Unconditional 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.

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.

Mapping developmental QTL for plant height in soybean [Glycine max (L.) Merr.] using a four-way recombinant inbred line population.

Plant height (PH) is an important trait in soybean, as taller plants may have higher yields but may also be at risk for lodging. Many genes act jointly to influence PH throughout development. To map the quantitative trait loci (QTL) controlling PH, we used the unconditional variable method (UVM) and conditional variable method (CVM) to analyze PH data for a four-way recombinant inbred line (FW-RIL) population derived from the cross of (Kenfeng14 × Kenfeng15) × (Heinong48 × Kenfeng19). We identified 7, 8, 16, 19, 15, 27, 17, 27, 22, and 24 QTL associated with PH at 10 developmental stages, respectively. These QTL mapped to 95 genomic regions. Among these QTL, 9 were detected using UVM and CVM, and 89 and 66 were only detected by UVM or CVM, respectively. In total, 36 QTL controlling PH were detected at multiple developmental stages and these made unequal contributions to genetic variation throughout development. Among 19 novel regions discovered in our study, 7 could explain over 10% of the phenotypic variation and contained only one single QTL. The unconditional and conditional QTL detected here could be used in molecular design breeding across the whole developmental procedure.

Identification of a gene encoding a sugar transporter as a potential target of a novel LysR‐type transcriptional regulator rluD from rhizobium required for symbiotic nodulation in soybean (Glycine max Merr.)

AbstractRhizobia can fine‐tune activating key genes involved in background regulation network of nodulation in effective symbiosis processes. Detecting the mechanism of the soybean‐rhizobium interaction plays a pivotal role in aiding the agricultural environment. Here, we report that LysR family transcriptional factor rluD is a novel putative gene that supports symbiosis of broad host‐range strain Sinorhizobium fredii HH103 (S. fredii HH103) during soybean nodulation. The nodule numbers (NN) and nodule dry weight (NDW) of ‘Charleston’ and ‘Dongnong594’ (parents of recombinant inbred line [RIL] population) were reduced after inoculation with S. fredii HH103ΩrluD compared to HH103. To further elucidate the mechanism of rluD in the establishment of symbiosis, unconditional and conditional quantitative trait loci (QTLs) underlying NN and NDW were detected via a high‐generation RILs inoculated with HH103ΩrluD and wild‐type HH103. A major locus of the overlapping region between unconditional and conditional QTLs on Chromosome 6 was identified. The candidate gene Glyma.06g166800 encodes a novel sugar transporter, which might interact with rluD based on qRT‐PCR analysis. This result is valuable for elucidating the mechanism of symbiosis.

A high-density genetic map and QTL mapping on growth and latex yield-related traits in Hevea brasiliensis Müll.Arg

Hevea brasiliensis Müll.Arg., the rubber tree, is an important industrial crop, which is the major source of nature rubber. Recently, Simple Sequence Repeat (SSR)- and Single Nucleotide Polymorphism (SNP)-based genetic maps have been reported, however further trait-related markers are needed for efficient molecular breeding in rubber tree. In this study, a total of 206 F1 progeny derived from a cross of CATAS 8–79 × MT/C/11 9/67 were sequenced and genotyped by specific–locus amplified fragment sequencing (SLAF-seq) technology. Based on 268,592 SNPs obtained from SLAF-seq, a high-density genetic map with 4,543 SNPs was constructed, covering 2,670.27 cM of the whole rubber tree genome. This map has an average marker distance of 0.59 cM.Based on the high-density genetic map, dynamic quantitative trait loci (QTL) analysis of latex yield and stem growth was carried out by conditional and unconditional QTL mapping. To our knowledge, it is the first time to perform conditional QTL mapping of rubber tree for exploring QTLs expressed selectively at different growth stages. In total, eleven QTLs for stem growth and 12 QTLs for latex yield, distributing in 15 linkage groups (LGs), were detected, explaining 3.1%–18.4% of the phenotypic variance. Two QTLs, qSG-8-2 for stem growth and qLD-8-3 for latex yield, were mapped within the same marker interval of LG 8 with a genetic distance of 3.0 cM. Herein the high-density genetic map and trait-related QTLs in rubber tree were reported, which will facilitate the genetic research of rubber tree and enhance rubber tree molecular breeding.

