ArticleEuropean journal of human genetics : EJHG2016
FAPI: Fast and accurate P-value Imputation for genome-wide association study.
Article in European journal of human genetics : EJHG, 2016. 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.
- Detailed stratified GWAS analysis for severe COVID-19 in four European populations.Human molecular genetics · 2022Pooled it
- PYRAMA: an open-source tool for advanced meta-analysis of genome wide association studies.Bioinformatics (Oxford, England) · 2026Article
- PRED-LD: efficient imputation of GWAS summary statistics.BMC bioinformatics · 2025Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Knowledge-based analyses reveal new candidate genes associated with risk of hepatitis B virus related hepatocellular carcinoma.BMC cancer · 2020Article
- Determining the impact of uncharacterized inversions in the human genome by droplet digital PCR.Genome research · 2020Article
- Understanding HLA associations from SNP summary association statistics.Scientific reports · 2019Article
- Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases.Nucleic acids research · 2016Article
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4 authors.
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Abstract
Imputing individual-level genotypes (or genotype imputation) is now a standard procedure in genome-wide association studies (GWAS) to examine disease associations at untyped common genetic variants. Meta-analysis of publicly available GWAS summary statistics can allow more disease-associated loci to be discovered, but these data are usually provided for various variant sets. Thus imputing these summary statistics of different variant sets into a common reference panel for meta-analyses is impossible using traditional genotype imputation methods. Here we develop a fast and accurate P-value imputation (FAPI) method that utilizes summary statistics of common variants only. Its computational cost is linear with the number of untyped variants and has similar accuracy compared with IMPUTE2 with prephasing, one of the leading methods in genotype imputation. In addition, based on the FAPI idea, we develop a metric to detect abnormal association at a variant and showed that it had a significantly greater power compared with LD-PAC, a method that quantifies the evidence of spurious associations based on likelihood ratio. Our method is implemented in a user-friendly software tool, which is available at http://statgenpro.psychiatry.hku.hk/fapi.
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