油菜油分、蛋白质和硫苷含量相关性分析及QTL定位

为定位与油分、蛋白质和硫苷含量等品质性状相关的数量性状位点（QTL）,以2个含油量较高的甘蓝型油菜（Brassica napus）品系8908B和R1为研究材料,配置正反交组合。在正反交F2代群体中,含油量和蛋白质含量都存在极显著的负相关,相关系数分别为-0．68和-0．81,含油量和硫苷含量相关性不显著：蛋白质含量和硫苷含量在正交群体中相关性不显著,但在反交群体中存在显著负相关（相关系数r=-0．45）。利用正交F2代群体中的118个单株,构建了包含121个标记的遗传连锁图谱,图谱长1298．7cM,有21个连锁群（LGs）。采用复合区间作图法,在连锁图上定位了2个与含油量有关的QTL,分别位于LG8和LG10,其贡献率分别为4．8％和13．7％,增效基因都来源于R1;定位了2个与蛋白质含量有关的QTL：pr01和pr02,分别位于LG1和LG3,其贡献率分别为15．2％和14．1％,位点pr07由8908B提供增效基因,pro2则由R1提供增效基因：定位了4个与硫苷含量有关的QTL,其中LG20上有2个,LG4和LG8上各1个,它们的贡献率在1．9％-25．4％之间,除LG20上glu7的增效基因来自R1外,其余3个QTL位点均由8908B提供增效基因。     

油菜硫苷组分含量及抑菌活性研究

对油菜不同器官硫苷的组分与含量进行分析,结果表明根系和茎中的硫苷总量显著高于叶片,根系中的硫苷以4甲-氧基-3吲-哚甲基硫苷和1-甲氧基-3吲-哚甲基硫苷为主,茎和叶中以2羟-基-3丁-烯基硫苷和苯乙基硫苷为主。油菜根系中的硫苷组分从冬前期到终花期基本保持稳定,硫苷各组分的含量在不同生育时期的变化基本一致,终花期含量最高,成熟期含量最低。19个油菜品种根系中硫苷组分与含量存在较大差异;以草莓灰霉菌为靶标物,研究油菜根系的挥发性水解产物和水溶性水解产物对土传病原真菌的抑制作用,发现不同油菜品种根系水解产物的抑菌活性与其硫苷组分与含量有关,草莓灰霉菌对丁烯基ITC非常敏感,吲哚类ITCs对草莓灰霉生长的抑制作用要低于芳香族ITCs。     

陕西省双低油菜硫苷含量的稳定性分析

采用HPLC法对陕西省冬油菜区5个试区种植的双低油菜籽粒中硫苷总量和组分含量进行了分析,结果表明: 1 陕西省不同地区的生态条件对双低油菜总硫苷含量有显著的影响,相差达13.14μmol/g,CV为13.30%,5个试区硫苷含量的排序为宝鸡>安康>咸阳>大荔>汉中,地区间达显著或极显著差异;品种间硫苷含量的稳定性有明显的差异,以P01-10品种稳定性最好; 2 在测定的8个硫苷组分中,变幅最大的5个组分依次为2-羟基-3-丁烯基硫苷、4-羟基-3吲哚甲基硫苷、4-戊烯基硫苷、3-丁烯基硫苷和2羟基-4-戊烯基硫苷,极差分别为:5.70、2.69、1.70、1.31和1.03μmol/g,变异系数分别为:17.59%、16.41%、58.84%、10.21%和50.32%,其中以3-丁烯基硫苷稳定性最好,4-戊烯基硫苷稳定性最差; 3 籽粒中硫苷与含油量、千粒重及容重均有显著的相关性,相关系数分别为-0.783、-0.948和0.656,通径和偏相关分析表明,仅硫苷与千粒重偏相关显著为-0.859,含油量和容重是通过千粒重来影响硫苷的.     

