Efficient genome ancestry inference in complex pedigrees with inbreeding.

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• High-density SNP data in model animals enables fine-resolution genetic variation studies.
• Model animal resources are generated via multi-generational breeding schemes from founder animals.
• Inferring genotype ancestry from founder genotypes is computationally challenging, especially with large, complex pedigrees.

Purpose of the Study:

• To develop an accurate and efficient method for inferring genome ancestry in model animal resources.
• To address the computational difficulties of existing methods with large pedigrees and complex inbreeding structures.
• To enable precise genetic variation studies using high-density SNP data.

Main Methods:

• Developed a hidden Markov model (HMM) to infer genome ancestry.
• The HMM derives ancestry probabilities through the inbreeding process without explicit generational modeling.
• The method is designed to handle complex pedigrees with inbreeding and repetitive substructures.

Main Results:

• The method accurately infers genome ancestry in complex pedigrees, including those with inbreeding.
• Inference speed is independent of the number of generations, offering significant scalability.
• Achieved comparable accuracy to explicit pedigree models but with substantially better scalability on large datasets like the Collaborative Cross (CC).

Conclusions:

• The presented method offers an accurate and efficient solution for genome ancestry inference in model animal resources.
• It overcomes computational limitations of existing approaches, particularly for large pedigrees with inbreeding.
• Facilitates advanced genetic variation studies by enabling precise genotype ancestry determination.