贝叶斯统计在QTL作图中的应用研究进展

在许多复杂情况下, 贝叶斯统计方法比经典数理统计方法能更直接解决问题, 且可有效整合部分先验信息, 但其需要高强度计算的特性曾限制了其广泛应用。近几十年来, 随着高速计算机的发展以及MCMC算法的不断提出, 贝叶斯方法已被用于群体遗传学、分子进化、连锁作图和数量遗传学等研究领域, 文章综述了数量遗传学中QTL作图的贝叶斯方法从简单到复杂的发展历程。     

塔里木盆地北缘盐土有机碳含量的贝叶斯地统计预测

准确预测土壤有机碳的空间分布,对于土壤资源开发和保护、应对气候变化和生态系统健康都具有重要意义.本文以塔里木盆地北缘盐土1300 m×1700 m样地为试验区,采集5～10 cm深度土壤样品144个,构建土壤有机碳含量的贝叶斯地统计空间预测模型,并以普通克里格、序惯高斯模拟和逆距离加权方法为对照,评价贝叶斯地统计对土壤有机碳含量的预测性能.结果表明: 研究区土壤有机碳含量处于1.59～9.30 g·kg^-1,平均值为4.36 g·kg^-1,标准偏差为1.62 g·kg^-1;半方差函数符合指数模型,空间结构比参数值为0.57;利用贝叶斯地统计方法,获得了土壤有机碳含量的空间分布图以及评价预测不确定性的预测方差、上95%分位数、下95%分位数分布图;与普通克里格、序惯高斯模拟和逆距离加权方法相比,贝叶斯地统计方法具有更高的土壤有机碳含量空间预测精度,显示出该方法对土壤有机碳含量预测的优越性.     

基于模型数据融合的长白山阔叶红松林碳循环模拟

 充分、有效地利用各种陆地生态系统碳观测数据改善陆地生态系统模型, 是当前我国陆地生态系统碳循环研究领域亟待解决的重要问题之一。该研究以2003~2005年长白山阔叶红松林的6组生物计量观测数据和涡度相关技术测定的碳通量数据为基础, 利用马尔可夫链-蒙特卡罗方法对陆地生态系统模型的关键参数(即碳滞留时间)进行了反演, 进而预测了长白山阔叶红松林生态系统碳库、碳通量及其不确定性。反演结果表明, 长白山阔叶红松林叶凋落物和微生物碳的平均滞留时间最短, 为2~6个月; 其次是叶和细根生物量碳, 二者的平均滞留时间为1~2 a; 慢性土壤有机碳的平均滞留时间为8~16 a; 碳在木质生物量和惰性土壤有机质库中的滞留时间最长, 平均滞留时间分别为77~109 a和409~1 879 a。模拟结果显示, 碳库和累积碳通量模拟值的不确定性将随着模拟时间的延长而增大。当气温升高10%和20%时, 长白山阔叶红松林总初级生产力年总量将分别增加6.5%和9.9%, 净生态系统生产力(NEP)年总量的变化取决于土壤温度的变化。若土壤温度保持不变, NEP年总量将分别增加11.4%~21.9%和17.6%~33.1%; 若土壤温度也相应升高10%和20%, NEP年总量的增幅反而下降甚至低于原来的水平。假设气候和植被保持在2003~2005年的状态, 2020年长白山阔叶红松林NEP年总量为(163±12) g C·m^–2·a^–1, 土壤呼吸年总量为(721±14) g C·m^–2·a^–1。马尔可夫链-蒙特卡罗方法是反演模型参数、优化模拟结果和评估模拟结果不确定性的有效方法, 但今后仍需在惰性土壤碳滞留时间的估计、驱动数据和模型结构的不确定性分析、模型数据融合方法方面进行深入研究, 以进一步提高碳循环模拟的准确性。     

