Emotion recognition links to reactive and proactive aggression across childhood: A multi-study design

Difficulty recognizing negative emotions is linked to aggression in children. However, it remains unclear how certain types of emotion recognition (insensitivities vs. biases) are associated with functions of aggression and whether these relations change across childhood. We addressed these gaps in two diverse community samples (study 1: aged 4 and 8; N = 300; study 2: aged 5 to 13, N = 374). Across studies, children performed a behavioral task to assess emotion recognition (sad, fear, angry, and happy facial expressions) while caregivers reported children's overt proactive and reactive aggression. Difficulty recognizing fear (especially in early childhood) and sadness was associated with greater proactive aggression. Insensitivity to anger - perceiving angry faces as showing no emotion - was associated with increased proactive aggression, especially in middle-to-late childhood. Additionally, greater happiness bias - mistaking negative emotions as being happy - was consistently related to higher reactive aggression only in early childhood. Together, difficulty recognizing negative emotions was related to proactive aggression, however, the strength of these relations varied based on the type of emotion and developmental period assessed. Alternately, difficulty determining emotion valence was related to reactive aggression in early childhood. These findings demonstrate that distinct forms of emotion recognition are important for understanding functions of aggression across development.

Batch Correction and Harmonization of -Omics Datasets with a Tunable Median Polish of Ratio

Large scale -omics datasets can provide new insights into normal and disease-related biology when analyzed through a systems biology framework. However, technical artefacts present in most -omics datasets due to variations in sample preparation, batching, platform settings, personnel, and other experimental procedures prevent useful analyses of such data without prior adjustment for these technical factors. Here, we demonstrate a tunable median polish of ratio (TAMPOR) approach for batch effect correction and agglomeration of multiple, multi-batch, site-specific cohorts into a single analyte abundance data matrix that is suitable for systems biology analyses. We illustrate the utility and versatility of TAMPOR through four distinct use cases where the method has been applied to different proteomic datasets, some of which contain a specific defect that must be addressed prior to analysis. We compare quality control metrics and sources of variance before and after application of TAMPOR to show that TAMPOR is effective at removing batch effects and other unwanted sources of variance in -omics data. We also show how TAMPOR can be used to harmonize -omics datasets even when the data are acquired using different analytical approaches. TAMPOR is a powerful and flexible approach for cleaning and harmonization of -omics data prior to downstream systems biology analysis.

Associations between the CADM2 gene, substance use, risky sexual behavior, and self-control: A phenome-wide association study

Risky behaviors, such as substance use and unprotected sex, are associated with various physical and mental health problems. Recent genome-wide association studies indicated that variation in the cell adhesion molecule 2 (CADM2) gene plays a role in risky behaviors and self-control. In this phenome-wide scan for risky behavior, it was tested if underlying common vulnerability could be (partly) explained by pleiotropic effects of this gene and how large the effects were. Single nucleotide polymorphism (SNP)-level and gene-level association tests within four samples (25 and Up, Spit for Science, Netherlands Twin Register, and UK Biobank and meta-analyses over all samples (combined sample of 362,018 participants) were conducted to test associations between CADM2, substance- and sex-related risk behaviors, and various measures related to self-control. We found significant associations between the CADM2 gene, various risky behaviors, and different measures of self-control. The largest effect sizes were found for cannabis use, sensation seeking, and disinhibition. Effect sizes ranged from 0.01% to 0.26% for single top SNPs and from 0.07% to 3.02% for independent top SNPs together, with sufficient power observed only in the larger samples and meta-analyses. In the largest cohort, we found indications that risk-taking proneness mediated the association between CADM2 and latent factors for lifetime smoking and regular alcohol use. This study extends earlier findings that CADM2 plays a role in risky behaviors and self-control. It also provides insight into gene-level effect sizes and demonstrates the feasibility of testing mediation. These findings present a good starting point for investigating biological etiological pathways underlying risky behaviors.

