Rapid and Accurate Multiple Testing Correction and Power Estimation for Millions of Correlated Markers
Year: 2009
Type: article
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 null 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 null 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.
Source: PLoS Genetics
Authors: Buhm Han, Hyun Min Kang, Eleazar Eskin
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Awards: INFRASTRUCTURE OF BIOENGINEERING DEVELOPMENT, RI: FWGrid: A Research Infrastructure with Fast Wireless, Wired, Compute, and Data Infrastructure for Next Generation Systems and Applications, Discovering the Genetic Basis of Hypertension, Collaborative Research: SEIII: Estimating Haplotype Frequencies, Collaborative Research: SEIII: Estimating Haplotype Frequencies, NATIONAL BIOMEDICAL COMPUTATION RESOURCE, Collaborative Research: Design and Analysis of Compressed Sensing DNA Microarrays
OpenAlex ID: W2018617521