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 F_8:9 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.     

A genetic analysis of the quality of northern-style Chinese steamed bread

Dissecting the genetic basis for the traits of northern-style Chinese steamed bread (NCSB) is of great significance for wheat quality breeding. Quantitative trait loci (QTLs) for the processing quality of NCSB were studied using a recombinant inbred line (RIL) consisting of 173 lines derived from a “Shannong01–35 × Gaocheng9411” cross. Twenty-four putative additive QTLs were detected on chromosomes 1A, 1B, 1D, 3A, 3B, 4A, 4B, 5B, 6B, and 7B. Of these QTLs, QTex1A.1-27, QHei5B.5-488, and QGum4B.4-17 had the highest contribution and accounted for 9.33, 10.9, and 12.0% of the phenotypic variations, respectively. Several co-located QTLs with additive effects were detected on chromosomes 1A, 1D, 4B, and 5B. Two clusters (RFL_CONTIG2160_524-WSNP_CAP12_C2438_1180601 and EX_C101685_705-RAC875_C27536_611) for height, total score, and texture and for chewiness, gumminess, and hardness were detected on chromosomes 1A and 4B, respectively. Two QTLs for chewiness and hardness (QCh1D-4, QHa1D-4) with additive effects were detected; these alleles could be good targets for improving the processing quality of steamed bread from common wheat (Triticum aestivum L.). In addition, QTLs for wheat flour quality and the associated correlations with NCSB were simultaneously analyzed. Negative correlations were detected between chewiness and the wet/dry gluten content (WGC/DGC) or protein content. Two QTLs (QCh4B.4-17 and QPr4B.4-17) and three QTLs (QCh4B.4-13, QWG4B.4-13, and QDG4B.4-13) clustered in the same chromosomal region. The detected QTL clusters should be further investigated during wheat breeding and could be used by breeders to improve wheat quality and especially the processing quality of NCSB.     

Molecular genetic analysis of grain protein content and flour whiteness degree using RILs in common wheat

Grain protein content (GPC) and flour whiteness degree (FWD) are important qualitative traits in common wheat. Quantitative trait locus (QTL) mapping for GPC and FWD was conducted using a set of 131 recombinant-inbred lines derived from the cross ‘Chuan 35050 × Shannong 483’ in six environmental conditions. A total of 22 putative QTLs (nine GPC and 13 FWD) were identified on 12 chromosomes with individual QTL explaining 4.5–34.0% phenotypic variation. Nine QTLs (40.9%) were detected in two or more environments. The colocated QTLs were on chromosomes 1B and 4B. Among the QTLs identified for GPC, QGpc.sdau-4A from the parent Shannong 483 represented some important favourable QTL alleles. QGpc.sdau-2A.1 and QFwd.sdau-2A.1 had a significant association with both GPC and FWD. The markers detected on top of QTL regions could be potential targets for marker-assisted selection.     

Unconditional and conditional QTL analysis of kernel weight related traits in wheat (Triticum aestivum L.) in multiple genetic backgrounds

Wheat thousand kernel weight (TKW) is a complex trait, and is largely controlled by several kernel traits, including kernel length (KL) and kernel width (KW). In order to reveal the genetic relationship between TKW and these kernel traits (KW and KL) as accurate as possible, we applied both unconditional and conditional mapping analyses to three distinct genetic populations, one DH population and two RIL populations. This report describes the identifications of 36 unconditional and conditional additive QTLs and 30 pairs of unconditional and conditional epistatic QTLs, all of which are closely associated with TKW. While the conditional additive locus Qtkw1B, detected in the RIL2 population, exhibited the largest contribution, explaining 14.12 % of TKW variance, the unconditional epistatic QTLs Qtkw3A-2/Qtkw5B.1, detected in the DH population, accounted for 11.95 % of phenotypic variance. This study also showed that, compared with unconditional mapping, conditional mapping resulted in very different numbers and different extent of effects of additive and epistatic QTLs that were associated with TKW when TKW was conditioned on kernel traits (KW and KL). These data strongly suggest that KW and KL indeed play a significant role in determining TKW. Furthermore, we demonstrated that the effects of the 25 additive QTLs for TKW were either entirely or largely determined by KW, while the effects of the other 25 additive QTLs for TKW were either entirely or largely affected by KL. We conclude that the conditional mapping can be useful for a better understanding of the interrelationship between the yield contributing traits at the QTL level.     

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.     

