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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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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. |
format | Online Article Text |
id | pubmed-4666664 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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