Genetic dissection of ten photosynthesis-related traits based on InDel- and SNP-GWAS in soybean.

KEY MESSAGE: A total of 416 InDels and 112 SNPs were significantly associated with soybean photosynthesis-related traits. GmIWS1 and GmCDC48 might be related to chlorophyll fluorescence and gas-exchange parameters, respectively. Photosynthesis is one of the main factors determining crop yield. A better understanding of the genetic architecture for photosynthesis is of great significance for soybean yield improvement. Our previous studies identified 5,410,112 single nucleotide polymorphisms (SNPs) from the resequencing data of 219 natural soybean accessions. Here, we identified 634,106 insertions and deletions (InDels) from these 219 accessions and used these InDel variations to perform principal component and linkage disequilibrium analysis of this population. The genome-wide association study (GWAS) were conducted on six chlorophyll fluorescence parameters (chlorophyll content, light energy absorbed per reaction center, quantum yield for electron transport, probability that a trapped exciton moves an electron into the electron transport chain beyond primary quinone acceptor, maximum quantum yield of photosystem II primary photochemistry in the dark-adapted state, performance index on absorption basis) and four gas-exchange parameters (intercellular carbon dioxide concentration, stomatal conductance, net photosynthesis rate, transpiration rate) and revealed 416 significant InDels and 112 significant SNPs. Based on GWAS results, GmIWS1 (encoding a transcription elongation factor) and GmCDC48 (encoding a cell division cycle protein) with the highest expression in the mapping region were determined as the candidate genes responsible for chlorophyll fluorescence and gas-exchange parameters, respectively. Further identification of favorable haplotypes with higher photosynthesis, seed weight and seed yield were carried out for GmIWS1 and GmCDC48. Overall, this study revealed the natural variations and candidate genes underlying the photosynthesis-related traits based on abundant phenotypic and genetic data, providing valuable insights into the genetic mechanisms controlling photosynthesis and yield in soybean.

Genome-wide association study of ear tip barrenness in waxy maize.

Ear tip-barrenness (ETB), which results from aborted kernels or infertile florets at the ear tip, is an undesirable factor affecting the yield and quality of waxy maize. To uncover the genetic basis of ETB, a genome-wide association study (GWAS) was conducted using the genotype with 27,354 SNPs and phenotype with three environments. Five SNPs that distributed on chromosomes 1, 3 and 6, were identified to be significantly associated with ETB based on the threshold of false discovery rate (FDR) at 0.05. Among these significant loci, three SNPs were clustered together and colocalized with genomic regions previously reported. The average length of ETB decreased almost linearly from the inbred lines containing no favorable alleles across the three loci (1.75 cm) to those with one (1.18 cm), two (0.94 cm) and three (0.65 cm) favorable alleles. Moreover, three important genes, Zm00001d030028, Zm00001d041510 and Zm00001d038676 were predicted for three significant QTLs, respectively. These results promote the understanding genetic basis for ETB and will be useful for breeding waxy maize varieties with high-quality and high-yield.

Comparison of the physicochemical properties of starches from maize reciprocal F1 hybrids and their parental lines.

Heterosis on maize yield and quality is highly variable and depends on parental selection. This study investigated and compared the starch structure and physicochemical properties among four sweet-waxy maize lines, four waxy maize lines, and their eight reciprocal F1 hybrids. Compared with the sweet-waxy maize, waxy maize and F1 hybrids had lower extent of branching of amylopectin and relative crystallinity, and larger starch granule size. Waxy maize starch had higher breakdown viscosity and retrogradation percentage, and lower setback viscosity and gelatinization enthalpy than the sweet-waxy maize starch. Meanwhile, the peak and setback viscosities, and retrogradation enthalpy of most F1 hybrid starches were higher than those of their female parent, while gelatinization enthalpy was the opposite. The F1 hybrid starches had higher onset temperature and retrogradation percentage and lower gelatinization enthalpy than their male parent in general. In conclusion, this study provides a framework for the production of new hybrids.

Downregulation of a gibberellin 3ß-hydroxylase enhances photosynthesis and increases seed yield in soybean.

