大豆蛋白含量新位点qPRO-19-1的定位

大豆是重要的粮食作物和经济作物,其籽粒蛋白约为40%,是植物蛋白的重要来源之一。国产大豆主要用于食用,提高大豆蛋白含量是主要的育种目标。因此,发掘大豆蛋白含量相关基因,对开发分子标记并培育高蛋白食用大豆具有重要意义。本研究以低蛋白大豆品种中黄35为母本,以源自日本的高蛋白大豆十胜长叶为父本,构建了重组自交系(RIL,recombination inbred lines)群体。利用集群分离分析法(BSA,bulked segregant analysis)在3条染色体筛选出9个与蛋白含量相关的SSR标记,其中位于19号染色体的QTL尚未见报道。进一步利用完备区间作图法(ICIM-ADD)分析RIL群体F_2:15和F_2:16,在19号染色体重复定位了1个蛋白质含量相关QTL qPRO-19-1,位于分子标记SSR_19_38和SSR_19_59之间,LOD值分别为3.43和3.98,贡献率分别为7.81%和14.87%,高蛋白等位基因来自于高蛋白亲本十胜长叶。qPRO-19-1的定位区间长度为385 kb,共有注释基因36个。本研究定位了蛋白质含量相关的新位点qPRO-19-1,为大豆高蛋白基因的图位克隆及分子标记育种奠定了基础。     

Locating QTLs controlling several adult root traits in an elite Chinese hybrid rice

This study aimed to elucidate the genetics of the adult root system in elite Chinese hybrid rice. Several adult root traits in a recombinant inbred line (RIL) population of Xieyou 9308 and two backcross F₁ (BCF₁) populations derived from the RILs were phenotyped under hydroponic culture at heading stage for quantitative trait locus (QTL) mapping and other statistical analysis. There a total of eight QTLs detected for the root traits. Among of them, a pleiotropic QTL was repeatedly flanked by RM180 and RM5436 on the short arm of chromosome 7 for multiple traits across RILs and its BCF₁ populations, accounting for 6.88% to 25.26% of the phenotypic variances. Only additive/dominant QTLs were detected for the root traits. These results can serve as a foundation for facilitating future cloning and molecular breeding.     

Molecular marker dissection of rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) plant architecture under temperate and tropical climates

Rice (Oryza sativa L.) plants develop vertically with shoot elongation and horizontally with tillering. The purpose of this study was to identify and characterize genomic regions influencing the rice plant architecture by quantitative trait locus (QTL) analysis for the component traits: culm length (CL), panicle length (PnL), panicle number (PnN) and tiller number (TN). For this QTL analysis, 191 recombinant inbred lines (F₇) derived from a cross of Milyang 23 (M23) and Akihikari (AK) were grown in 1995, 1996 and 1997 (May–Oct) in Joetsu, Japan (temperate climate), and in the 2000 dry season (Jan–Apr), the 2000 wet season (Jun–Oct) and the 2001 dry season in Los Baños, The Philippines (tropical climate). Results showed that rice plant architecture was influenced by 19 genomic regions categorized into five groups. In Group I, two regions (on chrs. 6 and 11) affected shoot elongation (CL and PnL) and tillering (PnN and TN) in opposite directions more significantly in Los Baños than in Joetsu. In Group II, two regions (chrs. 3 and 12) affected shoot elongation, whereas in Group III, five regions [chrs. 1 (two), 2, 3 and 9] affected only culm length (CL). Expressions of four regions of Group III were influenced by either tropical or temperate environments. In Group IV, seven regions (chrs. 1, 2, 4, 5, 6, 8 and 9) controlled panicle development (PnN or PnL), and in Group V, three regions (chrs. 1, 2 and 3) regulated tillering (PnN or TN). Characterizing these 19 genomic regions provided a detailed analysis of rice plant architecture with emphasis on the multiple effect and environmental responsive regions.Communicated by D. Mackill     

Identification of QTLs for seed germination capability after various storage periods using two RIL populations in rice

