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Gene-Wise Burden of Coding Variants Correlates to Noncoding Pharmacogenetic Risk Variants
Genetic variability can modulate individual drug responses. A significant portion of pharmacogenetic variants reside in the noncoding genome yet it is unclear if the noncoding variants directly influence protein function and expression or are present on a haplotype including a functionally relevant...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7247590/ https://www.ncbi.nlm.nih.gov/pubmed/32349395 http://dx.doi.org/10.3390/ijms21093091 |
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author | Park, Jihye Lee, Soo Youn Baik, Su Youn Park, Chan Hee Yoon, Jun Hee Ryu, Brian Y. Kim, Ju Han |
author_facet | Park, Jihye Lee, Soo Youn Baik, Su Youn Park, Chan Hee Yoon, Jun Hee Ryu, Brian Y. Kim, Ju Han |
author_sort | Park, Jihye |
collection | PubMed |
description | Genetic variability can modulate individual drug responses. A significant portion of pharmacogenetic variants reside in the noncoding genome yet it is unclear if the noncoding variants directly influence protein function and expression or are present on a haplotype including a functionally relevant genetic variation (synthetic association). Gene-wise variant burden (GVB) is a gene-level measure of deleteriousness, reflecting the cumulative effects of deleterious coding variants, predicted in silico. To test potential associations between noncoding and coding pharmacogenetic variants, we computed a drug-level GVB for 5099 drugs from DrugBank for 2504 genomes of the 1000 Genomes Project and evaluated the correlation between the long-known noncoding variant-drug associations in PharmGKB, with functionally relevant rare and common coding variants aggregated into GVBs. We obtained the area under the receiver operating characteristics curve (AUC) by comparing the drug-level GVB ranks against the corresponding pharmacogenetic variants-drug associations in PharmGKB. We obtained high overall AUCs (0.710 ± 0.022–0.734 ± 0.018) for six different methods (i.e., SIFT, MutationTaster, Polyphen-2 HVAR, Polyphen-2 HDIV, phyloP, and GERP(++)), and further improved the ethnicity-specific validations (0.759 ± 0.066–0.791 ± 0.078). These results suggest that a significant portion of the long-known noncoding variant-drug associations can be explained as synthetic associations with rare and common coding variants burden of the corresponding pharmacogenes. |
format | Online Article Text |
id | pubmed-7247590 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72475902020-06-10 Gene-Wise Burden of Coding Variants Correlates to Noncoding Pharmacogenetic Risk Variants Park, Jihye Lee, Soo Youn Baik, Su Youn Park, Chan Hee Yoon, Jun Hee Ryu, Brian Y. Kim, Ju Han Int J Mol Sci Article Genetic variability can modulate individual drug responses. A significant portion of pharmacogenetic variants reside in the noncoding genome yet it is unclear if the noncoding variants directly influence protein function and expression or are present on a haplotype including a functionally relevant genetic variation (synthetic association). Gene-wise variant burden (GVB) is a gene-level measure of deleteriousness, reflecting the cumulative effects of deleterious coding variants, predicted in silico. To test potential associations between noncoding and coding pharmacogenetic variants, we computed a drug-level GVB for 5099 drugs from DrugBank for 2504 genomes of the 1000 Genomes Project and evaluated the correlation between the long-known noncoding variant-drug associations in PharmGKB, with functionally relevant rare and common coding variants aggregated into GVBs. We obtained the area under the receiver operating characteristics curve (AUC) by comparing the drug-level GVB ranks against the corresponding pharmacogenetic variants-drug associations in PharmGKB. We obtained high overall AUCs (0.710 ± 0.022–0.734 ± 0.018) for six different methods (i.e., SIFT, MutationTaster, Polyphen-2 HVAR, Polyphen-2 HDIV, phyloP, and GERP(++)), and further improved the ethnicity-specific validations (0.759 ± 0.066–0.791 ± 0.078). These results suggest that a significant portion of the long-known noncoding variant-drug associations can be explained as synthetic associations with rare and common coding variants burden of the corresponding pharmacogenes. MDPI 2020-04-27 /pmc/articles/PMC7247590/ /pubmed/32349395 http://dx.doi.org/10.3390/ijms21093091 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Park, Jihye Lee, Soo Youn Baik, Su Youn Park, Chan Hee Yoon, Jun Hee Ryu, Brian Y. Kim, Ju Han Gene-Wise Burden of Coding Variants Correlates to Noncoding Pharmacogenetic Risk Variants |
title | Gene-Wise Burden of Coding Variants Correlates to Noncoding Pharmacogenetic Risk Variants |
title_full | Gene-Wise Burden of Coding Variants Correlates to Noncoding Pharmacogenetic Risk Variants |
title_fullStr | Gene-Wise Burden of Coding Variants Correlates to Noncoding Pharmacogenetic Risk Variants |
title_full_unstemmed | Gene-Wise Burden of Coding Variants Correlates to Noncoding Pharmacogenetic Risk Variants |
title_short | Gene-Wise Burden of Coding Variants Correlates to Noncoding Pharmacogenetic Risk Variants |
title_sort | gene-wise burden of coding variants correlates to noncoding pharmacogenetic risk variants |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7247590/ https://www.ncbi.nlm.nih.gov/pubmed/32349395 http://dx.doi.org/10.3390/ijms21093091 |
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