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Estimating genome-wide significance for whole-genome sequencing studies.

Xu C, Tachmazidou I, Walter K, Ciampi A, Zeggini E, Greenwood CM, UK10K Consorti - Genet. Epidemiol. (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada.

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Estimates of genome-wide significance thresholds for a combined strategy including window-based tests of rare variants and single-marker tests of common variants. Results are derived from simulations, for three MAF thresholds and three test statistics. The horizontal axis is −log10(0.05/m), for m tests. Each dot is a single estimated value for −log10 of the FWER at 5% for sections of chromosome 3 of varying size. A linear regression was fit through all the data. The gray line is the line of equality, y = x.
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fig04: Estimates of genome-wide significance thresholds for a combined strategy including window-based tests of rare variants and single-marker tests of common variants. Results are derived from simulations, for three MAF thresholds and three test statistics. The horizontal axis is −log10(0.05/m), for m tests. Each dot is a single estimated value for −log10 of the FWER at 5% for sections of chromosome 3 of varying size. A linear regression was fit through all the data. The gray line is the line of equality, y = x.

Mentions: The simulation approach also enables us to study the significance thresholds for a combination of window-based tests of rare genetic variation and single-marker tests of common variation. In Figure4, the necessary significance thresholds for controlling FWER at 5% are shown for genomic sections of varying size for this combined strategy. The variability across chromosomal sections of the same size is shown, as well as the linear relationship. All estimates here are well below the line of equality, y = x, demonstrating the well-known effect of linkage disequilibrium between common variants.


Estimating genome-wide significance for whole-genome sequencing studies.

Xu C, Tachmazidou I, Walter K, Ciampi A, Zeggini E, Greenwood CM, UK10K Consorti - Genet. Epidemiol. (2014)

Estimates of genome-wide significance thresholds for a combined strategy including window-based tests of rare variants and single-marker tests of common variants. Results are derived from simulations, for three MAF thresholds and three test statistics. The horizontal axis is −log10(0.05/m), for m tests. Each dot is a single estimated value for −log10 of the FWER at 5% for sections of chromosome 3 of varying size. A linear regression was fit through all the data. The gray line is the line of equality, y = x.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4489336&req=5

fig04: Estimates of genome-wide significance thresholds for a combined strategy including window-based tests of rare variants and single-marker tests of common variants. Results are derived from simulations, for three MAF thresholds and three test statistics. The horizontal axis is −log10(0.05/m), for m tests. Each dot is a single estimated value for −log10 of the FWER at 5% for sections of chromosome 3 of varying size. A linear regression was fit through all the data. The gray line is the line of equality, y = x.
Mentions: The simulation approach also enables us to study the significance thresholds for a combination of window-based tests of rare genetic variation and single-marker tests of common variation. In Figure4, the necessary significance thresholds for controlling FWER at 5% are shown for genomic sections of varying size for this combined strategy. The variability across chromosomal sections of the same size is shown, as well as the linear relationship. All estimates here are well below the line of equality, y = x, demonstrating the well-known effect of linkage disequilibrium between common variants.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada.

Show MeSH
Related in: MedlinePlus