Browsing by Author "Kauwe, John S. K."

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    Assembly of 809 whole mitochondrial genomes with clinical, imaging, and fluid biomarker phenotyping
    (Elsevier, 2018-04) Ridge, Perry G.; Wadsworth, Mark E.; Miller, Justin B.; Saykin, Andrew J.; Green, Robert C.; Alzheimer’s Disease Neuroimaging Initiative; Kauwe, John S. K.; Radiology and Imaging Sciences, School of Medicine
    INTRODUCTION: Mitochondrial genetics are an important but largely neglected area of research in Alzheimer's disease. A major impediment is the lack of data sets. METHODS: We used an innovative, rigorous approach, combining several existing tools with our own, to accurately assemble and call variants in 809 whole mitochondrial genomes. RESULTS: To help address this impediment, we prepared a data set that consists of 809 complete and annotated mitochondrial genomes with samples from the Alzheimer's Disease Neuroimaging Initiative. These whole mitochondrial genomes include rich phenotyping, such as clinical, fluid biomarker, and imaging data, all of which is available through the Alzheimer's Disease Neuroimaging Initiative website. Genomes are cleaned, annotated, and prepared for analysis. DISCUSSION: These data provide an important resource for investigating the impact of mitochondrial genetic variation on risk for Alzheimer's disease and other phenotypes that have been measured in the Alzheimer's Disease Neuroimaging Initiative samples.
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    Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans
    (Elsevier, 2015-07) Saykin, Andrew J.; Shen, Li; Yao, Xiaohui; Kim, Sungeun; Nho, Kwangsik; Risacher, Shannon L.; Ramanan, Vijay K.; Foroud, Tatiana M.; Faber, Kelly M.; Sarwar, Nadeem; Munsie, Leanne M.; Hu, Xiaolan; Soares, Holly D.; Potkin, Steven G.; Thompson, Paul M.; Kauwe, John S. K.; Kaddurah-Daouk, Rima; Green, Robert C.; Toga, Arthur W.; Weiner, Michael W.; Alzheimer's Disease Neuroimaging Initiative; Department of Radiology and Imaging Sciences, IU School of Medicine
    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.
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    Genome-wide association study identifies four novel loci associated with Alzheimer's endophenotypes and disease modifiers
    (Springer Verlag, 2017-05) Deming, Yuetiva; Li, Zeran; Kapoor, Manav; Harari, Oscar; Del-Aguila, Jorge L.; Black, Kathleen; Carrell, David; Cai, Yefei; Fernandez, Maria Victoria; Budde, John; Ma, Shengmei; Saef, Benjamin; Howells, Bill; Huang, Kuanlin; Bertelsen, Sarah; Fagan, Anne M.; Holtzman, David M.; Morris, John C.; Kim, Sungeun; Saykin, Andrew J.; De Jager, Philip L.; Albert, Marilyn; Moghekar, Abhay; O’Brien, Richard; Riemenschneider, Matthias; Petersen, Ronald C.; Blennow, Kaj; Zetterberg, Henrik; Minthon, Lennart; Van Deerlin, Vivianna M.; Lee, Virginia Man-Yee; Shaw, Leslie M.; Trojanowski, John Q.; Schellenberg, Gerard; Haines, Jonathan L.; Mayeux, Richard; Pericak-Vance, Margaret A.; Farrer, Lindsay A.; Peskind, Elaine R.; Li, Ge; Di Narzo, Antonio F.; Alzheimer’s Disease Neuroimaging Initiative (ADGC). The Alzheimer Disease Genetic Consortium (ADGC); Kauwe, John S. K.; Goate, Alison M.; Cruchaga, Carlos; Medicine, School of Medicine