Seed germination capability of rice is one of the important traits in the production and storage of seeds. Quantitative trait loci (QTL) associated with seed germination capability in various storage periods was identified using two sets of recombinant inbred lines (RILs) which derived from crosses between Milyang 23 and Tong 88-7 (MT-RILs) and between Dasanbyeo and TR22183 (DT-RILs). A total of five and three main additive effects (QTLs) associated with seed germination capability were identified in MT-RILs and DT-RILs, respectively. Among them, six QTLs were identified repeatedly in various seed storage periods designated as qMT-SGC5.1, qMT-SGC7.2, and qMT-SGC9.1 on chromosomes 5, 7, and 9 in MT-RILs, and qDT-SGC2.1, qDT-SGC3.1, and qDT-SGC9.1 on chromosomes 2, 3, and 9 in DT-RILs, respectively. The QTL on chromosome 9 was identified in both RIL populations under all three storage periods, explaining up to 40% of the phenotypic variation. Eight and eighteen pairs additive × additive epistatic effect (epistatic QTL) were identified in MT-RILs and DT-RILs, respectively. In addition, several near isogenic lines (NILs) were developed to confirm six repeatable QTL effects using controlled deterioration test (CDT). The identified QTLs will be further studied to elucidate the mechanisms controlling seed germination capability, which have important implications for long-term seed storage.     

Comparison of QTL controlling seedling vigour under different temperature conditions using recombinant inbred lines in rice (Oryza sativa)

BACKGROUND AND AIMS: Seedling vigour is one of the major determinants for stable stand establishment in rice (Oryza sativa), especially in a direct seeding cropping system. The objectives of this study were to identify superior alleles with consistent effects on seedling vigour across different temperature conditions and to investigate genotype x environmental temperature interactions for seedling vigour QTL. METHODS: A set of 282 F13 recombinant inbred lines (RILs) derived from a rice cross were assessed for four seedling vigour traits at three temperatures (25 degrees C, 20 degrees C and 15 degrees C). Using a linkage map with 198 marker loci, the main-effect QTL for the traits were mapped by composite interval mapping. KEY RESULTS: A total of 34 QTL for the four seedling vigour traits were identified. Of these QTL, the majority (82%) were clustered within five genomic regions, designated as QTL qSV-3-1, qSV-3-2, qSV-5, qSV-8-1 and qSV-8-2. All of these five QTL had small individual effects on the traits, explaining 3.1-15.8 % of the phenotypic variation with a mean of 7.3 %. QTL qSV-3-1, qSV-3-2 and qSV-8-1 showed almost consistent effects on the traits across all three temperatures while qSV-5 and qSV-8-2 had effects mainly at the 'normal' temperatures of 20 degrees C and 25 degrees C. Among the five QTL identified, all and four showed additive effects on shoot length and germination rate, respectively. The contributions of these five QTL to shoot length and germination rate were also much larger than those to the other two traits. CONCLUSIONS: A few of genomic regions (or QTL) were identified as showing effects on seedling vigour. For these QTL, significant genotype x environmental temperature interactions were found and these interactions appeared to be QTL-specific. Among the four seedling vigour traits measured, shoot length and germination rate could be used as relatively good indicators to evaluate the level of seedling vigour in rice.     

直播水稻茎鞘非结构碳水化合物积累与转运的遗传剖析

为了揭示水稻（Oryza sativa）茎鞘非结构碳水化合物（nonstructural carbohydrate, NSC）积累与转运的遗传基础, 在大田直播条件下, 利用来源于Lemont/特青的重组自交系群体, 对5个相关性状进行了QTL定位。始穗期和成熟期共检测到3个茎鞘NSC含量QTL, 分别位于第1、9和12染色体上, 贡献率分别为13%、7%和7%, 增效等位基因均来自特青。检测到的2个NSC转运率QTL均位于第12染色体上, 贡献率分别为8%和14%。检测到的结实率和千粒重QTL分别为3个和4个, 3个结实率QTL的贡献率分别为9%、24%和6%, 4个千粒重QTL的贡献率分别为14%、11%、12%和13%。进一步的分析表明,来自Lemont的等位基因降低成熟期茎鞘NSC含量的同时却能提高NSC转运率、结实率和千粒重, 而来自特青的等位基因对NSC转运率和结实率均有增效作用, 这为性状间表型相关提供了重要的遗传解释。     

A Set of Lotus japonicus Gifu x Lotus burttii Recombinant Inbred Lines Facilitates Map-based Cloning and QTL Mapping

Model legumes such as Lotus japonicus have contributed significantly to the understanding of symbiotic nitrogen fixation. This insight is mainly a result of forward genetic screens followed by map-based cloning to identify causal alleles. The L. japonicus ecotype 'Gifu' was used as a common parent for inter-accession crosses to produce F2 mapping populations either with other L. japonicus ecotypes, MG-20 and Funakura, or with the related species L. filicaulis. These populations have all been used for genetic studies but segregation distortion, suppression of recombination, low polymorphism levels, and poor viability have also been observed. More recently, the diploid species L. burttii has been identified as a fertile crossing partner of L. japonicus. To assess its qualities in genetic linkage analysis and to enable quantitative trait locus (QTL) mapping for a wider range of traits in Lotus species, we have generated and genotyped a set of 163 Gifu × L. burttii recombinant inbred lines (RILs). By direct comparisons of RIL and F2 population data, we show that L. burttii is a valid alternative to MG-20 as a Gifu mapping partner. In addition, we demonstrate the utility of the Gifu × L. burttii RILs in QTL mapping by identifying an Nfr1-linked QTL for Sinorhizobium fredii nodulation.     