利用杉木的F1代群体构建遗传连锁图谱

对于杉木 1∶1分离的分子标记位点 ,提出了一种新的构建遗传连锁图谱的策略。通过二点连锁分析 ,任意两个位点的连锁相和重组率可以得到推断和估计。对于一个连锁群中的最优排序 ,采用隐马尔可夫链模型的方法进行多位点的连锁分析。该作图方法比通常林木上所用的“拟测交”作图方法更有效。采用该作图策略 ,利用句容0号无性系 (♀ )×柔叶杉 (♂ )的F1代群体的AFLP分子标记数据重建了句容 0号无性系和柔叶杉的遗传连锁图谱。在句容 0号无性系的连锁图谱中 ,有 10 1个标记分布在 11个连锁群上 ,图谱的总长度为 2 2 82 6cM ,平均图距为 2 2 6cM ,单个连锁群上最多含有 17个标记 ,最少含有 5个标记 ;在柔叶杉的连锁图谱中 ,有 94个标记分布在 11个连锁群上 ,图谱的总长度为 2 5 6 5 8cM ,平均图距为 2 7 3cM ,单个连锁群上最多含有 16个标记 ,最少含有 4个标记。构建的句容 0号无性系和柔叶杉的遗传连锁图谱比原有的图谱分别增加了 2 6个标记和 2 8个标记 ,双亲的图谱共增加了 5 4个AFLP标记 ,使图谱上的分子标记总数达到 195个 ,双亲遗传图谱的跨度均超过了 2 0 0 0cM ,基本上达到了杉木基因组的长度 ,图谱的覆盖率接近于 10 0 %。利用新的作图方法可以较大提高分子标记在图谱上的分辨率 ,得到可认为是     

基于EM算法的遗传重组率估计方法

EM算法是在不完全信息资料下实现参数极大似然估计的一种通用方法．本文导出了双位点不同标记类型,包括共显性-共显性,共显性-显性和显性-显性3种模式下,估计遗传重组率的EM算法,以及获得重组率抽样方差的Bootstrap方法;并将之推广到部分个体缺失标记基因型(未检测到电泳谱带)下的重组率估计．通过大量Monte Carlo模拟研究发现: (1)连锁紧密时,样本容量对重组率的估计影响不大;连锁松散时,需要较大样本容量才可检测到连锁以及实现重组率的较精确估计．(2)用包含缺失标记的所有个体估计重组率比仅用其中的非缺失标记个体估计更准确,且可显著提高连锁检测的统计功效．     

基于贝叶斯二项式模型分析棕头鸥种群多度与环境因子间的关系

传统的种群多度调查方法由于默认观察率 p 等于 1,因此极有可能低估种群大小,进而误判种群多度与环境因子间的关系。为了了解禽流感爆发后青海湖棕头鸥种群与环境因子间的关系,为棕头鸥管理提供有效依据,于 2010 年和 2011 年的 4 ～6 月调查了青海湖保护区 23 个观测点的棕头鸥种群数量及环境因子。通过包含观察率的贝叶斯二项式混合模型分析棕头鸥种群多度与环境因子间的关系,采用 DIC 准则进行因子筛选。结果表明: 种群数量亚模型包含取样面积、放牧强度、距公路距离和植被盖度 4 个参数,种群数量随取样面积、距公路距离和植被盖度的增加而增加,随放牧强度的增加而减少; 观察率亚模型包含观察经验和棕头鸥的行为月节律 2 个参数,观察率随观察月的递增而降低,随观察经验的增加而升高,高经验观察者平均每千米观察到 18. 1 只棕头鸥,低经验观察者可以平均观察到 13. 7 只。天气状况不影响观察率,这可能与棕头鸥的觅食栖息地距岸边较近,不影响观察者的观察有关。     

