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Background <p>Genome-wide association studies have identified connections between genetic variations and diseases, but they only examine a small portion of single nucleotide polymorphisms. To enhance genetic findings, researchers suggest imputing genotypes for unmeasured SNPs to improve coverage and statistical power. When this is not possible, summary statistics imputation can be used as an alternative. The available summary statistics imputation tools rely on reference panels, such as the 1000 Genomes Project, to estimate linkage disequilibrium (LD) between variants for accurate imputation. Tools like FAPI and SSIMP use these reference panels in variant call format (VCF) for this purpose, though this process can be time-consuming. A more effective approach for processing reference panels in summary statistics imputation was proposed in RAISS. In this approach, the LD among the variants is precomputed from the reference panel, prior to imputation, thereby reducing computational time.</p> Results <p>We present PRED-LD, an imputation method for GWAS summary statistics that aims to enhance the resolution of genetic association analyses. The proposed method uses precomputed linkage disequilibrium statistics from HapMap, Pheno Scanner and TOP-LD to impute summary statistics, given beta coefficients and standard errors. The single-point approach that we describe provides a fast and accurate way to estimate associations for untyped single nucleotide polymorphisms that exhibit high linkage disequilibrium (LD). The proposed method is faster, provides accurate imputation compared to existing tools, and has been implemented in both a web service (<a href="https://compgen.dib.uth.gr/PRED-LD/">https://compgen.dib.uth.gr/PRED-LD/</a>) and a command-line tool (<a href="https://github.com/pbagos/PRED-LD">https://github.com/pbagos/PRED-LD</a>), making it a useful resource for the research community.</p> Conclusions <p>PRED-LD offers an efficient and accurate method for GWAS summary statistics imputation, providing faster performance, direct result interpretation, and the ability to use multiple reference panels. Also, the online version of PRED-LD simplifies obtaining LD information and performing imputation tasks without downloading reference panels and will be continuously updated to support tools for meta-analysis and fine-mapping in GWAS.</p>

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PRED-LD: efficient imputation of GWAS summary statistics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月16日

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  • Georgios A. Manios,
  • Aikaterini Michailidi,
  • Panagiota I. Kontou,
  • Pantelis G. Bagos

摘要

Background

Genome-wide association studies have identified connections between genetic variations and diseases, but they only examine a small portion of single nucleotide polymorphisms. To enhance genetic findings, researchers suggest imputing genotypes for unmeasured SNPs to improve coverage and statistical power. When this is not possible, summary statistics imputation can be used as an alternative. The available summary statistics imputation tools rely on reference panels, such as the 1000 Genomes Project, to estimate linkage disequilibrium (LD) between variants for accurate imputation. Tools like FAPI and SSIMP use these reference panels in variant call format (VCF) for this purpose, though this process can be time-consuming. A more effective approach for processing reference panels in summary statistics imputation was proposed in RAISS. In this approach, the LD among the variants is precomputed from the reference panel, prior to imputation, thereby reducing computational time.

Results

We present PRED-LD, an imputation method for GWAS summary statistics that aims to enhance the resolution of genetic association analyses. The proposed method uses precomputed linkage disequilibrium statistics from HapMap, Pheno Scanner and TOP-LD to impute summary statistics, given beta coefficients and standard errors. The single-point approach that we describe provides a fast and accurate way to estimate associations for untyped single nucleotide polymorphisms that exhibit high linkage disequilibrium (LD). The proposed method is faster, provides accurate imputation compared to existing tools, and has been implemented in both a web service (https://compgen.dib.uth.gr/PRED-LD/) and a command-line tool (https://github.com/pbagos/PRED-LD), making it a useful resource for the research community.

Conclusions

PRED-LD offers an efficient and accurate method for GWAS summary statistics imputation, providing faster performance, direct result interpretation, and the ability to use multiple reference panels. Also, the online version of PRED-LD simplifies obtaining LD information and performing imputation tasks without downloading reference panels and will be continuously updated to support tools for meta-analysis and fine-mapping in GWAS.