DOT: Gene-set analysis by combining decorrelated association statistics

Abstract

Historically, the majority of statistical association methods have been designed assuming availability of SNP-level information. However, modern genetic and sequencing data present new challenges to access and sharing of genotype-phenotype datasets, including cost management, difficulties in consolidation of records across research groups, etc. These issues make methods based on SNP-level summary statistics particularly appealing. The most common form of combining statistics is a sum of SNP-level squared scores, possibly weighted, as in burden tests for rare variants. The overall significance of the resulting statistic is evaluated using its distribution under the null hypothesis.Here, we demonstrate that this basic approach can be substantially improved by decorrelating scores prior to their addition, resulting in remarkable power gains in situations that are most commonly encountered in practice; namely, under heterogeneity of effect sizes and diversity between pairwise LD. In these situations, the power of the traditional test, based on the added squared scores, quickly reaches a ceiling, as the number of variants increases. Thus, the traditional approach does not benefit from information potentially contained in any additional SNPs, while our decorrelation by orthogonal transformation (DOT) method yields steady gain in power. We present theoretical and computational analyses of both approaches, and reveal causes behind sometimes dramatic difference in their respective powers. We showcase DOT by analyzing breast cancer data, in which our method strengthened levels of previously reported associations and implied the possibility of multiple new alleles that jointly confer breast cancer risk.

Detecting Weak Signals by Combining Small P-Values in Genetic Association Studies

“…which increases with the increased heterogeneity of µ. More details on the performance of the decorrelation approach are given by us elsewhere, 19 but here we briefly note that this finding is practically relevant because substantial heterogeneity of associations is expected among genetic variants, leading to a substantial power boost, as we next illustrate via re-analysis of published associations of genetic variants within the µ-opioid gene with pain sensitivity.…”

“…Theoretical properties and extensive numerical evaluation of DOT will be published elsewhere and currently these findings are available as a preprint. 19 Further, we illustrate an application of our methods with analyses of variants within the µ-opioid gene that have been shown to affect sensitivity to pain. We find strengthened evidence of overall association within the 11-SNP block.…”

A flexible summary-based colocalization method with application to the mucin Cystic Fibrosis lung disease modifier locus

“…[49][50][51] Summary statistics can also be decorrelated before being summed together, which is powerful under heterogeneity of effect sizes and variation between pairwise LD patterns. 49 The stage 1 test implemented here has the same functional form as that used in VEGAS 30 and fastBAT. 31 This set-based test can be more powerful than a test which takes the maximum of the chi-squared test statistics in a region, an approach implemented in GATES 52 and Pascal-Max, 53 especially when there are multiple independent association signals.…”

“…These include versions with weighted sums of summary statistics, known as gene set analysis (GSA) tests or burden tests for rare variants. [49][50][51] Summary statistics can also be decorrelated before being summed together, which is powerful under heterogeneity of effect sizes and variation between pairwise LD patterns. 49 The stage 1 test implemented here has the same functional form as that used in VEGAS 30 and fastBAT.…”

A New Functional F-Statistic for Gene-Based Inference Involving Multiple Phenotypes

“…Similar to other simulation studies [33], we utilized realistic linkage disequilibrium patterns by using data from the 1000 Genomes Project [34] for a 100 Kb region of chromosome 17 which included 12,735 SNPs spanning from the FGF11 gene to the NDEL1 gene. The selection of this region of the genome was arbitrary, but we expect the linkage disequilibrium structure of this region to be representative of, and generalizable to, other regions of the genome.…”