Soybean Recombinant Inbred Lines Research Articles

Identification of QTLs for symbiotic nitrogen fixation and related traits in a soybean recombinant inbred line population.

The genetic architecture of symbiotic N fixation and related traits was investigated in the field. QTLs were identified for percent N derived from the atmosphere, shoot [N] and C to N ratio. Soybean [Glycine max (L.) Merr.] is cultivated worldwide and is the most abundant source of plant-based protein. Symbiotic N2 fixation (SNF) in legumes such as soybean is of great importance; however, yields may still be limited by N in both high yielding and stressful environments. To better understand the genetic architecture of SNF and facilitate the development of high yielding cultivars and sustainable soybean production in stressful environments, a recombinant inbred line population consisting of 190 lines, developed from a cross between PI 442012A and PI 404199, was evaluated for N derived from the atmosphere (Ndfa), N concentration ([N]), and C to N ratio (C/N) in three environments. Significant genotype, environment and genotype × environment effects were observed for all three traits. A linkage map was constructed containing 3309 single nucleotide polymorphism (SNP) markers. QTL analysis was performed for additive effects of QTLs, QTL × environment interactions, and QTL × QTL interactions. Ten unique additive QTLs were identified across all traits and environments. Of these, two QTLs were detected for Ndfa and eight for C/N. Of the eight QTLs for C/N, four were also detected for [N]. Using QTL × environment analysis, six QTLs were detected, of which five were also identified in the additive QTL analysis. The QTL × QTL analysis identified four unique epistatic interactions. The results of this study may be used for genomic selection and introgression of favorable alleles for increased SNF, [N], and C/N via marker-assisted selection.

Genetic analysis and QTL mapping of seed hardness trait in a soybean (Glycine max) recombinant inbred line (RIL) population

Seed hardness is a critical quality trait impacting both the suitability of soybeans for consumption and their processing. The primary objective of this study was to explore the genetic foundations underlying seed hardness in soybeans. A 234 recombinant inbred line (RIL) population was evaluated for seed hardness across three years (2015 in Gansu, 2016, and 2017 in Hainan). Notably, the parent varieties, Zhonghuang35 and Jindou21, displayed significant differences in seed hardness. Also, the RIL population exhibited a wide range of genetic variation in seed hardness, with coefficients of variation between 70.53 % and 94.94 %. The frequency distribution of this trait conformed to a relatively normal distribution, making it suitable for QTL analysis. Six QTLs associated with seed hardness were identified with three located on chromosome 2 and three on chromosome 16. The major QTL, qHS-2-1, consistently exhibited the highest percentage of PVE and LOD in Gansu 2015, Hainan 2016, and Hainan 2017, suggesting its central role in determining seed hardness. Further investigation revealed four genes within the qHS-2-1 interval potentially related to seed hardness. GO enrichment analysis provided insights into their functions, including factors such as Glyma.02G307000, a pectin lyase-like superfamily protein, which could influence seed hardness through its role in pectin lyase enzyme activity. Expression analysis of these candidate genes demonstrated significant differences between the two parent varieties, further highlighting their potential role in seed coat hardness. This study offers valuable insights into the genetic mechanisms governing soybean seed coat hardness, providing a foundation for future research and crop improvement efforts.

Biochemical changes in recombinant inbred lines of soybean (Glycine max L. Merr.) under storage-related stresses

Biochemical changes in recombinant inbred lines of soybean (Glycine max L. Merr.) under storage-related stresses

Identification of major quantitative trait loci and candidate genes for seed weight in soybean

Key messageFour major quantitative trait loci for 100-seed weight were identified in a soybean RIL population under five environments, and the most likely candidate genes underlying these loci were identified.Seed weight is an important target of soybean breeding. However, the genes underlying the major quantitative trait loci (QTL) controlling seed weight remain largely unknown. In this study, a soybean population of 300 recombinant inbred lines (RILs) derived from a cross between PI595843 (PI) and WH was used to map the QTL and identify candidate genes for seed weight. The RIL population was genotyped through whole genome resequencing, and phenotyped for 100-seed weight under five environments. A total of 38 QTL were detected, and four major QTL, each explained at least 10% of the variation in 100-seed weight, were identified. Six candidate genes within these four major QTL regions were identified by analyses of their tissue expression patterns, gene annotations, and differential gene expression levels in soybean seeds during four developmental stages between two parental lines. Further sequence variation analyses revealed a C to T substitution in the first exon of the Glyma.19G143300, resulting in an amino acid change between PI and WH, and thus leading to a different predicted kinase domain, which might affect its protein function. Glyma.19G143300 is highly expressed in soybean seeds and encodes a leucine-rich repeat receptor-like protein kinase (LRR-RLK). Its predicted protein has typical domains of LRR-RLK family, and phylogenetic analyses reveled its similarity with the known LRR-RLK protein XIAO (LOC_Os04g48760), which is involved in controlling seed size. The major QTL and candidate genes identified in this study provide useful information for molecular breeding of new soybean cultivars with desirable seed weight.