甘蓝型油菜种子硫代葡萄糖苷含量的QTL定位及候选基因鉴定

【目的】为了解析油菜种子硫代葡萄糖苷性状的重要遗传位点及候选基因,【方法】本研究利用KN DH群体在冬性环境2015-2018连续4年的种子硫苷含量表型和KN 高密度SNP遗传连锁图谱,通过Wincart 2.5软件的符合区间作图法对甘蓝型油菜种子硫代葡萄糖苷含量进行QTL定位和潜在候选基因鉴定。【结果】共鉴定到47个硫苷含量QTL,单个QTL解释表型变异最大是qGC.16YL19-4(19.44%),解释表型变异最小的是qGC.15YL12-5(1.82%)。利用元分析的方法将初步鉴定的47个QTL整合为38个consensus QTL,其中7个consensus QTL(cqGC.A9-5、cqGC.A9-7、cqGC.A9-9、cqGC.C2-9、cqGC.C2-10、cqGC.C9-5和cqGC.C9-6)为环境稳定表达QTL,包括3个硫苷含量主效QTL(cqGC.A9-5、cqGC.C2-10和cqGC.C9-5)。在主效QTLcqGC.A9-5和cqGC.C9-5鉴定到3个候选基因BnaA09g05480D,BnaC09g05620D和BnaC09g05810D,其功能主要涉及了油菜硫苷生物合成途径中吲哚-3-乙醛肟(IAOx)的合成和将2-烷基-苹果酸异构化形成3-烷基-苹果酸酯,以及硫苷的转运与分配。【结论】本研究获得了油菜种子硫苷含量3个主效QTL及3个候选基因,该结果为硫苷含量相关基因的功能机械和优质油菜品种培育提供理论依据。     

甘蓝型油菜种子发芽率QTL定位及相关生理性状

利用GH06×P174组合衍生的183个重组自交系进行种子发芽率遗传分析及种子发芽期间种子生理性状分析。用复合区间作图法对在室温下保存两年的种子(STY)、保存1年的种子(SOY)与新收获种子(FS)发芽率进行QTL定位。另外对保存两年的种子及新收获的种子萌发期间脂肪酶活性、电导率、还原糖含量、总糖含量及根系活力进行了测定, 并对结果进行分析。结果表明3批种子的发芽率QTL位点各不相同, 保存两年的种子在第9、14、17条连锁群上分别检测到1个位点, 保存1年的种子在第5、9条连锁群上各检测到1个位点, 新收获的种子在第4、18条连锁群上分别测检到1个位点。研究发现, 3批种子的发芽率相关性不显著, 发芽率差异达到极显著水平, 同时保存不同年份种子的发芽率QTL各不相同, 这表明甘蓝型油菜发芽率受很多不同因素所控制。保存两年及新收获种子的发芽率与电导率之间的相关性均达到极显著负相关, 表明可以通过电导率的测定估测发芽率, 电导率的研究对种子发芽率的研究具有重要意义。     

甘蓝型油菜千粒重性状的QTL定位分析

该研究利用油菜双单倍体株系(348份)群体和已构建的遗传连锁图谱,采用复合区间作图法,对2009~2013年连续5年的千粒重性状表型数据进行QTL初步定位和分析,结果共获得46个显著性千粒重QTL,主要分布在A7、C1和C6等11条染色体上;其中qTSW-09 DL11-1的表型变异最高(19.63%),qTSW-11 DL9的表型变异最小(2.73%)。通过元分析方法将所获得的46个QTL进行整合,结果显示:cqTSW-C1-2的表型变异最大(10.64%),并发现多个整合后的一致性QTL能够在连续多年试验中被检测到,其中cqTSW-C1-3连续5年被检测到,表明控制千粒重的QTL在种植环境中能够稳定表达;同时,新发现位于C1染色体上的千粒重主效QTL cqTSW-C1-2,解释表型变异达到10.64%。油菜千粒重性状的QTL分析和主效QTL的获得,为进一步实现油菜大籽粒的分子育种和高产新品种的培育提供了重要的理论指导。     

