ArticleNature genetics2026
Fast and flexible joint fine-mapping of multiple traits via the Sum of Single Effects model.
Article in Nature genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A multi-ancestry meta genome-wide association study of migraine among veterans: associations with traumatic brain injury, depression, and post-traumatic stress disorder.Molecular psychiatry · 2026Pooled it
- FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies.PLoS genetics · 2026Article
- Mendelianization: Concentrating Polygenic Signal Into a Single Causal Locus.Genetic epidemiology · 2026Article
- Shared genetic and molecular architecture between insulin resistance and cognitive performance.medRxiv : the preprint server for health sciences · 2026Article
- Selection-guided discovery in South Asians implicates the MAPT locus in insulin resistance.medRxiv : the preprint server for health sciences · 2026Article
- A Multi-Context Regulome-Wide Association Atlas for Genetic Studies of Aging Brain Disorders.medRxiv : the preprint server for health sciences · 2026Article
- Leveraging cell-type specificity and similarity improves single-cell eQTL fine-mapping.Nature communications · 2026Article
- FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies.bioRxiv : the preprint server for biology · 2026Article
- Focus on single-gene effects limits discovery and interpretation of complex-trait-associated variants.American journal of human genetics · 2026Article
- Combining functional annotation and multi-trait fine-mapping methods improves fine-mapping resolution at glycaemic trait loci.Human molecular genetics · 2026Article
- Overlap between COPD genetic association results and transcriptional quantitative trait loci.HGG advances · 2026Article
- MAJA: multivariate Bayesian model for discovery of shared epigenetic pathways across human phenotypes.Bioinformatics advances · 2026Article
- Towards improved fine-mapping of candidate causal variants.Nature reviews. Genetics · 2025Review
- mfSuSiE enables multi-cell-type fine-mapping and multi-omic integration of chromatin accessibility QTLs in aging brain.bioRxiv : the preprint server for biology · 2025Article
- GWAS-informed data integration and non-coding CRISPRi screen illuminate genetic etiology of bone mineral density.Genome biology · 2025Article
- scTWAS: A powerful statistical framework for single-cell transcriptome-wide association studies.Research square · 2025Article
- Distinct patterns of genetic overlap among multimorbidities revealed with trivariate MiXeR.Genome medicine · 2025Article
- fSuSiE enables fine-mapping of QTLs from genome-scale molecular profiles.bioRxiv : the preprint server for biology · 2025Article
- BAYESIAN VARIABLE SELECTION IN A COX PROPORTIONAL HAZARDS MODEL WITH THE "SUM OF SINGLE EFFECTS" PRIOR.ArXiv · 2025Article
- Improved genetic discovery and fine-mapping resolution through multivariate latent factor analysis of high-dimensional traits.Cell genomics · 2025Article
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5 authors.
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Abstract
We introduce mvSuSiE, a multitrait fine-mapping method, to identify putative causal variants from genetic association data (individual-level or summary). mvSuSiE learns patterns of shared genetic effects from data, and exploits these patterns to improve power to identify causal single nucleotide polymorphisms (SNPs). Comparisons on simulated data show that mvSuSiE is competitive in speed, power and precision with existing multitrait methods, and uniformly improves over single-trait fine-mapping (Sum of Single Effects) performed separately for each trait. We applied mvSuSiE to jointly fine-map 16 blood cell traits using data from the UK Biobank. By jointly analyzing traits and modeling heterogeneous effect-sharing patterns, we identified a substantially larger number of causal SNPs (>3,000) than single-trait fine-mapping and achieved narrower credible sets. mvSuSiE also more comprehensively characterized how genetic variants affect blood cell traits; 68% of causal SNPs showed significant effects across more than one blood cell type.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.