The generalized multiplicative model for viability selection at multiple loci

Selection due to differential viability is studied in an n-locus two-allele model using a set indexation that allows the simplicity of the one-locus two-allele model to be carried to multi-locus models. The existence condition is analyzed for polymorphic equilibria with linkage equilibrium: Robbins' equilibria. The local stability condition is given for the Robbins' equilibria on the boundaries in the generalized non-epistatic selection regimes of Karlin and Liberman (1979). These generalized non-epistatic regimes include the additive selection model, the multiplicative selection model and the multiplicative interaction model, and their symmetric versions cover all the symmetric viability models.Research supported by grant no. 11-7805 from the Danish Natural Science Research Council, by NIH grant GM 28016, by a fellowship from the Research Foundation of Aarhus University, and by a visiting fellowship from the University of New England, N.S.W.     

On the Use of Bayesian Methods for Evaluating Compartmental Neural Models

Computational modeling is being used increasingly in neuroscience. In deriving such models, inference issues such as model selection, model complexity, and model comparison must be addressed constantly. In this article we present briefly the Bayesian approach to inference. Under a simple set of commonsense axioms, there exists essentially a unique way of reasoning under uncertainty by assigning a degree of confidence to any hypothesis or model, given the available data and prior information. Such degrees of confidence must obey all the rules governing probabilities and can be updated accordingly as more data becomes available. While the Bayesian methodology can be applied to any type of model, as an example we outline its use for an important, and increasingly standard, class of models in computational neuroscience—compartmental models of single neurons. Inference issues are particularly relevant for these models: their parameter spaces are typically very large, neurophysiological and neuroanatomical data are still sparse, and probabilistic aspects are often ignored. As a tutorial, we demonstrate the Bayesian approach on a class of one-compartment models with varying numbers of conductances. We then apply Bayesian methods on a compartmental model of a real neuron to determine the optimal amount of noise to add to the model to give it a level of spike time variability comparable to that found in the real cell.     

Globin gene transfer for the treatment of severe hemoglobinopathies: a paradigm for stem cell-based gene therapy

The prospect of treating blood disorders with genetically modified stem cells is highly promising. This therapeutic approach, however, raises a number of fundamental biological questions, spanning several research fields. Further investigation is required to better understand how to isolate and efficiently transduce hematopoietic stem cells (HSCs), while preserving optimal homing and self-renewing properties; how to design safe vectors permitting controlled expression of the transgene products; and how to promote host repopulation by engrafted HSCs. This article addresses basic issues in stem cell-based gene therapy from the perspective of regulating transgene expression, taking globin gene transfer for the treatment of severe hemoglobinopathies as a paradigm.     

THE POPULATION GENETICS OF ADAPTATION: THE DISTRIBUTION OF FACTORS FIXED DURING ADAPTIVE EVOLUTION

We know very little about the genetic basis of adaptation. Indeed, we can make no theoretical predictions, however heuristic, about the distribution of phenotypic effects among factors fixed during adaptation nor about the expected “size” of the largest factor fixed. Study of this problem requires taking into account that populations gradually approach a phenotypic optimum during adaptation via the stepwise substitution of favorable mutations. Using Fisher's geometric model of adaptation, I analyze this approach to the optimum, and derive an approximate solution to the size distribution of factors fixed during adaptation. I further generalize these results to allow the input of any distribution of mutational effects. The distribution of factors fixed during adaptation assumes a pleasingly simple, exponential form. This result is remarkably insensitive to changes in the fitness function and in the distribution of mutational effects. An exponential trend among factors fixed appears to be a general property of adaptation toward a fixed optimum.     

多QTL定位的压缩估计方法

本文综述了多标记分析和多QTL定位的压缩估计方法。对于前者,Xu（Genetics,2003,163：789—801）首先提出了Bayesian压缩估计方法。其关键在于让每个效应有一个特定的方差参数,而该方差又服从一定的先验分布,以致能从资料中估计之。由此,能够同时估计大量分子标记基因座的遗传效应,即使大多数标记的效应是可忽略的。然而,对于上位性遗传模型,其运算时间还是过长。为此,笔者将上述思想嵌入极大似然法,提出了惩罚最大似然方法。模拟研究显示：该方法能处理变量个数大于样本容量10倍左右的线性遗传模型。对于后者,本文详细介绍了基于固定区间和可变区间的Bayesian压缩估计方法。固定区间方法可处理中等密度的分子标记资料;可变区间方法则可分析高密度分子标记资料,甚至是上位性遗传模型。对于上位性检测,已介绍的惩罚最大似然方法和可变区间Bayesian压缩估计方法可供利用。应当指出,压缩估计方法在今后的eQTL和QTN定位以及基因互作网络分析等研究中也是有应用价值的。     

