Integrating brain imaging endophenotypes with GWAS for Alzheimer’s disease

Katherine A. Knutson, Wei Pan

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Background: Genome wide association studies (GWAS) have identified many genetic variants associated with increased risk of Alzheimer’s disease (AD). These susceptibility loci may effect AD indirectly through a combination of physiological brain changes. Many of these neuropathologic features are detectable via magnetic resonance imaging (MRI). Methods: In this study, we examine the effects of such brain imaging derived phenotypes (IDPs) with genetic etiology on AD, using and comparing the following methods: two-sample Mendelian randomization (2SMR), generalized summary statistics based Mendelian randomization (GSMR), transcriptome wide association studies (TWAS) and the adaptive sum of powered score (aSPU) test. These methods do not require individual-level genotypic and phenotypic data but instead can rely only on an external reference panel and GWAS summary statistics. Results: Using publicly available GWAS datasets from the International Genomics of Alzheimer’s Project (IGAP) and UK Biobank’s (UKBB) brain imaging initiatives, we identify 35 IDPs possibly associated with AD, many of which have well established or biologically plausible links to the characteristic cognitive impairments of this neurodegenerative disease. Conclusions: Our results highlight the increased power for detecting genetic associations achieved by multiple correlated SNP-based methods, i.e., aSPU, GSMR and TWAS, over MR methods based on independent SNPs (as instrumental variables). Availability: Example code is available at [Figure not available: see fulltext.]

Original languageEnglish (US)
JournalQuantitative Biology
StateAccepted/In press - 2020

Bibliographical note

Funding Information:
We thank the reviewers for many helpful comments. This work was supported by NIH grants T32GM108557, R01AG065636, R01HL116720, R01GM113250 and R01GM126002, and by the Minnesota Supercomputing Institute at the University of Minnesota.

Publisher Copyright:
© 2020, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature.


  • MRI
  • Mendelian randomization
  • SPU tests
  • Sum test
  • TWAS
  • aSPU test


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