Gene networks in hexaploid wheat: interacting quantitative trait loci for grain protein content

In hexaploid wheat, single-locus and two-locus quantitative trait loci (QTL) analyses for grain protein content (GPC) were conducted using two different mapping populations (PI and PII). Main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL x environment interactions (QE, QQE) were detected using two-locus analyses in both the populations. Only a few QTLs were common in both the analyses, and the QTLs and the interactions detected in the two populations differed, suggesting the superiority of two-locus analysis and the need for using several mapping populations for QTL analysis. A sizable proportion of genetic variation for GPC was due to interactions (28.59% and 54.03%), rather than to M-QTL effects (7.24% and 7.22%), which are the only genetic effects often detected in the majority of QTL studies. Even E-QTLs made a marginal contribution to genetic variation (2.68% and 6.04%), thus suggesting that the major part of genetic variation is due to changes in gene networks rather than the presence or absence of specific genes. This is in sharp contrast to the genetic dissection of pre-harvest sprouting tolerance conducted by us earlier, where interaction effects were not substantial, suggesting that the nature of genetic variation also depends on the nature of the trait.     

QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat

In bread wheat, single-locus and two-locus QTL analyses were conducted for seven yield and yield contributing traits using two different mapping populations (P I and P II). Single-locus QTL analyses involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated yield traits to detect the pleiotropic QTLs. Two-locus analyses were conducted to detect main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions (QE and QQE). Only a solitary QTL for spikelets per spike was common between the above two populations. HomoeoQTLs were also detected, suggesting the presence of triplicate QTLs in bread wheat. Relatively fewer QTLs were detected in P I than in P II. This may be partly due to low density of marker loci on P I framework map (173) than in P II (521) and partly due to more divergent parents used for developing P II. Six QTLs were important which were pleiotropic/coincident involving more than one trait and were also consistent over environments. These QTLs could be utilized efficiently for marker assisted selection (MAS).     

Identification of QTLs with main, epistatic and QTL?×?environment interaction effects for salt tolerance in rice seedlings under different salinity conditions

Salt tolerance of rice (Oryza sativa L.) at the seedling stage is one of the major determinants of its stable establishment in saline soil. One population of recombinant inbred lines (RILs, F (2:9)) derived from a cross between the salt-tolerant variety Jiucaiqing and the salt-sensitive variety IR26 was used to determine the genetic mechanism of four salt tolerance indices, seedling height (SH), dry shoot weight (DSW), dry root weight (DRW) and Na/K ratios (Na/K) in roots after 10 days in three salt concentrations (0.0, 0.5 and 0.7 % NaCl). The main effect QTLs (M-QTLs) and epistatic QTLs (E-QTLs) were detected by QTL IciMapping program using single environment phenotypic values. Eleven M-QTLs and 11 E-QTLs were identified for the salt tolerance indices. There were six M-QTLs and two E-QTLs identified for SH, three M-QTLs and five E-QTLs identified for DSW, two M-QTLs and one E-QTL identified for DRW, and three E-QTLs identified for Na/K. The phenotypic variation explained by each M-QTL and E-QTL ranged from 7.8 to 23.9 % and 13.3 to 73.7 %, respectively. The QTL-by-environment interactions were detected by QTLNetwork program in the joint analyses of multi-environment phenotypic values. Six M-QTLs and five E-QTLs were identified. The phenotypic variation explained by each QTL and QTL × environment interaction ranged from 0.95 to 6.90 % and 0.02 to 0.50 %, respectively. By comparing chromosomal positions of these M-QTLs with those previously identified, five M-QTLs qSH1.3, qSH12.1, qSH12.2, qDSW12.1 and qDRW11 might represent novel salt tolerance genes. Five selected RILs with high salt tolerance had six to eight positive alleles of the M-QTLs, indicating that pyramiding by marker-assisted selection (MAS) of M-QTLs can be applied in rice salt tolerance breeding programs.     

