Employing MCMC under the PPL framework to analyze sequence data in large pedigrees.

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Whole-genome and whole-exome sequencing are increasingly feasible.
• Family data is valuable for identifying disease mutations, especially in large pedigrees.
• Locus and allelic heterogeneity can complicate mutation discovery.

Purpose of the Study:

• To extend capabilities for positional mapping in large pedigrees.
• To fine-map disease mutations to individual sequence variants.
• To develop and validate a novel hybrid algorithm for linkage analysis.

Main Methods:

• Combined linkage analysis and within-pedigree linkage trait-variant disequilibrium analysis.
• Developed a hybrid approach integrating Kelvin's trait model integration with JPSGCS's Markov chain Monte Carlo (McSample) for marker likelihood approximation.
• Applied the method to a simulated large pedigree with a two-locus trait model.

Main Results:

• Demonstrated the positional mapping template for large pedigrees.
• Validated the efficacy of the novel hybrid algorithm.
• Successfully fine-mapped variants in a simulated complex trait.

Conclusions:

• The hybrid algorithm enhances the precision of positional mapping in large pedigrees.
• This approach is effective for identifying disease-causing mutations even with complex genetic architectures.
• The method provides a powerful tool for genetic studies in large families.