Genotypic and phenotypic characterization of genetic differentiation and diversity in the USDA rice mini-core collection

A rice mini-core collection consisting of 217 accessions has been developed to represent the USDA core and whole collections that include 1,794 and 18,709 accessions, respectively. To improve the efficiency of mining valuable genes and broadening the genetic diversity in breeding, genetic structure and diversity were analyzed using both genotypic (128 molecular markers) and phenotypic (14 numerical traits) data. This mini-core had 13.5 alleles per locus, which is the most among the reported germplasm collections of rice. Similarly, polymorphic information content (PIC) value was 0.71 in the mini-core which is the highest with one exception. The high genetic diversity in the mini-core suggests there is a good possibility of mining genes of interest and selecting parents which will improve food production and quality. A model-based clustering analysis resulted in lowland rice including three groups, aus (39 accessions), indica (71) and their admixtures (5), upland rice including temperate japonica (32), tropical japonica (40), aromatic (6) and their admixtures (12) and wild rice (12) including glaberrima and four other species of Oryza. Group differentiation was analyzed using both genotypic distance Fst from 128 molecular markers and phenotypic (Mahalanobis) distance D(2) from 14 traits. Both dendrograms built by Fst and D(2) reached similar-differentiative relationship among these genetic groups, and the correlation coefficient showed high value 0.85 between Fst matrix and D(2) matrix. The information of genetic and phenotypic differentiation could be helpful for the association mapping of genes of interest. Analysis of genotypic and phenotypic diversity based on genetic structure would facilitate parent selection for broadening genetic base of modern rice cultivars via breeding effort.     

Mapping QTLs for alpha-amylase activity in rye grain

Genetic control of alpha-amylase activity in rye grain was investigated by QTL mapping based on DS2 x RXL10 intercross consisting of 99 F5-6 families propagated at one location during four vegetation seasons. A wide range of variation in alpha-amylase activity and transgression effects were found among families and parental lines. This variation was shown to be determined in 40.1% by 7 significant (LOD score not less than 2.5) and 2 putative QTLs (2 < LOD < 2.5) distributed on all rye chromosomes except 4R. Two significant QTLs located on 3RL and 5RL chromosome arms were expressed each year. The third significant QTL was detected in three years (1RL). The other four significant QTLs (2RL, 5RS, 6RL, 7RL) were found in one year of study. The number and composition of QTLs were specific for a given year varying from three to six. QTLs were not correlated with isoenzyme polymorphisms at the structural alpha-Amy1 loci. A QTL associated with a region containing the alpha-Amy3 locus was detected on chromosome 5RL. Both high- and low-activity QTL alleles were found in each parental line, which explains the appearance of transgressive recombinants in the segregating population.     

Mapping QTLs controlling grain yield and its components on chromosome 5A of wheat

Chromosome 5A of wheat is known to carry a number of genes affecting adaptability and productivity. To localize quantitative trait loci (QTLs) controlling grain yield and its components, an RFLP map was constructed from 118 single-chromosome recombinant lines derived from the F₁ between Chinese Spring (Cappelle-Desprez 5A) and Chinese Spring (Triticum spelta 5A). The map was combined with the field-trial data scored over 3 years. A total of five regions in chromosome 5A contributed effects on yield traits. Increases in grain yield, 50-grain weight and spikelet number/ear were determined by complementary QTL alleles from both parents. The effects associated with the vernalization requirement gene Vrn-A1 or a closely linked QTL were significant only in the favorable growing season where the later-flowering vrn-A1 allele from Cappelle-Desprez 5A produced a higher tiller number/plant and spikelet number/ear. The effects of the ear morphology gene q or closely linked QTL(s) were detected for grain yield and ear grain weight. Three other QTLs with minor effects were dispersed along chromosome 5A. These QTLs had large interactions with years due to changes in the magnitude of the significant response. The alleles from T. spelta, however, conferred a higher yield performance. Received: 18 August 1999 / Accepted: 25 March 2000     

Mapping QTLs with main and epistatic effects underlying grain yield and heading time in soft winter wheat

There is increasing awareness that epistasis plays a role for the determination of complex traits. This study employed an association mapping approach in a large panel of 455 diverse European elite soft winter wheat lines. The genotypes were evaluated in multi-environment trials and fingerprinted with SSR markers to dissect the underlying genetic architecture of grain yield and heading time. A linear mixed model was applied to assess marker-trait associations incorporating information of covariance among relatives. Our findings indicate that main effects dominate the control of grain yield in wheat. In contrast, the genetic architecture underlying heading time is controlled by main and epistatic effects. Consequently, for heading time it is important to consider epistatic effects towards an increased selection gain in marker-assisted breeding.     