Seed yield, determined mainly by seed numbers and seed weight, is the primary target of soybean breeding. Identifying the genes underlying yield-related traits is of great significance. Through joint linkage mapping and a genome-wide association study for 100-seed weight, we cloned GmGA3ox1, a gene encoding gibberellin 3ß-hydroxylase, which is the key enzyme in the gibberellin synthesis pathway. Genome resequencing identified a beneficial GmGA3ox1 haplotype contributing to high seed weight, which was further confirmed by soybean transformants. CRISPR/Cas9-generated gmga3ox1 mutants showed lower seed weight, but promoted seed yield by increasing seed numbers. The gmga3ox1 mutants reduced gibberellin biosynthesis while enhancing photosynthesis. Knockout of GmGA3ox1 resulted in the upregulation of numerous photosynthesis-related genes, particularly the GmRCA family encoding ribulose-1,5-bispho-sphate carboxylase-oxygenase (Rubisco) activases. The basic leucine zipper transcription factors GmbZIP97 and GmbZIP159, which were both upregulated in the gmga3ox1 mutants and induced by the gibberellin synthesis inhibitor uniconazole, could bind to the promoter of GmRCAß and activate its expression. Analysis of genomic sequences with over 2700 soybean accessions suggested that GmGA3ox1 is being gradually utilized in modern breeding. Our results elucidated the important role of GmGA3ox1 in soybean yield. These findings reveal important clues for future high-yield breeding in soybean and other crops.

Nucleotide Diversity of the Maize ZmCNR13 Gene and Association With Ear Traits.

The maize (Zea mays L.) ZmCNR13 gene, encoding a protein of fw2.2-like (FWL) family, has been demonstrated to be involved in cell division, expansion, and differentiation. In the present study, the genomic sequences of the ZmCNR13 locus were re-sequenced in 224 inbred lines, 56 landraces and 30 teosintes, and the nucleotide polymorphism and selection signature were estimated. A total of 501 variants, including 415 SNPs and 86 Indels, were detected. Among them, 51 SNPs and 4 Indels were located in the coding regions. Although neutrality tests revealed that this locus had escaped from artificial selection during the process of maize domestication, the population of inbred lines possesses lower nucleotide diversity and decay of linkage disequilibrium. To estimate the association between sequence variants of ZmCNR13 and maize ear characteristics, a total of ten ear-related traits were obtained from the selected inbred lines. Four variants were found to be significantly associated with six ear-related traits. Among them, SNP2305, a non-synonymous mutation in exon 2, was found to be associated with ear weight, ear grain weight, ear diameter and ear row number, and explained 4.59, 4.61, 4.31, and 8.42% of the phenotypic variations, respectively. These results revealed that natural variations of ZmCNR13 might be involved in ear development and can be used in genetic improvement of maize ear-related traits.

Multi-Locus Genome-Wide Association Study and Genomic Selection of Kernel Moisture Content at the Harvest Stage in Maize.

Kernel moisture content at the harvest stage (KMC) is an important trait that affects the mechanical harvesting of maize grain, and the identification of genetic loci for KMC is beneficial for maize molecular breeding. In this study, we performed a multi-locus genome-wide association study (ML-GWAS) to identify quantitative trait nucleotides (QTNs) for KMC using an association mapping panel of 251 maize inbred lines that were genotyped with an Affymetrix CGMB56K SNP Array and phenotypically evaluated in three environments. Ninety-eight QTNs for KMC were detected using six ML-GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, PLARmEB, PKWmEB, and ISIS EM-BLASSO). Eleven of these QTNs were considered to be stable, as they were detected by at least four ML-GWAS models under a uniformed environment or in at least two environments and BLUP using the same ML-GWAS model. With qKMC5.6 removed, the remaining 10 stable QTNs explained <10% of the phenotypic variation, suggesting that KMC is mainly controlled by multiple minor-effect genetic loci. A total of 63 candidate genes were predicted from the 11 stable QTNs, and 10 candidate genes were highly expressed in the kernel at different time points after pollination. High prediction accuracy was achieved when the KMC-associated QTNs were included as fixed effects in genomic selection, and the best strategy was to integrate all KMC QTNs identified by all six ML-GWAS models. These results further our understanding of the genetic architecture of KMC and highlight the potential of genomic selection for KMC in maize breeding.

