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Genome-wide epistasis analysis for Alzheimer’s disease and implications for genetic risk prediction

BACKGROUND: Single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies only explain part of the heritability of Alzheimer’s disease (AD). Epistasis has been considered as one of the main causes of “missing heritability” in AD. METHODS: We performed genome-wide epistasis scr...

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Autores principales: Wang, Hui, Bennett, David A., De Jager, Philip L., Zhang, Qing-Ye, Zhang, Hong-Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7934265/
https://www.ncbi.nlm.nih.gov/pubmed/33663605
http://dx.doi.org/10.1186/s13195-021-00794-8
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author Wang, Hui
Bennett, David A.
De Jager, Philip L.
Zhang, Qing-Ye
Zhang, Hong-Yu
author_facet Wang, Hui
Bennett, David A.
De Jager, Philip L.
Zhang, Qing-Ye
Zhang, Hong-Yu
author_sort Wang, Hui
collection PubMed
description BACKGROUND: Single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies only explain part of the heritability of Alzheimer’s disease (AD). Epistasis has been considered as one of the main causes of “missing heritability” in AD. METHODS: We performed genome-wide epistasis screening (N = 10,389) for the clinical diagnosis of AD using three popularly adopted methods. Subsequent analyses were performed to eliminate spurious associations caused by possible confounding factors. Then, candidate genetic interactions were examined for their co-expression in the brains of AD patients and analyzed for their association with intermediate AD phenotypes. Moreover, a new approach was developed to compile the epistasis risk factors into an epistasis risk score (ERS) based on multifactor dimensional reduction. Two independent datasets were used to evaluate the feasibility of ERSs in AD risk prediction. RESULTS: We identified 2 candidate genetic interactions with P(FDR) <  0.05 (RAMP3-SEMA3A and NSMCE1-DGKE/C17orf67) and another 5 genetic interactions with P(FDR) <  0.1. Co-expression between the identified interactions supported the existence of possible biological interactions underlying the observed statistical significance. Further association of candidate interactions with intermediate phenotypes helps explain the mechanisms of neuropathological alterations involved in AD. Importantly, we found that ERSs can identify high-risk individuals showing earlier onset of AD. Combined risk scores of SNPs and SNP-SNP interactions showed slightly but steadily increased AUC in predicting the clinical status of AD. CONCLUSIONS: In summary, we performed a genome-wide epistasis analysis to identify novel genetic interactions potentially implicated in AD. We found that ERS can serve as an indicator of the genetic risk of AD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13195-021-00794-8.
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spelling pubmed-79342652021-03-08 Genome-wide epistasis analysis for Alzheimer’s disease and implications for genetic risk prediction Wang, Hui Bennett, David A. De Jager, Philip L. Zhang, Qing-Ye Zhang, Hong-Yu Alzheimers Res Ther Research BACKGROUND: Single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies only explain part of the heritability of Alzheimer’s disease (AD). Epistasis has been considered as one of the main causes of “missing heritability” in AD. METHODS: We performed genome-wide epistasis screening (N = 10,389) for the clinical diagnosis of AD using three popularly adopted methods. Subsequent analyses were performed to eliminate spurious associations caused by possible confounding factors. Then, candidate genetic interactions were examined for their co-expression in the brains of AD patients and analyzed for their association with intermediate AD phenotypes. Moreover, a new approach was developed to compile the epistasis risk factors into an epistasis risk score (ERS) based on multifactor dimensional reduction. Two independent datasets were used to evaluate the feasibility of ERSs in AD risk prediction. RESULTS: We identified 2 candidate genetic interactions with P(FDR) <  0.05 (RAMP3-SEMA3A and NSMCE1-DGKE/C17orf67) and another 5 genetic interactions with P(FDR) <  0.1. Co-expression between the identified interactions supported the existence of possible biological interactions underlying the observed statistical significance. Further association of candidate interactions with intermediate phenotypes helps explain the mechanisms of neuropathological alterations involved in AD. Importantly, we found that ERSs can identify high-risk individuals showing earlier onset of AD. Combined risk scores of SNPs and SNP-SNP interactions showed slightly but steadily increased AUC in predicting the clinical status of AD. CONCLUSIONS: In summary, we performed a genome-wide epistasis analysis to identify novel genetic interactions potentially implicated in AD. We found that ERS can serve as an indicator of the genetic risk of AD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13195-021-00794-8. BioMed Central 2021-03-04 /pmc/articles/PMC7934265/ /pubmed/33663605 http://dx.doi.org/10.1186/s13195-021-00794-8 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Research
Wang, Hui
Bennett, David A.
De Jager, Philip L.
Zhang, Qing-Ye
Zhang, Hong-Yu
Genome-wide epistasis analysis for Alzheimer’s disease and implications for genetic risk prediction
title Genome-wide epistasis analysis for Alzheimer’s disease and implications for genetic risk prediction
title_full Genome-wide epistasis analysis for Alzheimer’s disease and implications for genetic risk prediction
title_fullStr Genome-wide epistasis analysis for Alzheimer’s disease and implications for genetic risk prediction
title_full_unstemmed Genome-wide epistasis analysis for Alzheimer’s disease and implications for genetic risk prediction
title_short Genome-wide epistasis analysis for Alzheimer’s disease and implications for genetic risk prediction
title_sort genome-wide epistasis analysis for alzheimer’s disease and implications for genetic risk prediction
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7934265/
https://www.ncbi.nlm.nih.gov/pubmed/33663605
http://dx.doi.org/10.1186/s13195-021-00794-8
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