Joint estimation of recombination fractions and interference coefficients in multilocus linkage analysis.

Estimation of recombination fractions and interference coefficients is of importance in multilocus linkage analysis. With the development of molecular genetic technologies such as RFLP, multilocus data are readily available to researchers. Several methods have been developed to analyze such data, and each performs well under restrictive conditions. The present paper proposes a method based on a multiplicative model and maximum-likelihood estimation of recombination fractions and interference coefficients. The estimators are consistent regardless of the model assumptions and are efficient if the model is a good approximation. The estimators are tractable even when there are incomplete observations. Furthermore, the interference between nonadjacent chromosomal regions or those among three chromosomal regions can be modeled and tested by a simple Z-test. The proposed method was applied to linkage analysis of four-locus data obtained from Drosophila and that of seven-locus data obtained again from Drosophila. Reanalysis of the first example revealed that there is interference between chromosomal regions 2 and 3. Analysis of the second example suggested that there is triple interference as well as pairwise interference between nonadjacent chromosomal regions; the genetic interpretation of these findings remains to be developed.     

Patterns of recombination and MLH1 foci density along mouse chromosomes: modeling effects of interference and obligate chiasma

Crossover interference in meiosis is often modeled via stationary renewal processes. Here we consider a new model to incorporate the known biological feature of "obligate chiasma" whereby in most organisms each bivalent almost always has at least one crossover. The initial crossover is modeled as uniformly distributed along the chromosome, and starting from its position, subsequent crossovers are placed with forward and backward stationary renewal processes using a chi-square distribution of intercrossover distances. We used our model as well as the standard chi-square model to simulate the patterns of crossover densities along bivalents or chromatids for those having zero, one, two, or three or more crossovers; indeed, such patterns depend on the number of crossovers. With both models, simulated patterns compare very well to those found experimentally in mice, both for MLH1 foci on bivalents and for crossovers on genetic maps. However, our model provides a better fit to experimental data as compared to the standard chi-square model, particularly regarding the distribution of numbers of crossovers per chromosome. Finally, our model predicts an enhancement of the recombination rate near the extremities, which, however, explains only a part of the pattern observed in mouse.     

Sample size requirement for detecting interference under the chi-square model.

The chi-square model (CHS) of recombination has been studied extensively, in recent years, for its ability of capturing the process and estimating the level of crossover interference. Exploration thus far shows that this model yields much better fits to human genetic data than Haldane's no-interference model, and the explicit level of interference can be easily estimated as well. This paper provides calculations of sample sizes required to detect interference under CHS for a variety of settings. Two data types, fully informative meioses and phase-unknown backcross families, are studied. Under each setting, we calculate the number of meioses/families needed to ensure that the expected log-likelihood difference between the chi(2) interference model and Haldane's no-interference model exceeds a prespecified threshold. It is found that joint consideration of multiple (more than three) markers dramatically reduces the number of meioses/families needed when compared to an analysis based on three-point data, a traditional setting for detecting interference using three-point tests. The results indicate that the numbers of meioses needed to detect interference under CHS are well within the reach of most genetic mapping studies.     

Estimating Effective Population Size or Mutation Rate Using the Frequencies of Mutations of Various Classes in a Sample of DNA Sequences

Mutations resulting in segregating sites of a sample of DNA sequences can be classified by size and type and the frequencies of mutations of different sizes and types can be inferred from the sample. A framework for estimating the essential parameter θ = 4Nu utilizing the frequencies of mutations of various sizes and types is developed in this paper, where N is the effective size of a population and μ is mutation rate per sequence per generation. The framework is a combination of coalescent theory, general linear model and Monte-Carlo integration, which leads to two new estimators θ(ξ) and θ(η) as well as a general Watterson''s estimator θ(K) and a general Tajima''s estimator θ(π). The greatest strength of the framework is that it can be used under a variety of population models. The properties of the framework and the four estimators θ(K), θ(π), θ(ξ) and θ(η) are investigated under three important population models: the neutral Wright-Fisher model, the neutral model with recombination and the neutral Wright''s finite-islands model. Under all these models, it is shown that θ(ξ) is the best estimator among the four even when recombination rate or migration rate has to be estimated. Under the neutral Wright-Fisher model, it is shown that the new estimator θ(ξ) has a variance close to a lower bound of variances of all unbiased estimators of θ which suggests that θ(ξ) is a very efficient estimator.     

