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Your search keyword '"Kauwe, John S. K."' showing total 15 results
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15 results on '"Kauwe, John S. K."'

4. LATE-NC risk alleles (in TMEM106B, GRN, and ABCC9 genes) among persons with African ancestry

Author

Katsumata, Yuriko, primary, Fardo, David W, additional, Shade, Lincoln M P, additional, Bowen, James D, additional, Crane, Paul K, additional, Jarvik, Gail P, additional, Keene, C Dirk, additional, Larson, Eric B, additional, McCormick, Wayne C, additional, McCurry, Susan M, additional, Mukherjee, Shubhabrata, additional, Kowall, Neil W, additional, McKee, Ann C, additional, Honig, Robert A, additional, Lawrence, S, additional, Vonsattel, Jean Paul, additional, Williamson, Jennifer, additional, Small, Scott, additional, Burke, James R, additional, Hulette, Christine M, additional, Welsh-Bohmer, Kathleen A, additional, Gearing, Marla, additional, Lah, James J, additional, Levey, Allan I, additional, Wingo, Thomas S, additional, Apostolova, Liana G, additional, Farlow, Martin R, additional, Ghetti, Bernardino, additional, Saykin, Andrew J, additional, Spina, Salvatore, additional, Albert, Marilyn S, additional, Lyketsos, Constantine G, additional, Troncoso, Juan C, additional, Frosch, Matthew P, additional, Green, Robert C, additional, Growdon, John H, additional, Hyman, Bradley T, additional, Tanzi, Rudolph E, additional, Potter, Huntington, additional, Dickson, Dennis W, additional, Ertekin-Taner, Nilufer, additional, Graff-Radford, Neill R, additional, Parisi, Joseph E, additional, Petersen, Ronald C, additional, Duara, Ranjan, additional, Buxbaum, Joseph D, additional, Goate, Alison M, additional, Sano, Mary, additional, Masurkar, Arjun V, additional, Wisniewski, Thomas, additional, Bigio, Eileen H, additional, Mesulam, Marsel, additional, Weintraub, Sandra, additional, Vassar, Robert, additional, Kaye, Jeffrey A, additional, Quinn, Joseph F, additional, Woltjer, Randall L, additional, Barnes, Lisa L, additional, Bennett, David A, additional, Schneider, Julie A, additional, Yu, Lei, additional, Henderson, Victor, additional, Fallon, Kenneth B, additional, Harrell, Lindy E, additional, Marson, Daniel C, additional, Roberson, Erik D, additional, DeCarli, Charles, additional, Jin, Lee-Way, additional, Olichney, John M, additional, Kim, Ronald, additional, LaFerla, Frank M, additional, Monuki, Edwin, additional, Head, Elizabeth, additional, Sultzer, David, additional, Geschwind, Daniel H, additional, Vinters, Harry V, additional, Chesselet, Marie-Francoise, additional, Galasko, Douglas R, additional, Brewer, James B, additional, Boxer, Adam, additional, Karydas, Anna, additional, Kramer, Joel H, additional, Miller, Bruce L, additional, Rosen, Howard J, additional, Seeley, William W, additional, Burns, Jeffrey M, additional, Swerdlow, Russell H, additional, Abner, Erin, additional, Van Eldik, Linda J, additional, Albin, Roger L, additional, Lieberman, Andrew P, additional, Paulson, Henry L, additional, Arnold, Steven E, additional, Trojanowski, John Q, additional, Van Deerlin, Vivianna M, additional, Hamilton, Ronald L, additional, Kamboh, M Ilyas, additional, Lopez, Oscar L, additional, Becker, James T, additional, Cao, Chuanhai, additional, Raj, Ashok, additional, Smith, Amanda G, additional, Chui, Helena C, additional, Miller, Carol A, additional, Ringman, John