Genetic dissection of developmental behavior of grain weight in wheat under diverse temperature and water regimes

As a quantitatively inherited trait related to high yield potential, grain weight (GW) development in wheat is constrained by abiotic stresses such as limited water supply and high temperature. Data from a doubled haploid population, derived from a cross of (Hanxuan 10?×?Lumai 14), grown in four environments were used to explore the genetic basis of GW developmental behavior in unconditional and conditional quantitative trait locus (QTL) analyses using a mixed linear model. Thirty additive QTLs and 41 pairs of epistatic QTLs were detected, and were more frequently observed on chromosomes 1B, 2A, 2D, 4A, 4B and 7B. No single QTL was continually active during all stages or periods of grain growth. The QTLs with additive effects (A-QTLs) expressed in the period S1|S0 (the period from the flowering to the seventh day after) formed a foundation for GW development. GW development at these stages can be used as an index for screening superior genotypes under diverse abiotic stresses in a wheat breeding program. One QTL, i.e. Qgw.cgb-6A.2, showed high adaptability for water-limited and heat-stress environments. Many A-QTLs interacted with more than one other QTL in the two genetic models, such as Qgw.cgb-4B.2 interacted with five QTLs, showing that the genetic architecture underlying GW development involves a collective expression of genes with additive and epistatic effects.     

Chromosomes A07 and A05 associated with stable and major QTLs for pod weight and size in cultivated peanut (<Emphasis Type="Italic">Arachis hypogaea</Emphasis> L.)

Key message

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.

Abstract

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 1756.48 cM 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 5 cM interval (1.48 Mb 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 1.3 cM genetic interval (280 kb 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.

     

Identification of Early Vigor QTLs and QTL by Environment Interactions in Wheat (<Emphasis Type="Italic">Triticum eastivum</Emphasis> L.)

Crop productivity is highly dependent on successful seed germination and seedling establishment. This study evaluated two mapping populations, Batavia/Ernie (double haploid) and Synthetic/Opata (recombinant inbred lines), for early vigor under water stress and normal growing conditions. Significant gene, environment (water), and gene by environment interaction effects were observed. Broad sense heritability was 29 and 40% for the Batavia/Ernie and Synthetic/Opata populations, respectively. Quantitative trait loci (QTL) were analyzed based on single and multienvironment models. The two mapping populations differed in the number and locations of QTLs except qNev.uwa.4AL was identified in both populations under the non-stress condition, while qSev.uwa.3BL was specifically expressed under the stress condition in the Synthetic/Opata population. QTL by environment interaction (QEI) enabled identification of nine QTLs, including those identified by the single environment approach. Phenotypic variation expression (PVE) of QEIs ranged from 4.8 to 14.9% across the populations. Larger proportion of PVE of QEIs was explained by the additive components. Favorable alleles for three of the QTLs identified in the Synthetic/Opata population were derived from Synthetic, while Batavia contributed favorable alleles to a QTL on the long arm of chromosome 1D in the Batavia/Ernie population. QTL detected under water stress (qSev.uwa.3BL) co-located with the DREB 1 gene, which was mapped between markers Xmwg818 and Xfbb117 (58.1–77.6 cM). QTLs with high proportion of additive components can be validated for marker assisted gene pyramiding and selection.     

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.     

Mapping QTLs for yield and nitrogen-related traits in wheat: influence of nitrogen and phosphorus fertilization on QTL expression

Key message

The present study identified some new important genomic regions and demonstrated the availability of conditional analysis in dissecting QTLs induced by environmental factors.

Abstract

The high input and low use efficiency of nutrient fertilizers require knowledge of the genetic control of crop reaction to nutrient supplements. In this study, 14 morphological and 8 physiological traits of a set of 182 wheat (Triticum aestivum L.) recombinant inbred lines (Xiaoyan 54 × Jing 411) were investigated in six environments to map quantitative trait loci (QTLs). The influence of nitrogen (N) and phosphorus (P) fertilization on QTL expression was studied by unconditional and conditional analysis. A total of 117 and 30 QTLs were detected by unconditional and conditional analysis, respectively, among which 21 were common for both methods. Thirty-four QTL clusters were identified. Eighteen conserved QTLs (15.4 % of the 117 QTLs) between years, but within nutritional treatment were found. The three major QTLs on chromosomes 2D, 4B and 6A were coincident with Rht8, Rht-B1b and TaGW2, respectively. The other two important intervals on chromosomes 4B and 7A for yield component traits were newly detected QTLs that warrant further study. By conditional analysis, spikelet number per spike was found to be induced by P fertilization mostly, whereas N fertilization had more effects on the expression of the QTLs for nitrogen concentration and utilization efficiency traits. QTLs that respond to N and P interactions were also detected. The results are helpful for understanding the genetic basis of N utilization efficiency in wheat under different N and P supplement environments and provide evidence for the availability of conditional analysis in dissecting QTLs induced by environmental factors.     

