Detection of quantitative trait loci (QTLs) for seedling traits

合集下载

生物技术题库

一、名词解释1. 生物(shēngwù)技术：也称生物工程(shēnɡ wùɡōnɡ chénɡ)（bioengingineering），是指人们以现代生命科学为基础，结合先进的工程技术手段和其他基础学科的科学原理，按照预先的设计改造生物体或加工生物原料，为人类生产(shēngchǎn)出所需产品或达到某种目的。

2.植物(zhíwù)组织培养：是指在无菌条件下，把离体的植物器官、组织、细胞、原生质体等材料接种在人工培养基中，使其发育(fāyù)成完整植株的过程。

3、植物细胞全能性：任何一个拥有完整细胞核的植物细胞都具有形成一个完整植株所必需的全部遗传信息，在特定条件下表达可产生独立的个体。

4..脱分化：也叫去分化，已经停止分化的细胞、组织、器官在离体培养条件下，逐渐失去其原来的结构和功能而恢复其分生状态，形成无组织结构的细胞团的过程。

5.再分化：由脱分化产生的无组织结构的细胞团在离体培养条件的剌激下重新分化成具有不同结构和功能的细胞、组织、器官的过程，即再分化。

6.培养基：是植物组织培养的物质基础。

其成分是植物生长发育所必需的各种物质的来源，主要包括营养元素、植物生长调节剂、碳源、维生素、有机添加物等。

7.外植体：在植物组织培养中，用来进行培养的植物材料常称外植体(explant)，包括植物器官、组织、细胞等。

7.重组DNA技术：是指将一种生物体（供体）的基因与载体在体外进行拼接重组，然后转入另一种生物体（受体）内，使之按照人们的意愿稳定遗传并表达出新产物或新性状的DNA体外操作程序，也称为分子克隆技术。

8.基因工程：就是将目的基因插入到载体（质粒，病毒）中，再把携带目的基因的载体转入受体材料细胞中，使目的基因在受体细胞中表达。

进而使受体生物表现出目的基因的性状。

⑴限制性核酸内切酶：是一类能够识别双链DNA中的特定核苷酸序列，并在特定位点切割双链DNA的内切酶。

QTL精细定位和克隆的策略

QTL精细定位和克隆的策略QTL（Quantitative Trait Loci）精细定位和克隆是一种用于研究复杂性状遗传基础的策略。

QTL是指影响数量性状的基因或染色体区域，通过精细定位和克隆这些QTL，可以了解底层的基因机制以及其对数量性状的调控方式。

本文将介绍QTL精细定位和克隆的策略及其主要步骤。

1.QTL的初步定位：初步定位是通过建立遗传图谱或关联分析等方法，确定QTL所在的染色体区域。

常用的方法包括构建遗传连锁图和联合鉴定法。

遗传连锁图通过建立基因座之间的连锁关系，得到QTL大致所在的染色体区域。

联合鉴定法是基于多个遗传座的遗传效应与表型表达之间的关系，通过统计模型来确定QTL的位置。

2.QTL的精细定位：精细定位是在初步定位的基础上，进一步缩小QTL的定位区域。

常用的方法包括细分群体和QTL-候选基因关联分析。

细分群体是通过构建更多的染色体互换系或染色体片段替代系，并进行连锁鉴定，缩小QTL区域。

QTL-候选基因关联分析则是通过挖掘精密的关联信号，确定QTL所在的基因区域。

这些关联信号可以来自候选基因的DNA多态性标记或RNA表达水平等。

总之，QTL精细定位和克隆是一种通过缩小QTL区域，最终确定突变基因，揭示底层的基因机制的策略。

通过建立遗传图谱和进行关联分析等初步定位方法，缩小QTL的定位区域。

随后，通过细分群体和QTL-候选基因关联分析等精细定位方法，最终确定QTL所在的基因区域。

最后，通过基因克隆和功能验证，揭示QTL对数量性状的调控方式。

这些研究有助于深入理解数量性状的遗传基础，提高作物和动物的育种效率。

qtl名词解释

qtl名词解释QTL（QuantitativeTraitLoci）是指在染色体上能影响特定量化性状的细胞或组织的一些位点，它们可以被用来推断种群遗传变异的遗传机制，也可以分析多个植物的遗传和生理机制。

QTLs是基因组学研究的一个重要组成部分，在遗传学、分子生物学和生物信息学领域都有重要应用。

QTL实际上是一个抽象概念，指的是存在于基因组中的某一位置，这个位置上的基因会影响一个特定量化性状，比如颜色、抗病性、抗虫性、汁液分泌等。

QTL可以是一个简单的基因座，也可以是多个基因座的组合，由于不一定断定QTL对应哪一个实际的基因，也就不一定能说明它的表型产生方式，所以QTL的研究具有很大的挑战性和技术性。

QTL研究主要分为两大步骤，即定位和认识。

首先，利用数学方法定位QTL，即在遗传谱系中找出与特定性状相关联的染色体位点；第二步，进行QTL认识，即定义QTL的表型影响。

QTL认识可以通过分析已有数据和新技术进行获取，比如对QTL对应基因进行测序、克隆和表达谱分析，从而获取更多的关键信息。

QTL研究可以帮助科学家研究特定性状的遗传机理，由于此类研究的受益者比较多，比如农业、植物科学和医学等，所以QTL研究得到了越来越多的关注。

QTL研究在分子水稻育种领域的应用，尤其是对于基因定位的研究，取得了重要的成果，例如根瘤病抗性、米粒粗糙度、米质等特定性状。

了解QTLs在植物特定性状上所发挥的作用，可以提高植物种质的利用效率，培育优良品种，进而改善作物产量和质量，促进农作物的可持续发展。

QTL的研究也可以应用于其他生物，比如动物、微生物等，诸如特定疾病的发生、性状的发育等，都可以通过QTL研究来探究其遗传机制。

例如，在研究非洲黄热病病毒基因中，科学家们发现了一个重要的QTL，它可以预测昆虫是否会发病，从而开展防控研究，从而降低昆虫对病毒感染的风险。

综上所述，QTL是指染色体细胞上可以影响某一特定量化性状的位点，利用QTL可以获得更多的基因结构信息，这有助于我们深入理解植物的遗传和生理机制，也有助于提高农作物的产量和质量，QTL 的研究也可以应用于其他生物，用于研究特定疾病的发生等。

辣椒英文文献已读

2001年,C. Djian-Caporalino · L.Pijarowski · A. Fazari M. Samson · L.Gaveau · C.O’Byrne · V. Lefebvre C. Caranta · A. Palloix · P. Abad发表文章High-resolution genetic mapping of the pepper (Capsicum annuumL.)resistance loci Me3 and Me4 conferring heat-stable resistance to root-knot nematodes (Meloidogyne spp.)采用从C. annuum F1代得到的双单倍体(DH)群体构建了两张种内图谱,主要采用AFLP 和RAPD标记。

图谱长度1,582 cM ,共227 标记,18个连锁群,覆盖了67%的辣椒基因。

本研究采用的材料是C. annuum parents ‘Yolo Wonder’(‘YW’) and‘PM687’。

抗性品系PM687来源于印度,从PI322719群体中获得,YW是一个感病品种,Me3和Me4都是抗根结线虫基因。

PM687和YW杂交F1代有103个单双倍体(DH),163株F2代个体,‘Perennial’, another inbred line of C. annuum (see Table 1)used to generate the DH200 pepper map,另外一个亲本。

采用RAPD和AFLP标记,分析产生了与Me3连锁的8个相斥性标记和4个相引性标记。

在基因两边最近的距离是0.5,1.0,1.5和3cm,Me4与Me3相距100cm,Me3最近的基因名为Q04-0.3,距RAPD标记10.1cm,名为CT135d的距RFLP标记2.7cm.。

qtl定位原理和流程

qtl定位原理和流程下载温馨提示:该文档是我店铺精心编制而成，希望大家下载以后，能够帮助大家解决实际的问题。

文档下载后可定制随意修改，请根据实际需要进行相应的调整和使用，谢谢!并且，本店铺为大家提供各种各样类型的实用资料，如教育随笔、日记赏析、句子摘抄、古诗大全、经典美文、话题作文、工作总结、词语解析、文案摘录、其他资料等等，如想了解不同资料格式和写法，敬请关注!Download tips: This document is carefully compiled by theeditor.I hope that after you download them,they can help yousolve practical problems. The document can be customized andmodified after downloading,please adjust and use it according toactual needs, thank you!In addition, our shop provides you with various types ofpractical materials,such as educational essays, diaryappreciation,sentence excerpts,ancient poems,classic articles,topic composition,work summary,word parsing,copy excerpts,other materials and so on,want to know different data formats andwriting methods,please pay attention!QTL定位：原理与流程详解QTL，全称为Quantitative Trait Loci，即数量性状位点，是遗传学中一个重要的概念。

大麦抗网斑病QTL初步定位

大麦抗网斑病QTL初步定位近年来，我国大麦种植业面临着来自网斑病的严重威胁，该病害对大麦产量和质量造成了严重影响。

因此，大麦抗网斑病的研究显得尤为重要，这不仅有助于解决大麦病害问题，还能提高我国大麦的种植效益。

为了探索大麦的抗网斑病机制，研究人员开展了大麦抗网斑病QTL初步定位研究。

QTL（Quantitative Trait Loci）是指影响性状数量性状的基因座，通过QTL定位可以帮助我们理解基因与性状之间的关系。

首先，研究人员选择了对网斑病高度抗性的大麦品种作为研究对象，这些品种对网斑病的抗性高于普通品种。

然后，通过交叉杂交法获得了一组F2群体，该群体包含了网斑病高抗品种的遗传背景。

接下来，研究人员对F2群体进行了网斑病人工接种试验，并观察了不同个体的病情表现。

根据观察结果，研究人员将群体分为抗病和感病两个亚群。

利用分子标记技术，研究人员对这两个亚群的DNA样本进行了基因分型。

通过对基因分型数据的分析，研究人员在大麦染色体上发现了多个与网斑病抗性相关的QTL。

这些QTL位于不同的染色体位置，互相作用形成复杂的调控网络。

与普通品种相比，抗病亚群的DNA样本中特异的QTL片段数量明显增加，这进一步证明了这些QTL的抗病作用。

进一步的研究发现，这些与网斑病抗性相关的QTL可能参与了许多生物学过程，包括抗病基因的表达、信号转导和代谢途径等。

此外，这些QTL可能与其他抗性基因和调控因子之间存在着复杂的相互作用。

综上所述，大麦抗网斑病QTL初步定位的研究为我们理解大麦抗病性的机制提供了重要线索。

进一步的研究可以帮助我们识别出具体的抗病基因，并开发出抗网斑病的新品种。

这将为我国大麦种植业的发展提供有力的支撑，促进我国农业的可持续发展通过对大麦抗网斑病的研究，我们发现了多个与该病抗性相关的QTL。

这些QTL在不同染色体位置上发现，彼此之间形成复杂的调控网络。

抗病亚群的DNA样本中QTL片段数量明显增加，进一步验证了这些QTL的抗病作用。

小麦株高和千粒重QTL定位及其元分析

小麦株高和千粒重QTL定位及其元分析小麦是世界上最重要的粮食作物之一，其株高和千粒重影响着产量和质量。

因此，研究小麦株高和千粒重的遗传基础对于小麦的育种和改良具有重要意义。

本文将对小麦株高和千粒重的QTL（Quantitative Trait Loci，数量性状基因座）定位及其元分析进行探讨。

首先，我们需要了解什么是QTL定位。

QTL是影响数量性状的基因座，它们通常通过遗传连锁分析或关联分析来定位。

定位QTL的目的是寻找与数量性状相关的基因，从而了解数量性状的遗传机制和调控途径。

针对小麦株高和千粒重的QTL定位研究，研究者通常采用构建遗传连锁图谱、关联分析或候选基因筛选等方法。

首先，研究者会通过杂交等交配试验收集大量的小麦单株F2、BC1等群体，并测量其株高和千粒重等数量性状。

然后，通过分子标记技术（如SSR、SNP等）对这些群体进行遗传连锁图谱的构建，找出与株高和千粒重相关的分子标记。

接下来，使用连锁图谱定位方法，比如LOD（Logarithm of Odds，对数似然比）分析，来定位株高和千粒重的QTL。

除了遗传连锁分析，关联分析也是QTL定位的重要方法。

关联分析是通过分析群体中已知的分子标记和数量性状数据之间的关联关系，来确定QTL位点。

关联分析通常使用对群体中的每个个体进行基因型和数量性状测量，然后通过统计学方法来检测分子标记和数量性状之间的关联程度。

最常用的关联分析方法是GWAS（Genome-Wide Association Study，全基因组关联分析），它可以同时分析整个基因组的多个QTL位点。

在QTL定位的基础上，可以进行元分析。

元分析是将多个独立的研究结果进行系统性整合和统计分析。

通过元分析，我们可以更准确地确定小麦株高和千粒重的QTL位点以及其效应大小。

元分析还可以对不同研究结果的异质性和一致性进行评估，并探索潜在的因素和交互作用。

总之，小麦株高和千粒重的QTL定位及其元分析可以帮助我们深入了解小麦数量性状的遗传机制和调控途径。

qtl名词解释

qtl名词解释QTL是QuantitativeTraitLoci的缩写，它是指不同基因组上具有确定定量性状的位置。

QTL术使用染色体映射技术来定位与定量性状相关的基因。

它的恒定性意味着，测量的值在植物和动物的可观察性状态之间存在贯穿性，这就是为什么QTL技术可以用于研究定量性状的原因。

QTL技术在生物学研究中有重要的应用价值，它可以帮助研究人员了解植物、动物和其它物种的遗传结构，同时有助于检测和定位与定量性状相关的基因。

QTL技术可以帮助研究人员预测物种的发育，而且也可以用于改良植物和动物，改善物种的品质。

QTL技术的基本原理是将细胞核中的染色体绘制成染色体图，然后利用统计技术来识别与定量性状相关的基因。

这可以通过分子标记的技术实现，其中采用的方法包括：重叠法、快速适应性变异法、单核苷酸多态性检测（SNP）等。

QTL技术的优势在于，它可以检测到小片段内的单个基因或一系列基因，而且不受物种局限。

QTL技术应用于育种，可以提高植物和动物的质量，改善养殖成果。

比如，以前的育种策略仅局限于选择有利的性状，但QTL技术可以帮助改良品种，使其具有更好的性能和特征。

它可以检测和定位准确的遗传位点，这使得育种工作可以基于精确的数据进行讨论，从而提高育种效果。

此外，QTL技术还有助于研究定量性状的遗传机制，例如调节基因组结构与表型之间的关系，进而识别出参与定量性状影响的生物学过程，帮助研究人员理解遗传和发育之间的关系以及如何影响某个特定的性状。

综上所述，QTL词解释的重要性已经被提出，它可以被应用于生物学研究，以发展定量性状的育种策略，改善物种的品质，更好地理解生物学过程，以及更准确地研究定量性状的遗传机制。

QTL技术有助于科学研究，并为改良物种提供了可行的替代方案。

qtl名词解释

qtl名词解释QTL是全基因组定位(QuantitativeTraitLoci-QTL)的缩写，它是指与量性状之间的遗传相关性的特定染色体位点。

QTL是个体表现遗传学特性的重要部分，它可以帮助我们了解基因和基因产物对表型状态的影响。

它可以用来描述不同种群中对某种表型的差异，或者如何影响变异的性状。

QTL是基因定位的一种统计技术，它可以把定位反映在统计概率上，通过测定遗传位点与某个性状的相关性，可以显著改善定位精度。

基于QTL的定位，可以确定和某个性状相关的位点，从而在DNA序列中更准确地指出与性状有关的基因。

QTL研究可以帮助遗传学家们有效地定位实现一定功能的基因，而不必测定每一个基因，从而降低遗传学研究成本，并加快基因定位进程。

同时，QTL研究对早期对象基因筛选，节省时间，提高效率，有着重要的作用。

在近几十年来，QTL研究已经取得了显著的进步，从定位困难的大型量性状，到小型的变异性状，QTL研究现在几乎可以用于任何量性状的定位研究。

此外，新的技术，如二代序列学技术，分子生物学技术和统计学技术，也为QTL研究增添了新的推动力。

QTL研究可以帮助我们了解性状变异的遗传机制，并且也为育种工作提供了强有力的支持。

QTL定位仪器可用于分子标记辅助育种，可以精确的定位出在遗传组成中影响一个特定性状的基因，并将其映射到实验室中更容易识别的染色体形式，从而更容易进行培育和监测。

总的来说，QTL是遗传学表现的重要组成部分，它可以帮助我们了解表型状态的生物学机制，提高育种效率，加快定位基因的进程，为研究种群遗传变异性，以及基因组学研究、个体基因分析等提供有力的支撑。

