Mapping quantitative trait loci for traits defined as ratios

Many traits are defined as ratios of two quantitative traits. Methods of QTL mapping for regular quantitative traits are not optimal when applied to ratios due to lack of normality for traits defined as ratios. We develop a new method of QTL mapping for traits defined as ratios. The new method uses a special linear combination of the two component traits, and thus takes advantage of the normal property of the new variable. Simulation study shows that the new method can substantially increase the statistical power of QTL detection relative to the method which treats ratios as regular quantitative traits. The new method also outperforms the method that uses Box-Cox transformed ratio as the phenotype. A real example of QTL mapping for relative growth rate in soybean demonstrates that the new method can detect more QTL than existing methods of QTL mapping for traits defined as ratios.     

林木数量性状基因定位中的若干问题

随着ＤＮＡ分子标记技术的迅速发展,ＱＴＬ定位已成为当前生物学研究领域的前沿。迄今已对许多种动、植物定位了许多重要性状的ＱＴＬ。这些研究促进了遗传学的发展,并将作为育种的新策略应用。与作物相比,林木ＱＴＬ定位有其特性。本文详细讨论了林木的生物学特性对ＱＴＬ定位的影响、ＱＴＬ定位的系谱设计和统计分析方法。     

Joint mapping of quantitative trait Loci for multiple binary characters

Joint mapping for multiple quantitative traits has shed new light on genetic mapping by pinpointing pleiotropic effects and close linkage. Joint mapping also can improve statistical power of QTL detection. However, such a joint mapping procedure has not been available for discrete traits. Most disease resistance traits are measured as one or more discrete characters. These discrete characters are often correlated. Joint mapping for multiple binary disease traits may provide an opportunity to explore pleiotropic effects and increase the statistical power of detecting disease loci. We develop a maximum-likelihood method for mapping multiple binary traits. We postulate a set of multivariate normal disease liabilities, each contributing to the phenotypic variance of one disease trait. The underlying liabilities are linked to the binary phenotypes through some underlying thresholds. The new method actually maps loci for the variation of multivariate normal liabilities. As a result, we are able to take advantage of existing methods of joint mapping for quantitative traits. We treat the multivariate liabilities as missing values so that an expectation-maximization (EM) algorithm can be applied here. We also extend the method to joint mapping for both discrete and continuous traits. Efficiency of the method is demonstrated using simulated data. We also apply the new method to a set of real data and detect several loci responsible for blast resistance in rice.     

IMpRH server: an RH mapping server available on the Web

SUMMARY: The INRA-Minnesota Porcine Radiation Hybrid (IMpRH) Server provides both a mapping tool (IMpRH mapping tool) and a database (IMpRH database) of officially submitted results. The mapping tool permits the mapping of a new marker relatively to markers previously mapped on the IMpRH panel. The IMpRH database is the official database for submission of new results and queries. The database not only permits the sharing of public data but also semi-private and private data.     

A dynamic model for functional mapping of biological rhythms

Functional mapping is a statistical method for mapping quantitative trait loci (QTLs) that regulate the dynamic pattern of a biological trait. This method integrates mathematical aspects of biological complexity into a mixture model for genetic mapping and tests the genetic effects of QTLs by comparing genotype-specific curve parameters. As a way of quantitatively specifying the dynamic behavior of a system, differential equations have proven to be powerful for modeling and unraveling the biochemical, molecular, and cellular mechanisms of a biological process, such as biological rhythms. The equipment of functional mapping with biologically meaningful differential equations provides new insights into the genetic control of any dynamic processes. We formulate a new functional mapping framework for a dynamic biological rhythm by incorporating a group of ordinary differential equations (ODE). The Runge-Kutta fourth order algorithm was implemented to estimate the parameters that define the system of ODE. The new model will find its implications for understanding the interplay between gene interactions and developmental pathways in complex biological rhythms.     

