Conditional Quantitative Trait Loci Research Articles

Mapping of dynamic quantitative trait loci for plant height in a RIL population of foxtail millet (Setaria italica L.).

Plant height (PH) is a crucial trait for strengthening lodging resistance and boosting yield in foxtail millet. To identify quantitative trait loci (QTL) and candidate genes associated with PH, we first developed a genetic map using a recombinant inbred line (RIL) population derived from a cross between Aininghuang and Jingu 21. Then, PH phenotyping data and four variations of best linear unbiased prediction (BLUP) were collected from nine environments and three development stages. Next, QTL mapping was conducted using both unconditional and conditional QTL methods. Subsequently, candidate genes were predicted via transcriptome analysis of parental samples at three developmental stages. The results revealed that the genetic map, based on re-sequencing, consisted of 4,360 bin markers spanning 1,016.06 cM with an average genetic distance of 0.23 cM. A total of 19 unconditional QTL, accounting for 5.23%-35.36% of the phenotypic variation explained (PVE), which included 7 major and 4 stable QTL, were identified. Meanwhile, 13 conditional QTL, explaining 5.88%-40.35% of PVE, including 5 major and 3 stable QTL, were discovered. Furthermore, four consistent and stable QTL were identified. Finally, eight candidate genes were predicted through RNA-seq and weighted gene co-expression network analysis (WGCNA). Those findings provide a crucial foundation for understanding the genetic mechanisms underlying PH development and facilitate molecular marker-assisted breeding of ideal plant types in foxtail millet.

Unraveling the genetic basis of grain number-related traits in a wheat-Agropyron cristatum introgressed line through high-resolution linkage mapping

BackgroundGrain number per spike (GNS) is a pivotal determinant of grain yield in wheat. Pubing 3228 (PB3228), a wheat-Agropyron cristatum germplasm, exhibits a notably higher GNS.ResultsIn this study, we developed a recombinant inbred line (RIL) population derived from PB3228/Gao8901 (PG-RIL) and constructed a high-density genetic map comprising 101,136 loci, spanning 4357.3 cM using the Wheat 660 K SNP array. The genetic map demonstrated high collinearity with the wheat assembly IWGSC RefSeq v1.0. Traits related to grain number and spikelet number per spike were evaluated in seven environments for quantitative trait locus (QTL) analysis. Five environmentally stable QTLs were detected in at least three environments. Among these, two major QTLs, QGns-4A.2 and QGns-1A.1, associated with GNS, exhibited positive alleles contributed by PB3228. Further, the conditional QTL analysis revealed a predominant contribution of PB3228 to the GNS QTLs, with both grain number per spikelet (GNSL) and spikelet number per spike (SNS) contributing to the overall GNS trait. Four kompetitive allele-specific PCR (KASP) markers that linked to QGns-4A.2 and QGns-1A.1 were developed and found to be effective in verifying the QTL effect within a diversity panel. Compared to previous studies, QGns-4A.2 exhibited stability across different trials, while QGns-1A.1 represents a novel QTL. The results from unconditional and conditional QTL analyses are valuable for dissecting the genetic contribution of the component traits to GNS at the individual QTL level and for understanding the genetic basis of the superior grain number character in PB3228. The KASP markers can be utilized in marker-assisted selection for enhancing GNS.ConclusionsFive environmentally stable QTLs related to grain number and spikelet number per spike were identified. PB3228 contributed to the majority of the QTLs associated with GNS.

Dynamics and genetic regulation of macronutrient concentrations during grain development in maize

