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Identifying individual risk rare variants using protein structure guided local tests (POINT)
Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases. Due to the rarity of the mutant events, rare variants are routinely analyzed on an aggregate level. While aggregation analyses improve the detection of global...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6396946/ https://www.ncbi.nlm.nih.gov/pubmed/30779729 http://dx.doi.org/10.1371/journal.pcbi.1006722 |
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author | Marceau West, Rachel Lu, Wenbin Rotroff, Daniel M. Kuenemann, Melaine A. Chang, Sheng-Mao Wu, Michael C. Wagner, Michael J. Buse, John B. Motsinger-Reif, Alison A. Fourches, Denis Tzeng, Jung-Ying |
author_facet | Marceau West, Rachel Lu, Wenbin Rotroff, Daniel M. Kuenemann, Melaine A. Chang, Sheng-Mao Wu, Michael C. Wagner, Michael J. Buse, John B. Motsinger-Reif, Alison A. Fourches, Denis Tzeng, Jung-Ying |
author_sort | Marceau West, Rachel |
collection | PubMed |
description | Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases. Due to the rarity of the mutant events, rare variants are routinely analyzed on an aggregate level. While aggregation analyses improve the detection of global-level signal, they are not able to pinpoint causal variants within a variant set. To perform inference on a localized level, additional information, e.g., biological annotation, is often needed to boost the information content of a rare variant. Following the observation that important variants are likely to cluster together on functional domains, we propose a protein structure guided local test (POINT) to provide variant-specific association information using structure-guided aggregation of signal. Constructed under a kernel machine framework, POINT performs local association testing by borrowing information from neighboring variants in the 3-dimensional protein space in a data-adaptive fashion. Besides merely providing a list of promising variants, POINT assigns each variant a p-value to permit variant ranking and prioritization. We assess the selection performance of POINT using simulations and illustrate how it can be used to prioritize individual rare variants in PCSK9, ANGPTL4 and CETP in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data. |
format | Online Article Text |
id | pubmed-6396946 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-63969462019-03-09 Identifying individual risk rare variants using protein structure guided local tests (POINT) Marceau West, Rachel Lu, Wenbin Rotroff, Daniel M. Kuenemann, Melaine A. Chang, Sheng-Mao Wu, Michael C. Wagner, Michael J. Buse, John B. Motsinger-Reif, Alison A. Fourches, Denis Tzeng, Jung-Ying PLoS Comput Biol Research Article Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases. Due to the rarity of the mutant events, rare variants are routinely analyzed on an aggregate level. While aggregation analyses improve the detection of global-level signal, they are not able to pinpoint causal variants within a variant set. To perform inference on a localized level, additional information, e.g., biological annotation, is often needed to boost the information content of a rare variant. Following the observation that important variants are likely to cluster together on functional domains, we propose a protein structure guided local test (POINT) to provide variant-specific association information using structure-guided aggregation of signal. Constructed under a kernel machine framework, POINT performs local association testing by borrowing information from neighboring variants in the 3-dimensional protein space in a data-adaptive fashion. Besides merely providing a list of promising variants, POINT assigns each variant a p-value to permit variant ranking and prioritization. We assess the selection performance of POINT using simulations and illustrate how it can be used to prioritize individual rare variants in PCSK9, ANGPTL4 and CETP in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data. Public Library of Science 2019-02-19 /pmc/articles/PMC6396946/ /pubmed/30779729 http://dx.doi.org/10.1371/journal.pcbi.1006722 Text en © 2019 Marceau West 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 Marceau West, Rachel Lu, Wenbin Rotroff, Daniel M. Kuenemann, Melaine A. Chang, Sheng-Mao Wu, Michael C. Wagner, Michael J. Buse, John B. Motsinger-Reif, Alison A. Fourches, Denis Tzeng, Jung-Ying Identifying individual risk rare variants using protein structure guided local tests (POINT) |
title | Identifying individual risk rare variants using protein structure guided local tests (POINT) |
title_full | Identifying individual risk rare variants using protein structure guided local tests (POINT) |
title_fullStr | Identifying individual risk rare variants using protein structure guided local tests (POINT) |
title_full_unstemmed | Identifying individual risk rare variants using protein structure guided local tests (POINT) |
title_short | Identifying individual risk rare variants using protein structure guided local tests (POINT) |
title_sort | identifying individual risk rare variants using protein structure guided local tests (point) |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6396946/ https://www.ncbi.nlm.nih.gov/pubmed/30779729 http://dx.doi.org/10.1371/journal.pcbi.1006722 |
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