Gene actions of QTLs affecting several agronomic traits resolved in a recombinant inbred rice population and two backcross populations

To understand the types of gene action controlling seven quantitative traits in rice, we carried out quantitative trait locus (QTL) mapping in order to distinguish between the main-effect QTLs (M-QTLs) and digenic epistatic QTLs (E-QTLs) responsible for the trait performance of 254 recombinant inbred lines (RILs) from rice varieties Lemont/Teqing and two backcross hybrid (BCF₁) populations derived from these RILs. We identified 44 M-QTL and 95 E-QTL pairs in the RI and BCF₁ populations as having significant effects on the mean values and mid-parental heterosis of heading date, plant height, flag leaf length, flag leaf width, panicle length, spikelet number and spikelet fertility. The E-QTLs detected collectively explained a larger portion of the total phenotypic variation than the M-QTLs in both the RI and BCF₁ populations. In both BCF₁ populations, over-dominant (or under-dominant) loci were more important than additive and complete or partially dominant loci for M-QTLs and E-QTL pairs, thereby supporting prior findings that overdominance resulting from epistatic loci are the primary genetic basis of inbreeding depression and heterosis in rice.     

Comparison of molecular linkage maps and QTLs for morphological traits in two reciprocal backcross populations of rice

Comparison of maps and QTLs between populations may provide us with a better understanding of molecular maps and the inheritance of traits. We developed and used two reciprocal BC1F1 populations, IP/DS//IP and IP/DS//DS, for QTL analysis. DS (Dasanbyeo) is a Korean tongil-type cultivar (derived from an indica x japonica cross and similar to indica in its genetic make-up) and IP (Ilpumbyeo) is a Korean japonica cultivar. We constructed two molecular linkage maps corresponding to each backcross population using 196 markers for each map. The length of each chromosome was longer in the IP/DS//IP population than in the IP/DS//DS population, indicating that more recombinants were produced in the IP/DS//IP population. Distorted segregation was observed for 44 and 19 marker loci for the IP/DS//IP and IP/DS//DS populations, respectively; these were mostly skewed in favor of the indica alleles. A total of 36 main effect QTLs (M-QTLs) and 15 digenic epistatic interactions (E-QTLs) were detected for the seven traits investigated. The phenotypic variation explained (PVE) by M-QTLs ranged from 3.4% to 88.2%. Total PVE of the M-QTLs for each trait was significantly higher than that of the E-QTLs. The total number of M-QTLs identified in the IP/DS//IP population was higher than in the IP/DS//DS population. However, the total PVE by the M-QTLs and E-QTLs together for each trait was similar in the two populations, suggesting that the two BC1F1 populations are equally useful for QTL analysis. Maps and QTLs in the two populations were compared. Eleven new QTLs were identified for SN, SF, GL, and GW in this study, and they will be valuable in marker-assisted selection, particularly for improving grain traits in tongil-type varieties.     

Gene actions of QTLs affecting several agronomic traits resolved in a recombinant inbred rice population and two testcross populations

To understand the types of gene action controlling seven quantitative traits in rice, QTL mapping was performed to dissect the main effect (M-QTLs) and digenic epistatic (E-QTLs) QTLs responsible for the trait performance of 254 recombinant inbred lines (RILs) of "Lemont/Teqing", and two testcross (TC) F(1) populations derived from these RILs. The correlation analyses reveal a general pattern, i.e. trait heritability in the RILs was negatively correlated to trait heterosis in the TC hybrids. A large number of M-QTLs and E-QTLs affecting seven traits, including heading date (HD), plant height (PH), flag leaf length (FLL), flag leaf width (FLW), panicle length (PL), spikelet number per panicle (SN) and spikelet fertility (SF), were identified and could be classified into two predominant groups, additive QTLs detected primarily in the RILs, and overdominant QTLs identified exclusively in the TC populations. There is little overlap between QTLs identified in the RILs and in the TC populations. This result implied that additive gene action is largely independent from non-additive gene action in the genetic control of quantitative traits of rice. The detected E-QTLs collectively explained a much greater portion of the total phenotypic variation than the M-QTLs, supporting prior findings that epistasis has played an important role in the genetic control of quantitative traits in rice. The implications of these results to the development of inbred and hybrid cultivars were discussed.     

