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bioRxiv · 10.64898/2026.07.01.735858

Detecting domain-level organization in genome-wide association study summary statistics using frequency-impact-reliability profiles

Hongdong Hao, H.·Dian, C.·Zhang, X.·Xue, H.·Meng, T.
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Abstract

Genome-wide association studies (GWAS) identify trait-associated variants but are typically interpreted through single-variant significance and locus-level peaks, treating summary statistics as collections of independent signals. Here we show that GWAS summary statistics exhibit previously unrecognized local genetic-statistical organization along genomic coordinates. We develop FIR-GWAS, a framework that integrates allele frequency, effect magnitude and statistical reliability to define frequency-impact-reliability (FIR) profiles and quantify their spatial continuity. Across EUR height GWAS, ancestry-specific datasets and eight additional human complex traits, we find consistent enrichment of same-profile adjacency and coordinate-contiguous FIR domains beyond chromosome-preserving null expectations. These patterns persist after removal of genome-wide significant variants and are reproducible across SNP- and window-based analyses. We further show that FIR-domain architecture separates genome-wide significant structured regions from isolated association peaks and identifies subthreshold domains with coherent statistical organization. FIR-domain structure is consistently associated with regulatory annotations and trait-related gene sets, and highlights biologically plausible subthreshold candidate regions. Across Arabidopsis and chicken GWAS, we observe that FIR-domain architecture is not restricted to human traits but recurs across independent association-summary landscapes. Decomposition analyses suggest that this spatial regularity arises from coordinated local continuity in allele frequency, effect size and statistical reliability. Together, these results reveal GWAS summary statistics as structured genetic-statistical landscapes rather than collections of independent signals, defining a domain-level layer of organization that complements conventional single-variant and locus-based interpretation.

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Hongdong Hao, H., Dian, C., Zhang, X., Xue, H., Meng, T.. 2026-07-06. Detecting domain-level organization in genome-wide association study summary statistics using frequency-impact-reliability profiles. https://doi.org/10.64898/2026.07.01.735858

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