Regression Analysis for Constraining Free Parameters in Electrophysiological Models of Cardiac Cells

A major challenge in computational biology is constraining free parameters in mathematical models. Adjusting a parameter to make a given model output more realistic sometimes has unexpected and undesirable effects on other model behaviors. Here, we extend a regression-based method for parameter sensitivity analysis and show that a straightforward procedure can uniquely define most ionic conductances in a well-known model of the human ventricular myocyte. The model''s parameter sensitivity was analyzed by randomizing ionic conductances, running repeated simulations to measure physiological outputs, then collecting the randomized parameters and simulation results as “input” and “output” matrices, respectively. Multivariable regression derived a matrix whose elements indicate how changes in conductances influence model outputs. We show here that if the number of linearly-independent outputs equals the number of inputs, the regression matrix can be inverted. This is significant, because it implies that the inverted matrix can specify the ionic conductances that are required to generate a particular combination of model outputs. Applying this idea to the myocyte model tested, we found that most ionic conductances could be specified with precision (R² > 0.77 for 12 out of 16 parameters). We also applied this method to a test case of changes in electrophysiology caused by heart failure and found that changes in most parameters could be well predicted. We complemented our findings using a Bayesian approach to demonstrate that model parameters cannot be specified using limited outputs, but they can be successfully constrained if multiple outputs are considered. Our results place on a solid mathematical footing the intuition-based procedure simultaneously matching a model''s output to several data sets. More generally, this method shows promise as a tool to define model parameters, in electrophysiology and in other biological fields.     

Proposed genetic nomenclature rules for Tetrahymena thermophila,Paramecium primaurelia and Paramecium tetraurelia. The Seventh International Meeting on Ciliate Molecular Biology Genetics Nomenclature.

Ordered tetrad data yield information on chromatid interference, chiasma interference, and centromere locations. In this article, we show that the assumption of no chromatid interference imposes certain constraints on multilocus ordered tetrad probabilities. Assuming no chromatid interference, these constraints can be used to order markers under general chiasma processes. We also derive multilocus tetrad probabilities under a class of chiasma interference models, the chi-square models. Finally, we compare centromere map functions under the chi-square models with map functions proposed in the literature. Results in this article can be applied to order genetic markers and map centromeres using multilocus ordered tetrad data.     

A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation

While many models of biological object recognition share a common set of “broad-stroke” properties, the performance of any one model depends strongly on the choice of parameters in a particular instantiation of that model—e.g., the number of units per layer, the size of pooling kernels, exponents in normalization operations, etc. Since the number of such parameters (explicit or implicit) is typically large and the computational cost of evaluating one particular parameter set is high, the space of possible model instantiations goes largely unexplored. Thus, when a model fails to approach the abilities of biological visual systems, we are left uncertain whether this failure is because we are missing a fundamental idea or because the correct “parts” have not been tuned correctly, assembled at sufficient scale, or provided with enough training. Here, we present a high-throughput approach to the exploration of such parameter sets, leveraging recent advances in stream processing hardware (high-end NVIDIA graphic cards and the PlayStation 3''s IBM Cell Processor). In analogy to high-throughput screening approaches in molecular biology and genetics, we explored thousands of potential network architectures and parameter instantiations, screening those that show promising object recognition performance for further analysis. We show that this approach can yield significant, reproducible gains in performance across an array of basic object recognition tasks, consistently outperforming a variety of state-of-the-art purpose-built vision systems from the literature. As the scale of available computational power continues to expand, we argue that this approach has the potential to greatly accelerate progress in both artificial vision and our understanding of the computational underpinning of biological vision.     

