Challenges and opportunities in processing NanoString nCounter data

NanoString nCounter is a medium-throughput technology used in mRNA and miRNA differential expression studies. It offers several advantages, including the absence of an amplification step and the ability to analyze low-grade samples. Despite its considerable strengths, the popularity of the nCounter platform in experimental research stabilized in 2022 and 2023, and this trend may continue in the upcoming years. Such stagnation could potentially be attributed to the absence of a standardized analytical pipeline or the indication of optimal processing methods for nCounter data analysis. To standardize the description of the nCounter data analysis workflow, we divided it into five distinct steps: data pre-processing, quality control, background correction, normalization and differential expression analysis. Next, we evaluated eleven R packages dedicated to nCounter data processing to point out functionalities belonging to these steps and provide comments on their applications in studies of mRNA and miRNA samples.

Toxicity of ciprofloxacin and ofloxacin to Moina macrocopa and investigation of p-value adjustments for (eco)toxicological studies

Ciprofloxacin (CFX) and ofloxacin (OFX) are commonly found as residual contaminants in aquatic environments, posing potential risks to various species. To ensure the safety of aquatic wildlife, it is essential to determine the toxicity of these antibiotics and establish appropriate concentration limits. Additionally, in (eco)toxicological studies, addressing the issue of multiple hypothesis testing through p-value adjustments is crucial for robust decision-making. In this study, we assessed the no observed adverse effect concentration (NOAEC) of CFX and OFX on Moina macrocopa across a concentration range of 0-400 µg L-1. Furthermore, we investigated multiple p-value adjustments to determine the NOAECs. Our analysis yielded consistent results across seven different p-value adjustments, indicating NOAECs of 100 µg CFX L-1 for age at first reproduction and 200 µg CFX L-1 for fertility. For OFX treatment, a NOAEC of 400 µg L-1 was observed for both biomarkers. However, further investigation is required to establish the NOAEC of OFX at higher concentrations with greater certainty. Our findings demonstrate that CFX exhibits higher toxicity compared to OFX, consistent with previous research. Moreover, this study highlights the differential performance of p-value adjustment methods in terms of maintaining statistical power while controlling the multiplicity problem, and their practical applicability. The study emphasizes the low NOAECs for these antibiotics in the zooplanktonic group, highlighting their significant risks to ecological and environmental safety. Additionally, our investigation of p-value adjustment approaches contributes to a deeper understanding of their performance characteristics, enabling (eco)toxicologists to select appropriate methods based on their specific needs and priorities.

Integrative in silico approaches to analyse microRNA-mediated responses in human diseases

Advancements in sequencing technologies have facilitated omics level information generation for various diseases in human. High-throughput technologies have become a powerful tool to understand differential expression studies and transcriptional network analysis. An understanding of complex transcriptional networks in human diseases requires integration of datasets representing different RNA species including microRNA (miRNA) and messenger RNA (mRNA). This review emphasises on conceptual explanation of generalized workflow and methodologies to the miRNA mediated responses in human diseases by using different in silico analysis. Although, there have been many prior explorations in miRNA-mediated responses in human diseases, the advantages, limitations and overcoming the limitation through different statistical techniques have not yet been discussed. This review focuses on miRNAs as important gene regulators in human diseases, methodologies for miRNA-target gene prediction and data driven methods for enrichment and network analysis for miRnome-targetome interactions. Additionally, it proposes an integrative workflow to analyse structural components of networks obtained from high-throughput data. This review explains how to apply the existing methods to analyse miRNA-mediated responses in human diseases. It addresses unique characteristics of different analysis, its limitations and its statistical solutions influencing the choice of methods for the analysis through a workflow. Moreover, it provides an overview of promising common integrative approaches to comprehend miRNA-mediated gene regulatory events in biological processes in humans. The proposed methodologies and workflow shall help in the analysis of multi-source data to identify molecular signatures of various human diseases.

Effect of microphysics scheme and data assimilation on hydrometeor and radiative flux simulations in the Arctic

Although clouds are a major factor influencing atmospheric environments in the Arctic, numerical simulations of Arctic clouds are uncertain. In this study, the effects of microphysics scheme and data assimilation (DA) on the simulation of clouds, hydrometeors and radiative fluxes in the Arctic were investigated using the polar weather research and forecasting (WRF) model and three-dimensional variational DA. Compared with the WRF 5-class (WSM5) microphysics scheme, when the Morrison double-moment (Morrison) scheme was used, the simulated amount of cloud ice water decreased by approximately 68%. In contrast, the amount of water vapour, cloud liquid water, snow and rain in the atmosphere increased. With DA, the amount of water vapour increased, leading to increased hydrometeors. The cloud liquid water increased in the middle and low atmospheres when Morrison was used, whereas it increased in the low atmosphere when DA was used. The increase in cloud liquid water by using Morrison resulted in a decrease in the downward short-wave radiative flux at the surface, whereas using DA increased the downward long-wave radiative flux. Changing the microphysics scheme induced redistribution of the region and amounts of hydrometeors, whereas DA induced an increase in hydrometeors in specific regions by adding observation information to the model states.

Meta-analysis of RNA interaction profiles of RNA-binding protein using the RBPInper tool

Motivation

Recent RNA-centric experimental methods have significantly expanded our knowledge of proteins with known RNA-binding functions. However, the complete regulatory network and pathways for many of these RNA-binding proteins (RBPs) in different cellular contexts remain unknown. Although critical to understanding the role of RBPs in health and disease, experimentally mapping the RBP-RNA interactomes in every single context is an impossible task due the cost and manpower required. Additionally, identifying relevant RNAs bound by RBPs is challenging due to their diverse binding modes and function.

Results

To address these challenges, we developed RBP interaction mapper RBPInper an integrative framework that discovers global RBP interactome using statistical data fusion. Experiments on splicing factor proline and glutamine rich (SFPQ) datasets revealed cogent global SFPQ interactome. Several biological processes associated with this interactome were previously linked with SFPQ function. Furthermore, we conducted tests using independent dataset to assess the transferability of the SFPQ interactome to another context. The results demonstrated robust utility in generating interactomes that transfers to unseen cellular context. Overall, RBPInper is a fast and user-friendly method that enables a systems-level understanding of RBP functions by integrating multiple molecular datasets. The tool is designed with a focus on simplicity, minimal dependencies, and straightforward input requirements. This intentional design aims to empower everyday biologists, making it easy for them to incorporate the tool into their research.

Availability and implementation

The source code, documentation, and installation instructions as well as results for use case are freely available at https://github.com/AneneLab/RBPInper. A user can easily compile similar datasets for a target RBP.

Efficacy of chemotherapy plus immune checkpoint inhibitors in patients with non-small cell lung cancer who have rare oncogenic driver mutations: a retrospective analysis

BACKGROUND

Targeted therapy is now the standard of care in driver-oncogene-positive non-small cell lung cancer (NSCLC). Its initial clinical effects are remarkable. However, almost all patients experience treatment resistance to targeted therapy. Hence, chemotherapy is considered a subsequent treatment option. In patients with driver-oncogene-negative NSCLC, combined immune checkpoint inhibitors (ICIs) and chemotherapy as the first-line therapy has been found to be beneficial. However, the efficacy of ICI plus chemotherapy against driver-oncogene-positive NSCLC other than epidermal growth factor receptor mutation and anaplastic lymphoma kinase fusion is unclear.