Conditional and unconditional QTL analyses of seed hardness in vegetable soybean (Glycine max L. Merr.)

The seed hardness of vegetable soybean (VSB) is generally regarded as an important quality characteristic that contributes largely to its processing and mouthfeel. However, the genetic control of it is still a gray area. To identify the dynamic quantitative trait loci (QTLs) and detect the genetic relationship between seed hardness and growth period traits at the QTL level, conditional and unconditional QTL analyses were performed using a population of 184 recombinant inbred lines in 2015, 2016 and 2017. For seed hardness at stage 1 to stage 4 (W1–W4), 12 and 8 QTLs were identified by unconditional and conditional mapping, respectively. The comparative analyses between unconditional and conditional mapping uncovered three types of dynamic QTLs underlying seed hardness in the development of VSB. These results demonstrated that genes underlying seed hardness were actively expressed in the interval between stage 2 and stage 3, which was in parallel with the mass filling period in the development of soybean seeds. For seed hardness at R6 stage (W), 11 unconditional QTLs were detected. When W was conditioned on the number of days after sowing to R6 stage (DAS), the number of days after flowering to R6 stage (DAF) and flower time (FT), 11, 2, and 2 conditional QTLs were identified, respectively. Four types of unique QTLs distinguished the contributions of DAS, DAF and FT to the genetic effects of W. Of these, uqW.D1b-1 and uqW.A1-2 control W with independent of DAS or FT. Hence, they would be valuable for the selection softer seed lines, without the simultaneous depression of growth period traits even though there exist significant positive correlation between them. The loci identified by conditional and unconditional QTL analyses may be useful for the improvements of decreased seed hardness with the best balance of growth period through marker assisted selection (MAS).

QTL Detection for Kernel Size and Weight in Bread Wheat (Triticum aestivum L.) Using a High-Density SNP and SSR-Based Linkage Map.

High-density genetic linkage maps are essential for precise mapping quantitative trait loci (QTL) in wheat (Triticum aestivum L.). In this study, a high-density genetic linkage map consisted of 6312 SNP and SSR markers was developed to identify QTL controlling kernel size and weight, based on a recombinant inbred line (RIL) population derived from the cross of Shixin828 and Kenong2007. Seventy-eight putative QTL for kernel length (KL), kernel width (KW), kernel diameter ratio (KDR), and thousand kernel weight (TKW) were detected over eight environments by inclusive composite interval mapping (ICIM). Of these, six stable QTL were identified in more than four environments, including two for KL (qKL-2D and qKL-6B.2), one for KW (qKW-2D.1), one for KDR (qKDR-2D.1) and two for TKW (qTKW-5A and qTKW-5B.2). Unconditional and multivariable conditional QTL mapping for TKW with respect to TKW component (TKWC) revealed that kernel dimensions played an important role in regulating the kernel weight. Seven QTL-rich genetic regions including seventeen QTL were found on chromosomes 1A (2), 2D, 3A, 4B and 5B (2) exhibiting pleiotropic effects. In particular, clusters on chromosomes 2D and 5B possessing significant QTL for kernel-related traits were highlighted. Markers tightly linked to these QTL or clusters will eventually facilitate further studies for fine mapping, candidate gene discovery and marker-assisted selection (MAS) in wheat breeding.

