Shared and Distinct Features of Human Milk and Infant Stool Viromes.

Infants acquire many of their microbes from their mothers during the birth process. The acquisition of these microbes is believed to be critical in the development of the infant immune system. Bacteria also are transmitted to the infant through breastfeeding, and help to form the microbiome of the infant gastrointestinal (GI) tract; it is unknown whether viruses in human milk serve to establish an infant GI virome. We examined the virome contents of milk and infant stool in a cohort of mother-infant pairs to discern whether milk viruses colonize the infant GI tract. We observed greater viral alpha diversity in milk than in infant stool, similar to the trend we found for bacterial communities from both sites. When comparing beta diversity, viral communities were mostly distinguishable between milk and infant stool, but each was quite distinct from adult stool, urine, and salivary viromes. There were significant differences in viral families in the infant stool (abundant bacteriophages from the family Siphoviridae) compared to milk (abundant bacteriophages from the family Myoviridae), which may reflect significant differences in the bacterial families identified from both sites. Despite the differences in viral taxonomy, we identified a significant number of shared viruses in the milk and stool from all mother-infant pairs. Because of the significant proportion of bacteriophages transmitted in these mother-infant pairs, we believe the transmission of milk phages to the infant GI tract may help to shape the infant GI microbiome

Assessing the Forms and Functions of Aggression Using Self-Report: Factor Structure and Invariance of the Peer Conflict Scale in Youths

This study examined the structure of a self-report measure of the forms and functions of aggression in 855 adolescents (582 boys, 266 girls) aged 12 to 19 years recruited from high school, detained, and residential settings. The Peer Conflict Scale (PCS) is a 40-item measure that was developed to improve upon existing measures and provide an efficient, reliable, and valid assessment of four dimensions of aggression (i.e., reactive overt, reactive relational, proactive overt, and proactive relational) in youths. Confirmatory factor analyses showed that a 4-factor model represented a satisfactory solution for the data. The factor structure fit well for both boys and girls and across high school, detained, and residential samples. Internal consistency estimates were good for the 4 factors, and they showed expected associations with externalizing variables (i.e., arrest history, callous-unemotional traits, and delinquency). Reactive and proactive subtypes showed unique associations consistent with previous literature. Implications for the use of the PCS to assess aggression and inform intervention decisions in diverse samples of youths are discussed

KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response.

Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks; the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics

KG-Hub-building and exchanging biological knowledge graphs.

MOTIVATION: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. RESULTS: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. AVAILABILITY AND IMPLEMENTATION: https://kghub.org

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch\u27s APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch\u27s data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch\u27s analytic tools by developing a customized plugin for OpenAI\u27s ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app

Association between Interpersonal Trust, Reciprocity, and Depression in South Korea: A Prospective Analysis

Background: A growing body of empirical evidence indicates that low-level social capital is related to poor mental health outcomes. However, the prospective association between social capital and depression remains unclear, and no published studies have investigated the association with longitudinal data in East-Asian countries. Methods: We analyzed data from the ongoing Korean Welfare Panel Study to prospectively investigate association between social capital and depression. Social capital was measured at the individual level by two items specific to interpersonal trust and reciprocity. Depression was annually assessed as a dichotomous variable using the Center for Epidemiologic Studies Depression Scale. After excluding participants who had depression in 2006, logistic regression models were applied to estimate the association between each social capital indicator and new-onset depression developed in 2007 or long-term depression in both 2007 and 2008. We also examined the association in a subpopulation restricted to healthy participants after excluding individuals with any pre-existing disability, chronic disease, or poor self-rated health condition. Results: Compared to the high interpersonal trust group, the odds ratios of developing new-onset and long-term depression among the low interpersonal trust group were 1.22 (95% CI: 1.08∼1.38) and 1.23 (95% CI: 1.03∼1.50), respectively, and increased to 1.32 (95% CI: 1.10∼1.57) and 1.47 (95% CI: 1.05∼2.08) in the subpopulation analyses restricted to healthy individuals. Although the low and intermediate reciprocity group also had significantly higher odds of developing new-onset depression compared to the high reciprocity group, the effects were attenuated and statistically non-significant in the subpopulation analyses. Conclusion: Low interpersonal trust appears to be an independent risk factor for new-onset and long-term depression in South Korea

BRCA2 polymorphic stop codon K3326X and the risk of breast, prostate, and ovarian cancers

Background: The K3326X variant in BRCA2 (BRCA2*c.9976A>T; p.Lys3326*; rs11571833) has been found to be associated with small increased risks of breast cancer. However, it is not clear to what extent linkage disequilibrium with fully pathogenic mutations might account for this association. There is scant information about the effect of K3326X in other hormone-related cancers. Methods: Using weighted logistic regression, we analyzed data from the large iCOGS study including 76 637 cancer case patients and 83 796 control patients to estimate odds ratios (ORw) and 95% confidence intervals (CIs) for K3326X variant carriers in relation to breast, ovarian, and prostate cancer risks, with weights defined as probability of not having a pathogenic BRCA2 variant. Using Cox proportional hazards modeling, we also examined the associations of K3326X with breast and ovarian cancer risks among 7183 BRCA1 variant carriers. All statistical tests were two-sided. Results: The K3326X variant was associated with breast (ORw = 1.28, 95% CI = 1.17 to 1.40, P = 5.9x10- 6) and invasive ovarian cancer (ORw = 1.26, 95% CI = 1.10 to 1.43, P = 3.8x10-3). These associations were stronger for serous ovarian cancer and for estrogen receptor–negative breast cancer (ORw = 1.46, 95% CI = 1.2 to 1.70, P = 3.4x10-5 and ORw = 1.50, 95% CI = 1.28 to 1.76, P = 4.1x10-5, respectively). For BRCA1 mutation carriers, there was a statistically significant inverse association of the K3326X variant with risk of ovarian cancer (HR = 0.43, 95% CI = 0.22 to 0.84, P = .013) but no association with breast cancer. No association with prostate cancer was observed. Conclusions: Our study provides evidence that the K3326X variant is associated with risk of developing breast and ovarian cancers independent of other pathogenic variants in BRCA2. Further studies are needed to determine the biological mechanism of action responsible for these associations

A Simple Standard for Sharing Ontological Mappings (SSSOM).

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec

Evaluation of polygenic risk scores for breast and ovarian cancer risk prediction in BRCA1 and BRCA2 mutation carriers

Background: Genome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic mutation in the high-risk BC and OC genes BRCA1 or BRCA2. The combined effects of these variants on BC or OC risk for BRCA1 and BRCA2 mutation carriers have not yet been assessed while their clinical management could benefit from improved personalized risk estimates. Methods: We constructed polygenic risk scores (PRS) using BC and OC susceptibility SNPs identified through population-based GWAS: for BC (overall, estrogen receptor [ER]-positive, and ER-negative) and for OC. Using data from 15 252 female BRCA1 and 8211 BRCA2 carriers, the association of each PRS with BC or OC risk was evaluated using a weighted cohort approach, with time to diagnosis as the outcome and estimation of the hazard ratios (HRs) per standard deviation increase in the PRS. Results: The PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.27, 95% confidence interval [CI] = 1.23 to 1.31, P = 8.2 x 10(53)). In BRCA2 carriers, the strongest association with BC risk was seen for the overall BC PRS (HR = 1.22, 95% CI = 1.17 to 1.28, P = 7.2 x 10(-20)). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These translate to differences in absolute risks (more than 10% in each case) between the top and bottom deciles of the PRS distribution; for example, the OC risk was 6% by age 80 years for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS. Conclusions: BC and OC PRS are predictive of cancer risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management