Unconditional Quantitative Trait Loci Mapping Research Articles

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.

Mapping dynamic QTL dissects the genetic architecture of grain size and grain filling rate at different grain-filling stages in barley

Grain filling is an important growth process in formation of yield and quality for barley final yield determination. To explore the grain development behavior during grain filling period in barley, a high-density genetic map with 1962 markers deriving from a doubled haploid (DH) population of 122 lines was used to identify dynamic quantitative trait locus (QTL) for grain filling rate (GFR) and five grain size traits: grain area (GA), grain perimeter (GP), grain length (GL), grain width (GW) and grain diameter (GD). Unconditional QTL mapping is to detect the cumulative effect of genetic factors on a phenotype from development to a certain stage. Conditional QTL mapping is to detect a net effect of genetic factors on the phenotype at adjacent time intervals. Using unconditional, conditional and covariate QTL mapping methods, we successfully detected 34 major consensus QTLs. Moreover, certain candidate genes related to grain size, plant height, yield, and starch synthesis were identified in six QTL clusters, and individual gene was specifically expressed in different grain filling stages. These findings provide useful information for understanding the genetic basis of the grain filling dynamic process and will be useful for molecular marker-assisted selection in barley breeding.

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.

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.

Genetic Dissection of Wheat Kernel Hardness Using Conditional QTL Mapping of Kernel Size and Protein-Related Traits

Kernel hardness (KH) is one of the primary quality parameters for common wheat (Triticum aestivum L.) and has a major impact on milling, flour quality, and end-product properties. In addition to Puroindoline (Pin) mutations and differences in Pin expression, other factors, such as kernel size and protein-related traits, play noticeable roles in determining hardness, but at the quantitative trait locus (QTL) level, the influence of these factors remains unclear. In this study, genetic relationships between KH and kernel size traits and between KH and protein-related traits were demonstrated by unconditional and conditional mapping using a wheat 90K genotyping assay with a segregating population of 173 recombinant inbred lines in four environments. Eight additive QTL for KH were detected using unconditional QTL mapping analysis; these QTL were primarily distributed on chromosomes 4B, 5A, 5B, and 6D, with phenotypic variation that ranged from 0.2 to 17.7%. In addition, one pair of epistatic QTL (QKH3B.4-65/QKH4B.6-2) was identified by unconditional mapping, and this pair accounted for 1.6% of the phenotypic variation. Through conditional mapping, after excluding the influences of kernel size and protein-related traits, 14 QTL were discovered and accounted for 0.6–18.5% of the phenotypic variation. Of them, the stable QTL QKH4B.4-17 made the largest contribution, which was partially contributed by the kernel length (KL), kernel thickness (KT), and dry gluten content (DGC). Furthermore, QKH4B.4-17 was crucially contributed by the kernel width (KW), kernel diameter (KD), kernel protein content (KPC), and wet gluten content (WGC) and was independent of the sedimentation volume (SV) and gluten index (GI). Another major QTL, QKH5B.10-63, was independent of the KW and KT; partly due to the variations in KL, KD, DGC, and WGC; and conclusively contributed by the KPC, SV, and GI. Seven additional QTL were only detected in the conditional analysis and were crucially contributed by kernel size or protein-related traits. These results demonstrated that kernel size and protein-related traits play significant roles in determining KH. The present study increases the understanding of the relationships between KH and kernel size and between KH and protein-related traits at the QTL level.

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.

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.

Conditional Quantitative Trait Locus Mapping of Wheat Seed Protein-fraction in Relation to Starch Content

Protein and starch are important in wheat quality and yield. To understand the genetic relationship between protein and starch at the quantitative trait locus (QTL)/gene level, 168 doubled haploid (DH) lines were used at three locations over 2 years. The QTLs for proteinfraction contents and starch content were analyzed by unconditional and conditional QTL mapping. We detected 17 unconditional additive QTLs (four albumin QTLs, three globulin QTLs, six gliadin QTLs, four glutenin QTLs) controlling protein-fraction contents. We detected 19 conditional QTLs (five albumin QTLs, three globulin QTLs, five gliadin QTLs, six glutenin QTLs) based on starch content. Of these QTLs, QAlu1B, QGlo6A, QGli1B, QGli7A, QGlu1B and QGlu1D increased the protein-fraction contents independent of the starch content. These QTLs could regulate the usual inverse relationship between protein and starch in wheat seeds. The results could possibly be used in the simultaneous improvement of grain protein and starch content in wheat breeding.