The rational design of multiple molecular module-based assemblies for simultaneously improving rice yield and grain quality

正Rice is one of the most important staple food for over half of the world's population,and a substantial increase in productivity and quality of rice grain will be required to feed a growing human population.Grain size and shape are the two important components contributing to grain yield and quality,because they impact both yield potential and end-use quality.Over the past 50 years,the     

Identifying wheat genomic regions for improving grain protein concentration independently of grain yield using multiple inter-related populations

Grain yield (GY) and grain protein concentration (GPC) are two major traits contributing to the economic value of the wheat crop. These are, consequently, major targets in wheat breeding programs, but their simultaneous improvement is hampered by the negative correlation between GPC and GY. Identifying the genetic determinants of GPC and GY through quantitative trait loci (QTL) analysis would be one way to identify chromosomal regions, allowing improvement of GPC without reducing GY using marker-assisted selection. Therefore, QTL detection was carried out for GY and GPC using three inter-connected doubled haploid populations grown in a large multi-environment trial network. Chromosomes 2A, 2D, 3B, 7B and 7D showed co-location of QTL for GPC and GY with antagonistic effects, thus contributing to the negative GPC–GY relationship. Nonetheless, genomic regions determining GPC independently of GY across experiments were found on chromosomes 3A and 5D and could help breeders to move the GPC–GY relationship in a desirable direction.     

Fine mapping of QTLs for rice grain yield under drought reveals sub-QTLs conferring a response to variable drought severities

Fine-mapping studies on four QTLs, qDTY(2.1), qDTY(2.2), qDTY(9.1) and qDTY(12.1), for grain yield (GY) under drought were conducted using four different backcross-derived populations screened in 16 experiments from 2006 to 2010. Composite and bayesian interval mapping analyses resolved the originally identified qDTY(2.1) region of 42.3 cM into a segment of 1.6 cM, the qDTY(2.2) region of 31.0 cM into a segment of 6.7 cM, the qDTY(9.1) region of 32.1 cM into two segments of 9.4 and 2.4 cM and the qDTY(12.1) region of 10.6 cM into two segments of 3.1 and 0.4 cM. Two of the four QTLs (qDTY(9.1) and qDTY(12.1)) having effects under varying degrees of stress severity showed the presence of more than one region within the original QTL. The study found the presence of a donor allele at RM262 within qDTY(2.1) and RM24334 within qDTY(9.1) showing a negative effect on GY under drought, indicating the necessity of precise fine mapping of QTL regions before using them in marker-assisted selection (MAS). However, the presence of sub-QTLs together in close vicinity to each other provides a unique opportunity to breeders to introgress such regions together as a unit into high-yielding drought-susceptible varieties through MAS.     

Mapping QTLs associated with drought avoidance in upland rice

The identification of molecular markers linked to genes controlling drought resistance factors in rice is a necessary step to improve breeding efficiency for this complex trait. QTLs controlling drought avoidance mechanisms were analyzed in a doubled-haploid population of rice. Three trials with different drought stress intensities were carried out in two sites. Leaf rolling, leaf drying, relative water content of leaves and relative growth rate under water stress were measured on 105 doubled haploid lines in two trials and on a sub-sample of 85 lines in the third one. Using composite interval mapping with a LOD threshold of 2.5, the total number of QTLs detected in all trials combined was 11 for leaf rolling, 10 for leaf drying, 11 for relative water content and 10 for relative growth rate under stress. Some of these QTLs were common across traits. Among the eleven possible QTLs for leaf rolling, three QTLs (on chromosomes 1, 5 and 9) were common across the three trials and four additional QTLs (on chromosomes 3, 4 and 9) were common across two trials. One QTL on chromosome 4 for leaf drying and one QTL on chromosome 1 for relative water content were common across two trials while no common QTL was identified for relative growth rate under stress. Some of the QTLs detected for leaf rolling, leaf drying and relative water content mapped in the same places as QTLs controlling root morphology, which were identified in a previous study involving the same population. Some QTL identified here were also located similarly with other QTLs for leaf rolling as reported from other populations. This study may help to chose the best segments for introgression into rice varieties and improvement of their drought resistance.     