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 F_8:9 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 mapping of pre-harvest sprouting resistance in a white wheat cultivar Danby

Key message

One major and three minor QTLs for resistance to pre-harvest sprouting (PHS) were identified from a white wheat variety “Danby.” The major QTL on chromosome 3A is TaPHS1, and the sequence variation in its promoter region was responsible for the PHS resistance. Additive?×?additive effects were detected between two minor QTLs on chromosomes 3B and 5A, which can greatly enhance the PHS resistance.

Abstract

Pre-harvest sprouting (PHS) causes significant losses in yield and quality in wheat. White wheat is usually more susceptible to PHS than red wheat. Therefore, the use of none grain color-related PHS resistance quantitative trait loci (QTLs) is essential for the improvement in PHS resistance in white wheat. To identify PHS resistance QTLs in the white wheat cultivar “Danby” and determine their effects, a doubled haploid population derived from a cross of Danby?×?“Tiger” was genotyped using genotyping-by-sequencing markers and phenotyped for PHS resistance in two greenhouse and one field experiments. One major QTL corresponding to a previously cloned gene, TaPHS1, was consistently detected on the chromosome arm 3AS in all three experiments and explained 21.6–41.0% of the phenotypic variations. A SNP (SNP?222) in the promoter of TaPHS1 co-segregated with PHS in this mapping population and was also significantly associated with PHS in an association panel. Gene sequence comparison and gene expression analysis further confirmed that SNP?222 is most likely the causal mutation in TaPHS1 for PHS resistance in Danby in this study. In addition, two stable minor QTLs on chromosome arms 3BS and 5AL were detected in two experiments with allele effects consistently contributed by Danby, while one minor QTL on 2AS was detected in two environments with contradicted allelic effects. The two stable minor QTLs showed significant additive?×?additive effects. The results demonstrated that pyramiding those three QTLs using breeder-friendly KASP markers developed in this study could greatly improve PHS resistance in white wheat.

     

Dynamic QTL analysis of the Na+ content,K+ content,and Na+/K+ 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, F_2:3 and BC₁F_2:3 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 F_2:3 and BC1F_2:3 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.     

小麦幼苗耐热性的QTL定位分析

以小麦DH群体(‘旱选10号’ב鲁麦14’)为材料,在高温(热胁迫)及常温(对照)两种条件下考察小麦幼苗的根干重、苗干重、幼苗生物量、叶片叶绿素含量、叶绿素荧光参数及其耐热指数,并应用基于混合线性模型的复合区间作图法分析幼苗性状及其耐热指数QTL的数量、染色体分布及表达情况,以及QTL与环境的互作效应。结果显示:(1)亲本‘旱选10号’的耐热性明显优于‘鲁麦14’,且杂交后代的耐热性出现超亲分离。(2)控制幼苗耐热相关性状的QTL位点在染色体2D、6B、3A、4A、5A和7A上分布较多,而控制幼苗性状耐热指数的QTL在染色体6A、6B、3A、2D、5A和7A上分布较多,QTL位点在染色体上的分布有区域化的趋势。(3)控制幼苗性状的单个加性QTL和上位性QTL解释的表型变异分别平均为2.48%和2.65%;而控制耐热指数的单个加性QTL和上位性QTL解释的表型变异分别平均为8.84%和1.98%。(4)在热胁迫和对照条件下共检测到与幼苗性状及其耐热指数有关的加性效应QTL 13个和上位性效应QTL 28对,分布在除4D和6D以外的19条染色体上。研究表明,控制幼苗性状的QTL以上位性效应为主,而其耐热指数的QTL以加性效应为主。     

A major QTL for powdery mildew resistance is stable over time and at two development stages in winter wheat

