DNA水平自然选择作用的检测

上个世纪60年代,Kimura提出的“中性进化”假说使经典的达尔文自然选择学说遭遇了前所未有的挑战。但新近的研究表明：在DNA水平,越来越多的证据支持“自然选择”的进化理论。这些研究成果得益于近年来大量群体和基因组DNA数据的积累,以及理论群体遗传学的发展。在DNA水平检测选择作用是否存在的方法包括两大类：种内多态性检验和种间差异度检验。前者以Tajima(1989)提出的D检验为代表,后者大都基于“中性条件下,种内与种间进化速率一致”的原理。这些方法以中性假说作为零假设,结合统计检验方法分析DNA数据,被称为“中性检验”。这些方法对于解决一些有关进化的基础理论问题和人类遗传学及生物信息学的深入研究都具有重要意义。本文介绍几个应用广泛的检测方法,以使国内的读者了解它们的基本思路和操作方法。     

The Effect of Genomic Inversions on Estimation of Population Genetic Parameters from SNP Data

In recent years it has emerged that structural variants have a substantial impact on genomic variation. Inversion polymorphisms represent a significant class of structural variant, and despite the challenges in their detection, data on inversions in the human genome are increasing rapidly. Statistical methods for inferring parameters such as the recombination rate and the selection coefficient have generally been developed without accounting for the presence of inversions. Here we exploit new software for simulating inversions in population genetic data, invertFREGENE, to assess the potential impact of inversions on such methods. Using data simulated by invertFREGENE, as well as real data from several sources, we test whether large inversions have a disruptive effect on widely applied population genetics methods for inferring recombination rates, for detecting selection, and for controlling for population structure in genome-wide association studies (GWAS). We find that recombination rates estimated by LDhat are biased downward at inversion loci relative to the true contemporary recombination rates at the loci but that recombination hotspots are not falsely inferred at inversion breakpoints as may have been expected. We find that the integrated haplotype score (iHS) method for detecting selection appears robust to the presence of inversions. Finally, we observe a strong bias in the genome-wide results of principal components analysis (PCA), used to control for population structure in GWAS, in the presence of even a single large inversion, confirming the necessity to thin SNPs by linkage disequilibrium at large physical distances to obtain unbiased results.     

Improving Power for Testing Genetic Association in Case–Control Studies by Reducing the Alternative Space

Summary .  To detect association between a genetic marker and a disease in case–control studies, the Cochran–Armitage trend test is typically used. The trend test is locally optimal when the genetic model is correctly specified. However, in practice, the underlying genetic model, and hence the optimal trend test, are usually unknown. In this case, Pearson's chi-squared test, the maximum of three trend test statistics (optimal for the recessive, additive, and dominant models), and the test based on genetic model selection (GMS) are useful. In this article, we first modify the existing GMS method so that it can be used when the risk allele is unknown. Then we propose a new approach by excluding a genetic model that is not supported by the data. Using either the model selection or exclusion, the alternative space is reduced conditional on the observed data, and hence the power to detect a true association can be increased. Simulation results are reported and the proposed methods are applied to the genetic markers identified from the genome-wide association studies conducted by the Wellcome Trust Case–Control Consortium. The results demonstrate that the genetic model exclusion approach usually performs better than existing methods under its worst situation across scientifically plausible genetic models we considered.     

Characteristic gene selection via weighting principal components by singular values

Conventional gene selection methods based on principal component analysis (PCA) use only the first principal component (PC) of PCA or sparse PCA to select characteristic genes. These methods indeed assume that the first PC plays a dominant role in gene selection. However, in a number of cases this assumption is not satisfied, so the conventional PCA-based methods usually provide poor selection results. In order to improve the performance of the PCA-based gene selection method, we put forward the gene selection method via weighting PCs by singular values (WPCS). Because different PCs have different importance, the singular values are exploited as the weights to represent the influence on gene selection of different PCs. The ROC curves and AUC statistics on artificial data show that our method outperforms the state-of-the-art methods. Moreover, experimental results on real gene expression data sets show that our method can extract more characteristic genes in response to abiotic stresses than conventional gene selection methods.     

