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Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases
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Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases
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Journal Article
Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases
2024
Overview
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.
The cross-population Sum of Single Effects (SuSiEx) model is a robust and computationally efficient method for conducting multi-ancestry fine-mapping of genome-wide association signals, producing smaller credible sets and capturing population-specific causal variants.
Publisher
Nature Publishing Group US,Nature Publishing Group
Subject
/ Animal Genetics and Genomics
/ Biobanks
/ Biomedical and Life Sciences
/ Chromosome Mapping - methods
/ East Asian People - genetics
/ Genetic Predisposition to Disease
/ Genome-wide association studies
/ Genome-Wide Association Study - methods
/ Genomes
/ Genomics
/ Humans
/ Mapping
/ Methods
/ Multifactorial Inheritance - genetics
/ Polymorphism, Single Nucleotide
/ Power