M, additional, Schneider, Lon S, additional, Bird, Thomas D, additional, Sonnen, Joshua A, additional, Yu, Chang-En, additional, Grabowski, Thomas, additional, Peskind, Elaine, additional, Raskind, Murray, additional, Li, Ge, additional, Tsuang, Debby W, additional, Asthana, Sanjay, additional, Atwood, Craig S, additional, Carlsson, Cynthia M, additional, Sager, Mark A, additional, Chin, Nathaniel A, additional, Craft, Suzanne, additional, Cairns, Nigel J, additional, Morris, John C, additional, Cruchaga, Carlos, additional, Strittmatter, Stephen, additional, Reiman, Eric M, additional, Beach, Thomas G, additional, Huentelman, Matthew J, additional, Hardy, John, additional, Myers, Amanda J, additional, Kauwe, John S K, additional, Hakonarson, Hakon, additional, Blacker, Deborah, additional, Montine, Thomas J, additional, Baldwin, Clinton T, additional, Farrer, Lindsay A, additional, Jun, Gyungah, additional, Lunetta, Kathryn L, additional, Bush, William S, additional, Haines, Jonathan L, additional, Lerner, Alan J, additional, Zhou, Xiongwei, additional, Barral, Sandra, additional, Reitz, Christiane, additional, Vardarajan, Badri N, additional, Mayeux, Richard, additional, Beecham, Gary W, additional, Carney, Regina M, additional, Cuccaro, Michael L, additional, Gilbert, John R, additional, Hamilton-Nelson, Kara L, additional, Kunkle, Brian W, additional, Martin, Eden R, additional, Pericak-Vance, Margaret A, additional, Vance, Jeffery M, additional, Cantwell, Laura B, additional, Kuzma, Amanda P, additional, Malamon, John, additional, Naj, Adam C, additional, Qu, Liming, additional, Schellenberg, Gerard D, additional, Valladares, Otto, additional, Wang, Li-San, additional, Zhao, Yi, additional, Leverenz, James B, additional, De Jager, Philip L, additional, Evans, Denis A, additional, Katz, Mindy J, additional, Lipton, Richard B, additional, Boeve, Bradley F, additional, Allen, Mariet, additional, Carrasquillo, Minerva M, additional, Younkin, Steven G, additional, Kukull, Walter A, additional, Faber, Kelley M, additional, Foroud, Tatiana M, additional, Pavlik, Valory, additional, Massman, Paul, additional, Darby, Eveleen, additional, Rodriguear, Monica, additional, Khaleeq, Aisha, additional, Royall, Donald R, additional, Stevens, Alan, additional, Ory, Marcia, additional, DeToledo, John C, additional, Wilms, Henrick, additional, Johnson, Kim, additional, Perez, Victoria, additional, Hernandez, Michelle, additional, Wilhelmsen, Kirk C, additional, Tilson, Jeffrey, additional, Chasse, Scott, additional, Barber, Robert C, additional, Fairchild, Thomas J, additional, O’Bryant, Sid E, additional, Knebl, Janice, additional, Hall, James R, additional, Johnson, Leigh, additional, Mains, Douglas, additional, Alvarez, Lisa, additional, Gamboa, Adriana, additional, Paydarfar, David, additional, Bertelson, John, additional, Woon, Martin, additional, Ayres, Gayle, additional, Aguirre, Alyssa, additional, Palmer, Raymond, additional, Polk, Marsha, additional, Adams, Perrie M, additional, Huebinger, Ryan M, additional, Reisch, Joan S, additional, Rosenberg, Roger N, additional, Cullum, Munro, additional, Williams, Benjamin, additional, Quiceno, Mary, additional, Hynan, Linda, additional, Smith, Janet, additional, Davis, Barb, additional, Nguyen, Trung, additional, Rogaeva, Ekaterina, additional, George-Hyslop, Peter St, additional, and Nelson, Peter T, additional

