Estimating Genome-Wide Significance for Whole-Genome Sequencing Studies
Although a standard genome-wide significance level has been accepted for the testing of association between common genetic variants and disease, the era of whole-genome sequencing (WGS) requires a new threshold. The allele frequency spectrum of sequence-identified variants is very different from com...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Ltd
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4489336/ https://www.ncbi.nlm.nih.gov/pubmed/24676807 http://dx.doi.org/10.1002/gepi.21797 |
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author | Xu, ChangJiang Tachmazidou, Ioanna Walter, Klaudia Ciampi, Antonio Zeggini, Eleftheria Greenwood, Celia M T |
author_facet | Xu, ChangJiang Tachmazidou, Ioanna Walter, Klaudia Ciampi, Antonio Zeggini, Eleftheria Greenwood, Celia M T |
author_sort | Xu, ChangJiang |
collection | PubMed |
description | Although a standard genome-wide significance level has been accepted for the testing of association between common genetic variants and disease, the era of whole-genome sequencing (WGS) requires a new threshold. The allele frequency spectrum of sequence-identified variants is very different from common variants, and the identified rare genetic variation is usually jointly analyzed in a series of genomic windows or regions. In nearby or overlapping windows, these test statistics will be correlated, and the degree of correlation is likely to depend on the choice of window size, overlap, and the test statistic. Furthermore, multiple analyses may be performed using different windows or test statistics. Here we propose an empirical approach for estimating genome-wide significance thresholds for data arising from WGS studies, and we demonstrate that the empirical threshold can be efficiently estimated by extrapolating from calculations performed on a small genomic region. Because analysis of WGS may need to be repeated with different choices of test statistics or windows, this prediction approach makes it computationally feasible to estimate genome-wide significance thresholds for different analysis choices. Based on UK10K whole-genome sequence data, we derive genome-wide significance thresholds ranging between 2.5 × 10(−8) and 8 × 10(−8) for our analytic choices in window-based testing, and thresholds of 0.6 × 10(−8)–1.5 × 10(−8) for a combined analytic strategy of testing common variants using single-SNP tests together with rare variants analyzed with our sliding-window test strategy. |
format | Online Article Text |
id | pubmed-4489336 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | John Wiley & Sons, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-44893362015-07-07 Estimating Genome-Wide Significance for Whole-Genome Sequencing Studies Xu, ChangJiang Tachmazidou, Ioanna Walter, Klaudia Ciampi, Antonio Zeggini, Eleftheria Greenwood, Celia M T Genet Epidemiol Research Articles Although a standard genome-wide significance level has been accepted for the testing of association between common genetic variants and disease, the era of whole-genome sequencing (WGS) requires a new threshold. The allele frequency spectrum of sequence-identified variants is very different from common variants, and the identified rare genetic variation is usually jointly analyzed in a series of genomic windows or regions. In nearby or overlapping windows, these test statistics will be correlated, and the degree of correlation is likely to depend on the choice of window size, overlap, and the test statistic. Furthermore, multiple analyses may be performed using different windows or test statistics. Here we propose an empirical approach for estimating genome-wide significance thresholds for data arising from WGS studies, and we demonstrate that the empirical threshold can be efficiently estimated by extrapolating from calculations performed on a small genomic region. Because analysis of WGS may need to be repeated with different choices of test statistics or windows, this prediction approach makes it computationally feasible to estimate genome-wide significance thresholds for different analysis choices. Based on UK10K whole-genome sequence data, we derive genome-wide significance thresholds ranging between 2.5 × 10(−8) and 8 × 10(−8) for our analytic choices in window-based testing, and thresholds of 0.6 × 10(−8)–1.5 × 10(−8) for a combined analytic strategy of testing common variants using single-SNP tests together with rare variants analyzed with our sliding-window test strategy. John Wiley & Sons, Ltd 2014-04 2014-02-14 /pmc/articles/PMC4489336/ /pubmed/24676807 http://dx.doi.org/10.1002/gepi.21797 Text en © 2014 The Authors. Genetic Epidemiology published by Wiley Periodicals, Inc. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Xu, ChangJiang Tachmazidou, Ioanna Walter, Klaudia Ciampi, Antonio Zeggini, Eleftheria Greenwood, Celia M T Estimating Genome-Wide Significance for Whole-Genome Sequencing Studies |
title | Estimating Genome-Wide Significance for Whole-Genome Sequencing Studies |
title_full | Estimating Genome-Wide Significance for Whole-Genome Sequencing Studies |
title_fullStr | Estimating Genome-Wide Significance for Whole-Genome Sequencing Studies |
title_full_unstemmed | Estimating Genome-Wide Significance for Whole-Genome Sequencing Studies |
title_short | Estimating Genome-Wide Significance for Whole-Genome Sequencing Studies |
title_sort | estimating genome-wide significance for whole-genome sequencing studies |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4489336/ https://www.ncbi.nlm.nih.gov/pubmed/24676807 http://dx.doi.org/10.1002/gepi.21797 |
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