Mapping and characterization of seed dormancy QTLs using chromosome segment substitution lines in rice

Seed dormancy—the temporary failure of a viable seed to germinate under favorable conditions—is a complex characteristic influenced by many genes and environmental factors. To detect the genetic factors associated with seed dormancy in rice, we conducted a QTL analysis using chromosome segment substitution lines (CSSLs) derived from a cross between Nona Bokra (strong dormancy) and Koshihikari (weak dormancy). Comparison of the levels of seed dormancy of the CSSLs and their recurrent parent Koshihikari revealed that two chromosomal regions—on the short arms of chromosomes 1 and 6—were involved in the variation in seed dormancy. Further genetic analyses using an F₂ population derived from crosses between the CSSLs and Koshihikari confirmed the allelic differences and the chromosomal locations of three putative QTLs: Sdr6 on chromosome 1 and Sdr9 and Sdr10 on chromosome 6. The Nona Bokra alleles of the three QTLs were associated with decreased germination rate. We discuss the physiological features of the CSSLs and speculate on the possible mechanisms of dormancy in light of the newly detected QTLs.     

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.     

Mapping QTLs for submergence tolerance in rice by AFLP analysis and selective genotyping

By combining the amplified fragment length polymorphism (AFLP) technique with selective genotyping, we constructed a linkage map for rice and assigned each linkage group to a corresponding chromosome. The AFLP map, consisting of 202 AFLP markers, was generated from 74 recombinant inbred lines (RIL) which were selected from both extremes of the population (250 lines) with respect to the response to complete submergence. Map length was 1756 cM, with an average interval size of 8.5 cM. To assign linkage groups to chromosomes, we used 50 previously mapped AFLP markers as anchor markers distributed over the 12 chromosomes. Other AFLP markers were then assigned to specific chromosomes based on their linkage to anchor markers. This AFLP map is equivalent to the RFLP/AFLP map constructed previously as the anchors were in the same order in both maps. Furthermore, tests with two restriction fragment length polymorphism (RFLP) markers and two sequence-tagged site (STS) markers showed that they mapped in the expected positions. Using this AFLP map, a major gene for submergence tolerance was localized on chromosome 9. Quantitative trait loci (QTL) associated with submergence tolerance were detected on chromosomes 6, 7, 11, and 12. We conclude that the combination of AFLP mapping and selective genotyping provides a much faster and easier approach to QTL identification than the use of RFLP markers. Received: 20 December 1996 / Accepted: 21 January 1997     

Mapping of QTLs for phosphorus-deficiency tolerance in rice (Oryza sativa L.)

 Phosphorus (P) deficiency of soils is a major yield-limiting factor in rice production. Increasing the P-deficiency tolerance of rice cultivars may represent a more cost-effective solution than relying on fertilizer application. The objective of this study was to identify putative QTLs for P-deficiency tolerance in rice, using 98 backcross inbred lines derived from a japonica×indica cross and genotyped at 245 RFLP marker loci. Lines were grown on P-deficient soil and P uptake, internal P-use efficiency, dry weight, and tiller number were determined. Three QTLs were identified for dry weight and four QTLs for P uptake, together explaining 45.4% and 54.5% of the variation for the respective traits. Peaks for both traits were in good agreement which was to be expected considering the tight correlation of r=0.96 between dry weight and P uptake. For both traits the QTL linked to marker C443 on chromosome 12 had a major effect. Two of the three QTLs detected for internal P-use efficiency, including the major one on chromosome 12, coincided with QTLs for P uptake; however, whereas indica alleles increased P uptake they reduced P-use efficiency. We concluded that this was not due to the tight linkage of two genes in repulsion but rather due to an indirect effect of P uptake on P-use efficiency. Most lines with high use efficiency were characterized by very low P uptake and dry weight and apparently experienced extreme P-deficiency stress. Their higher P-use efficiency was thus the result of highly sub-optimal tissue-P concentrations and did not represent a positive adaptation to low P availability. The number of tillers produced under P deficiency is viewed as an indirect indicator of P-deficiency tolerance in rice. In addition to the major QTL on chromosome 12 already identified for all other traits, two QTLs on chromosome 4 and 12 were identified for tiller number. Their position, however, coincided with QTLs for tiller number reported elsewhere under P-sufficient conditions and therefore appear to be not related to P-deficiency tolerance. In this study P-deficiency tolerance was mainly caused by differences in P uptake and not in P-use efficiency. Using a trait indirectly related to P-deficiency tolerance such as tiller number, we detected a major QTL but none of the minor QTLs detected for P uptake or dry weight. Received: 9 February 1998 / Accepted: 29 April 1998     

Mapping QTLs for phosphorus deficiency tolerance in rice (Oryza sativa L.)

