Quantitative Trait Loci for Murine Growth

Body size is an archetypal quantitative trait with variation due to the segregation of many gene loci, each of relatively minor effect, and the environment. We examine the effects of quantitative trait loci (QTLs) on age-specific body weights and growth in the F(2) intercross of the LG/J and SM/J strains of inbred mice. Weekly weights (1-10 wk) and 75 microsatellite genotypes were obtained for 535 mice. Interval mapping was used to locate and measure the genotypic effects of QTLs on body weight and growth. QTL effects were detected on 16 of the 19 autosomes with several chromosomes carrying more than one QTL. The number of QTLs for age-specific weights varied from seven at 1 week to 17 at 10 wk. The QTLs were each of relatively minor, subequal effect. QTLs affecting early and late growth were generally distinct, mapping to different chromosomal locations indicating separate genetic and physiological systems for early and later murine growth.     

Quantitative Trait Loci and Candidate Genes for Neutrophil Recruitment in Sterile Inflammation Mapped in AXB-BXA Recombinant Inbred Mice

Neutrophil recruitment (NR) to sites of sterile inflammation plays a key role in tissue damage and healing potential of lesions characteristic to non-infectious inflammatory diseases. Previous studies suggested significant genetic control of neutrophil survival, function, and migration in inflammatory responses to endogenous and exogenous stimuli. We have mapped the murine genome for quantitative trait loci (QTLs) harbouring genetic determinants that regulate NR in SI using a murine model of chemically-induced peritonitis. NR was quantified in 16 AXB-BXA recombinant inbred strains and their progenitors, A/J (A) and C57BL/6J (B). A continuous distribution of NR was found among the strains, with parent B showing higher NR and parent A showing lower NR (3.0-fold difference, p=0.05). Within the progeny strains, a 5.5-fold difference in NR was observed between the lowest, BXA1, and the highest responders AXB19 (p<0.001). This data was analyzed using GeneNetwork, which linked NR to one significant QTL on chromosome 12 (Peritoneal Neutrophil Recruitment 1, PNR1) and two suggestive QTLs (PNR2, PNR3) on chromosomes 12 and 16 respectively. Sixty-four candidate genes within PNR1 were cross-referenced with currently published data, mRNA expression from two NR microarrays, and single nucleotide polymorphism analysis. The present study brings new light into the genetics of NR in response to cell injury and highlights potential candidate genes Hif1α, Fntb, and Prkch and their products for further studies on neutrophil infiltration and inflammation resolution in sterile inflammation.     

水稻QTL定位研究进展

水稻的许多重要农艺性状均属于数量性状,研究水稻数量性状遗传对水稻育种具有十分重要的意义.近年来大量的研究揭示了水稻QTL的基本特征,剖析了重要农艺性状的遗传基础,给水稻遗传改良带来了新策略,不断深入的研究已经完成了水稻特定数量基因的精细定位和克隆,到目前为止已经有一万多个水稻QTL进行了定位,其中有19个进行了克隆,这对水稻育种具有十分重要的意义.本文主要综述了QTL定位的理论基础,水稻QTL定位的研究进展,并对水稻QTL研究的趋势进行了展望.     

构建数量性状基因图谱的几种统计方法

以线性数学模型为线索,概述了用于构建数量性状基因图谱的几种主要统计方法,包括方差分析法、标记回归法、区间作图法、复合区间作图法、Jansen的复合区间作图法、双侧标记回归法以及新近发展的多区间作图法和多亲本作图法等.讨论了各种方法的优缺点. Abstract:Statistical methods for mapping QTLs were summarized, including one marker analysis, arker regression analysis,interval mapping (IM),composite interval mapping (CIM),Jansen's composite interval mapping, flanking marker regression analysis,multiple interval mapping (MIM) and multiple families mapping.Their advantages and disadvantages were discussed.     