METHODS

Using the hospital medical records, we retrospectively reviewed advanced or recurrent NSCLC patients who were treated with chemotherapy with or without ICIs at Aichi Cancer Center Hospital between January 2014 and January 2023. Patients with druggable rare mutations such as KRAS-G12C, MET exon 14 skipping, HER2 20 insertion, BRAF-V600E mutations, and ROS1 and RET rearrangements were analyzed.

RESULTS

In total, 61 patients were included in this analysis. ICI plus chemotherapy was administered in 36 patients (the ICI-chemo group) and chemotherapy in 25 patients (the chemo group). The median progression-free survival (PFS) rates were 14.0 months in the ICI-chemo group and 4.8 months in the chemo group (hazard ratio [HR] = 0.54, 95% confidence interval [CI] = 0.28-1.01). The median overall survival rates were 31.3 and 21.7 months in the ICI-chemo and chemo groups, respectively (HR = 0.70, 95% CI = 0.33-1.50). Multivariate Cox regression analysis of PFS revealed that HER2 exon 20 insertion mutation was significantly associated with a poorer PFS (HR: 2.39, 95% CI: 1.19-4.77, P = 0.014). Further, ICI-chemo treatment was significantly associated with a better PFS (HR: 0.48, 95% CI: 0.25-0.91, P = 0.025).

CONCLUSION

ICI plus chemotherapy improves treatment efficacy in rare driver-oncogene-positive NSCLC.

Mortality Prediction by Kinetic Parameters of Lactate and S-Adenosylhomocysteine in a Cohort of Critically Ill Patients

Tissue hypoxia is associated with the development of organ dysfunction and death in critically ill patients commonly captured using blood lactate. The kinetic parameters of serial lactate evaluations are superior at predicting mortality compared with single values. S-adenosylhomocysteine (SAH), which is also associated with hypoxia, was recently established as a useful predictor of septic organ dysfunction and death. We evaluated the performance of kinetic SAH parameters for mortality prediction compared with lactate parameters in a cohort of critically ill patients. For lactate and SAH, maxima and means as well as the normalized area scores were calculated for two periods: the first 24 h and the total study period of up to five days following ICU admission. Their performance in predicting in-hospital mortality were compared in 99 patients. All evaluated parameters of lactate and SAH were significantly higher in non-survivors compared with survivors. In univariate analysis, the predictive power for mortality of SAH was higher compared with lactate in all forms of application. Multivariable models containing SAH parameters demonstrated higher predictive values for mortality than models based on lactate parameters. The optimal models for mortality prediction incorporated both lactate and SAH parameters. Compared with lactate, SAH displayed stronger predictive power for mortality in static and dynamic application in critically ill patients.

HiPerMAb: a tool for judging the potential of small sample size biomarker pilot studies

Common statistical approaches are not designed to deal with so-called "short fat data" in biomarker pilot studies, where the number of biomarker candidates exceeds the sample size by magnitudes. High-throughput technologies for omics data enable the measurement of ten thousands and more biomarker candidates for specific diseases or states of a disease. Due to the limited availability of study participants, ethical reasons and high costs for sample processing and analysis researchers often prefer to start with a small sample size pilot study in order to judge the potential of finding biomarkers that enable - usually in combination - a sufficiently reliable classification of the disease state under consideration. We developed a user-friendly tool, called HiPerMAb that allows to evaluate pilot studies based on performance measures like multiclass AUC, entropy, area above the cost curve, hypervolume under manifold, and misclassification rate using Monte-Carlo simulations to compute the p-values and confidence intervals. The number of "good" biomarker candidates is compared to the expected number of "good" biomarker candidates in a data set with no association to the considered disease states. This allows judging the potential in the pilot study even if statistical tests with correction for multiple testing fail to provide any hint of significance.

Using routine health-care data to search for unknown transfusion-transmitted disease: a nationwide, agnostic retrospective cohort study

BACKGROUND

Identification and prevention of transfusion-transmitted disease is essential for blood transfusion safety. However, current surveillance systems are largely driven by reports of sentinel events, which is an approach that might be inadequate for identifying transmission of pathogens not known to be transmissible or pathogens with long incubation periods. Using a combination of health-data registers and blood-bank databases, we aimed to perform an agnostic search for potential transfusion-transmitted diseases and to identify unknown threats to the blood supply.

METHODS

In this nationwide, agnostic retrospective cohort study, we developed a systematic algorithm for performing a phenome-wide search for transfusion-transmitted disease without consideration of any a-priori suspicion of blood-borne transmissibility. We applied this algorithm to a nationwide Swedish transfusion database (SCANDAT-3S) to test for possible transmission of 1155 disease entities based on all relevant diagnostic coding systems in use during the period. We ascertained health outcomes of blood donors and transfusion recipients from the Swedish National Inpatient Register, Swedish Cause of Death Register, and Swedish Cancer Register. Analyses were two-pronged, studying both disease diagnosis concordance between donors and recipients and a possible shared increased disease risk among all recipients of a given donor. For both approaches, we used Cox proportional hazards regression models with time-dependent covariates. Adjustment for multiple comparisons was done using a false discovery rate method.

FINDINGS

The analyses included data on 1·72 million patients who had received 18·97 million transfusions (red blood cell, plasma, platelet, or whole blood units) between Jan 1, 1968, and Dec 31, 2017, from 1·04 million blood donors. The median follow-up was 4·5 (IQR 0·9-11·4) years for recipients and 18·5 (8·3-26·2) years for donors. We found evidence of transfusion-transmission for 15 diseases, of which 13 were validated using a second conceptually different approach. We identified transmission of viral hepatitis and its complications (eg, oesophageal varices) but also transmission of other conditions (eg, pneumonia of unknown origin). The diseases that could not be validated in this second approach, HIV and abnormal findings in specimens from male genital organs, were not statistically significant after adjustment for multiple testing. The effect sizes were small (close to 1) for other conditions.

INTERPRETATION

We find no strong evidence of unexpected, widespread transfusion-transmitted disease. This novel approach serves as a proof-of-concept for agnostic, data-driven surveillance for transfusion-transmitted disease using routinely collected blood-bank and health-care data.

FUNDING

Department of Health and Human Services, US National Heart, Lung, and Blood Institute, US National Institutes of Health, Swedish Research Council and Region Stockholm.

Ion entropy and accurate entropy-based FDR estimation in metabolomics

Accurate metabolite annotation and false discovery rate (FDR) control remain challenging in large-scale metabolomics. Recent progress leveraging proteomics experiences and interdisciplinary inspirations has provided valuable insights. While target-decoy strategies have been introduced, generating reliable decoy libraries is difficult due to metabolite complexity. Moreover, continuous bioinformatics innovation is imperative to improve the utilization of expanding spectral resources while reducing false annotations. Here, we introduce the concept of ion entropy for metabolomics and propose two entropy-based decoy generation approaches. Assessment of public databases validates ion entropy as an effective metric to quantify ion information in massive metabolomics datasets. Our entropy-based decoy strategies outperform current representative methods in metabolomics and achieve superior FDR estimation accuracy. Analysis of 46 public datasets provides instructive recommendations for practical application.

