ArticleAmerican journal of human genetics2022
Leveraging the local genetic structure for trans-ancestry association mapping.
Article in American journal of human genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Trans-ancestry meta-analysis of genome wide association studies of inhibitory control.Molecular psychiatry · 2023Pooled it
- A unified framework for cell-type-specific eQTL prioritization by integrating bulk and scRNA-seq data.American journal of human genetics · 2025Article
- A comprehensive framework for trans-ancestry pathway analysis using GWAS summary data from diverse populations.PLoS genetics · 2024Article
- Multi-trait GWAS for diverse ancestries: mapping the knowledge gap.BMC genomics · 2024Article
- PGS-Depot: a comprehensive resource for polygenic scores constructed by summary statistics based methods.Nucleic acids research · 2024Article
- XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias.Nature communications · 2023Article
- GCPBayes pipeline: a tool for exploring pleiotropy at the gene level.NAR genomics and bioinformatics · 2023Article
- PALM: a powerful and adaptive latent model for prioritizing risk variants with functional annotations.Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
7 authors.
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Abstract
Over the past two decades, genome-wide association studies (GWASs) have successfully advanced our understanding of the genetic basis of complex traits. Despite the fruitful discovery of GWASs, most GWAS samples are collected from European populations, and these GWASs are often criticized for their lack of ancestry diversity. Trans-ancestry association mapping (TRAM) offers an exciting opportunity to fill the gap of disparities in genetic studies between non-Europeans and Europeans. Here, we propose a statistical method, LOG-TRAM, to leverage the local genetic architecture for TRAM. By using biobank-scale datasets, we showed that LOG-TRAM can greatly improve the statistical power of identifying risk variants in under-represented populations while producing well-calibrated p values. We applied LOG-TRAM to the GWAS summary statistics of various complex traits/diseases from BioBank Japan, UK Biobank, and African populations. We obtained substantial gains in power and achieved effective correction of confounding biases in TRAM. Finally, we showed that LOG-TRAM can be successfully applied to identify ancestry-specific loci and the LOG-TRAM output can be further used for construction of more accurate polygenic risk scores in under-represented populations.
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