 The amplified fragment length polymorphism (AFLP) technique combined with selective genotyping was used to map quantitative trait loci (QTLs) associated with tolerance for phosphorus (P) deficiency in rice. P deficiency tolerant cultivar IR20 was crossed to IR55178-3B-9-3 (sensitive to P-deficiency) and 285 recombinant inbred lines (RILs) were produced by single-seed descent. The RILs were phenotyped for the trait by growing them in P-sufficient (10.0 mg/l) and P-deficient (0.5 mg/l) nutrient solution and determining their relative tillering ability at 28 days after seeding, and relative shoot dry weight and relative root dry weight at 42 days after seeding. Forty two of each of the extreme RILs (sensitive and tolerant) and the parents were subjected to AFLP analysis. A map consisting of 217 AFLP markers was constructed. Its length was 1371.8 cM with an average interval size of 7.62 cM. To assign linkage groups to chromosomes, 30 AFLP and 26 RFLP markers distributed over the 12 chromosomes were employed as anchor markers. Based on the constructed map, a major QTL for P-deficiency tolerance, designated PHO, was located on chromosome 12 and confirmed by RFLP markers RG9 and RG241 on the same chromosome. Several minor QTLs were mapped on chromosomes 1, 6, and 9. Received: 21 April 1998 / Accepted: 9 June 1998     

Identification of quantitative trait loci for grain size and the contributions of major grain-size QTLs to grain weight in rice

Grain weight, one of the three major components of rice yield, is largely determined by grain size, which is controlled by quantitative trait loci (QTLs). In a previous study, we identified qGS5 as a major QTL for grain width. Here, we report our identification of two more major grain-size QTLs (qGL3 and qGW2a) by using a recombinant inbred line (RIL) population from a cross of two indica varieties, ‘Zhenshan 97’ and ‘SLG’. To investigate the contribution of the three grain-size QTLs to final grain weight, we developed near-isogenic lines (NILs) NIL-qGL3, NIL-qGW2a, and NIL-qGS5 and used these to build the combined QTLs–NIL in the genetic background of ‘Zhenshan 97’ by marker-assisted selection and conventional backcrossing, respectively. A BCF₂ population of 957 individuals was developed from the combined QTLs-NIL for further study of the genetic control of grain size. The QTL analysis revealed that qGW2a and qGL3 played more important roles in grain weight gain than qGS5. All three QTLs showed additive effects with respect to grain weight, with no interaction. These results clearly indicate that pyramiding of major grain-size QTLs is a useful approach for improving rice yield.     

Stability of QTLs for rice grain dimension and endosperm chalkiness characteristics across eight environments

Rice appearance quality, including traits specifying grain dimension and endosperm chalkiness, represents a major problem in many rice-producing areas of the world. In this study, the genetic basis of six appearance quality traits of milled rice was dissected into quantitative trait loci (QTL) main effects, and the stability of these QTLs was assessed in a population of 66 chromosome segment substitution lines (CSSLs) across eight environments. The CSSLs showed transgressive segregation for many of the traits, and significant correlations were detected among most of the traits. Twenty-two QTLs were identified on eight chromosomes, and numerous QTLs affecting related traits were mapped in the same regions, probably reflecting pleiotropic effects. Nine QTLs, namely qGL-1,qGL-3, qGW-5,qLWR-3, qLWR-5,qPGWC-8, qPGWC-9, qACE-8, and qDEC-8, were consistently detected across the eight environments. The additive main effect and multiplicative interaction (AMMI) analysis showed that genotype (G) × environment (E) interaction was significant for all six traits, with the first three iPCA terms accounting for over 80% of the G × E variance. Both D_I values and the iPCA1-iPCA2 biplots showed that the CSSLs harboring the nine QTL alleles were more stable than those carrying any of the additional 13 QTL alleles, thereby confirming their environmental stability and pointing to their appropriateness as targets for marker-assisted selection for high-quality rice varieties.     

Mapping of QTLs conferring resistance to bacterial leaf streak in rice

A large F₂ and a RI population were separately derived from a cross between two indica rice varieties, one of which was highly resistant to bacterial leaf streak (BLS) and the other highly susceptible. Following artificial inoculation of the RI population and over 2 years of testing, 11 QTLs were mapped by composite interval mapping (CIM) on six chromosomes. Six of the QTLs were detected in both seasons. Eight of the QTLs were significant following stepwise regression analysis, and of these, 5 with the largest effects were significant in both seasons. The detected QTLs explained 84.6% of the genetic variation in 1997. Bulked segregant analysis (BSA) of the extremes of the F₂ population identified 3 QTLs of large effect. The 3 QTLs were dentical to 3 of the 5 largest QTLs detected by CIM. The independent detection of the same QTLs using two methods of analysis in separate mapping populations verifies the existence of the QTLs for BLS and provides markers to ease their introduction into elite varieties. Received: 13 October 1999 / Accepted: 29 October 1999     

Identifying and exploiting grain yield genes in rice

Improved grain yield has been a major focus of crop breeding programs around the world. With the accomplishments of the Rice Genome Project, genes regulating several agronomically important traits related to grain yield, such as tiller number, grain number, grain size, and plant height, have recently been identified. Although these findings have not been enough to fully characterize the mechanisms that regulate each trait, these genes and knowledge of the molecular mechanisms involved provide a set of tools that can be combined to achieve tailor-made breeding suitable for various programs aimed at higher grain yield.     

