Anming Ding

Conditional QTL mapping for plant height with respect to the length of the spike and internode in two mapping populations of wheat.

Plant height (PH) in wheat is a complex trait; its components include spike length (SL) and internode lengths. To precisely analyze the factors affecting PH, two F8:9 recombinant inbred line (RIL) populations comprising 485 and 229 lines were generated. Crosses were performed between Weimai 8 and Jimai 20 (WJ) and between Weimai 8 and Yannong 19 (WY). Possible genetic relationships between PH and PH components (PHC) were evaluated at the quantitative trait locus (QTL) level. PH and PHC (including SL and internode lengths from the first to the fourth counted from the top, abbreviated as FIITL, SITL, TITL, and FOITL, respectively) were measured in four environments. Individual and the pooled values from four trials were used in the present analysis. A QTL for PH was mapped using data on PH and on PH conditioned by PHC using IciMapping V2.2. All 21 chromosomes in wheat were shown to harbor factors affecting PH in two populations, by both conditional and unconditional QTL mapping methods. At least 11 pairwise congruent QTL were identified in the two populations. In total, ten unconditional QTL and five conditional QTL that could be detected in the conditional analysis only have been verified in no less than three trials in WJ and WY. In addition, three QTL on the short arms of chromosomes 4B, 4D, and 7B were mapped to positions similar to those of the semi-dwarfing genes Rht-B1, Rht-D1 and Rht13, respectively. Conditional QTL mapping analysis in WJ and WY proved that, at the QTL level, SL contributed the least to PH, followed by FIITL; TITL had the strongest influence on PH, followed by SITL and FOITL. The results above indicated that the conditional QTL mapping method can be used to evaluate possible genetic relationships between PH and PHC, and it can efficiently and precisely reveal counteracting QTL, which will enhance the understanding of the genetic basis of PH in wheat. The combination of two related populations with a large/moderate population size made the results authentic and accurate.

QTL detection of seven spike-related traits and their genetic correlations in wheat using two related RIL populations

Spike-related traits contribute greatly to grain yield in wheat. To localize wheat chromosomes for factors affecting the seven spike-related traits—i.e., the spike length (SL), the basal sterile spikelet number (BSSN), the top sterile spikelet number (TSSN), the sterile spikelet number in total (SSN), the spikelet number per spike (SPN), the fertile spikelet number (FSN) and the spike density (SD)—two F8:9 recombinant inbred line (RIL) populations were generated. They were derived from crosses between Weimai 8 and Jimai 20 (WJ) and between Weimai 8 and Yannong 19 (WY), comprising 485 and 229 lines, respectively. Combining the two new linkage maps and the phenotypic data collected from the four environments, we conducted quantitative trait locus (QTL) detection for the seven spike-related traits and evaluated their genetic correlations. Up to 190 putative additive QTL for the seven spike-related traits were detected in WJ and WY, distributing across all the 21 wheat chromosomes. Of these, at least nine pairwise QTL were common to the two populations. In addition, 38 QTL showed significance in at least two of the four different environments, and 18 of these were major stable QTL. Thus, they will be of great value for marker assisted selection (MAS) in breeding programs. Though co-located QTL were universal, every trait owned its unique QTL and even two closely related traits were not excluded. The two related populations with a large/moderate population size made the results authentic and accurate. This study will enhance the understanding of the genetic basis of spike-related traits.

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.

Kernel weight per spike: what contributes to it at the individual QTL level?

Spike length (SL), spikelet number (SPN) per spike, kernel number per spike (KNPS), and thousand-kernel weight (TKW) have strong genetic associations with kernel weight per spike (KWPS) in wheat. To investigate their genetic relationships at the individual quantitative trait locus (QTL) level, both unconditional and conditional QTL mapping for KWPS with respect to SL, SPN, KNPS, and TKW were conducted. Two related F8:9 recombinant inbred line populations, comprising 485 and 229 lines, respectively, were used. The trait phenotypic performances of each population were evaluated in four different environments. Unconditional QTL mapping analysis identified 22 putative additive QTL for KWPS, five of which were stable QTL, and only QKwps-WJ-1B.2 showed significant additive-by-environment interaction effects. In comparison with unconditional QTL mapping analysis, conditional QTL mapping analysis indicated that, at the QTL level, KNPS and TKW contributed more to KWPS than did SL and SPN. Any unconditional QTL for KWPS detected in this study was associated with at least one of its four related traits. The present study will provide assistance in the understanding of the genetic relationships between KWPS and its related traits.