Identification of salt-tolerant QTLs with strong genetic background effect using two sets of reciprocal introgression lines in rice

Effect of genetic background on detection of quantitative trait locus (QTL) governing salinity tolerance (ST) was studied using two sets of reciprocal introgression lines (ILs) derived from a cross between a moderately salinity tolerant japonica variety, Xiushui09 from China, and a drought tolerant but salinity susceptible indica breeding line, IR2061-520-6-9 from the Philippines. Salt toxicity symptoms (SST) on leaves, days to seedling survival (DSS), and sodium and potassium uptake by shoots were measured under salinity stress of 140?mmol/L of NaCl. A total of 47 QTLs, including 26 main-effect QTLs (M-QTLs) and 21 epistatic QTLs (E-QTLs), were identified from the two sets of reciprocal ILs. Among the 26?M-QTLs, only four (15.4%) were shared in the reciprocal backgrounds while no shared E-QTLs were detected, indicating that ST QTLs, especially E-QTLs, were very specific to the genetic background. Further, 78.6% of the M-QTLs for SST and DSS identified in the reciprocal ILs were also detected in the recombinant inbred lines (RILs) from the same cross, which clearly brings out the background effect on ST QTL detection and its utilization in ST breeding. The detection of ILs with various levels of pyramiding of nonallelic M-QTL alleles for ST from Xiushui09 into IR2061-520-6-9 allowed us to further improve the ST in rice.     

Additive and over-dominant effects resulting from epistatic loci are the primary genetic basis of heterosis in rice

A set of 148 F9 recombinant inbred lines (RILs) was developed from the cross of an indica cultivar 93-11 and japonica cultivar DTT13,showing strong F1 heterosis.Subsequently,two backcross F1 (BCF1) populations were constructed by backcrossing these 148 RILs to two parents,93-11 and DT713.These three related populations (281BCF1 lines,148 RILs) were phenotyped for six yield-related traits in two locations.Significant inbreeding depression was detected in the population of RILS and a high level of heterosis was observed in the two BCF1 populations.A total of 42 main-effect quantitative trait loci (M-QTLs) and 109 epistatic effect QTL pairs (E-QTLs) were detected in the three related populations using the mixed model approach.By comparing the genetic effects of these QTLs detected in the RILs,BCF1 performance and mid-parental heterosis (HMp),we found that,in both BCF1 populations,the QTLs detected could be classified into two predominant types:additive and over-domlnant loci,which indicated that the additive and over-dominant effect were more important than complete or partially dominance for M-QTLs and E-QTLs.Further,we found that the E-QTLs detected collectively explained a larger portion of the total phenotypic variation than the M-QTLs in both RILs and BCF1 populations.All of these results suggest that additive and over-dominance resulting from epistatic loci might be the primary genetic basis of heterosis in rice.     

Identification of main effect and epistatic quantitative trait loci for morphological and yield-related traits in peanut (<Emphasis Type="Italic">Arachis hypogaea</Emphasis> L.)

An effort was made in the present study to identify the main effect and epistatic quantitative trait locus (QTL) for the morphological and yield-related traits in peanut. A recombinant inbred line (RIL) population derived from TAG 24 × GPBD 4 was phenotyped in seven environments at two locations. QTL analysis with available genetic map identified 62 main-effect QTLs (M-QTLs) for ten morphological and yield-related traits with the phenotypic variance explained (PVE) of 3.84–15.06%. Six major QTLs (PVE >?10%) were detected for PLHT, PPP, YPP, and SLNG. Stable M-QTLs appearing in at least two environments were detected for PLHT, LLN, YPP, YKGH, and HSW. Five M-QTLs governed two traits each, and 16 genomic regions showed co-localization of two to four M-QTLs. Intriguingly, a major QTL reported to be linked to rust resistance showed pleiotropic effect for yield-attributing traits like YPP (15.06%, PVE) and SLNG (13.40%, PVE). Of the 24 epistatic interactions identified across the traits, five interactions involved six M-QTLs. Three interactions were additive × additive and remaining two involved QTL × environment (QE) interactions. Only one major M-QTL governing PLHT showed epistatic interaction. Overall, this study identified the major M-QTLs for the important productivity traits and also described the lack of epistatic interactions for majority of them so that they can be conveniently employed in peanut breeding.     

