使用哈迪-温伯格不平衡检验对QTL进行关联分析(英文)

多位点单体型的可用性对于数量性状位点(QTL)的遗传分析提供了有价值的工具。QTL定位可以通过检测极端群体中一个标记位点的哈迪-温伯格不平衡(HWD)来实现.本文拓展了HWD检验到多个标记位点,通过选择基因型对QTL进行单体型关联分析.我们用分析方法调查了不同遗传率,不同样本大小和不同样本选择阈值对HWD检验的统计功效影响.结果表明HWD检验具有高的功效.一个基于血管紧张素转换酶(ACE)基因的10个SNPs单体型频率的模拟研究用来评估HWD检验的性质.     

使用极端样本构建一个基于熵的对数量性状位点关联研究的统计量

基于熵理论,Zhao等提出了一个对复杂疾病易感基因进行关联研究的统计量．本文拓展了这一理论到数量性状,利用熵理论。获得了一个对数量性状位点进行关联研究的采用群体极端样本及稠密标记的统计量．     

数量性状基因座的动态定位策略

分子标记辅助数量性状基因（QTL）定位和效应分析技术为深入研究数量性状的遗传基础提供了一个有力手段．但目前的QTL定位策略是静态的,只估计各QTL在某观察时刻的累积效应,无法了解QTL的表达动态．本文提出一种新的QTL定位策略,称为“动态定位”,能够揭示QTI表达的动态过程,并能极大地提高QTL定位的统计功效.     

QTL形态标记定位的一种数学方法

根据家蚕中位于Z染色体上的伴性遗传的双形态标记和假定与其有连锁关系的一个具有一对主基因差异的数量性状在测交世代中,所作的理论分布,本文建立了QTL形态标记定位的数学方法,即频数分布面积法,并给出了相应的检测一对主基因在测交世代中的同分离比例及其与形态标记是否有连锁关系的X2统计量．这种定位方法亦适应于非伴性遗传方式的QTL形态标记定位．与单标记定位的极大似然方法相比,我们的方法所作的双标记定位能显示QTL与形态标记发生重组的交叉干步作用,并且定位结果不受作用于数量性状的环境效应所影响．     

玉米株高和穗位高的遗传基础与分子机制*

株高和穗位高是玉米重要育种性状,直接影响植株的养分利用效率及抗倒伏性,进而影响玉米产量。玉米株高和穗位高属于典型数量性状,目前通过数量性状位点(quantitative trait loci mapping,QTL)定位和全基因组关联分析(genome-wide association study, GWAS)等方法已挖掘到较多相关遗传位点,通过QTL精细定位及利用突变体克隆了一些调控株高和穗位高关键基因。但是由于各研究组所利用的群体类型和大小、标记类型和密度以及统计方法不同,所鉴定QTL差异较大,单个研究难以揭示玉米株高和穗位高遗传结构。早期QTL定位的结果多以遗传距离来展示,不同时期GWAS研究所使用参考基因组版本不同,这进一步增加了借鉴和利用前人研究结果的难度。首次将目前已鉴定株高和穗位高遗传定位信息统一锚定至玉米自交系B73参考基因组V4版本,构建了株高和穗位高性状定位的一致性图谱,并鉴定出可被多个独立研究定位的热点区间。进一步对已克隆玉米株高和穗位高调控基因进行总结与分类,揭示株高和穗位高性状调控机制,对深度解析株高和穗位高遗传结构、指导基因克隆和利用分子标记辅助选择优化玉米株高和穗位高性状均具有重要意义。     

植物QTL分析的理论研究进展

数量性状的表型是由数量性状基因座 ( Quantitative trait locus,QTL)和环境效应共同作用的结果。传统的数量遗传学采用统计学的方法由一级统计量和二级统计量描述处理 QTL的复合作用 ,估计各种遗传参数 (例如遗传力、遗传相关、遗传进度、有效因子数等 ) ,用于指导遗传育种实践。然而 ,在传统的数量遗传学分析中 ,往往假设数量性状受微效多基因控制 ,这些基因具有相同的并且是较微小的效应 ,所估计的遗传参数反映的是数量性状多基因系统的整体特征 ,其理论方法不能用于追踪研究和描述单个数量性状基因的作用。近年来 ,由于分子生物学技…     

QTL定位的研究方法

QTL 定位就是采用类似单基因定位的方法将QTL定位在遗传图谱上 ,确定 QTL与遗传标记间的距离 (以重组率表示 ) [1]。根据标记数目的不同 ,可分为单标记、双标记和多标记几种方法。根据统计分析方法的不同 ,可分为方差与均值分析法、回归及相关分析法、矩估计及最大似然法等。根据标记区间数可分为零区间作图、单区间作图和多区间作图。此外 ,还有将不同方法结合起来的综合分析方法 ,如 QTL复合区间作图 (CIM)、多区间作图 (MIM)、多 QTL作图、多性状作图 (MTM)等等。建立在标记与数量性状之间相互关联基础上的关联分析方法主要有…     