近红外光谱仪快速检测油菜硫苷、芥酸及油份含量数学模型的建立

近红外反射光谱技术是一种高效、快速的现代分析技术,应用过程中需要建立相应的数学模型.收集油菜籽样品600余份,采用国际(家)标准方法对芥酸、硫苷、含油率进行测试,根据各模型的要求,选择不同的具有代表性的样品,建立数学模型.内部交叉证实法对模型验证结果显示:高芥酸、低芥酸、硫苷、含油率模型复相关系数分别为0.9677、0.9318、0.9820、0.9626,内部验证均方差(RMSECV)分别为4.41、2.24、4.62、0.543.外部样品检测法分别用已建模型和国际(家)标准方法对油菜籽样品芥酸、硫苷、含油率进行检测,两者检测结果基本一致.综合内部和外部验证结果表明:建立的模型能够满足油菜籽硫甘、芥酸及油份含量快速检测的要求.另对油菜自身含水率对数学模型的影响做了初步探索.     

水稻叶绿素含量动态QTL分析

为剖析水稻不同生育时期叶绿素含量的变化动态及遗传机制,以‘Sasanishiki’（粳稻）、‘Habataki’（籼稻）及其杂交衍生的85个回交重组自交系（BILs）群体为材料,对控制水稻叶片叶绿素含量的数量性状基因位点（QTL）变化动态进行了分析。共检测到39个QTL,包括26个非条件QTL和13个条件QTL,分布在除第7和第11号染色体以外的10条染色体上,平均每个时期检测到3．25个非条件QTL。其中生育前期和生育中后期检测到的QTL位点较少,仅为1—3个;在生育中期（盛期）检测到的QTL位点相对较多,一般为4～5个。在生育期的中期和后期均能在第1和2号染色体上检测到控制叶绿素含量的QTL,并且这些QTL位点在1和2号染色体上呈现聚集现象。本研究同时发现了一些新的QTL位点,这些QTL将有助于我们更全面地了解叶绿素在不同发育时期的遗传基础。     

甘蓝型油菜千粒重性状的QTL初步定位研究

千粒重是油菜重要的产量相关性状之一,构建油菜遗传连锁图谱是研究其产量性状基因的前提。本研究利用小孢子培养技术,选育出了甘蓝型油菜大粒品系(G-42)和小粒品系(7-9)的纯合DH系DH-G-42和DH-7-9,其千粒重分别为6.24 g和2.42 g,二者比值达2.58。以DH-G-42为母本、DH-7-9为父本,构建了含190个单株的F2遗传作图群体,利用SSR和SRAP标记技术绘制遗传连锁图谱,该图谱共包含20个连锁群,涉及128个SSR标记和100个SRAP标记,图谱总长1546.6cM,标记间平均图距为6.78cM。本研究共检测到3个与千粒重性状相关的QTL,分别位于A9和C1连锁群,其中qSW-A9-1和qSW-A9-2贡献率分别达到10.98%和27.45%,均可视为控制粒重的主效QTL。本研究为后续进行油菜千粒重性状QTL的精细定位分析、分子标记辅助选择育种及新基因的克隆等奠定了基础。     

