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Principal Investigator  
Principal Investigator's Name: Annie Lee
Institution: Columbia University Irving Medical Center
Department: Neurology
Proposed Analysis: Genes associated vascular risk factors, cerebrovascular pathology and Alzheimer’s disease BACKGROUND Neuropathological studies indicate that in patients with Alzheimer’s disease (AD), the hallmark findings of neuritic plaques and neurofibrillary tangles can frequently be accompanied by varying degrees of cardiovascular and cerebrovascular disease in up to 70% of patients1-4. Caribbean Hispanics and African-Americans have an increased frequency of sporadic and familial AD5,6 and disparities in cardiovascular disease. Compared to white individuals, the cumulative incidence of AD to age 90 years was increased two-fold among Caribbean Hispanics and African-Americans5,6, and prevalence of hypertension and diabetes are higher and/or not as well controlled in Caribbean Hispanics and African-Americans7,8. Whether cardiovascular and cerebrovascular risk factors (e.g. hypertension, smoking, hyperlipidemia) increase the risk of AD while interacting with genetic factors remains unknown. APOE, CLU, ABCA7, and SORL1, typically associated with immune mechanisms in AD, are also part of the lipid metabolism pathway providing evidence for a putative molecular relationship. In fact, ABCA7 and SORL1 have been associated with brain infarcts9. Our recent study10,11 detected evidence of interaction between FMNL2 and cerebrovascular risk factors on AD using meta-analysis of multiple genome-wide associations studies across different ethnic cohorts in ~15,000 individuals. Despite the possible associations between genes, vascular factors and AD, further investigation needs to be done by using validated vascular risk factors in larger samples with more ethnic cohorts to identify additional genes that reach the statistical significance in the interaction analysis. We are collaborating with Lindsay Farrer and Jesse Mez at Boston University who will share summary data from the Framingham Study and with Arfan Ikram and Ahmad Shahzad who will also share summary data from the Rotterdam Study. These additional datasets will greatly augment our sample size. We have requested additional clinical data from the National Alzheimer Coordinating Center (NACC) and both clinical and genetic data from the Cardiovascular Health Study (CHS) and the Northern Manhattan Stroke Study (NOMAS). Aim. We will conduct a genome-wide gene-CVRF (cardio-vascular risk factor) interaction for association with AD and investigate whether these relationships differ by ethnic group in multi-ethnic cohorts. We will further conduct a meta-analysis of genome-wide association studies (GWAS) from different cohorts. We hypothesize that identifying specific causal genes that interact with cardio- and cerebrovascular risk factors will provide insight into how they may perturb pathways leading to AD and whether these relationships differ by ethnic group. DATASETS REQUESTED FOR ANALYSIS We request GWAS data assembled by ADGC on all participants from NACC (ADRCs). These datasets would include all European and non-European individuals, even if they have failed population filtering QC. Our recent study10,11 used the National Alzheimer Coordinating Center (NACC) samples described in Table 1, with a total sample size of 8,012 individuals. Since the publication of our previous report, we have requested updated cardiovascular risk data from NACC. We are in the process of constructing the CVRF score on the updated set of NACC participants. VARIABLES REQUESTED FOR ANALYSIS • Clinical AD status (yes or no) • Sex • Age: age of onset/age at last exam • APOE status • Population specific PCs (to be used a covariates in the analyses) • Genotypes: Both imputed and unimputed o Genotyped (we use this for individual level QC and for population filtering) o TopMed imputed genotypes for GWA analyses and subsequent meta-analysis. ANALYSES AND METHODS Data QC. After ADGC pre-imputation QC and imputation, we will perform post-imputation QC. This post-imputation QC will be conducted both within and across cohorts. Within cohorts, we will exclude any variants with missing rate greater than 5%, out of Hardy-Weinberg equilibrium (p<1x10-6), or with less than 1% minor allele frequency (MAF). Participants were excluded if they were missing more than 2% of variants present in the overall cohort. Participants from NACC cohort will be split into individuals or NHW, African American and Hispanic ancestry based on genetically inferred PCs and analysis will be conducted independently in each ethnic group (followed by meta-analysis). Post-imputation genotype data will be filtered for imputation quality (R2>0.8) and minor allele frequency (>1%). KING will be used to perform multidimensional scaling (MDS) to identify population substructure within each cohort. Participants more than six standard deviations from the mean within cohort in the first three calculated MDS components will be removed from analysis. The process will lean on the NACC guidelines. Statistical and Data Analysis. We will perform genome-wide gene-CVRF (cardio-vascular risk factor) score interaction analysis independently in each cohort and will summarize the results in a meta-analysis to improve analysis power and results interpretability. Self-reported data from each participant is recorded as a binary indicator (Yes-Ever Had or No-Never Had) for heart disease, hypertension, and diabetes. A quantitative variable is recorded for body mass index (BMI) from the last visit. We will compute the principal components (PCs) from the four cerebrovascular variables, a mixture of quantitative and qualitative data, using PCAMix package and the 1st principal component which captures the greatest amount of variance accounted for by the four cerebrovascular risk factors will be used