Optimal Weighting for Integrative Association Tests: Application to Whole-Genome Sequencing Studies

Music City Center 

The integrative association test utilizes a weighting scheme to combine prior information and increase statistical power. In whole-genome sequencing (WGS) studies, it facilitates the integration of biological characteristics of single nucleotide variants (SNVs) to improve the detection of novel disease genes. Despite recent applicational advances, determining optimal weights to fully leverage relevant information remains an open question. For a broad family of weighted integrative tests, this paper proposes optimal weights that maximize the tests' asymptotic efficiency, a dominant metric influencing statistical power. The study elucidates how weighting enhances statistical power and designs a practical approach for integrating effective information from SNV allele frequencies, annotations, and linkage disequilibrium. Extensive simulations demonstrate improved statistical power compared to existing methods. An osteoporosis case study further illustrates the method's application and potential for detecting more novel disease genes.

Data integration

p-value combination

signal detection

weighting

whole genome sequencing study 

Section on Statistics in Genomics and Genetics