Mining for genes encoding proteins associated with NopL of Sinorhizobium fredii HH103 using quantitative trait loci in soybean (Glycine max Merr.) recombinant inbred lines

In rhizobia, host specificity is controlled by the nodulation outer proteins (Nops). However, the host genes encoding proteins associated with Nops remain unknown. We constructed a Sinorhizobium fredii HH103ΩnopL mutant and analyzed the nodule number (NN) and nodule dry weight (NDW) of 10 germplasms inoculated with the mutant or wild-type strain. Using recombinant inbred lines (RILs), the conditional and unconditional quantitative trait loci (QTLs) associated with NopL were detected. The regions overlapping conditional and unconditional QTLs were identified. Additionally, the expression levels of selected candidate genes were analyzed by qRT-PCR. Depending on the germplasm, NopL positively or negatively affected NN and NDW. The NN and NDW of ‘Charleston’ and ‘Dongnong 594’ (RIL parents) were lower in samples inoculated with Sinorhizobium fredii HH103ΩnopL than in samples inoculated with the wild-type strain. In soybean inoculated independently with Sinorhizobium fredii HH103 and HH103ΩnopL, 11 and 5 unconditional QTLs were identified for NN and NDW, respectively, while five and two conditional QTLs were identified for NN and NDW, respectively. Two overlapping QTLs were identified among the conditional and unconditional QTLs. Furthermore, the enzymes encoded by Glyma.13g170500 and Glyma.07g099700 likely interacted with NopL. The Glyma.13g170500 and Glyma.07g099700 detected at QTLs represent candidate genes that encode enzymes that interact with NopL to regulate the establishment of symbiotic relationships between rhizobia and soybean.

Quantitative trait loci for the diurnal flag leaf starch content during the early grain-filling stage in wheat (Triticum aestivum L.)

Starch is a product of photosynthetic activities in leaves. Wheat yields largely depend on photosynthetic carbon fixation and carbohydrate metabolism in flag leaves. The mapping of quantitative trait loci (QTLs) associated with flag leaf starch content (FLSC) in wheat (Triticum aestivum L.) was completed using unconditional and conditional QTL analyses. The FLSC of this population during the early grain-filling stage was measured at six stages in six environments. Combining unconditional and conditional QTL mapping methods, eight unconditional QTLs and nine conditional QTLs were detected, with five QTLs identified as unconditional and conditional QTLs. Four unconditional QTLs (i.e. qFLS-1B, qFLS-1D-1, qFLS-4A, and qFLS-7D-1) and one conditional QTL (i.e. qFLS-3A-1) were identified in two of six environments. Two QTLs (qFLS-1D-2 and qFLS-7D-1), which significantly affected the FLSC, were identified using the unconditional QTL mapping method, while three QTLs (i.e. qFLS-1A, qFLS-3A-1, and qFLS-7D-1) were detected using the conditional QTL mapping method. Our findings provide new insights into the genetic mechanism and regulatory network underlying the diurnal FLSC in wheat.

Dynamic and epistatic QTL mapping reveals the complex genetic architecture of waterlogging tolerance in chrysanthemum.

37 unconditional QTLs, 51 conditional QTLs and considerable epistatic QTLs were detected for waterlogging tolerance, and six favourable combinations were selected accelerating the possible application of MAS in chrysanthemum breeding. Chrysanthemum is seriously impacted by soil waterlogging. To determine the genetic characteristics of waterlogging tolerance (WAT) in chrysanthemum, a population of 162 F1 lines was used to construct a genetic map to identify the dynamic and epistatic quantitative trait loci (QTLs) for four WAT traits: wilting index (WI), dead leaf ratio (DLR), chlorosis score (Score) and membership function value of waterlogging (MFVW). The h B2 for the WAT traits ranged from 0.49 to 0.64, and transgressive segregation was observed in both directions. A total of 37 unconditional consensus QTLs with 5.81-18.21% phenotypic variation explanation (PVE) and 51 conditional consensus QTLs with 5.90-24.56% PVE were detected. Interestingly, three unconditional consensus QTLs were consistently identified across different stages, whereas no conditional consensus QTLs were consistently expressed. In addition, considerable epistatic QTLs, all with PVE values ranging from 0.01 to 8.87%, were detected by a joint analysis of WAT phenotypes. These results illustrated that the QTLs (genes) controlling WAT were environmentally dependent and selectively expressed at different times and indicated that both additive and epistatic effects underlie the inheritance of WAT in chrysanthemum. The findings of the current study provide insights into the complex genetic architecture of WAT, and the identification of favourable alleles represents an important step towards the application of molecular marker-assisted selection (MAS) and QTL pyramiding in chrysanthemum WAT breeding programmes.