谷子SSR分子图谱的构建及主要农艺性状QTL定位

试验拟对谷子重要农艺性状进行数量性状位点QTL分析。以表型差异较大的沈3/晋谷20F2作图群体为材料,观测其株高、穗长等性状,选用SSR做分子标记,利用完备区间作图法(BASTEN C J)进行QTL分析。结果显示,表型数据在作图群体中呈现连续分布,表现为多基因控制的数量性状,被整合的54个SSR标记构建10个连锁群,LOD阈值设置为2.0,检测到与株高相关的主效QTL2个,联合贡献率45.9637%,穗长主效QTL1个,贡献率14.9647%,与穗重、粒重相关的主效QTL为同一位点,贡献率分别为11.9601%和10.1879%。有6组QTL位点之间存在基因互作效应,大小范围为-0.4986-16.6407,对性状的贡献率在2.2716％至6.7478％之间。谷子表型控制复杂,相关QTL的检测受环境影响较大,不同连锁群QTL间互作明显。     

大豆遗传图谱的构建和若干农艺性状的QTL定位分析

大豆许多重要农艺性状都是由微效多基因控制的数量性状,对这些数量性状进行QTL定位是大豆数量性状遗传研究领域的一个重要内容.本研究利用栽培大豆科新3号为父本、中黄20为母本杂交得到含192个单株的F2分离群体,构建了含122 个SSR标记、覆盖1719.6cM、由33个连锁群组成的连锁遗传图谱.利用复合区间作图法,对该群体的株高、主茎节数、单株粒重和蛋白质含量等农艺性状的调查数据进行QTL分析,共找到两个株高QTL,贡献率分别为9.15%和6.08%;两个主茎节数QTL,贡献率分别为10. 1%和8.6%;一个蛋白质含量QTL,贡献率为9.8%;一个单株粒重QTL,贡献率为11.4% .通过遗传作图共找到与所定位的4个农艺性状QTL连锁的6个SSR标记,这些标记可以应用于大豆种质资源的分子标记辅助选择,从而为大豆分子标记辅助育种提供理论依据.     

胚乳性状数量基因的定位方法

将三倍体胚乳性状的数量遗传模型和二倍体性状数量基因（QTL）图构建方法相结合,导出双侧标记基因型下有关胚乳性状QTL的遗传组成、平均数和遗传方差分量,据之提出以某一区间双侧标记基因型胚乳性状的平均值为依变数,以该区间内任一点假定存在的QTL的加性效应d、显性效应h1和／或h2的系数为自变数,进行有重复观察值的多元线性回归分析,根据多元线性回归的显著性测验该点是否存在QTL,并估计出QTL的遗传效应。给定区间内任一点,皆可以此进行分析,从而可在整条染色体上作图,并以之确定QTL的数目和最可能位置,同时,在检测某一区间时,利用多元线性回归方法将该区间外可能存在的QTL的干扰进行统计控制,以提高QTL检测的精度。此外,还讨论了如何将之推广应用于其他类型的DNA不对应资料以及具复杂遗传模型的胚乳性状资料。     

The application of the entropy-based statistic for genomic association study of QTL

An entropy-based statistic TPE has been proposed for genomic association study for disease-susceptibility locus.The statistic TPE may be directly adopted and/or extended to quantitative-trait locus (QTL)mapping for quantitative traits.In this article,the statistic TPE was extended and applied to quantitative trait for association analysis of QTL by means of selective genotyping.The statistical properties (the type I error rate and the power) were examined under a range of parameters and population-sampling strategies (e.g.,various genetic models,various heritabilities,and various sample-selection threshold values) by simulation studies.The results indicated that the statistic Tee is robust and powerful for genomic association study of QTL.A simulation study based on the haplotype frequencies of 10 single nucleotide polymorphisms (SNPs) of angiotensin-I converting enzyme genes was conducted to evaluate the performance of the statistic TPE for genetic association study.     

R/qtlDesign: inbred line cross experimental design

An investigator planning a QTL (quantitative trait locus) experiment has to choose which strains to cross, the type of cross, genotyping strategies, and the number of progeny to raise and phenotype. To help make such choices, we have developed an interactive program for power and sample size calculations for QTL experiments, R/qtlDesign. Our software includes support for selective genotyping strategies, variable marker spacing, and tools to optimize information content subject to cost constraints for backcross, intercross, and recombinant inbred lines from two parental strains. We review the impact of experimental design choices on the variance attributable to a segregating locus, the residual error variance, and the effective sample size. We give examples of software usage in real-life settings. The software is available at .     

