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

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

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

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 material The online version of this article (doi:10.1007/s11682-013-9262-z) contains supplementary material, which is available to authorized users

Using Clinical Scales and Digital Measures to Explore Falls in Patients with Lewy Body Dementia

Introduction: PRESENCE was a phase 2 clinical trial assessing the efficacy of mevidalen, a D1 receptor positive allosteric modulator, for symptomatic treatment of Lewy body dementia (LBD). Mevidalen demonstrated improvements in motor and non-motor features of LBD, global functioning, and actigraphy-measured activity and daytime sleep. Adverse events (AEs) of fall were numerically increased in mevidalen-treated participants. Methods: A subset of PRESENCE participants wore a wrist actigraphy device for 2-week periods pre-, during, and posttreatment. Actigraphy sleep and activity measures were derived per period and analyzed to assess for their association with participants’ reports of an AE of fall. Prespecified baseline and treatment-emergent clinical characteristics were also included in the retrospective analysis of falls. Independent-samples t test and χ2 test were performed to compare the means and proportions between individuals with/without falls. Results: A trend toward more falls was observed with mevidalen treatment (31/258 mevidalen-treated vs. 4/86 in placebo-treated participants: p = 0.12). Higher body mass index (BMI) (p < 0.05), more severe disease measured by baseline Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part II (p < 0.05), and a trend toward improved Alzheimer’s Disease Assessment Scale-Cognitive Subscale 13 (ADAS-Cog13) (p = 0.06) were associated with individuals with falls. No statistically significant associations with falls and treatment-emergent changes were observed. Conclusion: The association of falls with worse baseline disease severity and higher BMI and overall trend toward improvements on cognitive and motor scales suggest that falls in PRESENCE may be related to increased activity in mevidalen-treated participants at greater risk for falling. Future studies to confirm this hypothesis using fall diaries and digital assessments are necessary

Serotonin 2A Receptor SNP rs7330461 Association with Treatment Response to Pomaglumetad Methionil in Patients with Schizophrenia

This study aims to confirm the initial pharmacogenetic finding observed within the clinical proof-of-concept trial of an enhanced response to treatment with pomaglumetad methionil (LY2140023 monohydrate) in Caucasian schizophrenia patients homozygous for T/T at single nucleotide polymorphism rs7330461 in the serotonin (5-hydroxytryptamine) 2A receptor gene compared to A/A homozygous patients. The effect of the rs7330461 genotype on the response to pomaglumetad methionil treatment was assessed in three additional clinical trials and in an integrated analysis. Overall, this study includes data from 1115 Caucasian patients for whom genotyping information for rs7330461 was available, consisting of 513 A/A homozygous, 466 A/T heterozygous and 136 T/T homozygous patients. Caucasian T/T homozygous patients showed significantly (p ≤ 0.05) greater improvement in Positive and Negative Syndrome Scale (PANSS) total scores during treatment with pomaglumetad methionil 40 mg twice daily compared to A/A homozygous patients. Additionally, T/T homozygous patients receiving pomaglumetad methionil had significantly (p ≤ 0.05) greater improvements in PANSS total scores compared to placebo and similar improvements as T/T homozygous patients receiving standard-of-care (SOC) treatment. The findings reported here in conjunction with prior reports show that in Caucasian patients with schizophrenia, the T/T genotype at rs7330461 is consistently associated with an increased treatment response to pomaglumetad methionil compared to the A/A genotype

Calibrating longitudinal cognition in Alzheimer's disease across diverse test batteries and datasets

Background: We sought to identify optimal approaches by calibrating longitudinal cognitive performance across studies with different neuropsychological batteries. Methods: We examined four approaches to calibrate cognitive performance in nine longitudinal studies of Alzheimer's disease (AD) (n = 10,875): (1) common test, (2) standardize and average available tests, (3) confirmatory factor analysis (CFA) with continuous indicators, and (4) CFA with categorical indicators. To compare precision, we determined the minimum sample sizes needed to detect 25% cognitive decline with 80% power. To compare criterion validity, we correlated cognitive change from each approach with 6-year changes in average cortical thickness and hippocampal volume using available MRI data from the AD Neuroimaging Initiative. Results: CFA with categorical indicators required the smallest sample size to detect 25% cognitive decline with 80% power (n = 232) compared to common test (n = 277), standardize-and-average (n = 291), and CFA with continuous indicators (n = 315) approaches. Associations with changes in biomarkers changes were the strongest for CFA with categorical indicators. Conclusions: CFA with categorical indicators demonstrated greater power to detect change and superior criterion validity compared to other approaches. It has wide applicability to directly compare cognitive performance across studies, making it a good way to obtain operational phenotypes for genetic analyses of cognitive decline among people with AD. i 2014 S. Karger AG, Base

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

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