多个连锁数量性状位点的多性状最优化选择

一种扩展的方法能够在多个世代对具有多个数量性状位点的多性状选择进行最优化。这种方法的基础是在目标雨数中用综合遗传值替代单个性状遗传值,并在整个规划期内最大化所有世代选择反应的加权和。利用多阶段系统优化控制理论,整个最优化问题通过一个向前和向后的迭代循环解决。用一个实际育种猪群的育种参数来评价该方法的选择效果,并和标准QTL选择和常规BLUP选择进行比较。结果表明,优化选择要优于标准QTL选择和常规BLUP选择。经济权重对优化选择的影响较明显,随着达100kg日龄赋予的经济权重的增加,优化选择的优势越明显。优化选择通过两种方式增加总选择反应：1）选择早期减少一部分QTL选择反应;2）对达100kgH龄给予更大的权重。选择后期优化累积贴现选择比优化终端选择给予达100kgH龄更大的权重。     

基于基因型选择提高QTL作图的精度——以一个RIL群体为例

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

胚乳性状的世代遗传方差

本文论述由两个纯系杂交而衍生的各种世代群体的胚乳性状的遗传方差分量。根据有关世代胚乳的遗传组成和加性-显性模型下的基因效应,导出了它们的总的、株间的和自交世代的遗传方差,并分别列于表1—3。     

利用一个水稻RIL群体定位控制淀粉特性的QTL

利用个山籼粳(Oryza sativa L.)杂交发展成的重组自交系(RIL)群体研究影响淀粉特性的遗传因子,测定了一系列淀粉特性有关性状,包括直链淀粉含量、胶稠度、淀粉糊粘度、胶的质地、糊化温度、热学特性、回生特性等22个参数。共定位了44个QTL,分布在第2～6、8、9、11染色体上,每个性状所定位的QTL在1到4个不等。其中有2个是主基因,一个是第6染色体上的Wx基因,它控制直链淀粉含量、胶稠度、淀粉糊粘度、胶的质地、回生特性等性状,另一个足第6染色体上的alk基因,它控制糊化温度与热学特性等性状,其他QTL都是微效基因,在第9染色体上RZ404和G295区间系首次检测到,它控制淀粉胶的硬度(hardness)、胶粘性(gumminess)、咀嚼性(chewiness)、回生淀粉的最高糊化温度、回生率等性状,这些性状都未曾研究过。     

7个烤烟产量相关性状的QTL定位分析

以烤烟Y3和K326为亲本,采用单粒传法(SSD)获得一个包含262个F6株系的重组自交系群体(RILs)。基于该群体构建了一张含有24个连锁群、626个SSR标记,总长为1 120.45cM的遗传图谱。通过两年一点3次重复的随机区组田间试验,测定了株高、节距、叶数、茎围、茎叶角度、腰叶长和腰叶宽7个与产量相关农艺性状,采用混合线性模型的严格复合区间作图法(rMQM)在烟草全基因组范围内进行QTL扫描分析。结果显示:(1)烤烟7个目标性状在RILs群体内各株系间存在较大范围的连续变异,具有显著的双向超亲分离,且各性状的平均值很接近中亲值;7个农艺性状的平均广义遗传率为73.33%,其中株高和节距的广义遗传率在80%以上,而茎围和茎叶角度的广义遗传率则低于60%,表明7个与烤烟产量相关的农艺性状是既受微效多基因控制又受环境条件影响的数量性状。(2)共检测到30个QTLs分布在9条连锁群上,其中与株高、节距、叶数、腰叶长和腰叶宽相关的5个主效QTLs在连续两年中均可检测到,且具有较大的效应值,可解释大于10%的表型变异。(3)7个目标性状之间存在一定程度的相关性,与之对应在基因组中也存在一些较小区域,每个区域包含2个或2个以上紧密连锁的不同性状的QTLs。烟草产量相关农艺性状的QTL分析和主效QTLs的获得,为进一步利用分子标记辅助选择培育烟草高产新品种奠定了理论基础。     