Published
2023
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11. Pairwise Correlation Analysis of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Dataset Reveals Significant Feature Correlation

Author

Huckvale, Erik D., Hodgman, Matthew W., Greenwood, Brianna B., Stucki, Devorah O., Ward, Katrisa M., Ebbert, Mark T. W., Kauwe, John S. K., Initiative, The Alzheimer’s Disease Neuroimaging Initiative The Alzheimer’s Disease Neuroimaging, Consortium, The Alzheimer’s Disease Metabolomics Consortium The Alzheimer’s Disease Metabolomics, and Miller, Justin B.

Subjects
Biometrics, Computer science, Datasets as Topic, Neuroimaging, QH426-470, Machine learning, computer.software_genre, Article, Correlation, symbols.namesake, Feature correlation, Alzheimer Disease, Genetics, ADNI, Humans, Generalizability theory, Genetic Association Studies, Genetics (clinical), feature reduction, business.industry, Magnetic Resonance Imaging, Bonferroni correction, machine learning, Feature (computer vision), symbols, Artificial intelligence, pairwise feature correlation, Transcriptome, business, computer, Alzheimer’s disease, Biomarkers, Alzheimer's Disease Neuroimaging Initiative
Abstract

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer’s disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the extent to which this issue might impact large scale analyses using these data. We found that 93.457% of biomarkers, 92.549% of the gene expression values, and 100% of MRI features were strongly correlated with at least one other feature in ADNI based on our Bonferroni corrected α (p-value ≤ 1.40754 × 10−13). We provide a comprehensive mapping of all ADNI biomarkers to highly correlated features within the dataset. Additionally, we show that significant correlation within the ADNI dataset should be resolved before performing bulk data analyses, and we provide recommendations to address these issues. We anticipate that these recommendations and resources will help guide researchers utilizing the ADNI dataset to increase model performance and reduce the cost and complexity of their analyses.

Published
2021
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13. Knowledge Gaps, Challenges, and Opportunities in Health and Prevention Research for Asian Americans, Native Hawaiians, and Pacific Islanders: A Report From the 2021 National Institutes of Health Workshop.

Author

Kanaya AM, Hsing AW, Panapasa SV, Kandula NR, Araneta MRG, Shimbo D, Wang P, Gomez SL, Lee J, Narayan KMV, Mau MKLM, Bose S, Daviglus ML, Hu FB, Islam N, Jackson CL, Kataoka-Yahiro M, Kauwe JSK, Liu S, Ma GX, Nguyen T, Palaniappan L, Setiawan VW, Trinh-Shevrin C, Tsoh JY, Vaidya D, Vickrey B, Wang TJ, Wong ND, Coady S, and Hong Y

Subjects
Hawaii, Health Promotion, Humans, National Institutes of Health (U.S.), United States epidemiology, Asian, Native Hawaiian or Other Pacific Islander
Abstract

Asian Americans (AsA), Native Hawaiians, and Pacific Islanders (NHPI) comprise 7.7% of the U.S. population, and AsA have had the fastest growth rate since 2010. Yet the National Institutes of Health (NIH) has invested only 0.17% of its budget on AsA and NHPI research between 1992 and 2018. More than 40 ethnic subgroups are included within AsA and NHPI (with no majority subpopulation), which are highly diverse culturally, demographically, linguistically, and socioeconomically. However, data for these groups are often aggregated, masking critical health disparities and their drivers. To address these issues, in March 2021, the National Heart, Lung, and Blood Institute, in partnership with 8 other NIH institutes, convened a multidisciplinary workshop to review current research, knowledge gaps, opportunities, barriers, and approaches for prevention research for AsA and NHPI populations. The workshop covered 5 domains: 1) sociocultural, environmental, psychological health, and lifestyle dimensions; 2) metabolic disorders; 3) cardiovascular and lung diseases; 4) cancer; and 5) cognitive function and healthy aging. Two recurring themes emerged: Very limited data on the epidemiology, risk factors, and outcomes for most conditions are available, and most existing data are not disaggregated by subgroup, masking variation in risk factors, disease occurrence, and trajectories. Leveraging the vast phenotypic differences among AsA and NHPI groups was identified as a key opportunity to yield novel clues into etiologic and prognostic factors to inform prevention efforts and intervention strategies. Promising approaches for future research include developing collaborations with community partners, investing in infrastructure support for cohort studies, enhancing existing data sources to enable data disaggregation, and incorporating novel technology for objective measurement. Research on AsA and NHPI subgroups is urgently needed to eliminate disparities and promote health equity in these populations.