Identification of Quantitative Trait Locus and Candidate Genes for Drought Tolerance in a Soybean Recombinant Inbred Line Population.

With global warming and regional decreases in precipitation, drought has become a problem worldwide. As the number of arid regions in the world is increasing, drought has become a major factor leading to significant crop yield reductions and food crises. Soybean is a crop that is relatively sensitive to drought. It is also a crop that requires more water during growth and development. The aim of this study was to identify the quantitative trait locus (QTL) that affects drought tolerance in soybean by using a recombinant inbred line (RIL) population from a cross between the drought-tolerant cultivar ‘Jindou21’ and the drought-sensitive cultivar ‘Zhongdou33’. Nine agronomic and physiological traits were identified under drought and well-watered conditions. Genetic maps were constructed with 923,420 polymorphic single nucleotide polymorphism (SNP) markers distributed on 20 chromosomes at an average genetic distance of 0.57 centimorgan (cM) between markers. A total of five QTLs with a logarithm of odds (LOD) value of 4.035–8.681 were identified on five chromosomes. Under well-watered conditions and drought-stress conditions, one QTL related to the main stem node number was located on chromosome 16, accounting for 17.177% of the phenotypic variation. Nine candidate genes for drought resistance were screened from this QTL, namely Glyma.16G036700, Glyma.16G036400, Glyma.16G036600, Glyma.16G036800, Glyma.13G312700, Glyma.13G312800, Glyma.16G042900, Glyma.16G043200, and Glyma.15G100700. These genes were annotated as NAC transport factor, GATA transport factor, and BTB/POZ-MATH proteins. This result can be used for molecular marker-assisted selection and provide a reference for breeding for drought tolerance in soybean.

QTL mapping of genes related to Nod factor signalling using recombinant inbred lines of soybean (Glycine max)

AbstractSoybean, which originated in China, is an important oil crop. The soybean–rhizobium symbiotic relationship provides soybean plants with abundant nitrogen. The Nod factor, which is an early signalling factor, affects the establishment of this symbiosis. In this study, the generated Sinorhizobium fredii HH103ΩNodB mutant exhibited defective Nod factor synthesis. The nodulation of 30 core collections, including ‘Dongnong594’ and ‘Charleston’, by the NodB mutant differed from the nodulation by the wild‐type strain. Soybean recombinant inbred lines derived from a ‘Dongnong594’ × ‘Charleston’ cross were used for mapping soybean genes related to the Nod factor signalling pathway. Eleven QTLs related to nodule number and eight QTLs related to nodule dry weight were revealed. On the basis of a qRT‐PCR analysis, Glyma.03G120700, Glyma.08G296000 and Glyma.15G21560 were identified as candidate genes influencing the NF interaction. These genes were differentially expressed following the inoculations with the wild‐type rhizobium and the NodB mutant. This study provides important insights into the mechanism mediating soybean responses to the NF after the soybean–rhizobium symbiosis is established.

Improving Quantitative Traits in Self-Pollinated Crops Using Simulation-Based Selection With Minimal Crossing.

Genomic selection and marker-assisted recurrent selection have been applied to improve quantitative traits in many cross-pollinated crops. However, such selection is not feasible in self-pollinated crops owing to laborious crossing procedures. In this study, we developed a simulation-based selection strategy that makes use of a trait prediction model based on genomic information to predict the phenotype of the progeny for all possible crossing combinations. These predictions are then used to select the best cross combinations for the selection of the given trait. In our simulated experiment, using a biparental initial population with a heritability set to 0.3, 0.6, or 1.0 and the number of quantitative trait loci set to 30 or 100, the genetic gain of the proposed strategy was higher or equal to that of conventional recurrent selection method in the early selection cycles, although the number of cross combinations of the proposed strategy was considerably reduced in each cycle. Moreover, this strategy was demonstrated to increase or decrease seed protein content in soybean recombinant inbred lines using SNP markers. Information on 29 genomic regions associated with seed protein content was used to construct the prediction model and conduct simulation. After two selection cycles, the selected progeny had significantly higher or lower seed protein contents than those from the initial population. These results suggest that our strategy is effective in obtaining superior progeny over a short period with minimal crossing and has the potential to efficiently improve the target quantitative traits in self-pollinated crops.