甘蓝型油菜遗传图谱的构建及单株产量构成因素的QTL分析

采用常规品系04-1139与高产多角果品系05-1054构建的F2代群体为作图群体, 运用SSR(Simple sequence repeat)和SRAP(Sequence-related amplified polymorphism)构建分子标记遗传图谱并对甘蓝型油菜单株产量构成因素进行QTL分析。遗传图谱包含200个分子标记, 分布于19个连锁群上, 总长度1 700.23 cM, 标记间的平均距离8.50 cM。采用复合区间作图法(Composite interval mapping, CIM)对单株产量构成因素(单株有效角果数、每果粒数和千粒重)进行QTL分析, 共检测到12个QTL: 其中单株有效角果数4个QTL, 分别解释表型变异为35.64%、12.96%、28.71%和34.02%; 每果粒数获得5个QTL, 分别解释表型变异为8.41%、7.87%、24.37%、8.57%和14.31%; 千粒重获得３个QTL, 分别解释表型变异为2.33%、1.81%和1.86%。结果表明: 同一性状的等位基因增效作用可以同时来自高值亲本和低值亲本; 文章中与主效QTL连锁的标记可用于油菜产量性状的分子标记辅助选择和聚合育种。     

Unraveling the Complex Trait of Crop Yield With Quantitative Trait Loci Mapping in Brassica napus

Yield is the most important and complex trait for the genetic improvement of crops. Although much research into the genetic basis of yield and yield-associated traits has been reported, in each such experiment the genetic architecture and determinants of yield have remained ambiguous. One of the most intractable problems is the interaction between genes and the environment. We identified 85 quantitative trait loci (QTL) for seed yield along with 785 QTL for eight yield-associated traits, from 10 natural environments and two related populations of rapeseed. A trait-by-trait meta-analysis revealed 401 consensus QTL, of which 82.5% were clustered and integrated into 111 pleiotropic unique QTL by meta-analysis, 47 of which were relevant for seed yield. The complexity of the genetic architecture of yield was demonstrated, illustrating the pleiotropy, synthesis, variability, and plasticity of yield QTL. The idea of estimating indicator QTL for yield QTL and identifying potential candidate genes for yield provides an advance in methodology for complex traits.YIELD is the most important and complex trait in crops. It reflects the interaction of the environment with all growth and development processes that occur throughout the life cycle (Quarrie et al. 2006). Crop yield is directly and multiply determined by yield-component traits (such as seed weight and seed number). Yield-related traits (such as biomass, harvest index, plant architecture, adaptation, resistance to biotic and abiotic constraints) may also indirectly affect yield by affecting the yield-component traits or by other, unknown mechanisms. Increasing evidence suggests that “fine-mapped” quantitative trait loci (QTL) or genes identified as affecting crop yield involve diverse pathways, such as seed number (Ashikari et al. 2005; Tian et al. 2006b; Burstin et al. 2007; Xie et al. 2008; Xing et al. 2008; Xue et al. 2008), seed weight (Ishimaru 2003; Song et al. 2005; Shomura et al. 2008; Wang et al. 2008; Xie et al. 2006, 2008; Xing et al. 2008; Xue et al. 2008), flowering time (Cockram et al. 2007; Song et al. 2007; Xie et al. 2008; Xue et al. 2008), plant height (Salamini 2003; Ashikari et al. 2005; Xie et al. 2008; Xue et al. 2008), branching (Clark et al. 2006; Burstin et al. 2007; Xing et al. 2008), biomass yield (Quarrie et al. 2006; Burstin et al. 2007), resistance and tolerance to biotic and abiotic stresses (Khush 2001; Brown 2002; Yuan et al. 2002; Waller et al. 2005; Zhang 2007; Warrington et al. 2008), and root architecture (Hochholdinger et al. 2008).Many experiments have explored the genetic basis of yield and yield-associated traits (yield components and yield-related traits) in crops. Summaries of identified QTL have been published for wheat (MacCaferri et al. 2008), barley (Von Korff et al. 2008), rice, and maize (http://www.gramene.org/). The results show several common patterns. First, QTL for yield and yield-associated traits tend to be clustered in the genome, which suggests that the QTL of the yield-associated traits have pleiotropic effects on yield. Second, this kind of pleiotropy has not been well analyzed genetically. The QTL for yield (complicated factor), therefore, have not been associated with any yield-associated traits (relatively simple factors, such as plant height). Therefore, they are unlikely to predict accurately potential candidate genes for yield. Third, only a few loci (rarely >10) have been found for each of these traits. Thus, the genetic architecture of yield has remained ambiguous. Fourth, trials were carried out in a few environments and how the mode of expression of QTL for these complex traits might respond in different environments is unclear.In this study, the genetic architecture of crop yield was analyzed through the QTL mapping of seed yield and eight yield-associated traits in two related populations of rapeseed (Brassica napus) that were grown in 10 natural environments. The complexity of the genetic architecture of seed yield was demonstrated by QTL meta-analysis. The idea of estimating indicator QTL (QTL of yield-associated traits, which are defined as the potential genetic determinants of the colocalized QTL for yield) for yield QTL in conjunction with the identification of candidate genes is described.     

