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Crane PK, Groot C, Ossenkoppele R, Mukherjee S, Choi S, Lee M, Scollard P, Gibbons LE, Sanders RE, Trittschuh E, Saykin AJ, Mez J, Nakano C, Donald CM, Sohi H, Risacher S. Cognitively defined Alzheimer's dementia subgroups have distinct atrophy patterns. Alzheimers Dement 2024; 20:1739-1752. [PMID: 38093529 PMCID: PMC10984445 DOI: 10.1002/alz.13567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 10/16/2023] [Accepted: 11/03/2023] [Indexed: 03/03/2024]
Abstract
INTRODUCTION We sought to determine structural magnetic resonance imaging (MRI) characteristics across subgroups defined based on relative cognitive domain impairments using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and to compare cognitively defined to imaging-defined subgroups. METHODS We used data from 584 people with Alzheimer's disease (AD) (461 amyloid positive, 123 unknown amyloid status) and 118 amyloid-negative controls. We used voxel-based morphometry to compare gray matter volume (GMV) for each group compared to controls and to AD-Memory. RESULTS There was pronounced bilateral lower medial temporal lobe atrophy with relative cortical sparing for AD-Memory, lower left hemisphere GMV for AD-Language, anterior lower GMV for AD-Executive, and posterior lower GMV for AD-Visuospatial. Formal asymmetry comparisons showed substantially more asymmetry in the AD-Language group than any other group (p = 1.15 × 10-10 ). For overlap between imaging-defined and cognitively defined subgroups, AD-Memory matched up with an imaging-defined limbic predominant group. DISCUSSION MRI findings differ across cognitively defined AD subgroups.
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Affiliation(s)
- Paul K. Crane
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Colin Groot
- Clinical Memory Research UnitLund UniversityLundSweden
- Alzheimer centerAmsterdam UMC ‐ VU Medical CenterAmsterdamNetherlands
| | - Rik Ossenkoppele
- Clinical Memory Research UnitLund UniversityLundSweden
- Alzheimer centerAmsterdam UMC ‐ VU Medical CenterAmsterdamNetherlands
| | | | - Seo‐Eun Choi
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Michael Lee
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Phoebe Scollard
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Laura E. Gibbons
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | | | - Emily Trittschuh
- Department of Psychiatry and Behavioral SciencesUniversity of Washington, and Geriatrics ResearchEducation, and Clinical CenterVA Puget Sound Health Care SystemSeattleUSA
| | - Andrew J. Saykin
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisUSA
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisUSA
| | - Jesse Mez
- Department of NeurologyBoston UniversityBostonMassachusettsUSA
| | - Connie Nakano
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | | | - Harkirat Sohi
- Department of Biomedical Informatics and Medical EducationUniversity of WashingtonSeattleUSA
- Now Pacific Northwest National LaboratoryRichlandUSA
| | | | - Shannon Risacher
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisUSA
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisUSA
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Sohi H, Gennari J, Madhyastha T, Lee BE, Risacher SL, MacDonald C, Groot C, Ossenkoppele R, Mez J, Trittschuh EH, Saykin AJ, Mukherjee S, Gibbons LE, Sanders RE, Choi SE, Crane PK. P1-361: REGIONAL DIFFERENCES IN CORTICAL THICKNESS ACROSS COGNITIVELY DEFINED ALZHEIMER'S DISEASE SUBGROUPS. Alzheimers Dement 2019. [DOI: 10.1016/j.jalz.2019.06.916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
| | | | | | - Briana E. Lee
- University of Washington Medical Center; Seattle WA USA
| | | | | | - Colin Groot
- Amsterdam Neuroscience, VU University Medical Center; Amsterdam UMC; Amsterdam Netherlands
| | | | - Jesse Mez
- Boston University School of Medicine; Boston MA USA
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Nafikov RA, Nato AQ, Sohi H, Wang B, Brown L, Horimoto AR, Vardarajan BN, Barral SM, Tosto G, Mayeux RP, Thornton TA, Blue E, Wijsman EM. Analysis of pedigree data in populations with multiple ancestries: Strategies for dealing with admixture in Caribbean Hispanic families from the ADSP. Genet Epidemiol 2018; 42:500-515. [PMID: 29862559 PMCID: PMC6160322 DOI: 10.1002/gepi.22133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 05/04/2018] [Accepted: 05/14/2018] [Indexed: 11/12/2022]
Abstract