QTL研究已经取得了巨大的成功，它将为未来的研究和应用提供更多的可能性和机会。

限制性两阶段多位点全基因组关联分析方法的特点与计算程序

作物学报 ACTA AGRONOMICA SINICA 2018, 44(9): 1274 1289/ISSN 0496-3490; CN 11-1809/S; CODEN TSHPA9E-mail:*********************.cn本研究由国家自然科学基金项目(31701447, 31671718), 国家重点研发计划项目(2017YFD0101500), 教育部111项目(B08025), 教育部长江学者和创新团队项目(PCSIRT_17R55), 国家现代农业产业技术体系建设专项(CARS-04), 江苏省优势学科建设工程专项, 中央高校基本科研业务费和江苏省JCIC-MCP 项目资助。

This study was supported by the National Natural Science Foundation of China (31701447, 31671718), the National Key R&D Program for Crop Breeding in China (2017YFD0101500), the MOE 111 Project (B08025), the MOE Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT_17R55), the China Agriculture Research System (CARS-04), the Jiangsu Higher Education PAPD Program, the Fundamental Research Funds for the Central Universities and the Jiangsu JCIC-MCP.*通信作者(Corresponding author): 盖钧镒,E-mail:************.cn第一作者联系方式:E-mail:****************Received(收稿日期): 2018-03-19; Accepted(接受日期): 2018-06-12; Published online(网络出版日期): 2018-06-29. URL: /kcms/detail/11.1809.S.20180629.1035.002.htmlDOI: 10.3724/SP.J.1006.2018.01274限制性两阶段多位点全基因组关联分析方法的特点与计算程序贺建波刘方东邢光南王吴彬赵团结管荣展盖钧镒*南京农业大学大豆研究所 / 农业部大豆生物学与遗传育种重点实验室 / 国家大豆改良中心 / 作物遗传与种质创新国家重点实验室, 江苏南京 210095摘要: 全基因组关联分析(genome-wide association study, GWAS)的理论及应用是近十几年来国内外数量性状研究的热点, 但是以往GWAS 方法注重于个别主要QTL/基因的检测与发掘。