A biplot approach for investigating QTL-by-environment patterns

Due to the universal presence of genotype by environment interactions, understanding the pattern of quantitative trait loci (QTL)-by-environment interactions is a prerequisite for effective marker-assisted selection. In this report, we describe a biplot approach for investigating QTL-by-environment patterns. This approach involves two steps. It starts with a two-way table containing effects of individual QTLs in individual environments for the trait under investigation. This table is decomposed into principal components via singular value decomposition, and the first two principal components are plotted for both QTLs and environments to form a biplot. The resulting QQE biplot contains information on QTL main effects (Q) and QTL-by-environment interactions (QE). A QQE biplot displays the QTL-by-environment patterns and allows visualization of: (1) the magnitude of the effect of a QTL, (2) the average effect of a QTL and its stability across environments, (3) the effects of a QTL in individual environments, (4) the similarity/dissimilarity among QTLs in effect and response to the environments, (5) the similarity/dissimilarity among environments in modulating QTL effects, (6) any differentiation of mega-environments, and (7) the combination of QTL alleles for maximum/minimum expression of the trait for each environment or mega-environment. A case study is provided using the QTL-by-environment two-way table for barley yield.     

Mapping of QTLs governing agronomic and yield traits in chickpea

Chickpea is one of the most important leguminous cool season food crops, cultivated prevalently in South Asia and Middle East. The main objective of this study was to identify quantitative trait loci (QTLs) associated with seven agronomic and yield traits in two recombinant inbred line populations of chickpea derived from the crosses JG62 × Vijay (JV population) and Vijay × ICC4958 (VI population) from at least three environments. Single locus QTL analysis involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated traits to detect pleiotropic QTLs. Two-locus analysis was conducted to identify the main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions. Through CIM analysis, a total of 106 significant QTLs (41 in JV and 65 in VI populations) were identified for the seven traits, of which one QTL each for plant height and days to maturity was common in both the populations. Six pleiotropic QTLs that were consistent over the environments were also identified. LG2 in JV and LG1a in VI contained at least one QTL for each trait. Hence, concentrating on these LGs in molecular breeding programs is most likely to bring simultaneous improvement in these traits.     

QTL mapping for quantities of protein fractions in bread wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

One of the key targets of breeding programs in bread wheat is to improve the end-use quality. The relationships between quantities of protein fractions and dough rheological characters have been well established, but there is little information on the genetic control of quantities of protein fractions. Two hundred and forty F₆ recombinant inbred lines derived from a cross between two Chinese wheat cultivars, PH82-2 and Neixiang 188, were sown at Jiaozuo in Henan province in the 2005–2006 and 2006–2007 cropping seasons, and inclusive composite interval mapping was used to dissect main effect quantitative trait loci (M-QTLs) and digenic epistatic QTLs (E-QTLs) for quantities of protein fractions. A total of 55 M-QTLs and 77 pairs of E-QTLs affecting the quantities of protein fractions including GLU-A1 (QGA1), GLU-B1 (QGB1), GLU-D1 (QGD1), HMW-GS (QHMW), GLU-A3 (QGA3), GLU-B3 (QGB3), LMW-GS (QLMW), glutenin (QGLU) and the ratio of the quantity of glutenin to those of gliadin were identified, with M-QTLs contributing 39.3–95.6% of the phenotypic variance explained (PVE), and E-QTLs accounting for 1.4–33.5% of the PVE. Among the M-QTLs, 33 were consistent in two seasons and in the mean value of two seasons with similar effects in both magnitude and direction, including major genes on HMW and LMW glutenin loci linked to Sec1 and Glu-B1c, Glu-D1d, Glu-A3a, and grain hardness locus Ha, indicating that these genes were the most important determinants of gluten strength, and they might have significant effects on dough properties not only through effects on allelic composition, but also by influencing quantities of protein fractions. The effects of E-QTLs were more influenced by environments, compared with those of M-QTLs, with only two pairs of E-QTLs consistent in two seasons and in the mean value of two seasons. The M-QTLs were detected in 12 marker intervals, all of which involved E-QTLs on quantities of protein fractions, whereas only 40 of 77 pairs of E-QTLs involved intervals in which M-QTLs were detected. The results indicated that besides main effects, epistatic effects were also important factors in determining quantities of protein fractions in wheat.     

