean against a complex nutritional disorder caused by phosphorus deficiency. In addition, more QTLs were detected under low phosphorus condition and some QTLs were detected that specifically expressed under different phosphorus levels. These particular QTLs could help provide greater understanding of the genetic basis of phosphorus efficiency in soybean.

Tolerance to low-phosphorus soil is a desirable trait in soybean cultivars. Previous quantitative trait locus (QTL) studies for phosphorus-deficiency tolerance were mainly derived from bi-parental segregating populations and few reports from natural population. The objective of this study was to detect QTLs that regulate phosphorus-deficiency tolerance in soybean using association mapping approach. Phosphorus-deficiency tolerance was evaluated according to five traits (plant shoot height, shoot dry weight, phosphorus concentration, phosphorus acquisition efficiency and use efficiency) comprising a conditional phenotype at the seedling stage. Association mapping of the conditional phenotype detected 19 SNPs including 13 SNPs that were significantly associated with the five traits across two years. A novel cluster of SNPs, including three SNPs that consistently showed significant effects over two years, that associated with more than one trait was detected on chromosome 3. All favorable alleles, which were determined based on the mean of conditional phenotypic values of each trait over the two years, could be pyramided into one cultivar through parental cross combination. The best three cross combinations were predicted with the aim of simultaneously improving phosphorus acquisition efficiency and use efficiency. These results will provide a thorough understanding of the genetic basis of phosphorus deficiency tolerance in soybean.

Soil salinity is a serious threat to agriculture sustainability worldwide. Seed germination is a critical phase that ensures the successful establishment and productivity of soybeans in saline soils. However, little information is available regarding soybean salt tolerance at the germination stage. The objective of this study was to identify the genetic mechanisms of soybean seed germination under salt stress. One natural population consisting of 191 soybean landraces was used in this study. Soybean seeds produced in four environments were used to evaluate the salt tolerance at their germination stage. Using 1142 single-nucleotide polymorphisms (SNPs), the molecular markers associated with salt tolerance were detected by genome-wide association analysis. Eight SNP-trait associations and 13 suggestive SNP-trait associations were identified using a mixed linear model and the TASSEL 4.0 software. Eight SNPs or suggestive SNPs were co-associated with two salt tolerance indices, namely (1) the ratio of the germination index under salt conditions to the germination index under no-salt conditions (ST-GI) and (2) the ratio of the germination rate under salt conditions to the germination rate under no-salt conditions (ST-GR). One SNP (BARC-021347-04042) was significantly associated with these two traits (ST-GI and ST-GR). In addition, nine possible candidate genes were located in or near the genetic region where the above markers were mapped. Of these, five genes, Glyma08g12400.1, Glyma08g09730.1, Glyma18g47140.1, Glyma09g00460.1, and Glyma09g00490.3, were verified in response to salt stress at the germination stage. The SNPs detected could facilitate a better understanding of the genetic basis of soybean salt tolerance at the germination stage, and the marker BARC-021347-04042 could contribute to future breeding for soybean salt tolerance by marker-assisted selection.

Four major SPC-specific loci were identified, and these accounted for 8.5–15.1 % of the phenotypic variation, thus explaining why certain soybean varieties have a high PC but a low SPC. Water-soluble protein content (SPC) is a critical factor in both food quality and the production of isolated soybean proteins. However, few data are available regarding the genetic control and the mechanisms contributing to elevated SPC. In this study, a soybean collection of 192 accessions from a wide geographic range was used to identify genomic regions associated with soybean protein content (PC) and SPC using an association mapping approach employing 1,536 SNP makers and 232 haplotypes. The diverse panel revealed a large genetic variation in PC and SPC. Association mapping was performed using three methods to minimize false-positive associations. This resulted in 4/8 SNPs and 3/6 haplotypes that were significantly associated with soybean PC/SPC in two or more environments based on the mixed model. An SNP that was highly significantly associated with PC, BARC-021267-04016, was localized 0.28 cM away from a published glycinin gene, G7, and was detected across all four environments. Four major SPC-specific loci, BARC-029149-06088, BARC-018023-02499, BARC-041663-08059 and haplotype 15 (hp15), were stably identified on chromosomes five and eight and explained 8.5–15.1 % of the phenotypic variation. Moreover, a glutelin type-B 2-like gene was identified on chromosome eight and may be related to soybean protein solubility. These markers, which are located in previously reported QTL, reconfirmed previous findings and may be important targets for the identification of protein-related genes. These novel SNPs and haplotypes are important for further understanding the genetic basis of PC and SPC. In addition, by comparing the correlation and genetic loci between PC and SPC, we provide new insights into why certain soybean varieties have a high protein content but a low SPC.

Phosphorus (P) is essential for all living cells and organisms, and low-P stress is a major factor constraining plant growth and yield worldwide. In plants, P efficiency is a complex quantitative trait involving multiple genes, and the mechanisms underlying P efficiency are largely unknown. Combining linkage analysis, genome-wide and candidate-gene association analyses, and plant transformation, we identified a soybean gene related to P efficiency, determined its favorable haplotypes and developed valuable functional markers. First, six major genomic regions associated with P efficiency were detected by performing genome-wide associations (GWAs) in various environments. A highly significant region located on chromosome 8, qPE8, was identified by both GWAs and linkage mapping and explained 41% of the phenotypic variation. Then, a regional mapping study was performed with 40 surrounding markers in 192 diverse soybean accessions. A strongly associated haplotype (P = 10−7) consisting of the markers Sat_233 and BARC-039899-07603 was identified, and qPE8 was located in a region of approximately 250 kb, which contained a candidate gene GmACP1 that encoded an acid phosphatase. GmACP1 overexpression in soybean hairy roots increased P efficiency by 11–20% relative to the control. A candidate-gene association analysis indicated that six natural GmACP1 polymorphisms explained 33% of the phenotypic variation. The favorable alleles and haplotypes of GmACP1 associated with increased transcript expression correlated with higher enzyme activity. The discovery of the optimal haplotype of GmACP1 will now enable the accurate selection of soybeans with higher P efficiencies and improve our understanding of the molecular mechanisms underlying P efficiency in plants., Author Summary In soybean plants, low-phosphorus (P) stress is more detrimental than other nutrient deficiencies, toxicities or diseases; however, it is difficult to select soybean varieties with high P efficiencies based on phenotypes. Although QTL map-based cloning is a powerful method, it is time-consuming, and the QTLs underlying P efficiency in soybeans have not been identified. Combining linkage and association analyses, we identified a highly significant region on chromosome 8, qPE8, which is associated with soybean P efficiency. Gene expression and plant transformation experiments indicated that the gene GmACP1 within qPE8 affects P efficiency. A haplotype survey of GmACP1 identified 10 haplotypes that explained 33% of the variation in P efficiency. The discovery of the optimal haplotype of GmACP1 will improve the accuracy of selecting soybeans with higher P efficiencies and increase our understanding of the molecular mechanisms underlying P efficiency in plants.