Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans

Abstract

Introduction: Genetic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) have been crucial in advancing the understanding of Alzheimer's disease (AD) pathophysiology. Here, we provide an update on sample collection, scientific progress and opportunities, conceptual issues, and future plans.

Methods: Lymphoblastoid cell lines and DNA and RNA samples from blood have been collected and banked, and data and biosamples have been widely disseminated. To date, APOE genotyping, genome-wide association study (GWAS), and whole exome and whole genome sequencing data have been obtained and disseminated.

Results: ADNI genetic data have been downloaded thousands of times, and >300 publications have resulted, including reports of large-scale GWAS by consortia to which ADNI contributed. Many of the first applications of quantitative endophenotype association studies used ADNI data, including some of the earliest GWAS and pathway-based studies of biospecimen and imaging biomarkers, as well as memory and other clinical/cognitive variables. Other contributions include some of the first whole exome and whole genome sequencing data sets and reports in healthy controls, mild cognitive impairment, and AD.

Discussion: Numerous genetic susceptibility and protective markers for AD and disease biomarkers have been identified and replicated using ADNI data and have heavily implicated immune, mitochondrial, cell cycle/fate, and other biological processes. Early sequencing studies suggest that rare and structural variants are likely to account for significant additional phenotypic variation. Longitudinal analyses of transcriptomic, proteomic, metabolomic, and epigenomic changes will also further elucidate dynamic processes underlying preclinical and prodromal stages of disease. Integration of this unique collection of multiomics data within a systems biology framework will help to separate truly informative markers of early disease mechanisms and potential novel therapeutic targets from the vast background of less relevant biological processes. Fortunately, a broad swath of the scientific community has accepted this grand challenge.

Keywords: Alzheimer's disease (AD); Bioethical issues; Biomarkers; Cerebrospinal fluid (CSF); Cognition; Copy number variation (CNV); DNA; Genome-wide association studies (GWAS); Magnetic resonance imaging (MRI); Memory; Mild cognitive impairment (MCI); Next generation sequencing (NGS); Positron emission tomography (PET); Precision medicine; RNA.

Figure 1

ADNI genetic data usage and reports (2008–2014) (A) Total publications by year and (B) by phenotype category using ADNI genetic data are displayed. Note that papers analyzing more than one phenotype were counted multiple times in panel B. (advMRI = studies using advanced MRI techniques (diffusion tensor imaging, resting-state functional MRI, arterial spin labeling perfusion MRI); 18F-Florbetapir or 11C-PiB = studies using [¹⁸F]Florbetapir or [¹¹C]PiB; 18F-FDG = [¹⁸F]FDG studies; Cognitive = studies utilizing neuropsychological test performance data; Clinical = studies using clinical data, such as diagnosis; Fluid biomarkers (CSF/plasma) = studies using CSF or plasma-based fluid biomarkers; sMRI = structural MRI studies)

Figure 2

Common journals and reported genes in manuscripts using ADNI genetic data (A) A word cloud of journal names where papers utilizing ADNI genetic data were published is shown with the color and size of a journal name corresponding to the number of papers published in that journal. Word clouds of gene names appears in these paper abstracts, (B) with and (C) without including APOE, are displayed with the color and size of a gene name corresponding to the number of abstracts mentioning the gene.

Figure 3

Converging “multi-omics” in ADNI This figure illustrates the landscape of multiple “-omics” domains relevant to AD. Note that ADNI has collected data spanning a broad range of these domains (indicated by asterisks; * = data from ADNI-1, ** = data from ADNI-GO/2). Image sources include upload.wikimedia.org (indicated by ^†) and www.uphs.upenn.edu (indicated by ^≠).

Figure 4

Path from genetic signals to targeted therapeutics: key applications to drug discovery and development This figure shows an overview of the path from genetic signal detection to targeted therapeutics and implications for trial design. (eQTL = expression quantitative trait loci; pQTL = proteomic quantitative trait loci; mQTL = metabolic quantitative trait loci; iPSC = induced pluripotent stem cells)