A novel method with improved power to detect recombination hotspots from polymorphism data reveals multiple hotspots in human genes

We introduce a new method for detection of recombination hotspots from population genetic data. This method is based on (a) defining an (approximate) penalized likelihood for how recombination rate varies with physical position and (b) maximizing this penalized likelihood over possible sets of recombination hotspots. Simulation results suggest that this is a more powerful method for detection of hotspots than are existing methods. We apply the method to data from 89 genes sequenced in African American and European American populations. We find many genes with multiple hotspots, and some hotspots show evidence of being population-specific. Our results suggest that hotspots are randomly positioned within genes and could be as frequent as one per 30 kb.     

Estimating major gene effects with partial information using Gibbs sampling

A method for estimating major gene effects using Gibbs sampling to infer genotype of individuals with unknown values, was compared with a standard mixed-model analysis. The purpose of this study was to evaluate the effect of including information of individuals with unknown genotypes on the estimates and their error variances (Ve) of the single-gene effects. When genotypes were known for all the individuals, results using the Gibbs method (GS) were similar to those obtained with the mixed model (MM). In the absence of selection, when information from individuals with unknown genotypes was included, GS yielded unbiased estimates of the major gene effects while reducing the Ve associated with them. This reduction in Ve depended on the gene frequency and mode of action of the major locus. For the additive effect, the reduction in Ve ranged from 29 to 69% of the total reduction which would have been obtained if all individuals had had a known genotype. Similarly the reduction in Ve found for the dominance effect ranged from 12 to 58%. Estimates using GS generally had small detectable biases when the polygenic heritability used in the analysis was inflated or estimated simultaneously. However, the benefit of using information from individuals with unknown genotypes was still maintained when comparing the mean square error of the estimates using either GS or MM when genotypes are only known for a subset of the population. When the population has been under selection, the use of Gibbs sampling to incorporate information of individuals without genotypes reduced substantially the bias and mean square error found for MM analysis on partial data. Nevertheless, there was some bias detected using Gibbs sampling. The gene frequency of the major gene in the base population was also well estimated despite its change over generations due to selection.     

A Gibbs sampling approach to linkage analysis.

We present a Monte Carlo approach to estimation of the recombination fraction theta and the profile likelihood for a dichotomous trait and a single marker gene with 2 alleles. The method is an application of a technique known as 'Gibbs sampling', in which random samples of each of the unknowns (here genotypes, theta and nuisance parameters, including the allele frequencies and the penetrances) are drawn from their posterior distributions, given the data and the current values of all the other unknowns. Upon convergence, the resulting samples derive from the marginal distribution of all the unknowns, given only the data, so that the uncertainty in the specification of the nuisance parameters is reflected in the variance of the posterior distribution of theta. Prior knowledge about the distribution of theta and the nuisance parameters can be incorporated using a Bayesian approach, but adoption of a flat prior for theta and point priors for the nuisance parameters would correspond to the standard likelihood approach. The method is easy to program, runs quickly on a microcomputer, and could be generalized to multiple alleles, multipoint linkage, continuous phenotypes and more complex models of disease etiology. The basic approach is illustrated by application to data on cholesterol levels and an a low-density lipoprotein receptor gene in a single large pedigree.     

A method to detect major serotypes of foot-and-mouth disease virus

Nucleic acid sequence-based amplification (NASBA) is an isothermal technique that allows the rapid amplification of specific regions of nucleic acid obtained from a diverse range of sources. It is especially suitable for amplifying RNA sequences. A rapid and specific NASBA technique was developed, allowing the detection of foot-and-mouth disease virus genetic material in a range of sample material, including preserved skin biopsy material from infected animals, vaccines prepared from denatured cell-free material, and cell-free antigen-based detection kits. A single pair of DNA oligonucleotide primers was able to amplify examples of all major FMD virus subtypes. The amplified viral RNA was detected by electrochemiluminescence. The method was at least as sensitive as existing cell-free antigen detection methods.     

