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Functional annotation of genomic variants in studies of late-onset Alzheimer's disease.


ABSTRACT: Motivation:Annotation of genomic variants is an increasingly important and complex part of the analysis of sequence-based genomic analyses. Computational predictions of variant function are routinely incorporated into gene-based analyses of rare-variants, though to date most studies use limited information for assessing variant function that is often agnostic of the disease being studied. Results:In this work, we outline an annotation process motivated by the Alzheimer's Disease Sequencing Project, illustrate the impact of including tissue-specific transcript sets and sources of gene regulatory information and assess the potential impact of changing genomic builds on the annotation process. While these factors only impact a small proportion of total variant annotations (?5%), they influence the potential analysis of a large fraction of genes (?25%). Availability and implementation:Individual variant annotations are available via the NIAGADS GenomicsDB, at https://www.niagads.org/genomics/ tools-and-software/databases/genomics-database. Annotations are also available for bulk download at https://www.niagads.org/datasets. Annotation processing software is available at http://www.icompbio.net/resources/software-and-downloads/. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Butkiewicz M 

PROVIDER: S-EPMC6084586 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Functional annotation of genomic variants in studies of late-onset Alzheimer's disease.

Butkiewicz Mariusz M   Blue Elizabeth E EE   Leung Yuk Yee YY   Jian Xueqiu X   Marcora Edoardo E   Renton Alan E AE   Kuzma Amanda A   Wang Li-San LS   Koboldt Daniel C DC   Haines Jonathan L JL   Bush William S WS  

Bioinformatics (Oxford, England) 20180801 16


<h4>Motivation</h4>Annotation of genomic variants is an increasingly important and complex part of the analysis of sequence-based genomic analyses. Computational predictions of variant function are routinely incorporated into gene-based analyses of rare-variants, though to date most studies use limited information for assessing variant function that is often agnostic of the disease being studied.<h4>Results</h4>In this work, we outline an annotation process motivated by the Alzheimer's Disease S  ...[more]

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