Nitrogen (N), phosphorus (P), and potassium (K) are essential macronutrients that are crucial not only for maize growth and development, but also for crop yield and quality. The genetic basis of macronutrient dynamics and accumulation during grain filling in maize remains largely unknown. In this study, we evaluated grain N, P, and K concentrations in 206 recombinant inbred lines generated from a cross of DH1M and T877 at six time points after pollination. We then calculated conditional phenotypic values at different time intervals to explore the dynamic characteristics of the N, P, and K concentrations. Abundant phenotypic variations were observed in the concentrations and net changes of these nutrients. Unconditional quantitative trait locus (QTL) mapping revealed 41 non-redundant QTLs, including 17, 16, and 14 for the N, P, and K concentrations, respectively. Conditional QTL mapping uncovered 39 non-redundant QTLs related to net changes in the N, P, and K concentrations. By combining QTL, gene expression, co-expression analysis, and comparative genomic data, we identified 44, 36, and 44 candidate genes for the N, P, and K concentrations, respectively, including GRMZM2G371058 encoding a Dof-type zinc finger DNA-binding family protein, which was associated with the N concentration, and GRMZM2G113967 encoding a CBL-interacting protein kinase, which was related to the K concentration. The results deepen our understanding of the genetic factors controlling N, P, and K accumulation during maize grain development and provide valuable genes for the genetic improvement of nutrient concentrations in maize.

GmTNRP1, associated with rhizobial type‐III effector NopT, regulates nitrogenase activity in the nodules of soybean (Glycine max)

AbstractSoybean is a rich source of protein and oil for humans. It forms a symbiotic association with rhizobia to fix atmospheric nitrogen for its growth and development. Rhizobial type‐III effectors act as important signaling molecules in symbiosis. However, they are poorly studied. In this study, mutation of TtsI in Sinorhizobium fredii HH103 reduced nodule numbers and dry weights in SN14 (cultivated soybean) and ZYD00006 (wild soybean). Moreover, it reduced nitrogenase activity in SN14 but not in ZYD00006. Based on the differences in nitrogenase activity between SN14 and ZYD00006 inoculated with HH103 and the TtsI mutant, two conditional quantitative trait loci associated with nitrogenase activity were identified in chromosome segment substitution lines derived from SN14 and ZYD00006. Using transcriptome analysis, single‐nucleotide polymorphism (SNP) analysis, and qRT‐PCR, Glyma.13G150000 (GmTNRP1) was identified as a candidate gene in soybean that was associated with nitrogenase activity, along with NopT (a type‐III effector). In addition, GmTNRP1 (an LRR‐RK family protein located on the cell membrane) was observed to be induced by NopT to negatively regulate nitrogenase activity. In transgenic hairy roots with GmTNRP1 silencing, no significant difference was observed in terms of nitrogenase activity between nodules inoculated with the NopT mutant and those inoculated with HH103 (wild type). However, the nitrogenase activity in the nodules decreased after overexpression of GmTNRP1. SNP located in the GmTNRP1 promoter affected its expression in response to NopT. Haplotype analysis revealed that SN14 belonged to Hap1 and ZYD00006 belonged to Hap2, which explained the different results in the two varieties. To the best of our knowledge, this is the first study to identify a soybean gene associated with a rhizobial type‐III effector in the nodules. GmTNRP1 can negatively regulate nitrogenase activity in the nodules. This study enhanced the understanding of the mechanism underlying symbiotic association between soybean and rhizobia.

Identification and Pyramiding Major QTL Loci for Simultaneously Enhancing Aflatoxin Resistance and Yield Components in Peanut.

Peanut is susceptible to Aspergillus flavus infection, and the consequent aflatoxin contamination has been recognized as an important risk factor affecting food safety and industry development. Planting peanut varieties with resistance to aflatoxin contamination is regarded as an ideal approach to decrease the risk in food safety, but most of the available resistant varieties have not been extensively used in production because of their low yield potential mostly due to possessing small pods and seeds. Hence, it is highly necessary to integrate resistance to aflatoxin and large seed weight. In this study, an RIL population derived from a cross between Zhonghua 16 with high yield and J 11 with resistance to infection of A. flavus and aflatoxin production, was used to identify quantitative trait locus (QTL) for aflatoxin production (AP) resistance and hundred-seed weight (HSW). From combined analysis using a high-density genetic linkage map constructed, 11 QTLs for AP resistance with 4.61-11.42% phenotypic variation explanation (PVE) and six QTLs for HSW with 3.20-28.48% PVE were identified, including three major QTLs for AP resistance (qAFTA05.1, qAFTB05.2 and qAFTB06.3) and three for HSW (qHSWA05, qHSWA08 and qHSWB06). In addition, qAFTA05.1, qAFTB06.3, qHSWA05, qHSWA08 and qHSWB06 were detected in multiple environments. The aflatoxin contents under artificial inoculation were decreased by 34.77-47.67% in those segregated lines harboring qAFTA05.1, qAFTB05.2 and qAFTB06.3, while the HSWs were increased by 47.56-49.46 g in other lines harboring qHSWA05, qHSWA08 and qHSWB06. Conditional QTL mapping indicated that HSW and percent seed infection index (PSII) had no significant influence on aflatoxin content. Interestingly, the QT 1059 simultaneously harboring alleles of aflatoxin content including qAFTA05.1 and qAFTB05.2, alleles of PSII including qPSIIB03.1, qPSIIB03.2, and qPSIIB10 and alleles of HSW including qHSWA05, qHSWB06, qHSWA08 had better resistance to A. flavus infection and to toxin production and higher yield potential compared with the two parents of the RIL. The above identified major loci for AP resistance and HWS would be helpful for marker-assisted selection in peanut breeding.

