Unconditional QTL Research Articles

Genetic Dissection of the Mixing Properties of Wheat Flour (Triticum aestivum L.) Using Unconditional and Conditional QTL Mapping.

Wheat (Triticum aestivum L.) flour mixing properties are essential quality parameters in the dough development process. Limited research on superior alleles for mixing properties has restricted their molecular improvement, and other factors related to the complex traits have been ignored. A molecular map of 9576 polymorphic markers in the RIL population (F8:9) (Shannong01-35/Gaocheng9411) was constructed to evaluate mixing property effects in three environments. The parents were selected with markedly distinct high-molecular-weight glutenin subunits (HMW-GS). This study not only evaluated mixing properties using conventional unconditional QTL mapping but also evaluated the relationships between protein-related traits using conditional QTL mapping. The analyses identified most additive QTLs for major mixing properties on chromosomes 1A, 1B, and 1D. Two major loci (1A.1-15 and 1D-1) associated with mixing properties have confirmed the important contributions of Glu-A1 and Glu-D1 to wheat quality at the QTL level, which were mainly affected by the gluten index. Another important locus, 1B.1-24 (associated with midline peak value and midline peak width, with high phenotypic variations explained), might represent a new variation distinct from Glu-B1. The favored alleles came from Gaocheng9411. Several mixing properties shared the same QTLs (1B.1-6 and 1A.1-15), indicating tight linkage or pleiotropism. Genotype-by-environment (G×E) interactions were also investigated in the present study. The QTL results in our study may improve our understanding of the genetic interrelationships between mixing properties and protein-related traits.

Unconditional and conditional analysis of epistasis between tillering QTLs based on single segment substitution lines in rice

Epistasis plays an important role in manipulating rice tiller number, but epistatic mechanism still remains a challenge. Here we showed the process of epistatic analysis between tillering QTLs. A half diallel mating scheme was conducted based on 6 single segment substitution lines and 9 dual segment pyramiding lines to allow the analysis of 4 epistatic components. Additive-additive, additive-dominance, dominance-additive, and dominance-dominance epistatic effects were estimated at 9 stages of development via unconditional QTL analysis simultaneously. Unconditional QTL effect (QTL cumulative effect before a certain stage) was then divided into several conditional QTL components (QTL net effect in a certain time interval). The results indicated that epistatic interaction was prevalent, all QTL pairs harboring epistasis and one QTL always interacting with other QTLs in various component ways. Epistatic effects were dynamic, occurring mostly within 14d and 21–35d after transplant and exhibited mainly negative effects. The genetic and developmental mechanism on several tillering QTLs was further realized and perhaps was useful for molecular pyramiding breeding and heterosis utilization for improving plant architecture.

Four QTL clusters containing major and stable QTLs for saturated fatty acid contents in a dense genetic map of cultivated peanut (Arachis hypogaea L.)

Peanut is an important source of edible vegetable oils worldwide. Saturated fatty acid content is a key factor that affects the quality of peanut oil; thus, reducing its content is a key objective in peanut breeding. In this study, we constructed a dense genetic map of recombinant inbred line (RIL) population (Zhonghua 10 × ICG 12625) with 1443 SSR loci, covering a total length of 2279.10 cM. Using phenotypic data from three environments, we have identified 59 additive QTLs including eight stable ones and nine epistatic QTLs for various fatty acids. Combination of conditional and unconditional QTL analyses further revealed that there was a high genetic correlation between stearic acid and very long-chain saturated fatty acids. In addition, four QTL clusters (CLB04-1, CLB04-2, CLB04-3, and CLB06) harboring major and stable QTLs for saturated fatty acids were identified, which explained 10.77–36.73%, 35.10–41.89%, 10.79–39.18%, and 4.85–20.32% phenotypic variations, respectively. The physical interval of four QTL clusters was located at pseudomolecule B04 and B06 of Arachis ipaensis, containing putative genes that encode enzymes and transcription factors involved in fatty acid biosynthesis pathways. This study comprehensively analyzed the genetic components that controlled saturated fatty acid profiles and identified four QTL clusters containing stable QTLs with large effects. Our results could provide a platform for further gene discovery and guide the development of markers for genome-wide assisted selection of varieties with desired components of saturated fatty acids.