Shrinkage Estimation Method for Mapping Multiple Quantitative Trait Loci

In this article, shrinkage estimation method for multiple-marker analysis and for mapping multiple quantitative trait loci (QTL) was reviewed. For multiple-marker analysis, Xu (Genetics, 2003, 163:789-801) developed a Bayesian shrinkage estimation (BSE) method. The key to the success of this method is to allow each marker effect have its own variance parameter, which in turn has its own prior distribution so that the variance can be estimated from the data. Under this hierarchical model, a large number of markers can be handled although most of them may have negligible effects. Under epistatic genetic model, however, the running time is very long. To overcome this problem, a novel method of incorporating the idea described above into maximum likelihood, known as penalized likelihood method, was proposed. A simulated study showed that this method can handle a model with multiple effects, which are ten times larger than the sample size. For multiple QTL analysis, two modified versions for the BSE method were introduced: one is the fixed-interval method and another is the variable-interval method. The former deals with markers with intermediate density, and the latter can handle markers with extremely high density as well as model with epistatic effects. For the detection of epistatic effects, penalized likelihood method and the variable-interval approach of the BSE method are available.     

Recalculation of 23 mouse HDL QTL datasets improves accuracy and allows for better candidate gene analysis

In the past 15 years, the quantitative trait locus (QTL) mapping approach has been applied to crosses between different inbred mouse strains to identify genetic loci associated with plasma HDL cholesterol levels. Although successful, a disadvantage of this method is low mapping resolution, as often several hundred candidate genes fall within the confidence interval for each locus. Methods have been developed to narrow these loci by combining the data from the different crosses, but they rely on the accurate mapping of the QTL and the treatment of the data in a consistent manner. We collected 23 raw datasets used for the mapping of previously published HDL QTL and reanalyzed the data from each cross using a consistent method and the latest mouse genetic map. By utilizing this approach, we identified novel QTL and QTL that were mapped to the wrong part of chromosomes. Our new HDL QTL map allows for reliable combining of QTL data and candidate gene analysis, which we demonstrate by identifying Grin3a and Etv6, as candidate genes for QTL on chromosomes 4 and 6, respectively. In addition, we were able to narrow a QTL on Chr 19 to five candidates.     

The candidate gene approach in plant genetics: a review

The candidate gene (CG) approach has been applied in plant genetics in the past decade for the characterisation and cloning of Mendelian and quantitative trait loci (QTLs). It constitutes a complementary strategy to map-based cloning and insertional mutagenesis. The goal of this paper is to present an overview of CG analyses in plant genetics. CG analysis is based on the hypothesis that known-function genes (the candidate genes) could correspond to loci controlling traits of interest. CGs refer either to cloned genes presumed to affect a given trait (`functional CGs') or to genes suggested by their close proximity on linkage maps to loci controlling the trait (`positional CGs'). In plant genetics, the most common way to identify a CG is to look for map co-segregation between CGs and loci affecting the trait. Statistical association analyses between molecular polymorphisms of the CG and variation in the trait of interest have also been carried out in a few studies. The final validation of a CG will be provided through physiological analyses, genetic transformation and/or sexual complementation. Theoretical and practical applications of validated CGs in plant genetics and breeding are discussed.     

玉米株高和穗位高遗传基础的QTL剖析

利用玉米强优势组合(Mo17×黄早四)自交衍生的191个F2单株构建了由SSR和AFLP标记组成的分子连锁图谱.F2进一步自交产生的184个F2:3家系用于调查株高和穗位高的表型值.采用基于混合线性模型的复合区间作图法和相应的作图软件QTLmapper/V2.0,分别定位了7个株高和6个穗位高QTL;检测到18对控制株高和13对控制穗位高的上位性效应位点;同时发现了与环境存在显著互作的6个株高和8个穗位高单位点标记区域以及4对株高和4对穗位高上位性效应区域.分析了各种遗传因素在株高和穗位高遗传基础中的相对作用大小,指出了加性、显性和上位性是玉米株高和穗位高的重要遗传基础.并对所定位的QTL的真实性、株高和穗位高的关系以及研究结果对分子育种的启示予以讨论.     

