ArticleNature communications2023
XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Fine-mapping a genome-wide meta-analysis of 98,374 migraine cases identifies 181 sets of candidate causal variants.Nature communications · 2026Pooled it
- Genome-wide association studies in a large Korean cohort identify quantitative trait loci for 36 traits and illuminate their genetic architectures.Nature communications · 2025Pooled it
- Evolution and domestication-trait associations of ultra-long centromere haplotypes in pepper plants.Nature ecology & evolution · 2026Article
- Towards improved fine-mapping of candidate causal variants.Nature reviews. Genetics · 2025Review
- FINEMAP-miss: fine-mapping genome-wide association studies with missing genotype information.Bioinformatics (Oxford, England) · 2025Article
- A unified framework for identification of cell-type-specific spatially variable genes in spatial transcriptomic studies.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Reflection knockoffs via householder reflection: applications in proteomics and genetic fine mapping.Genetics · 2025Article
- Fine-mapping methods for complex traits: essential adaptations for samples of related individuals.Briefings in bioinformatics · 2025Article
- Improved multiancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk.Nature genetics · 2025Article
- Integrative multi-omics QTL colocalization maps regulatory architecture in aging human brain.medRxiv : the preprint server for health sciences · 2025Article
- Fine-mapping in admixed populations using CARMA-X, with applications to Latin American studies.American journal of human genetics · 2025Article
- A unified framework for cell-type-specific eQTL prioritization by integrating bulk and scRNA-seq data.American journal of human genetics · 2025Article
- Genetics-driven risk predictions leveraging the Mendelian randomization framework.Genome research · 2024Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Genetics, transcriptomics, metagenomics, and metabolomics in the pathogenesis and prediction of atrial fibrillation.European heart journal supplements : journal of the European Society of Cardiology · 2024Article
- SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals.Bioinformatics (Oxford, England) · 2024Article
- Improved multi-ancestry fine-mapping identifiesmedRxiv : the preprint server for health sciences · 2024Article
- A Bayesian fine-mapping model using a continuous global-local shrinkage prior with applications in prostate cancer analysis.American journal of human genetics · 2024Article
- XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias.Nature communications · 2023Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution.
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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.