Previous genetic studies of venous thromboembolism (VTE) have been largely limited to common variants, leaving the genetic determinants relatively incomplete. We performed an exome-wide association study of VTE among 14,723 cases and 334,315 controls. Fourteen known and four novel genes (SRSF6, PHPT1, CGN, and MAP3K2) were identified through protein-coding variants, with broad replication in the FinnGen cohort. Most genes we discovered exhibited the potential to predict future VTE events in longitudinal analysis. Notably, we provide evidence for the additive contribution of rare coding variants to known genome-wide polygenic risk in shaping VTE risk. The identified genes were enriched in pathways affecting coagulation and platelet activation, along with liver-specific expression. The pleiotropic effects of these genes indicated the potential involvement of coagulation factors, blood cell traits, liver function, and immunometabolic processes in VTE pathogenesis. In conclusion, our study unveils the valuable contribution of protein-coding variants in VTE etiology and sheds new light on its risk stratification.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10984941 | PMC |
http://dx.doi.org/10.1038/s41467-024-47178-8 | DOI Listing |
Alzheimers Dement
December 2024
Huashan Hospital, Fudan University, Shanghai, Shanghai, China.
Background: Cognition and its two critical proxies, socioeconomic status (SES) and educational attainment (EA), contribute substantially to human health and are heritable. Elucidating the genetic characteristics of SES/EA/Cognition not only helps to understand the innate individual differences in cognition, but also aids in unraveling the biological mechanisms of complex cognitive-related disorders such as Alzheimer's disease (AD). Here, we explored the rare and common protein-coding variants impacting the comprehensive cognition phenotypic spectrum by leveraging large-scale exomes.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA.
Background: Although high-throughput DNA/RNA sequencing technologies have generated massive genetic and genomic data in human disease, translation of these findings into new patient treatment has not materialized by lack of effective approaches, such as Artificial Intelligence (AL) and Machine Learning (ML) tools.
Method: To address this problem, we have used AI/ML approaches, Mendelian randomization (MR), and large patient's genetic and functional genomic data to evaluate druggable targets using Alzheimer's disease (AD) as a prototypical example. We utilized the genomic instruments from 9 expression quantitative trait loci (eQTL) and 3 protein quantitative trait loci (pQTL) datasets across five human brain regions from three biobanks.
Alzheimers Dement
December 2024
Department of Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.
Background: Recent advances in Alzheimer's Disease (AD) research have emphasized the importance of recruiting from diverse populations. Notably, African-descent individuals have an almost doubled risk of developing AD compared to European-descent individuals. Transcriptome-wide association studies (TWAS) have advanced the analysis of non-coding variants by integrating gene expression with GWAS data.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
John P. Hussman Institute for Human Genomics, Miller School of Medicine, Miami, FL, USA.
Background: We identified the missense variant Ser1038Cys (rs377155188) in the tetratricopeptide repeat domain 3 (TTC3) gene that segregate in a non-Hispanic white late onset Alzheimer disease (LOAD) family. This variant is predicted to be deleterious and extremely rare (MAF<0.01%).
View Article and Find Full Text PDFClin Chem
January 2025
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States.
Background: Disease-causing copy-number variants (CNVs) often encompass contiguous genes and can be detected using chromosomal microarray analysis (CMA). Conversely, CNVs affecting single disease-causing genes have historically been challenging to detect due to their small sizes.
Methods: A custom comprehensive CMA (Baylor College of Medicine - BCM v11.
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