Aphanomyces euteiches——加拿大豌豆生产的严重威胁

ResearchCrop Genetics and Breeding—ReviewAphanomyces euteiches :A Threat to Canadian Field PeaProductionLongfei Wu a ,Kan-Fa Chang b ,Robert L.Conner c ,Stephen Strelkov a ,Rudolph Fredua-Agyeman b ,Sheau-Fang Hwang b ,⇑,David Feindel baDepartment of Agricultural,Food and Nutritional Science,University of Alberta,Edmonton,AB T6G 2P5,Canada bCrop Diversiﬁcation Center North,Alberta Agriculture and Forestry,Edmonton,AB T5Y 6H3,Canada cAgriculture and Agri-Food Canada,Morden Research and Development Centre,Morden,MB R6M 1Y5,Canadaa r t i c l e i n f o Article history:Received 2February 2018Revised 2May 2018Accepted 8June 2018Available online 17July 2018Keywords:Field peaAphanomyces euteiches Root rotPathogenicity variability Quantitative trait locia b s t r a c tField pea (Pisum sativum var. arvense L.) is an important legume crop around the world. It produces grains with high protein content and can improve the amount of available nitrogen in the soil. Aphanomyces root rot (ARR), caused by the soil-borne oomycete Aphanomyces euteiches D rechs. (A. euteiches ), is a major threat to pea production in many pea-growing regions including Canada; it can cause severe root damage, wilting, and considerable yield losses under wet soil conditions. Traditional disease manage-ment strategies, such as crop rotations and seed treatments, cannot fully prevent ARR under conditions conducive for the disease, due to the longevity of the pathogen oospores, which can infect ﬁeld pea plants at any growth stage. The development of pea cultivars with partial resistance or tolerance to ARR may be a promising approach to analyze the variability and physiologic specialization of A. euteiches in ﬁeld pea and to improve the management of this disease. As such, the detection of quantitative trait loci (QTL) for resistance is essential to ﬁeld pea-breeding programs. In this paper, the pathogenic characteristics of A. euteiches are reviewed along with various ARR management strategies and the QTL associated with partial resistance to ARR.Ó 2018 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY-NC-ND license1.IntroductionField pea (Pisum sativum var.arvense L.),along with common bean (Phaseolus vulgaris L.),faba bean (Vicia faba L.),soybean (Gly-cine max (L.)Merr.),chickpea (Cicer arietinum L.),and lentil (Lens culinaris Medik.),belongs to the family Fabaceae.The interaction between pea and Rhizobium bacteria leads to the formation of root nodules,which enable pea roots to ﬁx nitrogen directly from the atmosphere,thereby beneﬁting production of the pea and subse-quent crops.Pea seeds have a high protein content,are rich in starch,dietary ﬁber,vitamins,minerals,and polyphenols,and provide a protein-rich food source for both humans and livestock [1].Garden peas are processed for canning or freezing by the food industry,while ﬁeld pea is one of the most widely cultivated crops for human consumption and livestock feed on the Canadian Prairies,with an export market value of 1.2billion CAD in 2016[2].World grain pea production peaked in 1990at 1.66Â107t;by 2014,it had decreased by 5.5Â106t due to a reduction in pea cultivation in Europe [3,4].Since then,European pea cultivation has once again increased as a result of new Common Agricultural Policy (CAP)greening measures [5].Pea cultivation was introduced to Canada more than a century ago [6],ﬁrst in limited areas in East-ern Canada in the late 1800s.In 1985,there were only 8.05Â104hm 2of ﬁeld peas seeded in Canada.There was a signiﬁcant increase in pea cultivation in North America (i.e.,Canada and the United States)starting in the 1990s.Because of its adaptation to cool climates and its nutritional value for human and livestock consumption,ﬁeld pea has become increasingly popular as a cash crop to meet demand for the export market in Canada.By 2014,Canada had become the largest ﬁeld pea producer in the world,which now accounts for 21%of global production [4].At present,Aphanomyces root rot (ARR)is one of the major limitations to pea production worldwide.This disease is caused by Aphanomyces euteiches Drechs.(A.euteiches ),which is distin-guished from most other soil-borne pathogens by the formation of thick-walled oospores [7].It can cause severe root damage at all growth stages of its host.The longevity of A.euteiches oospores,⇑Corresponding author.E-mail address:sheau-fang.hwang@gov.ab.ca (S.-F.Hwang).combined with the absence of fully resistant pea genotypes, makes the management of ARR difﬁcult.This review describes pathogenic variability in A.euteiches,and the application of tradi-tional management strategies and partial resistance to control ARR inﬁeld pea.Pea root rot complex(PRRC)has been reported to be a serious problem inﬁeld pea production in Canada[8]and worldwide[9]. When root rot is severe,yield reduction can be as high as70% [10,11].A number of soil-borne pathogens have been reported to be involved in PRRC,including A.euteiches,Fusarium spp., Pythium spp.,Phytophthora spp.,and Rhizoctonia solani Kühn [12–15].Fusarium solani(Mart.)Sacc.(F.solani)was the most common causal agent of pea root rot worldwide[16].In addition to Fusarium spp.,A.euteiches has been reported to occur in certain countries in North America and Europe,as well as in Japan,Aus-tralia,and New Zealand[17].Yield losses due to infection by Pythium ultimum and A.euteiches were reported in the United States[18,19].An estimated loss of2.4Â104t ofﬁeld pea caused by PRRC occurred in Southern Ontario in1983[10].F.avenaceum (Corda ex.Fr.)Sacc.was reported to be the main cause of Fusarium root rot in pea crops in Alberta and Manitoba,account-ing for as much as80%of the isolates collected fromﬁeld samples [20,21].Hwang and Chang[22]reported that PRRC was prevalent in the Canadian province of Alberta.Tu[23]noted that the amount of damage toﬁeld pea caused by Fusarium spp.is inﬂu-enced by soil compaction,temperature,and moisture levels, which may also impact the relative prevalence of F.solani[16] and F.avenaceum[20].Infection by PRRC is associated with seed decay,damping-off, seedling blight,root rot,and wilt;however,the identity of the causal organisms cannot be determined solely by examining the symptoms[24].This increases the difﬁculty of predicting and controlling pea root rot in western Canada and elsewhere.Direct invasion of the seeds by any of the fungi involved in the PRRC complex,but most often by Pythium spp.,is usually the cause of seed decay[25,26],which results in a soft,mushy appearance of the seeds and in their rapid deterioration.Damping-off and seedling blight reduce seedling emergence and plant density, limit pea growth,delay canopy closure,and therefore increase weed competition.All of these factors may cause yield reductions [27].Root rot also restricts the transport of water and nutrients in infected roots,and reduces canopy density and the uniformity of crop maturity[28].Root rot may also destroy rhizobial nodules,leading to a reduction in nitrogenﬁxation in the roots[29].2.ARR caused by A.euteichesA.euteiches belongs to the class Oomycota(oomycetes),which comprises a large group of eukaryotes that includes the most diverse,important,and earliest-known water molds[30].Oomy-cetes resemble fungi in morphology(i.e.,mycelial growth)and many are parasitic.Unlike true fungi,oomycetes produce motile, biﬂagellate zoospores[30,31].Cytological and biochemical studies indicate additional differences that distinguish oomycetes from fungi[32–34].At the vegetative stage,the mycelia of oomycetes consist of a coenocytic thallus that remains diploid[33](Fig.1 (a)).The formation of haploid nuclei only occurs through meiosis for gamete formation.At this stage,fungal thalli produce septate cells,each of which carry one haploid nucleus.In addition,in con-trast to fungal cell walls,which are composed mainly of chitin (acetylglucosamine polymers),along with glucans,polysaccha-rides,mucopolysaccharides,waxes,and pigments,the cell walls of oomycetes contain cellulose,b-glucans and hydroxyproline, but no chitin[35].The genus Aphanomyces includes a number of water molds that are saprophytes or parasites ofﬁsh,crayﬁsh,and plants[36].There are about40described species of Aphanomyces[37].Most have a wide range of hosts belonging to different families,although there are a few exceptions such as A.cochlioides Drechs.,which only affects sugar beet(Beta vulgaris L.)[37]and A.iridis Ichitani et Tak.Kodama,which only affects iris(Iris spp.)[36].Although A. euteiches has a broad host range within the family Fabaceae,it causes the greatest economic damage to pea and lentil crops [38–40].This parasite has been isolated from pea,alfalfa(Medicago sativa L.),snap and red kidney bean(Proteus vulgaris L.),faba bean, red clover(Trifolium pratense L.),white clover(Trifolium repens L.), lentil,and several weed species[38,41].Nevertheless,its occur-rence and degree of pathogenicity may differ from one host to another.Pea-infecting strains and alfalfa-infecting strains of A. euteiches from the United States and France have been identiﬁed, and some strains can infect both pea and alfalfa[39,42,43].Papavi-zas and Ayers[38]reported that infection by A.euteiches caused large economic losses in pea and alfalfa crops in North America and Europe.The wide host range of A.euteiches,combined with its long-lived oospores,makes the management of ARR with crop rotation difﬁcult.Since it wasﬁrst described by Jones and Drechsler[44]and extensively reviewed by Papavizas and Ayers[38], A.euteiches has been considered to be one of the most damaging soil-borne pathogens of legumes.At present,A.euteiches has been reported in all of the main pea cultivation regions of the world[17].In France,it affects pea crops in the northern regions of the country [41].In North America,it causes severe yield losses in the Great Lakes region in both Canada and the United States,as well as in the Northeastern[25]and the Paciﬁc Northwest[45]regions of the United States.A high incidence and severity of pea root rot caused by A.euteiches was recently reported in Alberta[46].Yield losses caused by this parasite can be as high as86%in heavily infested peaﬁelds[47].2.1.Favorable conditionsSymptoms of ARR can develop within7–14days afterﬁrst infection,depending on soil moisture,temperature,and the concentration of oospores[38,47].High inoculum densities of A.euteiches increase the incidence and severity of ARR.Chan and Close[7]observed a positive correlation between the num-ber of oospores per100g of soil and root rot severity.Oospores can form germ tubes,which directly penetrate the cortex of the pea roots.Soil moisture levels inﬂuence the formation of sporangia and the release of zoospores,and allow theﬂagellated zoospores to travel to the plant roots in the moistureﬁlms sur-rounding soil particles[48,49].Zoospore infection also facilitates the leakage of metabolites from pea roots[50],which stimulates the germination of oospores and attracts more zoospores[9]. High rainfall favors ARR outbreaks,and only a short period is required for the completion of the infection process by A.euteiches[25].The minimum level of soil moisture needed for the initiation of ARR is about30%of the water-holding capacity of the soil[51,52].ARR may occur over the same wide soil-temperature range that is conducive for pea growth[25];however,the optimal tempera-tures for infection are about16°C,and20–28°C for disease devel-opment[53,54].High temperatures may accelerate pea root decay following infection by A.euteiches,since severe infection further limits water and nutrient movement withinﬁeld peas[55].Gaulin et al.[56]reported that A.euteiches can infect legume hosts at any growth stage,while others have suggested that infection occurs most commonly at the seedling stage[57,58].L.Wu et al./Engineering4(2018)542–5515432.2.Life-cycle of A.euteichesThe life-cycle of A.euteiches includes both asexual and sexual stages,which allow for its efﬁcient dissemination via zoospores and its survival as oospores during harsh winter conditions [41].The oospores are 18–25l m in diameter,have a thick protective wall,and contain energy reserves in the form of a large oil globule [9,38].They can survive in the soil for over ten years [47]and may be spread over long distances by the transportation of infested soil and/or infected plant residue [38].When adjacent to pea roots,the oospores germinate under con-ducive temperature and moisture conditions,and form either a mycelium or a zoosporangium.The zoosporangium,which forms as long tubes on the oospores,may release a large number of zoos-pores [59].The biﬂagellate motile zoospores are attracted to a suit-able host by chemical signals in the root exudates [60],and encyst within minutes on the rhizoplane (Fig.1(a)).The resulting cysts germinate and penetrate the host cortical cells within hours [38].Once an infection site has been established,coenocytic hyphae develop rapidly in the intercellular spaces of the host root tissue and the pathogen spreads from the roots to the stem (hypocotyls and epicotyls),eventually colonizing the entire root system.The infected roots become soft and water-soaked,and take on a honey-brown or blackish-brown coloration,which turns orange-brown or blackish-brown during the later stages of disease devel-opment (Fig.2(b)and (c)).Within a few days of infection,A.euteiches may enter its sexual stage with the formation and fusion of haploid antheridia and oogonia [59](Fig.1(a)and (d)).Subsequently,thick-walled oos-pores are formed,which ensure the long-term survival of the pathogen and serve as the primary source of inoculum for new infections in subsequent years [61](Fig.1(e)).The parasite may progress from ﬁrst infection of the roots to formation of oospores in as few as 10–14days [62].The translocation of water and nutrients within infected plants can be restricted by severe root rot [63](Fig.2(a)and (f)).Infectedplants may become stunted during the early growth stages and then start to wilt,resulting in premature death [64](Fig.2(d)).Moreover,ARR may severely delay pea maturity,reduce pod size and seed number,and decrease seed quality [64](Fig.2(e)and (g)).2.3.Variability and physiological specialization in A.euteiches Information on pathogenic variability and physiologic speciali-zation in A.euteiches is limited.Given the absence of completely resistant or immune pea genotypes,it is difﬁcult to create a differ-ential set to distinguish races,and the races identiﬁed by the limited differential genotypes may exhibit atypism [38].Nonethe-less,differences among isolates have been detected based on zoos-pore and oospore size,the time required for sporulation and the ability to produce zoospores,growth rate on culture media,and the production of pectinolytic and cellulolytic enzymes [38].Physiological specialization in A.euteiches was ﬁrst examined by King and Bissonnette [65],who indicated that isolates of the parasite differed in their virulence patterns on various pea culti-vars in Minnesota.Carlson [55]tested ten isolates of A.euteiches ,which were isolated from infested soil from Minnesota,New York,and Wisconsin,by inoculating the root tips of tolerant and suscep-tible pea cultivars,and reported considerable differences in the ability of the isolates to infect plants and produce oospores.Vari-able virulence and growth characteristics on culture media were also observed among seven single-zoospore isolates obtained from germinated oospores [48].Beute and Lockwood [66]inoculated six differential cultivars with 15A.euteiches single-zoospore isolates,and identiﬁed two races based on their virulence on those pea cul-tivars (Table 1)[66–70].The two races displayed a different disease reaction pattern on the six pea cultivars,based on disease severity.Employing the same differentials as Beute and Lockwood [66],Sundheim and Wiggen [67]conﬁrmed the existence of four physiological races of A.euteiches in a collection of 14isolates from four counties in Norway.Sundheim and Wiggen [67]evaluated resistance by counting the number of dead plants ten daysafterFig.1.Structures of A.euteiches .(a)Coenocytic hyphae with no septa;(b)encysted zoospore losing both ﬂagella;(c)oogonium of A.euteiches ;(d)antheridium and oogonium of A.euteiches during the sexual stage;(e)thick-walled oospore for survival in unfavorable conditions.544L.Wu et al./Engineering 4(2018)542–551inoculation.The method of race identiﬁcation described by Sund-heim and Wiggen [67]was questioned by Manning and Menzies [68],who suggested that the irreversibly wilted plants 10d after inoculation could not fully reﬂect the virulence spectrum of A.euteiches.The inconsistencies between these studies underscore the difﬁculties associated with race identiﬁcation in A.euteiches .Malvick and Percich [69]developed a new differential set (con-sisting of the pea genotypes MN313,MN314,90-2079,WI-8904,Little Marvel,Saranac,and Early Gallatin)to evaluate pathogenic diversity among 114A.euteiches isolates from the United States (Table 1),and also examined genetic variation via random ampli-ﬁed polymorphic DNA (RAPD)analysis.All isolates were patho-genic on one or more pea cultivars,and 18%and 14%were pathogenic on alfalfa (Saranac)and bean (Early Gallatin),respec-tively.Malvick and Percich [71]concluded that A.euteiches popula-tions were genotypically (based on the RAPD analysis)and phenotypically variable in the central and western United States.In a subsequent study,four virulence groups were identiﬁed,in which a disease severity of greater than 3.0(i.e.,>90%of the roots brown or yellow,but no symptoms present on the epicotyl or hypocotyl)was used as the threshold for a clear pathogenic inter-action [72].Fig.2.Symptoms of pea ARR caused by A.euteiches.(a)Yellowing and stunting of pea stems in the ﬁeld;(b)comparison of healthy (left)and diseased (right)plants;(c)discoloration and water-soaking of diseased pea rootlets;(d)wilted pea plants in the ﬁeld near harvest;(e)comparison of healthy (left)and diseased (right)pods;(f)Seedling blight in a low area of a ﬁeld after heavy rainfall;(g)bleaching of leaﬂets and premature ripening of the pod.Table 1Studies on pathogenic variability and physiological specialization in A.euteiches isolates from pea using various sets of differential pea genotypes.MethodDifferential genotypesIdentiﬁed race/virulence type Isolate region Ref.Race identiﬁcation Miragreen;Early Perfection;PI 175232;PI 169604;PI 180693;PI 166159Races 1and 2United States [66]Race identiﬁcation Miragreen;Early Perfection;PI 175232;PI 169604;PI 180693;PI 166159Races 1–4Norway [67]Race identiﬁcation Miragreen;Early Perfection;PI 175232;PI 169604;PI 180693;PI 166159Race 5New Zealand [68]Pathogenic variability MN313;MN314;90-2079;WI-8904;Little Marvel;Saranac;Early GallatinVirulence groups I–IV United States[69]Pathogenic variabilityBaccara;Capella;90-2131;MN313;552;PI 180693Virulence types I–XINorth America,Europe,and Oceania[70]L.Wu et al./Engineering 4(2018)542–551545Later,Wicker and Rouxel[70]examined109isolates of A.eute-iches from France,Denmark,Sweden,Norway,the United States, Canada,and New Zealand on another differential set(Baccara, Capella,90-2131,MN313,552,and PI180693)and identiﬁed11 virulence types(Table1).In that study,the isolates belonging to virulence type I,which caused severe ARR symptoms on all of the differentials,were predominant and the most aggressive. Wicker and Rouxel[70]also calculated a disease severity index (DSI)based on the mean of the individual disease severity ratings (0–5),and regarded a DSI<1as indicative of resistance.In a later study,Wicker et al.[17]indicated that the differential pea genotypes used by Malvick and Percich[69]were inadequate to distinguish French strains of A.euteiches.To more accurately evaluate the virulence of the pathogen from different countries, Wicker et al.[17]evaluated33pea lines and theﬁve differentials originally described by Wicker and Rouxel[70].The resistance detected in the differential pea genotypes in these studies has been used in the development of commercial pea cultivars with ARR resistance[17].Wu[73]conducted greenhouse screening of eight A.euteiches isolates from Alberta and Manitoba on the same differ-ential set as Wicker and Rouxel[70].Most strains were classiﬁed as virulence type I,although one strain was identiﬁed as virulence type III.Further testing of additional isolates from other Canadian regions with more differential breeding lines is still needed in order to better understand physiological specialization in this pathogen.2.4.Isolation of A.euteichesThe isolation of A.euteiches strains is difﬁcult.Pea root and root-let samples easily slough off infected tissue into the soil[74]. Numerous fungi also interfere with the isolation of A.euteiches [75].Manning and Menzies[75]successfully isolated A.euteiches on potato dextrose agar(PDA)plates using soil baiting.To increase the isolation success rate,metalaxyl-benomyl-vancomycin(MBV) [25]medium has been widely used to isolate A.euteiches,since it suppresses the growth of Pythium spp.,Phytophthora spp.,and most bacteria.Wu[73]used both direct isolation from infected root samples and soil baiting.For direct isolation,root and soil samples were collected at2–3weeks after seeding,when roots were not yet com-pletely infected by PRRC.The soil samples were later used for pathogen baiting with susceptible pea cultivars[68].The tips were cut from water-soaked pea roots and examined under a micro-scope for the presence of oospores.The root tips were surface-sterilized in1%NaClO for30s,rinsed in sterilized water,and plated on MBV medium.However,A.euteiches was detected in <0.1%of the samples,based on the results of a real-time poly-merase chain reaction(PCR)assay described by Vandemark et al.[76].2.5.Inoculation methodsZoospores are the most common form of A.euteiches inoculum employed in greenhouse experiments[17,43,70,77–83],while oospore-based inoculum has also been used in both greenhouse andﬁeld trials[38,73].The zoospore-based inoculum has been used widely for the detection of partial resistance to ARR inﬁeld pea[77–83].Zoospore inoculum is usually produced in a broth made from corn kernels,maltose-peptone,and oat(Avena sativa L.),or from pea seeds suspended in water,which are inoculated with A.euteiches and incubated for5–7d in the dark at room tem-perature[84].The resulting mycelial mats are placed in a mineral salt solution and aerated overnight to produce a zoospore suspen-sion of3Â105–8Â105zoospores per milliliter.The zoospores are usually used to precisely inoculate seven-day-old pea seedlings,before the seedlings are transplanted into pots in a green-house,with a determined zoospore concentration,thus eliminating the undesirable effect of nutrient substances in the media.Oospore inocula have been produced on autoclaved rolled oats with sand,cornmeal,and water.This substrate is inoculated with A.euteiches and incubated in the dark for30days at room temper-ature[38].Wu[73]modiﬁed this method by replacing cornmeal with oat grain.The grain-sand inocula were often used inﬁeld trials,as well as in greenhouse experiments,which need intensely infected disease conditions.Thygesen et al.[85]incubated A.euteiches in an oatmeal broth at20°C in the dark for4–8weeks; the broth was homogenized in a blender and thenﬁltered and washed with a mineral salt solution.The suspension was mixed with sterilized sand,dried at room temperature,and stored at 4°C.The oospore suspension also provides a precise inoculation for both the pea seedlings and pea seeds,which could continuously release zoospores in a greenhouse experiment.3.Traditional disease managementARR has been recognized as one of the most damaging root diseases ofﬁeld pea for almost a century[86].The options for management of this disease,however,are limited.Pea cultivars completely resistant to ARR are not available[25,87]and only partial resistance and/or tolerance has been reported in several studies[80,81,88].Some studies have focused on the efﬁcacy of fungicidal seed treatments at the seedling stage,which have been shown to improve plant health[89,90].At present,the most widely recommended method to manage ARR is avoidance via crop rotation and evaluation of infestation levels in theﬁeld prior to seeding[91].Biological control,including seed and soil treatments,has also shown promise at the experimental stage [9,92].3.1.Cultural practicesCrop rotation is one of the oldest and most fundamental meth-ods to manage diseases caused by soil-borne pathogens,although its effectiveness directly coincides with the length of rotation [93].A positive relationship exists between the frequency of pea crops and root rot severity[86].Rotation with non-host crops can therefore reduce the density of A.euteiches in the soil and thereby reduce the severity of ARR.Long-term crop rotations can reduce A.euteiches inoculum density in the soil,but they are not always effective in eradicating the disease[94].Nonetheless,the practicality and effectiveness of crop rotation as a method to manage ARR is questionable,because the oospores can survive for10–15years in the absence of a host[95].Furthermore,many alternative hosts,including chickpea,lentil,alfalfa,and weedy species,can sustain inoculum levels in the absence of pea[38]. Hossain et al.[96]recommended a crop rotation interval of6–8 years.Williams-Woodward et al.[97]examined the effect of oats as a rotation crop with pea,and observed that oat residues improved ARR suppression.Therefore,increased crop diversity may represent a good long-term strategy for disease management [98].Soil conditions can be suppressive or conducive to ARR[99]. Heyman et al.[100]observed a strong negative correlation between calcium concentration and disease development,which indicated that free calcium was a major variable in the degree of soil suppression of A.euteiches.Thisﬁnding led to the suggestion that calcium might play a role in the inhibition of zoospore produc-tion from the oospores[100].Residues from two plant families,the Brassicaceae—such as cabbage(Brassica oleracea L.),mustard(Brassica nigra L.),turnip546L.Wu et al./Engineering4(2018)542–551(Brassica rapa L.),and rapeseed(Brassica napus)[7,8,63,101–103]—and the Poaceae—such as oats,rye(Secale cereale L.),and maize (Zea mays L.)[8,97,104–108]—can reduce the severity of ARR.Soil compaction can exacerbate the development of ARR, causing pea yield losses as high as63%[107].In contrast,the yield of pea plots covered with oat shoots and residues increased by48% relative to plots planted without residues,suggesting that oat residues provide a promising method for the cultural control of pea ARR.Allmaras et al.[87]conﬁrmed the effect of oats as a pre-crop in the suppression of ARR,and pointed out that excessive compaction related to tillage and trafﬁc management may impair internal soil drainage and thus reduce the effectiveness of oat residues in controlling the disease.Field indexing by sampling soils to determine the A.euteiches inoculum potential can be an effective method to manage ARR of ﬁeld pea prior to seeding.Studies have identiﬁed and distinguished heavily infestedﬁelds from non-infested or lightly infestedﬁelds under greenhouse conditions[109,110],and this method of prior land selection can be an economical and dependable practice for avoiding ARR[111].Real-time PCR analysis has also been used to measure populations of A.euteiches inﬁeld soil.Vandemark et al. [112]and Armstrong-Cho et al.[113]demonstrated that a positive relationship existed between ARR severity and the DNA concentra-tion of several isolates of A.euteiches in pea roots.3.2.Disease prediction and molecular detection of A.euteichesMolecular markers are useful tools for the identiﬁcation of fungal and oomycete plant pathogens.The testing of soil or plant samples for the presence of A.euteiches DNA by PCR analysis with species-speciﬁc primers has been widely used[76].Chatterton et al.[46]and Armstrong-Cho et al.[113]detected A.euteiches in peaﬁelds in Alberta and Saskatchewan,respectively,based on a PCR assay.A number of commercial kits have also been used to identify A.euteiches efﬁciently[46,76,112].Nonetheless,informa-tion on the use of molecular markers for the identiﬁcation of speciﬁc races or pathotypes of A.euteiches is still limited and preliminary.Malvick and Percich[69]conducted RAPD analyses to evaluate genotypic diversity among strains of A.euteiches in the United States,but none of the76polymorphic RAPD markers were associ-ated with pathogenic variation.In another study,the same researchers successfully distinguished one major group and two closely related minor groups in a collection of114isolates from four locations in the United States,based on a pathogenicity test ofﬁve pea genotypes and RAPD analysis[69].Sauvage et al. [111]used two sets of markers,136F/136R and11F/280R, to amplify different-sized PCR products from105isolates of A.euteiches.They demonstrated a close relationship between the quantity of soil inoculum and ARR severity.3.3.Seed and soil treatmentsCertain soil fungicides for ARR control are prohibited in some regions,including much of Europe[96].In addition,the cost and adverse environmental effects of treating the soil with chemicals makes this approach impractical and undesirable across the broad area over which pea crops are grown[114,115].Seed-coating treat-ments such as hymexazol can effectively improve seedling emer-gence[116].However,Tu[106]pointed out the limitations in the control of pea root rot using Captan(N-trichloromethylthio-4-cyclohexene-1,2-dicarboximide).Furthermore,A.euteiches is resis-tant to some of the fungicides that are registered for the control of other oomycetes.For example,metalaxyl is active against most oomycetes,but not against Aphanomyces.It is the main ingredient of the selective medium used to isolate A.euteiches[25].Neither the systemic acylalanine-type of oomycete fungicides,such as met-alaxyl,nor the ethyl phosphonates,such as fosetyl-Al or cymoxanil, effectively control ARR[117].Some chemicals effectively suppress A.euteiches under controlled conditions,but have limited beneﬁ-cial effects inﬁeld trials[89,90].Tachigaren(hydroxyisoxazole or hymexazol)was reported to reduce root rot severity and increase yield under experimentalﬁeld conditions[116];this compound is available commercially in Japan for the control of the Pythium and Aphanomyces diseases of sugar beets[117].The effectiveness of Tachigaren for the control of ARR,however,was variable in other studies[118–120].A recent study determined that Intego Solo (ethaboxam)(Valent,Guelph,ON,Canada),BAS516F,and BAS 720F reduced disease severity under greenhouse conditions,but not underﬁeld conditions[73].At present,ethaboxam is the only fungicide registered for Pythium root rot control and the suppres-sion of seed rot caused by Phytophthora spp.and Aphanomyces spp.in legumes in Canada.3.4.Biological controlAntagonistic microorganisms applied to the seeds or soil may help to protect pea plants from infection by fungal and oomycete pathogens.The spores of arbuscular mycorrhizal(AM)fungi and some spore-forming bacteria,which were applied as seed coatings to control ARR in peaﬁelds,signiﬁcantly suppressed the develop-ment of ARR in aﬁeld trial[121].The application of isothiocyanate, a compound produced by members of the Brassicaceae in shoot tis-sues,has also been shown to have potential for the management of ARR due to its toxic effects on A.euteiches under controlled condi-tions[96].Biocontrol and fungicide treatments are often integrated into seed treatments.Recent studies have demonstrated that some fun-gal and bacterial strains,such as Gliocladium roseum(Clonostachys rosea(Link)Schroers),Pseudomonasﬂuorescens(Flügge)Migula, and species involved in the Burkholderia cepacia(Palleroni and Holmes)Yabuuchi et plex,which are formulated for seed coat application in combination with a fungicide,improved seed-ling emergence inﬁelds infested with A.euteiches to a greater extent than treatments in which only a fungicide was applied [17,18,90].Xue[90]evaluated a seed treatment consisting of the fungal strain ACM941(Clonostachys rosea)and a fungicide(Thiram 75WP(thiram)or Apron FL(metalaxyl)),and found that a seed coating with ACM941+fungicide improved pea seed germination in an A.euteiches-infestedﬁeld.AM fungi have also been proven to increase the seedling emergence of peas when inoculated with A.euteiches in greenhouse experiments,but they were not always effective in theﬁeld[85,122].Several studies indicated that solar-ization was effective for the control of pea root rot in temperate regions,when used in combination with green manure crops,lower dosages of chemicals,or biological control organisms[123,124]. 4.Genetic resistance to A.euteiches4.1.Partial resistance to ARRGenetic resistance to ARR inﬁeld pea could be the most economical and effective strategy for managing this disease.A number of pea-breeding lines with partial resistance or tolerance to ARR have been developed,and are used to prevent yield losses in some pea-producing regions[78,79,88,125].Some differential pea genotypes,such as Capella,MN144,MN313,MN314, 90-2131,90-2079,552,and PI180693,have been reported to be partially resistant to certain races of A.euteiches[17,72,125].The differentials PI180693and552have drawn considerable attention due to their high level of stable partial resistance to ARR[17,126].L.Wu et al./Engineering4(2018)542–551547。

qtl原理

qtl原理QTL原理。

QTL（Quantitative Trait Loci）是指定位于染色体上的控制数量性状的基因或基因组区域。

数量性状是指受多个基因和环境因素影响的性状，如生长速率、产量等。

QTL分析是一种通过遗传连锁和相关分析，来确定数量性状的遗传基础的方法。

QTL的发现对于理解数量性状的遗传机制、育种改良和基因定位都具有重要意义。

QTL分析的基本原理是通过构建遗传连锁图谱，确定数量性状与分子标记之间的遗传关系。

首先，需要构建一个包含足够密度的分子标记的遗传图谱，这些分子标记可以是单核苷酸多态性（SNP）、简单重复序列（SSR）等。

然后，通过测定不同个体的数量性状表型值，结合分子标记的基因型数据，利用统计方法来分析数量性状与分子标记之间的遗传关系。

最终确定数量性状的QTL位置和效应大小。

QTL分析的关键是构建遗传图谱和选择合适的统计方法。

构建遗传图谱需要选择合适的分子标记，保证标记的分布均匀和密度足够。

同时，统计方法的选择也是至关重要的，常用的方法包括单因素方差分析、双因素方差分析、QTL定位等。

QTL分析的应用范围非常广泛，涉及到农业、医学、动植物育种等多个领域。

在农业领域，QTL分析可以帮助育种者快速筛选出具有优良数量性状的种质资源，加速育种进程。

在医学领域，QTL分析可以帮助研究者发现与疾病相关的基因，为疾病的预防和治疗提供理论依据。

在动植物育种领域，QTL分析可以帮助育种者理解数量性状的遗传机制，指导育种方向。

总之，QTL分析是一种重要的遗传分析方法，通过构建遗传图谱和统计分析，可以确定数量性状的遗传基础，为育种改良和基因定位提供重要依据。

随着分子标记技术和统计方法的不断发展，QTL分析将在更多领域发挥重要作用，推动遗传学研究和应用的进步。

湖羊品种资源的深度利用探索正文

摘要湖羊是中国东南近海湿热农区特有的优良羔皮用绵羊品种，具有繁殖力强、早期生长快，以及耐舍饲、耐高温高湿等独特的性状和巨大的经济价值。

湖羊羔皮曾是我国传统的出口创汇商品，在国际裘皮市场上享有盛誉，被称为中国的“软宝石”。

1.湖羊核心群绵羊多胎主效FecB基因检测采135 只经产湖羊的血样，对突变型和野生型个体材料检测SNP，比较湖羊群体之间SNP的差异。

研究发现，湖羊为FecB（BMPR-IB 突变）基因高度纯合的群体，试验群体中FecB 基因频率为90%，未发现野生型个体，高产湖羊（108头）均为Fec B 基因纯合个体。

