Jicha, Gregory A

Abstract Introduction Genetic loci for Alzheimer's disease (AD) have been identified in whites of European ancestry, but the genetic architecture of AD among other populations is less understood. Methods We conducted a transethnic genome‐wide association study (GWAS) for late‐onset AD in Stage 1 sample including whites of European Ancestry, African‐Americans, Japanese, and Israeli‐Arabs assembled by the Alzheimer's Disease Genetics Consortium. Suggestive results from Stage 1 from novel loci were followed up using summarized results in the International Genomics Alzheimer's Project GWAS dataset. Results Genome‐wide significant (GWS) associations in single‐nucleotide polymorphism (SNP)–based tests ( P < 5 × 10 −8 ) were identified for SNPs in PFDN1/HBEGF , USP6NL/ECHDC3 , and BZRAP1‐AS1 and for the interaction of the (apolipoprotein E) APOE ε4 allele with NFIC SNP. We also obtained GWS evidence ( P < 2.7 × 10 −6 ) for gene‐based association in the total sample with a novel locus, TPBG ( P = 1.8 × 10 −6 ). Discussion Our findings highlight the value of transethnic studies for identifying novel AD susceptibility loci.

AbstractBackground and purposeVascular dementia (VAD) is a complex diagnosis at times difficult to distinguish from Alzheimer's disease (AD). MRI scans often show white matter hyperintensities (WMH) in both conditions. WMH increase with age, and both VAD and AD are associated with aging, thus presenting an attribution conundrum. In this study, we sought to show whether the amount of WMH in deep white matter (dWMH), versus periventricular white matter (PVH), would aid in the distinction between VAD and AD, independent of age.MethodsBlinded semiquantitative ratings of WMH validated by objective quantitation of WMH volume from standardized MRI image acquisitions. PVH and dWMH were rated separately and independently by two different examiners using the Scheltens scale. Receiver operator characteristic (ROC) curves were generated using logistic regression to assess classification of VAD (13 patients) versus AD (129 patients). Clinical diagnoses were made in a specialty memory disorders clinic.ResultsUsing PVH rating alone, overall classification (area under the ROC curve, AUC) was 75%, due only to the difference in age between VAD and AD patients in our study and not PVH. In contrast, dWMH rating produced 86% classification accuracy with no independent contribution from age. A global Longstreth rating that combines dWMH and PVH gave an 88% AUC.ConclusionsIncreased dWMH indicate a higher likelihood of VAD versus AD. Assessment of dWMH on MRI scans using Scheltens and Longstreth scales may aid the clinician in distinguishing the two conditions.

AbstractMotivationDiseases that progress slowly are often studied by observing cohorts at different stages of disease for short periods of time. The Alzheimer's Disease Neuroimaging Initiative (ADNI) follows elders with various degrees of cognitive impairment, from normal to impaired. The study includes a rich panel of novel cognitive tests, biomarkers, and brain images collected every 6 months for as long as 6 years. The relative timing of the observations with respect to disease pathology is unknown. We propose a general semiparametric model and iterative estimation procedure to estimate simultaneously the pathological timing and long‐term growth curves. The resulting estimates of long‐term progression are fine‐tuned using cognitive trajectories derived from the long‐term “Personnes Agées Quid” study.ResultsWe demonstrate with simulations that the method can recover long‐term disease trends from short‐term observations. The method also estimates temporal ordering of individuals with respect to disease pathology, providing subject‐specific prognostic estimates of the time until onset of symptoms. When the method is applied to ADNI data, the estimated growth curves are in general agreement with prevailing theories of the Alzheimer's disease cascade. Other data sets with common outcome measures can be combined using the proposed algorithm.AvailabilitySoftware to fit the model and reproduce results with the statistical software R is available as the grace package. ADNI data can be downloaded from the Laboratory of NeuroImaging.