Effects of vicariant barriers, habitat stability, population isolation and environmental features on species divergence in the south-western Australian coastal reptile community

Identifying explicit hypotheses regarding the factors determining genetic structuring within species can be difficult, especially in species distributed in historically dynamic regions. To contend with these challenges, we use a framework that combines species distribution models, environmental data and multi-locus genetic data to generate and explore phylogeographic hypotheses for reptile species occupying the coastal sand-dune and sand-plain habitats of the south-western Australian biodiversity hotspot, a community which has both a high diversity of endemics and has varied dramatically in spatial extent over time. We use hierarchical amova, summary statistic and distance-based analyses to explicitly test specific phylogeographic hypotheses. Namely, we test if biogeographic vicariance across barriers, habitat stability, population isolation along a linear habitat or fragmentation across different environments can explain genetic divergence within five co-distributed squamate reptile species. Our results show that patterns of genetic variation reflect complex and species-specific interactions related to the spatial distribution of habitats present currently and during repeated glacial minima, as opposed to being associated with historical factors such as habitat stability between glacial and inter-glacial periods or vicariant barriers. We suggest that the large impact of habitat characteristics over time (i.e. relative levels of habitat connectivity, climatic gradients and spatial heterogeneity of soil types) reflects the ecological restrictions of the sand-dune and sand-plain reptile communities and may explain the lack of concordance across taxa. The study demonstrates the general utility of the approach for assemblage-level, as well as single species, phylogeographic study, including its usefulness for exploring biologically informed hypotheses about what factors have influenced patterns of genetic variation.     

A new approach for mapping quantitative trait loci using complete genetic marker linkage maps

A new approach based on nonlinear regression for the mapping of quantitative trait loci (QTLs) using complete genetic marker linkage maps is advanced in this paper. We call the approach joint mapping as it makes comprehensive use of the information from every marker locus on a chromosome. With this approach, both the detection of the existence of QTLs and the estimation of their positions, with corresponding confidence intervals, and effects can be realized simultaneously. This approach is widely applicable because only moments are used. It is simple and can save considerable computer time. It is especially useful when there are multiple QTLs and/or interactions between them on a chromosome.     

Restriction fragment alleles of the rabbit IGHG genes with reference to the rabbit IGHGCH2 or e locus polymorphism

Among domesticated mammals, rabbit (Oryctolagus cuniculus) is the only species possessing not more than one subclass of immunoglobulin (IgG) antibodies. The rabbit IGHGCH2 or e locus presents two serologically defined alleles, the e14 and e15 allotypes, which are correlated with amino acid variation at the IgG CH2-CH3 interface. Genetic studies, while revealing the adaptive value of this polymorphism, have relied so far entirely upon allo-antisera. Here we show how these alleles can be distinguished by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methods. The proposed PCR-RFLP approach allows the monitoring of IGHG locus diversity in rabbit.     

Nonparametric and semiparametric group sequential methods for comparing accuracy of diagnostic tests

SUMMARY: Comparison of the accuracy of two diagnostic tests using the receiver operating characteristic (ROC) curves from two diagnostic tests has been typically conducted using fixed sample designs. On the other hand, the human experimentation inherent in a comparison of diagnostic modalities argues for periodic monitoring of the accruing data to address many issues related to the ethics and efficiency of the medical study. To date, very little research has been done on the use of sequential sampling plans for comparative ROC studies, even when these studies may use expensive and unsafe diagnostic procedures. In this article we propose a nonparametric group sequential design plan. The nonparametric sequential method adapts a nonparametric family of weighted area under the ROC curve statistics (Wieand et al., 1989, Biometrika 76, 585-592) and a group sequential sampling plan. We illustrate the implementation of this nonparametric approach for sequentially comparing ROC curves in the context of diagnostic screening for nonsmall-cell lung cancer. We also describe a semiparametric sequential method based on proportional hazard models. We compare the statistical properties of the nonparametric approach with alternative semiparametric and parametric analyses in simulation studies. The results show the nonparametric approach is robust to model misspecification and has excellent finite-sample performance.     

