基于基因型选择提高QTL作图的精度——以一个RIL群体为例

以PCR为基础的分子标记以及其他检测技术的发展,使得大规模的标记分析成为现实。这也为通过大群体标记分析,然后基于基因型选择挑选合适的小群体,从而提高QTL定位准确性和精度提供了可能。以一个包含294个家系的重组自交系（RIL）群体为例,通过基因型选择和随机选择的办法产生了一系列大小不等的亚群体,比较了两类群体QTL定位的结果。分析表明：相同大小的基因型选择群体与随机群体相比性状的表型分布都符合正态分布;标记的偏分离情况也没有明显的差别,都随着群体大小的增大,偏分离的比例也逐渐增大。但同等大小的基因型选择群体比随机群体的交换富集率（CE）要大,且随着选择强度的增大不断增大,如群体大小为270时,CE=1．04,群体大小为30时,CE=1．45。总体上,随着群体大小的增加,不管是随机群体还是选择群体,其QTL检测能力、灵敏性和特异性也随之增加,但选择群体的检测能力、灵敏性和特异性总体上要好于随机群体。当群体大于或等于240时,其QTL检测能力基本没有差别;群体大小大于或等于210时,其QTL检测的灵敏性和特异性也没有什么差别。这也说明：选择强度越大,效果越明显。以QTLI—LOD区间作为衡量QTL精度的一个指标,结果显示所有基因型选择群体都比相同大小随机群体的QTL定位精度高。目前QTL定位研究中,基因型数据较表型数据而言更容易准确获得,因此通过基因型选择可以更好的优化群体结构,减少田间实验的工作量,提高全基因组水平QTL作图的精度,为随后的QTL辅助选择和精细定位以及克隆提供帮助。     

Seasonal and plant-density dependency for quantitative trait loci affecting flowering time in multiple populations of Arabidopsis thaliana

Multiple environmental cues regulate the transition to flowering. In natural environments, plants perceive seasonal progression by changes in day length and growth temperature, and plant density is monitored by changes in the light quality reflected from neighbouring vegetation. To understand the seasonal and plant-density dependence associated with natural allelic variation in flowering time, we conducted a quantitative trait loci (QTL) mapping study in Ler x Cvi, Bay x Sha and Ler x No-0 recombinant inbred line (RIL) populations of Arabidopsis thaliana. Days and total leaf number to bolting were examined under low and high plant density (200 or 1600 plants m(-2)) in autumn-winter and spring seasons. We found between 4 and 10 QTLs associated with seasonal and density variations in each RIL population. For Ler x Cvi and Bay x Sha RIL populations, a major proportion of QTLs showed seasonal and density interaction (up to 63%) and four QTLs were common to all environments (21%). Only three QTLs showed seasonal or density dependency. By aligning the linkage maps onto a common physical map, we detected at least one QTL at chromosome 2 and two QTLs at chromosome 5 that overlap between the three RIL populations, suggesting that these QTLs play a crucial role in the adaptive control of flowering time.     

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.     

QTL mapping of grain weight in rice and the validation of the QTL qTGW3.2

A recombinant inbred line (RIL) population bred from a cross between a javanica type (cv. D50) and an indica type (cv. HB277) rice was used to map seven quantitative trait loci (QTLs) for thousand grain weight (TGW). The loci were distributed on chromosomes 2, 3, 5, 6, 8 and 10. The chromosome 3 QTL qTGW3.2 was stably expressed over two years, and contributed 9–10% of the phenotypic variance. A residual heterozygous line (RHL) was selected from the RIL population and its selfed progeny was used to fine map qTGW3.2. In this “F₂” population, the QTL explained about 23% of the variance, rising to nearly 33% in the subsequent “F_2:3” generation. The physical location of qTGW3.2 was confined to a ~ 556 kb region flanked by the microsatellite loci RM16162 and RM16194. The region also contains other factors influencing certain yield-related traits, although it is also possible that qTGW3.2 affects these in a pleiotropic fashion.     