研究表明该基因突变可用于湖羊高产个体的早期选择。

2. BMPR-IB 基因的拷贝数变异与湖羊多胎性状的关联分析基因拷贝数的增加或减少会通过基因剂量效应引起mRNA的表达量变化。

早期Booroola羊上的研究表明FecB基因是对排卵数有加性效应的显性基因，一个FecB拷贝增加排卵数约1.5个，二个FecB拷贝增加排卵数约3。

本研究以51头湖羊为研究对象，使用相对定量的方法分析BMPR-IB基因是否发生拷贝数变异并探究其与湖羊产羔性状之间的联系。

3.马头湖羊与普通生长性能比较通过FecB基因的分子手段，检测湖州练市生态养殖场湖羊核心群，筛选FecB 核心种群，结合体型培育纯种马头湖羊，将“马头湖羊”与普通湖羊公羊体重、体长，体高、胸围、管围、睾丸周长及母羊繁殖性能与普通湖羊进行比较，结果表明以FecB基因作为遗传标记基因，结合表型选择，可做到培育湖羊新品系，产生了湖羊新的品种特性，同时也为地方肉羊品种内的品系培育提供参考。

4.探究杜湖F1杂交母羊的母本优势以杜泊公羊为父本，分别与杜湖杂交一代母羊、纯种湖羊母羊、纯种杜泊羊母羊进行自然发情配种，检验其产羔数、初生重、断奶重等繁殖性能差异，并在选各组6月龄后代10只羔羊进行屠宰（公母各半）。

结果表明，杜湖F1杂种母羊在产羔数上显著（P＜0.05）高于纯种杜泊羊，与纯种湖羊无显著差异（P＞0.05）；杜湖F1为母本后代羔羊初生重与断奶重均极显著（P＜0.01）高于纯湖羊母本，极显著（P＜0.01）低于纯种杜泊羊；杜湖F1杂种母羊羔羊6月龄羊羔体重、体高、胴体重极显著（P＜0.01）高于纯种湖羊，与纯种杜泊相比无明显差异(P＞0.05）；胸围显著（P＜0.05）高于纯种湖羊，与杜泊羊无显著差异(P＞0.05）；杜湖F1杂种母羊羔羊6月龄屠宰率显著（P＜0.05）高于纯种湖羊，显著(P＜0.05）低于纯种杜泊羊，净肉率显著（P＜0.05）高于纯种湖羊，与纯种杜泊羊无明显差异(P＞0.05）。

bsa法定位qtl的置信区间计算

bsa法定位qtl的置信区间计算以bsa法定位qtl的置信区间计算为标题QTL（Quantitative Trait Loci）是指控制数量性状的基因座，通过研究这些基因座与数量性状之间的关系，可以帮助我们了解数量性状的遗传机制。

而BSA（Bulked Segregant Analysis）是一种常用的遗传定位方法，可用来鉴定控制数量性状的QTL。

在利用BSA法定位QTL时，我们通常会计算QTL的置信区间。

置信区间是指在统计学上对于参数估计的不确定性给出的一个范围，它提供了对参数真值可能落在的范围的估计。

我们需要进行实验，收集数量性状的表型数据和相应的分子标记数据。

然后，通过遗传分析方法，我们可以确定与数量性状相关的QTL候选区域。

接下来，利用BSA法，我们可以将群体分为两个池，即高表型池和低表型池。

然后，对这两个池进行基因组DNA测序，获取SNP（Single Nucleotide Polymorphism）数据。

在得到SNP数据后，我们可以使用统计学方法来计算QTL的置信区间。

一种常用的方法是使用Bayesian方法。

Bayesian方法基于Bayes定理，通过计算后验概率来估计参数的不确定性。

具体操作时，我们可以使用软件包如R/qtl或WinQTLCartographer来进行计算。

在进行计算时，我们需要给定先验概率、QTL位置的先验分布以及观测到的数据。

根据这些输入，Bayesian方法可以计算出QTL位置的后验分布，从而得到QTL的置信区间。

置信区间的计算结果通常以概率的形式给出，例如95%置信区间表示在95%的概率下，QTL的真实位置落在该区间内。

除了Bayesian方法，还有其他一些常用的方法可以用于计算QTL 的置信区间，如Bootstrap法和F-test法。

这些方法基于不同的统计原理，可以给出类似的结果。

需要注意的是，计算QTL的置信区间并不是一个简单的问题，它涉及到多个参数和假设的选择。

蛋白QTL的组学分析及其应用

蛋白QTL的组学分析及其应用随着生物信息学和高通量测序技术的发展，基因组、转录组和蛋白质组等在分子水平上的研究正在变得越来越普遍。

其中，基因组研究不仅可以揭示物种的遗传演化和基因家族的组合，还可以深入挖掘多种性状相关的遗传变异。

而QTL （Quantitative Trait Loci）定位技术可以用于鉴定控制各种性状的数量性状基因。

在蛋白质组学研究中，QTL也有着重要的作用，被称作蛋白QTL。

本文将重点介绍蛋白QTL的组学分析及其应用。

一、蛋白QTL的定义蛋白QTL是指对某个数量性状的控制有显著影响的蛋白质基因座，它不仅能够直接影响性状的表现，还可以通过调节其他不同组分的作用以间接影响性状的表现。

与传统的QTL性状定位技术不同，蛋白QTL定位不能简单地根据DNA序列的差异来研究蛋白质基因座；相反，应该从蛋白质水平进行探究。

二、蛋白QTL的组学分析蛋白QTL的组学分析是指根据高通量质谱技术等手段，对大规模样品进行蛋白质组学分析，从而鉴定出与性状预测相关的蛋白质基因座的过程。

具体而言，主要分为以下几个步骤：1. 样品准备：在进行蛋白组学研究前，需要先对样品进行制备。

合适的样品制备，可以大大提高后续蛋白质鉴定的准确性和稳定性。

2. 质谱分析：在蛋白质组学研究中，通常采用液相色谱-串联质谱（LC-MS/MS）技术，对各个样品进行蛋白质谱分析，获得大量的蛋白质数据。

3. 数据处理：将获得的蛋白质质谱数据与数据库中的蛋白质序列进行比对，并进行统计分析，找出与性状预测相关的蛋白质，从而确定蛋白质的分型和表型信息。

4. 统计分析：对于确定的蛋白质进行进一步的统计学分析，找出与性状预测相关的蛋白质基因座，进行QTL定位和基因功能分析。

三、蛋白QTL的应用1. 性状显性基因的发掘QTL定位技术常常用于鉴定数量性状的显性或隐性遗传基因。

由于蛋白质QTL定位技术的高灵敏性和高分辨率，可以更准确地鉴定数量性状的显性和隐性基因。

遗传性持续性胎儿血红蛋白增高症(HPFH)的分子机制

·综述·《中国产前诊断杂志（电子版）》　２０１２年第４卷第２期遗传性持续性胎儿血红蛋白增高症（ＨＰＦＨ）的分子机制曾小红　综述　朱宝生校审（昆明医科大学附属昆华医院遗传诊断中心，云南昆明　６５００３２）【摘要】　遗传性持续性胎儿血红蛋白增高症（Ｈｅｒｅｄｉｔａｒｙｐｅｒｓｉｓｔｅｎｃｅｏｆｆｅｔａｌｈｅｍｏｇｌｏｂｉｎ，ＨＰＦＨ）是成人红细胞中持续存在过量的胎儿血红蛋白（Ｆｅｔａｌｈｅｍｏｇｌｏｂｉｎ，ＨｂＦ），血液学检查正常的遗传综合征。

携带者常无临床症状。

ＨＰＦＨ具有高度的遗传异质性，分子机制主要涉及１１ｐ１５上β 类珠蛋白基因的遗传缺陷导致的ＨｂＦ异常高表达。

最近的研究表明，ＨＰＦＨ具有数量性状遗传特点，其发生机制可能不局限于单纯的β 类珠蛋白基因上的遗传缺陷，ＨＰＦＨ还与多个基因座的异常有关，具有数量性状位点（ｑｕａｎｔｉｔａｔｉｖｅｔｒａｉｔｌｏｃｉ，ＱＴＬ）的遗传特征。

主要包括ＱＴＬ６ｑ２３和ＱＴＬ２ｐ１５等的异常。

通过ＨＰＦＨ来探索珠蛋白基因的网络化表达调控机制，为镰状细胞性贫血、重型地中海贫血等疾病的治疗研究开拓了新路径。

【关键词】　ＨＰＦＨ；分子机制；β 类珠蛋白基因；ＱＴＬ【中图分类号】　Ｒ３９４．３【文献标识码】　Ａ基金项目：本文受云南省科技厅昆明医学院联合专项重点项目“云南地中海贫血遗传异质性与防治对策研究”（项目编号：２０１１ＦＢ１６４）资助通讯作者：朱宝生，Ｅｍａｉｌ：ｂｓｚｈｕ＠ｙａｈｏｏ．ｃｎ犕狅犾犲犮狌犾犪狉犅犪狊犻狊狅犳狋犺犲犎犲狉犲犱犻狋犪狉狔犘犲狉狊犻狊狋犲狀犮犲狅犳犉犲狋犪犾犎犲犿狅犵犾狅犫犻狀（犎犘犉犎）　犣犲狀犵犡犻犪狅犺狅狀犵，犣犺狌犅犪狅狊犺犲狀犵．（犌犲狀犲狋犻犮犇犻犪犵狀狅狊犻狊犆犲狀狋犲狉，犃犳犳犻犾犻犪狋犲犱犓狌狀犺狌犪犎狅狊狆犻狋犪犾狅犳犓狌狀犿犻狀犵犕犲犱犻犮犪犾犮狅犾犾犲犵犲，犓狌狀犿犻狀犵　６５００３２，犆犺犻狀犪）【犃犫狊狋狉犪犮狋】Ｈｅｒｅｄｉｔａｒｙｐｅｒｓｉｓｔｅｎｃｅｏｆｆｅｔａｌｈｅｍｏｇｌｏｂｉｎ（ＨＰＦＨ）ｉｓａｇｒｏｕｐｏｆｇｅｎｅｔｉｃｓｙｎｄｒｏｍｅｓｗｉｔｈｎｏｒｍａｌｂｌｏｏｄｔｅｓｔａｎｄｔｈｅｐｅｒｓｉｓｔｅｎｃｅｏｆｅｘｃｅｓｓｉｖｅＨｂＦ（Ｆｅｔａｌｈｅｍｏｇｌｏｂｉｎ）ｉｎｔｈｅａｄｕｌｔｒｅｄｂｌｏｏｄｃｅｌｌｓ．ＭｏｓｔｃａｒｒｉｅｒｓｏｆＨＰＦＨｈａｖｅｎｏｃｌｉｎｉｃａｌｓｙｍｐｔｏｍｓ．ＨＰＦＨｈａｓａｈｉｇｈｄｅｇｒｅｅｏｆｇｅｎｅｔｉｃｈｅｔｅｒｏｇｅｎｅｉｔｙ．ＴｈｅｍｏｌｅｃｕｌａｒｍｅｃｈａｎｉｓｍｓｏｆＨＰＦＨｉｎｖｏｌｖｅｉｎａｂｎｏｒｍａｌｅｘｐｒｅｓｓｉｏｎｏｆＨｂＦｃａｕｓｅｄｂｙｇｅｎｅｔｉｃｄｅｆｅｃｔｓｏｆｂｅｔａｇｌｏｂｉｎｇｅｎｅｃｌｕｓｔｅｒｏｎ１１ｐ１５．Ｒｅｃｅｎｔｌｙ，ｓｅｖｅｒａｌｓｔｕｄｉｅｓｒｅｖｅａｌｅｄｔｈａｔＨＰＦＨｈａｓｃｈａｒａｃｔｅｒｉｓｔｉｃｓｏｆｑｕａｎｔｉｔａｔｉｖｅｔｒａｉｔｌｏｃｉ（ＱＴＬ）．ＴｈｅｍｅｃｈａｎｉｓｍｏｆＨＰＦＨｍａｙｎｏｔｏｎｌｙｂｅｌｉｍｉｔｅｄｔｏｇｅｎｅｔｉｃｄｅｆｅｃｔｓｗｉｔｈｉｎｔｈｅｂｅｔａｇｌｏｂｉｎｇｅｎｅｃｌｕｓｔｅｒ，ｂｕｔａｌｓｏｂｅｒｅｌａｔｅｄｔｏａｂｎｏｒｍａｌｉｔｉｅｓｉｎｍｕｌｔｉｐｌｅｌｏｃｉ，ｉｎｃｌｕｄｉｎｇＱＴＬ６ｑ２３ａｎｄＱＴＬ２ｐ１５．Ｔｈｒｏｕｇｈｅｘｐｌｏｒｉｎｇｔｈｅｅｘｐｒｅｓｓｉｏｎａｎｄｒｅｇｕｌａｔｉｏｎｍｅｃｈａｎｉｓｍｓｏｆｇｌｏｂｉｎｇｅｎｅｒｅｇｕｌａｔｉｏｎｎｅｔｗｏｒｋ，ａｎｅｗｇａｔｅｆｏｒｓｔｕｄｉｅｓｏｎｔｈｅｔｒｅａｔｍｅｎｔｏｆｓｉｃｋｌｅｃｅｌｌａｎｅｍｉａａｎｄｔｈａｌａｓｓｅｍｉａｈａｓｂｅｅｎｏｐｅｎｅｄ．【犓犲狔狑狅狉犱狊】　ＨＰＦＨ；ｍｏｌｅｃｕｌａｒｍｅｃｈａｎｉｓｍ；ｂｅｔａｇｌｏｂｉｎｇｅｎｅ；ｑｕａｎｔｉｔａｔｉｖｅｔｒａｉｔｌｏｃｉ血红蛋白（Ｈｅｍｏｇｌｏｂｉｎ）是人体红细胞内的一种主要蛋白质，由珠蛋白和血红素结合而成，通过携氧释氧实现氧气在人体内的运输［１］。