Controlling for false discoveries subsequently to large scale one-way ANOVA testing in proteomics: Practical considerations

In discovery proteomics, as well as many other "omic" approaches, the possibility to test for the differential abundance of hundreds (or of thousands) of features simultaneously is appealing, despite requiring specific statistical safeguards, among which controlling for the false discovery rate (FDR) has become standard. Moreover, when more than two biological conditions or group treatments are considered, it has become customary to rely on the one-way analysis of variance (ANOVA) framework, where a first global differential abundance landscape provided by an omnibus test can be subsequently refined using various post-hoc tests (PHTs). However, the interactions between the FDR control procedures and the PHTs are complex, because both correspond to different types of multiple test corrections (MTCs). This article surveys various ways to orchestrate them in a data processing workflow and discusses their pros and cons.

Candidate biomarkers and persistent transcriptional responses after low and high dose ionizing radiation at high dose rate

PURPOSE

Development of an integrated time and dose model to explore the dynamics of gene expression alterations and identify biomarkers for biodosimetry following low- and high-dose irradiations at high dose rate.

MATERIAL AND METHODS

We utilized multiple transcriptome datasets (GSE8917, GSE43151, and GSE23515) from Gene Expression Omnibus (GEO) for identifying candidate biological dosimeters. A linear mixed-effects model with random intercept was used to explore the dose-time dynamics of transcriptional responses and to functionally characterize the time- and dose-dependent changes in gene expression.

RESULTS

We identified genes that are correlated with dose and time and discovered two clusters of genes that are either positively or negatively correlated with both dose and time based on the parameters of the model. Genes in these two clusters may have persistent transcriptional alterations. Twelve potential transcriptional markers for dosimetry-ARHGEF3, BAX, BBC3, CCDC109B, DCP1B, DDB2, F11R, GADD45A, GSS, PLK3, TNFRSF10B, and XPC were identified. Of these genes, BAX, GSS, and TNFRSF10B are positively associated with both dose and time course, have a persistent transcriptional response, and might be better biological dosimeters.

CONCLUSIONS

With the proposed approach, we may identify candidate biomarkers that change monotonically in relation to dose, have a persistent transcriptional response, and are reliable over a wide dose range.

Too much information is no information: how machine learning and feature selection could help in understanding the motor control of pointing

The aim of this study was to develop the use of Machine Learning techniques as a means of multivariate analysis in studies of motor control. These studies generate a huge amount of data, the analysis of which continues to be largely univariate. We propose the use of machine learning classification and feature selection as a means of uncovering feature combinations that are altered between conditions. High dimensional electromyogram (EMG) vectors were generated as several arm and trunk muscles were recorded while subjects pointed at various angles above and below the gravity neutral horizontal plane. We used Linear Discriminant Analysis (LDA) to carry out binary classifications between the EMG vectors for pointing at a particular angle, vs. pointing at the gravity neutral direction. Classification success provided a composite index of muscular adjustments for various task constraints-in this case, pointing angles. In order to find the combination of features that were significantly altered between task conditions, we conducted a post classification feature selection i.e., investigated which combination of features had allowed for the classification. Feature selection was done by comparing the representations of each category created by LDA for the classification. In other words computing the difference between the representations of each class. We propose that this approach will help with comparing high dimensional EMG patterns in two ways; (i) quantifying the effects of the entire pattern rather than using single arbitrarily defined variables and (ii) identifying the parts of the patterns that convey the most information regarding the investigated effects.

Whole-genome resequencing of the native sheep provides insights into the microevolution and identifies genes associated with reproduction traits

BACKGROUND

Sheep genomes undergo numerous genes losses, gains and mutation that generates genome variability among breeds of the same species after long time natural and artificial selection. However, the microevolution of native sheep in northwest China remains elusive. Our aim was to compare the genomes and relevant reproductive traits of four sheep breeds from different climatic environments, to unveil the selection challenges that this species cope with, and the microevolutionary differences in sheep genomes. Here, we resequenced the genomes of 4 representative sheep breeds in northwest China, including Kazakh sheep and Duolang sheep of native breeds, and Hu sheep and Suffolk sheep of exotic breeds with different reproductive characteristics.

RESULTS

We found that these four breeds had a similar expansion experience from ~ 10,000 to 1,000,000 years ago. In the past 10,000 years, the selection intensity of the four breeds was inconsistent, resulting in differences in reproductive traits. We explored the sheep variome and selection signatures by F_ST and θπ. The genomic regions containing genes associated with different reproductive traits that may be potential targets for breeding and selection were detected. Furthermore, non-synonymous mutations in a set of plausible candidate genes and significant differences in their allele frequency distributions across breeds with different reproductive characteristics were found. We identified PAK1, CYP19A1 and PER1 as a likely causal gene for seasonal reproduction in native sheep through qPCR, Western blot and ELISA analyses. Also, the haplotype frequencies of 3 tested gene regions related to reproduction were significantly different among four sheep breeds.

CONCLUSIONS

Our results provide insights into the microevolution of native sheep and valuable genomic information for identifying genes associated with important reproductive traits in sheep.

Improved family-wise error rate control in multiple equivalence testing

Equivalence testing is an important component of safety assessments, used for example by the European Food Safety Authority, to allow new food or feed products on the market. The aim of such tests is to demonstrate equivalence of characteristics of test and reference crops. Equivalence tests are typically univariate and applied to each measured analyte (characteristic) separately without multiplicity correction. This increases the probability of making false claims of equivalence (type I errors) when evaluating multiple analytes simultaneously. To solve this problem, familywise error rate (FWER) control using Hochberg's method has been proposed. This paper demonstrates that, in the context of equivalence testing, other FWER-controlling methods are more powerful than Hochberg's. Particularly, it is shown that Hommel's method is guaranteed to perform at least as well as Hochberg's and that an "adaptive" version of Bonferroni's method, which uses an estimator of the proportion of non-equivalent characteristics, often substantially outperforms Hommel's method. Adaptive Bonferroni takes better advantage of the particular context of food safety where a large proportion of true equivalences is expected, a situation where other methods are particularly conservative. The different methods are illustrated by their application to two compositional datasets and further assessed and compared using simulated data.

Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle

Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.

Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges

BACKGROUND

In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions.

METHODS

Advances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 "High-dimensional data" of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD.

RESULTS

The paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided.

CONCLUSIONS

This review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses.

