The HLA-DRB1*09:01-DQB1*03:03 haplotype is associated with the risk for late-onset Alzheimer's disease in APOE [Formula: see text]4-negative Japanese adults.
Daichi ShigemizuKoya FukunagaAkiko YamakawaMutsumi SuganumaKosuke FujitaTetsuaki KimuraKen WatanabeTaisei MushirodaTakashi SakuraiShumpei NiidaKouichi OzakiPublished in: npj aging (2024)
Late-onset Alzheimer's disease (LOAD) is the most common cause of dementia among those older than 65 years. The onset of LOAD is influenced by neuroinflammation. The human leukocyte antigen (HLA) system is involved in regulating inflammatory responses. Numerous HLA alleles and their haplotypes have shown varying associations with LOAD in diverse populations, yet their impact on the Japanese population remains to be elucidated. Here, we conducted a comprehensive investigation into the associations between LOAD and HLA alleles within the Japanese population. Using whole-genome sequencing (WGS) data from 303 LOAD patients and 1717 cognitively normal (CN) controls, we identified four-digit HLA class I alleles (A, B, and C) and class II alleles (DRB1, DQB1, and DPB1). We found a significant association between the HLA-DRB1*09:01-DQB1*03:03 haplotype and LOAD risk in APOE [Formula: see text]4-negative samples (odds ratio = 1.81, 95% confidence interval = 1.38-2.38, P = 2.03[Formula: see text]). These alleles not only showed distinctive frequencies specific to East Asians but demonstrated a high degree of linkage disequilibrium in APOE [Formula: see text]4-negative samples (r 2 = 0.88). Because HLA class II molecules interact with T-cell receptors (TCRs), we explored potential disparities in the diversities of TCR α chain (TRA) and β chain (TRB) repertoires between APOE [Formula: see text]4-negative LOAD and CN samples. Lower diversity of TRA repertoires was associated with LOAD in APOE [Formula: see text]4-negative samples, irrespective of the HLA DRB1*09:01-DQB1*03:03 haplotype. Our study enhances the understanding of the etiology of LOAD in the Japanese population and provides new insights into the underlying mechanisms of its pathogenesis.
Keyphrases
- late onset
- cognitive decline
- smoking cessation
- human milk
- mild cognitive impairment
- early onset
- high fat diet
- type diabetes
- physical activity
- traumatic brain injury
- lymph node metastasis
- machine learning
- gene expression
- squamous cell carcinoma
- hepatitis c virus
- cognitive impairment
- skeletal muscle
- artificial intelligence
- deep learning
- ejection fraction
- genome wide
- lipopolysaccharide induced
- hiv infected
- patient reported outcomes
- risk assessment
- regulatory t cells
- health insurance
- human health
- prognostic factors
- cerebral ischemia