Genetic dissection of interactions between wheat flour starch and its components in two populations using two QTL mapping methods

Starch content and its components are important for determining wheat end-use quality and yield. However, little information is available about their interactions at the QTL/gene level in more than one population using different QTL mapping methods. Therefore, to dissect these interactions, two mapping populations from two locations over 2 years were used. The QTLs for the populations were analyzed by unconditional and conditional QTL mapping by two different analysis methods. In the two populations, there were a total of 24 unconditional additive QTLs detected for flour amylose (FAMS), flour amylopectin (FAMP), flour total starch (FTSC), and the ratio of FAMS to FAMP using ICIMapping4.1 methods, but 26 unconditional QTLs were found using QTLNetwork2.0 methods. Of these QTLs, 10 stable major additive QTLs were identified in more than one environment, mainly distributed on chromosomes 3B, 4A, 5A, and 7D. The maximum percentage of phenotypic variance explained (PVE) reached 54.31%. Two new unconditional major additive QTLs on chromosome 3B (Qftsc3B and Qfamp3B) were found. A total of 23 and 19 conditional additive QTLs were identified in the two populations using two different methods, respectively. Of which, eight and six stable major conditional QTLs were detected on chromosomes 3B, 4A, and 7D, respectively. New repressed QTLs were identified, such as Qftsc/fams5B-1 and Qftsc/fams5B-2. There were 20 epistatic unconditional and 15 conditional QTLs detected. In all, important QTLs on chromosomes 3B, 4A, and 7A were found in both populations. However, the number of important QTLs in the special recombinant inbred line (RIL) population was higher than that in the double haploid (DH) population, especially on chromosomes 7D and 5B. Moreover, the QTLs on chromosomes 4A, 7A, and 7D were close to the Wx-1 loci in the RIL population. These indicated better results can be obtained by a special population to target traits than by a common population. The important QTLs on key chromosomes can always be detected no matter what kinds of populations are used, such as the QTLs on chromosome 4A. In addition, QTL clusters were found on chromosomes 4A, 3B, 7A, 7D, and 5A in the two populations, indicating these chromosome regions were very important for starch biosynthesis.

Conditional QTL mapping of three yield components in common wheat (Triticum aestivum L.)

Spike number per m2 (SN), kernel number per spike (KNPS) and thousand-kernel weight (TKW) are the three main components determining wheat (Triticum aestivum L.) yield. To evaluate the relationships among them a doubled haploid (DH) population consisting of 168 lines grown at three locations for three years was analyzed by unconditional and conditional QTL mapping. Thirty-three unconditional QTL and fifty-nine conditional QTL were detected. Among them, two QTL (QSN-DH-2B and QSN-DH-3A-1.1) improved SN, with no effect on KNPS. QKNPS-DH-2B-2.1 improved KNPS, with no effect on SN. QKNPS-DH-1A-1.1, QKNPS-DH-2D-1.1 and QKNPS-DH-6A improved KNPS, with no effect on SN or TKW. QKNPS-DH-6B was associated with increased SN and TKW. In addition, QTKW-DH-4B, QTKW-DH-5B and QTKW-DH-7B increased TKW without decreasing KNPS. These results provide useful information for marker assisted selection (MAS) and improvement in wheat yield.

Identification of Quantitative Trait Loci and Water Environmental Interactions for Developmental Behaviors of Leaf Greenness in Wheat.