家养动物复杂性状基因定位的统计分析和实验设计

复杂性状基因定位的研究是人类、动植物研究中的1个热点领域。在畜禽的研究中,其目的是定位与生产性状、繁殖性状和疾病相关的基因。在人类中,复杂性状基因定位的研究具有极大的挑战性。尽管基因定位的结果积累得很快,但能得以确认的结果却很少。关于畜禽基因定位的研究结果同样也增长很快,目前在鸡、猪、奶牛等物种中几个大尺度的基因定位工作也正在开展中。虽然在不远的将来能够得到新的、可确信的结果,但是如何精确地理解这些复杂性状的基因仍然需要一定的时间。近来,复杂性状基因定位的方法已被用于通过基因表达的数据研究转录调节因子的定位工作中,这是基因定位研究中1个新的领域。基因定位的统计分析和实验设计是基因定位研究中的关键性步骤,研究的目的在于讨论畜禽复杂性状基因定位的统计分析和实验设计的研究进展及今后的发展。     

Molecular genetic mapping and plant evolutionary biology

With the advent of molecular genetic mapping, it is possible to study the genetic basis of natural heritable variation in new ways. Here, three potential uses of molecular genetic mapping in plant ecology and evolutionary biology are discussed; (1) accurate estimation of genetic parameters, (2) understanding speciation and/or adaptation, and (3) investigating whole genome organization. Basic methods for mapping genes and important mapping strategies are outlined. Recent studies are introduced to illustrate progress so far in applying the new methods in ecological and evolutionary research.     

Statistical tests for admixture mapping with case-control and cases-only data

Admixture mapping is a promising new tool for discovering genes that contribute to complex traits. This mapping approach uses samples from recently admixed populations to detect susceptibility loci at which the risk alleles have different frequencies in the original contributing populations. Although the idea for admixture mapping has been around for more than a decade, the genomic tools are only now becoming available to make this a feasible and attractive option for complex-trait mapping. In this article, we describe new statistical methods for analyzing multipoint data from admixture-mapping studies to detect "ancestry association." The new test statistics do not assume a particular disease model; instead, they are based simply on the extent to which the sample's ancestry proportions at a locus deviate from the genome average. Our power calculations show that, for loci at which the underlying risk-allele frequencies are substantially different in the ancestral populations, the power of admixture mapping can be comparable to that of association mapping but with a far smaller number of markers. We also show that, although "ancestry informative markers" (AIMs) are superior to random single-nucleotide polymorphisms (SNPs), random SNPs can perform quite well when AIMs are not available. Hence, researchers who study admixed populations in which AIMs are not available can perform admixture mapping with the use of modestly higher densities of random markers. Software to perform the gene-mapping calculations, "MALDsoft," is freely available on the Pritchard Lab Web site.     

A dynamic model for functional mapping of biological rhythms

Functional mapping is a statistical method for mapping quantitative trait loci (QTLs) that regulate the dynamic pattern of a biological trait. This method integrates mathematical aspects of biological complexity into a mixture model for genetic mapping and tests the genetic effects of QTLs by comparing genotype-specific curve parameters. As a way of quantitatively specifying the dynamic behaviour of a system, differential equations have proved to be powerful for modelling and unravelling the biochemical, molecular, and cellular mechanisms of a biological process, such as biological rhythms. The equipment of functional mapping with biologically meaningful differential equations provides new insights into the genetic control of any dynamic processes. We formulate a new functional mapping framework for a dynamic biological rhythm by incorporating a group of ordinary differential equations (ODE). The Runge–Kutta fourth-order algorithm was implemented to estimate the parameters that define the system of ODE. The new model will find its implications for understanding the interplay between gene interactions and developmental pathways in complex biological rhythms.     

Microsatellite loci for genetic mapping in the turkey (Meleagris gallopavo)

New microsatellite loci for the turkey (Meleagris gallopavo) were developed from two small insert DNA libraries. Polymorphism at these new loci was examined in domestic birds and two resource populations designed for genetic linkage mapping. The majority of loci (152 of 168) was polymorphic in domestic turkeys and informative in two mapping resource populations and thus will be useful for genetic linkage mapping.     