to summarize the cardiovascular risk factors into one summary score, called CVRF score. Gene-based tests will be performed using the adaptive gene-environment interaction (aGE) test12. Independently, we will validate the results using an alternative method implemented in the gene-environment set association test (GESAT)13. Covariates will be included in the model, including age at diagnosis for AD and age at last visit for the unaffected participants, sex, and the first three principal components to adjust for population substructure. We will analyze genes with a minimum of two variants with allele frequency greater than 0.01 in the gene-based test. The aGE R package and GESAT functions in the iSKAT R package will be performed with default settings. Summary statistics from gene-based tests will be meta-analyzed with the MetaSKAT package in R and/or weighted sum of Z-scores method through METAL. Individual SNPs will be tested for CVRF interactions within each gene when a significant gene by vascular risk factors interaction was found. The SNP by CVRF interaction test will be performed in each cohort for all SNPs within candidate genes by fitting a logistic regression for AD as the outcome and testing for the interaction term of SNP by CVRF score. The interaction tests were adjusted for the main genetic effect and the CVRF score as covariates in all models, in addition to age, sex, and the first three principal components. The models were tested with additive coding for the SNPs. For models used in the NACC cohort, batch effects were included as covariates because genotyping from ten waves were imputed separately. The results were then combined in a meta-analysis using inverse-variance weighted average method through METAL by calculating the weighted mean of the SNP-CVRF interaction effect sizes, with the inverse variance from each cohort as weights. Both single variant and gene-based tests will be followed up with a pathway and functional analysis. Statistical significance will be set at p = 5x10-6 for the genome-wide gene-based tests and use the false discovery rate (FDR) adjusted p-value or Bonferroni correction for the number of genes tested. DATA TO BE ADDED We will compute CVRF-score on all NACC participants and return the association results and individual level CVRF scores. MEMBERS OF ANALYSIS TEAM Annie J. Lee, Caghan Kizil, Badri N. Vardarajan, and Richard Mayeux, from Columbia University Irving Medical Center. TIMELINE Given the focused hypothesis of this work, we anticipate completing statistical analyses within 9 months from SAG approval. DELIVERABLES Our key deliverable is our analysis results on the genes or genetic loci that interacts with cardio- cerebrovascular risk factors on late-onset AD. We plan to submit a focused manuscript on this finding and the association results will be made available. We will publish at least one manuscript. REFERENCES 1. Attems J, Jellinger KA. The overlap between vascular disease and Alzheimer's disease--lessons from pathology. BMC Med. 2014;12:206. 2. Brenowitz WD, Keene CD, Hawes SE, et al. Alzheimer's disease neuropathologic change, Lewy body disease, and vascular brain injury in clinic- and community-based samples. Neurobiol Aging. 2017;53:83-92. 3. Kapasi A, DeCarli C, Schneider JA. Impact of multiple pathologies on the threshold for clinically overt dementia. Acta Neuropathol. 2017;134(2):171-186. 4. Schneider JA, Arvanitakis Z, Bang W, Bennett DA. Mixed brain pathologies account for most dementia cases in community-dwelling older persons. Neurology. 2007;69(24):2197-2204. 5. Vardarajan BN, Schaid DJ, Reitz C, et al. Inbreeding among Caribbean Hispanics from the Dominican Republic and its effects on risk of Alzheimer disease. Genetics in medicine : official journal of the American College of Medical Genetics. 2015;17(8):639-643. 6. Tang MX, Cross P, Andrews H, et al. Incidence of AD in African-Americans, Caribbean Hispanics, and Caucasians in northern Manhattan. Neurology. 2001;56(1):49-56. 7. Lackland DT. Racial differences in hypertension: implications for high blood pressure management. Am J Med Sci. 2014;348(2):135-138. 8. Spanakis EK, Golden SH. Race/ethnic difference in diabetes and diabetic complications. Curr Diab Rep. 2013;13(6):814-823. 9. Farfel JM, Yu L, Buchman AS, Schneider JA, De Jager PL, Bennett DA. Relation of genomic variants for Alzheimer disease dementia to common neuropathologies. Neurology. 2016;87(5):489-496. 10. Lee AJ, Raghavan NS, Bhattarai P, et al. FMNL2 regulates gliovascular interactions and is associated with vascular risk factors and cerebrovascular pathology in Alzheimer's disease. Acta Neuropathol. 2022. 11. Raghavan NS, Sariya, s., Lee, A.J. Gao, Y., Reyes-Dumeyer, D., De Jager, P.L, Bennett, D.A., Menon, V., Lantigua, R.A., Kukull, W.A., Brickman, A.M., Manly, J.J. Gutierrez, J., Vardarajan, B.B., Tosto, G., Mayeux, R. FMNL2 interacts with cerebrovascular risk factors to alter Alzheimer’s disease risk. medRxiv. 2020. 12. Yang T, Chen H, Tang H, Li D, Wei P. A powerful and data-adaptive test for rare-variant-based gene-environment interaction analysis. Stat Med. 2019;38(7):1230-1244. 13. Lin X, Lee S, Christiani DC, Lin X. Test for interactions between a genetic marker set and environment in generalized linear models. Biostatistics. 2013;14(4):667-681.
Additional Investigators  
Investigator's Name: Richard Mayeux
Proposed Analysis: same as Lee's current application: genome-wide gene-CVRF (cardio-vascular risk factor) interaction for association with Alzheimer’s disease
Investigator's Name: Badri Vardarajan
Proposed Analysis: same as Lee's current application: genome-wide gene-CVRF (cardio-vascular risk factor) interaction for association with Alzheimer’s disease
Investigator's Name: Caghan Kizil
Proposed Analysis: same as Lee's current application: genome-wide gene-CVRF (cardio-vascular risk factor) interaction for association with Alzheimer’s disease