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Incorporating Non-Coding Annotations into Rare Variant Analysis

BACKGROUND: The success of collapsing methods which investigate the combined effect of rare variants on complex traits has so far been limited. The manner in which variants within a gene are selected prior to analysis has a crucial impact on this success, which has resulted in analyses conventionall...

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Autores principales: Richardson, Tom G., Campbell, Colin, Timpson, Nicholas J, Gaunt, Tom R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4851421/
https://www.ncbi.nlm.nih.gov/pubmed/27128317
http://dx.doi.org/10.1371/journal.pone.0154181
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author Richardson, Tom G.
Campbell, Colin
Timpson, Nicholas J
Gaunt, Tom R.
author_facet Richardson, Tom G.
Campbell, Colin
Timpson, Nicholas J
Gaunt, Tom R.
author_sort Richardson, Tom G.
collection PubMed
description BACKGROUND: The success of collapsing methods which investigate the combined effect of rare variants on complex traits has so far been limited. The manner in which variants within a gene are selected prior to analysis has a crucial impact on this success, which has resulted in analyses conventionally filtering variants according to their consequence. This study investigates whether an alternative approach to filtering, using annotations from recently developed bioinformatics tools, can aid these types of analyses in comparison to conventional approaches. METHODS & RESULTS: We conducted a candidate gene analysis using the UK10K sequence and lipids data, filtering according to functional annotations using the resource CADD (Combined Annotation-Dependent Depletion) and contrasting results with ‘nonsynonymous’ and ‘loss of function’ consequence analyses. Using CADD allowed the inclusion of potentially deleterious intronic variants, which was not possible when filtering by consequence. Overall, different filtering approaches provided similar evidence of association, although filtering according to CADD identified evidence of association between ANGPTL4 and High Density Lipoproteins (P = 0.02, N = 3,210) which was not observed in the other analyses. We also undertook genome-wide analyses to determine how filtering in this manner compared to conventional approaches for gene regions. Results suggested that filtering by annotations according to CADD, as well as other tools known as FATHMM-MKL and DANN, identified association signals not detected when filtering by variant consequence and vice versa. CONCLUSION: Incorporating variant annotations from non-coding bioinformatics tools should prove to be a valuable asset for rare variant analyses in the future. Filtering by variant consequence is only possible in coding regions of the genome, whereas utilising non-coding bioinformatics annotations provides an opportunity to discover unknown causal variants in non-coding regions as well. This should allow studies to uncover a greater number of causal variants for complex traits and help elucidate their functional role in disease.
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spelling pubmed-48514212016-05-07 Incorporating Non-Coding Annotations into Rare Variant Analysis Richardson, Tom G. Campbell, Colin Timpson, Nicholas J Gaunt, Tom R. PLoS One Research Article BACKGROUND: The success of collapsing methods which investigate the combined effect of rare variants on complex traits has so far been limited. The manner in which variants within a gene are selected prior to analysis has a crucial impact on this success, which has resulted in analyses conventionally filtering variants according to their consequence. This study investigates whether an alternative approach to filtering, using annotations from recently developed bioinformatics tools, can aid these types of analyses in comparison to conventional approaches. METHODS & RESULTS: We conducted a candidate gene analysis using the UK10K sequence and lipids data, filtering according to functional annotations using the resource CADD (Combined Annotation-Dependent Depletion) and contrasting results with ‘nonsynonymous’ and ‘loss of function’ consequence analyses. Using CADD allowed the inclusion of potentially deleterious intronic variants, which was not possible when filtering by consequence. Overall, different filtering approaches provided similar evidence of association, although filtering according to CADD identified evidence of association between ANGPTL4 and High Density Lipoproteins (P = 0.02, N = 3,210) which was not observed in the other analyses. We also undertook genome-wide analyses to determine how filtering in this manner compared to conventional approaches for gene regions. Results suggested that filtering by annotations according to CADD, as well as other tools known as FATHMM-MKL and DANN, identified association signals not detected when filtering by variant consequence and vice versa. CONCLUSION: Incorporating variant annotations from non-coding bioinformatics tools should prove to be a valuable asset for rare variant analyses in the future. Filtering by variant consequence is only possible in coding regions of the genome, whereas utilising non-coding bioinformatics annotations provides an opportunity to discover unknown causal variants in non-coding regions as well. This should allow studies to uncover a greater number of causal variants for complex traits and help elucidate their functional role in disease. Public Library of Science 2016-04-29 /pmc/articles/PMC4851421/ /pubmed/27128317 http://dx.doi.org/10.1371/journal.pone.0154181 Text en © 2016 Richardson et al 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 use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Richardson, Tom G.
Campbell, Colin
Timpson, Nicholas J
Gaunt, Tom R.
Incorporating Non-Coding Annotations into Rare Variant Analysis
title Incorporating Non-Coding Annotations into Rare Variant Analysis
title_full Incorporating Non-Coding Annotations into Rare Variant Analysis
title_fullStr Incorporating Non-Coding Annotations into Rare Variant Analysis
title_full_unstemmed Incorporating Non-Coding Annotations into Rare Variant Analysis
title_short Incorporating Non-Coding Annotations into Rare Variant Analysis
title_sort incorporating non-coding annotations into rare variant analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4851421/
https://www.ncbi.nlm.nih.gov/pubmed/27128317
http://dx.doi.org/10.1371/journal.pone.0154181
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