Testing for Hardy–Weinberg equilibrium in structured populations using genotype or low‐depth next generation sequencing data

Testing for deviations from Hardy–Weinberg equilibrium (HWE) is a common practice for quality control in genetic studies. Variable sites violating HWE may be identified as technical errors in the sequencing or genotyping process, or they may be of particular evolutionary interest. Large‐scale genetic studies based on next‐generation sequencing (NGS) methods have become more prevalent as cost is decreasing but these methods are still associated with statistical uncertainty. The large‐scale studies usually consist of samples from diverse ancestries that make the existence of some degree of population structure almost inevitable. Precautions are therefore needed when analysing these data set, as population structure causes deviations from HWE. Here we propose a method that takes population structure into account in the testing for HWE, such that other factors causing deviations from HWE can be detected. We show the effectiveness of PCAngsd in low‐depth NGS data, as well as in genotype data, for both simulated and real data set, where the use of genotype likelihoods enables us to model the uncertainty.     

Plasmodium relictum infection and MHC diversity in the house sparrow (Passer domesticus)

Antagonistic coevolution between hosts and parasites has been proposed as a mechanism maintaining genetic diversity in both host and parasite populations. In particular, the high level of genetic diversity usually observed at the major histocompatibility complex (MHC) is generally thought to be maintained by parasite-driven selection. Among the possible ways through which parasites can maintain MHC diversity, diversifying selection has received relatively less attention. This hypothesis is based on the idea that parasites exert spatially variable selection pressures because of heterogeneity in parasite genetic structure, abundance or virulence. Variable selection pressures should select for different host allelic lineages resulting in population-specific associations between MHC alleles and risk of infection. In this study, we took advantage of a large survey of avian malaria in 13 populations of the house sparrow (Passer domesticus) to test this hypothesis. We found that (i) several MHC alleles were either associated with increased or decreased risk to be infected with Plasmodium relictum, (ii) the effects were population specific, and (iii) some alleles had antagonistic effects across populations. Overall, these results support the hypothesis that diversifying selection in space can maintain MHC variation and suggest a pattern of local adaptation where MHC alleles are selected at the local host population level.     

Effect of linkage on the control of inbreeding in selection programmes

Selection and mating methods for controlling inbreeding in selection programmes are based on relationships obtained from pedigrees. The efficiency of these methods has always been tested by studies using genetic models of independent loci. However, under linkage the rate of inbreeding obtained from pedigrees can be different from the probability of identity by descent of genes. We simulated a quantitative trait under artificial selection controlled by a large number of genes spread on genome regions of different sizes. A method to control inbreeding based on minimising the average coancestry of selected individuals with a restriction in the loss of selection response, and a mating procedure to control inbreeding were applied. These methods, that use coancestry relationships, were not effective in controlling inbreeding when the genome sizes were smaller than five morgans or so. However, for larger genome sizes the methods were sufficiently efficient. For very tight linkage, methods that utilise molecular information from markers should be used. We finally discuss the effects of the selection of individual major genes on the neutral variability of adjacent genome regions.     

Study of genetic control of radiosensitivity

The results of radiation genetics studies are reviewed. The first series of studies concerned the role of heterogeneity of the human population for radiosensitivity of chromosomes in determining the pattern of dose-response relationships; correctness of extrapolation of averaged experimental data to low doses was demonstrated. In the second series of experiments, the radiation-induced adaptive response and the contribution of different factors, including genetic ones, to its formation in human cells were studied. A conclusion was made about impossibility of extrapolating data obtained for cell cultures to an organism as a whole or to a population. The third part of the study was of applied character: cytogenetic methods of biological dosimetry were used to estimate the doses of internal and external irradiation of children living on the territory of the Bryansk oblast contaminated after the Chernobyl accident. The results are discussed in the context of the present-day concepts of genetic control of sensitivity to environmental factors.     