Quantitative Trait Loci for Lipid Content in Drosophila melanogaster

Recombinant inbred lines derived from a natural population were used to investigate natural genetic variation for lipid abundance, protein abundance, and weight of Drosophila melanogaster. Females were heavier and contained more lipid and soluble protein than males. Lipid and protein abundance were genetically correlated with female weight, but male weight was not correlated with lipid or protein. Lipid and protein abundance were genetically correlated in males, but not in females. Quantitative trait loci (QTLs) for weight and protein abundance were predominantly on the X chromosome, whereas QTLs for lipid abundance were found on the second and third chromosomes. QTLs for lipid proportion (lipid abundance normalized by weight or protein abundance) were present on all chromosomes; a lipid proportion QTL on the third chromosome correlated with a QTL for starvation resistance observed in a previous study using the same set of recombinant inbred lines, suggesting that it might underlie both traits. Candidate genes are discussed in relationship to lipid abundance, lipid proportion, and starvation resistance.     

Quantitative Trait Loci for Thermal Time to Flowering and Photoperiod Responsiveness Discovered in Summer Annual-Type Brassica napus L

Time of flowering is a key adaptive trait in plants and is conditioned by the interaction of genes and environmental cues including length of photoperiod, ambient temperature and vernalisation. Here we investigated the photoperiod responsiveness of summer annual-types of Brassica napus (rapeseed, canola). A population of 131 doubled haploid lines derived from a cross between European and Australian parents was evaluated for days to flowering, thermal time to flowering (measured in degree-days) and the number of leaf nodes at flowering in a compact and efficient glasshouse-based experiment with replicated short and long day treatments. All three traits were under strong genetic control with heritability estimates ranging from 0.85–0.93. There was a very strong photoperiod effect with flowering in the population accelerated by 765 degree-days in the long day versus short day treatments. However, there was a strong genetic correlation of line effects (0.91) between the long and short day treatments and relatively low genotype x treatment interaction indicating that photoperiod had a similar effect across the population. Bivariate analysis of thermal time to flowering in short and long days revealed three main effect quantitative trait loci (QTLs) that accounted for 57.7% of the variation in the population and no significant interaction QTLs. These results provided insight into the contrasting adaptations of Australian and European varieties. Both parents responded to photoperiod and their alleles shifted the population to earlier flowering under long days. In addition, segregation of QTLs in the population caused wide transgressive segregation in thermal time to flowering. Potential candidate flowering time homologues located near QTLs were identified with the aid of the Brassica rapa reference genome sequence. We discuss how these results will help to guide the breeding of summer annual types of B. napus adapted to new and changing environments.     

Quantitative Trait Loci for Murine Growth

Body size is an archetypal quantitative trait with variation due to the segregation of many gene loci, each of relatively minor effect, and the environment. We examine the effects of quantitative trait loci (QTLs) on age-specific body weights and growth in the F(2) intercross of the LG/J and SM/J strains of inbred mice. Weekly weights (1-10 wk) and 75 microsatellite genotypes were obtained for 535 mice. Interval mapping was used to locate and measure the genotypic effects of QTLs on body weight and growth. QTL effects were detected on 16 of the 19 autosomes with several chromosomes carrying more than one QTL. The number of QTLs for age-specific weights varied from seven at 1 week to 17 at 10 wk. The QTLs were each of relatively minor, subequal effect. QTLs affecting early and late growth were generally distinct, mapping to different chromosomal locations indicating separate genetic and physiological systems for early and later murine growth.     

