一个基于熵的对疾病基因精细定位的指数

针对人类疾病基因的精细定位,本文利用稠密的标记位点,通过比较标记的熵和条件熵,给出了一个基于熵的指数。该指数可以度量标记基因和性状位点间连锁不平衡(LD)程度。该指数的特性是它不依赖于标记基因的频率。同时它对应疾病易感位点(DSL)精细定位的哈迪-温伯格不平衡(HWD)指数。通过计算机模拟,文章调查了不同遗传参数下该指数的性质。模拟结果表明该指数用作疾病易感位点精细定位是有效的。     

基因型错误对数量性状精细定位的哈迪-温伯格不平衡指数的效应

使用紧密相邻的标记位点且与标记基因频率无关的哈迪-温伯格不平衡(HWD)指数被用来对数量性状位点(QTL)进行精细定位.本文讨论了当存在基因型错误时HWD指数的性质.文章指出,当存在基因型错误时,对于在群体的标记基因频率已知的情形使用的两个HWD指数尽管受基因型错误的影响但仍然有效;而仅仅极端样本的标记基因频率已知的情形下使用的两个HWD指数同时与基因型错误和标记基因频率有关.计算机模拟表明,仅仅极端样本的标记基因频率已知的情形下使用的两个HWD指数在精细定位时会产生偏差,不适宜作精细定位.     

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.     

使用哈迪-温伯格不平衡检验来检测多个标记-疾病关联(英文)

多位点单体型的可用性为疾病易感位点(DSL)的遗传分析提供了有价值的工具.DSL定位可以通过检测患病个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对DSL进行单体型关联分析.我们调查了HWD检验的统计功效并与常用的x~2统计量进行比较.结果表明HWD检验具有高的功效且比常用的x~2统计量的功效高.     

一个基于熵的指数精细定位人类复杂性状位点

近来,一个基于熵的指数被提出用来对人类复杂性状位点进行连锁不平衡定位．这个熵指数比较了患病个体与正常个体或极端样本之间标记基因频率的熵和条件熵．本文基于熵理论,提出了另一个备选指数．这个新的指数比较患病个体与正常个体之间标记基因型频率的熵和条件熵．计算机模拟结果表明本文提出的新指数平行于之前的熵指数．而基于遗传性血色病（hereditary haemochromatosis,HH）数据的分析表明了这个新指数能有效对人类复杂性状位点进行精细定位．     

通过熵理论使用核心家系进行精细定位(英文)

针对精细定位人类复杂性状基因位点,我们拓展了基于熵的连锁不平衡指数到使用病例-父母和无关的对照-父母设计的家系研究.这个指数比较杂合子父母传递给受累子代和非受累子代基因的熵和条件熵.在单一群体和混合群体两种情形下,我们通过模拟调查了该指数的性质.结果表明在不同遗传模型下利用该指数定位的概率随着样本容量的增大而增大.当样本容量超过200,显性模型和加性模型的概率在90%以上.当样本容量超过300,隐性模型和乘积模型的概率在80%以上.当存在群体混杂时,该指数仍然适用于精细定位.     

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．     

人2号染色体一个高血压病易感基因的精细定位

运用荧光标记-半自动基因扫描分型技术,在中国上海人群中对一个高血压病易感基因位点进行精细定位.结果显示:2号染色体从157.16～162.46cM这一5.3cM的范围内,出现了一个持续的LOD值高峰.该范围内所有LOD值均在2.0以上,对应P值均小于0.001.最大LOD值为2.24(P=0.00067,160.52cM).单点分析峰值在D2S151(LOD=1.86,NPL Z= 2.60).另外,有多个位点存在传递不平衡.最终,将该易感基因定位于2q14-q23(D2S151-D2S2370),这与Stoll等人用比较基因组法将大鼠数量性状位点(QTL)定位于人类基因组所得结果一致.以上结果提示,在中国上海人群中,2号染色体2q14-q23这一区域值得进一步研究.     

