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. 2024 Oct 23;20(10):e1011322.
doi: 10.1371/journal.pgen.1011322. eCollection 2024 Oct.

A comprehensive framework for trans-ancestry pathway analysis using GWAS summary data from diverse populations

Sheng Fu  1   2 , William Wheeler  3 , Xiaoyu Wang  4   5 , Xing Hua  4   5 , Devika Godbole  4   5 , Jubao Duan  6   7 , Bin Zhu  4 , Lu Deng  1 , Fei Qin  4 , Haoyu Zhang  4 , Jianxin Shi  4 , Kai Yu  4
Affiliations

A comprehensive framework for trans-ancestry pathway analysis using GWAS summary data from diverse populations

Sheng Fu et al. PLoS Genet. .

Abstract

As more multi-ancestry GWAS summary data become available, we have developed a comprehensive trans-ancestry pathway analysis framework that effectively utilizes this diverse genetic information. Within this framework, we evaluated various strategies for integrating genetic data at different levels-SNP, gene, and pathway-from multiple ancestry groups. Through extensive simulation studies, we have identified robust strategies that demonstrate superior performance across diverse scenarios. Applying these methods, we analyzed 6,970 pathways for their association with schizophrenia, incorporating data from African, East Asian, and European populations. Our analysis identified over 200 pathways significantly associated with schizophrenia, even after excluding genes near genome-wide significant loci. This approach substantially enhances detection efficiency compared to traditional single-ancestry pathway analysis and the conventional approach that amalgamates single-ancestry pathway analysis results across different ancestry groups. Our framework provides a flexible and effective tool for leveraging the expanding pool of multi-ancestry GWAS summary data, thereby improving our ability to identify biologically relevant pathways that contribute to disease susceptibility.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Strategic framework for trans-ancestry pathway analysis.
This diagram illustrates two strategies employed in trans-ancestry pathway analysis using GWAS summary data from three distinct populations. The analyzed pathway includes three genes containing 2, 3, and 4 SNPs, respectively. Each population’s GWAS data is color-coded: blue, purple, and red. Trans-ancestry SNP-level and gene-level data are depicted with a mixture of these three colors. (a) SNP-centric approach: SNP-level summary data from the three GWAS (denoted as S) are consolidated to generate trans-ancestry SNP-level p-values. These p-values are then aggregated within each gene to obtain trans-ancestry gene-level p-values. Subsequently, these gene-level p-values are integrated across the genes in the pathway using the Adaptive Rank Truncated Product (ARTP) framework to assess pathway significance. (b) Gene-centric approach: From each GWAS, SNP-level summary data within each gene are consolidated to generate single-ancestry gene-level p-values (G). These p-values are then unified across the three GWAS to form the trans-ancestry gene-level p-value for each gene. Finally, these trans-ancestry gene-level p-values are combined across the pathway using the ARTP framework to determine overall pathway significance.
Fig 2
Fig 2. Power comparisons for pathway analyses of a 100-gene pathway with 10 causal genes and a 1:1 case-control ratio.
Power is estimated from 2,000 replicates at a type I error rate of 0.05. Detailed method descriptions can be found in the footnotes of Table 1.
Fig 3
Fig 3. Venn diagram comparing significant pathways associated with schizophrenia identified by five different pathway analysis methods.
The global significance threshold is established at 7.17×10−6, calculated using the Bonferroni correction to account for multiple testing of 6,970 pathways. Detailed method descriptions can be found in the footnotes of Table 1.
Fig 4
Fig 4. Heatmap of gene-level p-values for selected genes across 33 significant REACTOME pathways associated with schizophrenia detected by the Gene-wFisher method.
This heatmap displays gene-level p-values for 115 unique genes, as detailed in S3 Table, across 33 significant REACTOME pathways listed in S2 Table. Each gene, with a p-value below 0.005 as estimated by the Gene-wFisher method, is featured on the x-axis, while pathways are displayed on the y-axis, organized by their respective p-values. Each row in the heatmap corresponds to one significant pathway, with color intensity of each cell reflecting the gene-level p-value on a -log10 scale. Cells for genes not included in a pathway are shaded blue.
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