关联分析及其在植物遗传学研究中的应用

植物的很多重要经济性状均属于复杂性状。基于连锁分析的QTL作图是研究复杂性状的有效手段,但其尚存在一定的局限性。随着现代生物学的发展,一种基于连锁不平衡的新剖分复杂性状方法——关联分析法,开始应用于植物遗传学研究。与QTL作图法相比,应用关联分析法具有不需要构建特殊的群体,可同时对多个等位基因进行分析,定位QTL精度可达到单基因水平等优势。该文介绍了关联分析方法学的基础和特性,简述了其在植物遗传学研究中的进展情况,并对其未来发展和在植物遗传学研究中的应用进行了展望。     

A nonlinear mixed-effect mixture model for functional mapping of dynamic traits

Functional mapping has emerged as a next-generation statistical tool for mapping quantitative trait loci (QTL) that affect complex dynamic traits. In this article, we incorporated the idea of nonlinear mixed-effect (NLME) models into the mixture-based framework of functional mapping, aimed to generalize the spectrum of applications for functional mapping. NLME-based functional mapping, implemented with the linearization algorithm based on the first-order Taylor expansion, can provide reasonable estimates of QTL genotypic-specific curve parameters (fixed effect) and the between-individual variation of these parameters (random effect). Results from simulation studies suggest that the NLME-based model is more general than traditional functional mapping. The new model can be useful for the identification of the ontogenetic patterns of QTL genetic effects during time course.     

Joint linkage and linkage disequilibrium mapping of quantitative trait loci in natural populations

Linkage analysis and allelic association (also referred to as linkage disequilibrium) studies are two major approaches for mapping genes that control simple or complex traits in plants, animals, and humans. But these two approaches have limited utility when used alone, because they use only part of the information that is available for a mapping population. More recently, a new mapping strategy has been designed to integrate the advantages of linkage analysis and linkage disequilibrium analysis for genome mapping in outcrossing populations. The new strategy makes use of a random sample from a panmictic population and the open-pollinated progeny of the sample. In this article, we extend the new strategy to map quantitative trait loci (QTL), using molecular markers within the EM-implemented maximum-likelihood framework. The most significant advantage of this extension is that both linkage and linkage disequilibrium between a marker and QTL can be estimated simultaneously, thus increasing the efficiency and effectiveness of genome mapping for recalcitrant outcrossing species. Simulation studies are performed to test the statistical properties of the MLEs of genetic and genomic parameters including QTL allele frequency, QTL effects, QTL position, and the linkage disequilibrium of the QTL and a marker. The potential utility of our mapping strategy is discussed.     

普通菜豆基因组学及抗炭疽病遗传研究进展

普通菜豆是重要的食用豆类之一,在世界各大洲普遍种植。近年来,普通菜豆在遗传图谱构建、新标记开发与利用、抗性基因定位以及比较基因组学等方面取得了很大进展。遗传连锁图谱的构建是基因定位与克隆的基础,是遗传研究中的重要内容;利用分子连锁图谱鉴定、标记和定位抗病基因将在种质改良和分子标记辅助育种方面发挥重要作用。豆科植物比较基因组学的研究成果为菜豆遗传连锁图谱的发展提供了新的思路。本文从普通菜豆遗传连锁图谱的获得、普通菜豆与大豆同线性比较以及抗炭疽病基因定位等方面进行了综述,以期为普通菜豆遗传改良和抗病育种提供参考。 关键词：普通菜豆;遗传连锁图;同线性比较;抗菜豆炭疽病     

Isolation and high-resolution mapping of new DNA markers from the pericentromeric region of chromosome 10.

The gene responsible for multiple endocrine neoplasia type 2A (MEN 2A) has been localized to the pericentromeric region of chromosome 10. Several markers that fail to recombine with MEN2A have been identified, including D10Z1, D10S94, D10S97, and D10S102. Meiotic mapping in the MEN2A region is limited by the paucity of critical crossovers identified and by the dramatically reduced rates of recombination in males. Additional approaches to mapping loci in the pericentromeric region of chromosome 10 are required. We have undertaken the generation of a detailed physical map by radiation hybrid mapping. Here we report the development of a radiation hybrid panel and its use in the mapping of new DNA markers in pericentromeric chromosome 10. The radiation-reduced hybrids used for mapping studies all retain small subchromosomal fragments that include both D10S94 and D10Z1. One hybrid was selected as the source of DNA for cloning. One hundred five human recombinant clones were isolated from a lambda library made with pp11A DNA. We have completed regional mapping of 22 of those clones using our radiation hybrid mapping panel. Seven markers have been identified and, when taken together with previously meiotically mapped markers, define eight radiation hybrid map intervals between D10S34 and RBP3. The identical order is found for a number of these using either the radiation hybrid mapping panel or the meiotic mapping panel. We believe that this combination cloning and mapping approach will facilitate the precise positioning of new markers in pericentromeric chromosome 10 and will help in refining further the localization of MEN2A.     

