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A systematic variant annotation approach for ranking genes associated with autism spectrum disorders
BACKGROUND: The search for genetic factors underlying autism spectrum disorders (ASD) has led to the identification of hundreds of genes containing thousands of variants that differ in mode of inheritance, effect size, frequency, and function. A major challenge involves assessing the collective evid...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5075177/ https://www.ncbi.nlm.nih.gov/pubmed/27790361 http://dx.doi.org/10.1186/s13229-016-0103-y |
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author | Larsen, Eric Menashe, Idan Ziats, Mark N. Pereanu, Wayne Packer, Alan Banerjee-Basu, Sharmila |
author_facet | Larsen, Eric Menashe, Idan Ziats, Mark N. Pereanu, Wayne Packer, Alan Banerjee-Basu, Sharmila |
author_sort | Larsen, Eric |
collection | PubMed |
description | BACKGROUND: The search for genetic factors underlying autism spectrum disorders (ASD) has led to the identification of hundreds of genes containing thousands of variants that differ in mode of inheritance, effect size, frequency, and function. A major challenge involves assessing the collective evidence in an unbiased, systematic manner for their functional relevance. METHODS: Here, we describe a scoring algorithm for prioritization of candidate genes based on the cumulative strength of evidence for each ASD-associated variant cataloged in AutDB (also known as SFARI Gene). We retrieved data from 889 publications to generate a dataset of 2187 rare and 711 common variants distributed across 461 genes implicated in ASD. Each individual variant was manually annotated with multiple attributes extracted from the original report, followed by score assignment using a set of standardized parameters yielding a single score for each gene. RESULTS: There was a wide variation in scores; SHANK3, CHD8, and ADNP had distinctly higher scores than all other genes in the dataset. Our gene scores were significantly correlated with other recently published rankings of ASD genes (R (Spearman) = 0.40–0.63; p< 0.0001), providing support for our scoring algorithm. CONCLUSIONS: This new resource, which is freely available, for the first time aggregates on one-platform variants identified from various study types (simplex, multiplex, multigenerational, and consanguineous families), from both common and rare variants, and also incorporates their putative functional consequences to arrive at a genetically and biologically driven ranking scheme. This work represents a major step in moving from simply cataloging autism variants to using data-driven approaches to gain insight into their significance. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13229-016-0103-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5075177 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-50751772016-10-27 A systematic variant annotation approach for ranking genes associated with autism spectrum disorders Larsen, Eric Menashe, Idan Ziats, Mark N. Pereanu, Wayne Packer, Alan Banerjee-Basu, Sharmila Mol Autism Research BACKGROUND: The search for genetic factors underlying autism spectrum disorders (ASD) has led to the identification of hundreds of genes containing thousands of variants that differ in mode of inheritance, effect size, frequency, and function. A major challenge involves assessing the collective evidence in an unbiased, systematic manner for their functional relevance. METHODS: Here, we describe a scoring algorithm for prioritization of candidate genes based on the cumulative strength of evidence for each ASD-associated variant cataloged in AutDB (also known as SFARI Gene). We retrieved data from 889 publications to generate a dataset of 2187 rare and 711 common variants distributed across 461 genes implicated in ASD. Each individual variant was manually annotated with multiple attributes extracted from the original report, followed by score assignment using a set of standardized parameters yielding a single score for each gene. RESULTS: There was a wide variation in scores; SHANK3, CHD8, and ADNP had distinctly higher scores than all other genes in the dataset. Our gene scores were significantly correlated with other recently published rankings of ASD genes (R (Spearman) = 0.40–0.63; p< 0.0001), providing support for our scoring algorithm. CONCLUSIONS: This new resource, which is freely available, for the first time aggregates on one-platform variants identified from various study types (simplex, multiplex, multigenerational, and consanguineous families), from both common and rare variants, and also incorporates their putative functional consequences to arrive at a genetically and biologically driven ranking scheme. This work represents a major step in moving from simply cataloging autism variants to using data-driven approaches to gain insight into their significance. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13229-016-0103-y) contains supplementary material, which is available to authorized users. BioMed Central 2016-10-21 /pmc/articles/PMC5075177/ /pubmed/27790361 http://dx.doi.org/10.1186/s13229-016-0103-y Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. |
spellingShingle | Research Larsen, Eric Menashe, Idan Ziats, Mark N. Pereanu, Wayne Packer, Alan Banerjee-Basu, Sharmila A systematic variant annotation approach for ranking genes associated with autism spectrum disorders |
title | A systematic variant annotation approach for ranking genes associated with autism spectrum disorders |
title_full | A systematic variant annotation approach for ranking genes associated with autism spectrum disorders |
title_fullStr | A systematic variant annotation approach for ranking genes associated with autism spectrum disorders |
title_full_unstemmed | A systematic variant annotation approach for ranking genes associated with autism spectrum disorders |
title_short | A systematic variant annotation approach for ranking genes associated with autism spectrum disorders |
title_sort | systematic variant annotation approach for ranking genes associated with autism spectrum disorders |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5075177/ https://www.ncbi.nlm.nih.gov/pubmed/27790361 http://dx.doi.org/10.1186/s13229-016-0103-y |
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