复杂疾病的遗传易感基因区域的精细定位

以单核苷酸多态性(Single-nucleotide polymorphism, SNP)为遗传标记, 采用全基因组关联研究(Genome-wide association studies, GWAS)的策略, 已经在660多种疾病(或性状)中发现了3800多个遗传易感基因区域。但是, 其中最显著关联的遗传变异或致病性的遗传变异位点及其生物学功能并不完全清楚。这些位点的鉴定有助于阐明复杂疾病的生物学机制, 以及发现新的疾病标记物。后GWAS时代的主要任务之一就是通过精细定位研究找到复杂疾病易感基因区域内最显著关联的易感位点或致病性的易感位点并阐明其生物学功能。针对常见变异, 可通过推断或重测序增加SNP密度, 寻找最显著关联的SNP位点, 并通过功能元件分析、表达数量性状位点(Expression quantitative trait locus, eQTL)分析和单体型分析等方法寻找功能性的SNP位点和易感基因。针对罕见变异, 则可采用重测序、罕见单体型分析、家系分析和负荷检验等方法进行精细定位。文章对这些策略和所面临的问题进行了综述。     

藏猪3个繁殖性状主效基因多态性研究

藏猪是我国高海拔特有猪种,为了解藏猪繁殖性能的遗传潜力和基因多样性,利用PCR－RFLP技术测定了202头藏猪ESR、FSHβ和PRLR 3个繁殖性状主效基因位点的基因型多态性,并与文献报道的国内外猪种进行比较。结果显示藏猪群体ESR、FSHβ和PRLR优势基因型分别为BB型、AB型和AA型,3个位点基因型分布均符合哈迪－温伯格平衡。藏猪群体3个繁殖性状主效基因位点都有报道的有利基因型,且有利等位基因频率较高,说明藏猪在这3个基因位点可能具有优良繁殖性能的遗传潜力。     

Robust indices of Hardy-Weinberg disequilibrium for QTL fine mapping

Hardy-Weinberg disequilibrium (HWD) measures have been proposed using dense markers to fine map a quantitative trait locus (QTL) to regions < approximately 1 cM. Earlier HWD measures may introduce bias in the fine mapping because they are dependent on marker allele frequencies across loci. Hence, HWD indices that do not depend on marker allele frequencies are desired for fine mapping. Based on our earlier work, here we present four new HWD indices that do not depend on marker allele frequencies. Two are for use when marker allele frequencies in a study population are known, and two are for use when marker allele frequencies in a study population are not known and are only known in the extreme samples. The new measures are a function of the genetic distance between the marker locus and a QTL. Through simulations, we investigated and compared the fine mapping performance of the new HWD measures with that of the earlier ones. Our results show that when marker allele frequencies vary across loci, the new measures presented here are more robust and powerful.     

An entropy-based measure of founder informativeness

Optimizing quantitative trait locus (QTL) mapping experiments requires a generalized measure of marker informativeness because variable information is obtained from different marker systems, marker distribution and pedigree types. Such a measure can be derived from the concept of Shannon entropy, a central concept in information theory. Here we introduce entropy-based founder informativeness (EFI), a new measure of information content generalized across pedigrees, maps, marker systems and mating configurations. We derived equations for inbred- and outbred-derived mapping populations. Mathematical properties of EFI include enhanced sensitivity to mapping population type and extension to any number of founders. To illustrate the use of EFI, we compared experimental designs for QTL mapping for three examples: (i) different marker systems for an F2 pedigree, (ii) different marker densities and sampling sizes for a BC1 pedigree and (iii) a comparison of haplotypic versus zygotic analyses of an outbred pedigree. As an a priori generalized measure of information content, EFI does not require phenotypic data for optimizing experimental designs for QTL mapping.     

