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

Emily Berger, Deniz Yorukoglu, Lillian Zhang, Sarah K. Nyquist, Alex K. Shalek, Manolis Kellis, Ibrahim Numanagić () and Bonnie Berger ()
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Emily Berger: Massachusetts Institute of Technology
Deniz Yorukoglu: Massachusetts Institute of Technology
Lillian Zhang: Massachusetts Institute of Technology
Sarah K. Nyquist: Massachusetts Institute of Technology
Alex K. Shalek: Massachusetts Institute of Technology
Manolis Kellis: Massachusetts Institute of Technology
Ibrahim Numanagić: Massachusetts Institute of Technology
Bonnie Berger: Massachusetts Institute of Technology

Nature Communications, 2020, vol. 11, issue 1, 1-9

Abstract: Abstract 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.

Date: 2020
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DOI: 10.1038/s41467-020-18320-z

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