A haplotype inference algorithm for trios based on deterministic sampling
 Alexandros Iliadis^{1},
 John Watkinson^{1},
 Dimitris Anastassiou^{1}Email author and
 Xiaodong Wang^{1}
https://doi.org/10.1186/147121561178
© Iliadis et al; licensee BioMed Central Ltd. 2010
Received: 25 November 2009
Accepted: 23 August 2010
Published: 23 August 2010
Abstract
Background
In genomewide association studies, thousands of individuals are genotyped in hundreds of thousands of single nucleotide polymorphisms (SNPs). Statistical power can be increased when haplotypes, rather than threevalued genotypes, are used in analysis, so the problem of haplotype phase inference (phasing) is particularly relevant. Several phasing algorithms have been developed for data from unrelated individuals, based on different models, some of which have been extended to fathermotherchild "trio" data.
Results
We introduce a technique for phasing trio datasets using a treebased deterministic sampling scheme. We have compared our method with publicly available algorithms PHASE v2.1, BEAGLE v3.0.2 and 2SNP v1.7 on datasets of varying number of markers and trios. We have found that the computational complexity of PHASE makes it prohibitive for routine use; on the other hand 2SNP, though the fastest method for small datasets, was significantly inaccurate. We have shown that our method outperforms BEAGLE in terms of speed and accuracy for small to intermediate dataset sizes in terms of number of trios for all marker sizes examined. Our method is implemented in the "TreeBased Deterministic Sampling" (TDS) package, available for download at http://www.ee.columbia.edu/~anastas/tds
Conclusions
Using a TreeBased Deterministic sampling technique, we present an intuitive and conceptually simple phasing algorithm for trio data. The trade off between speed and accuracy achieved by our algorithm makes it a strong candidate for routine use on trio datasets.
Keywords
Background
Large genetic association studies involving thousands of individuals are becoming increasingly available, providing opportunities for biological and medical discoveries using sophisticated computational and statistical analysis [1]. Typically, individuals are genotyped using high throughput platforms so that each of hundreds of thousands of single nucleotide polymorphisms (SNPs) is assigned one of three values: homozygous major, homozygous minor, and heterozygous.
Rather than examining SNPs independent of each other, simultaneously considering the values of multiple SNPs within haplotypes (combinations of alleles at multiple loci in individual chromosomes) can improve the power of detecting associations with disease and is helpful in several applications, such as evolutionary genetics [2–6]. Since there are numerous haplotype arrangements for heterozygous SNPs that are consistent with the available threelevel genotyped values, the problem of inferring haplotype phase ("phasing") becomes particularly relevant. Such inference is based on modelling the mechanisms and the biological processes generating sequence variation. Associated computational and statistical techniques can be used on population samples, based on parsimony, the HardyWeinberg principle that allele and genotype frequencies in a population remain constant in equilibrium, Markov chain Monte Carlo, hidden Markov models, Expectation Minimization (EM) algorithm, etc. Most algorithms are designed to be generally used for unrelated individuals. Use of pedigree information [7], if available, is useful. In particular, "trio" data consisting of genotypes given in fathermotherchild triplets are obtained in genomewide association studies and some phasing algorithms are adapted to be used in this type of data.
An algorithm (PHASE [8]) used in the HapMap project [9] uses a Bayesian approach attempting to capture the tendency that haplotypes cluster together over regions of the chromosome and that this clustering can change as we move along the chromosome because of recombination. It uses a flexible model for the decay of linkage disequilibrium (LD, the nonrandom association of alleles) with distance. Although PHASE is considered the most accurate method, its computational complexity makes it prohibitively slow even for intermediatesized datasets. Thus, it may not be the method of choice for routine use in large genomewide association studies. On the other extreme of the tradeoff between complexity and accuracy, a computationally simple method (2SNP [10]) uses maximum spanning trees to successively phase whole genotypes starting from SNP pairs. Other well known approaches include HAP [11], using imperfect phylogeny, HAP2 using a Markov Chain Monte Carlo (MCMC) scheme [12] and PLEM [13], which uses an Expectation Maximization (EM) algorithm. A Gibbs sampling method, Haplotyper, is proposed in [14], which introduces the partitionligation (PL) method to support haplotype