Importance of correlations among matrix entries in stochastic models in relation to number of transition matrices

Stochastic matrix models are used to predict population viability and the risk of extinction. Different stochastic methods require different amounts of estimation effort and may lead to divergent estimates. We used 16 transition matrices collected from ten populations of the perennial herb Primula veris to compare population estimates produced by different stochastic methods, such as selection of matrices, selection of vital rates, selection of matrix elements, and Tuljapurkar's approximation. Specifically, we tested the reliability of the methods using different numbers of transition matrices, and examined the importance of correlations among matrix entries. When correlations among matrix entries were included in the models, selection of vital rates produced the lowest and Tuljapurkar's approximation produced the highest estimates of mean population growth rates. Selection of matrices and matrix elements often produced nearly similar population estimates. Simulations based on incompletely estimated correlations among matrix entries considerably differed from those based on all correlations estimated, particularly when correlations were strong. The magnitude of correlations among matrix entries depended on the number of matrices, which made it difficult to generalize correlations within a species. Given that selection of vital rates or matrix elements is used, correlations among matrix entries should usually be included in the model, and they should preferably be estimated from the present data rather than according to other information of the species.     

Long-term response to genomic selection: effects of estimation method and reference population structure for different genetic architectures

Background

Genomic selection has become an important tool in the genetic improvement of animals and plants. The objective of this study was to investigate the impacts of breeding value estimation method, reference population structure, and trait genetic architecture, on long-term response to genomic selection without updating marker effects.

Methods

Three methods were used to estimate genomic breeding values: a BLUP method with relationships estimated from genome-wide markers (GBLUP), a Bayesian method, and a partial least squares regression method (PLSR). A shallow (individuals from one generation) or deep reference population (individuals from five generations) was used with each method. The effects of the different selection approaches were compared under four different genetic architectures for the trait under selection. Selection was based on one of the three genomic breeding values, on pedigree BLUP breeding values, or performed at random. Selection continued for ten generations.

Results

Differences in long-term selection response were small. For a genetic architecture with a very small number of three to four quantitative trait loci (QTL), the Bayesian method achieved a response that was 0.05 to 0.1 genetic standard deviation higher than other methods in generation 10. For genetic architectures with approximately 30 to 300 QTL, PLSR (shallow reference) or GBLUP (deep reference) had an average advantage of 0.2 genetic standard deviation over the Bayesian method in generation 10. GBLUP resulted in 0.6% and 0.9% less inbreeding than PLSR and BM and on average a one third smaller reduction of genetic variance. Responses in early generations were greater with the shallow reference population while long-term response was not affected by reference population structure.

Conclusions

The ranking of estimation methods was different with than without selection. Under selection, applying GBLUP led to lower inbreeding and a smaller reduction of genetic variance while a similar response to selection was achieved. The reference population structure had a limited effect on long-term accuracy and response. Use of a shallow reference population, most closely related to the selection candidates, gave early benefits while in later generations, when marker effects were not updated, the estimation of marker effects based on a deeper reference population did not pay off.     

Radiological Terrorism

We run the risk that terrorists will decide to detonate an explosive device laced with radioactive materials (a radiological dispersal device, or RDD). If such an attack occurs, it is unlikely that the affected population or emergency responders would be exposed to high levels of external radiation, although airborne radionuclides may present a health risk under some circumstances. However, the effects of radiation and radioactivity are not well known among the general population, emergency responders, or medical personnel. This could lead to unwarranted panic, refusal to respond to the incident, inappropriately delaying or denying treatment to injured victims, and other unfortunate reactions during the emergency phase of any response. During the recovery phase, current regulations may lead to costly and restrictive radiation safety requirements over very large areas, although there have been recent efforts to relax some of these regulations in the first year following a radiological attack. The wide spread of radioactive contamination can also lead to environmental contamination, particularly in low-flow areas and near storm sewer discharge points, but the total radiation dose to the environment should not be excessively high in most locations.     

