多元性状同胞对连锁分析方法及其在原发性高血压基因定位数据中的应用

提出新的以广义最小二乘法原理处理同胞对数据间的相关性,以多元响应回归的方法处理多个性状数据间的相关性的多元性状同胞对连锁分析方法,模型的参数估计使用MCMC方法．并把此模型应用于原发性高血压基因定位的实际数据中．结果表明,与把多元性状拆成单一性状进行分析的方法相比,本文的方法可以提高估计的精度和检验的效能．     

两种常用数量性状连锁分析方法的原理和进展

在复杂性状疾病的家系连锁研究中,Haseman-Elston回归分析和方差组成模型是常用的两种数量性状连锁分析方法。前者主要针对同胞对的性状值差或和的平方进行回归分析;后者引用方差组成模型,将数量性状分解为遗传方差和环境方差,可估计二者对表型的影响。两种方法可应用于同胞对、核心家系或扩展家系,定位数量性状基因座。本文对这两种模型的原理、算法及其进展进行了综述,并给出了常用的统计软件包。 Abstract：In this article, we discussed two model-free methods for detecting genetic linkage for quantitative traits, Haseman-Elston regression approach and variance components approach. The former is a regression approach for detecting linkage based on the squared difference or squared sums in quantitative trait values of sib-pairs and their estimated marker IBD scores. The latter can jointly model covariate effects along with variance components, including genetic component and non-genetic sources of variability. We have outlined the model assumption, the algorithm and the extensions for the both methods.     

连锁分析研究的Meta分析方法

目前,随着基因定位研究的普遍开展,由于复杂性状每个易感基因的弱效应及单个研究中家系资料的不足造成各研究间的结果常不一致。如何科学地分析这些众多的不一致的结果正是目前基因定位和克隆研究者普遍面临的一个问题。为此,针对不同的连锁分析研究设计,以实例阐述了有关的方法及技巧。     

多基因离散性状QTL连锁分析方法

动物中有许多重要的离散性状,与常规的数量性状类似,其遗传基础受多基因控制并受到环境因子的修饰。由于多基因离散性状的表型特殊性,利用常规的QTL连锁分析方法很难获得理想的统计效果,相应地发展了许多基于广义线性模型框架内的非线性方法。本文就目前离散性状的QTL连锁分析方法作简要综述,并对可预期的改进方法进行了展望。     

参数连锁分析方法

通过拟合的数据资料,对目前最常用的参数型连锁分析方法进行了比较,为有针对性地选择连锁分析方法提供了依据。 Abstract：We present here two parametric statistics for linkage analysis ( linkage and genehunter).Using the simulated pedigrees, we introduced the usage of the two methods.     

基于选择牵连效应的标记/性状关联分析方法简介

许多重要农艺性状如产量、品质、抗逆性等多表现为数量性状, 是由多个基因和环境共同作用的结果, 对其遗传基础的研究比较困难。近年发展起来的以选择牵连效应分析为基础, 通过标记/性状之间的关联分析方法为这些性状的作图和遗传解析提供了新的手段, 也为作物的分子设计育种提供了新的思路, 其与QTL作图结果互相验证、互相补充, 必将促进数量遗传学、应用基因组学和育种学的发展。文章对关联分析的思路、方法、优缺点及应用时应注意的问题进行了比较系统的介绍。     

复杂疾病/性状的基因定位

影响人类健康的主要是一些多发性的复杂疾病,如肥胖、哮喘、高血压等,这类复杂疾病相关性状的表型没有明显的孟德尔遗传模式,多表现为连续的数量性状变异,遗传机理较为复杂,受多基因与环境的协同调控,在医学上较难进行明确的诊断。数量性状基因座(quantitative trait loci,简称QTL)是染色体上影响性状表型变异的特定区段。随着DNA分子标记技术的发展和分子标记连锁图谱的建立,     

乳蛋白座位与泌乳性状座位连锁关系的研究

本文利用杂合度分析和连锁群分析对乳蛋白基因座位与产奶量及乳成分等数量性状基因间的连锁关系进行了探讨。结果表明,各乳蛋白基因均具有较高的纯合度,但３个酪蛋白基因同时考虑时纯合度较低。泌乳性状的变差对乳蛋白基因座位杂合度没有明显的回归关系。Ｋ－ＣＮ基因与乳脂率和乳蛋白率,α_（ｓ１）－ＣＮ基因与３０５无产奶量间有显著的连锁关系。     

