Genetic risk for AD and amyloid deposition

Objectives: Alzheimer’s disease AD is the most common form of dementia and is responsible for a huge and growing health care burden in the developed and developing world. The Polygenic Risk Score (PRS) approach has shown 75%84% prediction accuracy of identifying individuals with AD risk. Methods: In this study we tested the prediction accuracy of AD, MCI and amyloid deposition risks with PRS, including and excluding APOE genotypes in a large publicly available data set with extensive phenotypic data: the Alzheimer's Disease Neuroimaging Initiative cohort. Among MCI individuals with amyloid positive status we examined PRS prediction accuracy in those who converted to AD. In addition, we divided polygenic risk score by biological pathways and tested them independently for distinguishing between AD, MCI and amyloid deposition. Results: We found that AD and MCI are predicted by both APOE genotype and PRS (AUC=0.82% and 68%, respectively). Amyloid deposition is predicted by APOE only (AUC=79%). Further progression to AD of individuals with MCI and amyloid positive status is predicted by PRS over and above APOE (AUC=67%). In pathway-specific PRSs analyses the protein-lipid complex has the strongest association with AD and amyloid deposition even when genes in APOE region were removed (p=0.0055 and p=0.0079, respectively). Interpretation: The results showed different pattern of APOE contribution in PRS risk predictions of AD/MCI and amyloid deposition. Our study suggests that APOE mostly contributes to amyloid accumulation and the PRS affects risk of further conversion to AD. Genetic risk for AD and amyloid deposition Leonenko et al. 3 Introduction Alzheimer’s disease is the most common form of dementia in the elderly people and is a major health problem world-wide1. The clinical diagnosis is typically characterized by progressive loss of memory and cognitive function. In the last decade numerous relevant susceptibility loci, genes and pathways have been identified2–6 that have improved the understanding of this complex disease. However the risk for developing AD involves multiple genetic and environmental components, with the APOE genotype7 having the strongest genetic effect2. Amyloid-beta (Aβ plays a key role in the pathogenesis of AD but little is known about the process of its formation in a brain. Identification of earliest pathological signature of Alzheimer’s disease requires longitudinal measurements of Aβ deposition in the brain by positron-emission tomography (PET) imaging or by measurements of Aβ reduction in cerebrospinal fluid (CSF). Although Aβ is necessary for the pathologic diagnosis of AD it is not sufficient in itself to cause cognitive dysfunction and clinical AD. It has been shown that amyloid deposition has low specificity for predicting development of AD8,9. The pre-clinical stage of AD starts with mild impairment in cognitive domains (MCI) and includes a syndrome featuring relatively isolated memory deficits10. )n , the National )nstitute on Aging and Alzheimer’s Association N)A-AA) created separate sets of diagnostic guidelines for the symptomatic or clinical stages of AD11,12, where AD represents the "disease", and "dementia" represents the clinical syndrome. Thus a person may progress from MCI to dementia (due to AD); but both MCI and dementia cases may or may not be AD. Studying individuals that develop MCI and then further progress to AD requires detailed longitudinal datasets. ADNI is a multicentre study designed to assess the utility of various biomarkers for detecting early changes associated with MCI and AD. It includes collection of neuroimaging data, clinical and cognitive assessments, along with information on demographics and individual genetic profiles. The PRS approach aggregates the effects of multiple genetic markers identified through Genome-Wide Association Studies (GWAS)2 and has shown great potential in identifying an individual’s risk of developing AD13,14. A few studies Genetic risk for AD and amyloid deposition Leonenko et al. 4 have recently used AD PRS to predict mild cognitive functions and clinical MCI15, however, only one has suggested that PRS could identify MCI in middle aged adults16 more effectively than the APOE locus alone. The PRS approach has also been applied to biological pathways related to AD but was not more predictive than APOE alone17. The implementation of the Polygenic Hazard Score (PHS) (which is closely related to PRS18) analysis in the ADNI data showed that PHS is associated to AD biomarkers (CSF and PET) in individuals without AD19, and that higher PHS were associated with greater rates of cognitive and clinical decline, even after controlling for APOE status20: however, its predictive value was not quantified. In this study we estimate the predictive accuracy of PRS differentiating a) AD cases versus controls, b) MCI cases versus controls, c) amyloid positive versus amyloid negative individuals. We also investigate whether d) the AD PRS can predict individuals with MCI who will progress to AD and those who will remain MCI, with positive amyloid deposition. Recently GWAS studies and exome/genome sequencing have implicated, with varying degrees of confidence, lipid metabolism, the innate immune system and endosomal vesicle recycling in late-onset AD pathogenesis21,22. Therefore we also examined the pathway-specific PRS association using these recently identified pathway6 related to AD risk.