Identification of quantitative trait loci and candidate genes

Tree Physiology 32, 626–638doi:10.1093/treephys/tps032Research paperIdentification of quantitative trait loci and candidate genesfor cadmium tolerance in PopulusDownloaded from Brahma Reddy Induri1, Danielle R. Ellis1, Gancho T. Slavov1,2, Tongming Yin3, Xinye Zhang4,Wellington Muchero5, Gerald A. Tuskan5 and Stephen P. DiFazio1,61Department of Biology, West Virginia University, 53 Campus Drive, Morgantown, WV 26506-6057, USA; 2Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3EB, UK; 3The Key Lab of Forest Genetics and Gene Engineering, Nanjing Forestry University, Nanjing 210037, China; 4Hubei Forestry Academy, Wuhan 430079 China; 5Biosciences Division, Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, TN 37831-6422, USA; 6Corresponding author (spdifazio@) Received December 5, 2011; accepted March 8, 2012; published online April 21, 2012; handling Editor Chung-Jui Tsai /their cells naturally. However, their slow growth rates, annual habit, small stature, low biomass and/or narrow geographic adaptability make them unsuitable for phytoremediation on an operational scale (Eapen and D’Souza 2005). Populus, with its rapid growth, makes an ideal plant species for phytoremedia-tion of soils polluted by heavy metals (Cunningham and Ow 1996). Furthermore, Populus is readily propagated from vege-tative cuttings, and has a wide range of adaptability, high bulk root volume, high transpiration rate and large stature, all of which should enhance the efficacy of phytoremediation (Rockwood et al. 2004). Previous studies have assessed the potential of using Populus to remediate soils polluted by atra-zine (Burken and Schnoor 1997), trichloroethylene (Newman et al. 1997), Cd (Robinson et al. 2000), selenium (Pilon-Smits et al. 1998) and zinc (Di Baccio et al. 2009). In addition, some hybrid Populus clones have shown elevated tolerance of Cd in field plantings relative to other tree species (Migeon et al. 2009), and there is considerable natural variation within the genus that could be exploited to enhance Cd tolerance further using biotechnological approaches.To further characterize Cd tolerance in Populus, we (i) q uantified phenotypic and genetic variation for Cd tolerance and Cd accumulation in a pseudo-backcross multiple genera-tion Populus pedigree that was grown under greenhouse con-ditions; (ii) detected quantitative trait loci (QTL) for traits related to Cd tolerance including dry weight components and root volume; and (3) identified candidate genes in the QTL intervals based on transcriptional data from a microarray study. Material and methodsPlant materialsAn interspecific mapping population was developed at the University of Minnesota Natural Resources Research Institute by crossing Populus trichocarpa Torr. & Gray clone 93-968 from western Washington to Populus deltoides Bart. clone ILL-101 from southern Illinois. The resulting F1 hybrid, 52-225, was in turn backcrossed to P. deltoides clone D-124 from Minnesota. Two-hundred and fifty-two full-sib progeny from the ‘pseudobackcross’ pedigree (Family 52-124) were used for the current study. Dormant cuttings were collected from a stoolbed in early spring prior to bud break and were stored at 4 °C for 5 weeks to meet chilling requirements. This facilitated uniform rooting of the cuttings, and enabled selection of plants with approximately uniform sizes at the initiation of our experi-ment. Two ramets per clone were used for both control and Cd treatments. A total of 1008 vegetative cuttings (252 geno-types × 2 treatments ×2 cuttings per genotype) were exam-ined in this experiment.Cuttings with approximate lengths of 15 cm and diameters of 1 cm were dipped in 1 : 10 dilution of a commercial liquid rooting hormone solution (Dip N Grow 1% indole-3-butyric acid, 0.5% naphthalene acetic acid) for 15 s, surrounded by foam stoppers and plugged into 3 cm holes at 8 cm spacingin the lids of polyethylene containers. Approximately 5 cmof the base of the cuttings was submerged beneath a dilute,c ontinuously aerated nutrient solution, 0.1 ×Johnson’s solution (Siddique et al. 1990). The nutrient solutionwas composed of 400 µM NH4NO3, 400 µM KNO3, 200 µMCa(NO3)2⋅4H2O, 100 µM MgSO4⋅7H2O, 50 µM K2HPO4, 20 µM KCl, 25 µM H3BO3, 0.5 µM Na2MoO4⋅2H2O, 2 µM MnSO4⋅4H2O,0.5 µM CuSO4⋅5H2O, 2 µM ZnSO4⋅7H2O, 0.5 µM CoCl2 and20 µM Fe-Na EDTA. The pH was ~5.4. One primary shoot was maintained per cutting.Once the cuttings developed roots and shoots, they were transplanted into 126 polyethylene containers (24 cm × 12 cm× 38 cm) following a partially balanced incomplete block design, with two treatments (Cd and control), 126 containersas incomplete blocks and two replicates of each genotype ineach treatment. Each container had one ramet from each ofeight different genotypes at 9 cm spacing and contained 7 l ofthe 0.1× Johnson’s solution described above. The solutions were changed every 3 days throughout the experiment (from19 May 2007 to 10 July 2007) to maintain nutrient and oxygen levels and to avoid algal growth.The experiment was carried out with a target day tempera-ture of 25 °C and a night temperature of 21 °C and a photope-riod of 18 h. Artificial lighting was provided by high-density mixed halide lights and photosynthetically active radiation (PAR) was recorded for every container used in the study, atplant height (average PAR = 224.05 µmol/m2/s and standard deviation = 55.08). Temperature was maintained by an evapo-rative cooling system.Cuttings were grown for 40 days and one-half of the con-tainers were randomly selected to receive a 25 µM CdCl2 treatment for an additional 2 weeks. The remaining half ofthe containers were maintained as described above for 2 weeks and used as controls. The Cd concentration was selected based on pilot experiments that revealed substan-tial phenotypic variation in this family without excessive mor-tality. Fresh CdCl2 was added each time solutions were changed. Before and after 20 days of treatment, measure-ments were taken on shoot length, cutting diameter, shoot diameter, PAR, root collar diameter (the diameter recordedright above the origin point of the uppermost root on a cut-ting) and rooted length for each cutting (portion of the cut-ting occupied by roots). Furthermore, root volume was estimated using water displacement in graduated cylinders.After 2 weeks, roots and shoots were harvested, separatedinto paper bags and dried in an oven at 60 °C for 6 days. Roots were washed with deionized water and blotted on paper towels before being stored in the bags. Dry weightswere recorded for leaves, roots and shoots separately, andCd tolerance QTL and candidate genes in Populus627at South China Agricultural University on June 30, 2012/Downloaded fromTree Physiology Online at Tree Physiology Volume 32, 2012total dry weights were calculated by summing all components.Statistical analysis of phenotypic dataPhenotypic data were tested for normality using the Shapiro–Wilk test (Shapiro and Wilk 1965) and logarithmic transforma-tions were performed to mitigate heteroscedasticity of variance versus predicted values. Data were analyzed with mixed linear models using SAS JMP, version 8.0 (SAS Institute Inc., Cary, NC, USA). The general form of the statistical model wasY T B G ijk i j k ijk =++++µεwhere Y ijk was the measurement of response variable Y (total dry weight, root dry weight, leaf dry weight, stem dry weight or root volume) for genotype k in container j under treatment i , µ was the population mean, T i was the effect of treatment i , B j was the effect of incomplete block (container) j , G k was the effect of genotype k and εijk was the experimental error. When significant (P ≤ 0.05), the rooted length and dry weight of each cutting and PAR for each container were also included as covariates. Genotype and all interactions with genotype were treated as random effects in the model, whereas all remaining variables were treated as fixed effects.Broad-sense heritabilities (H 2) were calculated as H 2 = V G /(V G + V ε) based on the variance components for genotype (V G ) and error (V ε) estimated using restricted maximum likelihood (REML) in mixed linear models that were fitted separately for control and Cd treatments. This was done to assess d ifferences in variances and heritabilities under these two conditions.The effect of Cd on each genotype (hereafter referred to as ‘Cd effects’) was estimated by subtracting its best linear unbi-ased predictor (BLUP) under Cd conditions from that under control conditions (Coles et al. 2010) for all biomass traits described above. The genotypes with the largest BLUP differ-ences were identified as Cd sensitive and those with the small -est BLUP differences were identified as Cd tolerant. Genetic correlations among traits were calculated using BLUPs and the REML method in SAS JMP, version 8.0.Linkage map constructionTo construct a genetic map for Family 52-124, 188 progeny were genotyped using 590 amplified fragment length polymor -phism markers. In addition, 418 progeny were genotyped using 287 simple sequence repeat (SSR) markers (Yin et al. 2009) that were chosen based on their physical locations along chro-mosomes to achieve an even distribution of markers on each linkage group. Four aneuploid trees were identified by geno -typing with fully informative SSR markers and these individuals were later removed from the mapping data set. Markerg eneration, genotyping and nomenclature were performed as described previously (Tuskan et al. 2004, Yin et al. 2004).Map construction was conducted using JoinMap 3.0 under the CP cross type (Van Ooijen 2001).Quantitative trait loci analysisQuantitative trait loci were identified using MapQTL version 5.0 (Van Ooijen 2004). Interval mapping was performed ini-tially to identify markers associated with putative QTL. Markers with significant effects in this analysis were then used as cofactors in restricted multiple QTL model mapping (Van Ooijen 2004). Logarithm of odds (LOD) scores were calcu-lated at 1.0 cM intervals. Only QTL with LOD score > 2.5 are reported here.Microarray studyA whole-genome microarray study was conducted using two genotypes that demonstrated differential Cd effects in the above QTL hydroponic study: a Cd-tolerant genotype (1–183) and a Cd-susceptible genotype (182). The experiment con-sisted of six polyethylene containers as described above but with only four plants per container. Each genotype was repre-sented by two ramets per container with randomly assigned locations. Three containers were treated with 25 µM CdCl 2 and the remaining three containers were under control conditions. Three ramets from each genotype × treatment combination were harvested at 24 h, and three at 72 h after Cd treatment. Roots from each plant were thoroughly washed in deionized water and blotted dry and all material was quick frozen in liquid nitrogen and stored at −80 °C.Plant tissue was ground in liquid nitrogen, RNA was extracted using the Qiagen RNA Mini kit (Qiagen, Germantown, MD, USA), and double-stranded DNA was synthesized using the Invitrogen SuperScript™ double-stranded cDNA Synthesis Kit (Invitrogen, Carlsbad, CA, USA). Labeling, hybridization and scanning were performed by NimbleGen (Roche NimbleGen, Madison, WI, USA). The raw data were normalized using the robust multichip averaging procedure, which performs a convolution background correction, quantile normalization and summarization based on a multi-array model fit robustly using the median polish algo -rithm (Bolstad et al. 2005). Differentially expressed genes (Cd treated versus control) were determined using rank product analysis (Breitling et al. 2004) using 1000 permutations and a percentage of false positives cutoff of 0.05.ResultsCadmium effectsThe effect of Cd was evident for all measured traits (Table 1). Cadmium symptoms included necrotic spots on leaves and browning of roots (Figure 1). The genotypes under Cd condi-tions showed poor growth compared with those under control conditions (Table 1). The effects of genotype, treatment and628 Induri et al.at South China Agricultural University on June 30, 2012/Downloaded fromTree Physiology Online at their interaction were highly significant (P < 0.001) for total dry weight, root dry weight, leaf dry weight, stem dry weight and root volume. The covariate effects of cutting rooted length, cutting dry weight and PAR were also significant (P < 0.001) for all traits except for PAR for root volume (Table 2).Cd tolerance QTL and candidate genes in Populus 629Table 1. S ummary statistics and heritability estimates for total dry weight and other measured traits along with their variance components.Mean (g)SD 1Range SE 2V G 3V p 4H 2 BS 5ControlTotal dry weight C 6.35 4.1124.980.190.1700.4710.361Leaf dry weight C 3.57 2.2813.050.100.1600.4990.321Root dry weight C 1.140.887.340.040.2010.5810.346Stem dry weight C 1.63 1.167.390.050.2330.5970.390Root volume C 1.620.94 6.000.040.0450.2350.191CadmiumTotal dry weight Cd 2.52 1.638.240.070.1530.4500.340Leaf dry weight Cd 1.46 1.01 5.230.040.1470.5330.276Root dry weight Cd 0.450.33 2.510.010.1790.5580.322Stem dry weight Cd 0.600.43 2.700.020.1530.5290.288Root volume Cd 0.970.649.000.020.0240.2180.110Cd mg/g0.340.271.510.010.1080.8590.1261Standard deviation.2Standard error.3Genetic variance.4Phenotypic variance (V G + V ε).5Broad-sense heritability.Figure 1. M orphological effects of Cd treatment in a hydroponic experiment: (a) control roots; (b) Cd-treated roots; (c) control leaf (d) and Cd-treated leaf.at South China Agricultural University on June 30, 2012/Downloaded fromTree Physiology Volume 32, 2012There was extensive variation in Cd responses among geno-types (Figures 2 and 3). Broad-sense heritabilities of total dry weight and its components were relatively low and comparable between control (range H 2 = 0.19–0.39) and Cd conditions (range H 2 = 0.11–0.34, Table 1). Root volume showed the low-est heritabilities under either control (H 2 = 0.19) or Cd (H 2 = 0.11) conditions. In contrast, total dry weight had heritabilities of 0.36 and 0.34 in control and Cd conditions, respectively (Table 1).Quantitative trait loci identificationQuantitative trait loci were mapped for 16 phenotypes with a minimum LOD score of 2.5. Four of these QTL were identified630Induri et al.Figure 2. G enotypic variation in visible effects of Cd treatment. (a) Differences between control (right two plants) and Cd (left two plants) treat-ments. (b) Variation among genotypes under Cd conditions. Blue bar is 30.5 cm.Figure 3. D istributions of the difference in BLUPs of total dry weight between control (C) and cadmium (Cd) treatments. Genotypes from the left portion of the curve are relatively tolerant of Cd treatment.Table 2. R esults of ANOVA for traits measured in the greenhouse hydroponic study that showed significant differences between Cd and control conditions.Total dry weightLeaf dry weight Root dry weight Stem dry weight Root volume Trait F P F P F P F P F P Genotype 1.97<0.0001 1.86<0.0001 1.76<0.0001 1.97<0.0001 1.61<0.0001Treatment 589.94<0.0001525.75<0.0001335.03<0.0001555.28<0.0001215.37<0.0001Genotype × treatment 1.490.0002 1.410.0009 1.280.0124 1.53<0.0001 1.340.0044Rooted length of cutting10.080.00165.990.014716.76<0.00017.710.005713.920.0002Cutting dry weight 139.78<0.0001130.92<0.000156.23<0.0001146.18<0.000151.94<0.0001PAR6.870.00914.850.02814.300.03889.180.00260.700.4027at South China Agricultural University on June 30, 2012/Downloaded fromin control conditions and 12 under Cd conditions. Quantitative trait loci were located on LG III and XVI (Figure 4; Table 3). The phenotypic variation explained by QTL ranged from 5.9 to 11.6% and averaged 8.2% across all traits. Excluding co-local-izing QTL (±1 LOD) and accounting for correlations among phenotypic traits (Table 4; see Figure S1 available as Supplementary Data at Tree Physiology Online), these 16 QTL correspond to four independent positions within the genome (Table 3), and ranged in size from ~15 to 30 cM (Figure 4). Three QTL for dry weight and its components under control conditions were co-located on LG III along with three QTL for Cd effects on dry weight, collectively referred to as locus cd1 (Table 3). Two clusters of QTL were mapped to LG XVI at two discrete positions, the first of which (cd3) contained QTL for leaf dry weight under control conditions and Cd effects on total dry weight and its components (Table 3). For both cd1 and cd3, the allelic effects for dry weight measures under control conditions were in the same direction as those for QTL for Cd effects. However, the relative effect of the allele originating from P. trichocarpa was in opposite directions for these two loci, decreasing Cd tolerance at cd1 and enhancing Cd toler-ance at cd3. The second cluster (cd2) on LG XVI contained QTL for total dry weight and its components under Cd condi-tions, as well as three QTL for Cd effects. In contrast to the other two loci, the allelic effects were opposite for dry weight measures and Cd effects. In this case, the allele from P. tricho-carpa conferred reduced Cd tolerance on average. Finally, an apparently independent locus on LG XVI (cd4) explained 11% of the variation in leaf Cd concentration under Cd conditions (Table 3).Candidate gene identificationThe Cd response QTL intervals encompassed 1571 predicted genes from v2.0 of the P. trichocarpa genome annotation. In addition, across all genotypes and treatments there were 1748 different genes that showed up-regulation, and 672 genes that showed down-regulation in response to Cd t reatment (see Figures S2 and S3 available as Supplementary Data at Tree Physiology Online). Nine of these differentially expressed genes were also present in the Cd response QTL intervals (Table 5).DiscussionWe observed substantial phenotypic variation in Cd effects on plant biomass and leaf Cd accumulation in Family 52-124, a pseudo-backcross pedigree derived from P. trichocarpa×deltoides hybrids. The effects of genotype, Cd treatment and their interactions were significant for all response variables measured in this study, including total dry weight, root dry weight, leaf dry weight, stem dry weight and root volume. Substantial variation in Cd tolerance is commonly observed within and among species. For example, Pietrini et al. (2010) used 50 µM of Cd (i.e., twice as high as in our experiment) to compare responses of multiple hybrid Populus clones involv-ing six species. They observed significant variation among clones in the effects of Cd on biomass accumulation, photo-synthetic responses, Cd content of plant parts and concentra-tion of photosynthetic pigments. Interestingly, the variation in Cd responses observed among full siblings in our inter-spe-cific cross is of magnitude similar to the variation observed among highly diverse clones that were exposed to a higher concentration of Cd. This illustrates the tremendous pheno-typic variation captured by our crossing design and experi-mental conditions. The Cd-tolerant and Cd-susceptible genotypes identified in this study are a useful resource for future experiments targeted at physiological and molecular dissection of Cd tolerance.Cd tolerance QTL and candidate genes in Populus631Figure 4. L ogarithm of odds scores from the QTL analysis. (a) Total dry weight control: linkage group III. (b) Total dry weight Cd: linkage group XVI.(c) Total dry weight control–Cd: linkage group III. (d) Total dry weight control–Cd: linkage group XVI.at South China Agricultural University on June 30, 2012/Downloaded fromTree Physiology Online at Tree Physiology Volume 32, 2012Heritability estimatesOur broad-sense heritability estimate for total dry weight was somewhat lower than those for growth and yield traits mea-sured for Populus in recent field studies (Marron et al. 2006, 2010, Rae et al. 2008, Dillen et al. 2009), though certainly within the range of estimates in earlier studies (Riemenschneider et al. 1996). The broad-sense heritabilities calculated for total dry weight and its components under control conditions were not substantially different from the heritabilities calculated for these traits under Cd exposure. Root volume had the lowest heritabilities under both conditions, whereas root dry weight had heritabilities that were comparable to those for other dry weight components. This discrepancy may reflect difficul -ties in accurately measuring root volume through water displacement.Quantitative trait lociAlthough we detected 16 QTL in this study, these occurred at only four different chromosomal locations. Co-localization ofQTL for total dry weight and dry weight components was not surprising, given that these traits were highly intercorrelated and were co-segregating in the pedigree. However, it is poten-tially more interesting and informative that dry weight under control and Cd conditions mapped to different QTL, and had opposing relationships to the Cd response QTL (C–Cd dry weight). Dry weight under control conditions was positively correlated with Cd effect and allelic effects were in the same direction for these traits when they were co-located, suggest-ing that these QTL were driven by the size of plants under control conditions (i.e., large plants showed more sensitivity to the Cd treatment). In contrast, dry weight under Cd conditions was negatively correlated with Cd effect, and allelic effects were opposite for these traits at co-located QTL, suggesting that Cd tolerance at this locus was associated with growth in the presence of Cd irrespective of performance under control conditions.Quantitative trait loci for different traits may also be co-located even when they are not correlated (Wu et al. 1997,632 Induri et al.Table 3. L inkage group (LG), QTL positions, logarithm of odds ratio (LOD) scores, percent variation explained (PVE) and direction of effect of each QTL are given under the control (C) and cadmium-treated (Cd) conditions.Locus TraitLinkage group Position LOD PVE Left marker Right marker T effects 1 D effects 2cd1Total dry weight C–Cd 3III 109.267 3.338.9*CCCGA_8*CACAC_218−0.0470.043cd1Root volume C–Cd III 110.267 3.049*CCCGA_8*CACAC_218−0.0250.022cd1Leaf dry weight C–Cd III 111.267 3.067.7*CCCGA_8*CACAC_218−0.0460.039cd1Total dry weight CIII 111.676 2.51 6.1*CCCGA_8*CACAC_218−0.0250.022cd1Root volume C III 114.726 2.5 6.8*CCCGA_1*CCCAC_22−0.0140.013cd1Root dry weight C III 110.267 2.66 6.6*CCCGA_8*CACAC_218−0.0210.022cd2Root dry weight C–Cd XVI 22.784 2.997.4G_3141*CCCCT_202R 0.045−0.030cd2Total dry weight C–Cd XVI 26.609 2.81 6.8*CCCCT_202R *TCCGT_8R 0.047−0.033cd2Leaf dry weight C–Cd XVI 26.609 4.2610*CCCCT_202R *TCCGT_8R 0.056−0.042cd2Total dry weight Cd XVI 26.609 3.147.8*CCCCT_202R *TCCGT_8R −0.0290.021cd2Root dry weight Cd XVI 26.609 2.997.3*CCCCT_202R *TCCGT_8R −0.0270.017cd2Leaf dry weight Cd XVI 26.609 4.611.6*CCCCT_202R *TCCGT_8R −0.0370.028cd3Leaf dry weight C XVI 56.812 2.9 5.9*CCCCT_246R *CTCAG_356R 0.033−0.016cd3Total dry weight C–Cd XVI 56.812 3.617.4*CCCCT_246R *CTCAG_356R 0.059−0.028cd3Leaf dry weight C–Cd XVI 56.812 5.4111*CCCCT_246R *CTCAG_356R 0.072−0.036cd4Leaf dry weight CdXVI68.5584.6111.3*CGCCA_308R*CTCTC_100R−0.0400.0251The relative effect of the allele originating from P. trichocarpa .2The relative effect of the allele originating from P. deltoides .3Difference in BLUP between control and Cd conditions (Cd effect).at South China Agricultural University on June 30, 2012/Downloaded fromTree Physiology Online at Cd tolerance QTL and candidate genes in Populus 633T a b l e 4. G e n e t i c (a b o v e d i a g o n a l ) a n d p h e n o t y p i c c o r r e l a t i o n s a m o n g t h e v a r i a b l e s m e a s u r e d .T r a i t s a l l c o r r e l a t i o n s T o t a l d r y w e i g h t C R o o t d r y w e i g h t CL e a f d r yw e i g h t CS t e m d r y w e i g h t C R o o t v o l u m e C T o t a l d r y w e i g h t C d R o o t d r y w e i g h t C d L e a f d r y w e i g h t C d S t e m d r y w e i g h t C d R o o t v o l u m e C d T o t a l d r y w e i g h t C –C d R o o t d r y w e i g h t C –C d L e a f d r y w e i g h t C –C d S t e m d r y w e i g h t C –C dR o o t v o l u m e C –C d T o t a l d r y w e i g h t C 10.88380.97650.94630.77030.2680.25620.22420.28220.18590.91970.79180.87830.88250.5893R o o t d r y w e i g h t C 0.902210.7960.80160.78620.21060.20840.17690.21490.14010.82350.92710.71860.7570.6323L e a f d r y w e i g h t C 0.9730.831710.88410.71020.26480.25560.22590.26690.18160.89680.70360.9010.82320.537S t e m d r y w e i g h t C 0.94310.81520.889510.73130.26740.24540.21480.31030.19410.86460.71310.78990.92780.5485R o o t v o l u m e C 0.62940.6260.60590.581710.21650.2050.17930.23380.19610.70430.71310.63150.67580.7931T o t a l d r y w e i g h t C d −0.3557−0.3091−0.2795−0.2959−0.079310.80050.9610.88430.5168−0.1319−0.0947−0.1627−0.0663−0.1229R o o t d r y w e i g h t C d −0.2765−0.3656−0.1976−0.2234−0.05630.815710.6550.69650.4847−0.0628−0.1733−0.0356−0.0157−0.1135L e a f d r y w e i g h t C d −0.3188−0.2572−0.3007−0.2286−0.06240.91290.688610.75980.4666−0.161−0.0729−0.219−0.0727−0.1258S t e m d r y w e i g h t C d −0.3146−0.2638−0.2208−0.356−0.05690.85590.7210.68910.4719−0.0701−0.0505−0.071−0.0667−0.0792R o o t v o l u m e C d −0.079−0.1164−0.0457−0.0305−0.19630.47980.47240.41450.461−0.0194−0.0446−0.02590.0186−0.4417T o t a l d r y w e i g h t C –C d 0.82910.74090.76690.75820.4363−0.8175−0.6584−0.7426−0.706−0.335310.85330.96990.9350.6565R o o t d r y w e i g h t C –C d 0.71980.83290.62940.63470.4196−0.6752−0.8196−0.5678−0.591−0.35150.847510.73720.76830.6802L e a f d r y w e i g h t C –C d 0.79770.67230.8030.690.412−0.7424−0.5519−0.8099−0.5664−0.28690.93580.74210.85690.5939S t e m d r y w e i g h t C –C d 0.78240.67160.69410.84240.405−0.682−0.558−0.5429−0.8034−0.28390.89020.74520.766210.6068R o o t v o l u m e C –C d0.4740.49480.43760.41190.796−0.3495−0.3295−0.2978−0.3221−0.74980.50160.50030.45530.44881at South China Agricultural University on June 30, 2012/Downloaded fromZhang et al. 2006). For example, in this study leaf dry weight Cd and leaf dry weight C–Cd were mapped to the same posi-tion on LG XVI, but were not significantly correlated. The same was observed between root dry weight Cd and root dry weight C–Cd and between total dry weight Cd and total dry weight C–Cd (Tables 3 and 4). This could be due to a single gene or a regulatory element having pleiotropic effects on these traits and/or different genes within the QTL interval that might be independently controlling these traits, as one QTL encompasses hundreds of candidate genes (Wullschleger et al. 2005, Novaes et al. 2009). Identification of different QTLs on different linkage groups under control and Cd conditions for total dry weight and root dry weight suggested that the genes governing those traits were differ-entially controlled (Rae et al. 2006), and that the control exerted by these QTLs or genomic regions was dependent on Cd exposure. Finally, it is important to note that QTLs identified in this study account for a relatively small propor-tion of the total variation in Cd responses. Furthermore, since the population used for the current study was relatively small, QTL effects were probably overestimated (Beavis 1998), so much work remains to identify further mechanisms of Cd tolerance in Populus.Candidate genes from QTL and microarray analyses Transcriptional responses to experimental stimuli are one possible indicator of gene function. We therefore used micro-array analyses to gain further insights into the potential roles of the many candidate genes within the QTL intervals. Of the 1571 genes contained within the Cd response QTL intervals, only 12 showed differential expression under Cd treatment. Among these were homologs of AT3G59140.1 (also known as multidrug resistance-associated p rotein 14), which were significantly overexpressed in the Cd-tolerant genotype (1–183) relative to the control after 72 h of treatment. Two closely related sequences matching this gene occur in the Cd tolerance QTL on LG III, presumably the result of recent tan-dem duplication. This gene belongs to the MRP (multidrug resistance-associated protein) subfamily of the superfamily of ATP binding cassette (ABC) transporters, which has been implicated in Cd sequestration (Klein et al. 2006). ATP bind-ing cassette transporters facilitate the translocation of che-lates including glutathione with Cd and Pb by ATP-driven processes (Lu et al. 1998, Tommasini et al. 1998, Kolukisaoglu et al. 2002, Martinoia et al. 2002, Klein et al. 2003). Interestingly, expressions of AtMRPs 3, 4, 6, 7 and 14 were up-regulated by Cd in Arabidopsis (Kolukisaoglu et al. 2002,634Induri et al.Table 5. C d tolerance candidate genes in QTL intervals that also showed altered expression levels in the microarray experiment. Corresponding Arabidopsis genes were identified based on best reciprocal BLAST hits and the putative functions were derived from the TAIR 9 annotation. Fold change is the ratio of normalized expression levels under Cd versus control conditions. ns, not significant with a false discovery rate of 0.05.Populus gene Arabidopsisgene QTL LG GenesymbolFoldchangePfp Clone Time(h)Putative functiongw1.XVI.2928.1AT1G70280.2XVI None12.33E-04Both24NHL repeat-containingmembrane proteinfgenesh4_pg.C_LG_ III001134AT3G59140.1III MRP14 4.70.00318372Multidrug resistance-associated protein;ATP-type transportergw1.XVI.2910.1AT2G28305.1XVI LOG1 3.30.01318272Unknown function,expressed during 4 leafsenescence stage eugene3.00031003AT4G11450.1III F25E4.70 3.10.02118324,72Unknown function,expressed during leafsenescenceestExt_fgenesh4_ pg.C_LG_III1085AT4G23496.1III SP1L50.320.01618272Unknown function;potentially involved inanisotropic cell expansionestExt_fgenesh4_ pg.C_LG_III1187AT4G24015.1III None0.270.04118224Zinc finger/unknownfunctionfgenesh4_pg.C_LG_III001060No Hits III None0.270.02818224Nonegw1.XVI.2786.1AT5G03150.1XVI JKD0.240.01718224,72Zinc finger transcriptionfactor; root development,regulation of cell division,cell differentiation,meristem growthfgenesh4_pm.C_ LG_XVI000330AT2G37220.1XVI F3G5.10.130.00118224Chloroplast RNA bindingprotein, response to ABAstimulus, response to cold at South China Agricultural University on June 30, 2012/Downloaded fromTree Physiology Volume 32, 2012。

