Recursive Algorithms for Modeling Genomic Ancestral Origins in a Fixed Pedigree

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Gene flow analysis in pedigrees is crucial for advancing quantitative trait loci (QTL) mapping in multiparental populations.
• Existing methods for modeling ancestral origins often face computational challenges due to exponentially increasing state spaces with pedigree size.

Purpose of the Study:

• To develop an efficient Markovian framework for modeling ancestral origins along homologous chromosomes in fixed pedigrees.
• To introduce novel recursive algorithms for parameter estimation in the Markov process, applicable to both autosomes and sex chromosomes.
• To demonstrate the utility of the framework in optimizing breeding schemes and enhancing ancestral inference for QTL mapping.

Main Methods:

• Developed a Markovian framework to model ancestral origins within individuals in fixed pedigrees.
• Introduced two novel recursive algorithms for calculating Markov process parameters, considering founder exchangeability.
• Validated algorithm accuracy through extensive simulations (one million) on a representative pedigree.

Main Results:

• The developed framework's state space size scales linearly or quadratically with the number of pedigree founders, offering significant computational advantages over existing methods.
• Algorithms demonstrated accuracy and applicability to both autosomes and sex chromosomes.
• Applications in multiparental populations include designing breeding schemes for enhanced recombination breakpoint density and integrating pedigree information into hidden Markov models for ancestral inference.

Conclusions:

• The novel Markovian framework and recursive algorithms provide a computationally efficient and versatile tool for genetic analysis.
• The method significantly expands the application range of genetic analysis, particularly for ancestral inference and QTL mapping in multiparental populations.
• This approach facilitates improved breeding strategies and more precise genetic mapping studies.