Background Quantitative genome-wide association studies (GWAS) primarily rely on additive linear models that compare average phenotypic differences between genotype groups. While effective for detecting common variants of moderate effect in large sample sizes, such approaches inherently reduce high-resolution phenotypic data to summary statistics (group averages), potentially limiting the detection of subtle genotype–phenotype relationships. Genomic Informational Field Theory (GIFT) is a recently developed methodology that preserves the fine-grained informational structure of quantitative traits by analysing ranked phenotypic configurations rather than relying solely on mean differences.

Methods We applied GIFT to genetic and neuropathological data from the Brains for Dementia Research cohort, a well characterised dataset of 563 individuals, and compared its performance with conventional GWAS. Principal component analysis (PCA) derived matrix was used to derive independent quantitative traits linked to from Alzheimer’s disease (AD) neuropathology measures (CERAD, Thal, Braak staging), with and without inclusion of age at death. Principal component analyses were performed using GWAS and GIFT frameworks on the same filtered genotype dataset.

Results Both GWAS and GIFT identified genome-wide significant associations (pvalue<10-6) within the APOE locus (NECTIN2–TOMM40–APOE–APOC1), demonstrating concordance with established AD genetic variants. However, GIFT detected additional significant 19 SNPs beyond those identified by GWAS. Variants associated with AD pathology implicated genes involved in amyloid processing, neuronal apoptosis, synaptic function, neuroinflammation, and metabolic regulation. Notably, GIFT identified 29 loci associated with age at death–related variation that were not detected by GWAS, highlighting genes linked to lipophagy, mitochondrial quality control, sphingolipid metabolism, frailty, and aging-related processes.

Conclusions GIFT recapitulates canonical GWAS findings while uncovering additional biologically relevant associations. By preserving the fine-grained structure of phenotypic data distributions and detecting non-random genotype segregation across ranked trait values, GIFT enables the identification of associations that remained undetected by traditional average-based GWAS approaches. These results demonstrate that rethinking analytical representation, rather than solely increasing sample size, can expand discovery potential of genetic association studies, offering a transparent and complementary framework for quantitative genomics in deeply phenotyped datasets.

The authors have declared no competing interest.

Q-Life Project (Grant: PR1958)

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Source data for this study are publicly available from Brains for Dementia Research (https://www.brainsfordementiaresearch.org/).

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