Improved haplotype inference by exploiting long-range linking and allelic imbalance in RNA-seq datasets

Haplotype reconstruction of distant genetic variants remains an unsolved problem due to the short-read length of common sequencing data. Here, MIT researchers introduce HapTree-X, a probabilistic framework that utilizes latent long-range information to reconstruct unspecified haplotypes in diploid and polyploid organisms. It introduces the observation that differential allele-specific expression can link genetic variants from the same physical chromosome, thus even enabling using reads that cover only individual variants. The researchers demonstrate HapTree-X’s feasibility on in-house sequenced Genome in a Bottle RNA-seq and various whole exome, genome, and 10X Genomics datasets. HapTree-X produces more complete phases (up to 25%), even in clinically important genes, and phases more variants than other methods while maintaining similar or higher accuracy and being up to 10×  faster than other tools. The advantage of HapTree-X’s ability to use multiple lines of evidence, as well as to phase polyploid genomes in a single integrative framework, substantially grows as the amount of diverse data increases.

HapTree-X framework compared to read-based phasing

Fig. 1

Traditional whole-genome sequencing (WGS) based phasing methods (top panel) depend on sequence contiguity and thus require a pair of SNPs (in red) to be connected through a common read that overlaps both in order to be phased. RNA-seq reads provide longer distance phasing capability due to long introns in the genome that are spliced-out in the sequenced transcript fragments (middle panel), yet SNPs that are far apart within the transcript due to long homozygous exonic regions are still difficult to phase using RNA-seq reads. Our HapTree-X framework (lower panel) overcomes this limitation by integrating RNA-seq reads and differential allele-specific expression (DASE) available from the RNA-seq data into a single probabilistic framework for haplotype phasing. For genes that display differential haplotypic expression (DHE), the majority of alleles can be phased together to obtain a single haplotype block for the entire gene. Depending on the DHE and depth-coverage, DASE-based phasing performs accurate haplotype reconstruction, without requiring paired-end or long reads, maintaining or improving on accuracy independent of gene/exon lengths as long as differential haplotypic expression is consistent across the loci being phased.

Berger E, Yorukoglu D, Zhan L et al. (2020) Improved haplotype inference by exploiting long-range linking and allelic imbalance in RNA-seq datasets. Nat Commun 11, 4662. [article]

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