Incorporating prior information into association studies.

UnlabelledRecent technological developments in measuring genetic variation have ushered in an era of genome-wide association studies which have discovered many genes involved in human disease. Current methods to perform association studies collect genetic information and compare the frequency of variants in individuals with and without the disease. Standard approaches do not take into account any information on whether or not a given variant is likely to have an effect on the disease. We propose a novel method for computing an association statistic which takes into account prior information. Our method improves both power and resolution by 8% and 27%, respectively, over traditional methods for performing association studies when applied to simulations using the HapMap data. Advantages of our method are that it is as simple to apply to association studies as standard methods, the results of the method are interpretable as the method reports p-values, and the method is optimal in its use of prior information in regards to statistical power.AvailabilityThe method presented herein is available at http://masa.cs.ucla.edu

Rapid and Accurate Multiple Testing Correction and Power Estimation for Millions of Correlated Markers

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

Increasing power in association studies by using linkage disequilibrium structure and molecular function as prior information

The availability of various types of genomic data provides an opportunity to incorporate this data as prior information in genetic association studies. This information includes knowledge of linkage disequilibrium structure as well as which regions are likely to be involved in disease. In this paper, we present an approach for incorporating this information by revisiting how we perform multiple-hypothesis correction. In a traditional association study, in order to correct for multiple-hypothesis testing, the significance threshold at each marker, t, is set to control the total false-positive rate. In our framework, we vary the threshold at each marker ti and use these thresholds to incorporate prior information. We present a numerical procedure for solving for thresholds that maximizes association study power using prior information. We also present the results of benchmark simulation experiments using the HapMap data, which demonstrate a significant increase in association study power under this framework. We provide a Web server for performing association studies using our method and provide thresholds optimized for the Affymetrix 500k and Illumina HumanHap 550 chips and demonstrate the application of our framework to the analysis of the Wellcome Trust Case Control Consortium data