Brain Structure and Allelic Associations in Alzheimer’s Disease

Background This paper examines late‐onset dementia‐related cognitive impairment utilizing neuroimaging‐genetics biomarker associations. Method The participants, ages 65 to 85, included 266 normal controls (CN’s), 572 mild cognitive impairment (MCI’s), and 188 Alzheimer’s disease (AD) patients. Genotype dosage data for AD‐associated single nucleotide polymorphisms (SNPs) were extracted from the imputed ADNI genetics dosage data using sample‐major additive coding. 29 such SNPs were selected, representing a subset of independent SNPs reported to be highly associated with AD in a recent AD meta‐GWAS study by Jansen et al . Result The expected significant correlations between the SNPs and the neuroimaging phenotypes were confirmed using the top 29 genomic markers (GMs) and 200 neuroimaging markers (NIMs). Relative to CN, odds ratios and relative risks for AD and MCI were employed for prediction using multinominal linear modeling of diagnosis. In the CN and MCI cohorts, mainly cortical thickness measures were associated with GMs among thickness and the other measures, even though it was not in the AD cohort. Network patterns within the CN and AD groups were shown as separated 3 group metrics including thickness, volume, and pct (the proportion of White to Gray Matter), but not in the MCI cohort. Multinominal linear model of clinical diagnosis showed precisely the specific NIMs and GMs that were most impactful in discriminating between AD and CN and between MCI and CN. Conclusion Advanced analytics provide mechanisms for exploring the interrelations between morphometric biomarkers and genomic indicators. This facilitates clinical investigation of phenotypic associations and facilitates deep systematic understanding of AD pathogenesis.