Human Genetics

, Volume 138, Issue 3, pp 271–285 | Cite as

Shared genetic architecture between metabolic traits and Alzheimer’s disease: a large-scale genome-wide cross-trait analysis

  • Zhaozhong Zhu
  • Yifei Lin
  • Xihao Li
  • Jane A. Driver
  • Liming LiangEmail author
Original investigation


A growing number of studies clearly demonstrate a substantial link between metabolic dysfunction and the risk of Alzheimer’s disease (AD), especially glucose-related dysfunction; one hypothesis for this comorbidity is the presence of a common genetic etiology. We conducted a large-scale cross-trait GWAS to investigate the genetic overlap between AD and ten metabolic traits. Among all the metabolic traits, fasting glucose, fasting insulin and HDL were found to be genetically associated with AD. Local genetic covariance analysis found that 19q13 region had strong local genetic correlation between AD and T2D (P = 6.78 × 10− 22), LDL (P = 1.74 × 10− 253) and HDL (P = 7.94 × 10− 18). Cross-trait meta-analysis identified 4 loci that were associated with AD and fasting glucose, 3 loci that were associated with AD and fasting insulin, and 20 loci that were associated with AD and HDL (Pmeta < 1.6 × 10− 8, single trait P < 0.05). Functional analysis revealed that the shared genes are enriched in amyloid metabolic process, lipoprotein remodeling and other related biological pathways; also in pancreas, liver, blood and other tissues. Our work identifies common genetic architectures shared between AD and fasting glucose, fasting insulin and HDL, and sheds light on molecular mechanisms underlying the association between metabolic dysregulation and AD.



We thank IGAP consortium, GIANT consortium, DIAGRAM consortium, MAGIC Consortium and ENGAGE Consortium for providing GWAS summary statistic data. We also thank Dr. Huwenbo Shi for statistical advice. This study was supported by grants from National Institute of Environmental Health Sciences (NIEHS) P30ES000002 (Zhu), A Merit Review Award Clinical Science R&D I01CX000934-01A1 (Driver) and Dr. Liang is a collaborator funded by this award.

Author contributions

ZZ, JAD, and LL designed the study. ZZ and XL performed the statistical analysis. ZZ, YL, JAD, and LL wrote the manuscript. All authors helped interpret the data, reviewed and edited the final paper, and approved the submission.

Compliance with ethical standards

Conflict of interest

The authors declare no competing interests.

Supplementary material

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Supplementary material 1 (PNG 152 KB)
439_2019_1988_MOESM2_ESM.xlsx (519 kb)
Supplementary material 2 (XLSX 518 KB)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Zhaozhong Zhu
    • 1
    • 2
  • Yifei Lin
    • 1
  • Xihao Li
    • 3
  • Jane A. Driver
    • 4
    • 5
  • Liming Liang
    • 1
    • 3
    Email author
  1. 1.Program in Genetic Epidemiology and Statistical Genetics, Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonUSA
  2. 2.Department of Environmental HealthHarvard T.H. Chan School of Public HealthBostonUSA
  3. 3.Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonUSA
  4. 4.Geriatric Research Education and Clinical Center and Massachusetts Veterans Epidemiology Research and Information CenterVA Medical CenterBostonUSA
  5. 5.Division of Aging, Brigham and Women’s HospitalHarvard Medical SchoolBostonUSA

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