Saddlepoint approximations to score test statistics in logistic regression for analyzing genome-wide association studies

We investigate saddlepoint approximations of tail probabilities of the score test statistic in logistic regression for genome-wide association studies. The inaccuracy in the normal approximation of the score test statistic increases with increasing imbalance in the response and with decreasing minor allele counts. Applying saddlepoint approximation methods greatly improve the accuracy, even far out in the tails of the distribution. By using exact results for a simple logistic regression model, as well as simulations for models with nuisance parameters, we compare double saddlepoint methods for computing two-sided P $$ P $$ -values and mid- P $$ P $$ -values. These methods are also compared to a recent single saddlepoint procedure. We investigate the methods further on data from UK Biobank with skin and soft tissue infections as phenotype, using both common and rare variants.

Exploring the forensic effectiveness and population genetic differentiation by self-constructed 41 multi-InDel panel in Yunnan Zhuang group

Yunnan is one of the main residences of the Zhuang group which is one of the 55 ethnic minorities in China. At present, there are relatively few researches on population genetics and forensic science of the Yunnan Zhuang group. Therefore, this study used a self-constructed panel containing 41 multi-InDel markers to analyze the genetic polymorphisms of 173 individuals from Yunnan Zhuang group. The results indicated that these 41 multi-InDels in Yunnan Zhuang group were highly polymorphic markers expect for three markers. The cumulative match probability and combined exclusion probability values of the 40 multi-InDels (MI38 marker was excluded) were 8.0671E-26 and 0.9999995959, respectively. In addition, population genetic analyses were performed on genotyping data of 41 multi-InDel markers among the Yunnan Zhuang and 26 reference populations, revealing that the Yunnan Zhuang group was genetically close to the five populations in East Asia. According to the STRUCTURE analysis, the Yunnan Zhuang group presented similar ancestral compositions to the five populations from East Asia, and when the K value was three, the five intercontinental populations showed their different genetic structures. In conclusion, the 41 multi-InDel markers could be used as an effective tool for individual identification and paternity testing of the Zhuang group in Yunnan province, as well as for their ancestry information inference studies.

Procrustes-based distances for exploring between-matrices similarity

The statistical shape analysis called Procrustes analysis minimizes the Frobenius distance between matrices by similarity transformations. The method returns a set of optimal orthogonal matrices, which project each matrix into a common space. This manuscript presents two types of distances derived from Procrustes analysis for exploring between-matrices similarity. The first one focuses on the residuals from the Procrustes analysis, i.e., the residual-based distance metric. In contrast, the second one exploits the fitted orthogonal matrices, i.e., the rotational-based distance metric. Thanks to these distances, similarity-based techniques such as the multidimensional scaling method can be applied to visualize and explore patterns and similarities among observations. The proposed distances result in being helpful in functional magnetic resonance imaging (fMRI) data analysis. The brain activation measured over space and time can be represented by a matrix. The proposed distances applied to a sample of subjects—i.e., matrices—revealed groups of individuals sharing patterns of neural brain activation. Finally, the proposed method is useful in several contexts when the aim is to analyze the similarity between high-dimensional matrices affected by functional misalignment.

β3AR-Dependent Brain-Derived Neurotrophic Factor (BDNF) Generation Limits Chronic Postischemic Heart Failure

BACKGROUND

Loss of brain-derived neurotrophic factor (BDNF)/TrkB (tropomyosin kinase receptor B) signaling accounts for brain and cardiac disorders. In neurons, β-adrenergic receptor stimulation enhances local BDNF expression. It is unclear if this occurs in a pathophysiological relevant manner in the heart, especially in the β-adrenergic receptor-desensitized postischemic myocardium. Nor is it fully understood whether and how TrkB agonists counter chronic postischemic left ventricle (LV) decompensation, a significant unmet clinical milestone.

METHODS

We conducted in vitro studies using neonatal rat and adult murine cardiomyocytes, SH-SY5Y neuronal cells, and umbilical vein endothelial cells. We assessed myocardial ischemia (MI) impact in wild type, β3AR knockout, or myocyte-selective BDNF knockout (myoBDNF KO) mice in vivo (via coronary ligation [MI]) or in isolated hearts with global ischemia-reperfusion (I/R).

RESULTS

In wild type hearts, BDNF levels rose early after MI (<24 hours), plummeting at 4 weeks when LV dysfunction, adrenergic denervation, and impaired angiogenesis ensued. The TrkB agonist, LM22A-4, countered all these adverse effects. Compared with wild type, isolated myoBDNF KO hearts displayed worse infarct size/LV dysfunction after I/R injury and modest benefits from LM22A-4. In vitro, LM22A-4 promoted neurite outgrowth and neovascularization, boosting myocyte function, effects reproduced by 7,8-dihydroxyflavone, a chemically unrelated TrkB agonist. Superfusing myocytes with the β3AR-agonist, BRL-37344, increased myocyte BDNF content, while β3AR signaling underscored BDNF generation/protection in post-MI hearts. Accordingly, the β1AR blocker, metoprolol, via upregulated β3ARs, improved chronic post-MI LV dysfunction, enriching the myocardium with BDNF. Last, BRL-37344-imparted benefits were nearly abolished in isolated I/R injured myoBDNF KO hearts.

CONCLUSIONS

BDNF loss underscores chronic postischemic heart failure. TrkB agonists can improve ischemic LV dysfunction via replenished myocardial BDNF content. Direct cardiac β3AR stimulation, or β-blockers (via upregulated β3AR), is another BDNF-based means to fend off chronic postischemic heart failure.

Evaluation of the Association between FGFR2 Gene Polymorphisms and Breast Cancer Risk in the Bangladeshi Population

Breast cancer is considered the most frequent cause of mortality from malignancy among females. Fibroblast growth factor receptor 2 (FGFR2) gene polymorphisms are highly related to the risk of breast cancer. However, no investigation has been carried out to determine the association of FGFR2 gene polymorphisms in the Bangladeshi population. Based on polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP), this study was performed to evaluate the association of FGFR2 (rs1219648, rs2420946, and rs2981582) variants in 446 Bangladeshi women (226 cases and 220 controls). A significant association of the FGFR2 rs1219648 variant with breast malignancy was reported in additive model 1 (aOR = 2.87, p < 0.0001), additive model 2 (aOR = 5.62, p < 0.0001), the dominant model (aOR = 2.87, p < 0.0001), the recessive model (aOR = 4.04, p < 0.0001), and the allelic model (OR = 2.16, p < 0.0001). This investigation also explored the significant association of the rs2981582 variant with the risk of breast cancer in additive model 2 (aOR = 2. 60, p = 0.010), the recessive model (aOR = 2.47, p = 0.006), and the allelic model (OR = 1.39, p = 0.016). However, the FGFR2 rs2420946 polymorphism showed no association with breast cancer except in the overdominant model (aOR = 0.62, p = 0.048). Furthermore, GTT (p < 0.0001) haplotypes showed a correlation with breast cancer risk, and all variants showed strong linkage disequilibrium. Moreover, in silico gene expression analysis showed that the FGFR2 level was upregulated in BC tissues compared to healthy tissues. This study confirms the association of FGFR2 polymorphisms with breast cancer risk.

Volumetric visceral fat machine learning phenotype on CT for differential diagnosis of inflammatory bowel disease

OBJECTIVES

To investigate whether volumetric visceral adipose tissue (VAT) features extracted using radiomics and three-dimensional convolutional neural network (3D-CNN) approach are effective in differentiating Crohn's disease (CD) and ulcerative colitis (UC).

