ExomePicks

ExomePicks selects individuals within large pedigrees for exome sequencing to maximize the utility of genotype imputation and improve detection of rare and population-specific variants.

Key Features:

• Genotype Imputation Optimization: Evaluates the impact of study-specific and general reference panels on imputation performance to guide selection of individuals for exome sequencing.
• Reference Panel Utilization: Compares study-specific reference panels with the 1000 Genomes Project (1000G) to identify scenarios where tailored panels improve imputation accuracy, especially for rare variants.
• Custom Array Integration: Assesses combinations of genome-wide and custom (including gene-centered) arrays to determine baseline genotypes that maximize imputation coverage.
• Population-Specific Guidelines: Analyzes allele frequency ranges and population genetic backgrounds (e.g., Sardinian) to tailor sequencing recommendations for distinct ancestries.
• Imputation Accuracy Metrics: Employs and adjusts imputation quality metrics such as MACH-Rsq and IMPUTE-INFO, recommending alternative cutoffs with study-specific reference panels to filter poorly imputed rare variants.

Scientific Applications:

• GWAS sequencing design: Optimizes selection of sequenced individuals for GWAS to improve imputation-based genotype coverage and power to detect associations.
• Rare variant discovery in isolated populations: Enhances discovery and accurate imputation of rare, population-specific variants in isolated cohorts such as Sardinians.
• Cost-effective pedigree sequencing: Informs cost-effective exome sequencing strategies within large pedigrees by selecting individuals who maximize imputation utility.
• Reference panel evaluation: Enables assessment of when study-specific reference panels outperform general panels for imputation.

Methodology:

Integrates genotype imputation results from study-specific and 1000 Genomes reference panels, compares imputation performance across reference panels and array configurations (genome-wide and gene-centered custom arrays), assesses allele-frequency–specific imputation performance in populations (e.g., Sardinian), and evaluates imputation quality using MACH-Rsq and IMPUTE-INFO with alternative cutoffs.