APL

APL performs statistical association testing of genetic markers and haplotypes in pedigree data while accounting for linkage disequilibrium and complex inheritance patterns.

Key Features:

• Handling Missing Parental Genotypes: Accommodates missing parental genotypes within nuclear families to enable analysis when parental data are incomplete.
• Generalization for Complex Structures: Extends beyond two-affected-sibling models to mixed nuclear family structures and multiple-marker haplotype analyses.
• Bootstrap Variance Estimation: Employs a bootstrap variance estimator as an alternative to the original robust variance estimator for variance estimation across varying family structures and sizes.
• Haplotype Phase Estimation via EM: Integrates haplotype phase estimation using an Expectation-Maximization (EM) algorithm for multiple-marker haplotype analysis.
• Simulation-Based Robustness Evaluation: Validated by computer simulations under non-standard scenarios, including violations of Hardy–Weinberg Equilibrium (HWE) in single-marker tests.
• Powerful Association Testing: Demonstrates increased power for detecting single-marker and multiple-marker haplotype associations compared with other association methods.

Scientific Applications:

• Association Testing in Pedigrees: Detects associations between genetic markers or haplotypes and traits in pedigree data where linkage disequilibrium is present.
• Analysis with Incomplete Parental Data: Enables analysis of family datasets that contain missing parental genotypes.
• Rare Allele and Haplotype Studies: Supports detection of associations involving rare alleles or haplotypes through haplotype-informed family-based analysis.
• Familial Disease Genetics: Facilitates investigation of hereditary contributions to diseases within familial contexts using linkage-aware association testing.

Methodology:

Uses an Expectation-Maximization (EM) algorithm for haplotype phase estimation, a bootstrap variance estimator for variance estimation, and computer simulations for robustness evaluation.