METHODS

This retrospective study enrolled 316 patients (mean age, 36.25 ± 13.58 [standard deviation]; 219 men) with confirmed diagnosis of CD and UC who underwent CT enterography between 2012 and 2021. Volumetric VAT was semi-automatically segmented on the arterial phase images. Radiomics analysis was performed using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. We developed a 3D-CNN model using VAT imaging data from the training cohort. Clinical covariates including age, sex, modified body mass index, and disease duration that impact VAT were added to the machine learning model for adjustment. The model's performance was evaluated on the testing cohort separating from the model's development process by its discrimination and clinical utility.

RESULTS

Volumetric VAT radiomics analysis with LASSO had the highest AUC value of 0.717 (95% CI, 0.614-0.820), though difference of diagnostic performance among the 3D-CNN model (AUC = 0.693; 95% CI, 0.587-0.798) and radiomics analysis with PCA (AUC = 0.662; 95% CI, 0.548-0.776) and LASSO have not reached statistical significance (all p > 0.05). The radiomics score was higher in UC than in CD on the testing cohort (mean ± SD, UC 0.29 ± 1.05 versus CD -0.60 ± 1.25; p < 0.001). The LASSO model with adjustment of clinical covariates reached an AUC of 0.775 (95%CI, 0.683-0.868).

CONCLUSION

The developed volumetric VAT-based radiomics and 3D-CNN models provided comparable and effective performance for the characterization of CD from UC.

KEY POINTS

• High-output feature data extracted from volumetric visceral adipose tissue on CT enterography had an effective diagnostic performance for differentiating Crohn's disease from ulcerative colitis. • With adjustment of clinical covariates that cause difference in volumetric visceral adipose tissue, adjusted clinical machine learning model reached stronger performance when distinguishing Crohn's disease patients from ulcerative colitis patients.

Inferring feature importance with uncertainties with application to large genotype data

Estimating feature importance, which is the contribution of a prediction or several predictions due to a feature, is an essential aspect of explaining data-based models. Besides explaining the model itself, an equally relevant question is which features are important in the underlying data generating process. We present a Shapley-value-based framework for inferring the importance of individual features, including uncertainty in the estimator. We build upon the recently published model-agnostic feature importance score of SAGE (Shapley additive global importance) and introduce Sub-SAGE. For tree-based models, it has the advantage that it can be estimated without computationally expensive resampling. We argue that for all model types the uncertainties in our Sub-SAGE estimator can be estimated using bootstrapping and demonstrate the approach for tree ensemble methods. The framework is exemplified on synthetic data as well as large genotype data for predicting feature importance with respect to obesity.

Genomic landscape of 891 RET fusions detected across diverse solid tumor types

In this study, we report the clinicopathologic and genomic profiles of 891 patients with RET fusion driven advanced solid tumors. All patient samples were tested using a tissue-based DNA hybrid capture next generation sequencing (NGS) assay and a subset of the samples were liquid biopsies tested using a liquid-based hybrid capture NGS assay. RET fusions were found in 523 patients with NSCLC and in 368 patients with other solid tumors. The two tumor types with the highest number of RET fusion were lung adenocarcinoma and thyroid papillary carcinoma, and they had a prevalence rate 1.14% (455/39,922) and 9.09% (109/1199), respectively. A total of 61 novel fusions were discovered in this pan-tumor cohort. The concordance of RET fusion detection across tumor types among tissue and liquid-based NGS was 100% (8/8) in patients with greater than 1% composite tumor fraction (cTF). Herein, we present the clinicopathologic and genomic landscape of a large cohort of RET fusion positive tumors and we observed that liquid biopsy-based NGS is highly sensitive for RET fusions at cTF ≥1%.

Pairwise ratio-based differential abundance analysis of infant microbiome 16S sequencing data

Differential abundance analysis of infant 16S microbial sequencing data is complicated by challenging data properties, including high sparsity, extreme dispersion and the relative nature of the information contained within the data. In this study, we propose a pairwise ratio analysis that uses the compositional data analysis principle of subcompositional coherence and merges it with a beta-binomial regression model. The resulting method provides a flexible and easily interpretable approach to infant 16S sequencing data differential abundance analysis that does not require zero imputation. We evaluate the proposed method using infant 16S data from clinical trials and demonstrate that the proposed method has the power to detect differences, and demonstrate how its results can be used to gain insights. We further evaluate the method using data-inspired simulations and compare its power against related methods. Our results indicate that power is high for pairwise differential abundance analysis of taxon pairs that have a large abundance. In contrast, results for sparse taxon pairs show a decrease in power and substantial variability in method performance. While our method shows promising performance on well-measured subcompositions, we advise strong filtering steps in order to avoid excessive numbers of underpowered comparisons in practical applications.

A Q-Q plot aids interpretation of the false discovery rate

False discovery rates are routinely controlled by application of the Benjamini-Hochberg step-up procedure to a set of p-values. A method is demonstrated for representing the values so obtained (the BH-FDRs) on a quantile-quantile (Q-Q) plot of the p-values transformed to the negative-logarithmic scale. Recognition of this connection between the BH-FDR and the Q-Q plot facilitates both understanding of the meaning of the BH-FDR and interpretation of the BH-FDR in a particular data set.

Validation of a Multivariate Prediction Model of the Clinical Progression of Alzheimer's Disease in a Community-Dwelling Multiethnic Cohort

BACKGROUND

The major aims of the three Predictors Studies have been to further our understanding of Alzheimer's disease (AD) progression sufficiently to predict the length of time from disease onset to major disease outcomes in individual patients with AD.

OBJECTIVES

To validate a longitudinal Grade of Membership (L-GoM) prediction algorithm developed using clinic-based, mainly white patients from the Predictors 2 Study in a statistically representative community-based sample of Hispanic (N = 211) and non-Hispanic (N = 62) older adults (with 60 males and 213 females) from the Predictors 3 Study and extend the algorithm to mild cognitive impairment (MCI).

METHODS

The L-GoM model was applied to data collected at the initial Predictors 3 visit for 150 subjects with AD and 123 with MCI. Participants were followed annually for up to seven years. Observed rates of survival and need for full-time care (FTC) were compared to those predicted by the algorithm.

RESULTS

Initial MCI/AD severity in Predictors 3 was substantially higher than among clinic-based AD patients enrolled at the specialized Alzheimer's centers in Predictors 2. The observed survival and need for FTC followed the L-GoM model trajectories in individuals with MCI or AD, except for N = 32 subjects who were initially diagnosed with AD but reverted to a non-AD diagnosis on follow-up.

CONCLUSION

These findings indicate that the L-GoM model is applicable to community-dwelling, multiethnic older adults with AD. They extend the use of the model to the prediction of outcomes for MCI. They also justify release of our L-GoM calculator at this time.

Overview of frequent pattern mining

Various methods of frequent pattern mining have been applied to genetic problems, specifically, to the combined association of two genotypes (a genotype pattern, or diplotype) at different DNA variants with disease. These methods have the ability to come up with a selection of genotype patterns that are more common in affected than unaffected individuals, and the assessment of statistical significance for these selected patterns poses some unique problems, which are briefly outlined here.

