Pedigree likelihood ratio for lineage markers.

Area of Science:

• Forensic Genetics
• Population Genetics
• Computational Biology

Background:

• Lineage-based markers (Y chromosome STRs, mitochondrial DNA) are crucial for kinship analysis, complementing autosomal markers.
• Current methods often simplify probability calculations for lineage-based hypotheses, potentially limiting accuracy.
• Specialized applications like database searching require robust kinship inference methods.

Purpose of the Study:

• To introduce a novel kinship hypothesis for analyzing lineage-based genetic data.
• To develop a graphical model for calculating genotype data probabilities under this new hypothesis.
• To address the inference of untyped individuals and computational complexity in pedigree analysis.

Main Methods:

• A fixed relationship kinship hypothesis is proposed for the questioned individual within a reference family.
• A graphical model is utilized to compute the probability of genotype data, incorporating founder haplotype frequency and transmission probability.
• Mutation models for Y chromosome STRs and mitochondrial DNA SNPs are suggested for transmission probability calculations.
• Methods for inferring genotypes of untyped individuals in pedigrees are developed.

Main Results:

• The study presents a new framework for kinship analysis using lineage-based markers.
• The proposed graphical model provides a probabilistic approach to genotype data given the kinship hypothesis.
• Algorithms are developed to handle untyped individuals and assess computational complexity.
• Numerical examples demonstrate the practical application of the developed methods.

Conclusions:

• The introduced kinship hypothesis and graphical model offer a more refined approach to kinship analysis with lineage-based markers.
• The methods enhance the accuracy of probability calculations in forensic genetics and database searching.
• The study provides a comprehensive framework for inferring relationships using complex pedigree data.