Genome-wide association mapping for protein, oil and water-soluble protein contents in soybean.

As a globally important legume crop, soybean provides excellent sources of protein and oil for human and livestock nutrition. Improving seed protein and oil contents has always been an important objective in soybean breeding. Water-soluble protein plays a significant role in the processing and efficacy of soybean protein. Here, a genome-wide association study (GWAS) of seed compositions (protein, oil, and water-soluble protein contents) was conducted using 211 diverse soybean accessions genotyped with a 355 K SoySNP array. Three, four, and five QTLs were identified related to the protein, oil, and water-soluble protein contents, respectively. Furthermore, five QTLs (qPC-15-1, qOC-8-1, qOC-12-1, qOC-20-1 and qWSPC-8-1) were detected in multiple environments. Analysis of the favorable alleles for oil and water-soluble protein contents showed that qOC-8-1 (qWSPC-8-1) exerted inverse effects on oil and water-soluble protein synthesis. Relative expression analysis suggested that Glyma.15G049200 in qPC-15-1 affects protein synthesis and Glyma.08G107800 in qOC-8-1 and qWSPC-8-1 might be involved in oil and water-soluble protein synthesis, producing opposite effects. The candidate genes and significant SNPs detected in the present study will allow a deeper understanding of the genetic basis for the regulation of protein, oil and water-soluble protein contents and provide important information that could be utilized in marker-assisted selection for soybean quality improvement.

Genetic and genomic analysis of the seed-filling process in maize based on a logistic model.

Seed filling is a dynamic process that determines seed size and nutritional quality. This time-dependent trait follows a logistic (S-shaped) growth curve that can be described by a logistic function, with parameters of biological relevance. When compared between genotypes, the filling dynamics variations are explained by the differences of parameter values; as such, the parameter estimates can be considered as "traits" for genetic analysis to identify loci that are associated with the seed-filling process. We carried out genetic and genomic analysis of the seed-filling process in maize, using a recombinant inbred line (RIL) population derived from the two inbred lines with contrasting seed-filling dynamics. We recorded seed dry weight at 14 time points after pollination, spanning the early filling phases to the late maturation stages. Fitting these data to a logistic model allowed for estimating 12 characteristic parameters that can be used to meaningfully describe the seed-filling process. Quantitative trait locus (QTL) mapping of these parameters identified a total of 90 nonredundant loci. Using bulked segregant RNA-sequencing (BSR-seq) analysis, we identified eight genes that showed differential gene expression patterns at multiple time points between the extreme pools, and these genes co-localize with the mapped QTL regions. Two of the eight genes, GRMZM2G391936 and GRMZM2G008263, are implicated in starch and sucrose metabolism, and biosynthesis of secondary metabolites that are well known for playing a vital role in seed filling. This study suggests that the logistic model-based approach can efficiently identify genetic loci that regulate dynamic developing traits.

Combined linkage and association mapping reveal candidate loci for kernel size and weight in maize.

Yield improvement is a top priority for maize breeding. Kernel size and weight are important determinants of maize grain yield. In this study, a recombinant inbred line (RIL) population and an association panel were used to identify quantitative trait loci (QTLs) for four maize kernel-related traits: kernel length, width, thickness and 100-kernel weight. Twenty-seven QTLs were identified for kernel-related traits across three environments and the best linear unbiased predictions (BLUPs) of each trait by linkage analysis, and four QTLs were stably detected in more than two environments. Additionally, 29 single nucleotide polymorphisms (SNPs) were identified as significantly associated with the four kernel-related traits and BLUPs by genome-wide association study, and two loci could be stably detected in both environments. In total, four QTLs/SNPs were co-associated with various traits in both populations. Using combined-linkage analysis and association mapping, PZE-101066560 on chromosome 1, associated with kernel width and with 100-kernel weight in the association panel, was co-localized within the QTL interval of qKW1-3 for kernel width in the RILs. Two annotated genes in the candidate region were considered as potential candidate genes. The QTLs and candidate genes identified here will facilitate molecular breeding for grain yield improvement in maize.

Identification of Loci and Candidate Genes Responsible for Pod Dehiscence in Soybean via Genome-Wide Association Analysis Across Multiple Environments.