利用杉木的F1代群体构建遗传连锁图谱

对于杉木11分离的分子标记位点,提出了一种新的构建遗传连锁图谱的策略.通过二点连锁分析,任意两个位点的连锁相和重组率可以得到推断和估计.对于一个连锁群中的最优排序,采用隐马尔可夫链模型的方法进行多位点的连锁分析.该作图方法比通常林木上所用的"拟测交"作图方法更有效.采用该作图策略,利用句容0号无性系(♀)×柔叶杉(♂)的F1代群体的AFLP分子标记数据重建了句容0号无性系和柔叶杉的遗传连锁图谱.在句容0号无性系的连锁图谱中,有101个标记分布在11个连锁群上,图谱的总长度为2 282.6 cM,平均图距为22.6 cM,单个连锁群上最多含有17个标记,最少含有5个标记;在柔叶杉的连锁图谱中,有94个标记分布在11个连锁群上,图谱的总长度为2 565.8 cM,平均图距为27.3 cM,单个连锁群上最多含有16个标记,最少含有4个标记.构建的句容0号无性系和柔叶杉的遗传连锁图谱比原有的图谱分别增加了26个标记和28个标记,双亲的图谱共增加了54个AFLP标记,使图谱上的分子标记总数达到195个,双亲遗传图谱的跨度均超过了2 000 cM,基本上达到了杉木基因组的长度,图谱的覆盖率接近于100%.利用新的作图方法可以较大提高分子标记在图谱上的分辨率,得到可认为是覆盖了整个基因组的遗传连锁框架图.     

简单贝叶斯决策的计算神经模型

大脑根据贝叶斯理论处理信息已经得到诸多心理学和神经生理学实验的支持.贝叶斯推理过程往往需要处理复杂的概率计算,最近Shadlen和Gold提出基于对数似然比可以简化基于贝叶斯的两可决策任务.然而,目前并不清楚如何在神经回路中实现基于对数似然比的贝叶斯决策.通过建立具有信息整合与胜者独享特性的决策神经回路,结合奖励调制的突触可塑性学习算法,得到决策行为与突触可塑性之间的对应关系,由此可实现简单贝叶斯决策的计算神经模型.最后,利用该模型模拟出最近Yang和Shadlen关于恒河猴可进行贝叶斯决策的实验结果.     

基于马尔可夫链的基因芯片特异性探针选择方法

对于基因表达芯片,特异性探针的选择是探针设计的重要环节,由于基因组序列数据量极大,不可能对每个候选探针都在全序列中进行特异性评价并进行取舍。对此问题,提出了一种采用马尔可夫链概率准则的探针特异性选择方法,即把基因组序列看作马尔可夫链,任何探针序列的互补序列作为它的一个子序列,都具有一定的出现概率,概率越小,越可能具有特异性。据此,选择其中概率最小的N个候选探针,能够大大减少进行特异性评价的探针数量,缩短探针设计的计算时间。对实际数据的测试结果表明,该方法选择的探针具有很高的特异性。     

Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses

Throughout the last two decades, Bayesian statistical methods have proliferated throughout ecology and evolution. Numerous previous references established both philosophical and computational guidelines for implementing Bayesian methods. However, protocols for incorporating prior information, the defining characteristic of Bayesian philosophy, are nearly nonexistent in the ecological literature. Here, I hope to encourage the use of weakly informative priors in ecology and evolution by providing a ‘consumer's guide’ to weakly informative priors. The first section outlines three reasons why ecologists should abandon noninformative priors: 1) common flat priors are not always noninformative, 2) noninformative priors provide the same result as simpler frequentist methods, and 3) noninformative priors suffer from the same high type I and type M error rates as frequentist methods. The second section provides a guide for implementing informative priors, wherein I detail convenient ‘reference’ prior distributions for common statistical models (i.e. regression, ANOVA, hierarchical models). I then use simulations to visually demonstrate how informative priors influence posterior parameter estimates. With the guidelines provided here, I hope to encourage the use of weakly informative priors for Bayesian analyses in ecology. Ecologists can and should debate the appropriate form of prior information, but should consider weakly informative priors as the new ‘default’ prior for any Bayesian model.     