Detection of inbreeding effects by the chi-square test on genotypic and phenotypic frequencies.

The problem of detection of inbreeding effects by the chi-square goodness-of-fit test is investigated. The noncentrality parameter of the limiting chi-square distribution is considered as a function of the inbreeding coefficient in two models: (1) a gene locus with m codominant alleles and (2) the generalized ABO model. Previous results by Ward and Sing and by Chakraborty and Rao were based on inadequate statistical reasoning. In the first model, the resulting noncentrality parameter is identical with that found by Ward and Sing. For the second model, the limiting noncentrality parameter is found to be zero.     

The Viscoelastic Properties of Passive Eye Muscle in Primates. II: Testing the Quasi-Linear Theory

We have extensively investigated the mechanical properties of passive eye muscles, in vivo, in anesthetized and paralyzed monkeys. The complexity inherent in rheological measurements makes it desirable to present the results in terms of a mathematical model. Because Fung''s quasi-linear viscoelastic (QLV) model has been particularly successful in capturing the viscoelastic properties of passive biological tissues, here we analyze this dataset within the framework of Fung''s theory.We found that the basic properties assumed under the QLV theory (separability and superposition) are not typical of passive eye muscles. We show that some recent extensions of Fung''s model can deal successfully with the lack of separability, but fail to reproduce the deviation from superposition.While appealing for their elegance, the QLV model and its descendants are not able to capture the complex mechanical properties of passive eye muscles. In particular, our measurements suggest that in a passive extraocular muscle the force does not depend on the entire length history, but to a great extent is only a function of the last elongation to which it has been subjected. It is currently unknown whether other passive biological tissues behave similarly.     

The evolutionary genetics of speciation.

The last decade has brought renewed interest in the genetics of speciation, yielding a number of new models and empirical results. Defining speciation as ''the origin of reproductive isolation between two taxa'', we review recent theoretical studies and relevant data, emphasizing the regular patterns seen among genetic analyses. Finally, we point out some important and tractable questions about speciation that have been neglected.     

Genome-Wide siRNA-Based Functional Genomics of Pigmentation Identifies Novel Genes and Pathways That Impact Melanogenesis in Human Cells

Melanin protects the skin and eyes from the harmful effects of UV irradiation, protects neural cells from toxic insults, and is required for sound conduction in the inner ear. Aberrant regulation of melanogenesis underlies skin disorders (melasma and vitiligo), neurologic disorders (Parkinson''s disease), auditory disorders (Waardenburg''s syndrome), and opthalmologic disorders (age related macular degeneration). Much of the core synthetic machinery driving melanin production has been identified; however, the spectrum of gene products participating in melanogenesis in different physiological niches is poorly understood. Functional genomics based on RNA-mediated interference (RNAi) provides the opportunity to derive unbiased comprehensive collections of pharmaceutically tractable single gene targets supporting melanin production. In this study, we have combined a high-throughput, cell-based, one-well/one-gene screening platform with a genome-wide arrayed synthetic library of chemically synthesized, small interfering RNAs to identify novel biological pathways that govern melanin biogenesis in human melanocytes. Ninety-two novel genes that support pigment production were identified with a low false discovery rate. Secondary validation and preliminary mechanistic studies identified a large panel of targets that converge on tyrosinase expression and stability. Small molecule inhibition of a family of gene products in this class was sufficient to impair chronic tyrosinase expression in pigmented melanoma cells and UV-induced tyrosinase expression in primary melanocytes. Isolation of molecular machinery known to support autophagosome biosynthesis from this screen, together with in vitro and in vivo validation, exposed a close functional relationship between melanogenesis and autophagy. In summary, these studies illustrate the power of RNAi-based functional genomics to identify novel genes, pathways, and pharmacologic agents that impact a biological phenotype and operate outside of preconceived mechanistic relationships.     