作物数量性状(QTL)基因研究进展

从作物数量性状基因座QTL(quantitative trait locus)作图群体类型及特点,QTL定位的原理和方法,作物QTL研究现状,以及QTL精细定位、克隆、利用等方面进行了综述。对作物QTL分子标记辅助选择育种进行了探讨,并对目前QTL定位中存在的问题和今后QTL的研究方向提出了一些思考。     

利用一个水稻RIL群体定位控制淀粉特性的QTL

利用一个由籼粳(Oryza sativa L.)杂交发展成的重组自交系(RIL)群体研究影响淀粉特性的遗传因子,测定了一系列淀粉特性有关性状,包括直链淀粉含量、胶稠度、淀粉糊粘度、胶的质地、糊化温度、热学特性、回生特性等22个参数.共定位了44个QTL,分布在第2～6、8、9、11染色体上,每个性状所定位的QTL在1到4个不等.其中有2个是主基因,一个是第6染色体上的Wx基因,它控制直链淀粉含量、胶稠度、淀粉糊粘度、胶的质地、回生特性等性状,另一个是第6染色体上的alk基因,它控制糊化温度与热学特性等性状,其他QTL都是微效基因,在第9染色体上RZ404和G295区间系首次检测到,它控制淀粉胶的硬度(hardness)、胶粘性(gumminess)、咀嚼性(chewiness)、回生淀粉的最高糊化温度、回生率等性状,这些性状都未曾研究过.     

3B染色体短臂小麦赤霉病抗性主效QTL的分析

采用区间作图和复合区间作图方法对重组自交系群体宁894037／Alondram、望水白／Alondra和苏麦3号／A1ondra进行了抗赤霉病QTL分析,结果表明,用在田间和温室的赤霉病抗性鉴定资料,在3个赤霉病抗源宁894037、望水白和苏麦3号的3B染色体短臂上均检测到主效QTL的存在。宁894037主效QTL位于标记BARCl33与Xgwm493之间的5．0cM的区间内,最高可解释42．8％的赤霉病抗性;望水白的主效QTL位于标记BARCl47与Xgwm493之间11．5cM的区间内,最高可解释15．1％的赤霉病抗性;苏麦3号的主效QTL位于Xgwm533a与Xgwm493之间13．0cM的区间内,最高可解释10．6％的赤霉病抗性。与赤霉病抗性主效QTL紧密连锁的标记均为SSR标记,可直接用于分子辅助育种。     

西瓜果实性状QTL定位及其遗传效应分析

用可溶性固形物含量高、薄皮、感枯萎病的栽培西瓜自交系（Ｃｉｔｒｕｌｌｕｓ ｌａｎａｔｕｓ ｖａｒ．ｌａｎａｔｕｓ）９７１０３和可溶性固形物含量低、皮厚、抗病的野生西瓜种质（Ｃｉｔｒｕｌｌｕｓ ｌａｎａｔｕｓ ｖａｒ．ｃｉｔｒｏｉｄｅｓ）ＰＩ２９６３４１杂交所得Ｆ２的１１８个单株为作图群体,通过构建分子连锁图谱,对西瓜主要果实性状可溶性固形物含量、果皮硬度、果皮厚度、单果重、种子千粒重进行区间作图,     

Optimizing Selection on Multiple Identified Quantitative Trait Loci in Population with Overlapping Generations