Identification of a Minimal 3-Transcript Signature to Differentiate Viral from Bacterial Infection from Best Genome-Wide Host RNA Biomarkers: A Multi-Cohort Analysis

The fight against the spread of antibiotic resistance is one of the most important challenges facing health systems worldwide. Given the limitations of current diagnostic methods, the development of fast and accurate tests for the diagnosis of viral and bacterial infections would improve patient management and treatment, as well as contribute to reducing antibiotic misuse in clinical settings. In this scenario, analysis of host transcriptomics constitutes a promising target to develop new diagnostic tests based on the host-specific response to infections. We carried out a multi-cohort meta-analysis of blood transcriptomic data available in public databases, including 11 different studies and 1209 samples from virus- (n = 695) and bacteria- (n = 514) infected patients. We applied a Parallel Regularized Regression Model Search (PReMS) on a set of previously reported genes that distinguished viral from bacterial infection to find a minimum gene expression bio-signature. This strategy allowed us to detect three genes, namely BAFT, ISG15 and DNMT1, that clearly differentiate groups of infection with high accuracy (training set: area under the curve (AUC) 0.86 (sensitivity: 0.81; specificity: 0.87); testing set: AUC 0.87 (sensitivity: 0.82; specificity: 0.86)). BAFT and ISG15 are involved in processes related to immune response, while DNMT1 is related to the preservation of methylation patterns, and its expression is modulated by pathogen infections. We successfully tested this three-transcript signature in the 11 independent studies, demonstrating its high performance under different scenarios. The main advantage of this three-gene signature is the low number of genes needed to differentiate both groups of patient categories.

A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene Signatures

Although massive amounts of condition-specific molecular profiles are being accumulated in public repositories every day, meaningful interpretation of these data remains a major challenge. In an effort to identify the biomarkers that describe the key biological phenomena for a given condition, several approaches have been developed over the past few years. However, the majority of these approaches either (i) do not consider the known intermolecular interactions, or (ii) do not integrate molecular data of multiple types (e.g., genomics, transcriptomics, proteomics, epigenomics, etc.), and thus potentially fail to capture the true biological changes responsible for complex diseases (e.g., cancer). In addition, these approaches often ignore the heterogeneity and study bias present in independent molecular cohorts. In this manuscript, we propose a novel multi-cohort and multi-omics meta-analysis framework that overcomes all three limitations mentioned above in order to identify robust molecular subnetworks that capture the key dynamic nature of a given biological condition. Our framework integrates multiple independent gene expression studies, unmatched DNA methylation studies, and protein-protein interactions to identify methylation-driven subnetworks. We demonstrate the proposed framework by constructing subnetworks related to two complex diseases: glioblastoma and low-grade gliomas. We validate the identified subnetworks by showing their ability to predict patients' clinical outcome on multiple independent validation cohorts.

Sickness absence diagnoses among abstainers, low-risk drinkers and at-risk drinkers: consideration of the U-shaped association between alcohol use and sickness absence in four cohort studies

AIMS

To estimate differences in the strength and shape of associations between alcohol use and diagnosis-specific sickness absence.

DESIGN

A multi-cohort study. Participants (n = 47 520) responded to a survey on alcohol use at two time-points, and were linked to records of sickness absence. Diagnosis-specific sickness absence was followed for 4-7 years from the latter survey.

SETTING AND PARTICIPANTS

From Finland, we had population cohort survey data from 1998 and 2003 and employee cohort survey data from 2000-02 and 2004. From France and the United Kingdom, we had employee cohort survey data from 1993 and 1997, and 1985-88 and 1991-94, respectively.

MEASUREMENTS

We used standard questionnaires to assess alcohol intake categorized into 0, 1-11 and > 11 units per week in women and 0, 1-34 and > 34 units per week in men. We identified groups with stable and changing alcohol use over time. We linked participants to records from sickness absence registers. Diagnoses of sickness absence were coded according to the International Classification of Diseases. Estimates were adjusted for sex, age, socio-economic status, smoking and body mass index.

FINDINGS

Women who reported drinking 1-11 units and men who reported drinking 1-34 units of alcohol per week in both surveys were the reference group. Compared with them, women and men who reported no alcohol use in either survey had a higher risk of sickness absence due to mental disorders [rate ratio = 1.51, 95% confidence interval (CI) = 1.22-1.88], musculoskeletal disorders (1.22, 95% CI = 1.06-1.41), diseases of the digestive system (1.35, 95% CI = 1.02-1.77) and diseases of the respiratory system (1.49, 95% CI = 1.29-1.72). Women who reported alcohol consumption of > 11 weekly units and men who reported alcohol consumption of > 34 units per week in both surveys were at increased risk of absence due to injury or poisoning (1.44, 95% CI = 1.13-1.83).

CONCLUSIONS

In Finland, France and the United Kingdom, people who report not drinking any alcohol on two occasions several years apart appear to have a higher prevalence of sickness absence from work with chronic somatic and mental illness diagnoses than those drinking below a risk threshold of 11 units per week for women and 34 units per week for men. Persistent at-risk drinking in Finland, France and the United Kingdom appears to be related to increased absence due to injury or poisoning.