Identification and validation of plant height, spike length and spike compactness loci in common wheat (Triticum aestivum L.)

BackgroundPlant height (PH), spike length (SL) and spike compactness (SCN) are important agronomic traits in wheat due to their strong correlations with lodging and yield. Thus, dissection of their genetic basis is essential for the improvement of plant architecture and yield potential in wheat breeding. The objective of this study was to map quantitative trait loci (QTL) for PH, SL and SCN in a recombinant inbred line (RIL) population derived from the cross ‘PuBing3228 × Gao8901’ (PG-RIL) and to evaluate the potential values of these QTL to improve yield.ResultsIn the current study, Five, six and ten stable QTL for PH, SL, and SCN, respectively, were identified in at least two individual environments. Five major QTL QPh.cas-5A.3, QPh.cas-6A, QSl.cas-6B.2, QScn.cas-2B.2 and QScn.cas-6B explained 5.58–25.68% of the phenotypic variation. Notably, two, three and three novel stable QTL for PH, SL and SCN were identified in this study, which could provide further insights into the genetic factors that shape PH and spike morphology in wheat. Conditional QTL analysis revealed that QTL for SCN were mainly affected by SL. Moreover, a Kompetitive Allele Specific PCR (KASP) marker tightly linked to stable major QTL QPh.cas-5A.3 was developed and verified using the PG-RIL population and a natural population.ConclusionsTwenty-one stable QTL related to PH, SL, and SCN were identified. These stable QTL and the user-friendly marker KASP8750 will facilitate future studies involving positional cloning and marker-assisted selection in breeding.

Conditional QTL mapping for seed germination and seedling traits under salt stress and candidate gene prediction in wheat

Breeding new wheat varieties with salt resistance is one of the best ways to solve a constraint on the sustainability and expansion of wheat cultivation. Therefore, understanding the molecular components or genes related to salt tolerance must contribute to the cultivation of salt-tolerant varieties. The present study used a recombinant inbred line (RIL) population to genetically dissect the effects of different salt stress concentrations on wheat seed germination and seedling traits using two quantitative trait locus (QTL) mapping methods. A total of 31 unconditional and 11 conditional QTLs for salt tolerance were identified on 11 chromosomes explaining phenotypic variation (PVE) ranging from 2.01 to 65.76%. Of these, 15 major QTLs were found accounting for more than 10% PVE. QTL clusters were detected on chromosomes 2A and 3B in the marker intervals ‘wPt-8328 and wPt-2087’ and ‘wPt-666008 and wPt-3620’, respectively, involving more than one salt tolerance trait. QRdw3B and QSfw3B.2 were most consistent in two or more salt stress treatments. 16 candidate genes associated with salt tolerance were predicted in wheat. These results could be useful to improve salt tolerance by marker-assisted selection (MAS) and shed new light on understanding the genetic basis of salt tolerance in wheat.

QTL analysis of the developmental changes in cell wall components and forage digestibility in maize (Zea mays L.)