Multipoint linkage analysis is an important approach for localizing disease-associated loci in pedigrees. Linkage analysis, however, is sensitive to misspecification of marker allele frequencies. Pedigrees from recently admixed populations are particularly susceptible to this problem because of the challenge of accurately accounting for population structure. Therefore, increasing emphasis on use of multiethnic samples in genetic studies requires reevaluation of best practices, given data currently available. Typical strategies have been to compute allele frequencies from the sample, or to use marker allele frequencies determined by admixture proportions averaged over the entire sample. However, admixture proportions vary among pedigrees and throughout the genome in a family-specific manner. Here, we evaluate several approaches to model admixture in linkage analysis, providing different levels of detail about ancestral origin. To perform our evaluations, for specification of marker allele frequencies, we used data on 67 Caribbean Hispanic admixed families from the Alzheimer's Disease Sequencing Project. Our results show that choice of admixture model has an effect on the linkage analysis results. Variant-specific admixture proportions, computed for individual families, provide the most detailed regional admixture estimates, and, as such, are the most appropriate allele frequencies for linkage analysis. This likely decreases the number of false-positive results, and is straightforward to implement.
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Affiliation(s)
- Rafael A Nafikov
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington
| | - Alejandro Q Nato
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington
| | - Harkirat Sohi
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington
| | - Bowen Wang
- Department of Statistics, University of Washington, Seattle, Washington
| | - Lisa Brown
- Department of Biostatistics, University of Washington, Seattle, Washington
| | - Andrea R Horimoto
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington
| | | | - Sandra M Barral
- Department of Neurology, Columbia University, New York, Washington
| | - Giuseppe Tosto
- Department of Neurology, Columbia University, New York, Washington
| | - Richard P Mayeux
- Department of Neurology, Columbia University, New York, Washington
| | - Timothy A Thornton
- Department of Biostatistics, University of Washington, Seattle, Washington
| | - Elizabeth Blue
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington
| | - Ellen M Wijsman
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington.,Department of Biostatistics, University of Washington, Seattle, Washington
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4
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Blue EE, Bis JC, Dorschner MO, Tsuang D, Barral SM, Beecham G, Below JE, Bush WS, Butkiewicz M, Cruchaga C, DeStefano A, Farrer LA, Goate A, Haines J, Jaworski J, Jun G, Kunkle B, Kuzma A, Lee JJ, Lunetta K, Ma Y, Martin E, Naj A, Nato AQ, Navas P, Nguyen H, Reitz C, Reyes D, Salerno W, Schellenberg GD, Seshadri S, Sohi H, Thornton TA, Valladares O, van Duijn C, Vardarajan BN, Wang LS, Boerwinkle E, Dupuis J, Pericak-Vance MA, Mayeux R, Wijsman EM. Genetic Variation in Genes Underlying Diverse Dementias May Explain a Small Proportion of Cases in the Alzheimer's Disease Sequencing Project. Dement Geriatr Cogn Disord 2018; 45:1-17. [PMID: 29486463 PMCID: PMC5971141 DOI: 10.1159/000485503] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 11/20/2017] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND/AIMS The Alzheimer's Disease Sequencing Project (ADSP) aims to identify novel genes influencing Alzheimer's disease (AD). Variants within genes known to cause dementias other than AD have previously been associated with AD risk. We describe evidence of co-segregation and associations between variants in dementia genes and clinically diagnosed AD within the ADSP. METHODS We summarize the properties of known pathogenic variants within dementia genes, describe the co-segregation of variants annotated as "pathogenic" in ClinVar and new candidates observed in ADSP families, and test for associations between rare variants in dementia genes in the ADSP case-control study. The participants were clinically evaluated for AD, and they represent European, Caribbean Hispanic, and isolate Dutch populations. RESULTS/CONCLUSIONS Pathogenic variants in dementia genes were predominantly rare and conserved coding changes. Pathogenic variants within ARSA, CSF1R, and GRN were observed, and candidate variants in GRN and CHMP2B were nominated in ADSP families. An independent case-control study provided evidence of an association between variants in TREM2, APOE, ARSA, CSF1R, PSEN1, and MAPT and risk of AD. Variants in genes which cause dementing disorders may influence the clinical diagnosis of AD in a small proportion of cases within the ADSP.
