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Related Experiment Video

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

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

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Benchmarking phasing software with a whole-genome sequenced cattle pedigree.

Claire Oget-Ebrad¹, Naveen Kumar Kadri², Gabriel Costa Monteiro Moreira³

¹Unit of Animal Genomics, GIGA-R and Faculty of Veterinary Medicine, University of Liège (B34), 4000, Liège, Belgium. claire.oget@uliege.be.

|February 15, 2022

Summary

Accurate haplotype reconstruction was assessed in Holstein cattle using whole-genome sequence data. ShapeIT4.1 and Beagle5.2 demonstrated superior performance, achieving high phasing accuracy for genomic applications.

Keywords:

Cattle Haplotype Phasing Sequencing data

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Area of Science:

Genomics
Population Genetics
Bioinformatics

Background:

Accurate haplotype reconstruction is crucial for quantitative and population genomics.
Evaluating phasing methods is essential for diverse sample types.
Livestock populations present unique challenges like low effective population size and high relatedness.

Purpose of the Study:

To evaluate the accuracy of population-based haplotype phasing methods.
To compare the performance of different phasing software using whole-genome sequence data from a Holstein cattle pedigree.
To assess phasing accuracy under different scenarios, including varying numbers of individuals and pedigree information.

Main Methods:

Utilized whole-genome sequence data from 264 Holstein cattle, including 98 trios.
Filtered sequence data and applied population-based phasing programs (AlphaPhase, ShapeIT, Beagle, Eagle, FImpute).
Assessed accuracy using switch error counts/rates, haplotype block lengths, and error probability as a function of SNP distance, with trio-based phasing as the gold standard.

Main Results:

ShapeIT4.1 and Beagle5.2 were the most accurate phasing methods across most metrics and scenarios.
ShapeIT4.1 achieved a median switch error count of 50 per individual and a mean haplotype block length of 24.1 Mb in scenario 2.
FImpute3.0 excelled at reconstructing long, error-free segments when more relatives were included (scenario 2).

Conclusions:

Extremely high haplotype phasing accuracies were achieved in a typical livestock population.
ShapeIT4.1 and Beagle5.2 are recommended for their superior accuracy, especially for long segments.
Most evaluated tools offer high accuracy for short distances, suitable for applications needing local haplotypes.