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.

QTL-By-Environment Interaction in the Response of Maize Root and Shoot Traits to Different Water Regimes.

Drought is a major abiotic stress factor limiting maize production, and elucidating the genetic control of root system architecture and plasticity to water-deficit stress is a crucial problem to improve drought adaptability. In this study, 13 root and shoot traits and genetic plasticity were evaluated in a recombinant inbred line (RIL) population under well-watered (WW) and water stress (WS) conditions. Significant phenotypic variation was observed for all observed traits both under WW and WS conditions. Most of the measured traits showed significant genotype-environment interaction (GEI) in both environments. Strong correlations were observed among traits in the same class. Multi-environment (ME) and multi-trait (MT) QTL analyses were conducted for all observed traits. A total of 48 QTLs were identified by ME, including 15 QTLs associated with 9 traits showing significant QTL-by-Environment interactions (QEI). QTLs associated with crown root angle (CRA2) and crown root length (CRL1) were identified as having antagonistic pleiotropic effects, while 13 other QTLs showed signs of conditional neutrality (CN), including 9 and 4 QTLs detected under WW and WS conditions, respectively. MT analysis identified 14 pleiotropic QTLs for 13 traits, SNP20 (1@79.2 cM) was associated with the length of crown root (CR), primary root (PR), and seminal root (SR) and might contribute to increases in root length under WS condition. Taken together, these findings contribute to our understanding of the phenotypic and genotypic patterns of root plasticity in response to water deficiency, which will be useful to improve drought tolerance in maize.

Genome-wide association study of kernel moisture content at harvest stage in maize.

Kernel moisture content at harvest stage (KMC) is an important factor affecting maize production, especially for mechanical harvesting. We investigated the genetic basis of KMC using an association panel comprising of 144 maize inbred lines that were phenotypically evaluated at two field trial locations. Significant positive or negative correlations were identified between KMC and a series of other agronomic traits, indicating that KMC is associated with other such traits. Combining phenotypic values and the Maize SNP3K Beadchip to perform a genome-wide association study revealed eight single nucleotide polymorphisms (SNPs) associated with KMC at P ≤ 0.001 using a mixed linear model (PCA+K). These significant SNPs could be converted into five quantitative trait loci (QTLs) distributed on chromosomes 1, 5, 8, and 9. Of these QTLs, three were colocalized with genomic regions previously reported. Based on the phenotypic values of the alleles corresponding to significant SNPs, the favorable alleles were mined. Eight maize inbred lines with low KMC and harboring favorable alleles were identified. These QTLs and elite maize inbred lines with low KMC will be useful in maize breeding.

Identification of Genetic Differentiation between Waxy and Common Maize by SNP Genotyping.

Waxy maize (Zea mays L. var. ceratina) is an important vegetable and economic crop that is thought to have originated from cultivated flint maize and most recently underwent divergence from common maize. In this study, a total of 110 waxy and 110 common maize inbred lines were genotyped with 3072 SNPs to evaluate the genetic diversity, population structure, and linkage disequilibrium decay as well as identify putative loci that are under positive selection. The results revealed abundant genetic diversity in the studied panel and that genetic diversity was much higher in common than in waxy maize germplasms. Principal coordinate analysis and neighbor-joining cluster analysis consistently classified the 220 accessions into two major groups and a mixed group with mixed ancestry. Subpopulation structure in both waxy and common maize sets were associated with the germplasm origin and corresponding heterotic groups. The LD decay distance (1500-2000 kb) in waxy maize was lower than that in common maize. Fourteen candidate loci were identified as under positive selection between waxy and common maize at the 99% confidence level. The information from this study can assist waxy maize breeders by enhancing parental line selection and breeding program design.

Association mapping of soybean seed germination under salt stress.

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.

Use of single nucleotide polymorphisms and haplotypes to identify genomic regions associated with protein content and water-soluble protein content in soybean.

KEY MESSAGE: 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.

Characterization of Rubisco activase genes in maize: an α-isoform gene functions alongside a ß-isoform gene.

