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Decision tree analysis of genetic risk for clinically heterogeneous Alzheimer’s disease

BACKGROUND: Heritability of Alzheimer’s disease (AD) is estimated at 74% and genetic contributors have been widely sought. The ε4 allele of apolipoprotein E (APOE) remains the strongest common risk factor for AD, with numerous other common variants contributing only modest risk for disease. Variabil...

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Autores principales: Yokoyama, Jennifer S, Bonham, Luke W, Sears, Renee L, Klein, Eric, Karydas, Anna, Kramer, Joel H, Miller, Bruce L, Coppola, Giovanni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4459447/
https://www.ncbi.nlm.nih.gov/pubmed/25880661
http://dx.doi.org/10.1186/s12883-015-0304-6
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author Yokoyama, Jennifer S
Bonham, Luke W
Sears, Renee L
Klein, Eric
Karydas, Anna
Kramer, Joel H
Miller, Bruce L
Coppola, Giovanni
author_facet Yokoyama, Jennifer S
Bonham, Luke W
Sears, Renee L
Klein, Eric
Karydas, Anna
Kramer, Joel H
Miller, Bruce L
Coppola, Giovanni
author_sort Yokoyama, Jennifer S
collection PubMed
description BACKGROUND: Heritability of Alzheimer’s disease (AD) is estimated at 74% and genetic contributors have been widely sought. The ε4 allele of apolipoprotein E (APOE) remains the strongest common risk factor for AD, with numerous other common variants contributing only modest risk for disease. Variability in clinical presentation of AD, which is typically amnestic (AmnAD) but can less commonly involve visuospatial, language and/or dysexecutive syndromes (atypical or AtAD), further complicates genetic analyses. Taking a multi-locus approach may increase the ability to identify individuals at highest risk for any AD syndrome. In this study, we sought to develop and investigate the utility of a multi-variant genetic risk assessment on a cohort of phenotypically heterogeneous patients with sporadic AD clinical diagnoses. METHODS: We genotyped 75 variants in our cohort and, using a two-staged study design, we developed a 17-marker AD risk score in a Discovery cohort (n = 59 cases, n = 133 controls) then assessed its utility in a second Validation cohort (n = 126 cases, n = 150 controls). We also performed a data-driven decision tree analysis to identify genetic and/or demographic criteria that are most useful for accurately differentiating all AD cases from controls. RESULTS: We confirmed APOE ε4 as a strong risk factor for AD. A 17-marker risk panel predicted AD significantly better than APOE genotype alone (P < 0.00001) in the Discovery cohort, but not in the Validation cohort. In decision tree analyses, we found that APOE best differentiated cases from controls only in AmnAD but not AtAD. In AtAD, HFE SNP rs1799945 was the strongest predictor of disease; variation in HFE has previously been implicated in AD risk in non-ε4 carriers. CONCLUSIONS: Our study suggests that APOE ε4 remains the best predictor of broad AD risk when compared to multiple other genetic factors with modest effects, that phenotypic heterogeneity in broad AD can complicate simple polygenic risk modeling, and supports the association between HFE and AD risk in individuals without APOE ε4. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12883-015-0304-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-44594472015-06-09 Decision tree analysis of genetic risk for clinically heterogeneous Alzheimer’s disease Yokoyama, Jennifer S Bonham, Luke W Sears, Renee L Klein, Eric Karydas, Anna Kramer, Joel H Miller, Bruce L Coppola, Giovanni BMC Neurol Research Article BACKGROUND: Heritability of Alzheimer’s disease (AD) is estimated at 74% and genetic contributors have been widely sought. The ε4 allele of apolipoprotein E (APOE) remains the strongest common risk factor for AD, with numerous other common variants contributing only modest risk for disease. Variability in clinical presentation of AD, which is typically amnestic (AmnAD) but can less commonly involve visuospatial, language and/or dysexecutive syndromes (atypical or AtAD), further complicates genetic analyses. Taking a multi-locus approach may increase the ability to identify individuals at highest risk for any AD syndrome. In this study, we sought to develop and investigate the utility of a multi-variant genetic risk assessment on a cohort of phenotypically heterogeneous patients with sporadic AD clinical diagnoses. METHODS: We genotyped 75 variants in our cohort and, using a two-staged study design, we developed a 17-marker AD risk score in a Discovery cohort (n = 59 cases, n = 133 controls) then assessed its utility in a second Validation cohort (n = 126 cases, n = 150 controls). We also performed a data-driven decision tree analysis to identify genetic and/or demographic criteria that are most useful for accurately differentiating all AD cases from controls. RESULTS: We confirmed APOE ε4 as a strong risk factor for AD. A 17-marker risk panel predicted AD significantly better than APOE genotype alone (P < 0.00001) in the Discovery cohort, but not in the Validation cohort. In decision tree analyses, we found that APOE best differentiated cases from controls only in AmnAD but not AtAD. In AtAD, HFE SNP rs1799945 was the strongest predictor of disease; variation in HFE has previously been implicated in AD risk in non-ε4 carriers. CONCLUSIONS: Our study suggests that APOE ε4 remains the best predictor of broad AD risk when compared to multiple other genetic factors with modest effects, that phenotypic heterogeneity in broad AD can complicate simple polygenic risk modeling, and supports the association between HFE and AD risk in individuals without APOE ε4. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12883-015-0304-6) contains supplementary material, which is available to authorized users. BioMed Central 2015-03-28 /pmc/articles/PMC4459447/ /pubmed/25880661 http://dx.doi.org/10.1186/s12883-015-0304-6 Text en © Yokoyama et al. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Yokoyama, Jennifer S
Bonham, Luke W
Sears, Renee L
Klein, Eric
Karydas, Anna
Kramer, Joel H
Miller, Bruce L
Coppola, Giovanni
Decision tree analysis of genetic risk for clinically heterogeneous Alzheimer’s disease
title Decision tree analysis of genetic risk for clinically heterogeneous Alzheimer’s disease
title_full Decision tree analysis of genetic risk for clinically heterogeneous Alzheimer’s disease
title_fullStr Decision tree analysis of genetic risk for clinically heterogeneous Alzheimer’s disease
title_full_unstemmed Decision tree analysis of genetic risk for clinically heterogeneous Alzheimer’s disease
title_short Decision tree analysis of genetic risk for clinically heterogeneous Alzheimer’s disease
title_sort decision tree analysis of genetic risk for clinically heterogeneous alzheimer’s disease
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4459447/
https://www.ncbi.nlm.nih.gov/pubmed/25880661
http://dx.doi.org/10.1186/s12883-015-0304-6
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