Improving predictive ability in sparse testing designs in soybean populations
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ABSTRACT:
SUBMITTER: Persa R
PROVIDER: S-EPMC10701390 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:The development of improved cultivars requires establishing multi-environment trials (METs) to evaluate their performance under a wide range of environmental conditions. However, the high phenotyping costs often limit the capacity to evaluate genotypes in all the target environments. Our main objective was to explore the potential of implementing sparse testing in cassava breeding programs to reduce the cost of phenotyping in METs. The population used in this study consisted of 435 cassava genotypes evaluated in five environments in Nigeria for dry matter (dm) and fresh root yield (fyld). Sparse testing designs were developed based on non-overlapping (NOL), completely overlapping (OL), and intermediates between NOL and OL genotypes. Three prediction models were assessed (one based on phenotypes only, while two had genomic data). All the three models had a higher predictive ability and a lower mean square error (MSE) when a large training set was used. Predictive ability increased and MSE reduced when genotype-by-environment interaction (G × E) was modeled for the same training set sizes and allocations. Predictive ability decreased while MSE increased with the increasing OL genotypes across the environments, suggesting that only a few OL genotypes may be required to set up METs for model training. Sparse testing using a model incorporating G × E could be implemented to reduce cost of phenotyping in cassava METs. If data were available, integrating crop growth models (CGMs) with genomic prediction holds the potential to improve predictive ability. The training population used for sparse testing could be optimized to determine the optimal size and distribution of genotypes to increase the predictive ability and reduce cost under a fixed budget.
| S-EPMC11800058 | biostudies-literature
Project description:With the abundance of large data, sparse penalized regression techniques are commonly used in data analysis due to the advantage of simultaneous variable selection and estimation. A number of convex as well as non-convex penalties have been proposed in the literature to achieve sparse estimates. Despite intense work in this area, how to perform valid inference for sparse penalized regression with a general penalty remains to be an active research problem. In this paper, by making use of state-of-the-art optimization tools in stochastic variational inequality theory, we propose a unified framework to construct confidence intervals for sparse penalized regression with a wide range of penalties, including convex and non-convex penalties. We study the inference for parameters under the population version of the penalized regression as well as parameters of the underlying linear model. Theoretical convergence properties of the proposed method are obtained. Several simulated and real data examples are presented to demonstrate the validity and effectiveness of the proposed inference procedure.
| S-EPMC7716883 | biostudies-literature
Project description:Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer "targeted" drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research.
| S-EPMC4737867 | biostudies-other
Project description:It is estimated that over 40 % of the 218,000 people with chronic hepatitis B (CHB) in Australia in 2011 are undiagnosed. A disproportionate number of those with undiagnosed infection were born in the Asia-Pacific region. Undiagnosed CHB can lead to ongoing transmission and late diagnosis limits opportunities to prevent progression to hepatocellular carcinoma (HCC) and cirrhosis. Strategies are needed to increase testing for hepatitis B virus (HBV) (including culturally and linguistically diverse communities, Aboriginal and/or Torres Strait Islander (Indigenous) people and people who inject drugs). General practitioners (GPs) have a vital role in increasing HBV testing and the timely diagnosis CHB. This paper describes the impact of a GP-based screening intervention to improve CHB diagnosis among priority populations in Melbourne, Australia.A non-randomised, pre-post intervention study was conducted between 2012 and 2013 with three general practices in Melbourne, Australia. Using clinic electronic health records three priority populations known to be at increased CHB risk in Australia (1: Asian-born patients or patients of Asian ethnicity living in Australia; 2: Indigenous people; or 3): people with a history of injecting drugs were identified and their HBV status recorded. A random sample were then invited to attend their GP for HBV testing and/or vaccination. Baseline and follow-up electronic data collection identified patients that subsequently had a consultation and HBV screening test and/or vaccination.From a total of 33,297 active patients, 2674 (8 %) were identified as a priority population at baseline; 2275 (85.1 %) of these patients had unknown HBV status from which 338 (14.0 %) were randomly sampled. One-fifth (n = 73, 21.6 %) of sampled patients subsequently had a GP consultation during the study period; only four people (5.5 %) were subsequently tested for HBV (CHB detected in n = 1) and none were vaccinated against HBV.CHB infection is an important long-term health issue in Australia and strategies to increase appropriate and timely testing are required. The study was effective at identifying whether Asian-born patients and patients of Asian had been tested or vaccinated for HBV; however the intervention was not effective at increasing HBV testing.
| S-EPMC4735953 | biostudies-other
Project description:BackgroundGenetic risk models could potentially be useful in identifying high-risk groups for the prevention of complex diseases. We investigated the performance of this risk stratification strategy by examining epidemiological parameters that impact the predictive ability of risk models.MethodsWe assessed sensitivity, specificity, and positive and negative predictive value for all possible risk thresholds that can define high-risk groups and investigated how these measures depend on the frequency of disease in the population, the frequency of the high-risk group, and the discriminative accuracy of the risk model, as assessed by the area under the receiver-operating characteristic curve (AUC). In a simulation study, we modeled genetic risk scores of 50 genes with equal odds ratios and genotype frequencies, and varied the odds ratios and the disease frequency across scenarios. We also performed a simulation of age-related macular degeneration risk prediction based on published odds ratios and frequencies for six genetic risk variants.ResultsWe show that when the frequency of the high-risk group was lower than the disease frequency, positive predictive value increased with the AUC but sensitivity remained low. When the frequency of the high-risk group was higher than the disease frequency, sensitivity was high but positive predictive value remained low. When both frequencies were equal, both positive predictive value and sensitivity increased with increasing AUC, but higher AUC was needed to maximize both measures.ConclusionsThe performance of risk stratification is strongly determined by the frequency of the high-risk group relative to the frequency of disease in the population. The identification of high-risk groups with appreciable combinations of sensitivity and positive predictive value requires higher AUC.
