Identification and Validation of Major QTLs, Epistatic Interactions, and Candidate Genes for Soybean Seed Shape and Weight Using Two Related RIL Populations

Understanding the genetic mechanism underlying seed size, shape, and weight is essential for enhancing soybean cultivars. High-density genetic maps of two recombinant inbred line (RIL) populations, LM6 and ZM6, were evaluated across multiple environments to identify and validate M-QTLs as well as identify candidate genes behind major and stable quantitative trait loci (QTLs). A total of 239 and 43 M-QTLs were mapped by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM) approaches, from which 180 and 18, respectively, are novel QTLs. Twenty-two QTLs including four novel major QTLs were validated in the two RIL populations across multiple environments. Moreover, 18 QTLs showed significant AE effects, and 40 pairwise of the identified QTLs exhibited digenic epistatic effects. Thirty-four QTLs associated with seed flatness index (FI) were identified and reported here for the first time. Seven QTL clusters comprising several QTLs for seed size, shape, and weight on genomic regions of chromosomes 3, 4, 5, 7, 9, 17, and 19 were identified. Gene annotations, gene ontology (GO) enrichment, and RNA-seq analyses of the genomic regions of those seven QTL clusters identified 47 candidate genes for seed-related traits. These genes are highly expressed in seed-related tissues and nodules, which might be deemed as potential candidate genes regulating the seed size, weight, and shape traits in soybean. This study provides detailed information on the genetic basis of the studied traits and candidate genes that could be efficiently implemented by soybean breeders for fine mapping and gene cloning, and for marker-assisted selection (MAS) targeted at improving these traits individually or concurrently.

Comparative QTL analysis and candidate genes identification of seed size, shape and weight in soybean (Glycine max L.)

Dissecting the genetic mechanism underlying seed size, shape and weight is essential to these traits for enhancing soybean cultivars. High-density genetic maps of two recombinant inbred line populations, LM6 and ZM6, evaluated in multiple environments to identify candidate genes behind seed-related traits major and stable QTLs. A total of 239 and 43 M-QTL were mapped by composite interval mapping and mixed-model based composite interval mapping approaches, respectively, from which 22 common QTLs including four major and novel QTLs. CIM and MCIM approaches identified 180 and 18 novel M-QTLs, respectively. Moreover, 18 QTLs showed significant AE effects, and 40 pairwise of the identified QTLs exhibited digenic epistatic effects. Seed flatness index QTLs (34 QTLs) were identified and reported for the first time. Seven QTL clusters underlying the inheritance of seed size, shape and weight on genomic regions of chromosomes 3, 4, 5, 7, 9, 17 and 19 were identified. Gene annotations, gene ontology (GO) enrichment and RNA-seq analyses identified 47 candidate genes for seed-related traits within the genomic regions of those 7 QTL clusters. These genes are highly expressed in seed-related tissues and nodules, that might be deemed as potential candidate genes regulating the above traits in soybean. This study provides detailed information for the genetic bases of the studied traits and candidate genes that could be efficiently implemented by soybean breeders for fine mapping and gene cloning as well as for MAS targeted at improving these traits individually or concurrently.

High-Resolution Mapping in Two RIL Populations Refines Major “QTL Hotspot” Regions for Seed Size and Shape in Soybean (Glycine max L.)

Seed size and shape are important traits determining yield and quality in soybean. However, the genetic mechanism and genes underlying these traits remain largely unexplored. In this regard, this study used two related recombinant inbred line (RIL) populations (ZY and K3N) evaluated in multiple environments to identify main and epistatic-effect quantitative trait loci (QTLs) for six seed size and shape traits in soybean. A total of 88 and 48 QTLs were detected through composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively, and 15 QTLs were common among both methods; two of them were major (R2 > 10%) and novel QTLs (viz., qSW-1-1ZN and qSLT-20-1K3N). Additionally, 51 and 27 QTLs were identified for the first time through CIM and MCIM methods, respectively. Colocalization of QTLs occurred in four major QTL hotspots/clusters, viz., “QTL Hotspot A”, “QTL Hotspot B”, “QTL Hotspot C”, and “QTL Hotspot D” located on Chr06, Chr10, Chr13, and Chr20, respectively. Based on gene annotation, gene ontology (GO) enrichment, and RNA-Seq analysis, 23 genes within four “QTL Hotspots” were predicted as possible candidates, regulating soybean seed size and shape. Network analyses demonstrated that 15 QTLs showed significant additive x environment (AE) effects, and 16 pairs of QTLs showing epistatic effects were also detected. However, except three epistatic QTLs, viz., qSL-13-3ZY, qSL-13-4ZY, and qSW-13-4ZY, all the remaining QTLs depicted no main effects. Hence, the present study is a detailed and comprehensive investigation uncovering the genetic basis of seed size and shape in soybeans. The use of a high-density map identified new genomic regions providing valuable information and could be the primary target for further fine mapping, candidate gene identification, and marker-assisted breeding (MAB).

