On set-based association tests: insights from a regression using summary statistics

Abstract

9Motivated by, but not limited to, association analyses of multiple genetic variants, we propose here 10 a summary statistics-based regression framework. The proposed method requires only variant-11 specific summary statistics, and it unifies earlier methods based on individual-level data as spe-12 cial cases. The resulting score test statistic, derived from a linear mixed-effect regression model, 13 inherently transforms the variant-specific statistics using the precision matrix to improve power 14 for detecting sparse alternatives. Furthermore, the proposed method can incorporate additional 15 variant-specific information with ease, facilitating omic-data integration. We study the asymptotic 16 properties of the proposed tests under the null and alternatives, and we investigate efficient p-value 17 calculation in finite samples. Finally, we provide supporting empirical evidence from extensive 18 simulation studies and two applications. 19

A flexible summary statistics-based colocalization method with application to the mucin cystic fibrosis lung disease modifier locus

“…The alternative gene/set-based tests include versions with weighted sums of summary statistics, known as gene set analysis (GSA) tests or burden tests for rare variants. 57 , 58 , 59 Summary statistics can also be decorrelated before being summed together, which is powerful under heterogeneity of effect sizes and variation between pairwise LD patterns. 57 The stage 1 test implemented here has the same functional form as that used in VEGAS 34 and fastBAT.…”

The X factor: A robust and powerful approach to X‐chromosome‐inclusive whole‐genome association studies

“…Thus, our work here also highlights new challenges associated with other analyses of X‐chromosomal SNPs. For example, how to aggregate association evidence across multiple SNPs or multiple traits (Zhao & Sun, 2021 ), and how to perform X‐chromosome‐inclusive polygenic risk score (PRS) analysis (Dudbridge et al, 2018 ), both of which we will address in future research.…”

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

“…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.…”