Challenging Targets or Describing Mismatches? A Comment on Common Decoy Distribution by Madej et al

In their recent article, Madej et al. (Madej, D.; Wu, L.; Lam, H.Common Decoy Distributions Simplify False Discovery Rate Estimation in Shotgun Proteomics. J. Proteome Res.2022, 21 (2), 339-348) proposed an original way to solve the recurrent issue of controlling for the false discovery rate (FDR) in peptide-spectrum-match (PSM) validation. Briefly, they proposed to derive a single precise distribution of decoy matches termed the Common Decoy Distribution (CDD) and to use it to control for FDR during a target-only search. Conceptually, this approach is appealing as it takes the best of two worlds, i.e., decoy-based approaches (which leverage a large-scale collection of empirical mismatches) and decoy-free approaches (which are not subject to the randomness of decoy generation while sparing an additional database search). Interestingly, CDD also corresponds to a middle-of-the-road approach in statistics with respect to the two main families of FDR control procedures: Although historically based on estimating the false-positive distribution, FDR control has recently been demonstrated to be possible thanks to competition between the original variables (in proteomics, target sequences) and their fictional counterparts (in proteomics, decoys). Discriminating between these two theoretical trends is of prime importance for computational proteomics. In addition to highlighting why proteomics was a source of inspiration for theoretical biostatistics, it provides practical insights into the improvements that can be made to FDR control methods used in proteomics, including CDD.

Powerful and interpretable control of false discoveries in two-group differential expression studies

MOTIVATION

The standard approach for statistical inference in differential expression (DE) analyses is to control the false discovery rate (FDR). However, controlling the FDR does not in fact imply that the proportion of false discoveries is upper bounded. Moreover, no statistical guarantee can be given on subsets of genes selected by FDR thresholding. These known limitations are overcome by post hoc inference, which provides guarantees of the number of proportion of false discoveries among arbitrary gene selections. However, post hoc inference methods are not yet widely used for DE studies.

RESULTS

In this article, we demonstrate the relevance and illustrate the performance of adaptive interpolation-based post hoc methods for two-group DE studies. First, we formalize the use of permutation-based methods to obtain sharp confidence bounds that are adaptive to the dependence between genes. Then, we introduce a generic linear time algorithm for computing post hoc bounds, making these bounds applicable to large-scale two-group DE studies. The use of the resulting Adaptive Simes bound is illustrated on a RNA sequencing study. Comprehensive numerical experiments based on real microarray and RNA sequencing data demonstrate the statistical performance of the method.

AVAILABILITY AND IMPLEMENTATION

A cross-platform open source implementation within the R package sanssouci is available at https://sanssouci-org.github.io/sanssouci/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A shared tissue transcriptome signature and pathways in psoriasis and ulcerative colitis

Despite multiple efficacious therapies in common between psoriasis (PS) and Ulcerative Colitis (UC), mechanisms underlying their common pathophysiology remain largely unclear. Here we sought to establish a link by evaluating expression differences and pathway alterations in diseased tissues. We identified two sets of differentially expressed genes (DEGs) between lesional and nonlesional tissues in meta-analyses of data collected from baseline samples in 3 UC and then 3 PS available clinical studies from Pfizer. A shared gene signature was defined by 190 DEGs common to both diseases. Commonly dysregulated pathways identified via enrichment analysis include interferon signaling, partly driven by genes IFI6, CXCL9, CXCL10 and CXCL11, which may attract chemotaxis of Th1 cells to inflammatory sites; IL-23 pathway (IL-23A, CCL20, PI3, CXCL1, LCN2); and Th17 pathway except IL-17A. Elevated expression of costimulatory molecules ICOS and CTLA4 suggests ongoing T-cell activation in both diseases. The clinical value of the shared signature is demonstrated by a gene set improvement score reflecting post-treatment molecular improvement for each disease. This is the first study using transcriptomic meta-analysis to define a tissue gene signature and pathways dysregulated in both PS and UC. These findings suggest immune mechanisms may initiate and sustain inflammation similarly in the two diseases.

Multivariate equivalence testing for food safety assessment

Products for food and feed derived from genetically modified (GM) crops are only allowed on the market when they are deemed to be safe for human health and the environment. The European Food Safety Authority (EFSA) performs safety assessment including a comparative approach: the compositional characteristics of a GM genotype are compared to those of reference genotypes that have a history of safe use. Statistical equivalence tests are used to carry out such a comparative assessment. These tests are univariate and therefore only consider one measured variable at a time. Phenotypic data, however, often comprise measurements on multiple variables that must be integrated to arrive at a single decision on acceptance in the regulatory process. The surge of modern molecular phenotyping platforms further challenges this integration, due to the large number of characteristics measured on the plants. This paper presents a new multivariate equivalence test that naturally extends a recently proposed univariate equivalence test and allows to assess equivalence across all variables simultaneously. The proposed test is illustrated on plant compositional data from a field study on maize grain and on untargeted metabolomic data of potato tubers, while its performance is assessed on simulated data.

Efficacy of metacognitive training on symptom severity, neurocognition and social cognition in patients with schizophrenia: A single-blind randomized controlled trial

Over the past decades, a number of complementary treatments for schizophrenia have emerged. One of these is metacognitive training (MCT), which combines the principles of cognitive-behavioral therapies, cognitive remediation, and psychoeducation into a hybrid approach placing emphasis on increasing metacognitive awareness. The aim of our study was to investigate the efficacy of MCT on symptom severity, and neurocognitive and social cognitive functioning in schizophrenia; also, attention was paid to the assessment of subjective acceptability. Forty-six patients diagnosed with schizophrenia were included in our single-blind randomized controlled trial, who were assigned to the intervention or control group. The intervention group was provided standard MCT, while the control group received treatment as usual. We assessed symptom severity and cognitive functions before and after the training, as well as after a 6-month follow-up period. Compared to the control group, the intervention group showed improvement in overall symptom severity, and positive and disorganized symptoms. Training participans showed further improvement at the follow-up assessment. Regarding neurocognitive functions, improvement in visuospatial functions was observed between pre- and post-intervention assessments compared to the control group. Patients showed excellent adherence, and evaluated the training as useful and interesting. In line with the results of previous studies, our results demonstrate the efficacy of MCT on symptom severity in schizophrenia. Improvements in cognitive functions that are closely related to the onset and prevalence of symptoms of schizophrenia were also found.

Combination of sunitinib and 177Lu-labeled antibody cG250 targeted radioimmunotherapy: A promising new therapeutic strategy for patients with advanced renal cell cancer

Sunitinib is an effective treatment for patients with metastatic Renal Cell Carcinoma (mRCC) but ultimately resistance occurs. The aim of this study was to investigate sunitinib resistance in RCCs and to develop therapeutic combination strategies with targeted radioimmunotherapy (RIT).