Mapping Environment-Specific Quantitative Trait Loci

Environment-specific quantitative trait loci (QTL) refer to QTL that express differently in different environments, a phenomenon called QTL-by-environment (Q × E) interaction. Q × E interaction is a difficult problem extended from traditional QTL mapping. The mixture model maximum-likelihood method is commonly adopted for interval mapping of QTL, but the method is not optimal in handling QTL interacting with environments. We partitioned QTL effects into main and interaction effects. The main effects are represented by the means of QTL effects in all environments and the interaction effects are represented by the variances of the QTL effects across environments. We used the Markov chain Monte Carlo (MCMC) implemented Bayesian method to estimate both the main and the interaction effects. The residual error covariance matrix was modeled using the factor analytic covariance structure. A simulation study showed that the factor analytic structure is robust and can handle other structures as special cases. The method was also applied to Q × E interaction mapping for the yield trait of barley. Eight markers showed significant main effects and 18 markers showed significant Q × E interaction. The 18 interacting markers were distributed across all seven chromosomes of the entire genome. Only 1 marker had both the main and the Q × E interaction effects. Each of the other markers had either a main effect or a Q × E interaction effect but not both.GENOTYPE-BY-ENVIRONMENT (G × E) interaction is a very important phenomenon in quantitative genetics. With the advanced molecular technology and statistical methods for quantitative trait loci (QTL) mapping (Lander and Botstein 1989; Jansen 1993; Zeng 1994), G × E interaction analysis has shifted to QTL-by-environment (Q × E) interaction. In the early stage of QTL mapping, almost all statistical methods were developed in a single environment (Paterson et al. 1991; Stuber et al. 1992). Data from different environments were analyzed separately and the conclusions were drawn from the separate analyses of QTL across environments. These methods do not consider the correlation of data under different environments and thus may not extract maximum information from the data. Composite interval mapping for multiple traits can be used for Q × E interaction if different traits are treated as the same trait measured in different environments (Jiang and Zeng 1995). This multivariate composite interval mapping approach makes good use of all data simultaneously and increases statistical power of QTL detection and accuracy of the estimated QTL positions. However, the number of parameters of this method increases dramatically as the number of environments increases. Therefore, the method may not be applied when the number of environments is large. Several other models have been proposed to solve the problem of a large number of environments (Jansen et al. 1995; Beavis and Keim 1996; Romagosa et al. 1996). These methods were based on some special situations and assumptions. One typical assumption was independent errors or constant variances across environments. These assumptions are often violated in real QTL mapping experiments.Earlier investigators realized the problem and adopted the mixed-model methodology to solve the problem (Piepho 2000; Boer et al. 2007). Under the mixed-model framework, people can choose which model effects are random and which are fixed. The mixed-model methodology is very flexible, leading to an easy way to model genetic and environmental correlation between environments using a suitable error structure. Piepho (2000) proposed a mixed model to detect QTL main effect across environments. Similar to the composite interval mapping analysis, his model incorporated one putative QTL and a few cofactors. The Q × E effects in the model were assumed to be random, which greatly reduced the number of estimated parameters. However, the fact that only one QTL is included in the model means that Piepho''s (2000) model remains a single-QTL model rather than a multivariate model. Boer et al. (2007) proposed a step-by-step mixed-model approach to detecting QTL main effects, Q × E interaction effects, and QTL responses to specific environmental covariates. In the final step, Boer et al. (2007) rewrote the model to include all QTL in a multiple-QTL model and reestimated their effects.In this study, we extended the Bayesian shrinkage method (Xu 2003) to map Q × E interaction effects of QTL. In the original study (Xu 2003), we treated each marker as a putative QTL and used the shrinkage method to simultaneously estimate marker effects of the entire genome. In the multiple-environment case, we can still use this approach to simultaneously evaluate marker effects under multiple environments but we can further partition the marker effects into main and Q × E interaction effects. For any particular marker, the mean of the marker effects represents the main effect and the variance of the marker effects represents the Q × E interaction effect for that marker. Under the Bayesian framework, we assigned a normal prior with zero mean and an unknown variance to each marker main effect and a multivariate normal prior with zero vector mean and homogeneous diagonal variance–covariance matrix to the Q × E interaction effects of each maker. In multiple environments, the structure of the error terms might be very complicated since we need to consider the correlation of the same genotype under different environments. In our analysis, we used different variance–covariance structures to model the error terms. The simplest case was the homogeneous diagonal matrix, and the most complex choice was an unstructured matrix. We also used a heterogeneous diagonal matrix whose parameters are somewhere between the two models. Finally, we considered several factor analytic models. The reason to use the factor analytic structure is that it can separate genetic effects into common effects and environment-specific effects. In addition, the factor analytic structure is parsimonious and thus can substantially reduce the computational burden of the mixed-model analyses.     

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。     

Multiple Trait Analysis of Genetic Mapping for Quantitative Trait Loci

We present in this paper models and statistical methods for performing multiple trait analysis on mapping quantitative trait loci (QTL) based on the composite interval mapping method. By taking into account the correlated structure of multiple traits, this joint analysis has several advantages, compared with separate analyses, for mapping QTL, including the expected improvement on the statistical power of the test for QTL and on the precision of parameter estimation. Also this joint analysis provides formal procedures to test a number of biologically interesting hypotheses concerning the nature of genetic correlations between different traits. Among the testing procedures considered are those for joint mapping, pleiotropy, QTL by environment interaction, and pleiotropy vs. close linkage. The test of pleiotropy (one pleiotropic QTL at a genome position) vs. close linkage (multiple nearby nonpleiotropic QTL) can have important implications for our understanding of the nature of genetic correlations between different traits in certain regions of a genome and also for practical applications in animal and plant breeding because one of the major goals in breeding is to break unfavorable linkage. Results of extensive simulation studies are presented to illustrate various properties of the analyses.     