A semiparametric empirical likelihood method for data from an outcome-dependent sampling scheme with a continuous outcome

Outcome-dependent sampling (ODS) schemes can be a cost effective way to enhance study efficiency. The case-control design has been widely used in epidemiologic studies. However, when the outcome is measured on a continuous scale, dichotomizing the outcome could lead to a loss of efficiency. Recent epidemiologic studies have used ODS sampling schemes where, in addition to an overall random sample, there are also a number of supplemental samples that are collected based on a continuous outcome variable. We consider a semiparametric empirical likelihood inference procedure in which the underlying distribution of covariates is treated as a nuisance parameter and is left unspecified. The proposed estimator has asymptotic normality properties. The likelihood ratio statistic using the semiparametric empirical likelihood function has Wilks-type properties in that, under the null, it follows a chi-square distribution asymptotically and is independent of the nuisance parameters. Our simulation results indicate that, for data obtained using an ODS design, the semiparametric empirical likelihood estimator is more efficient than conditional likelihood and probability weighted pseudolikelihood estimators and that ODS designs (along with the proposed estimator) can produce more efficient estimates than simple random sample designs of the same size. We apply the proposed method to analyze a data set from the Collaborative Perinatal Project (CPP), an ongoing environmental epidemiologic study, to assess the relationship between maternal polychlorinated biphenyl (PCB) level and children's IQ test performance.     

Bayesian model to detect phenotype-specific genes for copy number data

ABSTRACT: BACKGROUND: An important question in genetic studies is to determine those genetic variants, in particular CNVs, that arespecific to different groups of individuals. This could help in elucidating differences in disease predispositionand response to pharmaceutical treatments. We propose a Bayesian model designed to analyze thousands of copynumber variants (CNVs) where only few of them are expected to be associated with a specific phenotype. RESULTS: The model is illustrated by analyzing three major human groups belonging to HapMap data. We also show howthe model can be used to determine specific CNVs related to response to treatment in patients diagnosed withovarian cancer. The model is also extended to address the problem of how to adjust for confounding covariates(e.g., population stratification). Through a simulation study, we show that the proposed model outperforms otherapproaches that are typically used to analyze this data when analyzing common copy-number polymorphisms(CNPs) or complex CNVs. We have developed an R package, called bayesGen, that implements the model andestimating algorithms. CONCLUSIONS: Our proposed model is useful to discover specific genetic variants when different subgroups of individuals areanalyzed. The model can address studies with or without control group. By integrating all data in a unique modelwe can obtain a list of genes that are associated with a given phenotype as well as a different list of genes that areshared among the different subtypes of cases.     

Adaptive thresholds to detect differentially expressed genes in microarray data

To detect changes in gene expression data from microarrays, a fixed threshold for fold difference is used widely. However, it is not always guaranteed that a threshold value which is appropriate for highly expressed genes is suitable for lowly expressed genes. In this study, aiming at detecting truly differentially expressed genes from a wide expression range, we proposed an adaptive threshold method (AT). The adaptive thresholds, which have different values for different expression levels, are calculated based on two measurements under the same condition. The sensitivity, specificity and false discovery rate (FDR) of AT were investigated by simulations. The sensitivity and specificity under various noise conditions were greater than 89.7% and 99.32%, respectively. The FDR was smaller than 0.27. These results demonstrated the reliability of the method.     

Application of Gibbs sampling for inference in a mixed major gene-polygenic inheritance model in animal populations

The application of Gibbs sampling is considered for inference in a mixed inheritance model in animal populations. Implementation of the Gibbs sampler on scalar components, as used for human populations, appeared not to be efficient, and an approach with blockwise sampling of genotypes was proposed for use in animal populations. The blockwise sampling of genotypes was proposed for use in animal populations. The blockwise sampling by which genotypes of a sire and its final progeny were sampled jointly was effective in improving mixing, although further improvements could be looked for. Posterior densities of parameters were visualised from Gibbs samples; from the former highly marginalised Bayesian point and interval estimates can be obtained.     

A polymerase chain reaction-based method to detect cisplatin adducts in specific genes.

Every bulky lesion in DNA can potentially inhibit the Taq DNA polymerase and thereby decrease the amplification produced in the polymerase chain reaction. We investigated the feasibility of using this inhibition to quantify DNA lesions produced by the anticancer drug cisplatin. Products were detected by electrophoresis followed by ethidium bromide staining. Quantitation was obtained by including [32P]dCTP in the amplification reaction and subsequently assessing the incorporated radioactivity. Hamster genomic DNA was platinated in vitro to defined levels and amplified with primers that produce either a 150, 750 or 2,000 base pair fragment. The degree of inhibition of PCR agreed with the predicted level of DNA platination in each size of fragment, suggesting that the polymerase was inhibited by every cisplatin-induced lesion. This method was used to detect cisplatin-induced lesions in the adenine phosphoribosyltransferase gene of CHO cells. Cells were incubated with 0-125 microM cisplatin for 2 h, the DNA was purified and subjected to PCR. A significant decrease in amplification of the 2 kbp fragment was observed in DNA from cells incubated with cisplatin at 75 microM. The degree of inhibition agreed closely with the amount of DNA damage in the overall genome as measured by atomic absorption. No change was detected in amplification of the 150 base fragment which can therefore be used to normalize data for any variations between DNA samples. This assay has the same sensitivity as other methods currently used for the analysis of gene-specific damage. The advantage of this assay is that it obviates the need for specific endonuclease complexes to recognize and cleave DNA adducts as previously required when analyzing damage in specific genomic sequences.     

