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Can Genetic Analysis of Putative Blood Alzheimer’s Disease Biomarkers Lead to Identification of Susceptibility Loci?

Although 24 Alzheimer’s disease (AD) risk loci have been reliably identified, a large portion of the predicted heritability for AD remains unexplained. It is expected that additional loci of small effect will be identified with an increased sample size. However, the cost of a significant increase in...

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Autores principales: Barber, Robert C., Phillips, Nicole R., Tilson, Jeffrey L., Huebinger, Ryan M., Shewale, Shantanu J., Koenig, Jessica L., Mitchel, Jeffrey S., O’Bryant, Sid E., Waring, Stephen C., Diaz-Arrastia, Ramon, Chasse, Scott, Wilhelmsen, Kirk C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4666664/
https://www.ncbi.nlm.nih.gov/pubmed/26625115
http://dx.doi.org/10.1371/journal.pone.0142360
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author Barber, Robert C.
Phillips, Nicole R.
Tilson, Jeffrey L.
Huebinger, Ryan M.
Shewale, Shantanu J.
Koenig, Jessica L.
Mitchel, Jeffrey S.
O’Bryant, Sid E.
Waring, Stephen C.
Diaz-Arrastia, Ramon
Chasse, Scott
Wilhelmsen, Kirk C.
author_facet Barber, Robert C.
Phillips, Nicole R.
Tilson, Jeffrey L.
Huebinger, Ryan M.
Shewale, Shantanu J.
Koenig, Jessica L.
Mitchel, Jeffrey S.
O’Bryant, Sid E.
Waring, Stephen C.
Diaz-Arrastia, Ramon
Chasse, Scott
Wilhelmsen, Kirk C.
author_sort Barber, Robert C.
collection PubMed
description Although 24 Alzheimer’s disease (AD) risk loci have been reliably identified, a large portion of the predicted heritability for AD remains unexplained. It is expected that additional loci of small effect will be identified with an increased sample size. However, the cost of a significant increase in Case-Control sample size is prohibitive. The current study tests whether exploring the genetic basis of endophenotypes, in this case based on putative blood biomarkers for AD, can accelerate the identification of susceptibility loci using modest sample sizes. Each endophenotype was used as the outcome variable in an independent GWAS. Endophenotypes were based on circulating concentrations of proteins that contributed significantly to a published blood-based predictive algorithm for AD. Endophenotypes included Monocyte Chemoattractant Protein 1 (MCP1), Vascular Cell Adhesion Molecule 1 (VCAM1), Pancreatic Polypeptide (PP), Beta2 Microglobulin (B2M), Factor VII (F7), Adiponectin (ADN) and Tenascin C (TN-C). Across the seven endophenotypes, 47 SNPs were associated with outcome with a p-value ≤1x10(-7). Each signal was further characterized with respect to known genetic loci associated with AD. Signals for several endophenotypes were observed in the vicinity of CR1, MS4A6A/MS4A4E, PICALM, CLU, and PTK2B. The strongest signal was observed in association with Factor VII levels and was located within the F7 gene. Additional signals were observed in MAP3K13, ZNF320, ATP9B and TREM1. Conditional regression analyses suggested that the SNPs contributed to variation in protein concentration independent of AD status. The identification of two putatively novel AD loci (in the Factor VII and ATP9B genes), which have not been located in previous studies despite massive sample sizes, highlights the benefits of an endophenotypic approach for resolving the genetic basis for complex diseases. The coincidence of several of the endophenotypic signals with known AD loci may point to novel genetic interactions and should be further investigated.
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spelling pubmed-46666642015-12-10 Can Genetic Analysis of Putative Blood Alzheimer’s Disease Biomarkers Lead to Identification of Susceptibility Loci? Barber, Robert C. Phillips, Nicole R. Tilson, Jeffrey L. Huebinger, Ryan M. Shewale, Shantanu J. Koenig, Jessica L. Mitchel, Jeffrey S. O’Bryant, Sid E. Waring, Stephen C. Diaz-Arrastia, Ramon Chasse, Scott Wilhelmsen, Kirk C. PLoS One Research Article Although 24 Alzheimer’s disease (AD) risk loci have been reliably identified, a large portion of the predicted heritability for AD remains unexplained. It is expected that additional loci of small effect will be identified with an increased sample size. However, the cost of a significant increase in Case-Control sample size is prohibitive. The current study tests whether exploring the genetic basis of endophenotypes, in this case based on putative blood biomarkers for AD, can accelerate the identification of susceptibility loci using modest sample sizes. Each endophenotype was used as the outcome variable in an independent GWAS. Endophenotypes were based on circulating concentrations of proteins that contributed significantly to a published blood-based predictive algorithm for AD. Endophenotypes included Monocyte Chemoattractant Protein 1 (MCP1), Vascular Cell Adhesion Molecule 1 (VCAM1), Pancreatic Polypeptide (PP), Beta2 Microglobulin (B2M), Factor VII (F7), Adiponectin (ADN) and Tenascin C (TN-C). Across the seven endophenotypes, 47 SNPs were associated with outcome with a p-value ≤1x10(-7). Each signal was further characterized with respect to known genetic loci associated with AD. Signals for several endophenotypes were observed in the vicinity of CR1, MS4A6A/MS4A4E, PICALM, CLU, and PTK2B. The strongest signal was observed in association with Factor VII levels and was located within the F7 gene. Additional signals were observed in MAP3K13, ZNF320, ATP9B and TREM1. Conditional regression analyses suggested that the SNPs contributed to variation in protein concentration independent of AD status. The identification of two putatively novel AD loci (in the Factor VII and ATP9B genes), which have not been located in previous studies despite massive sample sizes, highlights the benefits of an endophenotypic approach for resolving the genetic basis for complex diseases. The coincidence of several of the endophenotypic signals with known AD loci may point to novel genetic interactions and should be further investigated. Public Library of Science 2015-12-01 /pmc/articles/PMC4666664/ /pubmed/26625115 http://dx.doi.org/10.1371/journal.pone.0142360 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
spellingShingle Research Article
Barber, Robert C.
Phillips, Nicole R.
Tilson, Jeffrey L.
Huebinger, Ryan M.
Shewale, Shantanu J.
Koenig, Jessica L.
Mitchel, Jeffrey S.
O’Bryant, Sid E.
Waring, Stephen C.
Diaz-Arrastia, Ramon
Chasse, Scott
Wilhelmsen, Kirk C.
Can Genetic Analysis of Putative Blood Alzheimer’s Disease Biomarkers Lead to Identification of Susceptibility Loci?
title Can Genetic Analysis of Putative Blood Alzheimer’s Disease Biomarkers Lead to Identification of Susceptibility Loci?
title_full Can Genetic Analysis of Putative Blood Alzheimer’s Disease Biomarkers Lead to Identification of Susceptibility Loci?
title_fullStr Can Genetic Analysis of Putative Blood Alzheimer’s Disease Biomarkers Lead to Identification of Susceptibility Loci?
title_full_unstemmed Can Genetic Analysis of Putative Blood Alzheimer’s Disease Biomarkers Lead to Identification of Susceptibility Loci?
title_short Can Genetic Analysis of Putative Blood Alzheimer’s Disease Biomarkers Lead to Identification of Susceptibility Loci?
title_sort can genetic analysis of putative blood alzheimer’s disease biomarkers lead to identification of susceptibility loci?
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4666664/
https://www.ncbi.nlm.nih.gov/pubmed/26625115
http://dx.doi.org/10.1371/journal.pone.0142360
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