We studied two RCC models, analyzed Vascular endothelial growth factor (VEGF) and its receptor (VEGFR) and AXL/MET expression and performed therapy studies in Balb/c^nu/nu mice combining sunitinib and [¹⁷⁷Lu]Lu-cG250 RIT (6.5 MBq/10 μg), specifically targeting RCC cells.

pAXL and pMET were expressed in sunitinib-resistant SK-RC-52 and absent in sunitinib-sensitive NU12. NGS evaluation showed that expression of VEGFA, VEGFB, VEGFD, PGF and VEGFR1,2,3 was higher and expression of VEGFC and PDGFA was lower in NU12 than in SK-RC-52.

Therapy studies combining sunitinib with [¹⁷⁷Lu]Lu-cG250 RIT showed that the best response in mice with “resistant” SK-RC-52 tumors was observed with two cycles of Sunitinib and ^[177Lu]Lu-cG250 RIT, probably due to increased vascular permeability by sunitinib treatment. In the “sensitive” NU12 model, two cycles of [¹⁷⁷Lu]Lu-cG250 RIT and two cycles of combination treatment were equally effective.

Enhanced therapeutic efficacy was achieved when two agents ([¹⁷⁷Lu]Lu-cG250 RIT and sunitinib) that on their own did not induce satisfactory response levels, are combined. Our findings provide a promising new therapeutic strategy for patients with advanced RCC.

Deglycosylation of pathological specimens alters performance of diagnostic PDL1 antibodies

Immunohistochemical (IHC) predictive quantitation of PDL1 expression is obligatory in many cancer entities with improved response to immune checkpoint inhibition in PDL1-positive subgroups. With recent demonstration of increased positivity rates after enzymatic deglycosylation in breast cancer specimens, a comparative analysis with two different antibodies and extended controls was performed in a cohort of head and neck squamous cell cancer samples (HNSCC).Formalin-fixed paraffin-embedded tissue from HNSCC specimens was used for initial on-slide method optimization based on the PNGase F assay. SDS-PAGE and immunoblotting with the PDL1 antibody 28-8 was performed to evaluate deglycosylation efficiency. A tissue micro array of n = 527 tissue cores of 181 patients with HNSCC was used to determine the effects of deglycosylation on staining pattern and intensity with PDL1 antibodies 28-8 and E1L3N.Successful on-slide deglycosylation with PNGase F was confirmed by immunoblot but varied across replicates. Using E1L3N (intracellular binding domain, most probably not glycosylated), mean signal intensity as well as the fraction of PDL1 positive cells was increased by deglycosylation. Opposite effects were observed with 28-8 (extracellular binding domain, glycosylated).Deglycosylation reduces diagnostic performance of the PDL1 antibody 28-8. In contrast, effects for E1L3N are complex and probably involve reduction of off-target binding leading to specifically improved signal intensity. However, enzymatic deglycosylation adds further variance to IHC.

Identifying desired qualifications, tasks, and organizational characteristics of practice managers-a cross-sectional survey among group practice physicians in Germany

Background

The increase of centralization developments in primary and secondary care practices may cause the organizational needs to increase as well, as the practices grow in size. This continuous change is observed in different stages in various countries since, from the perspective of a physician, it is reinforced by the benefits it adds to flexible work configuration, professional exchange and specialization. However, in order to benefit from the joint practice system, the proper managerial skills of practice managers are required, as doctors are not naturally prepared to fulfill such tasks. This study thus aims to gain insight into physicians' views in group practices and acquire a greater understanding of expectations towards practice management and the emerging role of practice managers (PM).

Methods

A cross-sectional study design was employed which utilized an anonymous online questionnaire. In total, 3,456 physicians were invited to participate in the study between February 8^th and March 17^th 2021 by the Association of Statutory Health Insurance Physicians of Baden-Württemberg, Germany. Bivariate and multivariate analyses were applied to characterize the expectations of physicians towards practice management.

Results

The survey yielded 329 replies (9,5%). 50% of the participating practices already had a PM employed. In general, these practices were larger than practices without a PM. Most physicians (85%) considered a medical background to be essential for the task of a PM. While practices without a PM considered it important for PMs to have medical qualifications, practices with a PM favored qualifications in business administration. 77.2% of physicians preferred to educate and recruit PMs out of their current practice staff. Competence in organizational tasks, such as coordination of tasks and quality management, was considered to be an essential skill of a PM and had the highest agreement levels among those surveyed, followed by staff management of non-physicians, billing, bookkeeping, staff management of physicians and recruiting. Based on multivariate regression analysis, larger practices valued the role of a PM more and were more likely to employ a PM. Notably, the effect that size had on these items was more substantial for generalists than specialists.

Conclusions

The benefits and importance of PMs as well as the potential for delegation are recognized, in particular, by larger practices. The positive feelings that physicians who already employ PMs have towards their contribution to ambulatory care are even more significant. Pre-existing medical support staff has been identified to be the most desirable candidates for taking on the role of PM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08199-5.

fdrci: FDR confidence interval selection and adjustment for large-scale hypothesis testing

Motivation

Approaches that control error by applying a priori fixed discovery thresholds such as 0.05 limit the ability of investigators to identify and publish weak effects even when evidence suggests that such effects exist. However, current false discovery rate (FDR) estimation methods lack a principled approach for post hoc identification of discovery thresholds other than 0.05.

Results

We describe a flexible approach that hinges on the precision of a permutation-based FDR estimator. A series of discovery thresholds are proposed, and an FDR confidence interval selection and adjustment technique is used to identify intervals that do not cover one, implying that some discoveries are expected to be true. We report an application to a transcriptome-wide association study of the MAVERICC clinical trial involving patients with metastatic colorectal cancer. Several genes are identified whose predicted expression is associated with progression-free or overall survival.

Availability and implementation

Software is provided via the CRAN repository (https://cran.r-project.org/web/packages/fdrci/index.html).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Genomic selection applications can improve the environmental performance of aquatics: A case study on the heat tolerance of abalone

Aquaculture is one of the world's fastest-growing and most traded food industries, but it is under the threat of climate-related risks represented by global warming, marine heatwave (MHW) events, ocean acidification, and deoxygenation. For the sustainable development of aquaculture, selective breeding may be a viable method to obtain aquatic economic species with greater tolerance to environmental stressors. In this study, we estimated the heritability of heat tolerance trait of Pacific abalone Haliotis discus hannai, performed genome-wide association studies (GWAS) analysis for heat tolerance to detect single nucleotide polymorphisms (SNPs) and candidate genes, and assessed the potential of genomic selection (GS) in the breeding of abalone industry. A total of 1120 individuals were phenotyped for their heat tolerance and genotyped with 64,788 quality-controlled SNPs. The heritability of heat tolerance was moderate (0.35-0.42) and the predictive accuracy estimated using BayesB (0.55 ± 0.05) was higher than that using GBLUP (0.40 ± 0.01). A total of 11 genome-wide significant SNPs and 2 suggestive SNPs were associated with heat tolerance of abalone, and 13 candidate genes were identified, including got2,znfx1,l(2)efl, and lrp5. Based on GWAS results, the prediction accuracy using the top 5K SNPs was higher than that using randomly selected SNPs and higher than that using all SNPs. These results suggest that GS is an efficient approach for improving the heat tolerance of abalone and pave the way for abalone selecting breeding programs in rapidly changing oceans.