Cell wall architecture plays a key role in stalk strength and forage digestibility. Lignin, cellulose, and hemicellulose are the three main components of plant cell walls, and they can impact stalk quality by affecting the structure and strength of the cell wall. To explore cell wall development during secondary cell wall lignification in maize stalks, conventional and conditional genetic mapping were used to identify the dynamic quantitative trait loci (QTLs) of the cell wall components and digestibility traits during five growth stages after silking. Acid detergent lignin (ADL), cellulose (CEL), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro dry matter digestibility (IVDMD) were evaluated in a maize recombinant inbred line (RIL) population. ADL, CEL, ADF, and NDF gradually increased from 10 to 40 days after silking (DAS), and then they decreased. IVDMD initially decreased until 40 DAS, and then it increased slightly. Seventy-two QTLs were identified for the five traits, and each accounted for 3.48–24.04% of the phenotypic variation. Six QTL hotspots were found, and they were localized in the 1.08, 2.04, 2.07, 7.03, 8.05, and 9.03 bins of the maize genome. Within the interval of the pleiotropic QTL identified in bin 1.08 of the maize genome, six genes associated with cell wall component biosynthesis were identified as potential candidate genes for stalk strength as well as cell wall-related traits. In addition, 26 conditional QTLs were detected in the five stages for all of the investigated traits. Twenty-two of the 26 conditional QTLs were found at 30 DAS conditioned using the values of 20 DAS, and at 50 DAS conditioned using the values of 40 DAS. These results indicated that cell wall-related traits are regulated by many genes, which are specifically expressed at different stages after silking. Simultaneous improvements in both forage digestibility and lodging resistance could be achieved by pyramiding multiple beneficial QTL alleles identified in this study.

Dynamic Quantitative Trait Loci Mapping for Plant Height in Recombinant Inbred Line Population of Upland Cotton

Plant height (PH) is a key plant architecture trait for improving the biological productivity of cotton. Ideal PH of cotton is conducive to lodging resistance and mechanized harvesting. To detect quantitative trait loci (QTL) and candidate genes of PH in cotton, a genetic map was constructed with a recombinant inbred line (RIL) population of upland cotton. PH phenotype data under nine environments and three best linear unbiased predictions (BLUPs) were used for QTL analyses. Based on restriction-site-associated DNA sequence (RAD-seq), the genetic map contained 5,850 single-nucleotide polymorphism (SNP) markers, covering 2,747.12 cM with an average genetic distance of 0.47 cM. Thirty-seven unconditional QTL explaining 1.03–12.50% of phenotypic variance, including four major QTL and seven stable QTL, were identified. Twenty-eight conditional QTL explaining 3.27–28.87% of phenotypic variance, including 1 major QTL, were identified. Importantly, five QTL, including 4 stable QTL, were both unconditional and conditional QTL. Among the 60 PH QTL (including 39 newly identified), none of them were involved in the whole period of PH growth, indicating that QTL related to cotton PH development have dynamic expression characteristics. Based on the functional annotation of Arabidopsis homologous genes and transcriptome data of upland cotton TM-1, 14 candidate genes were predicted within 10 QTL. Our research provides valuable information for understanding the genetic mechanism of PH development, which also increases the economic production of cotton.

Four-Year and Five-Developing-Stage Dynamic QTL Mapping for Tiller Number in the Hybrid Population of Agropyron Gaertn.

Tiller number (TN) is an important agronomic trait affecting gramineous crop yield. To understand the static and dynamic information of quantitative trait locus (QTLs) controlling TN of Agropyron Gaertn., both the unconditional and conditional quantitative trait loci (QTL) mapping of TN were conducted using a cross-pollinated (CP) hybrid population with a total of 113 plant lines from the cross between Agropyron cristatum (L.) Gaertn. Z1842 and Allium mongolicum Keng Z2098, based on the phenotypic data of TN at five developmental stages [i.e., recovering stage (RS), jointing stage (JS), heading stage (HS), flowering stage (FS), and maturity stage (MS)] in 4 years (i.e., 2017, 2018, 2020, and 2021) and the genetic map constructed of 1,023 single-nucleotide polymorphism (SNP) markers. Thirty-seven QTLs controlling TN were detected using two analysis methods in 4 years, which were distributed in six linkage groups. Each QTL explained 2.96–31.11% of the phenotypic variation, with a logarithum of odds (LOD) value of 2.51–13.95. Nine of these loci detected both unconditional and conditional QTLs. Twelve unconditional major QTLs and sixteen conditional major QTLs were detected. Three relatively major stable conditional QTLs, namely, cQTN1-3, cQTN1-5, and cQTN4-1, were expressed in 2020 and 2021. Meantime, two pairs of major QTLs cQTN1-5 and qTN1-4 and also cQTN2-4 and qTN2-3 were located at the same interval but in different years. Except for qTN2-2 and qTN3-5/cQTN3-5, other thirty-four QTLs were first detected in this study. This study provides a better interpretation of genetic factors that selectively control tiller at different developmental stages and a reference for molecular marker-assisted selection in the related plant improvement.

