A statistical framework for quantitative trait mapping.

Coupled with the reduction in sequencing costs, the number of RAD-seq analysis have been surging, generating vast genetic knowledge in relation with many crops. Specialized platforms might be intimidating to non-expert users and difficult to implement on each computer despite the growing interest in the usage of the dataset obtained by high-throughput sequencing. Therefore, RAD-R scripts were developed on Windows10 for RAD-seq analysis, allowing users who are not familiar with bioinformatics to easily analyze big sequence data. These RAD-R scripts that run a flow from raw sequence reads of F2 population for the self-fertilization plants to the linkage map construction as well as the QTL analysis can be also useful to many users with limited experience due to the simplicity of copying Excel cells into the R console. During the comparison of linkage maps constructed by RAD-R scripts and Stacks, RAD-R scripts were shown to construct the linkage map with less missing genotype data and a shorter total genetic distance. QTL analysis results can be easily obtained by selecting the reliable genotype data that is visually inferred to be appropriate for error correction from the genotype data files created by RAD-R scripts.

This article describes the R package mcglm implemented for fitting multivariate covariance generalized linear models (McGLMs). McGLMs provide a general statistical modeling framework for normal and non-normal multivariate data analysis, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link function and a matrix linear predictor involving known symmetric matrices. The models take non-normality into account in the conventional way by means of a variance function, and the mean structure is modeled by means of a link function and a linear predictor. The models are fitted using an estimating function approach based on second-moment assumptions. This provides a unified approach to a wide variety of different types of response variables and covariance structures, including multivariate extensions of repeated measures, time series, longitudinal, genetic, spatial and spatio-temporal structures. The mcglm package allows a flexible specification of the mean and covariance structures, and explicitly deals with multivariate response variables, through a user friendly formula interface similar to the ordinary glm function. Illustrations in this article cover a wide range of applications from the traditional one response variable Gaussian mixed models to multivariate spatial models for areal data using the multivariate Tweedie distribution. Additional features, such as robust and bias-corrected standard errors for regression parameters, residual analysis, measures of goodness-of-fit and model selection using the score information criterion are discussed through six worked examples. The mcglm package is a full R implementation based on the Matrix package which provides efficient access to BLAS (basic linear algebra subroutines), Lapack (dense matrix), TAUCS (sparse matrix) and UMFPACK (sparse matrix) routines for efficient linear algebra in R.

Many species on Earth can carry up to two different versions of a given gene, with each of these ‘alleles’ having only a 50/50 chance of being transmitted to the next generation via sexual reproduction. Certain ‘selfish’ sequences, however, can hijack this process and increase their probability of being passed on to an offspring. Known as transmission distortion, this phenomenon may result in alleles spreading through the population even if they are detrimental to fertility. Transmission distortion has been detected in many species such as flies, mice and some plants. It can take place at various stages during reproduction; for example, the selfish alleles may become overrepresented among eggs or sperm. However, scientists need to study a large number of offspring or reproductive cells to be able to detect whether an allele is inherited more often than expected. This has made it difficult to determine whether transmission distortion also happens in humans, and research so far has resulted in conflicting conclusions. A recently published dataset of human sperm from 25 donors offered Carioscia, Weaver et al. the opportunity to examine this question. Every volunteer had produced between 969 and 3377 sperm cells, each with about 1% of their genome sequenced. Carioscia, Weaver et al. developed a computational method, which they named rhapsodi, that allowed them to ‘fill in the gaps’ and infer missing regions of the genome for each cell. To do so, they relied on the fact that sperm cells from a given individual are highly related to one another. With this more complete data at hand, it became possible to look for evidence of transmission distortion by searching for alleles that were overrepresented in sperm from a given donor. No selfish sequence could be detected in any of the 25 individuals, suggesting that human sperm may not be subject to pervasive transmission distortion. Signatures of selfish alleles detected in previous human studies may have therefore not resulted from this mechanism taking place at the sperm level. Instead, transmission distortion in humans could primarily target eggs or happen at later stages (for instance, if embryos carrying the selfish allele have better chances of survival). The ‘rhapsodi’ method developed by Carioscia, Weaver et al. should allow other scientists to work with datasets for which large portions of the genetic information is missing. It may therefore become easier for researchers to track selfish alleles which are difficult to detect, and to examine bigger, more diverse samples which also include individuals with known fertility challenges.