Multiple Trait Analysis of Genetic Mapping for Quantitative Trait Loci

We present in this paper models and statistical methods for performing multiple trait analysis on mapping quantitative trait loci (QTL) based on the composite interval mapping method. By taking into account the correlated structure of multiple traits, this joint analysis has several advantages, compared with separate analyses, for mapping QTL, including the expected improvement on the statistical power of the test for QTL and on the precision of parameter estimation. Also this joint analysis provides formal procedures to test a number of biologically interesting hypotheses concerning the nature of genetic correlations between different traits. Among the testing procedures considered are those for joint mapping, pleiotropy, QTL by environment interaction, and pleiotropy vs. close linkage. The test of pleiotropy (one pleiotropic QTL at a genome position) vs. close linkage (multiple nearby nonpleiotropic QTL) can have important implications for our understanding of the nature of genetic correlations between different traits in certain regions of a genome and also for practical applications in animal and plant breeding because one of the major goals in breeding is to break unfavorable linkage. Results of extensive simulation studies are presented to illustrate various properties of the analyses.     

Dissecting Quantitative Trait Loci for Boron Efficiency across Multiple Environments in Brassica napus

High yield is the most important goal in crop breeding, and boron (B) is an essential micronutrient for plants. However, B deficiency, leading to yield decreases, is an agricultural problem worldwide. Brassica napus is one of the most sensitive crops to B deficiency, and considerable genotypic variation exists among different cultivars in response to B deficiency. To dissect the genetic basis of tolerance to B deficiency in B. napus, we carried out QTL analysis for seed yield and yield-related traits under low and normal B conditions using the double haploid population (TNDH) by two-year and the BQDH population by three-year field trials. In total, 80 putative QTLs and 42 epistatic interactions for seed yield, plant height, branch number, pod number, seed number, seed weight and B efficiency coefficient (BEC) were identified under low and normal B conditions, singly explaining 4.15–23.16% and 0.53–14.38% of the phenotypic variation. An additive effect of putative QTLs was a more important controlling factor than the additive-additive effect of epistatic interactions. Four QTL-by-environment interactions and 7 interactions between epistatic interactions and the environment contributed to 1.27–4.95% and 1.17–3.68% of the phenotypic variation, respectively. The chromosome region on A2 of SYLB-A2 for seed yield under low B condition and BEC-A2 for BEC in the two populations was equivalent to the region of a reported major QTL, BE1. The B. napus homologous genes of Bra020592 and Bra020595 mapped to the A2 region and were speculated to be candidate genes for B efficiency. These findings reveal the complex genetic basis of B efficiency in B. napus. They provide a basis for the fine mapping and cloning of the B efficiency genes and for breeding B-efficient cultivars by marker-assisted selection (MAS).     

猪肌肉蛋白质含量的QTL分析

用酶山猪与大白猪构建的资源家系检测影响肌肉蛋白质含量的数量性状座位（QTL）。测定135头F2代个体及其相应父母和祖父母在分布1号、2号和6号染色体上的24个微卫星基因型。用这些微卫星标记计算每个F2代个体固定位置的加快和显性系数,以1cM的间距肌肉蛋白质含量与这些系数进行回归分析,发现3个与肌肉蛋白质含量具有显著效应的QTL（P＜0．05）,它们分别于位于1号染色体的Sw1332和Sw2130之间,2号染色体的Sw2516和Sw1201之间,6号染色体的Sw2516和Sw1201之间,分别解释0．91％、12．76％和4．60％的肌肉蛋白质含量变异方差。结果提示,影肌肉蛋白质含量的QTL分布在多条染色体上。