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Infinium Assay for Large-scale SNP Genotyping Applications

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Published on: November 19, 2013

Extending long-range phasing and haplotype library imputation algorithms to large and heterogeneous datasets.

Daniel Money¹, David Wilson¹, Janez Jenko¹

¹The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK.

Genetics, Selection, Evolution : GSE

|July 10, 2020

Summary

Improved AlphaPhase algorithms enable rapid phasing of large, diverse genomic datasets. This accelerates livestock breeding and genetics research by making large-scale phasing computationally feasible.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Previous long-range phasing (LRP) and haplotype library imputation (HLI) algorithms struggled with large datasets and heterogeneous single nucleotide polymorphism (SNP) data.
Computational demands of traditional LRP, particularly exhaustive searches for surrogate parents, limited scalability.
Existing methods were not optimized for the missing data prevalent in multi-SNP array datasets.

Purpose of the Study:

To enhance LRP and HLI algorithms for efficient phasing of large-scale genomic datasets.
To adapt algorithms for datasets with varying SNP sets and high missing data rates.
To improve the computational speed and accuracy of genomic data phasing.

Main Methods:

Developed a novel LRP approach by phasing subsets of individuals and concatenating results, avoiding exhaustive searches.
Extended LRP and HLI to accommodate diverse marker sets and missing data for surrogate parent identification and haplotype determination.
Implemented and validated these improvements in an updated AlphaPhase version, comparing performance against Eagle2.

Main Results:

The updated AlphaPhase achieved significantly faster phasing times for large, simulated datasets compared to Eagle2.
Phasing accuracy for AlphaPhase reached 90.2% on a one-million-individual dataset with identical SNPs, while Eagle2 achieved 99.9%.
Accuracy slightly decreased with heterogeneous SNP sets (1.5% incorrect phasing) compared to uniform sets (0.4% incorrect phasing).

Conclusions:

The enhanced AlphaPhase algorithms provide a scalable and rapid solution for phasing large, heterogeneous genomic datasets.
AlphaPhase offers an order-of-magnitude speed improvement over other packages, facilitating large-scale genomic research in livestock.
While Eagle2 demonstrated higher accuracy, AlphaPhase's speed enables previously infeasible analyses in breeding and genetics.