Adaptive-weight burden test for associations between quantitative traits and genotype data with complex correlations

Abstract

High-throughput sequencing has often been used to screen samples from pedigrees or with population structure, producing genotype data with complex correlations rendered from both familial relation and linkage disequilibrium. With such data, it is critical to account for these genotypic correlations when assessing the contribution of variants by gene or pathway. Recognizing the limitations of existing association testing methods, we propose Adaptive-weight Burden Test (ABT), a retrospective, mixed-model test for genetic association of quantitative traits on genotype data with complex correlations. This method makes full use of genotypic correlations across both samples and variants, and adopts “data-driven” weights to improve power. We derive the ABT statistic and its explicit distribution under the null hypothesis, and demonstrate through simulation studies that it is generally more powerful than the fixed-weight burden test and family-based SKAT in various scenarios, controlling for the type I error rate. Further investigation reveals the connection of ABT with kernel tests, as well as the adaptability of its weights to the direction of genetic effects. The application of ABT is illustrated by a whole genome analysis of genes with common and rare variants associated with fasting glucose from the NHLBI “Grand Opportunity” Exome Sequencing Project.

Empowering rare variant burden-based gene-trait association studies via optimized computational predictor choice

“…For example, PTVs close to the end of the coding region of certain genes might not contribute to a given trait as much as PTVs at the beginning of the same genes [57, 58]. A weighted burden test approach [59, 60] has been developed to assign various weights to different variant types depending on their anticipated effect on a given trait. However, this approach is limited to human traits where the effect of different variant types has been established.…”

Gene‐based association analysis for bivariate time‐to‐event data through functional regression with copula models

“…For example, Wu et al . () proposed using mixed‐model tests to make full use of genetic correlations across both samples and variants, and to gain power through “data‐driven” weights, which are adaptive to the direction of an individual variant's effect. Another popular approach is the sequence kernel association test (SKAT), proposed by Wu et al .…”

“…Traditional burden test suffers from the lack of power when heterogeneous genetic effects exist within a region; therefore, several adaptive approaches have been proposed recently. For example, Wu et al (2018) proposed using mixed-model tests to make full use of genetic correlations across both samples and variants, and to gain power through "data-driven" weights, which are adaptive to the direction of an individual variant's effect. Another popular approach is the sequence kernel association test (SKAT), proposed by Wu et al (2011).…”

Gene-based Association Analysis for Bivariate Time-to-event Data through Functional Regression with Copula Models

“…Traditional burden test suffers from the lack of power when heterogeneous genetic effects exist within a region, therefore several adaptive approaches have been proposed recently. For example, Wu et al (2018) proposed to use mixed-model tests to make full use of genetic correlations across both samples and variants, and to gain power through "data-driven" weights which are adaptive to the direction of individual variant's effect. Another popular approach is the sequence kernel association test (SKAT), proposed by Wu et al (2011).…”