inference on long genotype vectors, a procedure adopted by some of the aforementioned methods so that they can be extended to large datasets. An obvious problem of the Gibbs sampler and of most of the previous frameworks is that when new data is introduced into the original dataset, the previous data also has to be reused in the estimation of the new data. Another drawback of using Gibbs sampler and EM algorithm in the haplotype inference problem is the lack of robustness of these two algorithms when the parameter space exhibits multimodality such as the one we encounter in the haplotype inference problem [15]. The performance of these methods has been evaluated in simulated datasets of both trio as well as unrelated individuals in a comparative review [16], providing some "gold standard" datasets for future algorithms to be compared upon. A more recent approach (BEAGLE [17, 18]) uses localized haplotype clustering and fits the data using an EMstyle update.
It is important for phasing methods that they scale well with the number of SNPs as well as the number of individuals. It is also important in terms of computational time that when new data is inserted in phased datasets, we do not have to reuse the previous data in the estimation of the new data. A deterministic sequential Monte Carlo (DSMC)  based phasing algorithm [19] has recently been proposed for unrelated individuals, allowing for large datasets. It jointly infers haplotype pair solutions and estimates haplotype frequencies based on HardyWeinberg equilibrium. It also uses a partition ligation method to allow processing of large SNP sets.
In this paper, we propose a related new TDS algorithm for haplotype phasing of trio data, in which trios are processed sequentially. All possible solutions for each haplotype are examined. Our algorithm uses the idea that within haplotype blocks there is limited haplotype diversity and thus attempts to phase each new trio using haplotypes that have already been encountered in the previously seen trios. The TDS framework allows us to effectively perform this search in the space of all possible solution combinations. The procedure can be seen as an efficient tree search procedure where in each step only "the most probable" solution streams are kept. Each of them contains one and only one solution for each trio already encountered. We show that our algorithm demonstrates an excellent tradeoff of speed and accuracy, making it ideal for routine use.
Results
The structure of this section is as follows: First we describe the datasets and figures of merit used to evaluate the method. Then we present the results from comparing our method to PHASE v2.1, BEAGLE v3.0.2 and 2SNP v1.7.
Datasets
We used a set of simulated datasets produced with the "COSI" software as provided in [16]. The haplotypes were simulated using a coalescent model that incorporates variation in recombination rates and demographic events and the parameters of the model were chosen to match aspects of data from a sample of Caucasian Americans [20, 16]. Three classes of dataset were provided, with each consisting of 20 sets of 30 trios spanning 1 Mb of sequence with a density of 1 SNP per 5 kb [16].
We also used the "COSI" software to create our own realistic simulated data sets to assess the performance of our method on large datasets. We created 20 datasets, each of them consisting of 4000 haplotypes with 20 Mb of marker data using the "bestfit" parameters obtained from fitting a coalescent model to the real data. Samples were taken from a European population and each simulated data set has a recombination rate sampled from a distribution matching the deCODE map, with recombination clustered into hotspots. For each simulated data set, we initially selected only those markers with minor allele frequency greater than 0.05. Markers were then randomly selected to obtain a density of about 1 SNP per 3 kb. In each dataset two sample sizes were created: 100 and 1000 trios. In each trio, each parent was randomly assigned a haplotype from the population so that no two individuals had the same haplotype and one of the haplotypes of each parent was selected to be transmitted to the child.
Definitions of criteria
Transmission Error Rate: The proportion of nonmissing parental genotypes with ambiguous phase that were incorrectly phased [18].
Incorrect Trios (IT): The number of trios for which phasing was not completely correct.
Computational Time: The average time to complete phasing. Our algorithm was implemented in Java for portability, memory efficiency and speed. For each method we recorded the average computational time in each dataset on a 3.66 GHz Xeon Intel PC with 8 GB of RAM.
Memory: The memory required by the software to complete haplotype inference.
Transmission Error Rate and Incorrect Trios
Average Transmission Error Rate For Phasing Trios
Average Transmission Error Rate (%)  