An entropy-based measure for QTL mapping using extreme samples of population

Quantitative trait locus (QTL) mapping can be accomplished through the method of selective genotyping, which is based on the differences of frequencies between an upper sample and a lower sample in population. However, amplifying the differences in marker allele frequencies in extreme samples may increase the probability for QTL mapping. Shannon entropy, which is a nonlinear function of allele frequencies, can be used to amplify the differences in marker allele frequencies. In this paper, we present a novel measure for linkage disequilibrium (LD) between a marker and single QTL, that is based on the comparison of the entropy and conditional entropy in a marker in extreme samples of population. This measure of LD between the marker and the trait locus can be used when the marker allele frequencies are known in the extreme samples of a population. We investigate the mapping performance in both analytic and simulation scenarios of a single QTL linked to a single marker. Our results show that the measure has very reasonable performance. In addition, a simulation study is performed on the basis of the haplotype frequencies of 10 SNPs of angiotensin-I converting enzyme (ACE) genes.     

Comparing linkage disequilibrium-based methods for fine mapping quantitative trait loci

Recently, a method for fine mapping quantitative trait loci (QTL) using linkage disequilibrium was proposed to map QTL by modeling covariance between individuals, due to identical-by-descent (IBD) QTL alleles, on the basis of the similarity of their marker haplotypes under an assumed population history. In the work presented here, the advantage of using marker haplotype information for fine mapping QTL was studied by comparing the IBD-based method with 10 markers to regression on a single marker, a pair of markers, or a two-locus haplotype under alternative population histories. When 10 markers were genotyped, the IBD-based method estimated the position of the QTL more accurately than did single-marker regression in all populations. When 20 markers were genotyped for regression, as single-marker methods do not require knowledge of haplotypes, the mapping accuracy of regression in all populations was similar to or greater than that of the IBD-based method using 10 markers. Thus for populations similar to those simulated here, the IBD-based method is comparable to single-marker regression analysis for fine mapping QTL.     

Linkage disequilibrium mapping of quantitative trait loci under truncation selection

As a dense map of single nucleotide polymorphism (SNP) markers are available, population-based linkage disequilibrium (LD) mapping or association study is becoming one of the major tools for identifying quantitative trait loci (QTL) and for fine gene mapping. However, in many cases, LD between the marker and trait locus is not very strong. Approaches that maximize the potential of detecting LD will be essential for the success of LD mapping of QTL. In this paper, we propose two strategies for increasing the probability of detecting LD: (1) phenotypic selection and (2) haplotype LD mapping. To provide the foundations for LD mapping of QTL under selection, we develop analytic tools for assessing the impact of phenotypic selection on allele and haplotype frequencies, and LD under three trait models: single trait locus, two unlinked trait loci, and two linked trait loci with or without epistasis. In addition to a traditional chi(2) test, which compares the difference in allele or haplotype frequencies in the selected sample and population sample, we present multiple regression methods for LD mapping of QTL, and investigate which methods are effective in employing phenotypic selection for QTL mapping. We also develop a statistical framework for investigating and comparing the power of the single marker and multilocus haplotype test for LD mapping of QTL. Finally, the proposed methods are applied to mapping QTL influencing variation in systolic blood pressure in an isolated Chinese population.     

Improving QTL Mapping Resolution Based on Genotypic Sampling—a Case Using a RIL Population

The QTL mapping results were compared with the genotypically selected and random samples of the same size on the base of a RIL population. The results demonstrated that there were no obvious differences in the trait distribution and marker segregation distortion between the genotypically selected and random samples with the same population size. However, a significant increase in QTL detection power, sensitivity, specificity, and QTL resolution in the genotypically selected samples were observed. Moreover, the highly significant effect was detected in small size of genotypically selected samples. In QTL mapping, phenotyping is a more sensitive limiting factor than genotyping so that the selection of samples could be an attractive strategy for increasing genome-wide QTL mapping resolution. The efficient selection of samples should be more helpful for QTL maker assistant selection, fine mapping, and QTL cloning.