Mapping QTLs for grain hardness and puroindoline content in wheat ( Triticum aestivum L.)

Genes for puroindoline-a (Pin-a), puroindoline-b (Pin-b) and grain-softness proteins (GSP) have been shown to be linked to the dominant Ha locus responsible for the soft texture of the grain. Though linkage has been demonstrated of the puroindoline genes to the Ha locus, there is no clear evidence that puroindoline content is the product of the gene Ha. A segregating population of 115 recombinant inbred lines (RILs) originating from a cross between the hexaploid Synthetic wheat ( Triticum durum x Aegilops tauschii, W 7984) and the cultivar 'Opata' (M 85) was studied in two different experimental years to detect Quantitative Trait Loci (QTLs) for three traits: grain hardness (Hard), puroindoline-a (Pin-a) and puroindoline-b (Pin-b) contents. The detection of QTLs was performed using marker linear regression. Negative correlation coefficients (-0.86 and -0.80) were identified between grain hardness and puroindoline content (a and b, respectively) on data obtained in 1996. Results obtained in 1999 confirmed the negative correlation between Hard and Pin-a (-0.73); however a positive correlation coefficient was found with Pin-b content (0.41). Total phenotypic variation explained by each QTL was calculated (R2). For each of the Hard, Pin-a and Pin-b traits one major QTL was detected on the short arm of chromosome 5D, located close to the mta9 allele (puroindoline-a). For the first year (1996) the QTL in this region explained around 63% of the phenotypic variability in grain hardness, 77% in Pin-a and 45% in Pin-b contents. These values were confirmed in trials carried out in 1999 with a R2 value of 0.71, 0.72 and 0.25 for Hard, Pin-a and Pin-b, respectively. In 1996 and 1999 a second major QTL was detected for grain hardness on the long arm of the same chromosome. Present results indicate that it cannot be definitely concluded that puroindoline content represents a linear explanation for variations in grain hardness.     

普通野生稻中增产相关QTL的发掘

普通野生稻(Oryza Rufipogon)是重要的遗传资源,发掘其优良等位基因将对水稻遗传改良产生重要影响。文章从以珍汕97为轮回亲本,普通野生稻为供体的BC2F1群体中选择一个与珍汕97表型明显不同的单株BC2F1-15,经过连续自交获得回交重组自交系BC2F5群体。均匀分布于12条染色体的126个多态性SSR(Simplesequence repeats)标记基因型分析,发现BC2F1-15单株在30%的标记位点为杂合基因型;利用该群体共检测到4个抽穗期、3个株高、4个每穗颖花数、2个千粒重和1个单株产量QTL。在第7染色体RM481-RM2区间,检测到抽穗期、每穗颖花数和产量QTL,野生稻等位基因表现增效作用;其他3个每穗颖花数QTL位点,野生稻等位基因也均具有增效作用。结果表明野生稻携带有增产相关的等位基因,这些有利等位基因无疑是水稻遗传改良可资利用的新资源。     

Mapping of QTLs associated with cold tolerance during the vegetative stage in rice

Low-temperature stress is an important factor affecting the growth and development of rice (Oryza sativa L.) in temperate and high-elevation areas. Cold stress may cause various seedling injuries, delayed heading and yield reduction due to spikelet sterility. In this study, 181 microsatellite marker loci were used to identify quantitative trait loci (QTLs) associated with cold tolerance at the vegetative stage in 191 recombinant inbred lines (RILs) derived from a cross of a cold-tolerant temperate japonica cultivar (M-202) with a cold-sensitive indica cultivar (IR50). Different temperature regimes were applied in growth chambers on 191 RILs. The temperature regimes imposed in the growth chamber simulated cold-stress injuries at the seedling and late vegetative stages. In this study a major QTL was identified on chromosome 12, designated as qCTS12a, that was closely associated with cold-induced necrosis and wilting tolerance, and accounted for 41% of the phenotypic variation. A number of QTLs with smaller effects were also detected on eight rice chromosomes.