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.     

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.     

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.     

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.     

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.     

Analysis of a Gibbs sampler method for model-based clustering of gene expression data

MOTIVATION: Over the last decade, a large variety of clustering algorithms have been developed to detect coregulatory relationships among genes from microarray gene expression data. Model-based clustering approaches have emerged as statistically well-grounded methods, but the properties of these algorithms when applied to large-scale data sets are not always well understood. An in-depth analysis can reveal important insights about the performance of the algorithm, the expected quality of the output clusters, and the possibilities for extracting more relevant information out of a particular data set. RESULTS: We have extended an existing algorithm for model-based clustering of genes to simultaneously cluster genes and conditions, and used three large compendia of gene expression data for Saccharomyces cerevisiae to analyze its properties. The algorithm uses a Bayesian approach and a Gibbs sampling procedure to iteratively update the cluster assignment of each gene and condition. For large-scale data sets, the posterior distribution is strongly peaked on a limited number of equiprobable clusterings. A GO annotation analysis shows that these local maxima are all biologically equally significant, and that simultaneously clustering genes and conditions performs better than only clustering genes and assuming independent conditions. A collection of distinct equivalent clusterings can be summarized as a weighted graph on the set of genes, from which we extract fuzzy, overlapping clusters using a graph spectral method. The cores of these fuzzy clusters contain tight sets of strongly coexpressed genes, while the overlaps exhibit relations between genes showing only partial coexpression. AVAILABILITY: GaneSh, a Java package for coclustering, is available under the terms of the GNU General Public License from our website at http://bioinformatics.psb.ugent.be/software     

Line and crossing effects in a diallel mating system with highly inbred lines of White Leghorn chickens

Summary Seven highly inbred lines of White Leghorn chickens were used in a near complete diallel mating plan during eight years. The lines originated from three different base populations selected for egg weight. Average inbreeding coefficients of parents of chicks hatching in successive years were 0.75, 0.80, 0.84, 0.86, 0.89, 0.91, 0.93 and 0.94. The composition of line, specific combining ability and reciprocal effects and their estimated values are given. These effects were estimated for age at first egg (AFE), average weight of all eggs laid to 40 weeks (EW40), body weight at 40 weeks (BW40), number of eggs to 40 weeks (EP40) and number of eggs between 41 and 60 weeks (EP60). Records of 3247 hens surviving to 40 weeks and of 3133 birds to 60 weeks could be used. Large differences between line effects could be found in all traits. They were only partly due to the preceding selection in the base populations. All specific combining effects were in the expected direction, negative for AFE and positive for EW40, BW40, EP40 and EP60. Recovery of inbreeding depression inflated these effects rather substancially. Average heterosis, defined as the relative superiority of a line combination over the mid parent value, was –11.3%, 5.8%, 7.8%, 45.1% and 35.8% for AFE, EW40, BW40, EP40 and EP60 respectively. One line showed a relative superiority in AFE of -19.3% compared to about –7.9% for all other combinations. Reciprocal or sex-linked effects were generally smaller in all traits than specific combining effects, they were considerably smaller in AFE, EP40 and EP60. General reciprocal effects could be found for several lines in one or more traits. Offspring of two lines, when used as sire lines, showed a negative correlation between reciprocal effects of egg weight and body weight.     

Estimating genetic correlations based on phenotypic data: a simulation-based method

Knowledge of genetic correlations is essential to understand the joint evolution of traits through correlated responses to selection, a difficult and seldom, very precise task even with easy-to-breed species. Here, a simulation-based method to estimate genetic correlations and genetic covariances that relies only on phenotypic measurements is proposed. The method does not require any degree of relatedness in the sampled individuals. Extensive numerical results suggest that the propose method may provide relatively efficient estimates regardless of sample sizes and contributions from common environmental effects.