The QIMR Berghofer Medical Research Institute (QIMR Berghofer) is an Australian medical research institute located in Herston, Brisbane, in the state of Queensland. QIMR was established in 1945 by the Government of Queensland through the enactment of the Queensland Institute of Medical Research Act 1945 (Qld). Previously known as the Queensland Institute of Medical Research (QIMR), the original purpose of the institute was to further the study of tropical diseases in North Queensland. The current director is Professor Fabienne Mackay. The institute is a registered charity. In 2021, the institute was named as one of the Queensland Greats by the Queensland Government..
This study tested whether adverse mood effect of the oral contraceptive pill (OCP) is associated with reproductive depressive episodes, including peripartum depression (PPD), premenstrual dysphoric disorder (PMDD), and perimenopausal depression. In a sample of 3,547 OCP users from the Australian Genetics of Depression Study, who reported a lifetime depression diagnosis, logistic regression was used to test the association of PPD, PMDD, and perimenopausal depression with OCP adverse mood effect. Polygenic scores (PGS) for major depression (MD) were also tested for association with adverse mood effect. Sensitivity analyses tested for modification of these associations by a history of depression prior to first OCP use (prior depression), or by depression onset before the age of twenty (child/teen depression onset). Adverse mood effect was reported by 1,342 OCP users (38
The major anxiety disorders (ANX; including generalized anxiety disorder, panic disorder and phobias) are highly prevalent, often onset early and cause substantial global disability. Although distinct in their clinical presentations, they probably represent differential expressions of a dysregulated threat-response system. Here, we present a genome-wide association meta-analysis comprising 122,341 European ancestry ANX cases and 729,881 controls. We identified 58 independent genome-wide significant risk variants and 66 genes with robust biological support. In an independent sample of 1,175,012 self-report ANX cases and 1,956,379 controls, 51 out of the 58 associations replicated. As predicted by twin studies, we found substantial genetic correlation between ANX and depression, neuroticism and other internalizing phenotypes. Follow-up analyses demonstrated enrichment in all major brain regions and highlighted GABAergic signaling as one potential mechanism implicated in ANX genetic risk. These results advance our understanding of the genetic architecture of ANX and prioritize genes for functional follow-up studies.
Interpretation of genetic variants is most accurate when gene- and disease-specific considerations are considered. The 2015 ACMG/AMP guidelines form the basis for the application of variant interpretation criteria for Mendelian disorders. The Hereditary Breast, Ovarian, and Pancreatic Cancer Variant Curation Expert Panel (HBOP VCEP) has undertaken the process for creating gene- and disease-specific specifications for the interpretation of PALB2 germline sequence variants. The HBOP VCEP is comprised of experts in the fields of clinical and molecular genetics, epidemiology, functional assays, and variant interpretation. The group met regularly to consider each of the codes from the 2015 ACMG/AMP guidelines to determine their relevance for PALB2. After criteria were created using database analysis, literature review, and expert opinion, they were vetted against a diverse set of pilot variants and ultimately finalized. The HBOP VCEP advised against using 13 codes, limited the use of six codes, and tailored nine codes to create the final PALB2 variant interpretation guidelines. Among the 39 pilot variants, 37 were in ClinVar, and using the new specifications concordant classifications resulted for 31 of the variants (84%). Of the 14 variants of uncertain significance/conflicting variants in ClinVar, four were classified by the VCEP, likely due to code combination modifications and refined population frequency cutoffs. The PALB2-specific guidelines put forward by the HBOP VCEP represent a conservative approach to classifying variants in PALB2 and lead to improved classifications relative to current ClinVar entries. Adoption of these specifications will help to harmonize classifications deposited in the public domain.
This cohort study evaluates the reduction in risk of high-grade serous carcinoma among women undergoing opportunistic bilateral salpingectomy in Canada.
The integration of multi-omics data, including genomics, transcriptomics, proteomics, epigenomics, and metabolomics, coupled with histological spatial data has transformed biomedical research, offering unprecedented insights into cellular functions and disease mechanisms. However, the sheer volume and complexity of these datasets present a significant challenge in terms of interpretation and clinical translation. Artificial intelligence (AI) and machine learning (ML) are transforming data analysis, enabling the extraction of meaningful patterns from high-dimensional datasets and facilitating the development of predictive models. This shift is particularly transformative in cancer research, where understanding the tumor microenvironment (TME) and its spatial dynamics is crucial for improving therapeutic outcomes. This review explores recent advancements in spatial omics (SO) including spatial transcriptomics (ST) and spatial proteomics (SP), and AI-driven computational models, focusing on their applications in oncology. We discuss key methodologies, including spatial barcoding, in situ sequencing, and digital spatial profiling, and highlight major platforms. AI-powered tools, including deep learning models and spatial graph-based analyses, enhance data interpretation, allowing for robust predictive modeling, biomarker discovery, and personalized therapeutic strategies. Despite their transformative potential, ST and AI-driven approaches face challenges, including high-dimensional data complexity, computational constraints, and standardization of analytical pipelines. Addressing these challenges requires advanced mathematical frameworks such as spatial graph theory, topological data analysis, and agent-based modeling, which refine data integration and improve biological insights. Future research should focus on enhancing spatial resolution, cross-platform data harmonization, and AI-driven predictive models to advance precision oncology. By integrating ST, SP, and AI, researchers can develop dynamic, patient-specific treatment strategies, ultimately improving clinical outcomes and deepening our understanding of cancer progression and immune system interactions.
