Haplotype Inference on Pedigrees with Recombinations, Errors, and Missing Genotypes via SAT solvers

TL;DR

This paper introduces HCRE, a generalized haplotype inference model that accounts for errors and missing data, and presents a SAT solver-based exact algorithm demonstrating practical effectiveness on simulated and real datasets.

Contribution

It extends the MRHC model to include errors and missing genotypes and develops a SAT-based exact algorithm for this more realistic and complex problem.

Findings

The SAT-based algorithm effectively solves HCRE instances.

HCRE provides more biologically accurate haplotype inference.

Experimental results show high accuracy and performance on real and simulated data.

Abstract

The Minimum-Recombinant Haplotype Configuration problem (MRHC) has been highly successful in providing a sound combinatorial formulation for the important problem of genotype phasing on pedigrees. Despite several algorithmic advances and refinements that led to some efficient algorithms, its applicability to real datasets has been limited by the absence of some important characteristics of these data in its formulation, such as mutations, genotyping errors, and missing data. In this work, we propose the Haplotype Configuration with Recombinations and Errors problem (HCRE), which generalizes the original MRHC formulation by incorporating the two most common characteristics of real data: errors and missing genotypes (including untyped individuals). Although HCRE is computationally hard, we propose an exact algorithm for the problem based on a reduction to the well-known Satisfiability problem. Our reduction exploits recent progresses in the constraint programming literature and, combined with the use of state-of-the-art SAT solvers, provides a practical solution for the HCRE problem. Biological soundness of the phasing model and effectiveness (on both accuracy and performance) of the algorithm are experimentally demonstrated under several simulated scenarios and on a real dairy cattle population.