Cerebrovascular diseases in nuclear workers first employed at the Mayak PA in 1948–1972

Incidence and mortality from cerebrovascular diseases (CVD) (430–438 ICD-9 codes) have been studied in a cohort of 18,763 workers first employed at the Mayak Production Association (Mayak PA) in 1948–1972 and followed up to the end of 2005. Some of the workers were exposed to external gamma-rays only while others were exposed to a mixture of external gamma-rays and internal alpha-particle radiation due to incorporated ²³⁹Pu. After adjusting for non-radiation factors, there were significantly increasing trends in CVD incidence with total absorbed dose from external gamma-rays and total absorbed dose to liver from internal alpha radiation. The CVD incidence was statistically significantly higher among workers with total absorbed external gamma-ray doses greater than 0.20 Gy compared to those exposed to lower doses; the data were consistent with a linear trend in risk with external dose. The CVD incidence was statistically significantly higher among workers with total absorbed internal alpha-radiation doses to liver from incorporated ²³⁹Pu greater than 0.025 Gy compared to those exposed to lower doses. There was no statistically significant trend in CVD mortality risk with either external gamma-ray dose or internal alpha-radiation dose to liver. The risk estimates obtained are generally compatible with those from other large occupational studies, although the incidence data point to higher risk estimates compared to those from the Japanese A-bomb survivors. Further studies of the unique cohort of Mayak workers chronically exposed to external and internal radiation will allow improving the reliability and validating the radiation safety standards for occupational and public exposure.     

Regularization network-based gene selection for microarray data analysis

Microarray data contains a large number of genes (usually more than 1000) and a relatively small number of samples (usually fewer than 100). This presents problems to discriminant analysis of microarray data. One way to alleviate the problem is to reduce dimensionality of data by selecting important genes to the discriminant problem. Gene selection can be cast as a feature selection problem in the context of pattern classification. Feature selection approaches are broadly grouped into filter methods and wrapper methods. The wrapper method outperforms the filter method but at the cost of more intensive computation. In the present study, we proposed a wrapper-like gene selection algorithm based on the Regularization Network. Compared with classical wrapper method, the computational costs in our gene selection algorithm is significantly reduced, because the evaluation criterion we proposed does not demand repeated training in the leave-one-out procedure.     

Effectiveness of different accelerated partial breast irradiation techniques for the treatment of breast cancer patients: Systematic review using indirect comparisons of randomized clinical trials

AimThis systematic review was conducted to compare the effectiveness of different accelerated partial breast irradiation (APBI) techniques for the treatment of breast cancer patients.BackgroundNumerous (APBI) techniques are available for clinical practice.Methods and materialsSystematic review of randomized controlled trials of APBI versus whole breast irradiation (WBI). The data from APBI studies were extracted for the analyses. Indirect comparisons were used to compare different APBI techniques.ResultsTen studies fulfilled the inclusion criteria. A total of 4343 patients were included, most of them with tumor stage T1-T2 and N0. Regarding APBI techniques, six trials used external beam radiation therapy; one intraoperative electrons; one intraoperative low-energy photons; one brachytherapy; and one external beam radiation therapy or brachytherapy. The indirect comparisons related to 5-years local control and 5-years overall survival were not significantly different between APBI techniques.ConclusionsBased on indirect comparisons, no differences in clinical outcomes were observed among diverse APBI techniques in published clinical trials that formally compared WBI to APBI. However wide confidence intervals and high risk of inconsistency precluded a sound conclusion. Further head-to-head clinical trials comparing different APBI techniques are required to confirm our findings. Studies comparing different techniques using individual participant data and/or real-life data from population-based studies/registries could also provide more robust results.     

Can transposable element copy number distribution parameters be estimated from natural populations of Drosophila melanogaster?

The copy frequency distribution of a transposable element family in a Drosophila melanogaster natural population is generally characterised by the values of the Charlesworths' model parameters α and β (Charlesworth & Charlesworth, 1983). The estimation of these parameters is made using the observed distribution of the occupied sites in a population sample. Several results have been interpreted as due either to the influence of stochastic factors or to deterministic factors (transposition, excision, selection…). The accuracy of this method was tested by estimations performed on samples from simulated populations. The results show that with the sample size usually used for natural population studies, the confidence intervals are too large to reasonably deduce either the element copy number distribution or the values of transposition and excision rate and selective coefficients.     