Chromosomes A07 and A05 associated with stable and major QTLs for pod weight and size in cultivated peanut (Arachis hypogaea L.).

Co-localized intervals and candidate genes were identified for major and stable QTLs controlling pod weight and size on chromosomes A07 and A05 in an RIL population across four environments. Cultivated peanut (Arachis hypogaea L.) is an important legume crops grown in>100 countries. Hundred-pod weight (HPW) is an important yield trait in peanut, but its underlying genetic mechanism was not well studied. In this study, a mapping population (Xuhua 13×Zhonghua 6) with 187 recombinant inbred lines (RILs) was developed to map quantitative trait loci (QTLs) for HPW together with pod length (PL) and pod width (PW) by both unconditional and conditional QTL analyses. A genetic map covering 1756.48cM was constructed with 817 markers. Additive effects, epistatic interactions, and genotype-by-environment interactions were analyzed using the phenotyping data generated across four environments. Twelve additive QTLs were identified on chromosomes A05, A07, and A08 by unconditional analysis, and five of them (qPLA07, qPLA05.1, qPWA07, qHPWA07.1, and qHPWA05.2) showed major and stable expressions in all environments. Conditional QTL mapping found that PL had stronger influences on HPW than PW. Notably, qHPWA07.1, qPLA07, and qPWA07 that explained 17.93-43.63% of the phenotypic variations of the three traits were co-localized in a 5cM interval (1.48Mb in physical map) on chromosome A07 with 147 candidate genes related to catalytic activity and metabolic process. In addition, qHPWA05.2 and qPLA05.1 were co-localized with minor QTL qPWA05.2 to a 1.3cM genetic interval (280kb in physical map) on chromosome A05 with 12 candidate genes. This study provides a comprehensive characterization of the genetic components controlling pod weight and size as well as candidate QTLs and genes for improving pod yield in future peanut breeding.

Conditional and unconditional QTLs mapping of gluten strength in common wheat (Triticum aestivum L.)

AbstractDissecting the genetic relationships among gluten-related traits is important for high quality wheat breeding. Quantitative trait loci (QTLs) analysis for gluten strength, as measured by sedimentation volume (SV) and gluten index (GI), was performed using the QTLNetwork 2.0 software. Recombinant inbred lines (RILs) derived from the winter wheat varieties Shannong 01-35×Gaocheng 9411 were used for the study. A total of seven additive QTLs for gluten strength were identified using an unconditional analysis. QGi1D-13 and QSv1D-14 were detected through unconditional and conditional QTLs mapping, which explained 9.15–45.08% of the phenotypic variation. QTLs only identified under conditional QTL mapping were located in three marker intervals: WPT-3743–GLU-D1 (1D), WPT-7001–WMC258 (1B), and WPT-8682–WPT-5562 (1B). Six pairs of epistatic QTLs distributed nine chromosomes were identified. Of these, two main effect QTLs (QGi1D-13 and QSv1D-14) and 12 pairs of epistatic QTLs were involved in interactions with the environment. The results indicated that chromosomes 1B and 1D are important for the improvement of gluten strength in common wheat. The combination of conditional and unconditional QTLs mapping could be useful for a better understanding of the interdependence of different traits at the QTL molecular level.

Characterization of the temporal and spatial expression of wheat (Triticum aestivum L.) plant height at the QTL level and their influence on yield-related traits.