QTL‐allele system of main stem node number in recombinant inbred lines of soybean (Glycine max) using association versus linkage mapping

AbstractMain stem node number (MSN) as a major plant architectural trait determines the plant canopy and seed yield potential in soybean. To identify the MSN QTL system, a recombinant inbred line (RIL) population consists of 427 lines derived from the cross of KF‐1 × NN1138–2 was tested at two locations in 2 years showing MSN ranging in 11.9–23.7, from which the MSN QTL system was explored using RTM‐GWAS (restricted two‐stage multi‐locus genome‐wide association study) compared with linkage mapping CIM (composite interval mapping). By RTM‐GWAS, 89 QTLs were detected with a phenotypic contribution of 84.50%, whereas by CIM, 24 QTLs detected, indicating the RTM‐GWAS more powerful than CIM. In the population, the best RIL genotypic value was ~24.5, whereas the predicted extreme potential was as high as 49.0. Of the 89 MSN QTLs, 31 ones overlap with those reported in literature, whereas the rest 58 ones are novel. From the QTLs, 74 candidate genes were annotated and GO‐grouped into six categories of biological processes. RTM‐GWAS is potential in exploration of MSN QTL/Gene system benefiting breeding for ideal canopy.

Genetic analysis and QTL mapping of growth period traits and plant height traits in soybean recombinant inbred lines from Dongnong 47 × PI 317334-B

High and stable yield is the main goal of soybean genetic improvement. In this study, association analysis was used to detect the quantitative trait loci (QTL) for the plant height, and soybean growth period using 182 SSR markers in the RIL population of 136 F4:8 lines, which developed from a cross between photoperiod-insensitive cultivar ‘Dongnong 47’ and photoperiod-sensitive variety PI317334–B. The results showed that 33 QTLs related to soybean growth period and plant height traits were detected by compound interval mapping, and were located on 12 linkage groups including N, C1, C2, J, D1a, B2, E, G, A2, O, L, I, with the contribution rate of 7.85–33.84%. These QTL loci and linkage markers related to soybean photoperiod sensitivity, would be helpful to identify key genes that control soybean photoperiod sensitivity, and provide an important basis for the breeding of new photoperiod-insensitive soybean varieties based on molecular design breeding.

A Major and Stable Quantitative Trait Locus qSS2 for Seed Size and Shape Traits in a Soybean RIL Population.

Seed size and shape traits are important determinants of seed yield and appearance quality in soybean [Glycine max (L.) Merr.]. Understanding the genetic architecture of these traits is important to enable their genetic improvement through efficient and targeted selection in soybean breeding, and for the identification of underlying causal genes. To map seed size and shape traits in soybean, a recombinant inbred line (RIL) population developed from K099 (small seed size) × Fendou 16 (large seed size), was phenotyped in three growing seasons. A genetic map of the RIL population was developed using 1,485 genotyping by random amplicon sequencing-direct (GRAS-Di) and 177 SSR markers. Quantitative trait locus (QTL) mapping was conducted by inclusive composite interval mapping. As a result, 53 significant QTLs for seed size traits and 27 significant QTLs for seed shape traits were identified. Six of these QTLs (qSW8.1, qSW16.1, qSLW2.1, qSLT2.1, qSWT1.2, and qSWT4.3) were identified with LOD scores of 3.80–14.0 and R2 of 2.36%–39.49% in at least two growing seasons. Among the above significant QTLs, 24 QTLs were grouped into 11 QTL clusters, such as, three major QTLs (qSL2.3, qSLW2.1, and qSLT2.1) were clustered into a major QTL on Chr.02, named as qSS2. The effect of qSS2 was validated in a pair of near isogenic lines, and its candidate genes (Glyma.02G269400, Glyma.02G272100, Glyma.02G274900, Glyma.02G277200, and Glyma.02G277600) were mined. The results of this study will assist in the breeding programs aiming at improvement of seed size and shape traits in soybean.

Genetic Relationships Among Physiological Processes, Phenology, and Grain Yield Offer an Insight Into the Development of New Cultivars in Soybean (Glycine max L. Merr).

