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

Nat Commun. 2020 Sep 16;11(1):4662. doi: 10.1038/s41467-020-18320-z.


Haplotype reconstruction of distant genetic variants remains an unsolved problem due to the short-read length of common sequencing data. Here, we 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. We 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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Allelic Imbalance*
  • Databases, Genetic
  • Diploidy
  • Haplotypes*
  • Humans
  • K562 Cells
  • Models, Genetic
  • Models, Statistical
  • Polymorphism, Single Nucleotide
  • Polyploidy
  • RNA-Seq
  • Sequence Analysis, RNA* / methods
  • Sequence Analysis, RNA* / statistics & numerical data