密固达与地舒单抗治疗原发性骨质疏松的成本-效用分析

摘要 目的：评价密固达与地舒单抗治疗原发性骨质疏松的经济性。方法：基于我国卫生体系角度,采用Excel2010软件构建Markov评估模型,利用成本-效用分析的方法评估密固达与地舒单抗治疗原发性骨质疏松的经济性。成本、健康效用值及药物治疗源自已发表的文献。模型循环周期为1年,时效为终生。采用单因素敏感性分析和概率分析评估模型参数变化对结果的影响。结果：地舒单抗用药方案比密固达方案给患者带来0.76质量调整生命年（QALYs）但同时用药成本也高于密固达方案2101.31元,其ICER为2764.88元/QALY。单因素敏感性分析发现药物成本对结果影响较大。概率敏感性分析结果显示,当采用3倍我国2022年人均国内生产总值（GDP）作为意愿支付阈值时,地舒单抗方案更具有经济性。结论：低于3倍我国2022年GDP阈值条件下,地舒单抗治疗原发性骨质疏松更具有经济性。     

Recombination and Gene Flux Caused by Gene Conversion and Crossing over in Inversion Heterokaryotypes

A theoretical analysis of the effects of inversions on recombination and gene flux between arrangements caused by gene conversion and crossing over was carried out. Two different mathematical models of recombination were used: the Poisson model (without interference) and the Counting model (with interference). The main results are as follows. (1) Recombination and gene flux are highly site-dependent both inside and outside the inverted regions. (2) Crossing over overwhelms gene conversion as a cause of gene flux in large inversions, while conversion becomes relatively significant in short inversions and in regions around the breakpoints. (3) Under the Counting model the recombination rate between two markers depends strongly on the position of the markers along the inverted segment. Two equally spaced markers in the central part of the inverted segment have less recombination than if they are in a more extreme position. (4) Inversions affect recombination rates in the uninverted regions of the chromosome. Recombination increases in the distal segment and decreases in the proximal segment. These results provide an explanation for a number of observations reported in the literature. Because inversions are ubiquitous in the evolutionary history of many Drosophila species, the effects of inversions on recombination are expected to influence DNA variation patterns.     

网络成瘾的生物学机制研究与展望

网络成瘾是指由于过度使用网络而导致社会及心理损害的现象,危害极大,故受到广泛关注。网络成瘾主要受生物学机制的调控。在脑神经机制方面,通过对成瘾者的自发脑电、事件相关电位以及成瘾者静息态BOLD-f MRI的分析,发现成瘾者脑功能区出现异常。同时,网络成瘾也受到自主神经功能的影响。另外,脑内奖赏系统和成瘾记忆模型也可能成为引发网络成瘾的脑神经机制。体内化学物质的失衡也能引发网络成瘾。鉴于此,一些治疗方法如药物干预、行为干预、认知干预以及综合干预疗法使得根治网络成瘾成为可能。本文拟从网络成瘾的概念、表现特点、不良影响及其发生的生物学机制等方面的研究进展进行阐述,以期为相关研究提供一些有价值的参考依据。     

The SMC′ Is a Highly Accurate Approximation to the Ancestral Recombination Graph

Two sequentially Markov coalescent models (SMC and SMC′) are available as tractable approximations to the ancestral recombination graph (ARG). We present a Markov process describing coalescence at two fixed points along a pair of sequences evolving under the SMC′. Using our Markov process, we derive a number of new quantities related to the pairwise SMC′, thereby analytically quantifying for the first time the similarity between the SMC′ and the ARG. We use our process to show that the joint distribution of pairwise coalescence times at recombination sites under the SMC′ is the same as it is marginally under the ARG, which demonstrates that the SMC′ is, in a particular well-defined, intuitive sense, the most appropriate first-order sequentially Markov approximation to the ARG. Finally, we use these results to show that population size estimates under the pairwise SMC are asymptotically biased, while under the pairwise SMC′ they are approximately asymptotically unbiased.     

Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data

We introduce a new statistical model for patterns of linkage disequilibrium (LD) among multiple SNPs in a population sample. The model overcomes limitations of existing approaches to understanding, summarizing, and interpreting LD by (i) relating patterns of LD directly to the underlying recombination process; (ii) considering all loci simultaneously, rather than pairwise; (iii) avoiding the assumption that LD necessarily has a "block-like" structure; and (iv) being computationally tractable for huge genomic regions (up to complete chromosomes). We examine in detail one natural application of the model: estimation of underlying recombination rates from population data. Using simulation, we show that in the case where recombination is assumed constant across the region of interest, recombination rate estimates based on our model are competitive with the very best of current available methods. More importantly, we demonstrate, on real and simulated data, the potential of the model to help identify and quantify fine-scale variation in recombination rate from population data. We also outline how the model could be useful in other contexts, such as in the development of more efficient haplotype-based methods for LD mapping.     

The evolution of evolvability in genetic linkage patterns

A number of factors have been proposed that may affect the capacity for an evolutionary system to generate adaptation. One that has received little recent attention among biologists is linkage patterns, or the ordering of genes on chromosomes. In this study, a simple model of genetic interactions, implemented in an evolutionary simulation, demonstrates that clustering of epistatically interacting genes increases the rate of adaptation. Moreover, long-term evolution with inversion can reorganize linkage patterns from random gene ordering into this more modular organization, thereby facilitating adaptation. These results are consistent with a large body of biological observations and some mathematical theory. Although linkage patterns are neutral with respect to individual fitness in this model, they are subject to lineage level selection for evolvability. At least two candidate mechanisms may contribute to improved evolvability under epistatic clustering: clustering may reduce interference between selection on different traits, and it may allow the simultaneous optimization of different recombination rates for gene pairs with additive and epistatic fitness effects.     

A segregation and linkage study of classical and nonclassical 21-hydroxylase deficiency.

The segregation of classical and nonclassical 21-hydroxylase deficiency (21-OHD) and its linkage to HLA-B was investigated in 220 families. First, the surprisingly high frequency of the nonclassical 21-OHD gene estimated elsewhere was confirmed using a different methodology which avoided particular assumptions concerning the classification of an individual''s genotype. In the present study the gene frequency was found to be .103 +/- .020 in an ethnically pooled sample and was as high as .223 +/- .062 among Ashkenazi Jews. Second, the segregation analysis of families ascertained through a nonclassical 21-OHD proband and those ascertained through a classical 21-OHD proband showed essentially identical results. A partial recessive model with no recombination between 21-OHD and HLA-B fitted the data better than did a complete recessive model with approximately 0.5% recombination between 21-OHD and HLA-B. The support for the partial over the complete recessive model depended on the assumed ascertainment probability, an unknown parameter in these data. Four families provided most of the evidence against the complete recessive model. All these included an unaffected sib who shared both HLA-B specificities in common with the affected proband. Possible explanations for the condition in these families include recombination, gene conversion, mutation in one of the parental gametes, or technical errors.     

HMMConverter 1.0: a toolbox for hidden Markov models

Hidden Markov models (HMMs) and their variants are widely used in Bioinformatics applications that analyze and compare biological sequences. Designing a novel application requires the insight of a human expert to define the model''s architecture. The implementation of prediction algorithms and algorithms to train the model''s parameters, however, can be a time-consuming and error-prone task. We here present HMMConverter, a software package for setting up probabilistic HMMs, pair-HMMs as well as generalized HMMs and pair-HMMs. The user defines the model itself and the algorithms to be used via an XML file which is then directly translated into efficient C++ code. The software package provides linear-memory prediction algorithms, such as the Hirschberg algorithm, banding and the integration of prior probabilities and is the first to present computationally efficient linear-memory algorithms for automatic parameter training. Users of HMMConverter can thus set up complex applications with a minimum of effort and also perform parameter training and data analyses for large data sets.