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Validating a Genomic Convergence and Network Analysis Approach Using Association Analysis of Identified Candidate Genes in Alzheimer’s Disease
Previously, we demonstrated an integrated genomic convergence and network analysis approach to identify the candidate genes associated with the complex neurodegenerative disorder, Alzheimer’s disease (AD). Here, we performed a pilot study to validate the in silico approach by studying the associatio...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8696000/ https://www.ncbi.nlm.nih.gov/pubmed/34956307 http://dx.doi.org/10.3389/fgene.2021.722221 |
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author | Talwar, Puneet Kushwaha, Suman Rawat, Chitra Kaur, Harpreet Srivastava, Ankit Agarwal, Rachna Chandna, Puneet Tucci, Paolo Saso, Luciano Kukreti, Ritushree |
author_facet | Talwar, Puneet Kushwaha, Suman Rawat, Chitra Kaur, Harpreet Srivastava, Ankit Agarwal, Rachna Chandna, Puneet Tucci, Paolo Saso, Luciano Kukreti, Ritushree |
author_sort | Talwar, Puneet |
collection | PubMed |
description | Previously, we demonstrated an integrated genomic convergence and network analysis approach to identify the candidate genes associated with the complex neurodegenerative disorder, Alzheimer’s disease (AD). Here, we performed a pilot study to validate the in silico approach by studying the association of genetic variants from three identified critical genes, APOE, EGFR, and ACTB, with AD. A total of 103 patients with AD and 146 healthy controls were recruited. A total of 46 single-nucleotide polymorphisms (SNPs) spanning the three genes were genotyped, of which only 19 SNPs were included in the final analyses after excluding non-polymorphic and Hardy–Weinberg equilibrium-violating SNPs. Apart from our previously reported APOE ε4, four other SNPs in APOE (rs405509, rs7259620, −rs769449, and rs7256173), one in EGFR (rs6970262), and one in ACTB (rs852423) showed a significant association with AD (p < 0.05). Our results validate the reliability of genomic convergence and network analysis approach in identifying the AD-associated candidate genes. |
format | Online Article Text |
id | pubmed-8696000 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86960002021-12-24 Validating a Genomic Convergence and Network Analysis Approach Using Association Analysis of Identified Candidate Genes in Alzheimer’s Disease Talwar, Puneet Kushwaha, Suman Rawat, Chitra Kaur, Harpreet Srivastava, Ankit Agarwal, Rachna Chandna, Puneet Tucci, Paolo Saso, Luciano Kukreti, Ritushree Front Genet Genetics Previously, we demonstrated an integrated genomic convergence and network analysis approach to identify the candidate genes associated with the complex neurodegenerative disorder, Alzheimer’s disease (AD). Here, we performed a pilot study to validate the in silico approach by studying the association of genetic variants from three identified critical genes, APOE, EGFR, and ACTB, with AD. A total of 103 patients with AD and 146 healthy controls were recruited. A total of 46 single-nucleotide polymorphisms (SNPs) spanning the three genes were genotyped, of which only 19 SNPs were included in the final analyses after excluding non-polymorphic and Hardy–Weinberg equilibrium-violating SNPs. Apart from our previously reported APOE ε4, four other SNPs in APOE (rs405509, rs7259620, −rs769449, and rs7256173), one in EGFR (rs6970262), and one in ACTB (rs852423) showed a significant association with AD (p < 0.05). Our results validate the reliability of genomic convergence and network analysis approach in identifying the AD-associated candidate genes. Frontiers Media S.A. 2021-12-09 /pmc/articles/PMC8696000/ /pubmed/34956307 http://dx.doi.org/10.3389/fgene.2021.722221 Text en Copyright © 2021 Talwar, Kushwaha, Rawat, Kaur, Srivastava, Agarwal, Chandna, Tucci, Saso and Kukreti. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Talwar, Puneet Kushwaha, Suman Rawat, Chitra Kaur, Harpreet Srivastava, Ankit Agarwal, Rachna Chandna, Puneet Tucci, Paolo Saso, Luciano Kukreti, Ritushree Validating a Genomic Convergence and Network Analysis Approach Using Association Analysis of Identified Candidate Genes in Alzheimer’s Disease |
title | Validating a Genomic Convergence and Network Analysis Approach Using Association Analysis of Identified Candidate Genes in Alzheimer’s Disease |
title_full | Validating a Genomic Convergence and Network Analysis Approach Using Association Analysis of Identified Candidate Genes in Alzheimer’s Disease |
title_fullStr | Validating a Genomic Convergence and Network Analysis Approach Using Association Analysis of Identified Candidate Genes in Alzheimer’s Disease |
title_full_unstemmed | Validating a Genomic Convergence and Network Analysis Approach Using Association Analysis of Identified Candidate Genes in Alzheimer’s Disease |
title_short | Validating a Genomic Convergence and Network Analysis Approach Using Association Analysis of Identified Candidate Genes in Alzheimer’s Disease |
title_sort | validating a genomic convergence and network analysis approach using association analysis of identified candidate genes in alzheimer’s disease |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8696000/ https://www.ncbi.nlm.nih.gov/pubmed/34956307 http://dx.doi.org/10.3389/fgene.2021.722221 |
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