ArticleNature genetics2024
Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases.
Article in Nature genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 12 of them syntheses that pooled it.
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Who cites it
82 citing papers in PubMed, 12 syntheses or guidelines pooled it.
- A multiancestry polygenic risk score improves stratification in patients with hypertrophic cardiomyopathy.Nature cardiovascular research · 2026Pooled it
- Multi-ancestry genome-wide association analyses provide insights into the genetic basis of Hashimoto's thyroiditis.Nature genetics · 2026Pooled it
- Genome-wide meta-analysis across East Asian and European populations provides insights into the molecular basis of lung cancer.Genome medicine · 2026Pooled it
- Multi-ancestry genome-wide association analyses of refractive error augment genetic discovery and polygenic prediction.Nature genetics · 2026Pooled it
- Genetic landscape and functional exploration of kidney cancer predisposition in cross-ancestral populations.Nature communications · 2026Pooled it
- Cross-ancestry analysis of gestational diabetes mellitus identifies novel loci, drug targets and biological pathways.Frontiers in endocrinology · 2026Pooled it
- Multi-ancestry genome-wide association analyses of polycystic ovary syndrome.Nature genetics · 2025Pooled it
- Multi-ancestry meta-analysis of keloids uncovers novel susceptibility loci in diverse populations.Nature communications · 2025Pooled 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
- Multi-ancestry sequencing-based genome-wide association study of C-reactive protein in 513,273 genomes.Nature communications · 2025Pooled it
- Cross-ancestry genome-wide association study identifies new susceptibility genes for preeclampsia.BMC pregnancy and childbirth · 2025Pooled it
- Shared genetic architectures of educational attainment in East Asian and European populations.Nature human behaviour · 2024Pooled it
- Prioritizing context-specific genetic risk mechanisms in 11 solid cancers.Journal of the National Cancer Institute · 2026Article
- Decomposing population differences in type 2 diabetes through a high-resolution GWAS of ~600,000 individuals with East Asian ancestry.Communications biology · 2026Article
- Systematic benchmarking of multi-ancestry fine-mapping under variant heterogeneity and LD mismatch.Briefings in bioinformatics · 2026Article
- Large-scale admixture mapping in the All of Us Research Program improves the characterization of cross-population phenotypic differences.Nature communications · 2026Article
- Article
- From GWAS Signals to Molecular Mechanisms: Explainable AI for Causal Gene Prioritization and Biomolecular Target Interpretation.Biomolecules · 2026Review
- Advancing risk gene discovery across the allele frequency spectrum.HGG advances · 2026Review
- Integrating multi-ancestry common and rare variant mapping accelerates therapeutic target discovery.medRxiv : the preprint server for health sciences · 2026Article
22 more citing papers are in PubMed but not listed here.
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Abstract
Genome-wide association studies (GWAS) of human complex traits or diseases often implicate genetic loci that span hundreds or thousands of genetic variants, many of which have similar statistical significance. While statistical fine-mapping in individuals of European ancestry has made important discoveries, cross-population fine-mapping has the potential to improve power and resolution by capitalizing on the genomic diversity across ancestries. Here we present SuSiEx, an accurate and computationally efficient method for cross-population fine-mapping. SuSiEx integrates data from an arbitrary number of ancestries, explicitly models population-specific allele frequencies and linkage disequilibrium patterns, accounts for multiple causal variants in a genomic region and can be applied to GWAS summary statistics. We comprehensively assessed the performance of SuSiEx using simulations. We further showed that SuSiEx improves the fine-mapping of a range of quantitative traits available in both the UK Biobank and Taiwan Biobank, and improves the fine-mapping of schizophrenia-associated loci by integrating GWAS across East Asian and European ancestries.
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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.