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 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.     

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.     

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.     

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.     

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.     

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.     

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).     

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.     

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.     

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.     

Genetic basis of pre-harvest sprouting tolerance using single-locus and two-locus QTL analyses in bread wheat

Quantitative trait loci (QTL) analysis for pre-harvest sprouting tolerance (PHST) in bread wheat was conducted following single-locus and two-locus analyses, using data on a set of 110 recombinant inbred lines (RILs) of the International Triticeae Mapping Initiative population grown in four different environments. Single-locus analysis following composite interval mapping (CIM) resolved a total of five QTLs with one to four QTLs in each of the four individual environments. Four of these five QTLs were also detected following two-locus analysis, which resolved a total of 14 QTLs including 8 main effect QTLs (M-QTLs), 8 epistatic QTLs (E-QTLs) and 5 QTLs involved in QTL × environment (QE) or QTL × QTL × environment (QQE) interactions, some of these QTLs being common. The analysis revealed that a major fraction (76.68%) of the total phenotypic variation explained for PHST is due to M-QTLs (47.95%) and E-QTLs (28.73%), and that only a very small fraction of variation (3.24%) is due to QE and QQE interactions. Thus, more than three-quarters of the genetic variation for PHST is fixable and would contribute directly to gains under selection. Two QTLs that were detected in more than one environment and at LOD scores above the threshold values were located on 3BL and 3DL presumably in the vicinity of the dormancy gene TaVp1. Another QTL was found to be located on 3B, perhaps in close proximity to the R gene for red grain colour. However, these associations of QTLs for PHST with genes for dormancy and grain colour are only suggestive. The results obtained in the present study suggest that PHST is a complex trait controlled by large number of QTLs, some of them interacting among themselves or with the environment. These QTLs can be brought together through marker-aided selection, leading to enhanced PHST.     

Genetic dissection of drought tolerance in chickpea (Cicer arietinum L.)

Key message

Analysis of phenotypic data for 20 drought tolerance traits in 1–7 seasons at 1–5 locations together with genetic mapping data for two mapping populations provided 9 QTL clusters of which one present on CaLG04 has a high potential to enhance drought tolerance in chickpea improvement.

Abstract

Chickpea (Cicer arietinum L.) is the second most important grain legume cultivated by resource poor farmers in the arid and semi-arid regions of the world. Drought is one of the major constraints leading up to 50 % production losses in chickpea. In order to dissect the complex nature of drought tolerance and to use genomics tools for enhancing yield of chickpea under drought conditions, two mapping populations—ICCRIL03 (ICC 4958 × ICC 1882) and ICCRIL04 (ICC 283 × ICC 8261) segregating for drought tolerance-related root traits were phenotyped for a total of 20 drought component traits in 1–7 seasons at 1–5 locations in India. Individual genetic maps comprising 241 loci and 168 loci for ICCRIL03 and ICCRIL04, respectively, and a consensus genetic map comprising 352 loci were constructed (http://cmap.icrisat.ac.in/cmap/sm/cp/varshney/). Analysis of extensive genotypic and precise phenotypic data revealed 45 robust main-effect QTLs (M-QTLs) explaining up to 58.20 % phenotypic variation and 973 epistatic QTLs (E-QTLs) explaining up to 92.19 % phenotypic variation for several target traits. Nine QTL clusters containing QTLs for several drought tolerance traits have been identified that can be targeted for molecular breeding. Among these clusters, one cluster harboring 48 % robust M-QTLs for 12 traits and explaining about 58.20 % phenotypic variation present on CaLG04 has been referred as “QTL-hotspot”. This genomic region contains seven SSR markers (ICCM0249, NCPGR127, TAA170, NCPGR21, TR11, GA24 and STMS11). Introgression of this region into elite cultivars is expected to enhance drought tolerance in chickpea.     

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.     

Genetic analysis of morphological index and its related taxonomic traits for classification of indica/japonica rice

A doubled haploid population derived from anther culture of ZYQ8/JX17 F1, a typical indica and japonica hybrid, was used in this study. Morphological index and its related taxonomic traits were investigated in 121 DH lines. The quantitative trait loci (QTLs) for morphological index and its related taxonomic traits were analyzed. Two major QTLs for leaf hairiness, three QTLs for length/width of grain, one QTL for color of hull when heading, one QTL for hairiness of hull, two QTLs for length of the first and second panicle internode, and one major QTL and two QTLs for phenol reaction were detected. Four QTLs for morphological index were also identified on chromosomes 1, 3, 4 and 6, respectively, three of which on chromosomes 1, 3 and 6, respectively, were found to be located in the same chromosome regions where some QTLs for the related taxonomic traits were located.     

QTL-based analysis of leaf senescence in an indica/japonica hybrid in rice (Oryza sativa L.)

In order to identify quantitative trait loci (QTLs) for leaf senescence and related traits in rice (Oryza sativa L.), we developed two backcross populations, indica/japonica// japonica and indica/japonica//indica, using IR36 as the indica parent and Nekken-2 as the japonica parent. The QTLs were mapped using a set of simple sequence-repeat markers (SSRs) in the BC₁F₁ population. Senescence was characterized in these plants by measuring the leaf chlorophyll content 25 days after flowering (DAF), the reduction in chlorophyll content (the difference between the chlorophyll content at flowering and at 25 DAF), and the number of late-discoloring leaves per panicle at 25 DAF in five plants from each BC₁F₂ line. These plants were moved into a temperature-controlled growth cabinet at the time of flowering and allowed to mature under identical conditions. Eleven QTLs were detected in the two populations. The major of QTLs for senescence were found on the short arm of chromosome 6 and on the long arm of chromosome 9. Of these, one QTL on chromosome 6 and two on chromosome 9 were verified by confirming the effects of the genotypes on the phenotypes of the BC₁F₃ lines. The japonica parent was found to contribute to late senescence at all but one QTL. Based on a comparison of the effects of heterozygotes and homozygotes on the phenotypic values of each QTL genotype, we concluded that the differential senescence observed in the indica-japonica hybrid was not due to over-dominance; rather, it was the result of partial-dominance genes that were donated from either of the parents.     