The maintenance of leaf greenness in wheat, highly responsible for yield potential and resistance to drought stress, has been proved to be quantitatively inherited and susceptible to interact with environments by traditional genetic analysis. In order to further dissect the developmental genetic behaviors of flag leaf greenness under terminal drought, unconditional and conditional QTL mapping strategies were performed with a mixed linear model in 120 F8-derived recombinant inbred lines (RILs) from two Chinese common wheat cultivars (Longjian 19 × Q9086) in different water environments. A total of 65 additive QTLs (A-QTLs) and 42 pairs of epistatic QTLs (AA-QTLs) were identified as distribution on almost all 21 chromosomes except 5A, explaining from 0.24 to 3.29 % of the phenotypic variation. Of these, 22 A-QTLs and 25 pairs of AA-QTLs were common in two sets of mapping methods but the others differed. These putative QTLs were essentially characteristic of time- and environmentally-dependent expression patterns. Indeed some loci were expressed at two or more stages, while no single QTL was continually active through whole measuring duration. More loci were detected in early growth periods but most of QTL × water environment interactions (QEIs) happened in mid-anaphase, where drought stress was more conducted with negative regulation on QTL expressions. Compared to other genetic components, epistatic effects and additive QEIs effects could be predominant in regulating phenotypic variations during the ontogeny of leaf greenness. Several QTL cluster regions were suggestive of tight linkage or expression pleiotropy in the inheritance of these traits. Some reproducibly-expressed QTLs or common loci consistent with previously detected would be useful to the genetic improvement of staygreen types in wheat through MAS, especially in water-deficit environments.

Unconditional and conditional QTL underlying the genetic interrelationships between soybean seed isoflavone, and protein or oil contents

AbstractSelection for soybean (Glycine max L. Merr.) rich in isoflavones, protein and oil has been difficult due to negative genetic interrelationships. In this study, genetic interrelationships among seed isoflavones and protein and oil contents were evaluated using both unconditional and conditional QTL mapping. Daidzein (DZ), genistein (GT), glycitein (GC) and total isoflavone (TI) contents were analysed in F5:6, F5:7 and F5:8 recombinant inbred lines (RILs) derived from a cross between ‘Zhongdou 27’(TI 3791 μg/g; protein content 42.84%; oil content 18.73%) and ‘Jiunong 20’ (TI 2061 μg/g; protein content 34.05%; oil content 21.42%). When DZ, GT, GC and TI were analysed for their genetic relationships with protein or oil contents, eight conditional QTL were detected, which included DZ|pro, GC|pro, GT|pro, TI|pro, DZ|oil, GC|oil, GT|oil and TI|oil. Seventeen QTL that had significant genetic associations between seed isoflavone, and seed protein or oil contents were found, including two for DZ conditioned on protein (qDZ|proK‐1, qDZ|proF‐2); one for GC conditioned on protein (qGC|proM‐1); three for GT conditioned on protein (qGT|proM‐1, qGT|proA2‐1, qGT|proL‐1); three for TI conditioned on protein (qTI|proM‐1, qTI|proA2‐1, qTI|proF‐2); one for DZ conditioned on oil (qDZ∣oil K_1); one for GC conditioned on oil (qGC∣oilI_1); four for GT conditioned on oil (qGT∣oil A2_1, qGT∣oil F_1, qGTF_2, qGT∣oilD2_1); three for TI conditioned on oil (qTI∣oilA2‐1, qTI∣oilE‐1, qTI∣oilL‐1). Few epistatic interactions among loci were detected. These loci may be valuable for improving seed isoflavone, protein and oil contents.