The temporal and spatial expression patterns of stable QTL for plant height and their influences on yield were characterized. Plant height (PH) is a complex trait in wheat (Triticum aestivum L.) that includes the spike length (SL) and the internode lengths from the first to the fifth internode, which are counted from the top and abbreviated as FIRITL, SECITL, THIITL, FOUITL, and FIFITL, respectively. This study identified eight putative additive quantitative trait loci (QTL) for PH. In addition, unconditional and conditional QTL mapping were used to analyze the temporal and spatial expression patterns of five stable QTL for PH. qPh-3A mainly regulated SL, FIRITL, and FIFITL to affect PH during the booting-heading stage (BS-HS); qPh-3D regulated all internode lengths to affect PH, especially during the BS-HS; before HS, qPh-4B mainly affected FIRITL, SECITL, THIITL, and FOUITL and qPh-5A.1 mainly affected SECITL, THIITL, and FOUITL to regulate PH; and qPh-6B mainly regulated FIRITL to affect the PH after the booting stage (BS). qPhdv-4B, a QTL for the response of PH to nitrogen stress, was stable and co-localized with qPh-4B. All five stable QTL, except for qPh-3A, were related to the 1000 kernel weight and yield per plant. Regions of qPh-3A, qPh-3D, qPh-4B, qPh-5A.1, and qPh-6B showed synteny to parts of rice chromosomes 1, 1, 3, 9, and 2, respectively. Based on comparative genomics analysis, Rht-B1b was cloned and mapped in the CI of qPh-4B. This report provides useful information for fine mapping of the stable QTL for PH and the genetic improvement of wheat plant type.

QTL mapping for yield and photosynthetic related traits under different water regimes in wheat

The improvement for drought tolerance requires understanding of the genetic control of wheat (Triticum aestivum L.) reaction to drought. In this study, a set of 131 recombinant inbred lines of wheat were investigated under well-watered (WW) and drought stress (DS) environments across 2 years to map quantitative trait loci (QTLs) for yield and physiological traits. A total of 225 QTLs were detected, including 32 non-environment-specific loci that were significant in both DS and WW, one drought-specific locus and two watering-specific loci. Three consistently-expressed QTLs (QTkw-3A.2, QTss-1A, and QScn-7A.1) were identified in at least three environments and the QTkw-1D.1 was significant in DS across the 2 years. By unconditional and conditional QTL analysis, spike number per plant and kernel number per spike were more important than thousand-kernel weight for grain yield (GY) at the given genetic background. Meta-analysis identified 67 meta-QTLs that contained QTLs for at least two traits. High frequency co-location of QTLs was found among either the spike-related traits or the six physiological traits. Four photosynthesis traits (CHL, LWUE, P N, and C i) were co-located with GY and/or yield components on various MQTLs. The results provided QTLs that warrant further study for drought tolerance breeding and are helpful for understanding the genetic basis of drought tolerance and the genetic contribution of yield components to GY at individual QTL level in wheat.

Dynamic quantitative trait loci underlies isoflavone accumulation in soybean seed

AbstractMost of quantitative trait loci (QTL) underlying soybean seed isoflavone contents were derived from the harvest stage of plant development, which uncover the genetic effects that were expressed in earlier seed developmental stages. The aim of this study was to detect conditional QTL associated with isoflavone accumulation during the entire seed development. A total of 112 recombinant inbred lines developed from the cross between ‘Zhongdou 27’ (higher seed isoflavone content) and ‘Jiunong 20’ (lower seed isoflavone content) were used for the conditional QTL analysis of daidzein (DZ), genistein (GT), glycitein (GC) and total isoflavone (TI) accumulations through composite interval mapping with mixed genetic model. The results indicated that the number and type of QTL and their additive effects for individual and total isoflavone accumulations were different among R3 to R8 developmental stages. Three unconditional QTL and six conditional QTL for DZ, four unconditional QTL and five conditional QTL for GT, six unconditional QTL and five conditional QTL for GC, six unconditional QTL and seven conditional QTL for TI were identified at different developmental stages, respectively. Unconditional and conditional QTL that affect individual and total isoflavone accumulations exhibited multiple expression patterns, implying that some QTL are active for long period and others are transient. Two genomic regions, Satt144‐Satt569 in linkage group F (LG F; chromosome 13, chr 13) for DZ, GC, GT and TI accumulations and Satt540‐Sat_240 in LG M (chr 07) for TI and GC accumulations, were found to significantly affect individual and total isoflavone accumulations in multiple developmental stages, suggesting that the accumulation of soybean seed isoflavones is governed by time‐dependent gene expression.

