Genome-Wide Association Study
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Related Articles from SNS
Genomic Dimensionality Bounds Mixed-Model Association Power and Fine-Mapping Resolution
Mixed-model genome-wide association studies (GWAS) behave differently in livestock than in humans, yet a unified explanation is lacking. Analyses using the full genomic relationship matrix (full-GRM; from genome-wide SNPs) yield only a few significant peaks even with hundreds of thousands of animals, whereas leave-one-chromosome-out (LOCO), numerator-relationship-matrix, and sparse-GRM approaches report many broad associations over similar data. Here we develop a framework that traces these...
Cell-specific regulatory circuits connect genetic variation to disease susceptibility
Genome-wide association studies have identified thousands of variants associated with immune-related diseases, yet most lie in non-coding regions, complicating mechanistic interpretation. Regulatory quantitative trait loci (QTLs), such as expression QTLs (eQTLs) and chromatin accessibility QTLs (caQTLs), offer a powerful framework for prioritization and interpretation of these disease-associated genetic variants. When analyzed together, they offer deeper insights into the regulatory...
From Genes to Tokens: a GWAS-inspired Approach for Interpretable Stylometric Analysis
Computer Science > Computation and Language [Submitted on 8 Jun 2026] Title:From Genes to Tokens: a GWAS-inspired Approach for Interpretable Stylometric Analysis View PDFAbstract:This short paper introduces a stylometric interpretation method inspired by genome-wide association studies (GWAS). Each "gene" token's association with "phenotype" authorship is tested using logistic regression with multiple-comparison correction.
Ultra-Fast Implementation of Multivariate GWAS in Genomic SEM Using Flexible Analytic Estimation
Many medical, physiological, and psychiatric traits and disorders are highly polygenic and exhibit complex patterns of genetic sharing and differentiation. In 2018, we introduced Genomic Structural Equation Modelling (Genomic SEM) as a formal framework and free, open source, R-based software for modelling the multivariate genetic architecture of both continuous and binary Genome-Wide Association Study (GWAS) phenotypes, interrogating their joint and distinct functional genomic pathways, and...
Cell-type-resolved genetic variation shapes inflammatory bowel disease risk
Nature, Published online: 03 June 2026; doi:10.1038/s41586-026-10627-zSingle-cell mapping of cis-expression quantitative trait loci in inflammatory bowel disease revealed distal, enhancer-enriched variants detected at the cell-type level more frequently co-localize with genome-wide association study loci than those identified at the tissue level.
Optical genome mapping identifies source-associated structural variant differences across early-passage human iPSCs
Background: Induced pluripotent stem cells (iPSCs) are an important model for studying human diseases in vitro. However, previous studies have shown that iPSC reprogramming and extended cell culture can introduce genomic structural variants (SVs). Technologies like karyotyping, CNV microarrays, and whole-genome sequencing have limitations in resolution, sensitivity, or the ability to detect large and complex structural variants compared to optical genome mapping (OGM).
The COPI coatomer influences LDL receptor activity, hepatic lipid storage, and apoB secretion
Background: Decreased hepatic removal of low density lipoproteins (LDL) and increased apolipoprotein B (apoB) production cause hypercholesterolemia, a major causal risk factor of atherosclerotic cardiovascular disease (ASCVD). By a genome-wide siRNA screen, we previously identified subunits of the Coat protein I (COPI) complex to limit LDL uptake into Huh-7 hepatocarcinoma cells. Methods: These findings were validated by targeted in vitro experiments as well as genetic association studies in...
An interpretable machine learning framework for dog breed inference and ancestry decomposition
The over 300 currently recognized breeds of domesticated dogs are the culmination of centuries of intense artificial selection and recurrent population bottlenecks. While breed labels are widely used in genetic and veterinary studies, inferring breed identity from genomic data remains challenging due to the high dimensionality of genotype data, uneven sampling across breeds, and admixture resulting in mixed-breed individuals. Here, we present an interpretable machine learning framework to...
Mapping genetic risk mechanisms for immune-mediated diseases across human dendritic cell differentiation
Defining the cell types and mechanisms through which genetic variation operates is essential to understand the biological basis of disease. Although human dendritic cells (DCs) are crucial in regulating immunity, their rarity and limitations in available genomic data have hampered efforts to link inherited disease risk to specific DC subsets. Here, we present a single-cell multi-omic atlas of human DC differentiation from hematopoietic stem and progenitor cells (HSPCs) that includes...