Genome-wide association studies (GWAS) have identified numerous genetic variants associated with Alzheimer's disease (AD) phenotypes. However, how these variants contribute to the etiology of AD remains largely elusive. Recent advances in genomic large language models (LLMs) have revolutionized regulatory genomic prediction tasks, offering new opportunities to interpret the genetic variation observed in personal genome. In this study, we propose epiBrainLLM, a novel computational framework that leverages genomic LLM to enhance our understanding of the causal pathways from genotypes to brain measures to AD-related clinical phenotypes. Our framework will first convert the personal DNA sequence into a diverse set of genomic and epigenomic features using a pretrained genomic LLM and then use these features to further predict phenotypes. Across various experimental settings, our results demonstrate that incorporating pretrained genomic LLMs significantly improves association analysis compared to using genotype information alone. We conclude that our proposed framework provides a novel perspective for understanding the regulatory mechanisms underlying the AD disease etiology, potentially offering insights into complex disease mechanisms beyond AD.

The authors have declared no competing interest.

The works of Q.L. was supported by NIH grant K99HG013661. The works of W.Z., and W.H.W were partially supported by NIH grants R01HG010359 and R01HG007735. The work of L.L. was supported by NSF grant CIF-2102227, and NIH grants R01AG061303 and R01AG062542.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics committee/IRB of Alzheimer's Disease Neuroimaging Initiative(ADNI) gave ethical approval for this work

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All data produced in the present study are available upon reasonable request to the authors

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