Genetic dissection of interaction between wheat protein and starch using three mapping populations

Protein and starch are important factors in determining wheat end-use quality and yield. However, these two traits are negatively correlated. So in order to dissect the genetic relationship between grain protein content (GPC) and grain starch content (GSC) at QTL/gene level, three mapping populations were used at three locations in 2 years. The QTLs for GPC and GSC were analyzed by unconditional and conditional QTL mappings. Thirteen QTLs were detected for GPC by each of the conditional and unconditional methods of QTL analyses. For GSC, seven and nine QTLs, respectively, were detected using unconditional and conditional QTL analyses methods. Of these QTLs, there was only one major QTL detected for GPC using unconditional QTL analysis. For GSC, two major QTLs were detected through each of the conditional and unconditional QTL mapping methods. Seven QTLs with minor effect on GSC were identified for GPC (Qgpc2A-12, Qgpc6D-3, Qgpc1D-15, Qgpc2D-23, Qgpc2D-31, Qgpc5B-19, and Qgpc4B-7). Four QTLs were found for GSC, named Qgsc1D-1, Qgsc4A-9, Qgsc7B-15, and Qgsc7D1-2, which had minor influence on GPC. The major QTL Qgsc7B-15 can directly be used for improving the GSC in molecular marker-assisted breeding. QTLs detected in the present study could possibly be used in simultaneous improvement of grain protein and starch content in wheat.

Genetic Analysis of Yield-Associated Traits by Unconditional and Conditional QTL in Brassica napus

Genetic Analysis of Yield-Associated Traits by Unconditional and Conditional QTL in <i>Brassica napus</i>

QTL mapping for salt tolerance based on snp markers at the seedling stage in maize (Zea mays L.)

Salinity is a major constraint to the sustainability and expansion of maize cultivation. Plant salt tolerance is a quantitative trait controlled by multiple genes. In the present study, we constructed a high density genetic map based on high quality SNP markers from 161 F2:5 recombinant inbred line populations derived from the cross between two maize inbred lines contrasting in salinity tolerance. QTL analysis was conducted in saline field and the hydroponic culture. For saline field, field germination rate and field salt tolerance ranking were used as salinity tolerance indicators to conduct QTL detection. For hydroponic culture, salt tolerance ranking, shoot fresh weight, shoot dry weight, tissue water content, shoot Na+ concentration, shoot K+ concentration, and shoot K+/Na+ ratio were used. Through unconditional QTL analysis, we detected 20 additive and nine epistatic QTLs, of which 12 and two showed significant QTL by treatment (Q × T) interaction effects, respectively. Moreover, the use of conditional analysis model allowed us to detect nine conditional QTLs. The QTLs were mainly clustered on chromosomes 1, 3 and 5. The five unconditional and three conditional QTLs reported here could individually explain more than 20 % of the phenotypic variation. The QTLs identified here could be helpful to improve salt tolerance in maize by marker-assisted selection and shed new light on understanding the genetic basis of salt tolerance in maize.

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.

Identification of unconditional and conditional QTL for oil, protein and starch content in maize

Oil, protein and starch are key chemical components of maize kernels. A population of 245 recombinant inbred lines (RILs) derived from a cross between a high-oil inbred line, By804, and a regular inbred line, B73, was used to dissect the genetic interrelationships among oil, starch and protein content at the individual QTL level by unconditional and conditional QTL mapping. Combined phenotypic data over two years with a genetic linkage map constructed using 236 markers, nine, five and eight unconditional QTL were detected for oil, protein and starch content, respectively. Some QTL for oil, protein and starch content were clustered in the same genomic regions and the direction of their effects was consistent with the sign of their correlation. In conditional QTL mapping, 37 (29/8) unconditional QTL were not detected or showed reduced effects, four QTL demonstrated similar effects under unconditional and conditional QTL mapping, and 17 additional QTL were identified by conditional QTL mapping. These results imply that there is a strong genetic relationship among oil, protein and starch content in maize kernels. The information generated in the present investigation could be helpful in marker-assisted breeding for maize varieties with desirable kernel quality traits.