Testing for Hardy-Weinberg equilibrium (HWE) is an important component in almost all analyses of population genetic data. Genetic markers that violate HWE are often treated as special cases; for example, they may be flagged as possible genotyping errors, or they may be investigated more closely for evolutionary signatures of interest. The presence of population structure is one reason why genetic markers may fail a test of HWE. This is problematic because almost all natural populations studied in the modern setting show some degree of structure. Therefore, it is important to be able to detect deviations from HWE for reasons other than structure. To this end, we extend statistical tests of HWE to allow for population structure, which we call a test of "structural HWE." Additionally, our new test allows one to automatically choose tuning parameters and identify accurate models of structure. We demonstrate our approach on several important studies, provide theoretical justification for the test, and present empirical evidence for its utility. We anticipate the proposed test will be useful in a broad range of analyses of genome-wide population genetic data.

This thesis concerns probability model development and analysis for genetic data. There are two studies involved. One study focuses on inference and study design for applications where objects of different types are observed. We apply a Bayesian hierarchical model to estimate the total number of categories in a region, and then use a Monte Carlo simulation approach to design future sampling. Specifically, the Monte Carlo simulation method is used to determine how large an extra sample is needed to guarantee that a certain proportion of all categories can be collected with a specified confidence. We apply the method to DNA sequence data. Some important properties of the proposed model are investigated through simulations. The second study uses genetic marker information to identify ancestors of a given individual in the presence of genotyping errors. We extend an existing probability model to calculate the probability that a particular inbred line is an ancestor of the given hybrid, accounting for genotyping errors. A simulation study indicates that if misclassification is ignored, ancestry probabilities can be overestimated. We use the maximum likelihood estimate (MLE) as the estimate for the error rate. The developed methodology is then applied to simulated data and a genetic data set containing maize Simple Sequence Repeats (SSR) profile markers.

Subcutaneous (sc) administration of long-acting insulin analogs is often employed in multiple daily injection (MDI) therapy of type 1 diabetes (T1D) to cover patient's basal insulin needs. Among these, insulin glargine 100U/mL (Gla-100) and 300 U/mL (Gla-300) are formulations indicated for once daily sc administration in MDI therapy of T1D. A few semi-mechanistic models of sc absorption of insulin glargine have been proposed in the literature, but were not quantitatively assessed on a large dataset. The aim of this paper is to propose a model of sc absorption of insulin glargine able to describe the data and provide precise model parameters estimates with a clear physiological interpretation. Three candidate models were identified on a total of 47 and 77 insulin profiles of T1D subjects receiving a single or repeated sc administration of Gla-100 or Gla-300, respectively. Model comparison and selection were performed on the basis of their ability to describe the data and numerical identifiability. The most parsimonious model is linear two-compartment and accounts for the insulin distribution between the two compartments after sc administration through parameter k. Between the two formulations, we report a lower fraction of insulin in the first versus second compartment (k = 86% versus 94% in Gla-100 versus Gla-300, p < 0.05), a lower dissolution rate from the first to the second compartment ([Formula: see text] versus 0.0008 min-1 in Gla-100 versus Gla-300, p << 0.001), and a similar rate of insulin absorption from the second compartment to plasma ([Formula: see text] versus 0.0016 min-1 in Gla-100 versus Gla-300, p = NS), in accordance with the mechanisms of insulin glargine protraction. The proposed model is able to both accurately describe plasma insulin data after sc administration and precisely estimate physiologically plausible parameters. The model can be incorporated in simulation platforms potentially usable for optimizing basal insulin treatment strategies.

The accuracy of the geomagnetic field secular variation (SV), obtained using different methods of analysis based on different data sets, has been estimated. A comparison of different SV models (developed by different groups of researchers) as candidates for compiling the IGRF 10th generation is the example of such an estimation. These models were obtained based on the Oersted and CHAMP satellite data. In the present work these models are compared in order to estimate the modeling algorithms and the effect of initial data selection on modeling results. To solve this problem, several SV models have been constructed based on the independent algorithms and data sets. The differences between the candidate models using different data proved to be of the same order. A comparison of the spatial power spectra indicated that all models constructed using data subjected to the frequency selection considerably differ from the models the data of which were not selected.