ST1  ST2  ST3  
PHASE  0.0013  0.0013  0.0145 
BEAGLE  
R = 1  0.0235  0.0318  0.0426 
R = 4  0.0150  0.0148  0.0344 
TDS  0.0039  0.0065  0.0320 
2SNP  0.4377  0.4868  0.4861 
Average number of Incorrect Trios per dataset
Incorrect Trios  

ST1  ST2  ST3  
PHASE  0.3  0.4  2.45 
BEAGLE  
R = 1  3.75  5.8  6.4 
R = 4  1.95  2.9  5.45 
TDS  0.95  1.6  5.4 
2SNP  25.9  28.6  28 
Average Transmission Error Rate For Phasing Trios with 1% Missing Rate
Average Transmission Error Rate (%)  

ST1  ST2  ST3  
PHASE  0.0031  0.0023  0.0161 
BEAGLE  
R = 1  0.0213  0.0248  0.0354 
R = 4  0.0093  0.0133  0.0278 
TDS  0.0094  0.0116  0.0348 
2SNP  0.3038  0.3486  0.3169 
Average number of Incorrect Trios per dataset with 1% Missing Rate
Incorrect Trios  

ST1  ST2  ST3  
PHASE  0.6  0.475  2.653 
BEAGLE  
R = 1  3.6054  5.25  6.4661 
R = 4  1.7464  3.1321  4.8893 
TDS  1.7521  2.7018  5.7768 
2SNP  26.05  28.55  28.2 
Average Transmission error rate for 100 and 1000 Trios as a function of the number of markers
Markers  

200  400  1000  6000  
TDS  100  0.00063  0.00075  0.0015  0.0023 
1000  0.00042  0.0008  0.0015  0.0023  
Beagle  100  0.0013  0.0013  0.0021  0.0024 
1000  0.00011  0.00033  0.0005  0.0007  
2SNP  100  0.1094  0.2855  0.3916  0.4315 
1000  0.1733  0.2524  0.3836  0.4117 
Timing Results
Timing Results
Time(s)  

ST1  ST2  ST3  
PHASE  8452  4932  5464 
BEAGLE  
R = 1  2.59  2.73  2.95 
R = 4  2.80  3.18  3.27 
TDS  1.99  2.48  2.61 
2SNP  0.63  0.6  0.59 
Timing Results with 1% Missing Rate
Time(s)  

ST1  ST2  ST3  
PHASE  8613  5220  5831 
BEAGLE  
R = 1  2.6744  2.9873  3.2409 
R = 4  2.9233  3.2858  3.4429 
TDS  2.0643  2.5815  2.7484 
2SNP  0.67  0.63  0.6 
Average Timing Results in seconds for 100 and 1000 Trios as a function of the number of markers
Markers  

200  400  1000  6000  
TDS  100  2.8  5  14.4  113.6 
1000  31.8  63.3  156.2  1257.4  
Beagle  100  3.7  5.6  15.2  118.4 
1000  12.7  31.6  291.8  1952.4  
2SNP  100  3  8.9  28.7  180.7 
1000  33.4  116.2  399.8  3008.2 
Memory Requirements
All methods could complete the experiments within the preallocated 1.5 Gb of RAM.
Discussion
An important feature of our algorithm is the partition of the whole genotype sequence in smaller blocks that exhibit limited haplotype diversity. We currently identify these haplotype blocks based on the genotype sequences (see Haplotype Block Partitioning section). However, we can have significant gain in the accuracy of our algorithm if we improve the accuracy in the estimation of the boundaries of the haplotype blocks. To achieve that, either the haplotype blocks should be already known from outside sources, or a set of phased haplotypes from the region at interest should be already available. In real applications, it is very often the case that studies are performed in populations that are already studied in the HapMap project. This means that for these populations we have accurately phased samples, which can be used as a basis for accurate definition of the haplotype blocks. Our methodology offers a unique framework that can easily incorporate prior knowledge in the form of haplotypes or trio genotypes from the same population as that from which the target samples were drawn. In the case of haplotypes (such as those available from the HapMap), they are introduced in the form of a prior for the counts in the TDS algorithm. In the case of unphased trio genotypes, the trios can be phased along with the target samples, with the result discarded at the end. The presence of the extra information will improve the phasing accuracy on the target samples.
A related problem to haplotype inference is imputation of missing SNPs. Several algorithms have been specifically developed to address this problem [21]. Some of the aforementioned algorithms have been extended and configured to complex imputation scenarios involving the use of prior information (in the form of known phased samples or extra genotype samples) for performing imputation in markers not typed in the original samples.
Average Allelic Imputation Error Rate For Simulated datasets
Average Allelic Imputation Error Rate (%)  

