Multigraph Cluster Analysis of AD Candidate Genes via Human Brain Mapping

Abstract

AbstractBackgroundBrain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer’s diseases (AD). Given the high dimensionality, the individual level SNP‐QT signals typically have small effect sizes and are hard to be detected and replicated. To overcome this limitation, this work proposes a new approach to identify high‐level imaging genetic associations through applying multi‐graph clustering to SNP‐QT association maps.MethodsParticipants include 255 cognitive normal (CN) and 218 late mild cognitive impairment (LMCI) subjects from the ADNI data. Linear regression is performed to regress regional AV‐45 measures on each of the 54 AD SNPs to obtain a brain association map across 116 AAL‐ROIs. To quantify the similarity between each SNP pair, 5 scoring functions are used to calculate 5 different similarity measures between the respective brain maps of the two SNPs. Each scoring function yields a similarity graph of all 54 SNPs. Multigraph min‐max clustering is applied to 5 different graphs simultaneously to group similar SNPs together. Functional annotation is performed on each SNP cluster and the corresponding average brain association map to provide high level interpretations.ResultsFigure 1 shows the genetic effect on regional AV‐45 measures for each pair of SNP and ROI. The multi‐graph clustering result is shown in Figure 2, where 54 AD‐related SNPs are clustered into two groups. Figure 3a shows the results of Enrichr Elsevier pathway analysis. SNP group 1 is associated with pathways such as amyloid beta clearance, while SNP group 2 is associated with pathways including amyloid beta formation and APP processing in AD. Figure 3b shows the NeuroSynth annotation of the average brain effect maps associated with the two SNP clusters, respectively.ConclusionWe proposed an approach that transforms individual level imaging genetic associations (SNP vs ROI) to high‐level associations (i.e., SNP cluster vs brain association map), and applied it to an LMCI study. These high‐level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts.