Modern quantitative trait locus (QTL) studies in multiparental populations offer opportunities to identify causal genes for thousands of clinical and molecular traits. Traditional analyses examine each trait by itself. However, to fully leverage this vast number of measured traits, the systems genetics community needs statistical tools to analyze multiple traits simultaneously (Jiang & Zeng, 1995; Korol, Ronin, & Kirzhner, 1995). A test of pleiotropy vs. separate QTL is one such tool that will aid dissection of complex trait genetics and enhance understanding of genetic architecture.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7380654 | PMC |
| http://dx.doi.org/10.21105/joss.01435 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The identification of plasma and cerebrospinal fluid metabolites causally associated with mental disorders from a genetic perspective.
J Affect Disord
January 2025
Department of Anesthesiology, Shengjing Hospital of China Medical University, Shenyang 110004, China. Electronic address:
Background: Metabolomics research is a promising orientation for the diagnosis and intervention of several diseases, and observational studies have found many metabolic profiles to be associated with mental disorders. However, the causal relationship between plasma and cerebrospinal fluid (CSF) metabolites and mental disorders has not been established.
Methods: We identified independent genetic variants associated with plasma, CSF metabolites, and mental disorders from pooled data in the published Genome-wide association studies (GWASs) and performed Mendelian randomization (MR) to investigate causal relationships.
Statin therapy in ischemic stroke patients with atrial fibrillation: Efficacy and safety outcomes.
Eur Stroke J
January 2025
Stroke Unit and Division of Cardiovascular Medicine, University of Perugia, Perugia, Italy.
Introduction: The efficacy and safety of statins for secondary prevention in patients who have experienced a cardioembolic stroke are not well-defined. However, previous observational data reported hyperlipidemia as a risk factor for both ischemic and bleeding complications in patients with AF and previous stroke. Based on these premises, we conducted a sub-analysis of the RAF and RAF-NOAC studies to evaluate the efficacy and safety of statins in secondary prevention in patients with acute ischemic stroke and AF.
View Article and Find Full Text PDFPleiotropic Associations with Alzheimer's Disease and Physical Activity: Sex Differences and the Effects of Environment.
Int J Mol Sci
November 2024
Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC 27705, USA.
Physical activity (PA) is a modifiable factor in mitigating/preventing Alzheimer's disease (AD). It is crucial to identify the conditions under which PA's effects on AD risk would be beneficial. This study aims to gain insights into pleiotropic predisposition to AD and PA within and across sexes and environmental effects.
View Article and Find Full Text PDFBidirectional two-sample Mendelian randomization study of atrial fibrillation and breast cancer.
Front Cardiovasc Med
November 2024
Department of Cardiovascular Unit, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Background: Observational studies have shown an association between Breast Cancer (BC) and Atrial Fibrillation (AF). However, due to confounding factors and reverse causality, the causal role between BC and AF remains unclear. In this study, bidirectional two-sample Mendelian randomization (MR) combined with meta-analysis was used to evaluate the causal association between BC and AF.
View Article and Find Full Text PDFPWAS Hub: exploring gene-based associations of complex diseases with sex dependency.
Nucleic Acids Res
January 2025
Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.
The Proteome-Wide Association Study (PWAS) is a protein-based genetic association approach designed to complement traditional variant-based methods like GWAS. PWAS operates in two stages: first, machine learning models predict the impact of genetic variants on protein-coding genes, generating effect scores. These scores are then aggregated into a gene-damaging score for each individual.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!