ST1  ST2  ST3  
PHASE  0.0063  0.0145  0.0133 
BEAGLE  
R = 1  0.0124  0.0255  0.0249 
R = 4  0.0101  0.0224  0.0223 
TDS  0.0124  0.0271  0.0266 
2SNP  0.0741  0.0855  0.0983 
Average Allelic Imputation Error Rate and Timing Results for HapMap datasets
Allelic Imputation Error Rate  Time(s)  

PHASE  0.0051  5360 
BEAGLE  
R = 1  0.0129  3.156 
R = 4  0.0112  3.339 
TDS  0.0134  2.53 
2SNP  0.0831  0.685 
Conclusions
We have introduced a new algorithm for inferring haplotype phase in nuclear families using a TreeBased Deterministic sampling scheme. PHASE, which is the most accurate algorithm for haplotype inference in trio families, is prohibitively slow for routine use, and 2SNP, which is the fastest algorithm for datasets up to 100 trios, is inaccurate. We have demonstrated that TDS is faster and more accurate than BEAGLE in almost all scenarios considered in small and intermediate dataset sizes in terms of trios and for all marker sizes. From a user's perspective, our implementation is friendlier since it is parameter  free, as all parameters are optimized inside the algorithm. This makes it ideal for routine tasks even for non specialized users. Furthermore, our TDS implementation provides a comprehensive, solid and straightforward framework to build upon for more complex phasing and imputation scenarios.
Methods
Brief Description
We first give an intuitive description of our algorithm highlighting its major concepts without going into detailed mathematical formalization. Suppose that we denote the major allele in a particular SNP locus in a haplotype as "0" and the minor allele as "1". Similarly in a genotype we denote by 0 that the individual is homozygous to the major allele at that SNP and with "1" that the individual is homozygous to the minor allele. We denote by "2" the heterozygous case. For example, the haplotype pair "10110" and "00100" would produce the genotype "20120".
Our algorithm processes nuclear families sequentially (Figure 1). In each family, multiple solutions are produced when we encounter a triple heterozygote SNP as explained earlier. Our algorithm examines all these different possible solutions.
Suppose we had n trios and each one of them had {K_{ l } , ..., K_{ n } } possible solutions. If we evaluate simultaneously all solutions for all haplotypes, which would obviously be the optimal way, we would end up with a total of $\prod _{i=1}^{n}{K}_{i}$ possible solutions each one of them having one and only one solution for each trio. To be consistent with the remaining sections we will call "solution" only the final solution and we will call these potential solutions as solution streams. Clearly this number of solution streams would be infeasible for all non trivial applications. Instead, in our algorithm we process trios sequentially and after processing each trio we keep only a prespecified K number of solution streams that would be the most probable ones (Figure 12b and 13b keeping only K = 2 streams in the end of these steps). Each one of these streams would have one and only one solution for each trio we have encountered (Figure 1).
To further explain this procedure, suppose that after processing a trio we have K streams. When the next trio is processed, which has, say, K^{ ext } possible solutions, we append each of these solutions to each of the previous K streams resulting in a total of K × K^{ ext } streams (Figure 12a and 13a). From these streams we keep only the K most probable ones (Figures 12b and 13b). So we always end up with K streams after processing each trio.
The idea for weighting the different streams is based on the concept that within a haplotype block we expect to have limited diversity and find only a subset of all the possible haplotypes. This means that most haplotypes should be encountered more than once. In terms of our procedure we would like to phase each new trio based on haplotypes that we have already encountered in that stream. Since the weight we assign to each node should capture this feature, it is a function of the weight that this node had prior to attaching one of the possible solutions of the current trio and of a factor that represents how the currently appended solution includes haplotypes that have already been seen (see Eq. (4) in Methods section).
Definitions and Model Selection
Let us assume that we have N subjects genotyped in L SNPs. Suppose that g_{ t }are the genotypes of the t^{th} trio, i.e., g_{ t } = {g_{ t,f }, g_{ t,m }, g_{ t,c } } where g_{ t,f }, g_{ t,m }, g_{ t,c } are the genotypes of the father the mother and the child of trio t respectively. Suppose also that G_{ t } = {g_{1}, ..., g_{ t } } is a set of genotypes of trios up to and including trio t. In each trio we consider the haplotypes of the parents denoted as h_{ t } = {h_{t,1}, h_{t,2}, h_{t,3}, h_{t,4}}, where {h_{t,1}, h_{t,2}} are the two haplotypes of the first parent and {h_{t,3}, h_{t,4}} are the two haplotypes of the second parent and similarly define H_{ t } = {h_{1}, ..., h_{ t } } Let us also define as θ = {θ_{1}, ..., θ_{ M } } a set of population haplotype frequencies for all the M haplotypes that appear in the population and Z = {z_{1}, ..., z_{ y } } as the set of haplotypes compatible with at least a genotype of any trio.
Let us consider the following dynamic model