Conditional Mapping Identified Quantitative Trait Loci for Grain Protein Concentration Expressing Independently of Grain Yield in Canadian Durum Wheat.

Grain protein concentration (GPC) is an important trait in durum cultivar development as a major determinant of the nutritional value of grain and end-use product quality. However, it is challenging to simultaneously select both GPC and grain yield (GY) due to the negative correlation between them. To characterize quantitative trait loci (QTL) for GPC and understand the genetic relationship between GPC and GY in Canadian durum wheat, we performed both traditional and conditional QTL mapping using a doubled haploid (DH) population of 162 lines derived from Pelissier × Strongfield. The population was grown in the field over 5 years and GPC was measured. QTL contributing to GPC were detected on chromosome 1B, 2B, 3A, 5B, 7A, and 7B using traditional mapping. One major QTL on 3A (QGpc.spa-3A.3) was consistently detected over 3 years accounting for 9.4–18.1% of the phenotypic variance, with the favorable allele derived from Pelissier. Another major QTL on 7A (QGpc.spa-7A) detected in 3 years explained 6.9–14.8% of the phenotypic variance, with the beneficial allele derived from Strongfield. Comparison of the QTL described here with the results previously reported led to the identification of one novel major QTL on 3A (QGpc.spa-3A.3) and five novel minor QTL on 1B, 2B and 3A. Four QTL were common between traditional and conditional mapping, with QGpc.spa-3A.3 and QGpc.spa-7A detected in multiple environments. The QTL identified by conditional mapping were independent or partially independent of GY, making them of great importance for development of high GPC and high yielding durum.

Integrating unconditional and conditional QTLs to dissect the genetic basis of stem mechanical strength in Brassica napus L

Stem mechanical strength (SMS) plays an important role in resisting stem dislodging. However, the genetic regulatory mechanisms underlying SMS in rapeseed remain unclear. In this study, a recombinant inbred line population containing 189 lines was used to investigate four SMS-related traits, namely stem breaking force (SBF), stem diameter (SD), stem weight (SW) and stem breaking strength (SBS). Accordingly, four conditional traits were also generated, namely SBF|SD, SBF|SW, SW|SD and SBS|SW. Quantitative trait locus (QTL) mapping for four unconditional SMS-related traits detected seven major QTLs, four of which were novel loci, with phenotypic contributions ranging from 10.41 to 27.22%. QTL mapping of conditional traits detected five major QTLs (including four novel loci), which explained 11.57 to 38.73% of the phenotypic variation. Comparative analyses between unconditional and conditional QTLs revealed that all 63 QTLs potentially govern biological processes (BPs) or unknown traits (UTs), which then influence SMS-related traits via 12 pathways. SBF was regulated by 13 unconditional QTLs via the QTL-BP-SD-SBF, QTL-BP-UTSD-SW-SBF, QTL-BP-SD-SW-SBF, and QTL-BP-UTSD&SW-SBF pathways, and by six conditional QTLs via the QTL-BP-SBF|SD-SBF and QTL-BP-SBF|SW-SBF pathways. SD was regulated by 18 unconditional QTLs via the QTL-BP-SD pathway, and SW by three regulatory pathways including QTL-BP-SD-SW, QTL-BP-UTSD-SW and QTL-BP-SW|SD-SW pathways. Finally, SBF potentially influences SMS via the SBF-SMS and SBF-SBS-SMS pathways. There were two additional regulatory pathways for SBS, namely QTL-BP-UTSW-SBS and QTL-BP-SBS|SW-SBS. In addition, 12 promising candidate genes were identified through multiple methods. These results contribute to our knowledge about the genetic regulatory mechanisms underlying SMS in Brassica napus.