Identification of several small main-effect QTLs and a large number of epistatic QTLs for drought tolerance related traits in groundnut (Arachis hypogaea L.)

Cultivated groundnut or peanut (Arachis hypogaea L.), an allotetraploid (2n = 4x = 40), is a self pollinated and widely grown crop in the semi-arid regions of the world. Improvement of drought tolerance is an important area of research for groundnut breeding programmes. Therefore, for the identification of candidate QTLs for drought tolerance, a comprehensive and refined genetic map containing 191 SSR loci based on a single mapping population (TAG 24 x ICGV 86031), segregating for drought and surrogate traits was developed. Genotyping data and phenotyping data collected for more than ten drought related traits in 2-3 seasons were analyzed in detail for identification of main effect QTLs (M-QTLs) and epistatic QTLs (E-QTLs) using QTL Cartographer, QTLNetwork and Genotype Matrix Mapping (GMM) programmes. A total of 105 M-QTLs with 3.48-33.36% phenotypic variation explained (PVE) were identified using QTL Cartographer, while only 65 M-QTLs with 1.3-15.01% PVE were identified using QTLNetwork. A total of 53 M-QTLs were such which were identified using both programmes. On the other hand, GMM identified 186 (8.54-44.72% PVE) and 63 (7.11-21.13% PVE), three and two loci interactions, whereas only 8 E-QTL interactions with 1.7-8.34% PVE were identified through QTLNetwork. Interestingly a number of co-localized QTLs controlling 2-9 traits were also identified. The identification of few major, many minor M-QTLs and QTL × QTL interactions during the present study confirmed the complex and quantitative nature of drought tolerance in groundnut. This study suggests deployment of modern approaches like marker-assisted recurrent selection or genomic selection instead of marker-assisted backcrossing approach for breeding for drought tolerance in groundnut.     

Quantitative trait loci for cold tolerance of rice recombinant inbred lines in low temperature environments

Low temperature is one of the major environmental stresses in rice cultivation in high-altitude and high-latitude regions. In this study, we cultivated a set of recombinant inbred lines (RIL) derived from Dasanbyeo (indica) / TR22183 (japonica) crosses in Yanji (high-latitude area), Kunming (high-altitude area), Chuncheon (cold water irrigation) and Suwon (normal) to evaluate the main effects of quantitative trait loci (QTL) and epistatic QTL (E-QTL) with regard to their interactions with environments for cold-related traits. Six QTLs for spikelet fertility (SF) were identified in three cold treatment locations. Among them, four QTLs on chromosomes 2, 7, 8, and 10 were validated by several near isogenic lines (NILs) under cold treatment in Chuncheon. A total of 57 QTLs and 76 E-QTLs for nine cold-related traits were identified as distributing on all 12 chromosomes; among them, 19 QTLs and E-QTLs showed significant interactions of QTLs and environments (QEIs). The total phenotypic variation explained by each trait ranged from 13.2 to 29.1% in QTLs, 10.6 to 29.0% in EQTLs, 2.2 to 8.8% in QEIs and 1.0% to 7.7% in E-QTL × environment interactions (E-QEIs). These results demonstrate that epistatic effects and QEIs are important properties of QTL parameters for cold tolerance at the reproductive stage. In order to develop cold tolerant varieties adaptable to wide-ranges of cold stress, a strategy facilitating marker-assisted selection (MAS) is being adopted to accumulate QTLs identified from different environments.     

Mapping quantitative trait loci controlling seed dormancy and heading date in rice, Oryza sativa L., using backcross inbred lines