    More than 20 genetic loci have been associated with risk for Alzheimer's disease (AD), but reported genome-wide significant loci do not account for all the estimated heritability and provide little information about underlying biological mechanisms. Genetic studies using intermediate quantitative traits such as biomarkers, or endophenotypes, benefit from increased statistical power to identify variants that may not pass the stringent multiple test correction in case-control studies. Endophenotypes also contain additional information helpful for identifying variants and genes associated with other aspects of disease, such as rate of progression or onset, and provide context to interpret the results from genome-wide association studies (GWAS). We conducted GWAS of amyloid beta (Aβ42), tau, and phosphorylated tau (ptau181) levels in cerebrospinal fluid (CSF) from 3146 participants across nine studies to identify novel variants associated with AD. Five genome-wide significant loci (two novel) were associated with ptau181, including loci that have also been associated with AD risk or brain-related phenotypes. Two novel loci associated with Aβ42 near GLIS1 on 1p32.3 (β = -0.059, P = 2.08 × 10-8) and within SERPINB1 on 6p25 (β = -0.025, P = 1.72 × 10-8) were also associated with AD risk (GLIS1: OR = 1.105, P = 3.43 × 10-2), disease progression (GLIS1: β = 0.277, P = 1.92 × 10-2), and age at onset (SERPINB1: β = 0.043, P = 4.62 × 10-3). Bioinformatics indicate that the intronic SERPINB1 variant (rs316341) affects expression of SERPINB1 in various tissues, including the hippocampus, suggesting that SERPINB1 influences AD through an Aβ-associated mechanism. Analyses of known AD risk loci suggest CLU and FERMT2 may influence CSF Aβ42 (P = 0.001 and P = 0.009, respectively) and the INPP5D locus may affect ptau181 levels (P = 0.009); larger studies are necessary to verify these results. Together the findings from this study can be used to inform future AD studies.
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    Genome-wide association study of brain arteriolosclerosis
    (Sage, 2022) Shade, Lincoln M. P.; Katsumata, Yuriko; Hohman, Timothy J.; Nho, Kwangsik; Saykin, Andrew J.; Mukherjee, Shubhabrata; Boehme, Kevin L.; Kauwe, John S. K.; Farrer, Lindsay A.; Schellenberg, Gerard D.; Haines, Jonathan L.; Mayeux, Richard P.; Schneider, Julie A.; Nelson, Peter T.; Fardo, David W.; Radiology and Imaging Sciences, School of Medicine
    Brain arteriolosclerosis (B-ASC) is characterized by pathologically altered brain parenchymal arterioles. B-ASC is associated with cognitive impairment and increased likelihood of clinical dementia. To date, no study has been conducted on genome-wide genetic risk of autopsy-proven B-ASC. We performed a genome-wide association study (GWAS) of the B-ASC phenotype using multiple independent aged neuropathologic cohorts. Included in the study were participants with B-ASC autopsy and genotype data available from the NACC, ROSMAP, ADNI, and ACT data sets. Initial Stage 1 GWAS (n = 3382) and Stage 2 mega-analysis (n = 4569) were performed using data from the two largest cohorts (NACC and ROSMAP). Replication of top variants and additional Stage 3 mega-analysis were performed incorporating two smaller cohorts (ADNI and ACT). Lead variants in the top two loci in the Stage 2 mega-analysis (rs7902929, p =  1.8×10−7 ; rs2603462, p =  4×10−7 ) were significant in the ADNI cohort (rs7902929, p =  0.012 ; rs2603462, p = 0.012 ). The rs2603462 lead variant colocalized with ELOVL4 expression in the cerebellum (posterior probability = 90.1%). Suggestive associations were also found near SORCS1 and SORCS3. We thus identified putative loci associated with B-ASC risk, but additional replication is needed.
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    The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores
    (Springer Nature, 2022-09-02) Page, Madeline L.; Vance, Elizabeth L.; Cloward, Matthew E.; Ringger, Ed; Dayton, Louisa; Ebbert, Mark T. W.; Alzheimer’s Disease Neuroimaging Initiative; Miller, Justin B.; Kauwe, John S. K.; Radiology and Imaging Sciences, School of Medicine
    The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.