A Comparison of Methods for Gene-Based Testing That Account for Linkage Disequilibrium

Abstract

Controlling the type I error rate while retaining sufficient power is a major concern in genome-wide association studies, which nowadays often examine more than a million single-nucleotide polymorphisms (SNPs) simultaneously. Methods such as the Bonferroni correction can lead to a considerable decrease in power due to the large number of tests conducted. Shifting the focus to higher functional structures (e.g., genes) can reduce the loss of power. This can be accomplished via the combination of p-values of SNPs that belong to the same structural unit to test their joint null hypothesis. However, standard methods for this purpose (e.g., Fisher’s method) do not account for the dependence among the tests due to linkage disequilibrium (LD). In this paper, we review various adjustments to methods for combining p-values that take LD information explicitly into consideration and evaluate their performance in a simulation study based on data from the HapMap project. The results illustrate the importance of incorporating LD information into the methods for controlling the type I error rate at the desired level. Furthermore, some methods are more successful in controlling the type I error rate than others. Among them, Brown’s method was the most robust technique with respect to the characteristics of the genes and outperformed the Bonferroni method in terms of power in many scenarios. Examining the genetic factors of a phenotype of interest at the gene-rather than SNP-level can provide researchers benefits in terms of the power of the study. While doing so, one should be careful to account for LD in SNPs belonging to the same gene, for which Brown’s method seems the most robust technique.

Statistical Study Design for Analyzing Multiple Gene Loci Correlation in DNA Sequences

“…Alternatively, the Bonferroni correction, a commonly employed method for error rate control, has well-documented limitations. It becomes overly conservative, especially when test independence assumptions are violated [15,16]. Moreover, when applied to a large number of tests, it necessitates exceptionally low nominal significance levels for individual tests to maintain an acceptable overall type I error rate [17].…”

“…In this research, a common challenge in genetic studies is tackled, where the causal SNP is often absent in the genotyped data. Instead, the genotyped SNPs are frequently in linkage disequilibrium (LD) with the causal SNP [15,23]. As a result, single-SNP analysis yields modest effects, given that each SNP inadequately represents the causal SNP.…”