Analysis of quantitative trait loci affecting chlorophyll content of rice leaves in a double haploid population and two backcross populations

Chlorophyll content, one of the most important physiological parameters related to plant photosynthesis, is usually used to predict yield potential. To map the quantitative trait loci (QTLs) underlying the chlorophyll content of rice leaves, a double haploid (DH) population was developed from an indica/japonica (Zhenshan 97/Wuyujing 2) crossing and two backcross populations were established subsequently by backcrossing DH lines with each of their parents. The contents of chlorophyll a and chlorophyll b were determined by using a spectrophotometer to directly measure the leaf chlorophyll extracts. To determine the leaf chlorophyll retention along with maturation, all measurements were performed on the day of heading and were repeated 30 days later. A total of 60 QTLs were resolved for all the traits using these three populations. These QTLs were distributed on 10 rice chromosomes, except chromosomes 5 and 10; the closer the traits, the more clustering of the QTLs residing on common rice chromosomal regions. In general, the majority of QTLs that specify chlorophyll a content also play a role in determining chlorophyll b content. Strangely, chlorophyll content in this study was found mostly to be lacking or to have a negative correlation with yield. In both backcross F₁ populations, overdominant (or underdominant) loci were more important than complete or partially dominant loci for main-effect QTLs and epistatic QTLs, thereby supporting previous findings that overdominant effects are the primary genetic basis for depression in inbreeding and heterosis in rice.     

黄瓜RIL群体瓜长和把长的QTL定位分析

以黄瓜野生变种‘PI183967’(Cucumis sativus L.hardwickii)和新泰密刺选系‘931’为亲本,通过单粒传法获得包含160个株系的F9代重组自交系群体(RIL)。结合分子遗传图谱和不同年份(2012年春、秋季和2013年春、秋季)的表型调查数据,利用MapQTL4.0软件进行黄瓜瓜长和把长的QTL定位。结果显示:(1)共检测到8个与瓜长和把长相关的QTLs,分布在染色体3、4、5、6、7上,LOD值在2.78~10.24之间,可解释7.4%~32.7%的表型变异率,贡献率≥10.0%的QTL位点4个,占QTLs总数的1/2,在春秋两季重复检测出的QTL位点有4个。(2)检测到4个与瓜长相关的QTLs位点Fl3.1、Fl4.1、Fl5.1、Fl6.1,4个与把长相关的QTLs位点Fsl3.1、Fsl3.2、Fsl5.1、Fsl6.1。(3)Fl6.1和Fsl6.1在2012、2013年春秋季中均可检测到,位于第6号染色体的109.2cM处,标记SSR17591~C80之间;Fl6.1在4次中的贡献率在13.8%~32.7%之间,Fsl6.1在4次中贡献率为12.1%~24.1%;(4)Fl3.1和Fsl3.2位于第6号染色体标记SSR16152~SSR07706之间,其中Fl3.1在2012年秋季和2013年春秋两季中均可检测到,贡献率总共为25.1%,Fsl3.2仅在2012年秋季中检测到,解释8.8%的表型变异率。研究表明,第6号染色体上标记SSR17591~C80和第3号染色体上的SSR16152~SSR07706等2个区域聚集了控制瓜长和把长的主效QTL位点,这2个区域应作为今后研究的重点。     

绿豆产量相关农艺性状的QTL定位

绿豆育种的目标性状大多是受多基因控制的数量性状,表现型受环境影响很大。为深入分析绿豆复杂数量性状的遗传特征,本试验以绿豆Berken/ACC41 F10重组近交系群体为作图群体,利用该群体已经构建的包含79个RFLP标记的分子连锁图谱对北京和广西2个种植环境下考察的11个绿豆产量相关农艺性状进行QTL定位。结果表明,2个环境下共检测到产量相关性状QTL 63个（其中北京25个,广西38个）,分布于除第13连锁群以外的12条连锁群。大部分QTL只在单一环境下被检测到,说明产量相关QTLs与环境之间存在明显的互作。2个环境均能检测到的QTL仅有6个,分别为控制荚长、百粒重、生育期的QTLs,这6个在不同生态环境下同时发挥效应的QTLs对于绿豆分子标记辅助育种具有特殊的意义。研究还发现2个QTLs富集区域和若干成束分布的QTLs,它们可能是发掘通用QTL的候选位点。     

大豆重组自交系群体（ZKS-HX）遗传图谱的构建和形态标记的定位

以大豆品种‘合丰25’为母本,半野生大豆‘新民6号’为父本杂交得到的F2-9代122个重组自交系为试验材料,构建了含有124个SSR标记、1个EST标记、3个形态学标记的大豆遗传图谱。此图谱覆盖的基因组长度为2348．3cM．标记间平均距离为18．3cM。每个连锁群长度范围为15．1～195．9cM之间,标记数范围2—10个。本文将控制茸毛色（Pb）基因定位于LG06-C2连锁群上,与Sat_40x2的遗传距离为39．6cM;控制叶耳g（Le）、花色（4W,）基因定位于LG12-F连锁群上,它们之间的遗传距离为9．9cM,与两边的Satt348、Sat_240标记遗传距离分别为13．3cM和10．5cM。