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.

Genetic dissection of flour whiteness by unconditional and conditional quantitative trait locus mapping in wheat

SUMMARYFlour whiteness (FW) is an important factor in assessing flour quality and determining the end product quality. It is an integrated sensory indicator reflecting flour colour and is negatively correlated with protein content. In order to dissect the genetic relationship between FW and its five related traits at the quantitative trait locus (QTL)/gene level, a recombinant inbred line population was evaluated under three environments. Quantitative trait loci for FW were analysed by unconditional and conditional QTL mapping. Four unconditional additive QTLs and 16 conditional additive QTLs were detected across the three environments. Of these QTLs, only one major additive QTL (Qfw1D1-1) was consistently identified using both unconditional and conditional QTL analysis. This QTL was independent of flour colour a* (a function of red-green with a positive a* for redness and negative for greenness) and b* (a green-blue value with positive value for yellowness and negative for blueness) and was only slightly affected by flour protein content. A minor additive QTL (Qfw4A-4) was also detected using these two QTL mapping methods, being independent of flour colour a* and b*. Five unconditional and ten conditional epistatic minor QTLs were detected, from which only one pair (Qfw3A-10/Qfw6B-6) was identified by both unconditional and conditional QTL mapping, also independent of flour colour a* and b*. The major QTL (Qfw1D1-1) identified in the current study for the first time can be used for improving wheat FW in marker-assisted breeding.

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.

Genetic analysis of kernel weight and kernel size in wheat (Triticum aestivum L.) using unconditional and conditional QTL mapping

Thousand kernel weight (TKW) is commonly regarded as a complex trait in wheat. Both unconditional and conditional quantitative trait locus (QTL) analyses were conducted using software QTLNetwork 2.0 in four environments to evaluate the genetic relationships between TKW and kernel size. These analyses involved a set of 182 F8:9 recombinant inbred lines derived from “Shannong01-35 × Gaocheng9411” with a genetic linkage map consisting of 503 molecular markers. Additive effects, epistatic effects, and genotype-by-environment (G × E) interactions of QTLs for TKW and kernel size were analyzed. A total of nine additive QTLs for TKW and kernel size were identified using unconditional analysis, which distributed on chromosomes 1B, 1D, 3D, 4B, 5B, and 6A. Among these, four QTLs were detected to be related to several different kernel traits. QTkw4B.1-7, QTkw5B.1-12, QTkw5B.1-17, and QTkw6A.1-29 were identified through both conditional and unconditional analyses. Seven QTLs for TKW were only identified under conditional QTL mapping. These included conditional QTLs for TKW influenced by or independent of the given kernel size traits. Twelve pairs of epistatic interaction QTLs involving 18 loci for the measured kernel traits were detected in unconditional analysis. The QTLs discovered in the present study through the combination of conditional and unconditional QTL mapping could increase the understanding of the genetic interrelationships between TKW and kernel size traits at the QTL level and provide the guiding information for breeding programs.

Genetic dissection of the maize kernel development process via conditional QTL mapping for three developing kernel-related traits in an immortalized F2 population.

