4.6 Article

Mendelian randomization highlights significant difference and genetic heterogeneity in clinically diagnosed Alzheimer's disease GWAS and self-report proxy phenotype GWAX

期刊

ALZHEIMERS RESEARCH & THERAPY
卷 14, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s13195-022-00963-3

关键词

Alzheimer's disease; Mendelian randomization; GWAS; GWAX; Genetic heterogeneity

资金

  1. National Natural Science Foundation of China [82071212, 81901181]
  2. Mathematical Tianyuan Fund of the National Natural Science Foundation of China [12026414]
  3. Beijing Natural Science Foundation [JQ21022]
  4. Beijing Ten Thousand Talents Project [2020A15]
  5. Science and Technology Beijing One Hundred Leading Talent Training Project [Z141107001514006]
  6. Beijing Municipal Administration of Hospitals' Mission Plan [SML20150802]
  7. Funds of Academic Promotion Programme of Shandong First Medical University & Shandong Academy of Medical Sciences [2019QL016, 2019PT007]

向作者/读者索取更多资源

Mendelian randomization studies have explored the causal association of risk factors with Alzheimer's disease using large-scale genetic datasets, revealing significant impacts of educational attainment on AD risk but genetic heterogeneity across different datasets. This highlights the importance of interpreting and further investigating findings from different types of datasets to better understand the relationship between education and AD risk.
Background Until now, Mendelian randomization (MR) studies have investigated the causal association of risk factors with Alzheimer's disease (AD) using large-scale AD genome-wide association studies (GWAS), GWAS by proxy (GWAX), and meta-analyses of GWAS and GWAX (GWAS+GWAX) datasets. However, it currently remains unclear about the consistency of MR estimates across these GWAS, GWAX, and GWAS+GWAX datasets. Methods Here, we first selected 162 independent educational attainment genetic variants as the potential instrumental variables (N = 405,072). We then selected one AD GWAS dataset (N = 63,926), two AD GWAX datasets (N = 314,278 and 408,942), and three GWAS+GWAX datasets (N = 388,324, 455,258, and 472,868). Finally, we conducted a MR analysis to evaluate the impact of educational attainment on AD risk across these datasets. Meanwhile, we tested the genetic heterogeneity of educational attainment genetic variants across these datasets. Results In AD GWAS dataset, MR analysis showed that each SD increase in years of schooling (about 3.6 years) was significantly associated with 29% reduced AD risk (OR=0.71, 95% CI: 0.60-0.84, and P=1.02E-04). In AD GWAX dataset, MR analysis highlighted that each SD increase in years of schooling significantly increased 84% AD risk (OR=1.84, 95% CI: 1.59-2.13, and P=4.66E-16). Meanwhile, MR analysis suggested the ambiguous findings in AD GWAS+GWAX datasets. Heterogeneity test indicated evidence of genetic heterogeneity in AD GWAS and GWAX datasets. Conclusions We highlighted significant difference and genetic heterogeneity in clinically diagnosed AD GWAS and self-report proxy phenotype GWAX. Our MR findings are consistent with recent findings in AD genetic variants. Hence, the GWAX and GWAS+GWAX findings and MR findings from GWAX and GWAS+GWAX should be carefully interpreted and warrant further investigation using the AD GWAS dataset.

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