Despite the large impact of powdery mildew in wheat cultivated areas, little has been done to study powdery mildew resistance by QTL analysis up to now. The objective of the present paper is to present how the genetic basis of powdery mildew resistance in the resistant wheat line RE714 have been studied by QTL analysis at the adult plant stage over the course of 3 years, and at the vernalized seedling plant stage, and a comparison between the results obtained. Two segregating populations (DH and F_2:3) were derived from the cross between the resistant line (RE714), and a susceptible line (Hardi); these were analysed for powdery mildew resistance at the adult plant stage in the field under natural infection conditions in 1996, 1997 and 1998. The DH population was also tested for powdery mildew resistance at the vernalized seedling stage with four different isolates of powdery mildew. At the adult plant stage, a total of three QTLs (on chromosomes 5D, 4A and 6A) and five QTLs (on chromosomes 5D, 6A, 7A and 7B) were found for the DH and F_2:3 populations, respectively. The genetic control of resistance was found to be polygenic but involved a major QTL (on chromosome 5D), which was detected each year and which explained a high proportion of the variability observed (28.1%–37.9%). At the vernalized seedling stage, two QTLs were found (on chromosomes 5D and 7B) and the QTL detected on chromosome 5D was common to the four isolates tested. The comparison between the two development stages showed that the QTL on chromosome 5D was detected in all the different environments tested and again explained a high proportion of the variability. Different molecular interpretations of this QTL have also been discussed. Received: 5 October 2000 / Accepted: 1 March 2001     

QTL identification and KASP marker development for productive tiller and fertile spikelet numbers in two high-yielding hard white spring wheat cultivars

Selecting high-yielding wheat cultivars with more productive tillers per unit area (PTN) combined with more fertile spikelets per spike (fSNS) is difficult. QTL mapping of these traits may aid understanding of this bottleneck and accelerate precision breeding for high yield via marker-assisted selection. PTN and fSNS were assessed in four to five trials from 2015 to 2017 in a doubled haploid population derived from two high-yielding cultivars “UI Platinum” and “SY Capstone.” Two QTL for PTN (QPTN.uia-4A and QPTN.uia-6A) and four QTL for fSNS (QfSNS.uia-4A, QfSNS.uia-5A, QfSNS.uia-6A, and QfSNS.uia-7A) were identified. The effects of the QTL were primarily additive and, therefore, pyramiding of multiple QTL may increase PTN and fSNS. However, the two QTL for PTN were positioned in the flanking regions for the two QTL for fSNS on chromosomes 4A and 6A, respectively, suggesting either possible pleiotropic effect of the same QTL or tightly linked QTL and explaining the difficulty of selecting both high PTN and fSNS in phenotypic selection. Kompetitive allele-specific PCR (KASP) markers for all identified QTL were developed and validated in a recombinant inbred line (RIL) population derived from the same two cultivars. In addition, KASP markers for three of the QTL (QPTN.uia-6A, QfSNS.uia-6A, and QfSNS.uia-7A) were further validated in a diverse spring wheat panel, indicating their usefulness under different genetic backgrounds. These KASP markers could be used by wheat breeders to select high PTN and fSNS.     

Dynamic Quantitative Trait Loci Analysis of Seed Reserve Utilization during Three Germination Stages in Rice

In this study, one rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of seed reserve utilization during the early (day 6), middle (day 10) and late (day 14) germination stages. The seedling dry weight (SDW) and weight of the mobilized seed reserve (WMSR) were increased, while the seed reserve utilization efficiency (SRUE) decreased, during the process of seed germination. The SDW and WMSR were affected by the seed weight, while the SRUE was not affected by the seed weight. A total of twenty unconditional and twenty-one conditional additive QTLs and eight epistatic QTLs were identified at three germination stages, and the more QTLs were expressed at the late germination stage. Among them, twelve additive and three epistatic QTLs for SDW, eight additive and three epistatic QTLs for WMSR and thirteen additive and two epistatic QTLs for SRUE were identified, respectively. The phenotypic variation explained by each additive QTL, epistatic QTL and QTL × development interaction ranged from 6.10 to 23.91%, 1.79 to 6.88% and 0.22 to 2.86%, respectively. Two major additive QTLs qWMSR7.1 and qSRUE4.3 were identified, and each QTL could explain more than 20% of the total phenotypic variance. By comparing the chromosomal positions of these additive QTLs with those previously identified, eleven QTLs might represent novel genes. The best four cross combinations of each trait for the development of RIL populations were selected. The selected RILs and the identified QTLs might be applicable to improve rice seed reserve utilization by the marker-assisted selection approach.     

