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Updated: Jun 3, 2026
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Infinium Assay for Large-scale SNP Genotyping Applications
Published on: November 19, 2013
A combined long-range phasing and long haplotype imputation method to impute phase for SNP genotypes
John M Hickey1, Brian P Kinghorn, Bruce Tier
1University of New England, Armidale, Australia. john.hickey@une.edu.au
Genetics, Selection, Evolution : GSE
|March 11, 2011
Summary
A new algorithm accurately phases single nucleotide polymorphism (SNP) data using long-range phasing and haplotype imputation, improving genome-wide association studies. This method is fast and efficient for large datasets.
Area of Science:
- Genomics
- Bioinformatics
- Statistical Genetics
Background:
- Phasing marker genotype data is crucial for advanced genome-wide association studies (GWAS).
- Accurate phasing enables identity-by-descent analysis and increases data utility through imputation.
- Long-range phasing and haplotype library imputation offer a fast and precise method for SNP data phasing.
Purpose of the Study:
- To develop and evaluate a novel algorithm for long-range phasing and haplotype library imputation.
- To create a method for resolving phase that is independent of family structure or pedigree information.
Main Methods:
- Developed a long-range phasing and haplotype library imputation algorithm.
- Combined information from surrogate parents and long haplotypes to determine allele phase.
- Algorithm's performance assessed on simulated and real livestock and human datasets.
Main Results:
- Achieved high phasing accuracy, with over 98% of alleles phased correctly in most datasets.
- Reported less than 0.5% incorrect phasing in simulated data.
- Demonstrated computational efficiency, running 26 times faster than fastPHASE on small datasets with fewer errors.
- Phasing accuracy was robust across varying population sizes, family structures, and SNP densities, though dataset size below 1000 showed lower accuracy.
Conclusions:
- The developed algorithm and software facilitate routine phasing of high-density SNP chips in large-scale genomic datasets.
- This advancement supports more powerful and comprehensive genome-wide association studies.
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