Haplotype phasing: Existing methods and new developments

By Sharon R. Browning, Brian L. Browning. Nature reviews genetics, 2011

628 citations recorded by the source corpus.

Determination of haplotype phase is becoming increasingly important as we enter the era of large-scale sequencing because many of its applications, such as imputing low-frequency variants and characterizing the relationship between genetic variation and disease susceptibility, are particularly relevant to sequence data. Haplotype phase can be generated through laboratory-based experimental methods, or it can be estimated using computational approaches. We assess the haplotype phasing methods that are available, focusing in particular on statistical methods, and we discuss the practical aspects of their application. We also describe recent developments that may transform this field, particularly the use of identity-by-descent for computational phasing.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7d6d1d011d0de7ade1cb898852834b1782906351

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Citations in the corpus

Robust and scalable inference of population history from hundreds of unphased whole-genomes (2016, background): “The other two contained artificially induced switch errors at rates of 1% and 5%, which are approximate upper and lower bounds on the accuracy of existing phasing algorithms 29,30 .”
A new approach for efficient genotype imputation using information from relatives (2014, background): “The relationship between reference and target groups significantly influences phasing and imputation performance [14].”
HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies (2017, background): “…phasing using population genotype data have proven successful for phasing common variants and for genotype imputation but are limited in their ability to phase rare variants and phase long stretches of the genome that cross recombination hot-spots (Browning and Browning 2011; Tewhey et al. 2011).”
A method for genome-wide genealogy estimation for thousands of samples (2019, methodology): “We combined the genome-wide significant GWAS hits (p < 5 ⇥ 10 8) of the GWAS catalogue [3] and the UK Biobank project [4].”
Stacks 2: Analytical Methods for Paired-end Sequencing Improve RADseq-based Population Genomics (2019, methodology): “Stacks v2 implements a read‐based phasing approach (as opposed to statistical phasing; Browning & Browning, 2011) that relies on the co‐observation, in a given read (or read pair), of the alleles at several SNPs.”
Reference-based phasing using the Haplotype Reference Consortium panel (2016, methodology): “We then applied each reference-based phasing method to phase chromosomes 1, 5, 10, 15, and 20 of the N target = 72 trio children using each simulated reference panel, and we compared the phased output against trio phase calls to compute switch error rates [2, 13].”
WhatsHap: fast and accurate read-based phasing (2016, methodology): “Many different implementations of this idea exist, as reviewed in [1].”
Piercing the dark matter: bioinformatics of long-range sequencing and mapping (2018, methodology): “Another popular method is to use statistical inference from large collections of genomes to impute the haplotypes 87 .”
HIBAG – HLA Genotype Imputation with Attribute Bagging (2013, neutral): “13 It is a computationally efﬁcient approach with high accuracy for thousands of samples and markers.”
Fast and accurate long-range phasing in a UK Biobank cohort (2015, positive): “For each SNP, we computed adjusted R2 between actual and imputed masked genotype values according to the formula adjusted ∶= − , (1)”
Haplotype estimation using sequencing reads. (2013, methodology): “The literature on phasing methods is extensive.(5) Most methods were designed to work on SNP genotypes derived from microarray genotyping chips.”