Soybean grain yield has steadily increased during the last century because of enhanced cultivars and better agronomic practices. Increases in the total biomass, shorter cultivars, late maturity, and extended seed-filling period are frequently reported as main contributors for better soybean performance. However, there are still processes associated with crop physiology to be improved. From the theoretical standpoint, yield is the product of efficiency of light interception (Ei), radiation use efficiency (RUE), and harvest index (HI). The relative contribution of these three parameters on the final grain yield (GY), their interrelation with other phenological–physiological traits, and their environmental stability have not been well established for soybean. In this study, we determined the additive–genetic relationship among 14 physiological and phenological traits including photosynthesis (A) and intrinsic water use efficiency (iWUE) in a panel of 383 soybean recombinant inbred lines (RILs) through direct (path analyses) and indirect learning methods [least absolute shrinkage and selection operator (LASSO) algorithm]. We evaluated the stability of Ei, RUE, and HI through the slope from the Finley and Wilkinson joint regression and the genetic correlation between traits evaluated in different environments. Results indicate that both supervised and unsupervised methods effectively establish the main relationships underlying changes in Ei, RUE, HI, and GY. Variations in the average growth rate of canopy coverage for the first 40 days after planting (AGR40) explain most of the changes in Ei. RUE is primarily influenced by phenological traits of reproductive length (RL) and seed-filling (SFL) as well as iWUE, light extinction coefficient (K), and A. HI showed a strong relationship with A, AGR40, SFL, and RL. According to the path analysis, an increase in one standard unit of HI promotes changes in 0.5 standard units of GY, while changes in the same standard unit of RUE and Ei produce increases on GY of 0.20 and 0.19 standard units, respectively. RUE, Ei, and HI exhibited better environmental stability than GY, although changes associated with year and location showed a moderate effect in Ei and RUE, respectively. This study brings insight into a group of traits involving A, iWUE, and RL to be prioritized during the breeding process for high-yielding cultivars.

Identification of Additive-Epistatic QTLs Conferring Seed Traits in Soybean Using Recombinant Inbred Lines.

Seed weight and shape are important agronomic traits that affect soybean quality and yield. In the present study, we used image analysis software to evaluate 100-seed weight and seed shape traits (length, width, perimeter, projection area, length/width, and weight/projection area) of 155 novel recombinant inbred soybean lines (NJRISX) generated by crossing “Su88-M21” and “XYXHD”. We examined quantitative trait loci (QTLs) associated with the six traits (except seed weight per projection area), and identified 42 additive QTLs (5–8 QTLs per trait) accounting for 24.9–37.5% of the phenotypic variation (PV). Meanwhile, 2–4 epistatic QTL pairs per trait out of a total of 18 accounted for 2.5–7.2% of the PV; and unmapped minor QTLs accounted for the remaining 35.0–56.7% of the PV. A total of 28 additive and 11 epistatic QTL pairs were concentrated in nine joint QTL segments (JQSs), indicating that QTLs associated with seed weight and shape are closely related and interacted. An interaction was also detected between additive and epistatic QTL pairs and environment, which made significant contributions of 1.4–9.5% and 0.4–0.8% to the PV, respectively. We annotated 18 candidate genes in the nine JQSs, which were important for interpreting the close relationships among the six traits. These findings indicate that examining the interactions between closely related traits rather than only analyzing individual trait provides more useful insight into the genetic system of the interrelated traits for which there has been limited QTL information.

Yield prediction by machine learning from UAS-based multi-sensor data fusion in soybean

BackgroundNowadays, automated phenotyping of plants is essential for precise and cost-effective improvement in the efficiency of crop genetics. In recent years, machine learning (ML) techniques have shown great success in the classification and modelling of crop parameters. In this research, we consider the capability of ML to perform grain yield prediction in soybeans by combining data from different optical sensors via RF (Random Forest) and XGBoost (eXtreme Gradient Boosting). During the 2018 growing season, a panel of 382 soybean recombinant inbred lines were evaluated in a yield trial at the Agronomy Center for Research and Education (ACRE) in West Lafayette (Indiana, USA). Images were acquired by the Parrot Sequoia Multispectral Sensor and the S.O.D.A. compact digital camera on board a senseFly eBee UAS (Unnamed Aircraft System) solution at R4 and early R5 growth stages. Next, a standard photogrammetric pipeline was carried out by SfM (Structure from Motion). Multispectral imagery serves to analyse the spectral response of the soybean end-member in 2D. In addition, RGB images were used to reconstruct the study area in 3D, evaluating the physiological growth dynamics per plot via height variations and crop volume estimations. As ground truth, destructive grain yield measurements were taken at the end of the growing season.ResultsAlgorithms and feature extraction techniques were combined to develop a regression model to predict final yield from imagery, achieving an accuracy of over 90.72% by RF and 91.36% by XGBoost.ConclusionsResults provide practical information for the selection of phenotypes for breeding coming from UAS data as a decision support tool, affording constant operational improvement and proactive management for high spatial precision.

