Exploring effective approaches for haplotype block phasing

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.

A novel haplotype-based eQTL approach identifies genetic associations not detected through conventional SNP-based methods

“…These results support the application of haplotype-based eQTL analysis on real datasets where SE is usually less than 2.5% [12] especially given there is little impact on the TPR.…”

“…To obtain realistic SEs, the formed genotypes were then phased using HAPI-UR [25] 100 times and all SEs were recorded. HAPI-UR was used as it is fast and non-deterministic by default [12]. The average SE of the 100 applications was 0.78%, while the maximum SE obtained when considering all unique SE locations was 2%.…”

“…Moreover, decisions also need to be made about how to form haplotype blocks and how to represent these blocks when evaluating their associations with gene expression levels. We have shown that when dealing with haplotype blocks, the choice of partitioning method has an impact on the phasing errors [12]. Previous approaches using haplotypes for eQTL analysis have defined blocks from pairs of SNPs [9], combinations of up to 4 SNPs [10] or regulatory regions [13] but it is unclear how the accuracy of the determined haplotypes is influenced by these partitioning approaches.…”

“…Previous approaches using haplotypes for eQTL analysis have defined blocks from pairs of SNPs [9], combinations of up to 4 SNPs [10] or regulatory regions [13] but it is unclear how the accuracy of the determined haplotypes is influenced by these partitioning approaches. In contrast, recent work has shown that existing phasing tools can achieve high accuracy within the haplotype blocks defined using linkage disequilibrium (LD) [14,12]. Such results encourage using LD-based blocks in this context.…”

Linked-read sequencing enables haplotype-resolved resequencing at population scale

“…Moreover, the phasing accuracies estimated here compare to values reported for human genomes sequenced with the same technology at 25x coverage (<0.4%, Porubsky et al 2017). Not even with parent-offspring trios current state-of-the-art statistical phasing approaches reach as high phasing accuracy as we find at this moderate coverage (Al Bkhetan et al 2019).…”