LDWP

LDWP identifies linked mutation regions from genotype and diagnostic data in groups of closely related individuals without requiring pedigree information.

Key Features:

• Pedigree-Independent Analysis: Performs linkage analysis when traditional pedigrees are unavailable, handling cases with a distant common ancestor (at least six generations ago).
• Mathematical Modeling: Models allele-sharing using the "shared center problem" and leverages a database of confirmed haplotypes as a reference.
• Algorithmic Approach: Frames the core problem as NP-complete and employs a ratio-2 polynomial-time approximation algorithm adapted into a practical heuristic.
• Heuristic Algorithm: Detects mutation regions by analyzing genotype data and diagnostic (diseased/normal) statuses among group members.
• Software Implementation: Implements the algorithms in a software package that has demonstrated efficacy through experimental validation.

Scientific Applications:

• Genetic Disease Research: Identifies genes responsible for genetic diseases by pinpointing mutation regions shared among affected individuals.
• Extended Linkage Analysis: Extends linkage analysis to cohorts lacking known pedigrees, broadening applicability in genetic studies.

Methodology:

Accepts genotype data and diagnostic information (diseased or normal) for a group of closely related individuals as input and processes these data using the described heuristic algorithm to identify regions where alleles are shared among affected members but not among unaffected ones.