Susceptibility of biallelic haplotype and genotype frequencies to genotyping error

With the availability of fast genotyping methods and genomic databases, the search for statistical association of single nucleotide polymorphisms with a complex trait has become an important methodology in medical genetics. However, even fairly rare errors occurring during the genotyping process can lead to spurious association results and decrease in statistical power. We develop a systematic approach to study how genotyping errors change the genotype distribution in a sample. The general M-marker case is reduced to that of a single-marker locus by recognizing the underlying tensor-product structure of the error matrix. Both method and general conclusions apply to the general error model; we give detailed results for allele-based errors of size depending both on the marker locus and the allele present. Multiple errors are treated in terms of the associated diffusion process on the space of genotype distributions. We find that certain genotype and haplotype distributions remain unchanged under genotyping errors, and that genotyping errors generally render the distribution more similar to the stable one. In case-control association studies, this will lead to loss of statistical power for nondifferential genotyping errors and increase in type I error for differential genotyping errors. Moreover, we show that allele-based genotyping errors do not disturb Hardy-Weinberg equilibrium in the genotype distribution. In this setting we also identify maximally affected distributions. As they correspond to situations with rare alleles and marker loci in high linkage disequilibrium, careful checking for genotyping errors is advisable when significant association based on such alleles/haplotypes is observed in association studies.     

Models and tests of linkage and association studies of quantitative trait locus for multi-allele marker Loci

In this paper, we explore models and tests for association and linkage studies of a quantitative trait locus (QTL) linked to a multi-allele marker locus. Based on the difference between an offspring's conditional trait means of receiving and not receiving an allele from a parent at marker locus, we propose three statistics T(m), T(m,row) and T(m,col) to test association or linkage disequilibrium between the marker locus and the QTL. These tests are composite tests, and use the offspring marginal sample means including offspring data of both homozygous and heterozygous parents. For the linkage study, we calculate the offspring's conditional trait mean given the allele transmission status of a heterozygous parent at the marker locus. Based on the difference between the conditional means of a transmitted and a nontransmitted allele from a heterozygous parent, we propose statistics T(parsi), T(satur), T(gen) and T(m,het) to perform composite tests of linkage between the marker locus and the quantitative trait locus in the presence of association. These tests only use the offspring data that are related to the heterozygous parents at the marker locus. T(parsi) is a parsimonious or allele-wise statistic, T(satur) and T(gen )are satured or genotype-wise statistics, and T(m,het) compares the row and column sample means for offspring data of heterozygous parents. After comparing the powers and the sample sizes, we conclude that T(parsi) has higher power than those of the bi-allele tests, T(satur), T(gen), and T(m,het). If there is tight linkage between the marker and the trait locus, T(parsi) is powerful in detecting linkage between the marker and the trait locus in the presence of association. By investigating the goodness-of-fit of T(parsi), we find that T(satur) does not gain much power compared to that of T(parsi). Moreover, T(parsi) takes into account the pattern of the data that is consistent with linkage and linkage disequilibrium. As the number of alleles at the marker locus increases, T(parsi) is very conservative, and can be useful even for sparse data. To illustrate the usefulness and the power of the methods proposed in this paper, we analyze the chromosome 6 data of the Oxford asthma data, Genetic Analysis Workshop 12.     

Selective genotyping for determination of linkage between a marker locus and a quantitative trait locus

Summary Selective genotyping is the term used when the determination of linkage between marker loci and quantitative trait loci (QTL) affecting some particular trait is carried out by genotyping only individuals from the high and low phenotypic tails of the entire sample population. Selective genotyping can markedly decrease the number of individuals genotyped for a given power at the expense of an increase in the number of individuals phenotyped. The optimum proportion of individuals genotyped from the point of view of minimizing costs for a given experimental power depends strongly on the cost of completely genotyping an individual for all of the markers included in the experiment (including the costs of obtaining a DNA sample) relative to the cost of rearing and trait evaluation of an individual. However, in single trait studies, it will almost never be useful to genotype more than the upper and lower 25% of a population. It is shown that the observed difference in quantitative trait values associated with alternative marker genotypes in the selected population can be much greater than the actual gene effect at the quantitative trait locus when the entire population is considered. An expression and a figure is provided for converting observed differences under selective genotyping to actual gene effects.     