A multiparent advanced generation inter-cross population for genetic analysis in wheat

We present the first results from a novel multiparent advanced generation inter-cross (MAGIC) population derived from four elite wheat cultivars. The large size of this MAGIC population (1579 progeny), its diverse genetic composition and high levels of recombination all contribute to its value as a genetic resource. Applications of this resource include interrogation of the wheat genome and the analysis of gene-trait association in agronomically important wheat phenotypes. Here, we report the utilization of a MAGIC population for the first time for linkage map construction. We have constructed a linkage map with 1162 DArT, single nucleotide polymorphism and simple sequence repeat markers distributed across all 21 chromosomes. We benchmark this map against a high-density DArT consensus map created by integrating more than 100 biparental populations. The linkage map forms the basis for further exploration of the genetic architecture within the population, including characterization of linkage disequilibrium, founder contribution and inclusion of an alien introgression into the genetic map. Finally, we demonstrate the application of the resource for quantitative trait loci mapping using the complex traits plant height and hectolitre weight as a proof of principle.     

不同发育时期小麦粒重性状QTL的动态分析

利用6044×01-35构建的重组自交系(RIL)群体为试验材料,对小麦粒重性状进行发育动态QTL分析。结果表明,在小麦花后子粒灌浆的7个不同时期,两个试验点共检测到16个与粒重性状相关的QTL。其中开花后20d检测到的单穗粒重QTL位于2A染色体上,解释率达12%,遗传效应超过10;两环境下控制千粒重QTL在7个时期均被检测到。花后的各个时期均能在Xgwm448-Xgpw7399标记区间定位到千粒重QTL。其中花后10d检测到1个千粒重QTL,位于2A染色体的Xgwm448-Xgpw7399标记区间,解释较大的表型变异,达到18%。Qtl8、Qtl13和Qtl14均定位在Xgwm448-Xgpw7399标记区间的同一位置,共同解释11%的表型变异。花后20d和花后25d均检测到1个QTL,位于2A染色体的Xgwm372-Xgwm95标记区间的不同位点,均能解释4%的表型变异。花后40d检测到1个QTL,位于1D染色体的Xwmc93-Xgpw2224标记区间,解释1%的表型变异。从连锁群的位置上看,控制千粒重的QTL主要集中在2A染色体的Xgwm448-Xgpw7399标记区间,这是一个控制千粒重QTL的富集区域,以期进行精细定位和图位克隆。     

Identification of metabolic and biomass QTL in Arabidopsis thaliana in a parallel analysis of RIL and IL populations

Plant growth and development are tightly linked to primary metabolism and are subject to natural variation. In order to obtain an insight into the genetic factors controlling biomass and primary metabolism and to determine their relationships, two Arabidopsis thaliana populations [429 recombinant inbred lines (RIL) and 97 introgression lines (IL), derived from accessions Col-0 and C24] were analyzed with respect to biomass and metabolic composition using a mass spectrometry-based metabolic profiling approach. Six and 157 quantitative trait loci (QTL) were identified for biomass and metabolic content, respectively. Two biomass QTL coincide with significantly more metabolic QTL (mQTL) than statistically expected, supporting the notion that the metabolic profile and biomass accumulation of a plant are linked. On the same basis, three out the six biomass QTL can be simulated purely on the basis of metabolic composition. QTL based on analysis of the introgression lines were in substantial agreement with the RIL-based results: five of six biomass QTL and 55% of the mQTL found in the RIL population were also found in the IL population at a significance level of P  ≤ 0.05, with >80% agreement on the allele effects. Some of the differences could be attributed to epistatic interactions. Depending on the search conditions, metabolic pathway-derived candidate genes were found for 24–67% of all tested mQTL in the database AraCyc 3.5. This dataset thus provides a comprehensive basis for the detection of functionally relevant variation in known genes with metabolic function and for identification of genes with hitherto unknown roles in the control of metabolism.