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Affiliation(s)
| | | | | | - Debby Tsuang
- University of Washington
- Veterans Administration Puget Sound Health Care
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Eric Boerwinkle
- Baylor College of Medicine
- University of Texas Health Sciences Center at Houston
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5
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Turner AM, Facelli JC, Jaspers M, Wetter T, Pfeifer D, Gatewood LC, Adam T, Li Y, Lin MC, Evans RS, Beukenhorst A, Theodoore van Mens HJ, Tensen E, Bock C, Fendrich L, Seitz P, Suleder J, Aldekhyyel R, Bridgeman K, Hu Z, Sattler A, Guo SY, Mohaimenul IM, Anggraini Ningrum DN, Tung HR, Bian J, Plasek JM, Rommel C, Burke J, Sohi H. Solving Interoperability in Translational Health. Perspectives of Students from the International Partnership in Health Informatics Education (IPHIE) 2016 Master Class. Appl Clin Inform 2017; 8:651-659. [PMID: 28636063 PMCID: PMC6241746 DOI: 10.4338/aci-2017-01-cr-0012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Accepted: 04/14/2017] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND In the summer of 2016 an international group of biomedical and health informatics faculty and graduate students gathered for the 16th meeting of the International Partnership in Health Informatics Education (IPHIE) masterclass at the University of Utah campus in Salt Lake City, Utah. This international biomedical and health informatics workshop was created to share knowledge and explore issues in biomedical health informatics (BHI). OBJECTIVE The goal of this paper is to summarize the discussions of biomedical and health informatics graduate students who were asked to define interoperability, and make critical observations to gather insight on how to improve biomedical education. METHODS Students were assigned to one of four groups and asked to define interoperability and explore potential solutions to current problems of interoperability in health care. RESULTS We summarize here the student reports on the importance and possible solutions to the "interoperability problem" in biomedical informatics. Reports are provided from each of the four groups of highly qualified graduate students from leading BHI programs in the US, Europe and Asia. CONCLUSION International workshops such as IPHIE provide a unique opportunity for graduate student learning and knowledge sharing. BHI faculty are encouraged to incorporate into their curriculum opportunities to exercise and strengthen student critical thinking to prepare our students for solving health informatics problems in the future.