Molecular phylogenetics of Linaceae with complete generic sampling and data from two plastid genes

The phylogeny of Linaceae is examined, with sampling from the 13 commonly recognized genera of the family and sequence data from the plastid genes matK and rbcL. Representatives of 24 additional families of the order Malpighiales are included in the analyses, with members of Celastrales, Fabales, Fagales, Oxalidales and Rosales used as outgroups. Linaceae and both subfamilies, the temperate Linoideae and the tropical Hugonioideae, are found to be monophyletic in likelihood‐ and parsimony‐based analyses, although the monophyly of Hugonioideae is not well supported. Average divergence time estimates using rbcL indicate that the subfamilies diverged from each other during the Palaeocene, approximately 60 million years ago. No sister group to Linaceae is consistently identified in these analyses, and relationships among families of Malpighiales are not well resolved. In accord with previous estimates of Linoideae phylogeny, Linum is shown to be nonmonophyletic, with several segregate genera nested within it, but the relationships of the south‐east Asian genera, Anisadenia, Reinwardtia and Tirpitzia, remain uncertain. In Hugonioideae, Indorouchera and Philbornea are found to be closely related to members of Hugonia section Durandea. Relationships of the neotropical genera Hebepetalum and Roucheria to the palaeotropical hugonioids are not consistently resolved. © 2010 The Linnean Society of London, Botanical Journal of the Linnean Society, 2011, 165 , 64–83.     

Developing an efficient multiplex PCR method to detect mcr-like genes

<正>Dear Editor,Since 2016, a growing number of mobile colistin resistance(mcr) genes have been identified and characterized (Liu et al., 2016). In addition to mcr-1 and its variants, mcr-2 to mcr-8 have now been reported, which reflects a significant threat to public health and agricultural production (Sun et al.,2018). These diverse new genes (mcr-2 to mcr-8) share     

Responses to selection on genotypic or phenotypic values in the presence of genes with major effects

Summary Average genotypic responses were compared after selection for genotypic values and for phenotypic values on the basis of single-gene models and multigene models in simulated livestock populations. Single-gene models dealt with single gene control of the genetic differences between animals, while multigene models considered a collection of genes with various magnitudes of effects on a trait. In each case, selection lasted through discrete generations until the fixation of the gene frequencies occurred. Generations to reach fixation were used to compare various models, and the two criteria for selection, for their efficiency in selection. Implications of using these models versus using infinitesimal models for selection in practice are presented.     

A Bayesian approach to finite mixture models in bioassay via data augmentation and Gibbs sampling and its application to-insecticide resistance

After continued treatment with an insecticide, within the population of the susceptible insects, resistant strains will occur. It is important to know whether there are any resistant strains, what the proportions are, and what the median lethal doses are for the insecticide. Lwin and Martin (1989, Biometrics 45, 721-732) propose a probit mixture model and use the EM algorithm to obtain the maximum likelihood estimates for the parameters. This approach has difficulties in estimating the confidence intervals and in testing the number of components. We propose a Bayesian approach to obtaining the credible intervals for the location and scale of the tolerances in each component and for the mixture proportions by using data augmentation and Gibbs sampler. We use Bayes factor for model selection and determining the number of components. We illustrate the method with data published in Lwin and Martin (1989).     

A maximum likelihood-based method for mining major genes affecting a quantitative character

In this article, we present a maximum likelihood-based analytical approach for detecting a major gene of large effect on a quantitative trait in a progeny population derived from a mating design. Our analysis is based on a mixed genetic model specifying both major gene and background polygenic inheritance. The likelihood of the data is formulated by combining the information about population behaviors of the major gene during hybridization and its phenotypic distribution densities. The EM algorithm is implemented to obtain maximum likelihood estimates for population and quantitative genetic parameters of the major locus. This approach is applied to detect an overdominant gene governing stem volume growth in a factorial mating design of aspen trees. It is suggested that further molecular genetic research toward mapping single genes affecting aspen growth and production based on the same experimental data has a high probability of success.