Genetic associations of in vivo pathology influence Alzheimer's disease susceptibility

Abstract

Introduction: Although the heritability of sporadic Alzheimer's disease (AD) is estimated to be 60-80%, addressing the genetic contribution to AD risk still remains elusive. More specifically, it remains unclear whether genetic variants are able to affect neurodegenerative brain features that can be addressed by in vivo imaging techniques.

Methods: Targeted sequencing analysis of the coding and UTR regions of 132 AD susceptibility genes was performed. Neuroimaging data using ¹¹C-Pittsburgh Compound B positron emission tomography (PET), ¹⁸F-fluorodeoxyglucose PET, and MRI that are available from the KBASE (Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's disease) cohort were acquired. A total of 557 participants consisted of 336 cognitively normal (CN) adults, 137 mild cognitive impairment (MCI), and 84 AD dementia (ADD) groups.

Results: We called 5391 high-quality single nucleotide variants (SNVs) on AD susceptibility genes and selected significant associations between variants and five in vivo AD pathologies: (1) amyloid β (Aβ) deposition, (2) AD-signature region cerebral glucose metabolism (AD-Cm), (3) posterior cingulate cortex (PCC) cerebral glucose metabolism (PCC-Cm), (4) AD-signature region cortical thickness (AD-Ct), and (5) hippocampal volume (Hv). The association analysis for common variants (allele frequency (AF) > 0.05) yielded several novel loci associated with Aβ deposition (PIWIL1-rs10848087), AD-Cm (NME8-rs2722372 and PSEN2-rs75733498), AD-Ct (PSEN1-rs7523) and, Hv (CASS4-rs3746625). Meanwhile, in a gene-based analysis for rare variants (AF < 0.05), cases carrying rare variants in LPL, FERMT2, NFAT5, DSG2, and ITPR1 displayed associations with the neuroimaging features. Exploratory voxel-based brain morphometry between the variant carriers and non-carriers was performed subsequently. Finally, we document a strong association of previously reported APOE variants with the in vivo AD pathologies and demonstrate that the variants exert a causal effect on AD susceptibility via neuroimaging features.

Conclusions: This study provides novel associations of genetic factors to Aβ accumulation and AD-related neurodegeneration to influence AD susceptibility.

Keywords: Alzheimer’s disease; Genetic association; In vivo AD pathologies; MRI; Neuroimaging; PET; Targeted panel sequencing.

Fig. 1

Study design and variant profile of the cohort. a Flowchart of the study design. b Phenotyping strategies of in vivo AD pathology. c Principal component analysis (PCA) of the KBASE cohort with individuals from the 1000 Genomes Project individuals across different populations. d Distribution of variants in the KBASE cohort by population frequency. e Distribution of variants by genetic regions

Fig. 2

Common variants that are significantly associated with neuroimaging features. For each signal, a circular Manhattan plot, quantile-quantile (Q-Q) plot, regional plot, regression plot with adjusted trait values, and voxel-based clustering analysis result are shown. a rs10848087 in PIWIL1 with cerebral Aβ deposition in global brain regions. b rs2722372 in NME8 with AD-Cm. c rs7523 in PSEN1 with AD-Ct. d rs4811697 in CASS4 with Hv

Fig. 3

Association of APOE variants with AD imaging biomarkers. a Log10-scaled coverage map of APOE in the KBASE cohort, along with the gene structure shown with gray boxes indicating exons. Black lines indicate the average coverage depths. On the right, AF of the three APOE variants in KBASE and major ethnic groups are displayed. b Regression plots for the three variant genotypes and imaging traits after adjusted with age and sex

Fig. 4

Genes with rare variants that are significantly associated with in vivo AD pathologies. Observed rare functional variants in the case or control groups defined by each clinical parameter are shown for each gene. a LPL with Aβ deposition in the global brain regions. b DSG2 with AD-signature cortical thickness. c ITPR1 with hippocampal volume. The right panel displays the exploratory voxel-based analyses of brain imaging to demonstrate the regional pattern differences in AD imaging biomarker phenotype between carriers and non-carriers