育种考试名词解释

1. 近交衰退与杂种优势：近交衰退指近交使繁殖性能、生理活动及与适应性相关的性状降低的现象; 杂种优势指不同种群个体杂交的后代往往在生活力、生长和生产性能等方面在一定程度上优于其亲本纯繁群平均值的现象。

近交衰退与杂种优势的遗传基础主要在于基因的非加性效应。

2. 个体育种值与EBV：个体育种值的简称育种值，指的是种用个体某一性状上能稳定遗传给下一代的基因的加性效应值。

虽然育种值可以稳定遗传，但不能直接度量的，只能利用统计学原理和方法，通过表型值和个体间的亲缘关系进行估计，由此得到的估计值称为估计育定牧场来统一测定。

优点：1）控制了环境条件的变异；2）客观性强；3）便于特殊设备的配备和管理（如自动计料器）。

缺点：1）成本较高；2）测定规模有限；3）易传播疾病；4）由于“遗传－环境互作”，使测定结果与实际情况产生偏差，代表性不强。

场内测定(on-farm test)：指直接在各个生产场内进行性能测定，不要求时间的一致。

通常强调建立场间遗传联系，以便于进行跨场际间的遗传评估。

3．畜禽育种过程中，影响选择成效的因素有哪些？（7分）可利用的遗传变异、选择强度、育种值估计的准确性、世代间隔等，此外遗传力、性状间的相关性、选择方案中的性状数目、近交、环境等也会影响到选择的效果。

4．什么是杂交育种？杂交育种的一般步骤是什么？（7分）杂交育种即育成杂交，指利用多品种间杂交能使彼此的优点结合在一起而创造新品种的杂交方法。

杂交育种的步骤主要包括1）杂交创新（采用杂交使基因重组，创造新的理想型个体）、2）自繁定型（将理想型个体停止杂交，改用杂种群内理想型个体自群繁育，稳定遗传基础获得固定的理想型）、3）扩群提高（迅速增加理想型个体数量和扩大其分布范围，培育新品系，建立品种整体结构和提高品种品质）等几个主要阶段。

1. 驯养与驯化：驯养指人类对野生动物的饲养；驯化指人类在野生动物驯养过程中，经过长期饲养、选择和培育，使动物的体型外貌、生活习性、生产性能等发生根本性变化（遗传基础发生改变），完全丧失野性而依赖于人类生存繁衍的过程。