Identification of quantitative trait loci conferring resistance to tan spot in a biparental population derived from two Nebraska hard red winter wheat cultivars

Tan spot, caused by Pyrenophora tritici-repentis (Ptr), is a destructive foliar disease in all types of cultivated wheat worldwide. Genetics of tan spot resistance in wheat is complex, involving insensitivity to fungal-produced necrotrophic effectors (NEs), major resistance genes, and quantitative trait loci (QTL) conferring race-nonspecific and race-specific resistance. The Nebraska hard red winter wheat (HRWW) cultivar ‘Wesley’ is insensitive to Ptr ToxA and highly resistant to multiple Ptr races, but the genetics of resistance in this cultivar is unknown. In this study, we used a recombinant inbred line (RIL) population derived from a cross between Wesley and another Nebraska cultivar ‘Harry’ (Ptr ToxA sensitive and highly susceptible) to identify QTL associated with reaction to tan spot caused by multiple races/isolates. Sensitivity to Ptr ToxA conferred by the Tsn1 gene was mapped to chromosome 5B as expected. The Tsn1 locus was a major susceptibility QTL for the race 1 and race 2 isolates, but not for the race 2 isolate with the ToxA gene deleted. A second major susceptibility QTL was identified for all the Ptr ToxC-producing isolates and located to the distal end of the chromosome 1A, which likely corresponds to the Tsc1 locus. Three additional QTL with minor effects were identified on chromosomes 7A, 7B, and 7D. This work indicates that both Ptr ToxA-Tsn1 and Ptr ToxC-Tsc1 interactions are important for tan spot development in winter wheat, and Wesley is highly resistant largely due to the absence of the two tan spot sensitivity genes.     

QTL mapping of flag leaf-related traits in wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Key message

QTL controlling flag leaf length, flag leaf width, flag leaf area and flag leaf angle were mapped in wheat.

Abstract

This study aimed to advance our understanding of the genetic mechanisms underlying morphological traits of the flag leaves of wheat (Triticum aestivum L.). A recombinant inbred line (RIL) population derived from ND3331 and the Tibetan semi-wild wheat Zang1817 was used to identify quantitative trait loci (QTLs) controlling flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), and flag leaf angle (FLANG). Using an available simple sequence repeat genetic linkage map, 23 putative QTLs for FLL, FLW, FLA, and FLANG were detected on chromosomes 1B, 2B, 3A, 3D, 4B, 5A, 6B, 7B, and 7D. Individual QTL explained 4.3–68.52% of the phenotypic variance in different environments. Four QTLs for FLL, two for FLW, four for FLA, and five for FLANG were detected in at least two environments. Positive alleles of 17 QTLs for flag leaf-related traits originated from ND3331 and 6 originated from Zang1817. QTLs with pleiotropic effects or multiple linked QTL were also identified on chromosomes 1B, 4B, and 5A; these are potential target regions for fine-mapping and marker-assisted selection in wheat breeding programs.

     

Quantitative trait loci mapping of yield and related traits using a high-density genetic map of maize

Improving grain yield is the ultimate goal of the maize-breeding programs. In this study, analyses of conditional and unconditional quantitative trait locus (QTL) and epistatic interactions were used to elucidate the genetic architecture of yield and its related traits. A total of 15 traits of a recombinant inbred line population, including yield per plant (YPP), seven ear-related traits, and seven kernel-related traits, were measured in six different environments. Based on the genetic linkage map constructed using 2091 bins as markers, 56 main-effect QTLs for these traits were identified. These QTLs were distributed across eight genomic regions (bin 1.06, bin 4.02/4.05/4.08, bin 5.04, bin 7.04, bin 8.08, and bin 9.04), within the marker intervals of 85.45–6260.66 kb, and the phenotypic variance explained ranging from 5.69 to 11.56 %. One gene (GRMZM2G168229) encoding SBP-box domain protein was located in the small interval of qKRN4-3 and may be involved in patterning of kernel row number. Seventeen conditional QTLs identified for YPP were conditioned on its related traits and explained 6.18–23.15 % of the phenotypic variance. Conditional mapping analysis revealed that qYPP4-1, qYPP6-1, and qYPP8-1 are partially influenced by YPP-related traits at the individual QTL level. Digenic epistatic analysis identified 12 digenic interactions involving 22 loci across the whole genome. In addition, conditional digenic epistatic analysis identified 14 digenic interactions involving 21 loci. This study provides valuable information for understanding the genetic relationship between YPP and related traits and constitutes the first step toward the cloning of the relevant genes.