Genetic dissection of photosynthetic performances in maize under drought-stressed and well-watered environments

BackgroundMaintaining photosynthetic capacity is a critical function that allows maize (Zea mays L.) to adapt to drought stress. The elucidation of genetic controls of photosynthetic performances, and tightly linked molecular markers under water stress are thus of great importance in marker-assisted selection (MAS) breeding. Meanwhile, little is known regarding their genetic controls under drought stress. Two F4 populations were developed to identify quantitative trait loci (QTLs) and dissect the genetic variation underlying six photosynthetic-related traits, namely, net photosynthetic rate (Pn), stomatal conductance (Gs), intercellular CO2 concentration (Ci), transpiration rate (Tr), ribulose 1,5-biphosphate carboxylase activity (RuBP), and water use efficiency (WUE) under drought-stressed and well-watered environments.ResultsFor two populations, we detected 54 QTLs under drought-stressed and well-watered environments by single-environment mapping with composite interval mapping (CIM), approximately 81.8~100 % QTLs displayed non-additive effects, and 43 of the 54 QTLs were identified under drought-stressed environment. We also dissected 54 QTLs via joint analysis of all environments with mixed-linear-model-based composite interval mapping (MCIM), 24 QTLs involved in QTL × environment interactions (QEIs), approximately 87.5 % QEIs were identified under drought-stressed environments, as well as 14 pair epistasis exhibited dominance-by-additive/dominance (DA/DD) effects under constracting environments. We further identified 8 constitutive QTLs (cQTLs) across two populations by CIM/MCIM under multiple environments. Remarkably, bin 1.07_1.10 (cQTL2), bin 6.05 (cQTL5), bin 7.02_7.04 (cQTL6), bin 8.03 (cQTL7), and bin 10.03 (cQTL8) exhibited 5 pleiotropic cQTLs that were consistent with phenotypic correlations among all photosynthetic-related traits. Additionally, 17 candidate genes were validated in above cQTLs.ConclusionsPhotosynthetic performances in maize were predominantly controlled by non-additive and QEIs effects, where more QEIs effects occurred in drought stress. 8 cQTLs affecting six photosynthetic-related traits could be useful for genetic improvement of these traits via QTL pyramiding, corresponding 5 QTLs clusters indicated tight linkage or pleiotropy in the inheritance of these traits, and 17 candidate genes involved in leaf morphology and development, photosynthesis, and stress reponse coincided with above corresponding cQTLs.

Genetic dissection of photosynthetic performances in maize under drought-stressed and well-watered environments

Background: Maintaining photosynthetic capacities is a critical function that allows maize ( Zea mays L.) to adapt to drought stress. The elucidation of genetic controls of photosynthetic performances, and tightly linked molecular markers under water stress are thus of great importance in marker-assisted selection (MAS) breeding. Meanwhile, little is known regarding their genetic controls under drought stress. Two F 4 populations were developed to identify quantitative trait loci (QTLs) and dissect the genetic variation underlying six photosynthetic-related traits, namely, net photosynthetic rate (Pn), stomatal conductance (Gs), intercellular CO 2 concentration (Ci), transpiration rate (Tr), ribulose 1,5-biphosphate carboxylase activity (RuBP), and water use efficiency (WUE) under drought-stressed and well-watered environments. Results: For two populations, we detected 54 QTLs under drought-stressed and well-watered environments by single-environment mapping with composite interval mapping (CIM), approximately 81.8~100 % QTLs displayed non-additive effects, and 43 of the 54 QTLs were identified under drought-stressed environment. We also dissected 54 QTLs via joint analysis of all environments with mixed-linear-model-based composite interval mapping (MCIM), 24 QTLs involved in QTL × environment interactions (QEIs), approximately 87.5 % QEIs were identified under drought-stressed environments, as well as 14 pair epistasis exhibited dominance-by-additive/dominance (DA/DD) effects under constracting environments. We further identified 8 constitutive QTLs (cQTLs) across two populations by CIM/MCIM under multiple environments. Remarkably, bin 1.07_1.10 (cQTL2), bin 6.05 (cQTL5), bin 7.02_7.04 (cQTL6), bin 8.03 (cQTL7), and bin 10.03 (cQTL8) exhibited 5 pleiotropic cQTLs that were consistent with phenotypic correlations among all photosynthetic-related traits. Additionally, 17 candidate genes were validated in above cQTLs. Conclusions: Photosynthetic performances in maize were predominantly controlled by non-additive and QEIs effects, where more QEIs effects occurred in drought stress. 8 cQTLs affecting six photosynthetic-related traits could be useful for genetic improvement of these traits via QTL pyramiding, corresponding 5 QTLs clusters indicated tight linkage or pleiotropy in the inheritance of these traits, and 17 candidate genes involved in leaf morphology and development, photosynthesis, and stress reponse coincided with above corresponding cQTLs.

Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize

Quantitative trait loci (QTLs) mapped in different genetic populations are of great significance for marker-assisted breeding. In this study, an F2:3 population were developed from the crossing of two maize inbred lines SG-5 and SG-7 and applied to QTL mapping for seven yield-related traits. The seven traits included 100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, ear weight, and grain weight per plant. Based on an ultra-high density linkage map, a total of thirty-three QTLs were detected for the seven studied traits with composite interval mapping (CIM) method, and fifty-four QTLs were indentified with genome-wide composite interval mapping (GCIM) methods. For these QTLs, Fourteen were both detected by CIM and GCIM methods. Besides, eight of the thirty QTLs detected by CIM were identical to those previously mapped using a F2 population (generating from the same cross as the mapping population in this study), and fifteen were identical to the reported QTLs in other recent studies. For the fifty-four QTLs detected by GCIM, five of them were consistent with the QTLs mapped in the F2 population of SG-5 × SG-7, and twenty one had been reported in other recent studies. The stable QTLs associated with grain weight were located on maize chromosomes 2, 5, 7, and 9. In addition, differentially expressed genes (DEGs) between SG-5 and SG-7 were obtained from the transcriptomic profiling of grain at different developmental stages and overlaid onto the stable QTLs intervals to predict candidate genes for grain weight in maize. In the physical intervals of confirmed QTLs qKW-7, qEW-9, qEW-10, qGWP-6, qGWP-8, qGWP-10, qGWP-11 and qGWP-12, there were 213 DEGs in total. Finally, eight genes were predicted as candidate genes for grain size/weight. In summary, the stable QTLs would be reliable and the candidate genes predicted would be benefit for maker assisted breeding or cloning.

Deciphering the Genetic Architecture of Plant Height in Soybean Using Two RIL Populations Sharing a Common M8206 Parent

Plant height (PH) is an important agronomic trait that is closely related to soybean yield and quality. However, it is a complex quantitative trait governed by multiple genes and is influenced by environment. Unraveling the genetic mechanism involved in PH, and developing soybean cultivars with desirable PH is an imperative goal for soybean breeding. In this regard, the present study used high-density linkage maps of two related recombinant inbred line (RIL) populations viz., MT and ZM evaluated in three different environments to detect additive and epistatic effect quantitative trait loci (QTLs) as well as their interaction with environments for PH in Chinese summer planting soybean. A total of eight and 12 QTLs were detected by combining the composite interval mapping (CIM) and mixed-model based composite interval mapping (MCIM) methods in MT and ZM populations, respectively. Among these QTLs, nine QTLs viz., QPH-2, qPH-6-2MT, QPH-6, qPH-9-1ZM, qPH-10-1ZM, qPH-13-1ZM, qPH-16-1MT, QPH-17 and QPH-19 were consistently identified in multiple environments or populations, hence were regarded as stable QTLs. Furthermore, Out of these QTLs, three QTLs viz., qPH-4-2ZM, qPH-15-1MT and QPH-17 were novel. In particular, QPH-17 could detect in both populations, which was also considered as a stable and major QTL in Chinese summer planting soybean. Moreover, eleven QTLs revealed significant additive effects in both populations, and out of them only six showed additive by environment interaction effects, and the environment-independent QTLs showed higher additive effects. Finally, six digenic epistatic QTLs pairs were identified and only four additive effect QTLs viz., qPH-6-2MT, qPH-19-1MT/QPH-19, qPH-5-1ZM and qPH-17-1ZM showed epistatic effects. These results indicate that environment and epistatic interaction effects have significant influence in determining genetic basis of PH in soybean. These results would not only increase our understanding of the genetic control of plant height in summer planting soybean but also provide support for implementing marker assisted selection (MAS) in developing cultivars with ideal plant height as well as gene cloning to elucidate the mechanisms of plant height.

QTL mapping of physiological traits at the booting stage in rice under low temperature combined with nitrogen fertilization

Further dissection of physiological molecular mechanisms is indispensable to alleviate rice yield losses resulting from cold injury. By using 105 near-isogenic lines (NILs) derived from a backcross between cv. Lijiangxintuanheigu (LTH) and cv. Towada, we detected quantitative trait loci (QTLs) for physiological traits of the rice flag leaf, based on polymorphic simple sequence repeat (SSR) markers, inclusive composite interval mapping (ICIM), mixed composite interval mapping (MCIM) approaches and phenotypic value subjected to combine with cold-water stress and three nitrogen application rates. By using ICIM, a total of 34 QTLs with additive effects (A-QTLs) were identified on chromosomes 1, 3, 4, 5, 6, 7 and 10, and the phenotypic variation (R²) explained by each QTL ranged from 8.46 to 29.14%. By using MCIM, 20 A-QTLs and 14 pairs of QTLs with epistatic × environment interaction effects (Epistatic QTLs) were detected, the contribution of environment interaction (H²AE) was 0.87 to 7.36%, while the contribution rates of E-QTL were from 0.97 to 3.58%. Fourteen A-QTLs were detected by ICIM and MCIM, which may serve as a basis for fine-mapping and candidate gene studies, and providing strategies for the development of cold-tolerant rice cultivars and nitrogen application to alleviate chilling stress.