Fine mapping QTL and mining genes for protein content in soybean by the combination of linkage and association analysis.

Soybean is one main source of dietary protein; therefore, improving protein content is an important objective in breeding programs. There is a significant negative correlation between protein and oil content, which influenced mapping quantitative trait locus (QTL) and quantitative trait nucleotides for these two traits. In this study, a linkage map was created with 2232 single-nucleotide polymorphism markers for the four-way recombinant inbred line (FW-RIL) population derived from the cross (Kenfeng 14 × Kenfeng 15) × (Heinong 48 × Kenfeng 19), and then conditional and unconditional QTL analyses were carried out by inclusive complete interval mapping based on the phenotypic data of protein and oil content collected in 10 different environments. As shown in the results of linkage analysis, a total of 85 QTL have been detected. We have performed association analysis using 109,676 markers after quality filtering for FW-RIL, and the results have shown that a total of 60 QTNs were detected. We have performed association analysis using 63,306 markers after quality filtering for resource population, and the results have shown that a total of 123 QTNs were detected. We have combined linkage and association analysis, and there are six QTNs verified by FW-RIL and resource population. We have performed pathway analysis on the genes in these six QTN attenuation regions, and the result shows that a total of four candidate genes are related to the synthesis or metabolism of soybean protein. These findings will facilitate marker-assisted selection and molecular breeding of soybean.

Identification and validation of quantitative trait loci for kernel traits in common wheat (Triticum aestivum L.)

BackgroundKernel weight and morphology are important traits affecting cereal yields and quality. Dissecting the genetic basis of thousand kernel weight (TKW) and its related traits is an effective method to improve wheat yield.ResultsIn this study, we performed quantitative trait loci (QTL) analysis using recombinant inbred lines derived from the cross ‘PuBing3228 × Gao8901’ (PG-RIL) to dissect the genetic basis of kernel traits. A total of 17 stable QTLs related to kernel traits were identified, notably, two stable QTLs QTkw.cas-1A.2 and QTkw.cas-4A explained the largest portion of the phenotypic variance for TKW and kernel length (KL), and the other two stable QTLs QTkw.cas-6A.1 and QTkw.cas-7D.2 contributed more effects on kernel width (KW). Conditional QTL analysis revealed that the stable QTLs for TKW were mainly affected by KW. The QTLs QTkw.cas-7D.2 and QKw.cas-7D.1 associated with TKW and KW were delimited to the physical interval of approximately 3.82 Mb harboring 47 candidate genes. Among them, the candidate gene TaFT-D1 had a 1 bp insertions/deletion (InDel) within the third exon, which might be the reason for diversity in TKW and KW between the two parents. A Kompetitive Allele-Specific PCR (KASP) marker of TaFT-D1 allele was developed and verified by PG-RIL and a natural population consisted of 141 cultivar/lines. It was found that the favorable TaFT-D1 (G)-allele has been positively selected during Chinese wheat breeding. Thus, these results can be used for further positional cloning and marker-assisted selection in wheat breeding programs.ConclusionsSeventeen stable QTLs related to kernel traits were identified. The stable QTLs for thousand kernel weight were mainly affected by kernel width. TaFT-D1 could be the candidate gene for QTLs QTkw.cas-7D.2 and QKw.cas-7D.1.

A novel, validated, and plant height-independent QTL for spike extension length is associated with yield-related traits in wheat.

