Data on likelihood ratios of two-person DNA mixtures interpreted using semi- and fully continuous systems.
Ontology highlight
ABSTRACT:
SUBMITTER: Rodriguez JJRB
PROVIDER: S-EPMC6811899 | biostudies-other | 2019 Oct
REPOSITORIES: biostudies-other
Publications
Data on likelihood ratios of two-person DNA mixtures interpreted using semi- and fully continuous systems.
Rodriguez Jae Joseph Russell B JJRB Bright Jo-Anne JA Salvador Jazelyn M JM Laude Rita P RP De Ungria Maria Corazon A MCA
Data in brief 20190904
In the paper, "Probabilistic approaches to interpreting two-person DNA mixtures from post-coital specimens" [1], we analysed 102 two-person DNA samples from simulated mixtures and male-female and male-male post-coital specimens. We report here data on profile characteristics of these samples and likelihood ratios (LRs) generated using semi- and fully continuous systems. Both log<sub>10</sub> LRs from true and non-contributor tests are presented. These data may supplement studies comparing perfor ...[more]
Similar Datasets
Project description:A likelihood ratio (LR) system is defined as the entire pipeline of the measurement and interpretation processes where probabilistic genotyping software (PGS) is a piece of the whole LR system. To gain understanding on how two LR systems perform, a total of 154 two-person, 147 three-person, and 127 four-person mixture profiles of varying DNA quality, DNA quantity, and mixture ratios were obtained from the filtered (.CSV) files of the GlobalFiler 29 cycles 15s PROVEDIt dataset and deconvolved in two independently developed fully continuous programs, STRmix v2.6 and EuroForMix v2.1.0. Various parameters were set in each software and LR computations obtained from the two software were based on same/fixed EPG features, same pair of propositions, number of contributors, theta, and population allele frequencies. The ability of each LR system to discriminate between contributor (H1-true) and non-contributor (H2-true) scenarios was evaluated qualitatively and quantitatively. Differences in the numeric LR values and their corresponding verbal classifications between the two LR systems were compared. The magnitude of the differences in the assigned LRs and the potential explanations for the observed differences greater than or equal to 3 on the log10 scale were described. Cases of LR < 1 for H1-true tests and LR > 1 for H2-true tests were also discussed. Our intent is to demonstrate the value of using a publicly available ground truth known mixture dataset to assess discrimination performance of any LR system and show the steps used to understand similarities and differences between different LR systems. We share our observations with the forensic community and describe how examining more than one PGS with similar discrimination power can be beneficial, help analysts compare interpretation especially with low-template profiles or minor contributor cases, and be a potential additional diagnostic check even if software in use does contain certain diagnostic statistics as part of the output.
| S-EPMC8448353 | biostudies-literature
Project description:Prioritizing individual rare variants within associated genes or regions often consists of an ad hoc combination of statistical and biological considerations. From the statistical perspective, rare variants are often ranked using Fisher's exact p values, which can lead to different rankings of the same set of variants depending on whether 1- or 2-sided p values are used.We propose a likelihood ratio-based measure, maxLRc, for the statistical component of ranking rare variants under a case-control study design that avoids the hypothesis-testing paradigm. We prove analytically that the maxLRc is always well-defined, even when the data has zero cell counts in the 2×2 disease-variant table. Via simulation, we show that the maxLRc outperforms Fisher's exact p values in most practical scenarios considered. Using next-generation sequence data from 27 rolandic epilepsy cases and 200 controls in a region previously shown to be linked to and associated with rolandic epilepsy, we demonstrate that rankings assigned by the maxLRc and exact p values can differ substantially.The maxLRc provides reliable statistical prioritization of rare variants using only the observed data, avoiding the need to specify parameters associated with hypothesis testing that can result in ranking discrepancies across p value procedures; and it is applicable to common variant prioritization.
| S-EPMC4759929 | biostudies-literature
Project description:Semi-continuous data, also known as zero-inflated continuous data, have a substantial portion of responses equal to a single value (typically 0) and a continuous, right-skewed distribution among the remaining positive values. For jointly modeling multivariate clustered semi-continuous responses, the covariate effects in the positive parts can be proportionally constrained to the covariate effects in the logistic part, yielding a multivariate two-part fixed effects model. It is shown that, both theoretically and experimentally, the proportionally constrained model is more efficient than the unconstrained model in terms of parameter estimation, and thus provides a deeper understanding of the data structure when the proportionality structure holds. A robust variance estimation method is also introduced and tested under various model mis-specified cases. The proposed model is applied to data from a randomized controlled trial evaluating potential preventive effects of meditation or exercise on duration and severity of acute respiratory infection illness. The new analysis infers that meditation not only has highly significant effects on reduction of acute respiratory infection severity and duration, but also has significant effects on preventing acute respiratory infection, which was not previously reported in the literature.