Evaluation of soybean RIL population for charcoal rot resistance

Charcoal rot disease caused by Macrophomina phaseolina (Tassi) Goid. is a major threat to soybean production.Identification of resistant sources and their deployment in resistance breeding programs is the best way to tackle this disease. Current study aimed to screen 125 Recombinant Inbred Lines (RILs) derived from the cross JS-97-52 × JS90-41 for charcoal rot resistance using cut-stem inoculation technique. Out of 125, 21 lines were found to be resistant compared to the better parent JS-97-52. These lines would further be confirmed for their resistance under field condition for their utilization in charcoal rot resistance breeding program.

Quantitative trait loci mapping for flooding tolerance at an early growth stage of soybean recombinant inbred line population

AbstractFlooding stress causes a significant yield reduction in soybean. The early growth of soybean in Korea coincides with the rainy season, potentially exposing to flooding stress. Greenhouse experiments were conducted to map the quantitative trait loci (QTL) for flooding tolerance in soybean and to identify and investigate candidate genes near the QTL hot spots. Flood stress was imposed at V1–V2 stage on a recombinant inbred line population (‘Paldalkong’ × ‘NTS1116’), and leaf chlorophyll content (CC) and shoot dry weight (DW) were measured under control and flooded conditions. The genetic map was constructed using 180K Axiom® SoyaSNP markers. The QTL were analysed under control and flooded conditions as well as for index (ratio of CC or DW under flooded to control, CCI and DWI) and flooding tolerance index (FTI, mean of CCI and DWI). A total of 20 QTL with LOD scores 3.59–19.73 causing 5.8%–33.3% phenotypic variation were identified on nine chromosomes. Chromosomes 10, 12 and 13 harboured relatively more stable QTL. Results of this study could be useful to further understand the genetic basis of soybean's flooding tolerance and applied in breeding programmes.

Natural variation and selection in GmSWEET39 affect soybean seed oil content.

Summary Soybean (Glycine max) is a major contributor to the world oilseed production. Its seed oil content has been increased through soybean domestication and improvement. However, the genes underlying the selection are largely unknown.The present contribution analyzed the expression patterns of genes in the seed oil quantitative trait loci with strong selective sweep signals, then used association, functional study and population genetics to reveal a sucrose efflux transporter gene, GmSWEET39, controlling soybean seed oil content and under selection. GmSWEET39 is highly expressed in soybean seeds and encodes a plasma membrane‐localized protein. Its expression level is positively correlated with soybean seed oil content. The variation in its promoter and coding sequence leads to different natural alleles of this gene. The GmSWEET39 allelic effects on total oil content were confirmed in the seeds of soybean recombinant inbred lines, transgenic Arabidopsis, and transgenic soybean hairy roots. The frequencies of its superior alleles increased from wild soybean to cultivated soybean, and are much higher in released soybean cultivars.The findings herein suggest that the sequence variation in GmSWEET39 affects its relative expression and oil content in soybean seeds, and GmSWEET39 has been selected to increase seed oil content during soybean domestication and improvement.

Genomic Regions Governing the Biosynthesis of Unsaturated Fatty Acids in Recombinant Inbred Lines of Soybean Raised across Multiple Growing Years

AbstractThe ratio of oleic acid to the combined value of linoleic and α‐linolenic acids determines the oxidative stability, and the ratio of linoleic acid to α‐linolenic acid is the key to the nutritional value of soybean oil. The present study was conducted to identify genomic regions associated with oleic, linoleic, and α‐linolenic acids in recombinant inbred lines (RIL), developed from LSb1 × NRC7, across 5 cropping years. These RIL were genotyped using 105 polymorphic SSR markers across soybean genome and analyzed for fatty acid composition. SSR markers, namely, Satt245 (LGp M), Satt556 (LGpB2), Sat_042 (LGp C1), Staga002 (LGp D1b), Satt684 (LGp A1), and AI856415 (LGpD1b) showed significant (P < 0.05) association with oleic acid for all the 5 years, though this association was weak in the years when the growing temperature during active seed formation stage was high. Quantitative trait loci (QTL) linked to Satt684 (LGp A1), Satt556 (LGp B2), Sat_042 (LGp C1), and AI856415 (LGp D1b) showed pleiotropic influence on the levels of unsaturated fatty acids. Complementation of favorable QTL from LSb1 and NRC7 generated 60% oleic acid and less than 4% α‐linolenic acid RIL, stable across 5 cropping years. New SSR markers, namely, Satt245, AI856415, and Staga002 identified to be associated with different unsaturated fatty acids may be useful in improving the efficiency of marker‐assisted breeding for enhancing the monounsaturated to polyunsaturated fatty acids ratio of soybean oil.