Conditional QTL mapping of protein content in wheat with respect to grain yield and its components

Grain protein content in wheat (Triticum aestivum L.) is generally considered a highly heritable character that is negatively correlated with grain yield and yield-related traits. Quantitative trait loci (QTL) for protein content was mapped using data on protein content and protein content conditioned on the putatively interrelated traits to evaluate possible genetic interrelationships between protein content and yield, as well as yield-related traits. Phenotypic data were evaluated in a recombinant inbred line population with 302 lines derived from a cross between the Chinese cultivar Weimai 8 and Luohan 2. Inclusive composite interval mapping using IciMapping 3.0 was employed for mapping unconditional and conditional QTL with additives. A strong genetic relationship was found between protein content and grain yield, and yield-related traits. Unconditional QTL mapping analysis detected seven additive QTL for protein content, with additive effects ranging in absolute size from 0.1898% to 0.3407% protein content, jointly accounting for 43.45% of the trait variance. Conditional QTL mapping analysis indicated two QTL independent from yield, which can be used in marker-assisted selection for increasing yield without affecting grain protein content. Three additional QTL with minor effects were identified in the conditional mapping. Of the three QTLs, two were identified when protein content was conditioned on yield, which had pleiotropic effects on those two traits. Conditional QTL mapping can be used to dissect the genetic interrelationship between two traits at the individual QTL level for closely correlated traits. Further, conditional QTL mapping can reveal additional QTL with minor effects that are undetectable in unconditional mapping.

Wheat kernel dimensions: how do they contribute to kernel weight at an individual QTL level?

Kernel dimensions (KD) contribute greatly to thousand-kernel weight (TKW) in wheat. In the present study, quantitative trait loci (QTL) for TKW, kernel length (KL), kernel width (KW) and kernel diameter ratio (KDR) were detected by both conditional and unconditional QTL mapping methods. Two related F8:9 recombinant inbred line (RIL) populations, comprising 485 and 229 lines, respectively, were used in this study, and the trait phenotypes were evaluated in four environments. Unconditional QTL mapping analysis detected 77 additive QTL for four traits in two populations. Of these, 24 QTL were verified in at least three trials, and five of them were major QTL, thus being of great value for marker assisted selection in breeding programmes. Conditional QTL mapping analysis, compared with unconditional QTL mapping analysis, resulted in reduction in the number of QTL for TKW due to the elimination of TKW variations caused by its conditional traits; based on which we first dissected genetic control system involved in the synthetic process between TKW and KD at an individual QTL level. Results indicated that, at the QTL level, KW had the strongest influence on TKW, followed by KL, and KDR had the lowest level contribution to TKW. In addition, the present study proved that it is not all-inclusive to determine genetic relationships of a pairwise QTL for two related/causal traits based on whether they were co-located. Thus, conditional QTL mapping method should be used to evaluate possible genetic relationships of two related/causal traits.

QTL detection of seven quality traits in wheat using two related recombinant inbred line populations

Grain protein content (GPC) and gluten quality are the most important factors determining the end-use quality of wheat for pasta-making. Both GPC and gluten quality are considered to be polygenic traits influenced by environmental factors and other agricultural practices. Two related F8:9 recombinant inbred line (RIL) populations were generated to localise genetic factors controlling seven quality traits: GPC, wet gluten content (WGC), flour whiteness (FW), kernel hardness (KH), water absorption (Abs), dough development time (DDT) and dough stability time (DST). These lines were derived by crossing Weimai 8 and Jimai 20 (WJ) and by crossing Weimai 8 and Yannong 19 (WY). In total, WJ comprised 485 lines, while WY comprised 229 lines. Data on these seven quality traits were collected from each line in five different environments. Up to 85 putative QTLs for the seven traits were detected in WJ and 65 putative QTLs were detected in WY. Of these QTLs, 31 QTLs (36.47%) were detected in at least two trials in WJ, while 24 QTLs (36.92%) were detected in at least two trials in WY. Three QTLs from WJ and 25 from WY accounted for more than 10% of the phenotypic variance. The total 150 QTLs were spread throughout all 21 wheat chromosomes. Of these, at least thirteen pairwise were common to both populations, accounting for 20.00 and 15.29% of the total QTLs in WJ and WY, respectively. A major QTL for GPC, accounting for 53.04% of the phenotypic variation, was detected on chromosome 5A. A major QTL for WGC also shared this interval, explained more than 36% of the phenotypic variation, and was significant in two environments. Though co-located QTLs were common, every trait had its unique control mechanism, even for two closely related traits. Due to the different sizes of the two line populations, we also assessed the effects of population size on the efficiency and precision of QTL detection. In sum, this study will enhance our understanding of the genetic basis of these seven pivotal quality traits and facilitate the breeding of improved wheat varieties.