Genetic analysis of morphological index and its related taxonomic traits for classification of indica/japonica rice

A doubled haploid population derived from anther culture of ZYQ8/JX17 F₁, a typical indica and japonica hybrid, was used in this study. Morphological index and its related taxonomic traits were investigated in 121 DH lines. The quantitative trait loci (QTLs) for morphological index and its related taxonomic traits were analyzed. Two major QTLs for leaf hairiness, three QTLs for length/width of grain, one QTL for color of hull when heading, one QTL for hairiness of hull, two QTLs for length of the first and second panicle internode, and one major QTL and two QTLs for phenol reaction were detected. Four QTLs for morphological index were also identified on chromosomes 1, 3, 4 and 6, respectively, three of which on chromosomes 1, 3 and 6, respectively, were found to be located in the same chromosome regions where some QTLs for the related taxonomic traits were located.     

QTL analysis for grain quality traits in 2 BC2F2 populations derived from crosses between Oryza sativa cv Swarna and 2 accessions of O. nivara

The appearance and cooking quality of rice determine its acceptability and price to a large extent. Quantitative trait loci (QTLs) for 12 grain quality traits were mapped in 2 mapping populations derived from Oryza sativa cv Swarna × O. nivara. The BC(2)F(2) population of the cross Swarna × O. nivara IRGC81848 (population 1) was evaluated during 2005 and that from Swarna × O. nivara IRGC81832 (population 2) was evaluated during 2006. Linkage maps were constructed using 100 simple sequence repeat (SSR) markers in population 1 and 75 SSR markers in population 2. In all, 21 QTLs were identified in population 1 (43% from O. nivara) and 37 in population 2 (38% QTLs from O. nivara). The location of O. nivara-derived QTLs mp1.2 for milling percent, kw6.1 for kernel width, and klac12.1 for kernel length after cooking coincided in the 2 populations and appear to be useful for Marker Assisted Selection (MAS). Four QTLs for milling percent, 1 QTL each for amylose content, water uptake, elongation ratio, 2 QTLs for kernel width, and 3 QTLs for gel consistency, each explained more than 20% phenotypic variance. Three QTL clusters for grain quality traits were close to the genes/QTLs for shattering and seed dormancy. QTLs for 4 quality traits were associated with 5 of the 7 major yield QTLs reported in the same 2 mapping populations. Useful introgression lines have been developed for several agronomic traits. It emerges that 40% O. nivara alleles were trait enhancing in both populations, and QTLs for grain quality overlapped with yield meta-QTLs and QTLs for dormancy and seed shattering.     

Analysis of quantitative trait loci which contribute to anther culturability in rice (Oryza sativa L.)

Anther culturability of rice is significantly different between indica and japonica varieties. A doubled haploid (DH) population was established via anther culture of an indica/japonica hybrid on SK₃ medium, which had been shown particularly suitable for anther culture of indica/japonica hybrids. For analyzing the quantitative trait loci (QTLs) responsible for anther culturability, anthers of the DH lines were again cultured with SK₃ medium and parameters for four traits representing the anther culturability were surveyed and analyzed with the molecular map constructed from the same DH population. The parameters for four major traits were as follows: callus induction frequency (CI), green plantlet differentiation frequency (GPD), albino plantlet differentiation frequency (APD), and green plantlet yield frequency (GPY). All four traits displayed continuous distributions among the DH lines. The correlation coefficients between these traits were also tested and showed that there was no relationship between callus induction and green plantlet differentiation frequencies, but both showed strong positive correlation with the frequency of green plantlet yield. For callus induction frequency, five QTLs were identified on chromosomes 6, 7, 8, 10 and 12. Two QTLs for green plantlet differentiation frequency were located on chromosomes 1 and 9. There was a major QTL for albino plantlet differentiation frequency on chromosome 9. No independent QTL was found for green plantlet yield frequency. The results may be useful in the selection of parents with high response to anther culture for rice haploid breeding and in the establishment of permanent DH populations for molecular mapping.     

水稻花药培养力的遗传分析及基因定位

在栽培稻的籼粳亚种间,花药培养力存在显著差异,这一差异主要是由遗传因素引起的。以适合籼粳稻杂种花药培养的SK_3培养基,经花药培养获得了一个籼粳交F_1代的加倍单倍体(DH)群体,对该群体的110个株系用同一种培养基进行花药培养,利用该群体构建的分子图谱进行有关水稻花药培养力的数量性状基因座位(QTLs)的分析。结果表明,与水稻花药培养力有关的4个性状在DH群体中均表现为连续分布,愈伤组织诱导率与绿苗分化率之间不存在相关性,而绿苗产率与愈伤组织诱导率和绿苗分化率均显著相关。在第6、7、8、10和12 5条染色体上分别检测到与愈伤组织诱导率有关的5个QTLs,其加性效应均为正。在第1和第9染色体上检测到与绿苗分化率有关的2个QTLs,这两个性状间的QTs不存在连锁。在第9染色体上有一个主效基因与白苗分化率有关,对绿苗产率则没有检测到特有的QTL。