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Affiliation(s)
- Anne M. Turner
- Department of Biomedical Informatics and Medical Education, University of Washington
| | - Julio C. Facelli
- Department of Biomedical Informatics, University of Utah and Intermountain Healthcare
| | | | - Thomas Wetter
- Department of Medical Informatics, University of Heidelberg, Heidelberg, Germany
| | - Daniel Pfeifer
- Department of Information Technology, Hochschule Heilbronn, Heilbronn, Germany
| | | | - Terry Adam
- Institute for Health Informatics, University of Minnesota
| | - YuChuan Li
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Ming-Chin Lin
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - R. Scott Evans
- Department of Biomedical Informatics, University of Utah and Intermountain Healthcare
| | | | | | - Esmée Tensen
- Amsterdam Medical Center, Amsterdam, Netherlands
| | - Christian Bock
- Department of Medical Informatics, University of Heidelberg, Heidelberg, Germany
| | - Laura Fendrich
- Department of Medical Informatics, University of Heidelberg, Heidelberg, Germany
| | - Peter Seitz
- Department of Medical Informatics, University of Heidelberg, Heidelberg, Germany
| | - Julian Suleder
- Department of Medical Informatics, University of Heidelberg, Heidelberg, Germany
| | | | - Kent Bridgeman
- Institute for Health Informatics, University of Minnesota
| | - Zhen Hu
- Institute for Health Informatics, University of Minnesota
| | - Aaron Sattler
- Institute for Health Informatics, University of Minnesota
| | - Shin-Yi Guo
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Islam Md. Mohaimenul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Dina Nur Anggraini Ningrum
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Hsin-Ru Tung
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Jiantao Bian
- Department of Biomedical Informatics, University of Utah and Intermountain Healthcare
| | - Joseph M. Plasek
- Department of Biomedical Informatics, University of Utah and Intermountain Healthcare
| | - Casey Rommel
- Department of Biomedical Informatics, University of Utah and Intermountain Healthcare
| | - Juandalyn Burke
- Department of Biomedical Informatics and Medical Education, University of Washington
| | - Harkirat Sohi
- Department of Biomedical Informatics and Medical Education, University of Washington
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Chapman NH, Nato AQ, Bernier R, Ankenman K, Sohi H, Munson J, Patowary A, Archer M, Blue EM, Webb SJ, Coon H, Raskind WH, Brkanac Z, Wijsman EM. Whole exome sequencing in extended families with autism spectrum disorder implicates four candidate genes. Hum Genet 2015. [PMID: 26204995 DOI: 10.1007/s00439-015-1585-y] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Autism spectrum disorders (ASDs) are a group of neurodevelopmental disorders, characterized by impairment in communication and social interactions, and by repetitive behaviors. ASDs are highly heritable, and estimates of the number of risk loci range from hundreds to >1000. We considered 7 extended families (size 12-47 individuals), each with ≥3 individuals affected by ASD. All individuals were genotyped with dense SNP panels. A small subset of each family was typed with whole exome sequence (WES). We used a 3-step approach for variant identification. First, we used family-specific parametric linkage analysis of the SNP data to identify regions of interest. Second, we filtered variants in these regions based on frequency and function, obtaining exactly 200 candidates. Third, we compared two approaches to narrowing this list further. We used information from the SNP data to impute exome variant dosages into those without WES. We regressed affected status on variant allele dosage, using pedigree-based kinship matrices to account for relationships. The p value for the test of the null hypothesis that variant allele dosage is unrelated to phenotype was used to indicate strength of evidence supporting the variant. A cutoff of p = 0.05 gave 28 variants. As an alternative third filter, we required Mendelian inheritance in those with WES, resulting in 70 variants. The imputation- and association-based approach was effective. We identified four strong candidate genes for ASD (SEZ6L, HISPPD1, FEZF1, SAMD11), all of which have been previously implicated in other studies, or have a strong biological argument for their relevance.
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Affiliation(s)
- Nicola H Chapman
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, WA, USA
| | - Alejandro Q Nato
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, WA, USA
| | - Raphael Bernier
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
| | - Katy Ankenman
- Department of Psychiatry, University of California, San Francisco, CA, USA
| | - Harkirat Sohi
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, WA, USA
| | - Jeff Munson
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA.,Center on Child Development and Disability, University of Washington, Seattle, WA, USA
| | - Ashok Patowary
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
| | - Marilyn Archer
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
| | - Elizabeth M Blue
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, WA, USA
| | - Sara Jane Webb
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA.,Center on Child Development and Disability, University of Washington, Seattle, WA, USA
| | - Hilary Coon
- Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA.,Department of Psychiatry, School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Wendy H Raskind
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, WA, USA.,Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA.,Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Zoran Brkanac
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
| | - Ellen M Wijsman
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, WA, USA. .,Department of Biostatistics, University of Washington, Seattle, WA, USA. .,Department of Genome Sciences, University of Washington, Seattle, WA, USA. .,University of Washington, University of Washington Tower, T15, 4333 Brooklyn Ave, NE, BOX 359460, Seattle, WA, 98195-9460, USA.
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