There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Sonya Kaur |
| Institution: | University of Miami |
| Department: | Neurology |
| Country: | |
| Proposed Analysis: | The overarching goal for this project is to evaluate the contribution of genetic short sleep duration to Alzheimer’s Disease (AD) risk using polygenic risk score. Cognitive function is the most important predictor of functional independence and quality of life in older adults1. Measures of sleep, including short sleep duration have consistently been associated with declines in memory and executive function and incident dementia 2-7.The prevalence of short sleep (<6-7 hour) duration has increased by 31% in the last 30 years8, disproportionately impacting the elderly with cognitive decline9. Meta- analysis of sleep characteristics across the lifespan show that total sleep time significantly decreases with advanced age10, a factor that likely contributes to age-associated cognitive decline. More recently, the discovery of the CLOCK genes involved in regulating circadian sleep rhythms supports the idea that a specific genetic underpinning of sleep duration that is not yet fully understood but offers insight into how short sleep may adversely impact brain health, Furthermore, given changes in sleep duration throughout the lifespan11, using genetic variants as proxies of lifetime sleep exposure may be useful to uncovering sleep-cognitive aging associations. Therefore, the biological basis for sleep duration may also help uncover critical missing information to identify the biological mechanism(s) by which short sleep exacerbates risk for AD. We hypothesize that genetic factors contributing to short sleep may also contribute to the development in cognitive, neuroimaging and cerebrospinal fluid markers of AD. Recent genome wide association studies (GWAS) have identified multiple genetic loci for sleep duration and proposed polygenic risk scores (PRS) for sleep duration phenotypes12. We aim to expand on this exciting work by leveraging data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. With the guidance of my mentors, I will construct an age related short sleep duration polygenic risk score (ARSS-PRS) based on 27 previously identified single nucleotide polymorphisms (SNPs) associated with short (<7 hours) sleep duration in the UK Biobank12 and examine its association with AD risk. Successful completion of this project will provide me with preliminary data and crucial initial training in genetic data analysis to compete for a larger NIH funded K08 study to examine genetic and epigenetic markers of sleep phenotypes in AD. SPECIFIC AIMS: Aim 1: Determine if the ARSS-PRS is associated with cognitive dysfunction at baseline and cognitive decline. Given the relationship between short sleep duration and cognitive decline3,13 we will examine the contribution of the ARSS-PRSS to impaired cognitive status at baseline, poorer scores on cognitive domains, decline in cognitive function over time in the ADNI-1, ADNI-2 and ADNI-GO cohort. We will also test for the interaction between APOE4 and ARSS-PRS on cognition. Hypothesis: Greater ARSS-PRS will be associated with cognitive dysfunction at baseline and cognitive decline during the follow-up period. These relationships will be more pronounced in individuals with the APOE4 allele. Aim 2: Determine if the ARSS-PRS is associated with diagnostic conversion. Given the strong association between sleep and AD risk14, we will examine the contribution of the ARSS-PRS to risk of conversion to mild cognitive impairment (MCI) or AD. Hypothesis: Greater ARSS-PRS will be associated with increased risk of conversion to MCI and AD as well as shorter time to conversion. These relationships will be more pronounced in individuals with the APOE4 allele. Data Source/Participants: The ADNI is an ongoing cohort of >1000 participants aged between 55 to 90 years recruited from over 50 sites across the US and Canada. Our analytical sample will include approximately 200 people classified as cognitively normal, 400 with MCI and 200 with AD28 who have complete cognitive, neuroimaging and CSF biomarker data available at baseline. Preliminary data in ADNI: We have previously reported associations between comorbid insomnia (aa disorder that can manifest with short sleep) and sleep disordered breathing with likelihood of being classified as MCI in ADNI29. Others have also reported that a history of sleep disorders is associated with early MCI and conversion to AD after adjustment for APOE4 status30. GWAS: ADNI 1 genetic samples were run on the Human610-Quad BeadChip (N = 818). The chip provides coverage of 924,000 randomly selected SNPs. ADNI 2 and ADNI GO samples were run on the Illumina HumanOmniExpress BeadChip (N = 432)31. All assays were performed according to manufacturer protocols. Bead intensity data were used to call genotypes using BeadStudio 3.2 (Illumina) for the first release of ADNI-1 data, GenomeStudio v2009.1 (Illumina) for the second release of ADNI-1 data, and GenomeStudio v2011.1 (Illumina) for ADNI-GO/2. All genotype data were quality controlled (QC), including checks for sex and identity32. APOE Genotyping: APOE4 allele status was determined using using DNA from blood samples from 818 ADNI-1, 128 ADNI-GO, and 778 ADNI-2 participants. For ADNI-1, APOE genotyping was carried out by polymerase chain reaction (PCR) amplification, Hhal restriction enzyme digestion, and subsequent standard gel resolution and visualization processes. For ADNI-GO and ADNI-2 DNA samples, genotyping was performed by Prevention Genetics (Marshfield, WI, USA) and LGC Genomics (Beverly, MA, USA). Assay kits were added to DNA samples, followed by thermal cycling reaction and an end-point fluorescent read. Genotypes were called using LGC Genomics' in-house Kraken software (http://www.lgcgroup.com/products/genotyping-software/kraken/) and were returned to the ADNI Genetics Core after manual QC. All APOE genotype data underwent further QC checks, including sex and identity checks, and potential problems were identified and corrected through communication with NCRAD and other cores32. Neuropsychological Assessment: Cognitive domains assessed included global cognitive function (Mini Mental State Examination Clinical Dementia Rating Scale Global), memory (Auditory Verbal Learning Test), processing speed (Trails A, Number Cancellation), working memory (Trails B, Digits backwards), language (Boston Naming Test, Category Fluency) and visuospatial skills (Clock Drawing). Functional independence was measured using the Functional Activities Questionnaire (FAQ). All evaluations were carried out in the participants’ primary language (English or Spanish). Composite scores for changes in the four domains will be computed using regression‐based change indices of the corresponding individual test after adjustment for age, years of education, and the time interval between the assessments. Diagnostic conversion to MCI and AD were determined using scores on the Mini Mental State Examination, Clinical Dementia Rating Scale and FAQ33. Covariates: Participants’ chronological age at baseline, sex, race/ethnicity, education level, global vascular risk score (GVRS, a weighted score that has been shown as a strong predictor of vascular events34 and cognitive decline35) and depression status (as measured by score on the Geriatric Depression Scale will be included in all statistical models as covariates. In addition, global genetic ancestry was calculated in ADNI using PCAs (Principal component analysis)36. PCA separates underlying structure among populations based on the principal components37. The first 3 PCAs of genetic ancestry will be entered into statistical models as covariates to adjust for population stratification. ANALYTICAL PLAN: Dr. Wang (genetics mentor) will oversee the construction of the ARSS-PRS and all analyses with the assistance of Chuanhui Dong, PhD (biostatistician). Dr. Kaur (PI) will complete graduate level coursework in human genetics and data analysis for human genomic studies in order to conduct the analyses under the supervision of Dr. Wang and Dr. Rundek. Aim 1: Determine if the ARSS-PRS is associated with cognitive dysfunction at baseline and cognitive decline. Using the summary statistics from the previously described UK Biobank study12 that identified 27 loci associated with short (<7 hours) sleep, we will generate the ARSS-PRS in ADNI. Specific genes covered by these SNPs include PAX8, SLC39A8, FOXP2, VRK2, TCF4, LINC01122. SEMA6D, SMAD5, METT5D1, LMOD1, RASGEF1B, MGAT3, DPYD, ZSCAN12, PDE4B, MAD1L1, ADCY2, HCRTR2, PAM, PCMTD1, GNA01, RBM5, SHISA6, USP49, PTPRJ, LAMA2, CSMD212. Individual participant scores will be created by summing the number of ‘risk’ alleles associated with short sleep duration at each genetic variant, which will be weighted by the respective allelic effect sizes expressed as the beta estimates for that allele from GWAS on the short sleep duration 12. We will run regression models with the ARSS-PRS as the predictor of cognitive outcomes as a global cognitive score and domain specific scores in memory, processing speed, working memory, language and visuospatial skills at baseline and change in these scores over time. Multiple linear regression models will be utilized for continuous outcomes and logistic regression models will be utilized for categorical outcomes. Clinically relevant covariates (age, sex, race/ethnicity, education, GVRS, depression status, global genetic ancestry as PCAs) will be included in all models. For longitudinal models, time from baseline to final follow up assessment will also be included. In order to assess the effect modification by APOE4 status on these relationships we will run linear regression models with interaction terms (ARSS-PRS x APOE4). If the interaction term is significant, we will stratify the analyses by APOE4 status. An alpha level 0.05 will be used to indicate statistically significant relationships between the ARSS-PRS and cognitive outcomes. A false discovery rate (FDR) adjustment will be used to mitigate Type I error due to multiple comparisons. Aim 2: Determine if the ARSS-PRS is associated with diagnostic conversion. Logistic regression models will be run to examine categorical outcomes (i.e., conversion to MCI and AD). Time to conversion will be examined as a continuous variable and analyzed using multiple regression models, with above mentioned clinically relevant covariates included in all models. A false discovery rate (FDR) adjustment will be used to mitigate Type I error due to multiple comparisons. Power Calculation: Our sample size is fixed to ADNI subjects with complete data on a GWAS, cognitive function, and APOE genotype. For the power calculation we rounded the sample to 1,000 subjects. ARSS-PRS will be the primary predictor of interest and calculated from a GWAS as a continuous variable, whereas multivariable generalized linear regression models will be employed to evaluate its associations. For continuous outcome variables, a total sample size of 1000 subjects will achieve 88% power to detect an R-Squared of 0.01 attributed to 2 independent variable(s) (ARSS-PRS and its interaction with APOE genotype) using an F-Test with a significance level (alpha) of 0.05, when the model is adjusted for an additional covariates with an R-Squared of 0.15. For binary outcomes, a logistic regression of a sample size of 1000 observations achieves 80% power at a 0.05 significance level to detect an odds ratio of 1.27 per SD change in ARSS-PRS, when the model is adjusted for other covariates with an R-Squared of 0.15. |
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