FLOSS

FLOSS performs ordered subset analysis (OSA) on MERLIN per-family linkage results from dense SNP genotyping in pedigree datasets to evaluate whether linkage evidence is strengthened within covariate-defined subsets of families.

Key Features:

• Implementation: Java-based analysis tool for post-linkage subset analysis.
• Input types: Ingests MERLIN per-family linkage output (.lod files) and covariate tables derived from MERLIN .ped/.dat files.
• Covariate handling: Supports quantitative covariates (e.g., age-of-onset, biomarker values) with numeric covariates flagged as type ‘C’.
• Ordering strategy: Orders families by covariate values to define ordered subsets for analysis.
• Scoring metrics: Assesses incremental linkage contributions using nonparametric linkage Z-scores or linear allele-sharing LOD scores.
• Biological objective: Targets detection of genetic heterogeneity and covariate-modulated linkage effects masked in aggregated analyses.
• Optimization: Optimized for high-density marker maps and dense SNP genotyping data.
• MERLIN integration: Leverages MERLIN’s inheritance-tree representations for computational efficiency while focusing on post-linkage covariate stratification.
• Analytical focus: Integrates dense linkage evidence with covariate-driven subset ordering to explore genotype–phenotype relationships in complex pedigrees.

Scientific Applications:

• Hypothesis-driven pedigree stratification: Stratifies pedigrees by quantitative covariates to test specific phenotype-related hypotheses.
• Genetic heterogeneity detection: Identifies subsets of families that strengthen linkage signals indicative of heterogeneity.
• Covariate-modulated linkage analysis: Evaluates how covariates modulate nonparametric Z-scores or linear allele-sharing LOD scores across subsets.
• Genotype–phenotype exploration in pedigrees: Enables fine-grained exploration of genotype–phenotype relationships in complex pedigrees using dense SNP linkage evidence.

Methodology:

FLOSS ingests MERLIN per-family .lod files and covariate tables derived from MERLIN .ped/.dat (numeric covariates flagged as type ‘C’), orders families by covariate values, and assesses incremental contributions to linkage signals using nonparametric linkage Z-scores or linear allele-sharing LOD scores while leveraging MERLIN’s inheritance-tree representations and optimization for high-density marker maps.