| S-EPMC6494713 | biostudies-literature
Project description:Despite increased recognition of the importance of evidence-based assessment in clinical psychology, utilization of gold-standard practices remains low, including during diagnostic assessments. One avenue to streamline evidence-based diagnostic assessment is to increase the use of diagnostic likelihood ratios (DLRs), derived from receiver operating characteristic curve analyses. DLRs allow for the adjustment of the likelihood that an individual has a disorder based on self-report data (e.g., questionnaires, psychosocial, family history). Although DLRs provide strong and readily implementable psychometric data to guide diagnostic decision-making, analyses necessary to derive DLRs are not commonplace in psychological curriculum and available resources require familiarity with specialized statistical methodologies and software. We developed a free, researcher-oriented dashboard, shinyDLRs (https://dlrs.shinyapps.io/shinyDLRs/), to facilitate the derivation of DLRs. shinyDLRs allows researchers to carry out multiple analyses while providing descriptive interpretations of statistics derived from receiver operating characteristic curves. We present the utility of this interface as applied to several freely available measures of mood and anxiety for the purposes of guiding diagnosis of psychopathology. The sample leveraged to accomplish this goal included 576 youth, 4-19 years of age, and a parent informant, both of whom completed several questionnaires and semi-structured interviews prior to participating in treatment at a university-based research clinic. Lastly, we provide recommendations for inclusion of DLRs in future research investigating the psychometric properties and diagnostic utility of assessments. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
| S-EPMC10167966 | biostudies-literature
Project description:Cationic alkyltrimethylammonium bromides (CnTAB, with n = 8, 12, 16, 18) and their mixtures with n-octanol as a nonionic surfactant were chosen as a model system to study the synergistic effect on foamability (two-phase system) and floatability (three-phase system) of quartz in the presence of binary mixtures of ionic/nonionic surfactants. The foam height of one-component solutions and binary mixtures and floatability of quartz particles were characterized as a function of the surfactant concentration and the number of carbons (n) in the alkyl chain of CnTAB. The experimental results of foamability and floatability measurements in one-component and mixed solutions revealed the synergistic effect, causing a significant enhancement in the foam height and recovery of quartz. In the presence of n-octanol, the height of foam increased remarkably for all CnTAB solutions studied, and this effect, whose magnitude depended on the CnTAB hydrophobic tail length, could not be justified by a simple increase in total surfactant concentration. A similar picture was obtained in the case of flotation response. The mechanism of synergistic effect observed in mixed CnTAB/n-octanol solutions was proposed. The discussion was supported by molecular dynamics simulations, and the probable mechanism responsible for synergism was discussed. In addition, an analysis allowing accurate determination of the concentration regimes, where the synergistic effect can be expected, was given. It was shown that for the two-phase system, the n-octanol molecule preadsorption at the liquid/gas interface causes an increase in CnTAB adsorption coverage over the level expected from its equilibrium value in the one-component solution. In the case of the three-phase system, the synergistic effect was related to the ionic surfactants serving as an anchor layer for n-octanol, which, in water/n-octanol solution (one-component system), do not adsorb on the surface of quartz.
| S-EPMC8154601 | biostudies-literature
Project description:In modern whole-genome scans, the use of stringent thresholds to control the genome-wide testing error distorts the estimation process, producing estimated effect sizes that may be on average far greater in magnitude than the true effect sizes. We introduce a method, based on the estimate of genetic effect and its standard error as reported by standard statistical software, to correct for this bias in case-control association studies. Our approach is widely applicable, is far easier to implement than competing approaches, and may often be applied to published studies without access to the original data. We evaluate the performance of our approach via extensive simulations for a range of genetic models, minor allele frequencies, and genetic effect sizes. Compared to the naive estimation procedure, our approach reduces the bias and the mean squared error, especially for modest effect sizes. We also develop a principled method to construct confidence intervals for the genetic effect that acknowledges the conditioning on statistical significance. Our approach is described in the specific context of odds ratios and logistic modeling but is more widely applicable. Application to recently published data sets demonstrates the relevance of our approach to modern genome scans.
| S-EPMC2665019 | biostudies-literature
Project description:BackgroundMissense (amino-acid changing) variants found in cancer predisposition genes often create difficulties when clinically interpreting genetic testing results. Although bioinformatics has developed approaches to predicting the impact of these variants, many of these approaches have not been readily applicable in the clinical setting. Bioinformatics approaches for predicting the impact of these variants have not yet found their footing in clinical practice because 1) interpreting the medical relevance of predictive scores is difficult; 2) the relationship between bioinformatics "predictors" (sequence conservation, protein structure) and cancer susceptibility is not understood.Methodology/principal findingsWe present a computational method that produces a probabilistic likelihood ratio predictive of whether a missense variant impairs protein function. We apply the method to a tumor suppressor gene, BRCA2, whose loss of function is important to cancer susceptibility. Protein likelihood ratios are computed for 229 unclassified variants found in individuals from high-risk breast/ovarian cancer families. We map the variants onto a protein structure model, and suggest that a cluster of predicted deleterious variants in the BRCA2 OB1 domain may destabilize BRCA2 and a protein binding partner, the small acidic protein DSS1. We compare our predictions with variant "re-classifications" provided by Myriad Genetics, a biotechnology company that holds the patent on BRCA2 genetic testing in the U.S., and with classifications made by an established medical genetics model [1]. Our approach uses bioinformatics data that is independent of these genetics-based classifications and yet shows significant agreement with them. Preliminary results indicate that our method is less likely to make false positive errors than other bioinformatics methods, which were designed to predict the impact of missense mutations in general.Conclusions/significanceMissense mutations are the most common disease-producing genetic variants. We present a fast, scalable bioinformatics method that integrates information about protein sequence, conservation, and structure in a likelihood ratio that can be integrated with medical genetics likelihood ratios. The protein likelihood ratio, together with medical genetics likelihood ratios, can be used by clinicians and counselors to communicate the relevance of a VUS to the individual who has that VUS. The approach described here is generalizable to regions of any tumor suppressor gene that have been structurally determined by X-ray crystallography or for which a protein homology model can be built.