Localization of multiple pleiotropic genes for lipoprotein metabolism in baboons

We employed a novel approach to identify the key loci that harbor genes influencing lipoprotein metabolism in approximately 2,000 pedigreed baboons fed various diets differing in levels of fat and cholesterol. In this study, 126 overlapping traits related to both LDL and HDL metabolism were normalized and subjected to genome-wide linkage screening. As was expected, the traits were highly, but not completely, correlated. We exploited the information in these correlated traits by focusing on those genomic regions harboring quantitative trait loci (QTL) for multiple traits, reasoning that the more influential genes would impact a larger number of traits. This study identified five major QTL clusters (each with at least two significant logarithm of the odds scores >4.7), two of which had not been previously reported in baboons. One of these mapped to the baboon ortholog of human chromosome 1p32-p34 and influenced concentrations of LDL-cholesterol on Basal and high-fat, low-cholesterol diets. The other novel QTL cluster mapped to the baboon ortholog of human chromosome 12q13.13-q14.1 and influenced LDL size properties on high-fat, low-cholesterol and high-fat, high-cholesterol, but not Basal, diets. Confirming the value of this approach, three of the QTL clusters replicated published linkage findings for the same or similar traits.     

Multimodel inference in ecology and evolution: challenges and solutions

Information theoretic approaches and model averaging are increasing in popularity, but this approach can be difficult to apply to the realistic, complex models that typify many ecological and evolutionary analyses. This is especially true for those researchers without a formal background in information theory. Here, we highlight a number of practical obstacles to model averaging complex models. Although not meant to be an exhaustive review, we identify several important issues with tentative solutions where they exist (e.g. dealing with collinearity amongst predictors; how to compute model-averaged parameters) and highlight areas for future research where solutions are not clear (e.g. when to use random intercepts or slopes; which information criteria to use when random factors are involved). We also provide a worked example of a mixed model analysis of inbreeding depression in a wild population. By providing an overview of these issues, we hope that this approach will become more accessible to those investigating any process where multiple variables impact an evolutionary or ecological response.     

Correlation and process in species distribution models: bridging a dichotomy

Within the field of species distribution modelling an apparent dichotomy exists between process‐based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlative–process spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the process–correlation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process‐based approaches to species distribution modelling lags far behind more correlative (process‐implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process‐explicit species distribution models and how they may complement current approaches to study species distributions.     

Bayesian prediction of spatial count data using generalized linear mixed models

Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, we demonstrate that so-called Langevin-Hastings updates are useful for efficient simulation of the posterior distributions, and we discuss computational issues concerning prediction.     

A Multiple Imputation Approach to Regression Analysis for Doubly Censored Data with Application to AIDS Studies

Sun, Liao, and Pagano (1999) proposed an interesting estimating equation approach to Cox regression with doubly censored data. Here we point out that a modification of their proposal leads to a multiple imputation approach, where the double censoring is reduced to single censoring by imputing for the censored initiating times. For each imputed data set one can take advantage of many existing techniques and software for singly censored data. Under the general framework of multiple imputation, the proposed method is simple to implement and can accommodate modeling issues such as model checking, which has not been adequately discussed previously in the literature for doubly censored data. Here we illustrate our method with an application to a formal goodness-of-fit test and a graphical check for the proportional hazards model for doubly censored data. We reanalyze a well-known AIDS data set.     

Early root growth plasticity in seedlings of three Mediterranean woody species

Using a 141 F₂ population generated from maize inbred B64 × teosinte Zea nicaraguensis cross, quantitative trait loci (QTLs) controlling aerenchyma formation in roots under non-flooding drained soil conditions were identified. Seedlings of Z. nicaraguensis formed clear aerenchyma in the cortex of adventitious roots in non-flooding conditions, whereas the maize inbred line B64 did not. In the F₂ population, the capacity to develop aerenchyma exhibited wide and continuous variation, suggesting the trait was controlled by multiple genes. A linkage map was developed using 85 SSR markers, covering 1,224 cM across all ten chromosomes. Composite interval mapping analysis revealed that four QTLs for aerenchyma formation under non-flooding conditions were located to two regions of chromosome 1 (identified as Qaer1.02-3 and Qaer1.07), chromosome 5 (Qaer5.09) and chromosome 8 (Qaer8.06-7), and these explained 46.5% of the total phenotypic variance. The multiple interval mapping approach identified additional QTLs on chromosomes 1 (Qaer1.01) and 5 (Qaer5.01). Using these results, it may be possible to use SSR markers linked to aerenchyma formation in a marker assisted selection approach to introduce aerenchyma formation in drained soil conditions into maize for the eventual development of flooding tolerant maize hybrids.