QTL underlying developmental behaviour of 100‐seed weight of soybean

With 4 tablesAbstractThe objective of this study was to determine QTL underlying seed weight from earlier developmental stage to postharvest in soybean, using a F5:8‐derived recombinant inbred line (RIL) (n = 125) population. Twenty‐three unconditional QTL and 17 conditional QTL associated with seed weight were identified in different developmental stages in 2008 and 2009. Four conditional QTL QSW J‐1 (on LG J, Gm 16), QSWF‐1(on LG F, Gm 13), QSWK‐2 (on LG K, Gm 9) and QSWA2‐2 (on LG A2, Gm 8) were identified across multiple stages and the two years. Fifteen QTL with conditional additive main effect (a) and/or conditional additive ×environment interaction effect (ae) at certain stages were identified. Of them, seven conditional QTL possessed significant additive main effect (a), and ten conditional QTL had significant ae effects. Twenty‐six epistatic pairwise QTL (aae) were identified in different developmental stages. Eleven pairs showed aa effect, and twelve pairs showed aae effect in different developmental stages.

Mining Applicable Elite Alleles of Growth Duration, Plant Height and Panicle Number per Plant by Conditional QTL Mapping in Japonica Rice

Unconditional and conditional QTL mapping were conducted for growth duration (GD), plant height (PH) and effective panicle number per plant (PN) using a recombinant inbred line (RIL) population derived from a cross between two japonica rice varieties Xiushui 79 and C Bao. The RIL population consisted of 254 lines was planted in two environments, Nanjing and Sihong, Jiangsu Province, China. Results showed that additive effects were major in all of QTLs for GD, PH and PN detected by the two methods, and the epistatic effects explained a small proportion of phenotypic variation. No interactions were detected between additive QTL and environment, and between epistatic QTL pairs and environment. After growth duration was adjusted to an identical level, RM80-160bp was detected as an applicable elite allele for PN, with an additive effect of 0.71. When effective panicle number per plant was adjusted to an identical level, RM448-240bp was detected as an applicable elite allele for GD, with an additive effect of 4.64. After plant height was adjusted to an identical level, RM80-160bp was detected as an applicable elite allele for PN, with an additive effect of 0.62, and RM448-240bp was detected as an applicable elite allele for GD, with an additive effect of 3.89. These applicable elite alleles could be used to improve target traits without influencing the other two traits.

Dynamic QTL Analysis of Rice Protein Content and Protein Index Using Recombinant Inbred Lines

Protein content (PC) and protein index (PI) play important roles in determining nutritional quality in rice (Oryza sativa L.). We used 71 lines derived from “Asominori/IR24” to analyze the developmental behavior of PC and PI through unconditional and conditional QTL mapping methods. In all, 10 unconditional QTLs and 6 conditional QTLs for PC, and 11 unconditional QTLs and 9 conditional QTLs for PI, were identified at four stages of grain filling. More were identified in the first three stages than at the final stage. Temporal patterns of gene expression for PC and PI differed over time, with several QTLs being expressed across two or three stages but many being expressed at only one stage. Some of these QTLs were closely linked with maturity QTLs reported previously. Many QTLs for PC and PI were co-localized, supporting the significant correlation found between PC and PI. Our results suggest that dynamic QTL mapping might be a valid means for revealing more genetic information about protein accumulations during seed development.

Unconditional and Conditional QTL Mapping for Tiller Numbers at Various Stages with Single Segment Substitution Lines in Rice ( Oryza sativa L.)

Tiller is one of the most important agronomic traits which influences quantity and quality of effective panicles and finally influences yield in rice. It is important to understand “static” and “dynamic” information of the QTLs for tillers in rice. This work was the first time to simultaneously map unconditional and conditional QTLs for tiller numbers at various stages by using single segment substitution lines in rice. Fourteen QTLs for tiller number, distributing on the corresponding substitution segments of chromosomes 1, 2, 3, 4, 6, 7 and 8 were detected. Both the number and the effect of the QTLs for tiller number were various at different stages, from 6 to 9 in the number and from 1.49 to 3.49 in the effect, respectively. Tiller number QTLs expressed in a time order, mainly detected at three stages of 0–7 d, 14–21 d and 35–42 d after transplanting with 6 positive, 9 random and 6 negative expressing QTLs, respectively. Each of the QTLs expressed one time at least during the whole duration of rice. The tiller number at a specific stage was determined by sum of QTL effects estimated by the unconditional method, while the increasing or decreasing number in a given time interval was controlled by the total of QTL effects estimated by the conditional method. These results demonstrated that it is highly effective and accurate for mapping of the QTLs by using single segment substitution lines and the conditional analysis methodology.

