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

Sequencing-based genetic association analysis is typically performed by first generating genotype calls from sequence data and then performing tests on the called genotypes. Standard approaches require accurate calling (GC), which can be achieved either with high sequencing depth (typically available in a small number of individuals) or via computationally intensive multi-sample linkage disequilibrium (LD)-aware methods. We propose efficient two-stage combination approach for analysis, single-nucleotide polymorphisms (SNPs) are screened stage rapid maximum likelihood (ML)-based method directly (without genotypes), selected SNPs evaluated second genotypes LD-aware calling. Extensive simulation- real data-based studies show that proposed save 80% computational costs still obtain more than 90% power classical to all markers at various depths d≥2.