| Title: | Novel Alzheimer's disease-associated variants and genetic interactions identified from UK biobank whole-exome sequencing data using IBI-DT |
| Journal: | Scientific Reports |
| Published: | 26 Jun 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42362650/ |
| DOI: | https://doi.org/10.1038/s41598-026-58159-w |
| URL: | https://www.nature.com/articles/s41598-026-58159-w_reference.pdf |
Publication 20526
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Abstract
Alzheimer's disease (AD) is a highly heritable neurodegenerative disorder whose genetic architecture remains incompletely understood, particularly with respect to rare variants and higher-order interactions. We applied Individualized Bayesian Inference-Decision Tree (IBI-DT) to 790,000 whole-exome sequencing variants from 8,292 unrelated White British individuals in the UK Biobank, using genome-wide association analysis (GWAS) based on Fisher's exact test as a marginal association comparator and evaluated the findings in an independent ADSP-Discovery ICE cohort of 1,560 unrelated individuals with 852,997 variants. In the discovery cohort, IBI-DT identified 178 significant variants using empirical null calibration, which mapped to 173 genes, whereas GWAS identified 16 significant variants that mapped to four genes. Compared with GWAS, IBI-DT prioritized more rare variants among the top 178 signals (147 vs. 66 with minor allele frequency ≤ 0.01) and identified variants with less correlated, less redundant linkage patterns. The IBI-DT-prioritized variants included established AD genes as well as additional biologically supported genes not prioritized by GWAS. Replication supported three variants and four genes across cohorts, including gene-level replication of KIF14 and ZNF90, which were replicated only by IBI-DT. IBI-DT also identified significant gene-gene and gene-environment interactions and biologically plausible enriched pathways. Neural networks trained on IBI-DT-prioritized variants outperformed GWAS-based models (AUC 0.67 vs. 0.63). Overall, these findings indicate that IBI-DT complements GWAS by recovering established AD signals while prioritizing rare, less redundant, and interaction-related AD-associated signals.</p>
4 Authors
- Jin Ren
- Rui Yin
- Jinying Zhao
- Jinling Liu
1 Application
| Application ID | Title |
|---|---|
| 95030 | Whole genome and exome sequencing to implicate genetic variants in rare dementia syndromes |
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