Open AccessIntegrating brain imaging endophenotypes with GWAS for Alzheimer’s disease
Author(s) -
Knutson Katherine A.,
Pan Wei
Publication year - 2021
Publication title -
quantitative biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.1007/s40484-020-0202-9
Subject(s) - mendelian randomization , genome wide association study , imaging genetics , neuroimaging , endophenotype , genetic association , genomics , computational biology , biobank , alzheimer's disease neuroimaging initiative , disease , single nucleotide polymorphism , biology , medicine , alzheimer's disease , neuroscience , bioinformatics , genetics , genotype , cognition , genetic variants , genome , pathology , gene
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 https://github.com/kathalexknuts/ADIDP .