Ischemic Heart Disease in Workers at Mayak PA: Latency of Incidence Risk after Radiation Exposure

We present an updated analysis of incidence and mortality from atherosclerotic induced ischemic heart diseases in the cohort of workers at the Mayak Production Association (PA). This cohort constitutes one of the most important sources for the assessment of radiation risk. It is exceptional because it comprises information on several other risk factors. While most of the workers have been exposed to external gamma radiation, a large proportion has additionally been exposed to internal radiation from inhaled plutonium. Compared to a previous study by Azizova et al. 2012, the updated dosimetry system MWDS-2008 has been applied and methods of analysis have been revised. We extend the analysis of the significant incidence risk and observe that main detrimental effects of external radiation exposure occur after more than about 30 years. For mortality, significant risk was found in males with an excess relative risk per dose of 0.09 (95% CI: 0.02; 0.16) while risk was insignificant for females. With respect to internal radiation exposure no association to risk could be established.     

Adjusting for selection bias in retrospective, case-control studies

Retrospective case–control studies are more susceptibleto selection bias than other epidemiologic studies as by designthey require that both cases and controls are representativeof the same population. However, as cases and control recruitmentprocesses are often different, it is not always obvious thatthe necessary exchangeability conditions hold. Selection biastypically arises when the selection criteria are associatedwith the risk factor under investigation. We develop a methodwhich produces bias-adjusted estimates for the odds ratio. Ourmethod hinges on 2 conditions. The first is that a variablethat separates the risk factor from the selection criteria canbe identified. This is termed the "bias breaking" variable.The second condition is that data can be found such that a bias-correctedestimate of the distribution of the bias breaking variable canbe obtained. We show by means of a set of examples that suchbias breaking variables are not uncommon in epidemiologic settings.We demonstrate using simulations that the estimates of the oddsratios produced by our method are consistently closer to thetrue odds ratio than standard odds ratio estimates using logisticregression. Further, by applying it to a case–controlstudy, we show that our method can help to determine whetherselection bias is present and thus confirm the validity of studyconclusions when no evidence of selection bias can be found.     

An evaluation of several methods for assessing the effects of occupational exposure to radiation

Several methods for the analysis of occupational radiation exposure data, including procedures based on Cox's proportional hazards model, are presented and evaluated. Issues of interest include the contribution of an external control, the effective handling of highly skewed exposure data, and the potential for detecting effects in populations occupationally exposed to radiation. Expressions for evaluating the power of various procedures are derived and applied to data from the Hanford population in order to determine power curves for detecting leukemia effects, with both additive and multiplicative linear models being used. It is found that the introduction of an external control can increase power, although not when an overall adjustment factor must be estimated from the data or when death rates for the study population are substantially lower than those for the control population. It is also found that very little power is lost if exposures are grouped. Finally, the power calculations indicate, as expected, that in analyses of occupationally exposed populations, such as the Hanford workers, there is very little chance of detecting radiation effects at the levels of our current estimates. However, power is reasonably good for detecting effects that are 10 to 15 times larger.     

Analysis of Matched Case–Control Data in Presence of Nonignorable Missing Exposure

Summary.   The present article deals with informative missing (IM) exposure data in matched case–control studies. When the missingness mechanism depends on the unobserved exposure values, modeling the missing data mechanism is inevitable. Therefore, a full likelihood-based approach for handling IM data has been proposed by positing a model for selection probability, and a parametric model for the partially missing exposure variable among the control population along with a disease risk model. We develop an EM algorithm to estimate the model parameters. Three special cases: (a) binary exposure variable, (b) normally distributed exposure variable, and (c) lognormally distributed exposure variable are discussed in detail. The method is illustrated by analyzing a real matched case–control data with missing exposure variable. The performance of the proposed method is evaluated through simulation studies, and the robustness of the proposed method for violation of different types of model assumptions has been considered.