GBIRP

GBIRP identifies genealogical relationships among individuals from genomewide scan data to support studies of hereditary disease and population genetics.

Key Features:

• Likelihood Methodology: GBIRP uses a likelihood-based approach to evaluate pairwise relatedness and can detect relatives up to degree eight (e.g., third cousins once removed).
• Genomewide Data Processing: The method processes genomewide scan data and was validated using microsatellite markers at an average density of 4 cM.
• Validation and Performance: In a study of 170 Tasmanians with multiple sclerosis, GBIRP identified known relative pairs and predicted 61 additional putative relative pairs among cases with an estimated false discovery rate of 10%.
• Enhanced Detection with SNPs: Power to identify genealogical links is expected to increase with integration of denser single nucleotide polymorphism (SNP) marker sets.

Scientific Applications:

• Gene Mapping: Applicable to linkage and association studies requiring identification of familial relationships in genetic datasets.
• Disease Genetics: Facilitates analyses of the genetic architecture of diseases such as multiple sclerosis by revealing unrecognized familial connections.
• Population Studies: Enhances mapping efforts in populations with extensive genealogical records, exemplified by Tasmania.

Methodology:

GBIRP computes likelihoods of pairwise relatedness by comparing genotypes between individuals from genomewide scan data to assess probabilities of relationships up to degree eight.