分子生物学和遗传学技术在家畜改良中的应用

分子生物学和遗传学技术在家畜改良中的应用近年来，基因科技和分子生物学技术正在快速发展和进步。

这些技术的应用已经对许多领域产生了深远的影响，其中包括家畜改良。

通过应用分子生物学和遗传学技术，我们能更好地了解家畜基因和其性状的关联，进一步促进生产性能、抗病能力、食用品质等方面的改良。

一、基因描述和显示现在我们已经能够了解无数基因的序列并且理解它们对生命的影响。

通过这些知识，研究者在所研究的动物上引入了许多有益的基因序列，这一过程称为转基因。

分析基因表达和遗传变异等信息是遗传学技术的首要内容。

其中转录组和蛋白质组学是非常重要的技术。

基因芯片技术通过同时分析大量基因表达情况，可以使我们了解不同品种之间的差异；同时，我们可以通过对大量样本进行分析，来判断特定基因序列与特定性状之间的关联。

二、分析遗传多样性目前，我们已经能够通过遗传多样性研究，了解到家畜品种的起源和演化过程。

这使得我们能够更好地利用血统信息，逐步提高品种的生产性能和适应性。

例如，一些家禽品种常用羽毛颜色和大小来区分；我们可以通过获得基因组信息，确定特定羽毛颜色和大小与其基因的关联。

可根据此信息祖先最可能有什么样子，或我们将有什么样子。

三、基因编辑CRISPR/Cas9是目前非常精确的基因编辑技术，它可以编辑特定基因序列，并删除或插入不同的DNA段。

使用CRISPR/Cas9技术来删除一些致病突变，或者导入有益基因的突变，可以在不使用转基因技术的情况下提高家畜生产力。

例如，我们可以通过引入耐寒基因，提高家畜在寒冷环境中的生存能力。

四、选择标记技术选择标记技术通过分析特定DNA序列与特定性状之间的关联，使得家畜选种过程更加精确。

这些基因通常被称为QTL或顺式基因变异。

QTL是Quantitative Trait Loci的首字母缩写，表示一组有益基因。

通过对大量样本进行分析，并确定特定QTL与特定性状之间的关联，可以帮助我们在家畜选种时取得理想的结果。

相关主题

1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。

Detection of quantitative trait loci (QTLs)for seedling traits and drought tolerance in wheat using three related recombinant inbred line (RIL)populationsHong Zhang •Fa Cui •Honggang WangReceived:27June 2013/Accepted:23November 2013/Published online:10December 2013ÓSpringer Science+Business Media Dordrecht 2013Abstract In order to detect quantitative trait loci (QTLs)for drought tolerance in wheat during seed germination conditional and unconditional QTL anal-yses of eight seedling traits were conducted under two water regimes using three related F 9recombinant inbred line populations with a common female parent.A total of 87QTLs for the eight seedlings traits and 34speciﬁc QTLs related to drought tolerance were detected.Seventy-one of these QTLs were major QTLs with contributions to phenotypic variance of [10%.Of the 34QTLs related to drought tolerance only eight were also detected by unconditional analysis of seedling traits under osmotic stress conditions indicating that most of the QTLs related to drought tolerance could not be detected by unconditional QTL analysis.Therefore,conditional QTL analysis of stress-tolerance traits such as drought tolerance was feasible and effective.Of 11important QTL clusters located on chromosomes 1BL,1D,2A,2B,2D,4A,6B,and 7B,nine were detected in multiple populations and eight were detected by both unconditional and conditional analyses.Keywords Triticum aestivum ÁMappingpopulation ÁUnconditional and conditional QTL analysisAbbreviations CL Coleoptile length PH Plant height RL Longest root length RN Root number SDW Seedling dry weight SLDW Stem-and-leaf dry weight RDW Root dry weightRSDWR Root-to-shoot dry weight ratio WL Recombinant inbred line populationderived from Weimai 89Luohan 2WY Recombinant inbred line populationderived from Weimai 89Yannong 19WJ Recombinant inbred line populationderived from Weimai 89Yannong 19IntroductionDrought imposes constraints on wheat productivity.Improvement of drought tolerance during germination and early development of wheat can overcome the inﬂuence of soil water deﬁcit,ensure seedling number,H.ZhangCollege of Ecology and Garden Architecture,Dezhou University,Dezhou 253023,Shandong,China e-mail:zhw718_0@F.CuiCenter for Agricultural Resources Research,Institute of Genetics and Developmental Biology,Chinese Academy of Sciences,Shijiazhuang 050021,Hebei,China H.Wang (&)State Key Laboratory of Crop Biology,Shandong Key Laboratory of Crop Biology,Taian Subcenter of National Wheat Improvement Center,College of Agronomy,Shandong Agricultural University,Tai’an 271018,China e-mail:hgwang@Euphytica (2014)196:313–330DOI 10.1007/s10681-013-1035-7and provide a solid base for obtaining high and stable yields.Drought tolerance is commonly measured by using high molecular mass polyethylene glycol(PEG) to mimic osmotic stress(Blum et al.1980;Almansouri et al.2001;Dhanda et al.2004;Mujtaba et al.2005),as this approach avoids much of the environmental noise associated withﬁeld experiments and induces a plantresponse similar to that induced by drought.Drought tolerance is a complex quantitative trait controlled by minor genes.Quantitative trait loci(QTL) mapping has become an effective tool for genetic analysis of quantitative factors such as agronomic traits (Perretant et al.2000;Sourdille et al.2000;Bo¨rner et al. 2002),disease tolerance(Anderson et al.2001;Simo´n et al.2004;Faris and Friesen2005;Schmolke et al. 2005),and abiotic stress tolerance(Galiba et al.2005; Ba´lint et al.2007).Several studies have documented QTL analysis of seedling drought tolerance of wheat (Spielmeyer2005;Zhou et al.2005;Rebetzke et al. 2007).However,most of them used a single mapping population and used only unconditional QTL analysis.Conditional genetic analysis can be used to exclude contributions of causal traits to variation of the resultant trait(Zhu1995).The remaining variation is deﬁned as conditional variation,and indicates the extra effects of genes that are independent of the causal trait.QTL analysis based on conditional variation is deﬁned as conditional QTL analysis,whereas that based on total variation is called unconditional or conventional QTL analysis.A comparison between unconditional and conditional QTL could provide an outline of the genetic relationships between the given causal trait and the resultant trait at the individual QTL level.This method has been widely used to identify QTL expressed at certain developmental stages of plants(Zhu1995; Atchley and Zhu1997;Yan et al.1998;Cao et al. 2001)or to analyze contributions of component traits to a complex trait(Guo et al.2005;Liu et al.2008;Cui et al.2011).This method was also recently proposed for analyzing the inﬂuence of different agrotechnologies or agronomic practices on crop growth and development with an aim of discovering QTLs expressed speciﬁcally in stress environments.Jiang et al.(2008)used the method to determine genes expressed speciﬁcally in low-nitrogen stress environments.However,determina-tion of QTLs expressed speciﬁcally under osmotic stress by conditional analysis has not been reported in wheat.Using three related recombinant inbred line(RIL) populations,we carried out both unconditional and conditional QTL analyses of eight seedling traits under both normal water and osmotic stress conditions during germination and early development of wheat. Our study aimed to(1)identify QTLs with signiﬁcant contributions to drought tolerance during germination, (2)improve our understanding of the genetic basis of drought tolerance,and(3)provide useful information for molecular marker-assisted selection(MAS)in breeding for increased drought tolerance.Materials and methodsExperimental materialsThree F9RIL populations derived from crosses between four common Chinese wheat varieties,namely,Weimai 89Luohan2(WL),Weimai89Yannong19(WY) and Weimai89Jimai20comprising179,175and172 lines,respectively,were used.Weimai8is a drought susceptible variety,released by the Weifang Municipal Academy of Agricultural Sciences,Shandong,in2003; Luohan2is a drought-tolerant variety,released by the Crop Research Institute,Luoyang Municipal Academy of Agricultural Sciences,Henan,in2001;Yannong19 is a water efﬁcient variety,released by the Yantai Municipal Academy of Agricultural Sciences,Shan-dong,in2001.Jimai20,also drought susceptible,was released by the Crop Research Institute,Shandong Academy of Agricultural Sciences in2003.All of the materials were created and conserved by Taian Sub-center of the National Wheat Improvement Center.An integrated genetic mapAn integrated genetic map constructed by our labora-tory in2011using the WL,WY and WJ populations will be published elsewhere.The map consists of1,127 loci distributed across all21wheat chromosomes and covers2,976.75cM with an average distance of 2.64cM between adjacent loci.The map includes 576DArT marker loci,496of which are common to at least two maps(Fig.1).Fig.1Locations of QTLs for wheat seedling traits and drought tolerance in three mapping populations.The positions of the marker loci and the QTL are listed to the left of individual chromosomes; marker loci are listed to the right.DArT markers are preﬁxed ‘‘wpt,’’and the remainders are PCR-based markers.Intervals for QTL are LOD[2.0with LOD peak values more than2.5.Black, green,and red colors of the QTL symbols indicate QTL detected in the WL,WY,and WJ,respectively.(Colorﬁgure online)cOsmotic stress testsOsmotic stress tests were performed in a completely randomized design with three replicates.Speciﬁc methods were as follows:well-ﬁlled seeds of lines in each population and their parents were packed in gauze,dipped into3%H2O2for10min for surface sterilization and washed two or three times in pure water.Soaked seeds were held at25°C for24h to hasten germination;ten germinating seeds were selected and placed uniformly on two layers ofﬁlter paper in a6cm diameter beaker.Subsequently,5ml of10%(W=-0.1MPa)PEG-6000solution or pure water were added to the beaker.The osmotic potential was calculated using the equation of Michel and Kaufmann(1973). Fig.1continuedW ¼À1:18Â10À2ÀÁC À1:18Â10À4ÀÁC 2þ2:67Â10À4ÀÁCT þ8:39Â10À7ÀÁC 2T where W is the osmotic potential in bars,C is the concentration of PEG-6000in g/kg H 2O and T is the temperature in °C.All the beakers were placed in a plastic box,covered with a thin plastic ﬁlm,and cultured at 25°C in the dark for three days.On the fourth day,5ml of water were added to each beaker with concurrent illumination.On the eighth day,ﬁve uniform seedlings were selected from each beaker to measure coleoptile length (CL),plant height (PH),the longest root length (RL),and root number (RN).The seedlings were placed in an oven for 20min at 100°C,and then dried to constant weight at 80°C.The following parameters were then measured:seedling dry weight (SDW),stem and leaf dry weight (SLDW)and root dry weight (RDW).The root-to-shoot dry weight ratio (RSDWR)was calculated.All weights and lengths reported herein were in grams and centimeters,respectively.QTL analysisStatistical analysis of the phenotypic data from the three RIL populations was carried out using the software SPSS13.0(SPSS,Chicago,IL,USA).The estimated broad-sense heritability of the corresponding trait wascalculated using the formula h 2¼r 2G =r 2G þr 2e ÀÁ,where r G 2is the genetic variance and r e 2is the experimental error.QTL screening was conducted using inclusive composite-interval mapping by Ici-Mapping 3.0(Li et al.2007;/).The threshold logarithm of odds ratio (LOD)scores were calculated using 1,000permutations,and the QTLs with LOD values \2.5were ignored to ensure the authenticity and reliability of the QTLs reported herein.Conditional QTL screening was conducted based on conditional phenotypic values of the eight traits obtained by software QGAStation 1.0(Zhu 1995).To obtain the conventional phenotypic value of each trait data were assembled according to the format of QTL data.The ﬁrst two columns in order were environment and genotype and the following columns were trait means detected under osmotic stress and normal water conditions.QTL data menus of QGAStation 1.0were selected as follows:‘Have environment effect (No)’;‘Have Block Effect (No)’.The selected method of analysis included ‘Ge Var’and ‘Conditional Final’.The assignment of a QTL name was based on the following rules:italicized uppercase ‘Q’denoting ‘QTL’,followed by trait abbreviation,chromosome designation and QTL sequence number for multiple QTL on the same chromosome.ResultsAnalysis of phenotypic dataThe trait values for the three RIL populations and their parents under the two water regimes are shown in Table 1.ANOVA indicated signiﬁcant differences (P \0.05)between Weimai 8and Luohan 2in PH,RL,RN,RDW,SLDW,and PDW under both water regimes,and RSDWR under osmotic stress.Weimai 8and Yannong 19differed signiﬁcantly in CL,RL,RN,PDW,and SLDW under both water regimes and RSDWR under normal water conditions.In regard to Weimai 8and Jimai 20,there were signiﬁcant differences in CL,RL,and RN under both water regimes,PH and PDW under normal water conditions,and SLDW under osmotic stress conditions.The variances for treatment effects of PH,RL,RN,PDW,SLDW,RDW,and RSDWR were signiﬁcant in the three populations,whereas CL was signiﬁcant only in the WY population.Estimated broad-sense heritabil-ities of the eight traits ranged from 15.4to 89.6%.Phenotypic variation among the eight traits in the RIL lines was obvious,and strong transgressive segregation occurred for each trait in both water regimes in all three populations,indicating that alleles with positive effects differed between the respective parents.All traits showed continuous variation in each population,indicating they were typical quantitative traits controlled by a few minor genes and that the data were suitable for QTL analysis (Table 1).Evaluations of phenotypic correlations of the eight traits under the two water regimes in all three popula-tions are shown in Table 2.All traits showed signiﬁcant positive correlations under both water regimes with correlation coefﬁcients ranging from 0.24to 0.91,and showing consistency between the populations.CL showed the highest correlation coefﬁcients,ranging from 0.83to 0.91in the three populations.The correlation coefﬁcients for RL,RDW,and RSDWRT a b l e 1S t a t i s t i c s f o r s e e d l i n g t r a i t s o f R I L s a n d p a r e n t s u n d e r n o r m a l w a t e r a n d o s m o t i c s t r e s s t r e a t m e n t sT r a i t (h 2%)a T r e a t m e n t bP a r e n tW L c W YW JW e i m a i 8L u o h a n 2Y a n n o n g 19J i m a i 20M e a n eS t d d S k e w n e s sK u r t o s i s M e a n S t d S k e w n e s s K u r t o s i s M e a n S t dS k e w n e s sK u r t o s i sC L (c m )(81.7/89.6/79.2)C 3.152.822.742.272.81a A 0.40-0.06-0.512.64a A 0.430.270.002.81a A 0.330.240.56T 3.463.463.212.242.84a A 0.430.03-0.412.56b A 0.410.640.322.86a A0.300.010.21P H (c m )(42.8/39.2/38.9)C 11.2712.5810.899.1912.64a A 1.21-0.150.5410.16a A 1.40-0.120.2512.99a A1.01-0.311.15T 8.9010.338.588.7411.23b B 1.33-0.360.248.35b B 1.30-0.160.2211.72b B1.100.03-0.15R L (c m )(31.0/34.7/19.0)C 10.579.198.648.169.49a A 1.80-0.05-0.127.50a A 1.980.01-0.4010.13a A1.600.050.21T 8.029.356.166.137.44b B 1.200.13-0.225.82b B 1.140.32-0.307.17b B0.980.04-0.30R N (64.8/68.4/38.2)C 3.004.204.604.95.35a A 0.46-0.080.454.70a A 0.53-0.21-0.075.08a A0.290.030.62T 3.605.805.404.85.57b B 0.560.000.394.84b B 0.49-0.380.445.26b B0.340.520.36P D W (m