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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Published on: December 7, 2021

An efficient algorithm for haplotype inference on pedigrees with recombinations and mutations.

Yuri Pirola¹, Paola Bonizzoni, Tao Jiang

¹DISCo, University Milano-Bicocca, Milan, and Parco Tecnologico Padano, Lodi.

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|March 9, 2011

Summary

This summary is machine-generated.

This study introduces the Minimum-Change Haplotype Configuration (MCHC) problem for genetic studies using pedigrees. A new heuristic algorithm offers an efficient and accurate solution for haplotype inference, improving upon existing methods.

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Area of Science:

Computational genetics
Bioinformatics
Population genetics

Background:

Haplotype inference (HI) is vital for genetic studies.
Pedigrees offer higher accuracy for HI than population data due to Mendelian inheritance constraints.
Existing methods like Minimum-Recombinant Haplotype Configuration (MRHC) have limitations.

Purpose of the Study:

Define a new Minimum-Change Haplotype Configuration (MCHC) problem for pedigrees.
Incorporate both recombination and mutation events in haplotype inference.
Develop an efficient and accurate heuristic algorithm for MCHC and MRHC.

Main Methods:

Formulated the MCHC problem, extending the MRHC problem.
Proved MCHC is APX-hard under specific restrictions.
Developed a heuristic algorithm for MCHC using an L-reduction to a coding problem.
Established O(nm/(log nm))-approximability for MCHC and MRHC on general pedigrees.

Main Results:

The proposed heuristic algorithm is efficient and accurate for MCHC.
The heuristic also effectively solves the MRHC problem.
Demonstrated O(nm/(log nm))-approximability for both MCHC and MRHC.
Experimental evaluation shows competitive performance against state-of-the-art methods.

Conclusions:

The MCHC problem provides a more comprehensive model for haplotype inference on pedigrees.
The developed heuristic algorithm is a significant advancement for accurate and efficient haplotype inference.
This work advances the field of computational genetics by providing new theoretical and algorithmic contributions.