Dynamic QTL analysis of seed reserve utilization in sh2 sweet corn germination stages

In this study, dynamic quantitative trait loci (QTL) were mapped in a recombinant inbred line (F2:4) population derived from a BF3109 x Q267 cross. This was done during the unconditional (4, 7, and 10 days) and conditional (0 to 4, 4 to 7, and 7 to 10 days) germination stages in sh2 sweet corn. The values of seedling dry weight, weight of mobilized seed reserve (WMSR), seed reserve depletion percentage (SRDP), seed reserve utilization efficiency (SRUE), and their heritability differed across the investigated stages. The heritabilities of these traits were lower at 7D/4D and 10D/7D compared with those at the 4- (4D/0D), 7- and 10-day (D) developmental stages. WMSR and SRDP were significantly negatively correlated with SRUE at the early stage. The unconditional QTL mapping can explain the accumulation of genetic effects of seed reserve utilization from the starting time, whereas the conditional QTL mapping can reveal genetic expression in the time intervals. Fifteen and fourteen additive QTLs were identified by the unconditional and conditional mapping, respectively. The number of additive QTLs and their effect values varied among the different stages. More additive QTLs were found at the later stage (7 to 10 days), based on the conditional mapping results. The identification of QTL mapping based on a combination of time-dependent measurements is important for a better understanding of the genetic bases of seed reserve utilization. It is also important for the improvement of relevant variety traits for subsequent sweet corn-breeding studies using marker-assisted selection.

QTLs for flag leaf size and their influence on yield-related traits in wheat (Triticum aestivum L.)

Flag leaf-related traits (FLRTs) are determinant traits affecting plant architecture and yield potential in wheat (Triticum aestivum L.). In this study, a recombinant inbred line population with 188 lines, derived from the cross between Kenong9204 and Jing411, was developed to identify quantitative trait loci (QTL) for flag leaf width (FLW), length (FLL), and area (FLA) under both low nitrogen and high nitrogen treatments. A total of 38 QTLs were detected in eight environments (year × location × treatment). Of these, two QTLs for FLW on chromosomes 4B and 6B (qFlw-4B.3 and qFlw-6B.2) and one for FLA on chromosome 5B (qFla-5B) were major stable QTLs. Both phenotypic and QTL mapping analyses indicated that FLW was the major contributor to flag leaf size. To investigate the genetic relationship between FLRTs and yield-related traits (YRTs) at the QTL level, both unconditional and multivariable conditional QTL mapping for YRTs with respect to FLRTs were conducted. Twelve QTL clusters simultaneously controlling FLRTs and YRTs were identified. In comparison with unconditional QTL mapping, conditional QTL mapping analysis revealed that most but not all the QTL for YRTs were improved by FLRTs. At the QTL level, FLA had the greatest contribution to YRTs, followed by FLW and FLL. This study provided a genetic foundation from which to obtain desirable plant architecture and improve yield potential in wheat breeding programs.