A novel, stably expressed, and plant height-independent QTL for spike extension length on 5AS was identified and validated in different populations using a newly developed and tightly linked KASP marker. As an important component of plant height (PH), spike extension length (SEL) plays a significant role in formation of an ideotype in wheat. Despite the fact that numerous loci for SEL in wheat have been reported, our knowledge on PH-independent loci remains to be limited. In this study, two recombinant inbred line (RIL) populations genotyped using the Wheat55K SNP were used to detect quantitative trait loci (QTL) controlling SEL across six environments. A total of 30 QTL for SEL were detected in these two RIL populations, and four of them, i.e., QSEL.sicau-2CN-4D, QSEL.sicau-2SY-4B.2, QSEL.sicau-2SY-4D.1, and QSEL.sicau-2CN-5A, were stably expressed. Genetic and conditional QTL analysis showed that the first three were significantly associated with PH, while the last one, QSEL.sicau-2CN-5A, is independent of PH. Comparison of genetic and physical maps suggested that only QSEL.sicau-2CN-5A located on chromosome arm 5AS is likely a novel QTL. A Kompetitive Allele-Specific PCR (KASP) marker, KASP-AX-110413733, tightly linked to this novel QTL was developed to successfully confirm its effect in three different genetic populations. Further, in the interval where QSEL.sicau-2CN-5A was located on 'Chinese Spring' wheat reference genome, three promising genes mainly expressed in wheat stem were predicated and they all encode the cytochrome P450 that was demonstrated to be closely associated with SEL elongation in rice. In addition, significant correlations between SEL and PH, spikelet number per spike, and thousand-grain weight were also detected. Altogether, our results broaden our understanding on genetic basis of SEL and will be useful for marker-based selection of lines with different SELs and fine mapping the novel and PH-independent QTL QSEL.sicau-2CN-5A.

The contribution of photosynthesis traits and plant height components to plant height in wheat at the individual quantitative trait locus level

Plant height is an important agronomic trait for morphogenesis and grain yield formation in wheat. In this study, we performed both normal and multivariate conditional quantitative trait locus (QTL) analyses for plant height with spike length, internode number, length of the first internode to the sixth internode from the top during harvest, and photosynthesis traits at the seedling stage and heading stage based on a recombinant inbred line population. A total of 49 normal QTLs were detected, as well as 312 conditional QTLs. The genetic region Xbcd1970-Xbcd262 on chromosome 2D harbored the most QTLs, with 6 normal QTLs and 39 conditional QTLs. A comparison between the normal and conditional QTL mapping analyses suggested that the length of the third internode, fourth internode, and fifth internode from the top showed a high genetic association with plant height, whereas all photosynthesis traits showed weaker associations. This comparative analysis could serve as a platform for dissecting the genetic relation between objective traits and other phenotypic traits before manipulation of genes collocated with QTL clusters.

QTL Mapping and Data Mining to Identify Genes Associated With the Sinorhizobium fredii HH103 T3SS Effector NopD in Soybean.

In some legume–rhizobium symbioses, host specificity is influenced by rhizobial type III effectors-nodulation outer proteins (Nops). However, the genes encoding host proteins that interact with Nops remain unknown. In this study, we aimed to identify candidate soybean genes associated with NopD, one of the type III effectors of Sinorhizobium fredii HH103. The results showed that the expression pattern of NopD was analyzed in rhizobia induced by genistein. We also found NopD can be induced by TtsI, and NopD as a toxic effector can induce tobacco leaf death. In 10 soybean germplasms, NopD played a positively effect on nodule number (NN) and nodule dry weight (NDW) in nine germplasms, but not in Kenjian28. Significant phenotype of NN and NDW were identified between Dongnong594 and Charleston, Suinong14 and ZYD00006, respectively. To map the quantitative trait locus (QTL) associated with NopD, a recombinant inbred line (RIL) population derived from the cross between Dongnong594 and Charleston, and chromosome segment substitution lines (CSSLs) derived from Suinong14 and ZYD00006 were used. Two overlapping conditional QTL associated with NopD on chromosome 19 were identified. Two candidate genes were identified in the confident region of QTL, we found that NopD could influence the expression of Glyma.19g068600 (FBD/LRR) and expression of Glyma.19g069200 (PP2C) after HH103 infection. Haplotype analysis showed that different types of Glyma.19g069200 haplotypes could cause significant nodule phenotypic differences, but Glyma.19g068600 (FBD/LRR) was not. These results suggest that NopD promotes S. fredii HH103 infection via directly or indirectly regulating Glyma.19g068600 and Glyma.19g069200 expression during the establishment of symbiosis between rhizobia and soybean plants.