A method was developed to model and optimize selection on multiple identified quantitative trait loci (QTLs) and polygenic estimated breeding value, in order to maximize a weighted sum of cumulative response to selection over multiple years in a population with overlapping generations. The model allows for a population with multiple sex-age classes, different number of age class between sires and dams, and varied genetic contribution of the age class. The optimization problem was formulated as a multiple-stage optimal control problem and solved by a forward and backward iteration loop. The practical utility of this method was illustrated in an example of pig breeding population with overlapping generations. The selection response of this method was compared with standard QTL selection and conventional best linear unbiased prediction (BLUP) selection. Simulation results show that optimal selection achieved greater selection response than either standard QTL or conventional BLUP selections. The influence of population structure on optimal selection was significant. Optimal QTL selection and standard QTL selection were more favorable in a population with overlapping generations than discrete generations, and obtained more benefits relative to conventional BLUP selection in a population with overlapping generations. Optimal QTL selection relative to conventional BLUP selection is also more favorable following increase of genetic contribution of two-year-old boars and sows in a population with overlapping generations.     

微卫星标记与奶牛数量性状QTL定位

本文对奶牛产奶性能（产奶量、乳脂率、乳脂量、蛋白率、蛋白量）、乳房炎及体细胞数、繁殖性能、生产寿命等数量性状QTL定位研究进展进行了综述。 Abstract:Mapping of quantitative trait loci for some important traits (milk yield,fat percentage,fat yield,protein percentage,protein yield,clinical mastitis and somatic cell count,reproductive performance,productive life,etc.) in dairy cattle was introduced in this review.     

猪部分内脏器官性状和乳头数的QTL检测

对内脏器官重量性状的QTL定位研究,所见报道不多;对于猪的繁殖性状,尚需做进一步的探讨。本研究在总共214头(180头F2个体)组成的资源家系中,在猪的SSC4、SSC6、SSC7、SSC8 和 SSC13上共选取39个微卫星标记,检测了8种内脏器官的重量性状:心重 (HW)、肺重 (LW)、肝 胆重 (LGW)、脾重 (SPW)、胃重 (STW)、小肠重(SIW)、大肠重(LIW) 和肾重(KW);其他一些胴体性状:胴体长性状1(自第一颈椎,CL1)、胴体长性状2(自第一胸椎,CL2)、肋骨数(RNS)和繁殖性状乳头数(TNS)的QTL定位。结果表明,检测到3个染色体极显著水平的QTL(P≤0.01),它们是HW QTL定位在SSC6上30 cM处,RNS QTL定位在SSC7上115 cM处和TNS QTL定位在SSC7上 110 cM处;另外6个染色体显著水平的QTL(P≤0.05)是:LW(SSC13上119 cM处)、LGW(SSC6上94 cM处)、SPW(SSC8上106 cM处)、SIW(SSC 4上0 cM处)、LIW(SSC 4上170 cM 处)和TNS(SSC 6上95 cM处)。上述QTL解释的表型变异从 0.04% 到 14.06%,有些位点的 QTL 可以解释表型变异的 10%以上,如 HW 的 QTL 解释表型变异的9.52%、SIW的QTL解释表型变异的13.47%、定位在SSC6上的TNS QTL解释表型变异的14.06%,而定位在 SSC7上的TNS QTL解释表型变异的11.30%。多数内脏器官重量性状的QTL定位结果未见报道。胴体长未见显著水平的QTL,而在SSC7上定位染色体极显著水平的肋骨数QTL。     

Quantitative Trait Loci for Panicle Layer Uniformity Identified in Doubled Haploid Lines of Rice in Two Environments

Uniformity of stem height in rice directly affects crop yield potential and appearance, and has become a vital index for rice improvement. In the present study, a doubled haploid (DH) population, derived from a cross between japonica rice Chunjiang 06 and indica rice TN1 was used to analyze the quantitative trait locus (QTL) for three related traits of panicle-layer-uniformity; that is, the tallest panicle height, the lowest panicle height and panicle layer disuniformity in two locations: Hangzhou (HZ) and Hainan (HN). A total of 16 QTLs for three traits distributed on eight chromosomes were detected in two different environments. Two QTLs, qTPH -4 and qTPH -8 were co-located with the QTLs for qLPH -4 and qLPH -8, which were only significant in the HZ environment, whereas the qTPH -6 and qLPH -6 located at the same interval were only significant in the HN environment. Two QTLs, qPLD -10-1 and qPLD -10-2, were closely linked to qTPH-10 , and they might have been at the same locus. One QTL, qPLD -3, was detected in both environments, explaining more than 23% of the phenotypic variations. The CJ06 allele of qPLD -3 could increase the panicle layer disuniformity by 9.23 and 4.74 cm in the HZ and HN environments. Except for qPLD -3, almost all other QTLs for the same trait were detected only in one environment, indicating that these three traits were dramatically affected by environmental factors. The results may be useful for elucidation of the molecular mechanism of panicle-layer-uniformity and marker assisted breeding for super-rice.     