基于贝叶斯统计的遗传连锁分析方法

贝叶斯学派是不同于经典数理统计的一个重要学派, 其发展的贝叶斯统计方法在现代科学的许多领域已有着广泛的应用。探讨了贝叶斯统计在遗传连锁分析中的应用, 包括遗传重组率的贝叶斯估计、遗传连锁的贝叶斯因子检验和基于马尔可夫链蒙特卡罗理论的遗传连锁图谱构建。用编制的SAS/IML程序进行了模拟研究和实例分析, 验证了贝叶斯方法在遗传连锁分析中的有效性和实用性。     

Statistics for Nonparametric Linkage Analysis of X-Linked Traits in General Pedigrees

We have compared the power of several allele-sharing statistics for "nonparametric" linkage analysis of X-linked traits in nuclear families and extended pedigrees. Our rationale was that, although several of these statistics have been implemented in popular software packages, there has been no formal evaluation of their relative power. Here, we evaluate the relative performance of five test statistics, including two new test statistics. We considered sibships of sizes two through four, four different extended pedigrees, 15 different genetic models (12 single-locus models and 3 two-locus models), and varying recombination fractions between the marker and the trait locus. We analytically estimated the sample sizes required for 80% power at a significance level of.001 and also used simulation methods to estimate power for a sample size of 10 families. We tried to identify statistics whose power was robust over a wide variety of models, with the idea that such statistics would be particularly useful for detection of X-linked loci associated with complex traits. We found that a commonly used statistic, S(all), generally performed well under various conditions and had close to the optimal sample sizes in most cases but that there were certain cases in which it performed quite poorly. Our two new statistics did not perform any better than those already in the literature. We also note that, under dominant and additive models, regardless of the statistic used, pedigrees with all-female siblings have very little power to detect X-linked loci.     

A Likelihood-Based Trait-Model-Free Approach for Linkage Detection of Binary Trait

Summary .  Trait-model-free (or "allele-sharing") approach to linkage analysis is a popular tool in genetic mapping of complex traits, because of the absence of explicit assumptions about the underlying mode of inheritance of the trait. The likelihood framework introduced by Kong and Cox (1997,  American Journal of Human Genetics   61, 1179–1188) allows calculation of accurate p-values and LOD scores to test for linkage between a genomic region and a trait. Their method relies on the specification of a model for the trait-dependent segregation of marker alleles at a genomic region linked to the trait. Here we propose a new such model that is motivated by the desire to extract as much information as possible from extended pedigrees containing data from individuals related over several generations. However, our model is also applicable to smaller pedigrees, and has some attractive features compared with existing models ( Kong and Cox, 1997 ), including the fact that it incorporates information on both affected and unaffected individuals. We illustrate the proposed model on simulated and real data, and compare its performance with the existing approach ( Kong and Cox, 1997 ). The proposed approach is implemented in the program lm_ibdtests within the framework of MORGAN 2.8 ( http://www.stat.washington.edu/thompson/Genepi/MORGAN/Morgan.shtml ).     

Bayesian Linkage Analysis of Categorical Traits for Arbitrary Pedigree Designs

Background

Pedigree studies of complex heritable diseases often feature nominal or ordinal phenotypic measurements and missing genetic marker or phenotype data.

Methodology

We have developed a Bayesian method for Linkage analysis of Ordinal and Categorical traits (LOCate) that can analyze complex genealogical structure for family groups and incorporate missing data. LOCate uses a Gibbs sampling approach to assess linkage, incorporating a simulated tempering algorithm for fast mixing. While our treatment is Bayesian, we develop a LOD (log of odds) score estimator for assessing linkage from Gibbs sampling that is highly accurate for simulated data. LOCate is applicable to linkage analysis for ordinal or nominal traits, a versatility which we demonstrate by analyzing simulated data with a nominal trait, on which LOCate outperforms LOT, an existing method which is designed for ordinal traits. We additionally demonstrate our method''s versatility by analyzing a candidate locus (D2S1788) for panic disorder in humans, in a dataset with a large amount of missing data, which LOT was unable to handle.

Conclusion

LOCate''s accuracy and applicability to both ordinal and nominal traits will prove useful to researchers interested in mapping loci for categorical traits.     