Bicistronic reporter assay systems have become a mainstay of molecular biology. While the assays themselves encompass a broad range of diverse and unrelated experimental protocols, the numerical data garnered from these experiments often have similar statistical properties. In general, a primary dataset measures the paired expression of two internally controlled reporter genes. The expression ratio of these two genes is then normalized to an external control reporter. The end result is a ‘ratio of ratios’ that is inherently sensitive to propagation of the error contributed by each of the respective numerical components. The statistical analysis of this data therefore requires careful handling in order to control for the propagation of error and its potentially misleading effects. A careful survey of the literature found no consistent method for the statistical analysis of data generated from these important and informative assay systems. In this report, we present a detailed statistical framework for the systematic analysis of data obtained from bicistronic reporter assay systems. Specifically, a dual luciferase reporter assay was employed to measure the efficiency of four programmed −1 frameshift signals. These frameshift signals originate from the L-A virus, the SARS-associated Coronavirus and computationally identified frameshift signals from two Saccharomyces cerevisiae genes. Furthermore, these statistical methods were applied to prove that the effects of anisomycin on programmed −1 frameshifting are statistically significant. A set of Microsoft Excel spreadsheets, which can be used as templates for data generated by dual reporter assay systems, and an online tutorial are available at our website (http://dinmanlab.umd.edu/statistics). These spreadsheets could be easily adapted to any bicistronic reporter assay system.

Integrative Bayesian analysis of neuroimaging-genetic data with application to cocaine dependence

We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc.

Natural tags based on DNA fingerprints or natural features of animals are now becoming very widely used in wildlife population biology. However, classic capture-recapture models do not allow for misidentification of animals which is a potentially very serious problem with natural tags. Statistical analysis of misidentification processes is extremely difficult using traditional likelihood methods but is easily handled using Bayesian methods. We present a general framework for Bayesian analysis of categorical data arising from a latent multinomial distribution. Although our work is motivated by a specific model for misidentification in closed population capture-recapture analyses, with crucial assumptions which may not always be appropriate, the methods we develop extend naturally to a variety of other models with similar structure. Suppose that observed frequencies f are a known linear transformation f=A'x of a latent multinomial variable x with cell probability vector pi=pi(theta). Given that full conditional distributions [theta | x] can be sampled, implementation of Gibbs sampling requires only that we can sample from the full conditional distribution [x | f, theta], which is made possible by knowledge of the null space of A'. We illustrate the approach using two data sets with individual misidentification, one simulated, the other summarizing recapture data for salamanders based on natural marks.

Equilibrium binding experiments are widely used for the accurate characterization of binding and competitive binding behavior in biological systems. Modern high-throughput discovery efforts in chemical biology rely heavily upon this principle. Here, we derive exact analytical expressions for general competitive binding models which can also explain a commonly encountered phenomenon in these types of experiments, anticooperative incomplete displacement. We explore the effects of nonspecific binding behavior and parameter misestimation. All expressions are derived in terms of total concentrations determined a priori. We discuss a general framework for high-throughput screening assays based on fluorescence polarization and strategies for assay development, sensitivity regimes, data quality control, analysis, and ranking. Theoretical findings are visualized by simulations using realistic parameter sets. Our results are the basis for the discovery of small-molecule inhibitors of the protein-protein interaction between human calcineurin and NFAT transcription factors, as discussed in the subsequent paper (31).

We present a general framework of exploratory data analysis defining a set of concepts and prototypes developed within the Lisp-Stat programming environment for a M-to-M links multidimensional approach. We overview the main domains and fundamentals on which we lay the developed interactive GIS. In a second stage, we detail the different prototypes we implemented in a software called ARPEGE' and how they collaborate in providing an interactive spatial data exploration. Then we show four examples of concrete geographical applications and their underlaying data models. We end on a discussion about the contribution and the limitations of our conceptual framework and its associated software, and open to future research.

Faculty Opinions recommendation of A general framework for development and data analysis of competitive high-throughput screens for small-molecule inhibitors of protein-protein interactions by fluorescence polarization.

In this paper, we propose a method of specific Correspondence Analysis which allows to treat questionnaires when some responses are missing, and thus to free oneself from the yoke of complete disjunctive encoding. The method of specific analysis is presented within the general framework of Geometric Data Analysis for a Euclidean cloud, then particularized to multinumerical protocols and to questionnaires. We show that, in this approach, beweighted Principal Component Analysis (PCA) is privileged and that Multiples Correspondence Analysis (MCA) is equivalent to a biweighted PCA on indicator variables. Finally, we compare the specific analysis to the conventional one by writing inequalities between eigenvalues and studying the rotation of principal subspaces when one goes from the global analysis to the specific one.