title: Leveraging reads that span multiple single nucleotide polymorphisms for haplotype inference from sequencing data.
abstract: BACKGROUND
  Haplotypes, defined as the sequence of alleles on one chromosome, are crucial for many genetic analyses. As experimental determination of haplotypes is extremely expensive, haplotypes are traditionally inferred using computational approaches from genotype data, i.e. the mixture of the genetic information from both haplotypes. Best performing approaches for haplotype inference rely on Hidden Markov Models, with the underlying assumption that the haplotypes of a given individual can be represented as a mosaic of segments from other haplotypes in the same population. Such algorithms use this model to predict the most likely haplotypes that explain the observed genotype data conditional on reference panel of haplotypes. With rapid advances in short read sequencing technologies, sequencing is quickly establishing as a powerful approach for collecting genetic variation information. As opposed to traditional genotyping-array technologies that independently call genotypes at polymorphic sites, short read sequencing often collects haplotypic information; a read spanning more than one polymorphic locus (multi-single nucleotide polymorphic read) contains information on the haplotype from which the read originates. However, this information is generally ignored in existing approaches for haplotype phasing and genotype-calling from short read data.
  
  
  RESULTS
  In this article, we propose a novel framework for haplotype inference from short read sequencing that leverages multi-single nucleotide polymorphic reads together with a reference panel of haplotypes. The basis of our approach is a new probabilistic model that finds the most likely haplotype segments from the reference panel to explain the short read sequencing data for a given individual. We devised an efficient sampling method within a probabilistic model to achieve superior performance than existing methods. Using simulated sequencing reads from real individual genotypes in the HapMap data and the 1000 Genomes projects, we show that our method is highly accurate and computationally efficient. Our haplotype predictions improve accuracy over the basic haplotype copying model by ∼20% with comparable computational time, and over another recently proposed approach Hap-SeqX by ∼10% with significantly reduced computational time and memory usage.
  
  
  BACKGROUND
  Publicly available software is available at http://genetics.cs.ucla.edu/harsh
  
  
  BACKGROUND
  bpasaniuc@mednet.ucla.edu or eeskin@cs.ucla.edu.
authors.affiliation: Department of Computer Science and Inter-Departmental Program in Bioinformatics, University of California, Los Angeles, CA 90095, USA.
authors.forename: Wen-Yun
authors.initials: WY
authors.lastname: Yang
authors.forename: Farhad
authors.initials: F
authors.lastname: Hormozdiari
authors.forename: Zhanyong
authors.initials: Z
authors.lastname: Wang
authors.forename: Dan
authors.initials: D
authors.lastname: He
authors.forename: Bogdan
authors.initials: B
authors.lastname: Pasaniuc
authors.forename: Eleazar
authors.initials: E
authors.lastname: Eskin
mesh_terms: D000465:Algorithms; D000483:Alleles; D015894:Genome, Human; D060005:Genotyping Techniques; D060148:HapMap Project; D006239:Haplotypes; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software