Initial state model p_{ θ }(h_{0})

State transition model p_{ θ }(h_{ t }h_{t1}) for t ≥ 1

Measurement model p_{ θ }(g_{ t }h_{ t }) for t ≥ 1
where p_{ θ } (•) are probability density functions depending on some unknown parameters θ.
In the next subsection, for the convenience of the reader, we present the form that the system update equations would have should the system parameters were known. Then we move forward and make the connection to the real scenario were the system parameters are not known.
TDS ESTIMATOR with known system parameters θ
where ${W}_{t1}={\displaystyle \sum _{k=1}^{K}{w}_{t1}^{(k)},}$
and I(•) is the indicator function such that I(x  y) = 1 for x = y and I(x  y) = 0 otherwise.
where [${H}_{t1}^{(k)},{h}_{t}^{(i)}$] represents the vector obtained by appending the element ${h}_{t}^{(i)}$ to the vector ${H}_{t1}^{(k)}$
TDS Estimator with unknown system parameters θ
However, the system parameters are not known. Suppose now that their posterior distribution given H_{ t }and G_{ t }only depends on a set of sufficient statistics T_{ t } = T_{ t } (H_{ t } G_{ t } ) = T_{ t } (T_{t1}, h_{ t } , g_{ t } ).
Conditional on the haplotype of the t^{th} trio the genotype of that trio is unique and is independent of all the previous observations G_{ t1 } and haplotypes H_{ t1 } that we have seen. So the term p_{ θ } (g_{ t } H_{ t },G_{t1}) and consequently the integral ∫ p(g_{ t } h_{ t },θ)p(θh_{ t } , H_{t1}, G_{t1}, Z)dθ are zero if the genotype is not compatible with haplotype h_{ t } and 1 otherwise.
The recursion now lies only in computing the integral in (2).
In order to calculate the integral in the previous equation we will define the prior distribution for the parameters θ and we will show how to update their posterior distribution.
Prior and Posterior Distribution for θ
where we denote ρ_{ m } (t) m = 1,...,M as the parameters of the distribution of θ after the t^{th} trio and I(z_{ m }  h_{ t,i } ) with i = 1,...,4 is the indicator function which equals 1 when z_{ m }  h_{ t,i } is a vector of zeros, and 0 otherwise.
TDSEstimator
where ${\rho}_{{h}_{t,i}}(t1)=\{{\rho}_{{z}_{m}}(t1):{h}_{t,i}={z}_{m}\}$
Haplotype Block Partitioning
Again, we use the idea that haplotypes exhibit block structures so that within each block the haplotype blocks exhibit limited diversity compared to the whole haplotype vectors. To define these blocks we use a Dynamic Programming (DP) algorithm similar to the one used in [19] so that we partition G into subsets of genotype segments. Our criterion for the DP algorithm partition would be that the sum of the entropies of the genotypes of the individual blocks would be minimum.
Let us define C (j) as the minimum total block entropy up to the j^{th} SNP, where total block entropy is the sum of the entropies of all the blocks. If G_{ i:j } is the set of genotypes that contains genotype segments from SNP i to SNP j, the entropy E (i,j) of that segment can be computed from the number of occurrences of each unique genotype segment in G_{ i:j } .
for j  i < W, where W is the maximum allowed haplotype block length.
When the DP algorithm was applied to the ST1,ST2 and ST3 datasets with the maximum allowed block size being 12, we obtained an average of 6 markers per block with the smallest block being a single marker and the largest equal to W. On average, we had 22 distinct haplotypes per block with their number ranging from 1 to 30.
Average Transmission Error Rate for Equal Block Partitioning TDS (Equal TDS)
Average Transmission Error Rate (%)  