Kernel development is an important dynamic trait that determines the final grain yield in maize. To dissect the genetic basis of maize kernel development process, a conditional quantitative trait locus (QTL) analysis was conducted using an immortalized F2 (IF2) population comprising 243 single crosses at two locations over 2years. Volume (KV) and density (KD) of dried developing kernels, together with kernel weight (KW) at different developmental stages, were used to describe dynamic changes during kernel development. Phenotypic analysis revealed that final KW and KD were determined at DAP22 and KV at DAP29. Unconditional QTL mapping for KW, KV and KD uncovered 97 QTLs at different kernel development stages, of which qKW6b, qKW7a, qKW7b, qKW10b, qKW10c, qKV10a, qKV10b and qKV7 were identified under multiple kernel developmental stages and environments. Among the 26 QTLs detected by conditional QTL mapping, conqKW7a, conqKV7a, conqKV10a, conqKD2, conqKD7 and conqKD8a were conserved between the two mapping methodologies. Furthermore, most of these QTLs were consistent with QTLs and genes for kernel development/grain filling reported in previous studies. These QTLs probably contain major genes associated with the kernel development process, and can be used to improve grain yield and quality through 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.

QTL mapping for traits related to P-deficient tolerance using three related RIL populations in wheat

Phosphorus (P) deficiency is one of the major constraints to wheat production worldwide. In this study, conditional and unconditional quantitative trait locus (QTL) analyses for 12 traits of wheat seedlings under P-normal and P-deficient conditions were conducted using three F9 recombinant inbred line (RIL) populations with a common female parent in wheat. Of the total 110 QTLs identified by unconditional QTL analysis, 29 were detected in multiple populations or under different P treatment, with 28 major QTLs. Notably, QRPU-4B on chromosome 4B in interval wPt-3608-wPt-667817 was detected in two populations and at two P levels, accounting for 44.08 % average value of phenotypic variance. Of 49 specific QTLs related to P-deficient tolerance detected by conditional QTL analysis, only nine were detected by unconditional QTL mapping under P-deficient condition, indicating that most QTLs directly concerned with P-deficient tolerance could be detected only by conditional QTL analysis. Eighteen important QTL clusters were mapped on 12 chromosomes 1D, 2A, 2B, 3A, 3B, 4B, 4D, 5A, 5D, 6A, 6B, and 7B, involving 85 QTLs that accounted for 53.4 % of total numbers of QTLs detected by unconditional and conditional mapping. Of these 18 QTL clusters, 14 were detected in multiple populations and 11 were detected by both unconditional and conditional analyses. Thus, these chromosome intervals were important areas controlling the expression of seedling traits related to P-deficient tolerance.

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.

Conditional QTL Mapping of Sedimentation Volume on Seven Quality Traits in Common Wheat

To evaluate the possible genetic interrelationships between flour components and the sedimentation volume (SD), a doubled haploid (DH) population comprising 168 lines were used to identify the conditional quantitative trait loci (QTLs) for SD in three environments. Ten additive QTLs and 15 pairs of epistatic QTLs were detected for SD through unconditional and conditional QTL mapping. Three major additive QTLs were detected for SD conditioned on the seven quality traits. Two additive QTLs were found to be independent of these traits. Three additive QTLs were suppressed by three of the seven traits because of non-detection in unconditional mapping. Three pairs of epistatic QTLs were completely affected by the seven traits because of detection in unconditional mapping but no-detection in conditional mapping. Twelve pairs of epistatic QTLs were detected in conditional mapping. Our results indicated that conditional mapping could contribute to a better understanding of the interdependence of different and closely correlated traits at the QTL molecular level, especially some minor QTLs were found. The conditional mapping approach provides new insights that will make it possible to avoid the disadvantages of different traits by breeding through molecular design.

Conditional and unconditional QTL mapping of drought-tolerance-related traits of wheat seedling using two related RIL populations