QTL mapping and gene mining to identify genes on soybean (Glycine max) associated with NopB of Sinorhizobium fredii HH103

AbstractSoybean is a special crop that can utilize N2 in the air via symbioses with Rhizobium spp. The formation of effective nodules is a complex process in which nodulation outer proteins (Nops) are determinants of establishment of a symbiotic relationship. We constructed a Sinorhizobium fredii HH103ΩnopB mutant. A nodulation test showed that the mutant had a negative effect on the Suinong14, ZYD00006, Dongnong594 and Charleston soybean lines. Recombinant inbred soybean lines were independently inoculated with the mutant and wild‐type strains, and five and four quantitative trait loci (QTLs) were identified by analysing the nodule number (NN) and nodule dry weight (NDW), respectively. We chose one QTL that overlapped with other studies and a novel QTL identified in our study and selected six candidate genes for further analysis. The qRT‐PCR analysis showed that only changes in Glyma.17G166200 expression depended on NopB. Further analysis showed that Glyma.17G166200 encoded a protein with a D‐glucose‐binding domain and a serine‐threonine/tyrosine protein kinase catalytic domain that was involved in the abscisic acid (ABA) pathway.

Effect of marker segregation distortion on high density linkage map construction and QTL mapping in Soybean (Glycine max L.)

Marker segregation distortion is a natural phenomenon. Severely distorted markers are usually excluded in the construction of linkage maps. We investigated the effect of marker segregation distortion on linkage map construction and quantitative trait locus (QTL) mapping. A total of 519 recombinant inbred lines of soybean from orthogonal and reciprocal crosses between LSZZH and NN493-1 were genotyped by specific length amplified fragment markers and seed linoleic acid content was measured in three environments. As a result, twenty linkage groups were constructed with 11,846 markers, including 1513 (12.77%) significantly distorted markers, on 20 chromosomes, and the map length was 2475.86 cM with an average marker-interval of 0.21 cM. The inclusion of distorted markers in the analysis was shown to not only improve the grouping of the markers from the same chromosomes, and the consistency of linkage maps with genome, but also increase genome coverage by markers. Combining genotypic data from both orthogonal and reciprocal crosses decreased the proportion of distorted markers and then improved the quality of linkage maps. Validation of the linkage maps was confirmed by the high collinearity between positions of markers in the soybean reference genome and in linkage maps and by the high consistency of 24 QTL regions in this study compared with the previously reported QTLs and lipid metabolism related genes. Additionally, linkage maps that include distorted markers could add more information to the outputs from QTL mapping. These results provide important information for linkage mapping, gene cloning and marker-assisted selection in soybean.

Phenotypic Variation and Genetic Architecture for Photosynthesis and Water Use Efficiency in Soybean (Glycine max L. Merr).

Photosynthesis (A) and intrinsic water use efficiency (WUE) are physiological traits directly influencing biomass production, conversion efficiency, and grain yield. Though the influence of physiological process on yield is widely known, studies assessing improvement strategies are rare due to laborious phenotyping and specialized equipment needs. This is one of the first studies to assess the genetic architecture underlying A and intrinsic WUE, as well as to evaluate the feasibility of implementing genomic prediction. A panel of 383 soybean recombinant inbred lines were evaluated in a multi-environment yield trial that included measurements of A and intrinsic WUE, using an infrared gas analyzer during R4–R5 growth stages. Genetic variability was found to support the possibility of genetic improvement through breeding. High genetic correlation between grain yield (GY) and A (0.80) was observed, suggesting increases in GY can be achieved through the improvement of A. Genome-wide association analysis revealed quantitative trait loci (QTLs) for these physiological traits. Cross-validation studies indicated high predictive ability (>0.65) for the implementation of genomic prediction as a viable strategy to improve physiological efficiency while reducing field phenotyping. This work provides core knowledge to develop new soybean cultivars with enhanced photosynthesis and water use efficiency through conventional breeding and genomic techniques.