Forensic Features and Genetic Structure Analyses of the Beijing Han Nationality Disclosed by a Self-Developed Panel Containing a Series of Ancestry Informative Deletion/Insertion Polymorphism Loci

The utilization of the ancestry informative markers to disclose the ancestral composition of a certain population and explore the genetic affinities between diverse populations is beneficial to inferring the biogeographic ancestry of unknown individuals and assisting in case detection, as well as avoiding the impacts of population stratification during genome-wide association analysis studies. In the present study, we applied an in-house ancestry informative deletion/insertion polymorphic multiplex amplification system to investigate the ancestral compositions of the Beijing Han population and analyze the genetic relationships between the Beijing Han population and 31 global reference populations. The results demonstrated that 32 loci of this self-developed panel containing 39 loci significantly contributed to the inference of genetic information for the Beijing Han population. The results of multiple population genetics statistical analyses indicated that the ancestral component and genetic architecture of the Beijing Han population were analogous to the reference East Asian populations, and that the Beijing Han population was genetically close to the reference East Asian populations.

Ensemble classification based signature discovery for cancer diagnosis in RNA expression profiles across different platforms

Molecular signatures have been excessively reported for diagnosis of many cancers during the last 20 years. However, false-positive signatures are always found using statistical methods or machine learning approaches, and that makes subsequent biological experiments fail. Therefore, signature discovery has gradually become a non-mainstream work in bioinformatics. Actually, there are three critical weaknesses that make the identified signature unreliable. First of all, a signature is wrongly thought to be a gene set, each component of which keeps differential expressions between or among sample groups. Second, there may be many false-positive genes expressed differentially found, even if samples derived from cancer or normal group can be separated in one-dimensional space. Third, cross-platform validation results of a discovered signature are always poor. In order to solve these problems, we propose a new feature selection framework based on ensemble classification to discover signatures for cancer diagnosis. Meanwhile, a procedure for data transform among different expression profiles across different platforms is also designed. Signatures are found on simulation and real data representing different carcinomas across different platforms. Besides, false positives are suppressed. The experimental results demonstrate the effectiveness of our method.

A Comparison of Methods for Gene-Based Testing That Account for Linkage Disequilibrium

Controlling the type I error rate while retaining sufficient power is a major concern in genome-wide association studies, which nowadays often examine more than a million single-nucleotide polymorphisms (SNPs) simultaneously. Methods such as the Bonferroni correction can lead to a considerable decrease in power due to the large number of tests conducted. Shifting the focus to higher functional structures (e.g., genes) can reduce the loss of power. This can be accomplished via the combination of p-values of SNPs that belong to the same structural unit to test their joint null hypothesis. However, standard methods for this purpose (e.g., Fisher’s method) do not account for the dependence among the tests due to linkage disequilibrium (LD). In this paper, we review various adjustments to methods for combining p-values that take LD information explicitly into consideration and evaluate their performance in a simulation study based on data from the HapMap project. The results illustrate the importance of incorporating LD information into the methods for controlling the type I error rate at the desired level. Furthermore, some methods are more successful in controlling the type I error rate than others. Among them, Brown’s method was the most robust technique with respect to the characteristics of the genes and outperformed the Bonferroni method in terms of power in many scenarios. Examining the genetic factors of a phenotype of interest at the gene-rather than SNP-level can provide researchers benefits in terms of the power of the study. While doing so, one should be careful to account for LD in SNPs belonging to the same gene, for which Brown’s method seems the most robust technique.

The socioeconomic landscape of the exposome during pregnancy

BACKGROUND

While socioeconomic position (SEP) is consistently related to pregnancy and birth outcome disparities, relevant biological mechanisms are manifold, thus necessitating more comprehensive characterization of SEP-exposome associations during pregnancy.

OBJECTIVES

We implemented an exposomic approach to systematically characterize the socioeconomic landscape of prenatal exposures in a setting where social segregation was less distinct in a hypotheses-generating manner.

METHODS

We described the correlation structure of 134 prenatal exogenous and endogenous sources (e.g., micronutrients, hormones, immunomodulatory metabolites, environmental pollutants) collected in a diverse, population-representative, urban, high-income longitudinal mother-offspring cohort (N = 1341; 2009-2011). We examined the associations between maternal, paternal, household, and areal level SEP indicators and 134 exposures using multiple regressions adjusted for precision variables, as well as potential effect measure modification by ethnicity and nativity. Finally, we generated summary SEP indices using Multiple Correspondence Analysis to further explore possible curved relationships.

RESULTS

Individual and household SEP were associated with anthropometric/adiposity measures, folate, omega-3 fatty acids, insulin-like growth factor-II, fasting glucose, and neopterin, an inflammatory marker. We observed paternal education was more strongly and consistently related to maternal exposures than maternal education. This was most apparent amongst couples discordant on education. Analyses revealed additional non-linear associations between areal composite SEP and particulate matter. Environmental contaminants (e.g., per- and polyfluoroalkyl substances) and micronutrients (e.g., folate and copper) showed opposing associations by ethnicity and nativity, respectively.

DISCUSSION

SEP-exposome relationships are complex, non-linear, and context specific. Our findings reinforce the potential role of paternal contributions and context-specific modifiers of associations, such as between ethnicity and maternal diet-related exposures. Despite weak presumed areal clustering of individual exposures in our context, our approach reinforces subtle non-linearities in areal-level exposures.

Engineering a Dynamic Controllable Infectivity Switch in Bacteriophage T7

Transcriptional repressors play an important role in regulating phage life cycle. Here, we examine how synthetic transcription repressors can be used in bacteriophage T7 to create a dynamic, controllable infectivity switch. We engineered T7 phage by replacing a large region of the early phage genome with different combinations of ligand-responsive promoters and ribosome binding sites (RBS) designed to control the phage RNA polymerase, gp1. Phages with engineered infectivity switch are fully viable at levels comparable to wildtype T7, when not repressed, indicating the phage can be engineered without loss of fitness. The most effective switch used a TetR-responsive promoter and an attenuated RBS, resulting in a 2-fold increase in latent period and a 10-fold decrease in phage titer when repressed. Phage activity can be further tuned using different inducer concentrations. Our study provides a proof of concept for how a simple synthetic circuit introduced into the phage genome enables user control over phage infectivity.

Pharmacological Inhibition of CETP (Cholesteryl Ester Transfer Protein) Increases HDL (High-Density Lipoprotein) That Contains ApoC3 and Other HDL Subspecies Associated With Higher Risk of Coronary Heart Disease

Supplemental Digital Content is available in the text.

Plasma total HDL (high-density lipoprotein) is a heterogeneous mix of many protein-based subspecies whose functions and associations with coronary heart disease vary. We hypothesize that increasing HDL by CETP (cholesteryl ester transfer protein) inhibition failed to reduce cardiovascular disease risk, in part, because it increased dysfunctional subspecies associated with higher risk such as HDL that contains apoC3.