Abstract
BackgroundKnowledge of phase, the specific allele sequence on each copy of homologous chromosomes, is increasingly recognized as critical for detecting certain classes of disease-associated mutations. One approach for detecting such mutations is through phased haplotype association analysis. While the accuracy of methods for phasing genotype data has been widely explored, there has been little attention given to phasing accuracy at haplotype block scale. Understanding the combined impact of the accuracy of phasing tool and the method used to determine haplotype blocks on the error rate within the determined blocks is essential to conduct accurate haplotype analyses.ResultsWe present a systematic study exploring the relationship between seven widely used phasing methods and two common methods for determining haplotype blocks. The evaluation focuses on the number of haplotype blocks that are incorrectly phased. Insights from these results are used to develop a haplotype estimator based on a consensus of three tools. The consensus estimator achieved the most accurate phasing in all applied tests. Individually, EAGLE2, BEAGLE and SHAPEIT2 alternate in being the best performing tool in different scenarios. Determining haplotype blocks based on linkage disequilibrium leads to more correctly phased blocks compared to a sliding window approach. We find that there is little difference between phasing sections of a genome (e.g. a gene) compared to phasing entire chromosomes. Finally, we show that the location of phasing error varies when the tools are applied to the same data several times, a finding that could be important for downstream analyses.ConclusionsThe choice of phasing and block determination algorithms and their interaction impacts the accuracy of phased haplotype blocks. This work provides guidance and evidence for the different design choices needed for analyses using haplotype blocks. The study highlights a number of issues that may have limited the replicability of previous haplotype analysis.
Highlights
Knowledge of phase, the specific allele sequence on each copy of homologous chromosomes, is increasingly recognized as critical for detecting certain classes of disease-associated mutations
Different error locations obtained by different phasing tools The switch errors observed across the six tools in chromosome 1 occurred at 12,145 different loci out of a possible 36,923 heterozygous single nucleotide polymorphism (SNP)
EAGLE2 and BEAGLE both had a switch error of 5.4% (20 switches/372 heterozygous SNPs) in the same example, yet the estimations are different. Such examples motivate the development of metrics that may be more relevant to phased haplotype association analysis and that capture the error rate of the haplotype blocks used for downstream statistical analysis
Summary
The specific allele sequence on each copy of homologous chromosomes, is increasingly recognized as critical for detecting certain classes of disease-associated mutations. While the accuracy of methods for phasing genotype data has been widely explored, there has been little attention given to phasing accuracy at haplotype block scale. Most genetic studies focus on analyzing genotypes to detect significant genetic associations with diseases [1]. It has long been recognized that some diseaseassociated haplotypes, the specific allele sequence on each copy of homologous chromosomes, may be undetectable with a focus on genotype alone [2, 3]. The different allocation of specific alleles on each copy of a chromosome pair (which is ignored by genotype analysis). The phased blocks are assessed statistically to determine significant association with disease
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