Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers

Abstract

The Genetics Core of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer’s disease (AD)-related phenotypes. Here, we provide a systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. Finally, we perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. Major ADNI findings included all the top 10 AD genes and several of these (e.g., APOE, BIN1, CLU, CR1, and PICALM) were corroborated by ADNI imaging, fluid and cognitive phenotypes. ADNI imaging genetics studies discovered novel findings (e.g., FRMD6) that were later replicated on different data sets. Several other genes (e.g., APOC1, FTO, GRIN2B, MAGI2, and TOMM40) were associated with multiple ADNI phenotypes, warranting further investigation on other data sets. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies employing next-generation sequencing and convergent multi-omics approaches, and for clinical drug and biomarker development.Electronic supplementary materialThe online version of this article (doi:10.1007/s11682-013-9262-z) contains supplementary material, which is available to authorized users.

The Worldwide Alzheimer's Disease Neuroimaging Initiative: An update

“…These efforts have led to the confirmation of several AD risk genes and the identification of novel risk loci, either alone [29] or as part of larger data sets provided by such consortia as Enhancing Neuro Imaging Genetics Through Meta Analysis or Cohorts for Heart and Aging Research in Genomic Epidemiology [30,31]. Further refinements to GWAS have included pathway and network analyses [32][33][34], the use of proteomics [35], and whole exome data [36], and copy number variant analysis.…”

Complexity across scales: a walkthrough to linking neuro-imaging readouts to molecular processes

“…Combining known genetic biomarkers with imaging data could improve the prediction pattern across all patients [72][73][74]. Neuroimaging genetics is an emerging field in which quantitative phenotypic features from brain imaging are used as readout to inspect the role of genetic variation in brain function [75,76].…”

“…Many successive studies also demonstrated the importance of CSF biomarkers, PET based biomarkers as early indicators of pre-clinical AD [99][100][101]. Another sister initiative of ADNI is called ADNI Genetics Core, which provides the possibility for researchers to estimate the genetic alterations using imaging features for understanding disease progression over time [102][103][104].…”

Perspective: The Alzheimer's Disease Neuroimaging Initiative and the role and contributions of the Private Partner Scientific Board (PPSB)

“…Through the release of ADNI WGS data, and progressing work on gene expression, an impactful integration of next generation sequencing and multiomics is made possible. Numerous ADNI genetics articles have been published, and the ADNI Genetics Core continues to have a critical engagement from scientists from the PPSB members companies [22].…”