Efficient Two-Stage Analysis Approaches for Complex Trait Association with Arbitrary Depth Sequencing Data

• Activity Number: 384 - Next-Generation Sequencing and High-Dimensional Data
• Type: Contributed
• Date/Time: Thursday, August 12, 2021 : 12:00 PM to 1:50 PM
• Sponsor: Biometrics Section
• Author(s): Zheng Xu* and Song Yan and Shuai Yuan and Zifang Guo and Yun Li
• Companies: Wright State University and University of North Carolina at Chapel Hill and Marinus Pharmaceuticals and Merck & Co. and UNC-Chapel Hill
• Keywords: Next generation sequencing data; association testing; maximum likelihood; sequencing depth; genotype calling; SNP

Abstract

Sequencing-based genetic association analysis is typically performed by first generating genotype calls from sequence data and then performing an association test on the called genotypes. Standard approaches require accurate genotype calling, which can be achieved either with high sequencing depth or via computationally intensive multi-sample linkage disequilibrium (LD) aware methods. Here, we propose a computationally efficient two-stage approach for association analysis, in which single nucleotide polymorphisms (SNPs) are screened in the first stage via a rapid maximum likelihood (ML) based method on sequence data directly (without first calling genotypes) and then the selected SNPs are evaluated in the second stage by performing association tests on genotypes from multi-sample LD-aware calling. Extensive simulations and real data analysis show that the proposed two-stage approaches can achieve ~90% maximal achievable power with ~20% computational costs when data are sequenced at various depths.