Genetic dissection of the developmental behaviour of total starch content and its components in wheat grain

Starch in wheat is an important component of flour and is related to grain yield and wheat end-products. In this study, a doubled haploid (DH) population with 168 lines derived from a cross of elite Chinese wheat (Triticum aestivum L.) cultivars Huapei 3 and Yumai 57 was used to identify dynamic quantitative trait loci (QTLs) for total starch content (TSC), amylose (AMS) and amylopectin (AMP) in wheat grain. Traits were measured at stages, grown under three treatments in two seasons, and were assessed by unconditional and conditional QTL analyses. Thirty-three additive QTLs and 21 pairs of epistatic QTLs for TSC, AMS and AMP were detected by unconditional mapping, whereas 19 additive QTLs and 15 pairs of epistatic QTLs were identified by conditional mapping. Of these, QTsc4A.1 and QAms4A.1 were detected continuously at five stages under three treatments in two seasons by unconditional mapping, indicating that the accumulated effects of these QTLs were expressed stably from 12 days after flowering (DAF) and were little affected by nitrogen and water agronomic treatment. These two QTLs also showed net expression from 12 to 22 DAF by conditional mapping. The results indicate that the two loci play an important role in starch synthesis. Most of the epistatic QTLs belonged to a minor QTL, but played an important role in the target traits. Therefore, the development of starch is mainly affected by additive effects besides the epistasis effect. The data are useful for potential marker-assisted selection and cloning of the target gene in further fine mapping, and provide a foundation to understand the genetic mechanism underlying the development of starch in wheat and to increase yield.

Quantitative trait locus mapping for growth duration and its timing components in wheat

Growth duration, the entire developmental period from seed emergence to maturity, plays an important role in wheat adaptation and is a complex trait controlled by multiple genes and influenced by environmental factors. In order to precisely detect quantitative trait loci (QTLs) for wheat growth duration and its timing components, and dissect their genetic relationship, we performed QTL mapping and conditional analysis in a recombinant inbred line population. A total of 17 conditional QTLs and 30 unconditional QTLs were identified in this study, which explained 3.3–34.7 % of the phenotypic variation. Of them, 17 explained more than 10 % of the phenotypic variation and eight were environment-independent. Eight common QTLs were identified on chromosomes 1D, 2A, 2D, 3D, 4A, 5A, 7A and 7D, and explained significant phenotypic variation for two to eight traits. Notably, the QTL on chromosome 2D (genetic region of wPt-7149-wPt-667843) explained major phenotypic variation for emergence time (ET), heading time (HT), maturity time (MT) and growth duration (GD). The QTL on chromosome 3D (wPt-741816-wPt-732611) accounted for significant phenotypic variation for ET, TT and FT. Conditional analysis and unconditional analysis suggested that utilizing the genetic region of wPt-7149-wPt-667843 on chromosome 2D can modify ET, HT, MT and GD simultaneously, whereas the genetic region of wPt-741816-wPt-732611 on chromosome 3D can modify GD components individually to avoid change to the whole GD. This QTL mapping study and genetic relationship analysis between growth duration and its component traits should be of great practical and theoretical value for wheat breeding.

Dynamic QTL analysis of the Na<sup>+</sup> content, K<sup>+</sup> content, and Na<sup>+</sup>/K<sup>+</sup> ratio in rice roots during the field growth under salt stress

Rice (Oryza sativa L.) is seriously impacted by global soil salinization. To determine the quantitative trait loci (QTLs) related to salt tolerance in rice roots, F2:3 and BC1F2:3 populations derived from a cross between the cv. Dongnong 425 of high quality and yield and the salt-tolerant cv. Changbai 10, were studied at different development stages. Two genetic linkage maps of F2:3 and BC1F2:3 populations were constructed. A 66 mM NaCl solution was used to irrigate the field and to analyze the dynamic QTL of some rice root traits. Using unconditional and conditional QTL mapping methods, 30 unconditional QTLs and 16 conditional QTLs related to the 6 root traits were detected on the 9 rice chromosomes during different developmental stages. Fourteen pairs of unconditional and conditional QTLs were detected at the identical developmental stage in the identical population. A number of QTLs were detected at different developmental stages, however, many did not appear at the last stage. Remarkably, qRKC1 appeared continuously at multiple stages in both the populations suggesting its key role in regulating the salt tolerance of rice roots.