Dynamic QTL mapping for plant height in the hybrid population of Agropyron Gaertn.

AbstractPlant height (PH) plays a very important role for plant breeding and also serves as a model trait to dynamic development study. The dynamic quantitative trait locus (QTL) analysis for PH of Agropyron Gaertn. was carried out in a cross‐pollination (CP) hybrid population of A. Gaertn. based on the phenotypic data of PH at different developmental stages in four year‐sites and the constructed single‐nucleotide polymorphism (SNP) genetic map. The results showed that 69 QTL and nine major QTL were detected by unconditional QTL mapping. A total of 107 QTL and 13 major QTL were found using conditional QTL mapping. Forty‐seven QTL were detected by two methods. Two unconditional QTL, Qph2‐5 and Qph3‐4, were expressed as major and stable QTL for PH. Four major stable conditional QTL for PH, cQph4‐1, cQph4‐8, cQph6‐2 and cQph6‐7, were detected. Two conditional PH QTL, cQph3‐5 and cQPh3‐7, were identified in four environments over multiple stages. The gene/QTL controlling PH was expressed in a certain spatiotemporal manner. These results could also provide a reference for genetic and breeding research of related plant.

Contribution of photosynthetic- and yield-related traits towards grain yield in wheat at the individual quantitative trait locus level

Grain yield improvement in wheat (Triticum aestivum L) requires understanding of the genetic control of grain yield components and other related traits. In this study, a total of 82 and 314 quantitative trait loci (QTLs) were associated with grain yield and grain yield components after harvest and photosynthetic traits at the seedling, heading and grain filling stages from a recombinant inbred line population by normal and multivariate QTLs analysis, respectively. Sixty-one QTL clusters explained from 4.42% to 27.72% of phenotypic variances for 2–76 traits. A comparison between the normal and conditional QTL mapping found that grain yield showed significant genetic correlation with grain yield components and photosynthetic traits on five QTLs. In the genetic region between Xbarc99 and Xbarc169 on chromosome 1D, grain yield was entirely contributed by thousand kernel weight and harvest index. At the same region, harvest index was contributed by transpiration rate at seedling stage, intercellular CO2 concentration, stomatal conductance, net photosynthetic rate and transpiration rate at heading stage; and intercellular CO2 concentration, net photosynthetic rate and transpiration rate at grain filling stage, whereas thousand kernel weight was suppressed by a normal QTL for chlorophyll content at grain filling stage in this region. The direct comparison analyses between grain yield with photosynthetic traits showed similar genetic relationships in this region. Manipulation of genes found within these QTL clusters might improve important agronomic traits and grain yield.

Mapping QTLs for protein and oil content in soybean by removing the influence of related traits in a four-way recombinant inbred line population

AbstractProtein content (PC) and oil content (OC) are important breeding traits of soybean [Glycine max(L.) Merr.]. Quantitative trait locus (QTL) mapping for PC and OC is important for molecular breeding in soybean; however, the negative correlation between PC and OC influences the accuracy of QTL mapping. In the current study, a four-way recombinant inbred lines (FW-RILs) population comprising 160 lines derived from the cross (Kenfeng14 × Kenfeng15) × (Heinong48 × Kenfeng19) was planted in eight different environments and PC and OC measured. Conditional and unconditional QTL analyses were carried out by interval mapping (IM) and inclusive complete IM based on linkage maps of 275 simple sequences repeat markers in a FW-RILs population. This analysis revealed 59 unconditional QTLs and 52 conditional QTLs among the FW-RILs. An analysis of additive effects indicated that the effects of 13 protein QTLs were not related to OC, whereas OC affected the expression of 13 and eight QTLs either partially or completely, respectively. Eight QTLs affecting OC were not influenced by PC, whereas six and 26 QTLs were partially and fully affected by PC, respectively. Among the QTLs detected in the current study, two protein QTLs and five oil QTLs had not been previously reported. These findings will facilitate marker-assisted selection and molecular breeding of soybean.