Abstract
Although methylation data continues to rise in popularity, much is still unknown about how to best analyze methylation data in genome-wide analysis contexts. Given continuing interest in gene-based tests for next-generation sequencing data, we evaluated the performance of novel gene-based test statistics on simulated data from GAW20. Our analysis suggests that most of the gene-based tests are detecting real signals and maintaining the Type I error rate. The minimum p value and threshold-based tests performed well compared to single-marker tests in many cases, especially when the number of variants was relatively large with few true causal variants in the set.
Highlights
Methylation data continues to grow in popularity owing to both its increasing availability and biological relevance, a result of increasing hypotheses about the contribution of epigenetic effects to the genetic architecture of common human diseases
We have identified a test statistic that is robust to situations where the majority of markers in the set are noncausal, which can be near optimal when combined with a variance components test [1]
Genes containing minor causal variants were only detected slightly more frequently than genes containing no causal variants, and so we focus the remainder of our analysis on genes containing major causal variants
Summary
Methylation data continues to grow in popularity owing to both its increasing availability (decline in cost) and biological relevance, a result of increasing hypotheses about the contribution of epigenetic effects to the genetic architecture of common human diseases. This rapid rise in popularity has meant that there are few “best practices” for the analysis of genome-wide epigenetic data. We have identified a test statistic that is robust to situations where the majority of markers in the set are noncausal, which can be near optimal when combined with a variance components test [1]
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