作物数量性状基因研究进展

分子生物技术的发展对作物数量性状基因(QTL)研究提供了条件,不同的定位群体各有其特点,相继出现的QTL定位方法也逐步完善.大量的研究揭示了QTL的基本特征,剖析了重要农艺性状的遗传基础,给作物遗传改良带来了新的策略,不断深入的研究已经完成了特定QTL的精细定位和克隆.本文从QTL的定位群体,定位方法,研究现状,精细定位与克隆,以及QTL利用等方面对作物数量性状基因的研究进行了综述。 Abstract:With the rapid development of molecular biotechnology,QTL analyses were executed for a lot of important agronomic traits in many crops.Different experimental populations and mapping methods had their own advantages in QTL analysis.Amounts of studies paid attention to locate the QTLs for important traits,and others tried to disect the genetic bases using molecular markers.Near isogenic lines were the best populations for QTL fine mapping and positional cloning,A few studies had been reported their results on materials with improvement traits using marker-assisted selection.This paper summarizes the recent progress on QTL mapping populations and methods,the status of QTL locating,QTL fine mapping and positional cloning,and QTL.application in breeding.     

作物数量性状基因研究进展

分子生物技术的发展对作物数量性状基因（QTL）研究提供了条件,不同的定位群体各有其特点,相继出现的QTL定位也逐步完善。大量的研究揭示了QTL的基本特征,剖析了重要农艺4性状的遗传基础,给作物遗传改良带来了新的策略,不断深入的研究已经完成了特定的QTL的精细定位和克隆。本从QTL的定位群体,定位方法,研究现状,精细定位与克隆,以及QTL利用等方面对作物数量性状基因的研究进行了综述。     

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。     

Identification of Quantitative Trait Loci Controlling Levels of Radiation-Induced Thymocyte Apoptosis in Mice

Thymocyte apoptosis levels are higher in C57BL/6J mice than in C3Hf/Kam mice. Low-dose irradiation increases the numbers of thymocytes undergoing apoptosis, but the strain difference persists. We mapped three loci controlling radiation-induced thymocyte apoptosis levels in F2 intercross progeny of these strains. The strongest association of a genomic region with an apoptosis level occurred in a region of chromosome 11 known to harbor a locus (or loci) important in the pathogenesis of several rodent models of autoimmune disease. Additional loci influencing radiation-induced thymocyte apoptosis were identified on chromosomes 9 and 16. The genetic polymorphisms underlying these loci may have an evolutionary role in fine-tuning the apoptotic response in T cells and may be important in the etiology of lymphoproliferative disorders and autoimmunity.     

A Nonparametric Approach for Mapping Quantitative Trait Loci

Genetic mapping of quantitative trait loci (QTLs) is performed typically by using a parametric approach, based on the assumption that the phenotype follows a normal distribution. Many traits of interest, however, are not normally distributed. In this paper, we present a nonparametric approach to QTL mapping applicable to any phenotypic distribution. The method is based on a statistic Z(w), which generalizes the nonparametric Wilcoxon rank-sum test to the situation of whole-genome search by interval mapping. We determine the appropriate significance level for the statistic Z(w), by showing that its asymptotic null distribution follows an Ornstein-Uhlenbeck process. These results provide a robust, distribution-free method for mapping QTLs.     

Bayes factors for detection of Quantitative Trait Loci

A fundamental issue in quantitative trait locus (QTL) mapping is to determine the plausibility of the presence of a QTL at a given genome location. Bayesian analysis offers an attractive way of testing alternative models (here, QTL vs. no-QTL) via the Bayes factor. There have been several numerical approaches to computing the Bayes factor, mostly based on Markov Chain Monte Carlo (MCMC), but these strategies are subject to numerical or stability problems. We propose a simple and stable approach to calculating the Bayes factor between nested models. The procedure is based on a reparameterization of a variance component model in terms of intra-class correlation. The Bayes factor can then be easily calculated from the output of a MCMC scheme by averaging conditional densities at the null intra-class correlation. We studied the performance of the method using simulation. We applied this approach to QTL analysis in an outbred population. We also compared it with the Likelihood Ratio Test and we analyzed its stability. Simulation results were very similar to the simulated parameters. The posterior probability of the QTL model increases as the QTL effect does. The location of the QTL was also correctly obtained. The use of meta-analysis is suggested from the properties of the Bayes factor.     

Marker-Assisted Introgression of Quantitative Trait Loci

The use of molecular markers for the introgression of one or several superior QTL alleles into a recipient line is investigated using analytic and simulation results. The positions of the markers devoted to the control of the genotype at the QTLs in a ``foreground selection' step are optimized given the confidence interval of the QTL position. Results demonstrate that using at least three markers per QTL allows a good control over several generations. Population sizes that should be recommended for various numbers of QTLs are calculated and are used to determine the limit in the number of QTLs that can be monitored simultaneously. If ``background selection' devoted to accelerate the return to the recipient parent genotype outside the QTL regions is applied, the positions of the markers devoted to the control of the QTLs have to be reconsidered. When several QTLs are monitored simultaneously, background selection among the limited number of individuals resulting from the foreground selection step accelerates the increase in genomic similarity with the recipient parent, with only limited costs. Background selection is even more efficient in a pyramidal backcross program where QTLs are first monitored one by one.