Published
2022
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14. Association between WWOX/MAF variants and dementia-related neuropathologic endophenotypes.

Author

Dugan AJ, Nelson PT, Katsumata Y, Shade LMP, Teylan MA, Boehme KL, Mukherjee S, Kauwe JSK, Hohman TJ, Schneider JA, and Fardo DW

Subjects
Aged, Aged, 80 and over, Female, Humans, Male, Alzheimer Disease genetics, Dementia genetics, Genetic Variation genetics, Genome-Wide Association Study methods, Phenotype, Proto-Oncogene Proteins c-maf genetics, TDP-43 Proteinopathies genetics, Tumor Suppressor Proteins genetics, WW Domain-Containing Oxidoreductase genetics
Abstract

The genetic locus containing the WWOX and MAF genes was implicated as a clinical Alzheimer's disease (AD) risk locus in two recent large meta-analytic genome wide association studies (GWAS). In a prior GWAS, we identified a variant in WWOX as a suggestive risk allele for hippocampal sclerosis. We hypothesized that the WWOX/MAF locus may be preferentially associated with non-plaque- and non-tau-related neuropathological changes (NC). Data from research participants with GWAS and autopsy measures from the National Alzheimer's Coordinating Center and the Religious Orders Study and the Rush Memory and Aging Project were meta-analyzed. Notably, no variants in the locus were significantly associated with ADNC. However, several WWOX/MAF variants had significant adjusted associations with limbic-predominant age-related TDP-43 encephalopathy NC (LATE-NC), HS, and brain arteriolosclerosis. These associations remained largely unchanged after adjustment for ADNC (operationalized with standard semiquantitative staging), suggesting that these associations are independent of ADNC. Thus, WWOX genetic variants were associated pathologically with LATE-NC, not ADNC., (Copyright © 2021. Published by Elsevier Inc.)

Published
2022
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15. Analysis of high-risk pedigrees identifies 11 candidate variants for Alzheimer's disease.

Author

Teerlink CC, Miller JB, Vance EL, Staley LA, Stevens J, Tavana JP, Cloward ME, Page ML, Dayton L, Cannon-Albright LA, and Kauwe JSK

Subjects
ATP-Binding Cassette Transporters genetics, Genetic Predisposition to Disease genetics, Genotype, Humans, Longevity, Membrane Proteins genetics, Pedigree, Alzheimer Disease genetics
Abstract

Introduction: Analysis of sequence data in high-risk pedigrees is a powerful approach to detect rare predisposition variants., Methods: Rare, shared candidate predisposition variants were identified from exome sequencing 19 Alzheimer's disease (AD)-affected cousin pairs selected from high-risk pedigrees. Variants were further prioritized by risk association in various external datasets. Candidate variants emerging from these analyses were tested for co-segregation to additional affected relatives of the original sequenced pedigree members., Results: AD-affected high-risk cousin pairs contained 564 shared rare variants. Eleven variants spanning 10 genes were prioritized in external datasets: rs201665195 (ABCA7), and rs28933981 (TTR) were previously implicated in AD pathology; rs141402160 (NOTCH3) and rs140914494 (NOTCH3) were previously reported; rs200290640 (PIDD1) and rs199752248 (PIDD1) were present in more than one cousin pair; rs61729902 (SNAP91), rs140129800 (COX6A2, AC026471), and rs191804178 (MUC16) were not present in a longevity cohort; and rs148294193 (PELI3) and rs147599881 (FCHO1) approached significance from analysis of AD-related phenotypes. Three variants were validated via evidence of co-segregation to additional relatives (PELI3, ABCA7, and SNAP91)., Discussion: These analyses support ABCA7 and TTR as AD risk genes, expand on previously reported NOTCH3 variant identification, and prioritize seven additional candidate variants., (© 2021 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Published
2022
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