一个DMD家系的Bayes分析和RFLP连锁分析

本文对一个DMD家系中先证者之妹的致病基因携带者风险用3种方法进行估计。单纯根据系谱分析,其风险为50%;以CPK值为条件概率作Bayes分析,其风险为25%;用RFLP连锁分析,推断其风险仅为5%。将RFLP连锁分析的结果作为又一个条件概率进行Bayes分析,其风险估计又进一步准确到不超过2%。三者结合,得到了最佳的结果。     

一种基于E-index方法区分复杂性状的分析工具

目前,生物遗传学领域在区分复杂性状的研究上正面临着巨大挑战,许多方法都被用来应对这项挑战,其中分子标记法,QTL作图法和序列分析法等就是用来区分控制复杂性状基因的主要应对策略。测定生物复杂性状对于研究生物多样性具有重要意义,也是进一步研究基因控制性状作用机理的重要途径,但是,现有的方法并不成熟也不完善,因此给有效区分复杂性状带来了一定难度。近年来,由于生长曲线能够有效地描述复杂性状,基于生长曲线来区分复杂性状的方法是目前常用的方式,Functional Mapping(FM)就是其中具有代表性的一种方法。在过去的十年间,FM方法是复杂性状区分效果最好的,但不能有效处理非单调类型的生长曲线。Earliness index(E-index)方法的问世,解决了非单调类型的曲线不能有效识别的难题,它能够将任意生物类型的复杂性状发展过程描述为生长曲线并加以区分。基于E-index方法的原理,开发了一套Eindex Application(EIA)分析工具,该工具中集成了E-index方法,利用生物数据可视化技术动态绘制生长曲线,包含数据获取、数据处理和结果输出等功能,为遗传工作者的研究提供了良好平台。仿真实验的结果证明了EIA分析工具具有高效、实时和准确的性能,是区分复杂性状的有力工具。     

Bayesian methods for estimating GEBVs of threshold traits

Estimation of genomic breeding values is the key step in genomic selection (GS). Manymethods have been proposed for continuous traits, but methods for threshold traits arestill scarce. Here we introduced threshold model to the framework of GS, and specifically,we extended the three Bayesian methods BayesA, BayesB and BayesCπ on the basis ofthreshold model for estimating genomic breeding values of threshold traits, and theextended methods are correspondingly termed BayesTA, BayesTB and BayesTCπ. Computingprocedures of the three BayesT methods using Markov Chain Monte Carlo algorithm werederived. A simulation study was performed to investigate the benefit of the presentedmethods in accuracy with the genomic estimated breeding values (GEBVs) for thresholdtraits. Factors affecting the performance of the three BayesT methods were addressed. Asexpected, the three BayesT methods generally performed better than the correspondingnormal Bayesian methods, in particular when the number of phenotypic categories was small.In the standard scenario (number of categories=2, incidence=30%,number of quantitative trait loci=50, h²=0.3), theaccuracies were improved by 30.4%, 2.4%, and 5.7% points,respectively. In most scenarios, BayesTB and BayesTCπ generated similar accuracies andboth performed better than BayesTA. In conclusion, our work proved that threshold modelfits well for predicting GEBVs of threshold traits, and BayesTCπ is supposed to be themethod of choice for GS of threshold traits.     

小麦农艺性状与品质特性的多元分析与评价

估算96个小麦品种(系)的11个农艺性状和10个品质特性参数的主成分,并以主成分和欧氏距离为基础,分别作二维排序分析和聚类分析。农艺性状的前4个主成分反映了85.3450%的原始数据信息量;品质特性的前4个主成分代表了89.1483%的原始数据信息量。以96个材料的主成分得分绘制二维排序图,27个小麦品种(系)表现为矮秆、子粒和旗叶较大,丰产性较好、综合农艺性状优良;32个小麦品种(系)表现为铁、锌含量较高,加工品质较好、综合品质特性优良。在系统聚类图中,农艺性状和品质特性分别被聚成5类。综合农艺性状较好的材料主要集中在第Ⅲ类和第Ⅳ类;综合品质特性较好的材料主要集中在第Ⅰ类和第Ⅱ类。综合分析发现,同时兼顾丰产性较好且子粒铁、锌含量较高,品质特性较好的小麦品种(系)有:泰山9818、西农822、轮选719、杨-31、西安837和中育9383。将聚类分析和二维排序分析结合起来,能较好的对小麦的性状组成做出综合评价,鉴定和评价出优质、高产、综合性状优良的小麦品种(系),为小麦遗传育种提供优良的种质资源,为合理选配亲本提供参考。