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Role of Genetic Interactions in Alzheimer’s Disease: Lessons from Long Life Family Study (LLFS)

Experimental and clinical studies of Alzheimer’s disease (AD) provide plentiful evidence of AD heterogeneity and involvement of many interacting genes and pathways in regulation of AD-related traits. However, detailed mechanisms of genetic interactions (GxG) involved in AD remain largely unknown. Un...

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Autores principales: Yashin, Anatoliy, Wu, Dequing, Arbeev, Konstantin, Stallard, Eric, Tan, Qihua, Kulminski, Alexander, Feitosa, Mary, Ukraintseva, Svetlana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7743337/
http://dx.doi.org/10.1093/geroni/igaa057.1589
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author Yashin, Anatoliy
Wu, Dequing
Arbeev, Konstantin
Stallard, Eric
Tan, Qihua
Kulminski, Alexander
Feitosa, Mary
Ukraintseva, Svetlana
author_facet Yashin, Anatoliy
Wu, Dequing
Arbeev, Konstantin
Stallard, Eric
Tan, Qihua
Kulminski, Alexander
Feitosa, Mary
Ukraintseva, Svetlana
author_sort Yashin, Anatoliy
collection PubMed
description Experimental and clinical studies of Alzheimer’s disease (AD) provide plentiful evidence of AD heterogeneity and involvement of many interacting genes and pathways in regulation of AD-related traits. However, detailed mechanisms of genetic interactions (GxG) involved in AD remain largely unknown. Uncovering hidden patterns of such interactions from human data will help better understand the nature of AD heterogeneity and find new targets for AD prevention. In this paper, we applied a newly developed method of evaluating joint GxG effects on AD to analysis of the Long Life Family Study data. The analysis included several steps: (i) selecting candidate genes from stress response pathways that are thought to be involved in AD; (ii) estimating interaction effects of SNP-pairs on AD risk, and selecting the top interacting SNPs; (iii) running GWAS-like interaction analysis for SNP-pairs, with one SNP fixed; (iv) using characteristics of the detected SNP-pairs interactions to construct the SNP-specific Interaction Polygenic Risk Scores (IPRS); and (v) evaluating the effects of IPRSs on AD. We found that SNP-specific IPRS have highly significant effects on AD risk. For most SNPs involved in the significant interaction effects on AD, their individual effects were statistically not significant. Male and female analyses yielded different subsets of the top interacting SNPs. These results support major role of genetic interactions in heterogeneity of AD, and indicate that AD mechanisms can involve different combinations of the interacting genetic variants in males and females, which may point to different pathways of resistance/response to stressors in two genders.
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spelling pubmed-77433372020-12-21 Role of Genetic Interactions in Alzheimer’s Disease: Lessons from Long Life Family Study (LLFS) Yashin, Anatoliy Wu, Dequing Arbeev, Konstantin Stallard, Eric Tan, Qihua Kulminski, Alexander Feitosa, Mary Ukraintseva, Svetlana Innov Aging Abstracts Experimental and clinical studies of Alzheimer’s disease (AD) provide plentiful evidence of AD heterogeneity and involvement of many interacting genes and pathways in regulation of AD-related traits. However, detailed mechanisms of genetic interactions (GxG) involved in AD remain largely unknown. Uncovering hidden patterns of such interactions from human data will help better understand the nature of AD heterogeneity and find new targets for AD prevention. In this paper, we applied a newly developed method of evaluating joint GxG effects on AD to analysis of the Long Life Family Study data. The analysis included several steps: (i) selecting candidate genes from stress response pathways that are thought to be involved in AD; (ii) estimating interaction effects of SNP-pairs on AD risk, and selecting the top interacting SNPs; (iii) running GWAS-like interaction analysis for SNP-pairs, with one SNP fixed; (iv) using characteristics of the detected SNP-pairs interactions to construct the SNP-specific Interaction Polygenic Risk Scores (IPRS); and (v) evaluating the effects of IPRSs on AD. We found that SNP-specific IPRS have highly significant effects on AD risk. For most SNPs involved in the significant interaction effects on AD, their individual effects were statistically not significant. Male and female analyses yielded different subsets of the top interacting SNPs. These results support major role of genetic interactions in heterogeneity of AD, and indicate that AD mechanisms can involve different combinations of the interacting genetic variants in males and females, which may point to different pathways of resistance/response to stressors in two genders. Oxford University Press 2020-12-16 /pmc/articles/PMC7743337/ http://dx.doi.org/10.1093/geroni/igaa057.1589 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America. http://creativecommons.org/licenses/by/4.0/ 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Abstracts
Yashin, Anatoliy
Wu, Dequing
Arbeev, Konstantin
Stallard, Eric
Tan, Qihua
Kulminski, Alexander
Feitosa, Mary
Ukraintseva, Svetlana
Role of Genetic Interactions in Alzheimer’s Disease: Lessons from Long Life Family Study (LLFS)
title Role of Genetic Interactions in Alzheimer’s Disease: Lessons from Long Life Family Study (LLFS)
title_full Role of Genetic Interactions in Alzheimer’s Disease: Lessons from Long Life Family Study (LLFS)
title_fullStr Role of Genetic Interactions in Alzheimer’s Disease: Lessons from Long Life Family Study (LLFS)
title_full_unstemmed Role of Genetic Interactions in Alzheimer’s Disease: Lessons from Long Life Family Study (LLFS)
title_short Role of Genetic Interactions in Alzheimer’s Disease: Lessons from Long Life Family Study (LLFS)
title_sort role of genetic interactions in alzheimer’s disease: lessons from long life family study (llfs)
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7743337/
http://dx.doi.org/10.1093/geroni/igaa057.1589
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