Dimension reduction for mapping mRNA abundance as quantitative traits

The advent of sophisticated genomic techniques for gene mapping and microarray analysis has provided opportunities to map mRNA abundance to quantitative trait loci (QTL) throughout the genome. Unfortunately, simple mapping of each individual mRNA trait on the scale of a typical microarray experiment is computationally intensive, subject to high sample variance, and therefore underpowered. However, this problem can be addressed by capitalizing on correlation among the large number of mRNA traits. We present a method to reduce the dimensionality for mapping gene expression data as quantitative traits. We used a blind method, principal components, and a sighted method, hierarchical clustering seeded by disease relevant traits, to define new traits composed of a small collection of promising mRNAs. We validated the principle of our approach by mapping the expression levels of metabolism genes in a population of F(2)-ob/ob mice derived from the BTBR and C57BL/6J strains. We found that lipogenic and gluconeogenic mRNAs, which are known targets of insulin action, were closely associated with the insulin trait. Multiple interval mapping and Bayesian interval mapping of this new trait revealed significant linkages to chromosome regions that were contained in loci associated with type 2 diabetes in this same mouse sample. As a further statistical refinement, we show that principal component analysis also effectively reduced dimensions for mapping phenotypes composed of mRNA abundances.     

Statistical methods for QTL mapping in cereals

This paper gives an overview of the statistical theory suitable for mapping quantitative trait loci in experimental populations derived from inbred parents, with a particular emphasis on methodology for cereal crops. The basic theory is described, and some new areas of statistical research appropriate for mapping in cereal crops are discussed.     

Fine-scale mapping of disease susceptibility locus with Bayesian partition model

The causal relationship between genes and diseases has been investigated with the development of DNA sequence. Polymorphisms incorporated in the HapMap Project have enabled fine mapping with linkage disequilibrium (LD) and prior clustering of the haplotypes on the basis of a similarity measure has often been performed in an attempt to capture coalescent events because they can reduce the amount of computation. However an inappropriate choice of similarity measure can lead to wrong conclusions and we propose a new haplotype-based clustering algorithm for fine-scale mapping by using a Bayesian partition model. To handle phase-unknown genotypes, we propose a new algorithm based on a Metropolized Gibbs sampler and it is implemented in C++. Our simulation studies found that the proposed method improves the accuracy of the estimator for the disease susceptibility locus. We illustrated the practical implication of the new analysis method by an application to fine-scale mapping of CYP2D6 in drug metabolism.     

A semiparametric approach for composite functional mapping of dynamic quantitative traits

Functional mapping has emerged as a powerful tool for mapping quantitative trait loci (QTL) that control developmental patterns of complex dynamic traits. Original functional mapping has been constructed within the context of simple interval mapping, without consideration of separate multiple linked QTL for a dynamic trait. In this article, we present a statistical framework for mapping QTL that affect dynamic traits by capitalizing on the strengths of functional mapping and composite interval mapping. Within this so-called composite functional-mapping framework, functional mapping models the time-dependent genetic effects of a QTL tested within a marker interval using a biologically meaningful parametric function, whereas composite interval mapping models the time-dependent genetic effects of the markers outside the test interval to control the genome background using a flexible nonparametric approach based on Legendre polynomials. Such a semiparametric framework was formulated by a maximum-likelihood model and implemented with the EM algorithm, allowing for the estimation and the test of the mathematical parameters that define the QTL effects and the regression coefficients of the Legendre polynomials that describe the marker effects. Simulation studies were performed to investigate the statistical behavior of composite functional mapping and compare its advantage in separating multiple linked QTL as compared to functional mapping. We used the new mapping approach to analyze a genetic mapping example in rice, leading to the identification of multiple QTL, some of which are linked on the same chromosome, that control the developmental trajectory of leaf age.