 To detect quantitative trait loci (QTLs) controlling seed dormancy, 98 BC₁F₅ lines (backcross inbred lines) derived from a backcross of Nipponbare (japonica)/Kasalath (indica)//Nipponbare were analyzed genetically. We used 245 RFLP markers to construct a framework linkage map. Five putative QTLs affecting seed dormancy were detected on chromosomes 3, 5, 7 (two regions) and 8, respectively. Phenotypic variations explained by each QTL ranged from 6.7% to 22.5% and the five putative QTLs explained about 48% of the total phenotypic variation in the BC₁F₅ lines. Except for those of the QTLs on chromosome 8, the Nipponbare alleles increased the germination rate. Five putative QTLs controlling heading date were detected on chromosomes 2, 3, 4, 6 and 7, respectively. The phenotypic variation explained by each QTL for heading date ranged from 5.7% to 23.4% and the five putative QTLs explained about 52% of the total phenotypic variation. The Nipponbare alleles increased the number of days to heading, except for those of two QTLs on chromosomes 2 and 3. The map location of a putative QTL for heading date coincided with that of a major QTL for seed dormancy on chromosome 3, although two major heading-date QTLs did not coincide with any seed dormancy QTLs detected in this study. Received: 10 October 1997 / Accepted: 12 January 1998     

Mapping QTLs with epistatic effects and QTL×environment interactions by mixed linear model approaches

A new methodology based on mixed linear models was developed for mapping QTLs with digenic epistasis and QTL×environment (QE) interactions. Reliable estimates of QTL main effects (additive and epistasis effects) can be obtained by the maximum-likelihood estimation method, while QE interaction effects (additive×environment interaction and epistasis×environment interaction) can be predicted by the-best-linear-unbiased-prediction (BLUP) method. Likelihood ratio and t statistics were combined for testing hypotheses about QTL effects and QE interactions. Monte Carlo simulations were conducted for evaluating the unbiasedness, accuracy, and power for parameter estimation in QTL mapping. The results indicated that the mixed-model approaches could provide unbiased estimates for both positions and effects of QTLs, as well as unbiased predicted values for QE interactions. Additionally, the mixed-model approaches also showed high accuracy and power in mapping QTLs with epistatic effects and QE interactions. Based on the models and the methodology, a computer software program (QTLMapper version 1.0) was developed, which is suitable for interval mapping of QTLs with additive, additive×additive epistasis, and their environment interactions. Received: 23 October 1998 / Accepted: 11 May 1999     

Quantitative trait locus analysis of tuber dormancy in diploid potato (Solanum spp.)

Quantitative trait locus (QTL) analysis for tuber dormancy was performed in a diploid potato population (TRP133) consisting of 110 individuals. The female parent was a hybrid between haploid S. tuberosum (2x) and S. chacoense, while the male parent was a S. phureja clone. The population was characterized for ten isozyme loci, 44 restriction fragment length polymorphisms (RFLPs) and 63 random amplified polymorphic DNAs (RAPDs). Eighty-seven of these loci segregating from the female parent were utilized to develop a linkage map that comprised 10 of the 12 chromosomes in the genome. Dormancy, as measured by days-to-sprouting after harvest, ranged from 10 to 90 days, with a mean of 19 days. QTLs were mapped by conducting one-way analyses of variance for each marker locus by dormancy combination. Twenty-two markers had a significant association with dormancy, identifying six putative QTLs localized on each of chromosomes 2, 3, 4, 5, 7 and 8. The QTL with the strongest effect on dormancy was detected on chromosome 7. A multilocus model was developed using the locus with highest R² value in each QTL. This model explained 57.5% of the phenotypic variation for dormancy. Seven percent of possible epistatic interactions among significant markers were significant when tested through two-way analyses of variance. When these were included in the main-effects model, it explained 72.1% of the phenotypic variation for dormancy. QTL analysis in potato, the methodology to transfer traits and interactions into the 4x level, and QTLs of value for marker-assisted selection, are discussed.     

QTL analysis of potato tuber dormancy

The potential loss of chemical sprout inhibitors because of public concern over the use of pesticides underscores the desirability of breeding for long dormancy of potato (Solanum tuberosum L.) tubers. Quantitative trait locus (QTL) analyses were performed in reciprocal backcrosses between S. tuberosum and S. berthaultii toward defining the complexity of dormancy. S. berthaultii is a wild Bolivian species characterized by a short-day requirement for tuberization, long tuber dormancy, and resistance to several insect pests. RFLP alleles segregating from the recurrent parents as well as from the interspecific hybrid were monitored in two segregating progenies. We detected QTLs on nine chromosomes that affected tuber dormancy, either alone or through epistatic interactions. Alleles from the wild parent promoted dormancy, with the largest effect at a QTL on chromosome 2. Long dormancy appeared to be recessive in the backcross to S. berthaultii (BCB). In BCB the additive effects of dormancy QTLs accounted for 48% of the measured phenotypic variance, and adding epistatic effects to the model explained only 4% more. In contrast, additive effects explained only 16% of the variance in the backcross to S. tuberosum (BCT), and an additional 24% was explained by the inclusion of epistatic effects. In BCB variation at all QTLs detected was associated with RFLP alleles segregating from the hybrid parent; in BCT all QTLs except for two found through epistasis were detected through RFLP alleles segregating from the recurrent parent. At least three dormancy QTLs mapped to markers previously found to be associated with tuberization in these crosses.Paper number 55 of the Department of Fruit and Vegetable Science, Cornell University     