Mapping QTLs for Yield Related Traits Using Two Associated RIL Populations of Wheat

Abstract The objectives of this study were to map quantitative trait loci (QTLs) for yield related trait in wheat ( Triticum aestivum L.) grown in multiple environments, identify chromosomal regions harboring important loci, and validate the stability of these chromosomal regions in different environments. The QTLs for spikelet number per spike (SN), grain number per spike (GN), spike number per plant (PN), 1000-grain weight (GW), and grain yield per plant (GY) were detected using inclusive composite interval mapping method. The 2 mapping populations were the F 8:9 generations of Weimai 8 × Yannong 19 (WY population) and Weimai 8 × Jimai 20 (WJ population), which contained 229 and 485 lines, respectively. Both populations were grown in 4 environments. Numerous QTLs for the 5 traits were identified on 21 chromosomes of wheat, including 9 for SN, 9 for GN, 4 for PN, 7 for GW, and 5 for GY in the WY population and 20 for SN, 16 for GN, 11 for PN, 14 for GW, and 9 for GY in the WJ population. Sixteen and 3 major QTLs with the phenotypic contribution larger than 10% were detected in the WY and WJ populations, respectively. In addition, 5 and 17 QTLs were identified in at least 2 environments in the WY and WJ populations, respectively. Some QTLs were mapped in the same or closely linked marker intervals in both populations. Nine pairs of QTLs and 2 chromosomal regions were inferred to be identical between the 2 populations. These results may enrich the QTL information for yield components of wheat and facilitate marker-assisted selection.

QTL Detection of Internode Length and its Component Index in Wheat Using Two Related RIL Populations

To comprehensively understand the genetic basis of plant height (PH), quantitative trait locus (QTL) analysis for internode lengths, internode component indices and plant height component index (PHCI) were firstly conducted in the present study. Two related F8:9 recombinant inbred line (RIL) populations comprising 485 and 229 lines were used. Two hundred and nine putative additive QTL for the eight traits were identified, 35 of which showed significance in at least three trials. Of these, at least 11 pairwise QTL were common to the two populations. PH components at the QTL level had different effects on PH, confirming our previous multivariate conditional analysis (Cui et al. 2011). Eleven major QTL that showed consistency in expression across environments should be of great value in the genetic improvement of PH in wheat. The results above will enhance the understanding of the genetic basis of PH in wheat.

Length of internode and spike: How do they contribute to plant height of wheat at an individual QTL level?

A recombinant inbred line (RIL) population with 302 lines derived from a cross of Weimai 8 × Luohan 2 was used to identify the quantitative trait loci (QTL) for plant height (PH) in wheat (Triticum aestivum L.). Possible genetic relationships between PH and PH components (PHC), including spike length (SL) and internode length from the first to the fourth node counted from the top, abbreviated as FIITL, SITL, TITL and FOITL, respectively, were evaluated at the QTL level. A QTL for PH was mapped using data on PH and on PH conditioned by PHC using the IciMapping V3.0 software. Conditional QTL mapping proved that, at the QTL level, SL contributed the least to PH, followed by FIITL and FOITL, while TITL had the strongest influence on PH, followed by SITL. These results indicate that the conditional QTL mapping method can be used to evaluate possible genetic relationships between PH and PHC, and that it can efficiently and precisely reveal counteracting QTL, which will enhance our understanding of the genetic basis of PH in wheat.