Rubisco activase (RCA) catalyzes the activation of Rubisco in vivo and plays a crucial role in regulating plant growth. In maize (Zea mays), only ß-form RCA genes have been cloned and characterized. In this study, a genome-wide survey revealed the presence of an α-form RCA gene and a ß-form RCA gene in the maize genome, herein referred to as ZmRCAα and ZmRCAß, respectively. An analysis of genomic DNA and complementary DNA sequences suggested that alternative splicing of the ZmRCAß precursor mRNA (premRNA) at its 3' untranslated region could produce two distinctive ZmRCAß transcripts. Analyses by electrophoresis and matrix-assisted laser desorption/ionization-tandem time-of-flight mass spectrometry showed that ZmRCAα and ZmRCAß encode larger and smaller polypeptides of approximately 46 and 43 kD, respectively. Transcriptional analyses demonstrated that the expression levels of both ZmRCAα and ZmRCAß were higher in leaves and during grain filling and that expression followed a specific cyclic day/night pattern. In 123 maize inbred lines with extensive genetic diversity, the transcript abundance and protein expression levels of these two RCA genes were positively correlated with grain yield. Additionally, both genes demonstrated a similar correlation with grain yield compared with three C4 photosynthesis genes. Our data suggest that, in addition to the ß-form RCA-encoding gene, the α-form RCA-encoding gene also contributes to the synthesis of RCA in maize and support the hypothesis that RCA genes may play an important role in determining maize productivity.

The acid phosphatase-encoding gene GmACP1 contributes to soybean tolerance to low-phosphorus stress.

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.

Variation in Rubisco activase (RCAß) gene promoters and expression in soybean [Glycine max (L.) Merr].

Understanding the genetic basis of Rubisco activase (RCA) gene regulation and altering its expression levels to optimize Rubisco activation may provide an approach to enhance plant productivity. However, the genetic mechanisms and the effect of RCA expression on phenotype are still unknown in soybean. This work analysed the expression of RCA genes and demonstrated that two RCA isoforms presented different expression patterns. Compared with GmRCAα, GmRCAß was expressed at higher mRNA and protein levels. In addition, GmRCAα and GmRCAß were positively correlated with chlorophyll fluorescence parameters and seed yield, suggesting that changes in expression of RCA has a potential applicability in breeding for enhanced soybean productivity. To identify the genetic factors that cause expression level variation of GmRCAß, expression quantitative trait loci (eQTL) mapping was combined with allele mining in a natural population including 219 landraces. The eQTL mapping showed that a combination of both cis- and trans-acting eQTLs might control GmRCAß expression. As promoters can affect both cis- and trans-acting eQTLs by altering cis-acting regulatory elements or transcription factor binding sites, this work subsequently focused on the promoter region of GmRCAß. Single-nucleotide polymorphisms in the GmRCAß promoter were identified and shown to correlate with expression level diversity. These SNPs were classified into two groups, A and B. Further transient expression showed that GUS expression driven by the group A promoter was stronger than that by the group B promoter, suggesting that promoter sequence types could influence gene expression levels. These results would improve understanding how variation within promoters affects gene expression and, ultimately, phenotypic diversity in natural populations.

Identification of single nucleotide polymorphisms and haplotypes associated with yield and yield components in soybean (Glycine max) landraces across multiple environments.

Genome-wide association analysis is a powerful approach to identify the causal genetic polymorphisms underlying complex traits. In this study, we evaluated a population of 191 soybean landraces in five environments to detect molecular markers associated with soybean yield and its components using 1,536 single-nucleotide polymorphisms (SNPs) and 209 haplotypes. The analysis revealed that abundant phenotypic and genetic diversity existed in the studied population. This soybean population could be divided into two subpopulations and no or weak relatedness was detected between pair-wise landraces. The level of intra-chromosomal linkage disequilibrium was about 500 kb. Genome-wide association analysis based on the unified mixed model identified 19 SNPs and 5 haplotypes associated with soybean yield and yield components in three or more environments. Nine markers were found co-associated with two or more traits. Many markers were located in or close to previously reported quantitative trait loci mapped by linkage analysis. The SNPs and haplotypes identified in this study will help to further understand the genetic basis of soybean yield and its components, and may facilitate future high-yield breeding by marker-assisted selection in soybean.