| S-EPMC3221548 | biostudies-literature
Project description:ObjectivesThis article explores the applicability of the accelerated stability assessment program (ASAP) in stability studies for parenteral medications. Conventional stability testing requires extensive evaluation over the entire shelf life of a product, which can be very time-consuming. In contrast, ASAP provides an efficient approach to support drug product development and expedite regulatory procedures.MethodsThe study involved subjecting the medication to different stress and long-term stability conditions and monitoring the formation of degradation products. A systematic methodology was employed to evaluate the stress stability data of the parenteral medication using various designs (full and reduced). ASAP models were then developed from these data and assessed using the statistical parameters R2 (coefficient of determination) and Q2 (predictive relevance). To validate the accuracy of the models, the predicted levels of degradation products from each of the 13 models were compared with the actual long-term stability results using the relative difference parameter.ResultsThe results confirmed the suitability of the evaluated full model and 11 reduced models for predicting degradation products, except for the two-temperature model, demonstrating the effectiveness of ASAP in stability studies and providing reliable predictions. However, the three-temperature model was identified as the most appropriate model for the parenteral medication under investigation. The statistical analyses showed high R2 and Q2 values, indicating robust model performance and predictive accuracy. Consequently, we applied the selected model on various formulations, demonstrating the suitability of the model and impurity levels below the ICH specification limit.ConclusionsThis research enhances understanding of how ASAP designs can be applied to stability studies for parenteral medications and demonstrates the significance of the application of ASAP during drug product development to expedite the initiation of procedures and implement post-approval variations.
| S-EPMC11858995 | biostudies-literature
Project description:Venous thromboembolism (VTE) is a complex, multifactorial problem, the development of which depends on a combination of genetic and acqfiguired risk factors. In a Spanish population, the Thrombo inCode score (or TiC score), which combines clinical and genetic risk components, was recently proven better at determining the risk of VTE than the commonly used model involving the analysis of two genetic variants associated with thrombophilia: the Factor V Leiden (F5 rs6025) and the G20210A prothrombin (F2 rs1799963). The aim of the present case-control study was to validate the VTE risk predictive capacity of the TiC score in a Northern European population (from Sweden). The study included 173 subjects with VTE and 196 controls. All were analyzed for the genetic risk variants included in the TiC gene panel. Standard measures -receiver operating characteristic (ROC) area under the curve (AUC), sensitivity, specificity, and odds ratio (OR)-were calculated. The TiC score returned an AUC value of 0.673, a sensitivity of 72.25%, a specificity of 60.62%, and an OR of 4.11. These AUC, sensitivity, and OR values are all greater than those associated with the currently used combination of genetic variants. A TiC version adjusted for the allelic frequencies of the Swedish population significantly improved its AUC value (0.783). In summary, the TiC score returned more reliable risk estimates for the studied Northern European population than did the analysis of the Factor V Leiden and the G20210A genetic variations in combination. Thus, the TiC score can be reliably used with European populations, despite differences in allelic frequencies.
| S-EPMC8266419 | biostudies-literature
Project description:To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes.
| S-EPMC8259603 | biostudies-literature
Project description:BackgroundGrouping samples with low prevalence of positives into pools and testing these pools can achieve considerable savings in testing resources compared with individual testing in the context of COVID-19. We review published pooling matrices, which encode the assignment of samples into pools and describe decoding algorithms, which decode individual samples from pools. Based on the findings we propose new one-round pooling designs with high compression that can efficiently be decoded by combinatorial algorithms. This expands the admissible parameter space for the construction of pooling matrices compared to current methods.ResultsBy arranging samples in a grid and using polynomials to construct pools, we develop direct formulas for an Algorithm (Polynomial Pools (PP)) to generate assignments of samples into pools. Designs from PP guarantee to correctly decode all samples with up to a specified number of positive samples. PP includes recent combinatorial methods for COVID-19, and enables new constructions that can result in more effective designs.ConclusionFor low prevalences of COVID-19, group tests can save resources when compared to individual testing. Constructions from the recent literature on combinatorial methods have gaps with respect to the designs that are available. We develop a method (PP), which generalizes previous constructions and enables new designs that can be advantageous in various situations.
| S-EPMC9872308 | biostudies-literature
Project description:Touch information is central to sensorimotor integration, yet little is known about how cortical touch and movement representations interact. Touch- and movement-related activity is present in both somatosensory and motor cortices, making both candidate sites for touch-motor interactions. We studied touch-motor interactions in layer 2/3 of the primary vibrissal somatosensory and motor cortices of behaving mice. Volumetric two-photon calcium imaging revealed robust responses to whisker touch, whisking, and licking in both areas. Touch activity was dominated by a sparse population of broadly tuned neurons responsive to multiple whiskers that exhibited longitudinal stability and disproportionately influenced interareal communication. Movement representations were similarly dominated by sparse, stable, reciprocally projecting populations. In both areas, many broadly tuned touch cells also produced robust licking or whisking responses. These touch-licking and touch-whisking neurons showed distinct dynamics suggestive of specific roles in shaping movement. Cortical touch-motor interactions are thus mediated by specialized populations of highly responsive, broadly tuned neurons.
| S-EPMC10542548 | biostudies-literature