Haplotype phase may be generated through either computational or experimental methods.
Computational phasing is simple and inexpensive and results in good accuracy for common variants over small regions.
Computational phasing of closely related individuals (such as parent–offspring trios) results in high accuracy at a high proportion of sites because of the additional information provided by Mendelian constraints.
Although specialized software for analysing complex relationships is somewhat limited, good results can be obtained by treating the related individuals as if they were unrelated when performing computational phasing.
A new development in computational phasing of unrelated individuals is the detection and use of segments of identity-by-descent that arise from distant relationships. In their current form, these methods are only suitable for small, isolated populations, but improvements in algorithms may lead to applicability to large samples from outbred populations.
Experimental phasing has a very high accuracy at a high proportion of sites and can phase de novo or very rare variants without the need to obtain data from closely related individuals.
Experimental phasing currently adds substantially to the cost of generating the genotype or sequence data (at least doubling the cost) and requires technical expertise, additional preparation time and, in some cases, specialized equipment.
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.
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This study was supported by the US National Institutes of Health (NIH) awards R01HG005701 and R01HG004960. This study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the data is available from http://www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under awards 076113 and 085475. The content of this study is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the Wellcome Trust.
The authors declare no competing financial interests.
In the context of this article, this is the estimation of missing genotype values by using the genotypes at nearby SNPs and the haplotype frequencies seen in other individuals.
- Calling genotypes
Estimating genotype values from raw data. Genotyping technology provides information about the underlying genotype, typically in the form of signal intensities or read counts of the two alleles. Statistical techniques are used to resolve this information into genotype calls. Typically, information across individuals is used, and correlation across SNPs (that is, haplotype phase) is also helpful.
Two haplotypes are identical-by-descent if they are identical copies of a haplotype inherited from a common ancestor.
- Cryptic relatedness
The undocumented existence of relatives within a sample.
- Posterior distribution
Probabilities that account for the prior information and the information in the data. For haplotype phase estimation, the posterior distribution accounts for all available information, including the genotypes and the estimated haplotype frequencies in the population.
- Expectation maximization algorithm
(EM algorithm). An iterative approach for finding the values of the unobserved data (such as haplotype phase) that maximize the statistical likelihood of the observed incomplete data. Although the likelihood increases with each iteration, the approach is not guaranteed to find the global maximum.
A divide-and-conquer strategy that is designed to reduce the computational burden for phasing methods that do not scale well with increasing region size. A large region is divided up into smaller regions, and haplotype phase estimates from the smaller regions are used to limit the possibilities when phasing the large region.
- Hidden Markov model
(HMM). A mathematically elegant and computationally tractable class of models in which the observed data are generated by an unobserved Markov process. A Markov process is a probabilistic process in which the distribution of future states (for example, states that are further along the chromosome) depends only on the current state and not on previous states.
- Haplotype block
A short genomic region within which inter-marker linkage disequilibrium is strong.
- Approximate coalescent
The coalescent is a model for the process by which the ancestry of alleles converges when looking back in time. An approximate coalescent is a model that generates patterns of genetic variation that are similar to patterns generated by the coalescent but that is computationally simpler.
- Linkage disequilibrium
(LD). Non-independence (correlation) between genetic variants at the population level. In general, LD decreases with genomic distance and is not present between variants on different chromosomes.
- Effective population size
The size of a population of randomly mating individuals that would show the same amount of genetic drift as is found in the actual population. The effective population size is usually smaller than the actual population size.
- Compound heterozygosity
The presence of two deleterious variants located in the same gene but on different chromosome copies of an individual. It is possible to distinguish between compound heterozygosity and the occurrence of two variants on the same chromosome copy by determining the haplotype phase.
A measure of linkage disequilibrium (LD) between two markers. D′ takes values between 0 and 1. Absence of LD is indicated by 0, and 1 indicates maximum possible LD given the allele frequency of the markers.
- Reference panel
A collection of samples that are not of direct interest but that are included in an analysis for the purposes of increasing statistical power or accuracy for the samples of interest. Reference panels are commonly used for genotype imputation and can also be used for haplotype phasing.
- Genotype likelihoods
Statistical likelihoods that encapsulate the relative evidence for each possible genotype call.
- Fluorescence-activated cell sorting
(FACS). A type of flow cytometry in which individual particles (such as chromosomes) are separated and fluorescence intensities (from earlier staining) are measured.
- Barcode labelling
Tagging of each sample with a unique short sequence (barcode) before pooling samples. After sequencing, the sample corresponding to each read can be determined from the barcode.
- Admixed ancestry
An individual has admixed ancestry if he or she has recent ancestors deriving from different continental populations.
- Large-insert clones
Large haplotype fragments that are inserted into, for example, bacterial artificial chromosomes (BACs).
- Shotgun sequencing
A sequencing method in which DNA is randomly sheared into small fragments before being sequenced.
A type of hybrid DNA molecule comprising bacterial DNA and a section of genomic DNA of ~40 kb in length.
The manipulation of fluids on a very small scale. This approach can be used to separate individual chromosomes before sequencing for experimental phasing.
A stage of mitosis at which chromosomes are highly condensed, facilitating their separation for some experimental phasing methods.
- Paired-end sequencing
Sequencing of haplotype fragments from each end. The two sequenced ends are typically separated by a gap.
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Browning, S., Browning, B. Haplotype phasing: existing methods and new developments. Nat Rev Genet 12, 703–714 (2011). https://doi.org/10.1038/nrg3054
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