Selective DNA Pooling for Determination of Linkage between a Molecular Marker and a Quantitative Trait Locus

Selective genotyping is a method to reduce costs in marker-quantitative trait locus (QTL) linkage determination by genotyping only those individuals with extreme, and hence most informative, quantitative trait values. The DNA pooling strategy (termed: ``selective DNA pooling') takes this one step further by pooling DNA from the selected individuals at each of the two phenotypic extremes, and basing the test for linkage on marker allele frequencies as estimated from the pooled samples only. This can reduce genotyping costs of marker-QTL linkage determination by up to two orders of magnitude. Theoretical analysis of selective DNA pooling shows that for experiments involving backcross, F(2) and half-sib designs, the power of selective DNA pooling for detecting genes with large effect, can be the same as that obtained by individual selective genotyping. Power for detecting genes with small effect, however, was found to decrease strongly with increase in the technical error of estimating allele frequencies in the pooled samples. The effect of technical error, however, can be markedly reduced by replication of technical procedures. It is also shown that a proportion selected of 0.1 at each tail will be appropriate for a wide range of experimental conditions.     

Marker-based mapping of quantitative trait loci using replicated progenies

Summary When heritability of the trait under investigation is low, replicated progenies can bring about a major reduction in the number of individuals that need to be scored for marker genotype in determining linkage between marker loci and quantitative trait loci (QTL). Savings are greatest when heritability of the trait is low, but are much reduced when heritability of the quantitative trait is moderate to high. Required numbers for recombinant inbred lines will be greater than those required for a simple F₂ population when heritabilities are moderate to high and the proportion of recombination between marker locus and quantitative trait locus is substantial.Contribution No. 2613-E of the Agricultural Research Organization, 1989 series     

Optimal sibship selection for genotyping in quantitative trait locus linkage analysis

In this paper we present a novel method for selecting optimally informative sibships of any size for quantitative trait locus (QTL) linkage analysis. The method allocates a quantitative index of potential informativeness to each sibship on the basis of observed trait scores and an assumed true QTL model. Any sample of phenotypically screened sibships can therefore be easily rank-ordered for selective genotyping. The quantitative index is the sibship's expected contribution to the non-centrality parameter. This expectation represents the weighted sum of chi(2) test statistics that would be obtained given the observed trait values over all possible sibship genotypic configurations; each configuration is weighted by the likelihood of it occurring given the assumed true genetic model. The properties of this procedure are explored in relation to the accuracy of the assumed true genetic model and sibship size. In comparison to previous methods of selecting phenotypically extreme sibships for genotyping, the proposed method is considerably more efficient and is robust with regard to the specification of the genetic model.     

Selective genotyping to detect quantitative trait loci affecting multiple traits: interval mapping analysis

 Segregating quantitative trait loci can be detected via linkage to genetic markers. By selectively genotyping individuals with extreme phenotypes for the quantitative trait, the power per individual genotyped is increased at the expense of the power per individual phenotyped, but linear-model estimates of the quantitative-locus effect will be biased. The properties of single- and multiple-trait maximum-likelihood estimates of quantitative-loci parameters derived from selectively genotyped samples were investigated using Monte-Carlo simulations of backcross populations. All individuals with trait records were included in the analyses. All quantitative-locus parameters and the residual correlation were unbiasedly estimated by multiple-trait maximum-likelihood methodology. With single-trait maximum-likelihood, unbiased estimates for quantitative-locus effect and location, and the residual variance, were obtained for the trait under selection, but biased estimates were derived for a correlated trait that was analyzed separately. When an effect of the QTL was simulated only on the trait under selection, a “ghost” effect was also found for the correlated trait. Furthermore, if an effect was simulated only for the correlated trait, then the statistical power was less than that obtained with a random sample of equal size. With multiple-trait analyses, the power of quantitative-trait locus detection was always greater with selective genotyping. Received: 23 February 1998 / Accepted: 15 May 1998     

Power and sample size calculations for case-control genetic association tests when errors are present: application to single nucleotide polymorphisms

The purpose of this work is to quantify the effects that errors in genotyping have on power and the sample size necessary to maintain constant asymptotic Type I and Type II error rates (SSN) for case-control genetic association studies between a disease phenotype and a di-allelic marker locus, for example a single nucleotide polymorphism (SNP) locus. We consider the effects of three published models of genotyping errors on the chi-square test for independence in the 2 x 3 table. After specifying genotype frequencies for the marker locus conditional on disease status and error model in both a genetic model-based and a genetic model-free framework, we compute the asymptotic power to detect association through specification of the test's non-centrality parameter. This parameter determines the functional dependence of SSN on the genotyping error rates. Additionally, we study the dependence of SSN on linkage disequilibrium (LD), marker allele frequencies, and genotyping error rates for a dominant disease model. Increased genotyping error rate requires a larger SSN. Every 1% increase in sum of genotyping error rates requires that both case and control SSN be increased by 2-8%, with the extent of increase dependent upon the error model. For the dominant disease model, SSN is a nonlinear function of LD and genotyping error rate, with greater SSN for lower LD and higher genotyping error rate. The combination of lower LD and higher genotyping error rates requires a larger SSN than the sum of the SSN for the lower LD and for the higher genotyping error rate.