g )(46.3/40.1/32.1)C 18.5424.3618.8915.921.18a A 2.630.26-0.1614.65a A 2.79-0.230.2320.64a A2.22-0.030.44T 14.2420.0616.9412.618.29b B 2.83-0.200.1712.34b B 2.450.140.2718.30b B2.11-0.170.20S L D W (m g )(47.9/44.9/46.0)C 11.8315.2312.8511.314.07a A 1.760.300.429.75a A2.110.110.7113.60a A1.560.190.10T 9.4112.2311.308.212.34b B 1.98-0.160.148.36b B1.650.130.1912.61b B1.58-0.160.01R D W (m g )(50.1/41.5/15.4)C 6.719.136.046.67.12a A 1.28-0.01-0.294.84a A1.140.060.317.04a A1.160.270.16T 4.837.835.644.45.94b B 1.15-0.10-0.363.97b B1.070.691.485.69b B0.880.250.80R S D W R (%)(54.6/45.6/19.1)C 56.7259.8747.0053.6650.84a A 8.460.17-0.1050.92a A12.530.550.7952.13a A8.830.540.26T 51.3363.9349.9147.7148.53b B 8.050.360.3247.85b B10.100.551.2745.50b B6.980.410.23aA r a b i c n u m e r a l s i n p a r e n t h e s e s a r e e s t i m a t e d b r o a d -s e n s e h e r i t a b i l i t i e s o f t h e c o r r e s p o n d i n g t r a i t s f o r W L W Y a n d W J i n t u r nbC ,T n o r m a l a n d o s m o t i c s t r e s s c o n d i t i o n s ,r e s p e c t i v e l ycW L W Y a n d W J ,c r o s s e s W e i m a i 89L u o h a n 2,W e i m a i 89Y a n n o n g 19,a n d W e i m a i 89J i m a i 20,r e s p e c t i v e l ydS t d S t a n d a r d d e v i a t i o neS m a l l l e t t e r s i n d i c a t e s i g n i ﬁc a n t d i f f e r e n c e s a t P =0.05,l a r g e l e t t e r s i n d i c a t e s i g n i ﬁc a n t d i f f e r e n c e s a t P =0.01were the smallest,indicating a larger inﬂuence of osmotic stress on root growth of wheat seedlings.The signiﬁcant correlations of all the traits under the two water treatments made it possible and meaningful to conduct conditional QTL analysis and determine the speciﬁc expression of QTL for the corresponding traits in wheat seedlings under osmotic stress.Unconditional QTL mappingUp to87QTLs distributed across all21wheat chromo-somes were identiﬁed by unconditional QTL analysis for the eight traits,individually explaining2.1–26.4%of the phenotypic variation(Table3;Fig.1).Among them,18 QTLs were detected under both water regimes,whereas 38and31,respectively,were detected only under normal water and osmotic stress conditions.19,25,and31 QTLs,respectively,were detected in the WL,WY and WJ populations,with10,20,and20QTL increasing the corresponding traits originating from the common parent Wemai8in WL,WY,and WJ,respectively.Of the18QTLs detected under both water regimes, 11were major QTLs individually accounting for[10% of the phenotypic variation.These QTLs were QCL-3B, QPH-4A,QRL-2A,QRN-4B,QRN-2B.1,QRN-2B.2, QRN-7B.1,QSLDW-1D,QRDW-2B,QPDW-2A.2,and QPDW-2B(Table3).The stability of these QTLs across both water environments implied expression of drought tolerance and indicated that they were potentially important for breeding for increased drought tolerance.Twelve QTLs were detected in at least two populations,accounting for13.8%of unconditional QTLs.Of them,QPH-1BL,QRL-2A,QRL-4B, QSLDW-2B.2,and QSLDW-6B.2,were major QTLs individually accounting for[10%of the phenotypic variation(Table3).Notably,QCL-4A.2and QSLDW-6B were jointly detected in all three populations.The coincidence of these QTLs in chromosomal regions across different mapping populations not only implied the reliability of the QTLs reported herein,but also indicated a potentially high efﬁciency of the corre-spondingﬂanking markers in MAS.Conditional QTL mappingWhen the eight trait values detected under osmotic stress were conditioned on those found under normal water conditions,conditional analysis detected a total of34conditional QTLs,individually explaining 4.9–28.6%of the phenotypic variation(Table4). These QTLs were located on all21chromosomes except1A,1B,4B,4D,and5D,19of them were major QTLs individually accounting for[10%of the phenotypic variation.Eight QTLs on chromosomes2A,2B,3D,6B,7A, and7D were detected in the WL population;all increasing the corresponding traits originating from Luohan2.Only QRLT/QRLC-2B had been detected by unconditional analysis showing reduced additive effects compared to the unconditional QTL.Nine QTLs located on chromosomes2B,2D,4A,5A, 5B,6A,6B,and7B were detected in the WY population; four alleles increasing the corresponding traits originat-ing from Yannong19,andﬁve from Weimai8.Only QPDWT/PDWC was detected by unconditional analysis of PDW,again showing decreased additive effects compared to the unconditional QTL analysis.Seventeen QTLs located on chromosomes1D,2B, 2D,3A,3B,4A,6A,6B,and6D were detected in the WJ population;eight alleles increased the corresponding traits originating from Weimai8,and nine originating from Jimai20.Five QTLs were also detected by the unconditional analysis;of those,QRSDWRT/RSDWRC-1D.1and QRSDWRT/RSDWRC-1D.2showed decreasedTable2Phenotypic correlations of wheat seedling traits under normal water and osmotic stress conditions in three populations Population a CL PH RL RN PDW SLDW RDW RSDWRWL0.906**0.660**0.590**0.704**0.687**0.649**0.696**0.608** WY0.897**0.769**0.586**0.686**0.604**0.626**0.557**0.468** WJ0.831**0.633**0.325**0.466**0.508**0.541**0.287**0.239** a WL,WY and WJ represent populations derived from Weimai89Luohan2,Weimai89Yannong19,and Weimai89Jimai20, respectively**Signiﬁcant at P=0.01Table3Summary of unconditional QTL for wheat seedling traits under two water regimesTrait QTL Treat a Population Chr b Left marker c Right marker LOD PVE(%)d Add eCL QCL-1A C WL1A wPt-731282Xwmc120 2.698.1-0.11 QCL-3B C/T WL3B Xmag3356wPt-1336 6.59/4.1017.5/9.90.19/0.15QCL-4A.1C WJ4A Xcft3034.2Xapr1.2.1 5.8115.6-0.16QCL-4A.2C WL/WY/WJ4A wPt-2084wPt-1362 2.78/3.69/3.71 6.7/8.3/8.5-0.11/-0.12/-0.13 QCl-5A T/T WJ/WL5A Xcwm216wPt-0605 3.50/3.568.9/11.20.08/0.14QCl-5B(C/T)/C WL/WY5B wPt-5175wPt-8449(3.03/4.33)/3.21(6.5/9.2)/10.0(-0.16/-0.13)/0.14 QCL-6B T WY6B wPt-664250wPt-666793 5.3518.70.20QCL-7A C WL7A wPt-6668Xmag2931.3 2.819.7-0.15QCL-7B.1C WY7B Xwmc517.2wPt-3833 2.7210.10.15QCL-7B.2C WY7B wPt-7108wPt-9925 2.6113.60.18QCL-7B.3T WY7B ww121wPt-1553 3.8010.2-0.13PH QPH-1BL C/C WY/WJ1BL wPt-0260wPt-2230 6.29/3.6513.5/15.2-0.51/0.39 QPH-2A.1C WJ2A Xgwm382.2wPt-665330 5.5420.60.61QPH-2A.2C WY2A Xcfe175.2wPt-3565 3.879.20.47QPH-2B C WJ2B wPt-9736wPt-80047.5021.4-0.67QPH-3B C WL3B Xmag3356wPt-1336 3.189.30.42QPH-4A C/T WY4A Xbarc1047wPt-7354 5.61/5.0112.7/11.3-0.49/-0.46QPH-4B T WJ4B wPt-6209wPt-6149 3.8510.2-0.38QPH-6A C/T WY/WJ6A wPt-0259wPt-4230 3.89/2.56 6.1/4.1-0.35/0.21QPH-7B C/T WY7B wPt-7934wPt-9467 4.78/3.738.9/8.10.41/0.37RL QRL-1A C WY1A wPt-4029wPt-668160 5.7014.5-0.83 QRL-1BL C/T WY/WL1BL wPt-6975Glu-b1 2.88/3.72 5.9/10.0-0.48/-0.38QRL-2A C/T WY/WJ2A Xcfe175.2wPt-3653 5.33/3.2715.2/7.60.88/-0.29QRL-2B T WL2B wPt-3109wPt-84608.1115.8-0.50QRL-3A T WJ3A wPt-671711wPt-18887.0419.70.46QRL-4A T WL4A wPt-6997wPt-4680 3.2811.0-0.40QRL-4B C/T WL/WJ4B Xgwm495wPt-5265 3.27/3.5510.7/11.3-0.62/0.67QRL-4D T WY4D Xcfd71wPt-2379 2.78 6.5-0.29QRL-5B C/T WY/WJ5B wPt-730009wPt-9103 2.53/4.62 6.0/12.50.57/0.63QRL-7B C WJ7B wPt-6936Xgwm344 3.3312.90.61RN QRN-1D C WY1D wPt-7697wPt-4988 4.01 6.80.14 QRN-2A C WJ2A Xgwm558Xgwm372 3.6910.10.09QRN-2B.1C/T WL2B wPt-669273wPt-97367.79/6.6415.5/11.30.20/0.21QRN-2B.2C/T WY2B wPt-3132wPt-2106 2.60/8.477.6/18.5-0.05/-0.26QRN-2D.1C WY2D Xcwm83wPt-4144 3.40 5.9-0.14QRN-2D.2C WY2D Xcfd168.1Xcfd168.2 3.39 6.3-0.13QRN-3A.1C WY3A wPt-2866Xcfa2163.1 2.598.5-0.15QRN-3A.2T WY3A wPt-7890wPt-8855 4.7615.8-0.20QRN-3B T WL3B wPt-7225wPt-4209 3.59 6.60.15QRN-4A T WJ4A wPt-0610wPt-669526 3.628.40.10QRN-4D C WY4D Xcau17.2Xcau17.1 5.088.9-0.18QRN-5A C WL5A Xmag3166.2wPt-3563 2.70 4.5-0.10QRN-5D C WY5D Xgdm99.2Xbarc320 6.0210.80.17QRN-5D T WJ5D Xswes558.4Xcfe242.1 2.689.9-0.11QRN-6A(C/T)/T WL/WY6A wPt-8124wPt-3524(4.14/5.65)/3.20(7.6/8.8)/12.8(0.13/0.17)/-0.21 QRN-6B T WJ6B wPt-663764wPt-3733 3.629.1-0.10QRN-7B.1C/T WL7B Xmag1036wPt-9467 5.28/9.214.4/18.8-0.18/-0.27QRN-7B.2T WL7B wPt-6936wPt-666615 5.259.2-0.17additive effects,whereas QSDWT/SDWC-1D and QRD WT/RDWC-2B showed increased effects,and QRDWT/ RDWC-6B showed additive effects similar to that of the unconditional QTL.QTL clustersEleven QTL clusters related to more than three traits were observed on chromosomes1BL,1D,2A,2B,2D,Table3continuedTrait QTL Treat a Population Chr b Left marker c Right marker LOD PVE(%)d Add eSLDW QSLDW-1D C/T WJ1D wPt-7946Xwmc429.3 2.92/6.1810.4/21.7-0.5/-0.8 QSLDW-2A T WJ2A wPt-3565wPt-4197 4.6910.8-0.6QSLDW-2B.1T WJ2B Xwmc617.1wPt-2314 3.4311.0-0.6QSLDW-2B.2C/C WL/WY2B wPt-3132wPt-4301 2.71/3.1312.4/20.4-0.7/1.2QSLDW-4A T WJ4A Xcft3034.2Xapr1.2.1 2.7420.10.9QSLDW-4B C WL4B wPt-8292wPt-6209 2.7512.5-0.6QSLDW-5D C WY5D wPt-5870Xgdm99.2 4.4912.20.8QSLDW-6B C/C/C WY/WJ/WL6B wPt-6282wPt-9881 5.16/3.49/3.2115.0/15.8/14.20.7/0.7/0.7QSLDW-6D.1C WJ6D Xissr844.1Xissr817 4.3617.20.7QSLDW-6D.2C WJ6D Xapr1.5.1Xapr1.2.3 2.85 6.4-0.6RDW QRDW-1A C/T WL1A wPt-668160wPt-664778 3.07/3.667.8/10.7-0.4/-0.4 QRDW-1BL C WY1BL wPt-8267wPt-7094 2.747.4-0.3QRDW-1D T WY1D Xcfd48.2Xcfe78.2 3.8812.1-0.4QRDW-2B C/T WL2B wPt-3132wPt-43017.51/5.9326.3/17.9-0.7/-0.5QRDW-4A T WJ4A wPt-4064wPt-9675 3.288.60.3QRDW-4B T WJ4B Xwmc349Xcfd54 2.508.40.3QRDW-6A C WJ6A wPt-0259wPt-4230 2.7110.20.4QRDW-6B T WJ6B wPt-3581wPt-663764 3.3711.6-0.3PDW QPDW-1D T WJ1D wPt-7946Xwmc429.3 3.8214.4-0.8 QPDW-2A.1T WJ2A wPt-3565wPt-4197 4.1810.2-0.7QPDW-2A.2C/T WY2A Xwmc177Xcwm109.7 4.57/3.8312.5/9.6 1.1/0.9QPDW-2B C/T WL2B wPt-3132wPt-4301 6.51/3.7926.4/14.8-1.4/-1.2QPDW-4A.1T WJ4A Xcft3034.2Xapr1.2.1 2.8626.2 1.4QPDW-4A.2T WY4A Xcfe89.4Xcfe89.3 3.409.00.8QPDW-5B T WJ5B Xissr854.1Xgwm335 2.688.70.7QPDW-5D C WJ5D Xswes558.4Xcfe242.1 3.037.8-0.6QPDW-6B C WL6B wPt-6282wPt-9881 2.7313.71QPDW-6D.1C WJ6D Xissr844.1Xissr817 4.5811.00.8QPDW-6D.2C WJ6D Xapr1.2.3Xcft3103 2.569.9-1RSDWR QRSDWR-1BL C WL1BL wPt-8627wPt-665375 5.2713.1 3.05 QRSDWR-1D T WY1D Xcfd48.2Xcfe78.2 3.308.8–3.12QRSDWR-1D T WJ1D wPt-671990wPt-4196 5.5613.5 2.75QRSDWR-1D T WJ1D wPt-671990wPt-4196 3.558.5-2.21QRSDWR-2D C WJ2D wPt-731134wPt-1554 2.817.3-2.78QRSDWR-3B C WL3B Xgwm30.2Xgpw1146 3.949.4-2.62QRSDWR-3B C WY3B wPt-2416Xcfe127 4.7316.3-6.27QRSDWR-4A T WL4A wPt-4620wPt-672107 2.84 6.4-2.11QRSDWR-4A T WJ4A Xcft3034.2Xapr1.2.1 3.4122.2-4.62QRSDWR-7B T WY7B wPt-2994wPt-669158 2.5212.2-3.68a Letters indicate different treatments;C normal water conditions,T osmotic stressb Chromosome on which the QTL was detectedc Flanking markerd Percentage phenotypic variance explained by additive effectse Estimate of the additive effect of the QTL.Positive values indicate that Weimai8alleles increase the trait value;negative values indicate that Weimai8alleles reduce the trait value4A,6B,and7B,explaining7.2–28.6%of the phenotypic variation in single traits,involving44 QTLs.Among them,eight clusters involved QTLs for both seedling traits and drought tolerance,and three involved QTLs only for seedling traits(Table5; Fig.1).Four QTL clusters were detected in three populations,ﬁve in two populations,and two in one population.The most important QTL clusters involving QTLs for drought tolerance were C9,C10,and C11 (Table5)and were detected in all three populations. Cluster C9on chromosome4A in marker interval wpt-1047–wpt-9675involved QTLs for PH,CL,RDW and drought tolerance.Cluster C10on chromosome6B in marker interval wpt-6282–wpt-3733involved seven QTLs for SDW,PDW,RDW,RN,and droughtTable4Summary of conditional QTL for wheat seedling traitsTrait a QTL Population Chr b Position Left marker Right marker LOD PVE(%)Add cRSDWRT|RSDWRC QRSDWRT|RSDWRC-1D.1WJ1D45.79wPt-671990wPt-4196 3.849.021.9(-) RSDWRT|RSDWRC QRSDWRT|RSDWRC-1D.2WJ1D52.79wPt-671990wPt-4196 3.147.2-19.7(?) SLDWT|SLDWC QSDWT|SDWC-1D WJ1D57.79wPt-7946Xwmc429.3 5.1718.5-0.6(-) RDWT|RDWC QRDWT|RDWC-2A WL2A62.00wPt-666857Xcfa2263.2 2.589.3-0.3 RNT|RNC QRNT|QRNC-2A WJ2A69.00Xdupw210Xgwm558 2.517.6-0.08 RSDWRT|RSDWRC QRSDWRT|RSDWRC-2B WJ2B12.65wPt-2314wPt-665550 4.8617.347.8 PHT|PHC QPHT|PHC-2B WJ2B21.65Xbarc98Xcfd188 2.6512.7-0.54 RLT|RLC QRLT|QRLC-2B WL2B38.00wPt-3109wPt-8460 3.518.4-0.29(-) RNT|RNC QRNT|QRNC-2B WJ2B42.65wPt-6144Xedm97.1 3.9310.4-0.1 RDWT|RDWC QRDWT|RDWC-2B WY2B63.61wPt-3132wPt-2106 2.5228.6 1.5(-) RDWT|RDWC QRDWT|RDWC-2D WY2D93.00wPt-2781wPt-666887 3.318.8-0.4 PDWT|PDWC QPDWT|PDWC-2D WJ2D98.34wPt-8713Xmag3596 4.1910.60.6 RLT|RLC QRLT|QRLC-3A WJ3A38.34wPt-7341wPt-6234 3.959.10.29 PDWT|PDWC QPDWT|PDWC-3B WJ3B64.23wPt-4220wPt-8238 2.627.30.4 SLDWT|SLDWC QSDWT|SDWC-3B WJ3B64.23wPt-4220wPt-8238 3.29.20.5 CLT|CLC QCLT|CLC-3D WL3D114.95wPt-666676wPt-5313 4.7518.4-0.08 PHT|PHC QPHT|PHC-3D WL3D119.95wPt-666676wPt-5313 3.489.1-0.37 PDWT|PDWC QPDWT|PDWC-4A WY4A92.17Xcfe89.4Xcfe89.3 2.527.50.6(-) RDWT|RDWC QRDWT|RDWC-4A WJ4A119.00wPt-4064wPt-9675 4.6512.10.3(=) PDWT|PDWC QPDWT|PDWC-5A WY5A90.48Xcfa2163.2wPt-3563 2.8821.5 1.3 RNT|RNC QRNT|QRNC-5B WY5B72.95wPt-669837wPt-5896 2.9818.5-0.17 RDWT|RDWC QRDWT|RDWC-6A WY6A34.44wPt-7599Xgwm169 2.557.2-0.4 RLT|RLC QRLT|QRLC-6A WJ6A71.00wPt-667405wPt-668031 4.1914.8-0.42 PHT|PHC QPHT|PHC-6A WY6A86.44Xbarc204.1wPt-8117 3.8213.9-0.34 RSDWRT|RSDWRC QRSDWRT|RSDWRC-6B WL6B32.00wPt-6282wPt-9881 3.1611.923.4 PDWT|PDWC QPDWT|PDWC-6B WJ6B37.00wPt-3581wPt-663764 3.5711.5-0.3 RDWT|RDWC QRDWT|RDWC-6B WJ6B37.00wPt-3581wPt-663764 3.2211.0-0.6(=) RNT|RNC QRNT|QRNC-6B WJ6B69.00wPt-665036wPt-1541 4.2714.4-0.11 PHT|PHC QPHT|PHC-6B WY6B97.00wPt-0171Xbarc178 2.517.10.22 PHT|PHC QPHT|PHC-6D WJ6D 2.00Xcfe87.1Pr119.1 2.6123.3-0.87 RLT|RLC QRLT|QRLC-7A WL7A73.00Xbarc49Yp-7A 3.4610.3-0.31 RNT|RNC QRNT|QRNC-7A WL7A98.00wPt-1928wPt-6495 2.648.6-0.12 RDWT|RDWC QRDWT|RDWC-7B WY7B58.27wPt-669158wPt-665293 2.7415.0 1.4 CLT|CLC QCLT|CLC-7D WL7D60.99wPt-0934wPt-1405 2.56 4.9-0.04a The trait value surveyed under osmotic stress was subjected to conditional QTL analysis on that detected under normal water conditionsb Chromosome on which the QTL was detectedc(-),(?),and(=)indicate that the QTL was also detected by unconditional analysis,and showed a reduced,increased,and similar additive effect, respectively,compared with that of the unconditional QTL analysis。

Detection of quantitative trait loci (QTLs) for seedling traits

生物技术题库

QTL精细定位和克隆的策略

qtl名词解释

辣椒英文文献已读

qtl定位原理和流程

大麦抗网斑病QTL初步定位

小麦株高和千粒重QTL定位及其元分析

qtl名词解释

qtl名词解释

限制性两阶段多位点全基因组关联分析方法的特点与计算程序

Aphanomyces euteiches——加拿大豌豆生产的严重威胁

qtl原理

湖羊品种资源的深度利用探索 正文

bsa法定位qtl的置信区间计算

蛋白QTL的组学分析及其应用

遗传性持续性胎儿血红蛋白增高症(HPFH)的分子机制

Identification of quantitative trait loci and candidate genes

育种考试名词解释

分子生物学和遗传学技术在家畜改良中的应用

湖羊品种资源的深度利用探索正文