Bayesian phylogeny analysis via stochastic approximation Monte Carlo

Monte Carlo methods have received much attention in the recent literature of phylogeny analysis. However, the conventional Markov chain Monte Carlo algorithms, such as the Metropolis–Hastings algorithm, tend to get trapped in a local mode in simulating from the posterior distribution of phylogenetic trees, rendering the inference ineffective. In this paper, we apply an advanced Monte Carlo algorithm, the stochastic approximation Monte Carlo algorithm, to Bayesian phylogeny analysis. Our method is compared with two popular Bayesian phylogeny software, BAMBE and MrBayes, on simulated and real datasets. The numerical results indicate that our method outperforms BAMBE and MrBayes. Among the three methods, SAMC produces the consensus trees which have the highest similarity to the true trees, and the model parameter estimates which have the smallest mean square errors, but costs the least CPU time.     

Bayesian phylogenetic model selection using reversible jump Markov chain Monte Carlo

A common problem in molecular phylogenetics is choosing a model of DNA substitution that does a good job of explaining the DNA sequence alignment without introducing superfluous parameters. A number of methods have been used to choose among a small set of candidate substitution models, such as the likelihood ratio test, the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and Bayes factors. Current implementations of any of these criteria suffer from the limitation that only a small set of models are examined, or that the test does not allow easy comparison of non-nested models. In this article, we expand the pool of candidate substitution models to include all possible time-reversible models. This set includes seven models that have already been described. We show how Bayes factors can be calculated for these models using reversible jump Markov chain Monte Carlo, and apply the method to 16 DNA sequence alignments. For each data set, we compare the model with the best Bayes factor to the best models chosen using AIC and BIC. We find that the best model under any of these criteria is not necessarily the most complicated one; models with an intermediate number of substitution types typically do best. Moreover, almost all of the models that are chosen as best do not constrain a transition rate to be the same as a transversion rate, suggesting that it is the transition/transversion rate bias that plays the largest role in determining which models are selected. Importantly, the reversible jump Markov chain Monte Carlo algorithm described here allows estimation of phylogeny (and other phylogenetic model parameters) to be performed while accounting for uncertainty in the model of DNA substitution.     

Bayesian Updating of Model-Based Risk Estimates Using Imperfect Public Health Surveillance Data

Model-based estimation of the human health risks resulting from exposure to environmental contaminants can be an important tool for structuring public health policy. Due to uncertainties in the modeling process, the outcomes of these assessments are usually probabilistic representations of a range of possible risks. In some cases, health surveillance data are available for the assessment population over all or a subset of the risk projection period and this additional information can be used to augment the model-based estimates. We use a Bayesian approach to update model-based estimates of health risks based on available health outcome data. Updated uncertainty distributions for risk estimates are derived using Monte Carlo sampling, which allows flexibility to model realistic situations including measurement error in the observable outcomes. We illustrate the approach by using imperfect public health surveillance data on lung cancer deaths to update model-based lung cancer mortality risk estimates in a population exposed to ionizing radiation from a uranium processing facility.     

A Bayesian approach to disease gene location using allelic association

A Bayesian approach to analysing data from family-based association studies is developed. This permits direct assessment of the range of possible values of model parameters, such as the recombination frequency and allelic associations, in the light of the data. In addition, sophisticated comparisons of different models may be handled easily, even when such models are not nested. The methodology is developed in such a way as to allow separate inferences to be made about linkage and association by including theta, the recombination fraction between the marker and disease susceptibility locus under study, explicitly in the model. The method is illustrated by application to a previously published data set. The data analysis raises some interesting issues, notably with regard to the weight of evidence necessary to convince us of linkage between a candidate locus and disease.     

Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models

The problem of evaluating the goodness of the predictive distributionsof hierarchical Bayesian and empirical Bayes models is investigated.A Bayesian predictive information criterion is proposed as anestimator of the posterior mean of the expected loglikelihoodof the predictive distribution when the specified family ofprobability distributions does not contain the true distribution.The proposed criterion is developed by correcting the asymptoticbias of the posterior mean of the loglikelihood as an estimatorof its expected loglikelihood. In the evaluation of hierarchicalBayesian models with random effects, regardless of our parametricfocus, the proposed criterion considers the bias correctionof the posterior mean of the marginal loglikelihood becauseit requires a consistent parameter estimator. The use of thebootstrap in model evaluation is also discussed.