| S-EPMC2587343 | biostudies-literature
Project description:PurposeTo present a methodology for calculating likelihood ratios for glaucoma diagnosis for continuous retinal nerve fiber layer (RNFL) thickness measurements from spectral-domain optical coherence tomography (spectral-domain OCT).DesignObservational cohort study.MethodsA total of 262 eyes of 187 patients with glaucoma and 190 eyes of 100 control subjects were included in the study. Subjects were recruited from the Diagnostic Innovations Glaucoma Study. Eyes with preperimetric and perimetric glaucomatous damage were included in the glaucoma group. The control group was composed of healthy eyes with normal visual fields from subjects recruited from the general population. All eyes underwent RNFL imaging with Spectralis spectral-domain OCT. Likelihood ratios for glaucoma diagnosis were estimated for specific global RNFL thickness measurements using a methodology based on estimating the tangents to the receiver operating characteristic (ROC) curve.ResultsLikelihood ratios could be determined for continuous values of average RNFL thickness. Average RNFL thickness values lower than 86 μm were associated with positive likelihood ratios (ie, likelihood ratios greater than 1), whereas RNFL thickness values higher than 86 μm were associated with negative likelihood ratios (ie, likelihood ratios smaller than 1). A modified Fagan nomogram was provided to assist calculation of posttest probability of disease from the calculated likelihood ratios and pretest probability of disease.ConclusionThe methodology allowed calculation of likelihood ratios for specific RNFL thickness values. By avoiding arbitrary categorization of test results, it potentially allows for an improved integration of test results into diagnostic clinical decision making.
| S-EPMC4081013 | biostudies-literature
Project description:ObjectivesRotator cuff tear is a common cause of shoulder diseases. Correct diagnosis of rotator cuff tears can save patients from further invasive, costly and painful tests. This study used predictive data mining and Bayesian theory to improve the accuracy of diagnosing rotator cuff tears by clinical examination alone.MethodsIn this retrospective study, 169 patients who had a preliminary diagnosis of rotator cuff tear on the basis of clinical evaluation followed by confirmatory MRI between 2007 and 2011 were identified. MRI was used as a reference standard to classify rotator cuff tears. The predictor variable was the clinical assessment results, which consisted of 16 attributes. This study employed 2 data mining methods (ANN and the decision tree) and a statistical method (logistic regression) to classify the rotator cuff diagnosis into "tear" and "no tear" groups. Likelihood ratio and Bayesian theory were applied to estimate the probability of rotator cuff tears based on the results of the prediction models.ResultsOur proposed data mining procedures outperformed the classic statistical method. The correction rate, sensitivity, specificity and area under the ROC curve of predicting a rotator cuff tear were statistical better in the ANN and decision tree models compared to logistic regression. Based on likelihood ratios derived from our prediction models, Fagan's nomogram could be constructed to assess the probability of a patient who has a rotator cuff tear using a pretest probability and a prediction result (tear or no tear).ConclusionsOur predictive data mining models, combined with likelihood ratios and Bayesian theory, appear to be good tools to classify rotator cuff tears as well as determine the probability of the presence of the disease to enhance diagnostic decision making for rotator cuff tears.
| S-EPMC3986413 | biostudies-literature
Project description:In this paper, we present a framework for automated shape differentiation in the finite element software NGSolve. Our approach combines the mathematical Lagrangian approach for differentiating PDE-constrained shape functions with the automated differentiation capabilities of NGSolve. The user can decide which degree of automatisation is required, thus allowing for either a more custom-like or black-box–like behaviour of the software. We discuss the automatic generation of first- and second-order shape derivatives for unconstrained model problems as well as for more realistic problems that are constrained by different types of partial differential equations. We consider linear as well as nonlinear problems and also problems which are posed on surfaces. In numerical experiments, we verify the accuracy of the computed derivatives via a Taylor test. Finally, we present first- and second-order shape optimisation algorithms and illustrate them for several numerical optimisation examples ranging from nonlinear elasticity to Maxwell’s equations. Supplementary Information The online version contains supplementary material available at 10.1007/s00158-020-02742-w.
| S-EPMC8550642 | biostudies-literature