For discovering the quantitative trait loci (QTLs) contributing to early seedling growth and drought tolerance during germination, conditional and unconditional analyses of 12 traits of wheat seedlings: coleoptile length, seedling height, longest root length, root number, seedling fresh weight, stem and leaves fresh weight, root fresh weight, seedling dry weight, stem and leaves dry weight, root dry weight, root to shoot fresh weight ratio, root-to-shoot dry weight ratio, were conducted under two water conditions using two F8:9 recombinant inbred line (RIL) populations. The results of unconditional analysis are as follows: 88 QTLs accounting for 3.33-77.01% of the phenotypic variations were detected on chromosomes 1A, 1B, 1D, 2A, 2B, 2D, 3A, 3B, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B and 7D. Among these QTLs, 19 were main-effect QTLs with a contribution rate greater than 10%. The results of the conditional QTL analysis of 12 traits under osmotic stress on normal water conditions were as follows: altogether 22 QTLs concerned with drought tolerance were detected on chromosomes 1B, 2A, 2B, 3B, 4A, 5D, 6A, 6D, 7B, and 7D. Of these QTLs, six were main-effect QTLs. These 22 QTLs were all special loci directly concerned with drought tolerance and most of them could not be detected by unconditional analysis. The finding of these QTLs has an important significance for fine-mapping technique, map-based cloning, and molecular marker-assisted selection of early seedling traits, such as growth and drought tolerance.

Conditional QTL mapping for waterlogging tolerance in two RILs populations of wheat

Waterlogging is a widespread limiting factor for wheat production throughout the world, specially irrigated and high rainfall environments. Only few studies reported QTLs for waterlogging tolerance. To identify quantitative trait loci (QTLs) for waterlogging tolerance, root dry weight index (RDWI), shoot dry weight index (SDWI), total dry weight index (TDWI) were measured at seedling stage in two unrelated recombinant inbred lines (RILs) populations. These populations were International Triticeae Mapping Initiative (ITMI) population ‘W7984 / Opata85’, and ‘SHW-L1 × Chuanmai 32’ (SC) population. Conditional QTL mapping and unconditional QTL mapping were studied to dissect the genetic relationship between TDWI and its components of SDWI and TDWI. Total of 36 QTLs for waterlogging tolerance in ITMI population and 10 QTLs in SC population were identified in present study. Of them, 17 alleles from synthetic hexaploid wheat ‘W7984’ and 3 alleles from synthetic hexaploid wheat ‘SHW-L1’ contribute positively to waterlogging tolerance. Combinations of conditional and unconditional mapping methods indicate that SDWI showed tighter genetic correlation with TDWI than RDWI. This QTL identification study and dissection provide theoretical basis and application foundation to Marker-assisted selection (MAS) of waterlogging tolerance improvement in wheat.

Genetic Analysis of Grain Filling Rate Using Conditional QTL Mapping in Maize

The grain filling rate (GFR) is an important dynamic trait that determines the final grain yield and is controlled by a network of genes and environment factors. To determine the genetic basis of the GFR, a conditional quantitative trait locus (QTL) analysis method was conducted using time-related phenotypic values of the GFR collected from a set of 243 immortalized F2 (IF2) population, which were evaluated at two locations over 2 years. The GFR gradually rose in the 0–15 days after pollination (DAP) and 16–22 DAP, reaching a maximum at 23–29 DAP, and then gradually decreasing. The variation of kernel weight (KW) was mainly decided by the GFR, and not by the grain filling duration (GFD). Thirty-three different unconditional QTLs were identified for the GFR at the six sampling stages over 2 years. Among them, QTLs qGFR7b, qGFR9 and qGFR6d were identified at the same stages at two locations over 2 years. In addition, 14 conditional QTLs for GFR were detected at five stages. The conditional QTL qGFR7c was identified at stage V|IV (37–43 DAP) at two locations over 2 years, and qGFR7b was detected at the sixth stage (44–50 DAP) in all four environments, except at Anyang location in 2009. QTLs qQTL7b and qQTL6f were identified by unconditional and conditional QTL mapping at the same stages, and might represent major QTLs for regulating the GFR in maize in the IF2 population. Moreover, most of the QTLs identified were co-located with QTLs from previous studies that were associated with GFR, enzyme activities of starch synthesis, soluble carbohydrates, and grain filling related genes. These results indicated that the GFR is regulated by many genes, which are specifically expressed at different grain filling stages, and the specific expression of the genes between 16–35 DAP might be very important for deciding the final kernel weight.