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Shared genetic architecture and bidirectional clinical risks within the psycho-metabolic nexus.
EBioMedicine
December 2024
Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Nanhu Brain-Computer Interface Institute, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory of Precision Psychiatry, Hangzhou, 310003, China; Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 311121, China; Brain Research Institute of Zhejiang University, Hangzhou, 310058, China; MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University School of Medicine, Hangzhou, 310058, China; Department of Psychology and Behavioral Sciences, Graduate School, Zhejiang University, Hangzhou, 310058, China. Electronic address:
Background: Increasing evidence suggests a complex interplay between psychiatric disorders and metabolic dysregulations. However, most research has been limited to specific disorder pairs, leaving a significant gap in our understanding of the broader psycho-metabolic nexus.
Methods: This study leveraged large-scale cohort data and genome-wide association study (GWAS) summary statistics, covering 8 common psychiatric disorders and 43 metabolic traits.
Genetic modifiers of asthma response to air pollution in children: An African ancestry GWAS and PM polygenic risk score study.
Environ Res
December 2024
Perelman School of Medicine, University of Pennsylvania, 3451 Walnut St, Philadelphia, PA, 19104, USA; The Center of Applied Genomics, The Children's Hospital of Philadelphia, 3615 Civic Center Blvd, 19104, Philadelphia, PA, USA; Division of Pulmonary and Sleep Medicine, The Children's Hospital of Philadelphia, 3615 Civic Center Blvd, 19104, Philadelphia, PA, USA.
Rationale: Ambient air pollution (AAP) is linked to asthma outcomes, but predicting individual risk remains challenging. Understanding genetic contributors to AAP sensitivity may help overcome this gap.
Objectives: To determine if single nucleotide polymorphisms (SNPs) are associated with AAP sensitivity in children with asthma.
One score to rule them all: regularized ensemble polygenic risk prediction with GWAS summary statistics.
bioRxiv
December 2024
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI.
Ensemble learning has been increasingly popular for boosting the predictive power of polygenic risk scores (PRS), with almost every recent multi-ancestry PRS approach employing ensemble learning as a final step. Existing ensemble approaches rely on individual-level data for model training, which severely limits their real-world applications, especially in non-European populations without sufficient genomic samples. Here, we introduce a statistical framework to construct regularized ensemble PRS, which allows us to combine a large number of candidate PRS models using only summary statistics from genome-wide association studies.
View Article and Find Full Text PDFModeling gene interactions in polygenic prediction via geometric deep learning.
Genome Res
January 2025
Department of Epidemiology, University of Florida, Gainesville, Florida 32603, USA;
Polygenic risk score (PRS) is a widely used approach for predicting individuals' genetic risk of complex diseases, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall short in capturing the intricate relationships between genotype and phenotype. In this study, we present PRS-Net, an interpretable geometric deep learning-based framework that effectively models the nonlinearity of biological systems for enhanced disease prediction and biological discovery.
View Article and Find Full Text PDFGenomic insights for personalised care in lung cancer and smoking cessation: motivating at-risk individuals toward evidence-based health practices.
EBioMedicine
December 2024
Department of Psychiatry, Washington University School of Medicine, St. Louis, USA. Electronic address:
Article Synopsis
- - The text discusses the serious global health issues of lung cancer and tobacco use and introduces the GREAT care paradigm, which uses polygenic risk scores (PRSs) to enhance cancer prevention and encourage healthier behaviors in patients.
- - Researchers developed standardized PRSs using extensive genetic data from diverse populations and validated them in a large sample, revealing significant risk factors for lung cancer and challenges in quitting smoking across different groups.
- - The PRS-based intervention aims to integrate genetic risk assessments into primary care, with plans for evaluation through clinical trials, potentially leading to better prevention strategies for lung cancer and more effective tobacco treatments.
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