The genetic relationship between popping expansion volume and two yield components in popcorn using unconditional and conditional QTL analysis

Popping expansion volume (PEV) is the most important quality trait in popcorn, while its germplasm is inferior to normal dent/flint corn in yield. In this study, 259 F2:3 families, developed from the cross between a dent corn inbred Dan232 and a popcorn inbred N04, were evaluated for their PEV, grain weight per plant (GWP) and 100-grain weight (100 GW) in two environments. The genetic relationship between PEV and GWP, and 100 GW on individual gene loci were evaluated using unconditional and conditional QTL mapping methods. In total, five, one and three unconditional QTL were identified for PEV, GWP, and 100 GW, respectively. The positive alleles of all QTL for PEV were from N04, while positive alleles of all QTL for GWP and 100 GW were from Dan232. In conditional mapping, one and two QTL failed to be detected, and all four additional QTL were detected. Nevertheless, three QTL were identified, which controlled PEV independently from GWP/100 GW. They seemed to be potential candidates in popcorn breeding to increase PEV without decreasing GWP/100 GW. The results suggested that for significantly correlated traits, the conditional QTL mapping method could be used to dissect the genetic interrelationship between traits at the level of individual QTL, as well as reveal additional QTL that were undetectable by unconditional mapping.

Conditional QTL mapping of oil content in rapeseed with respect to protein content and traits related to plant development and grain yield

Oil content in rapeseed (Brassica napus L.) is generally regarded as a character with high heritability that is negatively correlated with protein content and influenced by plant developmental and yield related traits. To evaluate possible genetic interrelationships between these traits and oil content, QTL for oil content were mapped using data on oil content and on oil content conditioned on the putatively interrelated traits. Phenotypic data were evaluated in a segregating doubled haploid population of 282 lines derived from the F(1) of a cross between the old German cultivar Sollux and the Chinese cultivar Gaoyou. The material was tested at four locations, two each in Germany and in China. QTLMapper version 1.0 was used for mapping unconditional and conditional QTL with additive (a) and locus pairs with additive x additive epistatic (aa) effects. Clear evidence was found for a strong genetic relationship between oil and protein content. Six QTL and nine epistatic locus pairs were found, which had pleiotropic effects on both traits. Nevertheless, two QTL were also identified, which control oil content independent from protein content and which could be used in practical breeding programs to increase oil content without affecting seed protein content. In addition, six additional QTL with small effects were only identified in the conditional mapping. Some evidence was apparent for a genetic interrelationship between oil content and the number of seeds per silique but no evidence was found for a genetic relationship between oil content and flowering time, grain filling period or single seed weight. The results indicate that for closely correlated traits conditional QTL mapping can be used to dissect the genetic interrelationship between two traits at the level of individual QTL. Furthermore, conditional QTL mapping can reveal additional QTL with small effects that are undetectable in unconditional mapping.

Quantitative trait loci analysis for the developmental behavior of tiller number in rice (Oryza sativa L.)

A doubled-haploid rice population of 123 lines from Azucena/IR64 was used for analyzing the developmental behavior of tiller number by conditional and unconditional QTL mapping methods. It was indicated that the number of QTLs significantly affecting tiller number was different at different measuring stages. Many QTLs controlling tiller growth identified at the early stages were undetectable at the final stage. Only one QTL could be detected across the whole growth period. By conditional QTL mapping, more QTLs for tiller number could be detected than that by unconditional mapping. The temporal patterns of gene expression for tiller number could be different at different stages. Even an individual gene or genes at the same genomic region might have opposite genetic effects at various growth stages.