雄性不交换条件下F2群体区间标记——QTL定位的相关方法（英文）

提出了雄性不交换条件下F2群体区间标记定位QTL的相关方法,并且对其适用的条件进行了讨论,通过对分子区间标记进行赋值,计算在无交叉干涉条件下分子标记与表型值的简单相关系数,并在此基础上进行连锁检验,在特定条件下可以估计数量性状座位（QTL）与分子标记座位间的连锁值。     

Quantitative Trait Loci for Panicle Layer Uniformity Identified in Doubled Haploid Lines of Rice in Two Environments

Uniformity of stem height in rice directly affects crop yield potential and appearance, and has become a vital index for rice improvement. In the present study, a doubled haploid （DH） population, derived from a cross between japonica rice Chunjiang 06 and indica rice TN1 was used to analyze the quantitative trait locus （QTL） for three related traits of paniclelayer-uniformity; that is, the tallest panicle height, the lowest panicle height and panicle layer disuniforrnity in two locations：Hangzhou （HZ） and Hainan （HN）. A total of 16 QTLs for three traits distributed on eight chromosomes were detected in two different environments. Two QTLs, qTPH-4 and qTPH-8 were co-located with the QTLs for qLPH-4 and qLPH-8, which were only significant in the HZ environment, whereas the qTPH-6 and qLPH-6 located at the same interval were only significant in the HN environment. Two QTLs, qPLD-10.1 and qPLD-10.2, were closely linked to qTPH-10, and they might have been at the same locus. One QTL, qPLD-3, was detected in both environments, explaining more than 23% of the phenotypic variations. The CJ06 allele of qPLD-3 could increase the panicle layer disuniformity by 9.23 and 4.74 cm in the HZ and HN environments. Except for qPLD-3, almost all other QTLs for the same trait were detected only in one environment, indicating that these three traits were dramatically affected by environmental factors. The results may be useful for elucidation of the molecular mechanism of panicle-layer-uniformity and marker assisted breeding for super-rice.     

Quantitative Trait Loci for Lipid Content in Drosophila melanogaster

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

Quantitative Trait Loci Identification by Estimating the Genetic Model based on the Extremal Samples

Background In genetic association studies with quantitative trait loci (QTL), the association between a candidate genetic marker and the trait of interest is commonly examined by the omnibus F test or by the t-test corresponding to a given genetic model or mode of inheritance. It is known that the t-test with a correct model specification is more powerful than the F test. However, since the underlying genetic model is rarely known in practice, the use of a model-specific t-test may incur substantial power loss. Robust-efficient tests, such as the Maximin Efficiency Robust Test (MERT) and MAX3 have been proposed in the literature.Methods In this paper, we propose a novel two-step robust-efficient approach, namely, the genetic model selection (GMS) method for quantitative trait analysis. GMS selects a genetic model by testing Hardy-Weinberg disequilibrium (HWD) with extremal samples of the population in the first step and then applies the corresponding genetic model-specific t-test in the second step.Results Simulations show that GMS is not only more efficient than MERT and MAX3, but also has comparable power to the optimal t-test when the genetic model is known.Conclusion Application to the data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort demonstrates that the proposed approach can identify meaningful biological SNPs on chromosome 19.