Genetic analysis of adaptive syndromes interrelated with seed dormancy in weedy rice (Oryza sativa)

Seed dormancy in rice interrelates to the weedy characteristics shattering, awn, black hull color, and red pericarp color. A cross between the weedy strain SS18-2 and the breeding line EM93-1 was developed to investigate the genetic basis and adaptive significance of these interrelationships. These characteristics or their components differed in average degree of dominance from –0.8 to 1.5, in heritability from 0.5 to 0.96, and in their contribution to phenotypic or genotypic variation in dormancy by up to 25%. Five dormancy, four shattering, and three awn-length quantitative trait loci (QTLs) were detected in the BC₁ population replicated in 2 years. Two QTLs for hull color were identified, and the SS18-2-derived and EM93-1-derived alleles increased the intensity of black, and red or yellow pigmentations, respectively. The only QTL for pericarp color co-located with the red pericarp gene Rc, with the SS18-2-derived allele increasing the intensity of black and red pigmentations. Four of the five dormancy QTLs were flanked or bracketed by one to four QTLs for the interrelated characteristics. The QTL organization pattern indicates the central role of seed dormancy in adaptive syndromes for non-domesticated plants, implies that the elimination of dormancy from cultivars could arise from the selections against multiple interrelated characteristics, and challenges the use of dormancy genes at these loci in breeding varieties for resistance to pre-harvest sprouting (PHS). However, another QTL (qSD12) provides candidate gene(s) for PHS resistance because it has a large effect in the population and it is independent of the loci for interrelated characteristics.     

Genetic control of dormancy in a Triumph/Morex cross in barley

Seed dormancy in barley (Hordeum vulgare L.) is one of the most important parameters affecting malting. Seed dormancy is quantitatively inherited and variously influenced by the environment. The objectives of the present study were to determine the genome location and effects of quantitative trait loci (QTLs) involved in the expression of seed dormancy in a barley cross between two varieties derived from different germplasm pools. Using a doubled-haploid population of 107 lines of the cross between the malting types Triumph (two-row, dormant) and Morex (six-row, non-dormant), seed dormancy phenotypic data sets from five environments and a 147-marker linkage map were developed in order to perform QTL analyses with simple interval mapping and simplified composite interval mapping procedures. Two different types of variables were considered for seed dormancy characterization: (1) level of dormancy induced during seed development, which was indirectly measured as germination percentage at 3 days and 7 days, GP3 and GP7 respectively; (2) rate of dormancy release in the course of a period after seed harvest (after-ripening). Different mechanisms of genetic control were detected for these two types of dormancy-related traits. A major and consistent dormancy QTL near the centromere on chromosome 7(5H) was associated with the establishment of dormancy during seed development and accounted for 52% and 33% of the variability for GP3 and GP7, respectively. Two other QTLs located in the vicinity of the vrs1 locus on chromosome 2(2H) and near the long arm telomere on chromosome 7(5H) explained 9% and 19% of variation, respectively, for the rate of dormancy release during after-ripening. Likewise, seed dormancy was assessed in an F₂ population derived from the cross between two dormant types of distinct germplasm groups, Triumph (European, two-row, malt) and Steptoe (North American, six-row, feed), which showed similar but not identical genetic control for dormancy. Interestingly, there is remarkable dormancy QTL conservation in both regions on chromosome 7(5H) identified in this study and among other barley mapping populations. These widely conserved QTLs show potential as targets for selection of a moderate level of seed dormancy in breeding programs.Communicated by P. Langridge