ST1  ST2  ST3  
TDS  0.0039  0.0065  0.0320 
Equal TDS  0.0113  0.0085  0.0360 
Average number of Incorrect Trios per dataset for Equal Block Partitioning TDS (Equal TDS)
Incorrect Trio  

ST1  ST2  ST3  
TDS  0.95  1.6  5.4 
Equal TDS  1.6  1.7  5.6 
PartitionLigation
In the partition phase the dataset is divided into small segments of consecutive loci using the haplotype block partitioning method described above. Once the blocks are phased, they are ligated together using the following method (an extension of the original method described in [14]).
The result of phasing for each block is a set of haplotype solutions, paired with their associated weights. Two neighbouring blocks are ligated by creating merged solutions from all combinations of the block solutions, each associated with the product of the individual weights, called the ligation weight. The TDS algorithm is then repeated in the same manner as it was for the individual blocks. However, the weights of the solutions are scaled by the associated ligation weight for that solution. In this way, no information content is lost in the process of ligating.
Furthermore, the order in which the individual blocks are ligated is not predetermined. We first ligate the blocks that would produce in each step the minimum entropy ligation. This procedure allows us to ligate first the most homogenous blocks so that we have more certainty in the solutions that we produce while moving in the ligation procedure.
Summary of the proposed algorithm
In the partition phase the dataset is divided into small segments of consecutive loci using the haplotype block partitioning.
Routine 1:

Enumerate the set of all possible haplotype vectors, Z, based on the given dataset G.

Initialization: Find all possible haplotype assignments for each trio and rearrange the trios in ascending order according to the number of distinct haplotype solutions each one of them has. Use the first n trios to enumerate all the possible streams, where n is the largest number such that the total number of streams enumerated from the n subjects does not exceed K, and compute their weights

Update: For i = n+1, n+2 ...

∘ Find the K^{ ext }possible haplotypes compatible with the genotype of the i^{th} trio.

∘ For k = 1,2,..., K^{ ext }

■ Enumerate all possible stream extensions ${H}_{i}^{(k,j)}=[{H}_{i1}^{(k)},{h}_{j}]$ with h_{ j }= {h_{j,1}, h_{j,2}, h_{j,3}, h_{j,4}}

■ ∀_{ j }compute the weights ${w}_{i}^{(k,j)}$ according to (3)


∘ Select and preserve K distinct sample streams {${H}_{i}^{(k)}$, k = 1,...,K} with the highest importance weights {${w}_{i}^{(k)}$, k = 1,...,K} from the set {${H}_{i}^{(k,j)},{w}_{i}^{(k,j)}$, k = 1,...,K, j = 1,..., K^{ ext }}
∘ ∀k, update the sufficient statistics ${T}_{i}^{(k)}={T}_{i}({T}_{i1},{h}_{i}^{(k)},{g}_{i})$

TDS ALGORITHM

Partition the genotype dataset G into S subsets using the procedure described in the "HaplotypeBlock partitioning subsection".

For s = 1,...,S apply Routine 1 so that all segments are phased and for each one keep all the solutions contained in the top K streams.

Until all blocks are ligated

∘ Find the blocks that if ligated would produce the minimum entropy
∘ Ligate the blocks, following the procedure described in the PartitionLigation section

Declarations
Acknowledgements
We would like to thank the referees for their helpful suggestions that let to important improvement of the manuscript.
Authors’ Affiliations
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