AbCD

AbCD calculates pre-estimated effective sample sizes for sequencing-based genetic study design across minor allele frequency (MAF) categories, sequencing coverages (0.5–30X), sample sizes (20–20,000), and four ethnic groups (Europeans, Africans, Asians, African Americans).

Key Features:

• Pre-estimated effective sample sizes: Provides effective sample size estimates stratified by MAF categories.
• Coverage and sample size range: Covers arbitrary sequencing depths from 0.5–30X and sample sizes from 20 to 20,000.
• Ethnic group-specific estimates: Supplies estimates tailored to Europeans, Africans, Asians, and African Americans.
• ShotGun short-read simulation: Simulates short reads with user-specified read length, average depth, and cycle-specific sequencing error rates and generates realistic read depth distributions for low- and high-depth scenarios.
• DesignPlanner integration and LD-aware genotype calling: Integrates ShotGun-generated sequence data with initial SNP discovery and employs a linkage disequilibrium-aware method to call genotypes and produce MAF-specific effective sample sizes.
• Evaluation of combined sequencing and genotyping data: Supports assessment of combined designs such as whole-genome low-depth with exonic high-depth sequencing and integration with existing GWAS genotyping data.

Scientific Applications:

• Study design optimization: Optimizes allocation of sequencing coverage and sample size to achieve target statistical power across MAF strata.
• Low-coverage sequencing strategies: Informs use of low-depth sequencing in large cohorts by providing MAF-specific effective sample size estimates.
• Genome-wide association studies (GWAS): Guides design choices for large-scale GWAS and other association studies relying on sequencing data.
• Combined sequencing and integration with GWAS: Assists planning of studies combining whole-genome low-depth and exonic high-depth sequencing or integrating sequence data with existing GWAS genotypes.

Methodology:

ShotGun performs short-read simulation with parameters for read length, average depth, and cycle-specific sequencing error rates to generate read depth distributions; DesignPlanner integrates ShotGun output with initial SNP discovery and uses a linkage disequilibrium-aware genotype-calling method to produce MAF-specific effective sample sizes.