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Rapid and accurate multiple testing correction and power estimation for millions of correlated markers.

Han B, Kang HM, Eskin E - PLoS Genet. (2009)

Bottom Line: Our method accounts for all correlation within a sliding window and corrects for the departure of the true distribution of the statistic from the asymptotic distribution.In simulations using the Wellcome Trust Case Control Consortium data, the error rate of SLIDE's corrected p-values is more than 20 times smaller than the error rate of the previous MVN-based methods' corrected p-values, while SLIDE is orders of magnitude faster than the permutation test and other competing methods.We also extend the MVN framework to the problem of estimating the statistical power of an association study with correlated markers and propose an efficient and accurate power estimation method SLIP.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA.

ABSTRACT
With the development of high-throughput sequencing and genotyping technologies, the number of markers collected in genetic association studies is growing rapidly, increasing the importance of methods for correcting for multiple hypothesis testing. The permutation test is widely considered the gold standard for accurate multiple testing correction, but it is often computationally impractical for these large datasets. Recently, several studies proposed efficient alternative approaches to the permutation test based on the multivariate normal distribution (MVN). However, they cannot accurately correct for multiple testing in genome-wide association studies for two reasons. First, these methods require partitioning of the genome into many disjoint blocks and ignore all correlations between markers from different blocks. Second, the true distribution of the test statistic often fails to follow the asymptotic distribution at the tails of the distribution. We propose an accurate and efficient method for multiple testing correction in genome-wide association studies--SLIDE. Our method accounts for all correlation within a sliding window and corrects for the departure of the true distribution of the statistic from the asymptotic distribution. In simulations using the Wellcome Trust Case Control Consortium data, the error rate of SLIDE's corrected p-values is more than 20 times smaller than the error rate of the previous MVN-based methods' corrected p-values, while SLIDE is orders of magnitude faster than the permutation test and other competing methods. We also extend the MVN framework to the problem of estimating the statistical power of an association study with correlated markers and propose an efficient and accurate power estimation method SLIP. SLIP and SLIDE are available at http://slide.cs.ucla.edu.

Show MeSH
Discrepancy between asymptotic p-value and true p-value in a single SNP experiment.Given a  threshold , the asymptotic p-value is . The true p-value is obtained by listing all possible contingency tables. The number of individuals (N) denotes the number of haplotypes, half control and half case.
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pgen-1000456-g003: Discrepancy between asymptotic p-value and true p-value in a single SNP experiment.Given a threshold , the asymptotic p-value is . The true p-value is obtained by listing all possible contingency tables. The number of individuals (N) denotes the number of haplotypes, half control and half case.

Mentions: This phenomenon can be illustrated with a single-SNP experiment using the test. For a threshold , the asymptotically approximated p-value (asymptotic p-value) is . Assume 1,000 case and 1,000 control haplotypes. Given a fixed number of minor alleles, we can list every possible 2×2 table. The true p-value is the sum of the probabilities of the tables whose statistic is . If the asymptotic approximation is accurate, then . We compare these two p-values for many different thresholds and plot the ratio in Figure 3. We repeat the experiments for various MAFs and sample sizes.


Rapid and accurate multiple testing correction and power estimation for millions of correlated markers.

Han B, Kang HM, Eskin E - PLoS Genet. (2009)

Discrepancy between asymptotic p-value and true p-value in a single SNP experiment.Given a  threshold , the asymptotic p-value is . The true p-value is obtained by listing all possible contingency tables. The number of individuals (N) denotes the number of haplotypes, half control and half case.
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2663787&req=5

pgen-1000456-g003: Discrepancy between asymptotic p-value and true p-value in a single SNP experiment.Given a threshold , the asymptotic p-value is . The true p-value is obtained by listing all possible contingency tables. The number of individuals (N) denotes the number of haplotypes, half control and half case.
Mentions: This phenomenon can be illustrated with a single-SNP experiment using the test. For a threshold , the asymptotically approximated p-value (asymptotic p-value) is . Assume 1,000 case and 1,000 control haplotypes. Given a fixed number of minor alleles, we can list every possible 2×2 table. The true p-value is the sum of the probabilities of the tables whose statistic is . If the asymptotic approximation is accurate, then . We compare these two p-values for many different thresholds and plot the ratio in Figure 3. We repeat the experiments for various MAFs and sample sizes.

Bottom Line: Our method accounts for all correlation within a sliding window and corrects for the departure of the true distribution of the statistic from the asymptotic distribution.In simulations using the Wellcome Trust Case Control Consortium data, the error rate of SLIDE's corrected p-values is more than 20 times smaller than the error rate of the previous MVN-based methods' corrected p-values, while SLIDE is orders of magnitude faster than the permutation test and other competing methods.We also extend the MVN framework to the problem of estimating the statistical power of an association study with correlated markers and propose an efficient and accurate power estimation method SLIP.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA.

ABSTRACT
With the development of high-throughput sequencing and genotyping technologies, the number of markers collected in genetic association studies is growing rapidly, increasing the importance of methods for correcting for multiple hypothesis testing. The permutation test is widely considered the gold standard for accurate multiple testing correction, but it is often computationally impractical for these large datasets. Recently, several studies proposed efficient alternative approaches to the permutation test based on the multivariate normal distribution (MVN). However, they cannot accurately correct for multiple testing in genome-wide association studies for two reasons. First, these methods require partitioning of the genome into many disjoint blocks and ignore all correlations between markers from different blocks. Second, the true distribution of the test statistic often fails to follow the asymptotic distribution at the tails of the distribution. We propose an accurate and efficient method for multiple testing correction in genome-wide association studies--SLIDE. Our method accounts for all correlation within a sliding window and corrects for the departure of the true distribution of the statistic from the asymptotic distribution. In simulations using the Wellcome Trust Case Control Consortium data, the error rate of SLIDE's corrected p-values is more than 20 times smaller than the error rate of the previous MVN-based methods' corrected p-values, while SLIDE is orders of magnitude faster than the permutation test and other competing methods. We also extend the MVN framework to the problem of estimating the statistical power of an association study with correlated markers and propose an efficient and accurate power estimation method SLIP. SLIP and SLIDE are available at http://slide.cs.ucla.edu.

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