Empirical vs Bayesian approach for estimating haplotypes from genotypes of unrelated individuals
- Shuying Sue Li^{1}Email author,
- Jacob Jen-Hao Cheng^{2} and
- Lue Ping Zhao^{1}
DOI: 10.1186/1471-2156-8-2
© Li et al; licensee BioMed Central Ltd. 2007
Received: 31 July 2006
Accepted: 29 January 2007
Published: 29 January 2007
Abstract
Background
The completion of the HapMap project has stimulated further development of haplotype-based methodologies for disease associations. A key aspect of such development is the statistical inference of individual diplotypes from unphased genotypes. Several methodologies for inferring haplotypes have been developed, but they have not been evaluated extensively to determine which method not only performs well, but also can be easily incorporated in downstream haplotype-based association analyses. In this paper, we attempt to do so. Our evaluation was carried out by comparing the two leading Bayesian methods, implemented in PHASE and HAPLOTYPER, and the two leading empirical methods, implemented in PL-EM and HPlus. We used these methods to analyze real data, namely the dense genotypes on X-chromosome of 30 European and 30 African trios provided by the International HapMap Project, and simulated genotype data. Our conclusions are based on these analyses.
Results
All programs performed very well on X-chromosome data, with an average similarity index of 0.99 and an average prediction rate of 0.99 for both European and African trios. On simulated data with approximation of coalescence, PHASE implementing the Bayesian method based on the coalescence approximation outperformed other programs on small sample sizes. When the sample size increased, other programs performed as well as PHASE. PL-EM and HPlus implementing empirical methods required much less running time than the programs implementing the Bayesian methods. They required only one hundredth or thousandth of the running time required by PHASE, particularly when analyzing large sample sizes and large umber of SNPs.
Conclusion
For large sample sizes (hundreds or more), which most association studies require, the two empirical methods might be used since they infer the haplotypes as accurately as any Bayesian methods and can be incorporated easily into downstream haplotype-based analyses such as haplotype-association analyses.
Background
The completion of the HapMap project has stimulated further interest in haplotype inference from un-phased single nucleotide polymorphism (SNP) genotypes [1]. Recent evidence indicates the human genome has hot spots and cold spots for recombination, and it is divided into multiple haplotype blocks, each of which has only a limited number of haplotypes [2–5]. Such haplotype block structure in the human genome suggests that haplotype-based methods may play an important role in genetic association studies [6, 7]. Haplotypes can be generated experimentally by dissecting out single chromosomes and inserting the entire chromosome into a yeast artificial chromosome [8] or by using rodent-human hybrid techniques to physically separate two chromosomes [5]. However, both technologies are experimentally challenging and cost prohibitive for use in population research at this time. The most commonly used technologies generate un-phased SNP genotypes. One way to resolve individual haplotypes is via family data, which is expensive to collect [9]. Another option is to resolve individual haplotypes statistically. Clark's heuristic algorithm is probably among the first statistical methods for inferring haplotypes from genotypes of unrelated individuals [10].
Many maximum likelihood methods have been developed, and almost all share the same scientific objectives and likelihood framework. The fundamental difference among these methods is a prior assumption for the distribution of haplotypes: methods with prior assumption for distribution of haplotypes are referred to as Bayesian methods, and the methods without any prior assumption are called empirical methods.
Estimation-maximization (EM) algorithm, a maximum likelihood method, was first introduced to infer haplotypes from unrelated individuals [11–13], but those earlier works were computationally demanding when processing large number of SNPs. More recently Qin et al. [14] discussed a new strategy for estimating haplotype frequencies using the EM algorithm, which largely improved performance, especially when analyzing data with large numbers of SNPs. Li et al. [15] have applied the estimating equation (EE) technique and further improved the statistical and computational efficiency in the estimation of haplotype frequencies and their standard errors. Both EM and EE methods are empirical methods.
Stephens et al. were probably among the first groups to propose a model-based Bayesian method [16] under the assumption of coalescence of haplotypes. Later it was modified to improve statistical and computational efficiency [17, 18]. Niu et al. [19] took a Bayesian approach but chose a Dirichlet distribution for haplotypes as their prior, and published a computational algorithm to handle a large number of SNPs, which was referred to as partition-ligation (PL).
Which method performs the best? Several papers have attempted to address this question and their conclusions are not without controversy [14, 19–22]. These papers compared the performances of haplotyping methods based on a limited number of available haplotype data sets and some simulated data. Recently, Marroni et al. [23] used genotype data provided by Illumina and Affymetrix for Genetic Analysis Workshop 14. The data include genotypes of 104 mother-son pairs with Caucasian ancestry on 313 SNPs of the X-chromosome. Because males have only one copy of the X-chromosome, mother haplotypes can be resolved from their sons' genotypes. Instead of evaluating the performances of the methods for analyzing SNPs within haplotype blocks, Marroni et al. investigated the 14 series of unphased genotypes of 5 or 10 SNPs with different values of linkage disequilibrium (LD). In this paper, we used the dense genotypes on the X-chromosome of 30 European and 30 African trios provided by the International HapMap project. The X-chromosome was chosen because mother haplotypes can be unambiguously resolved from the genotypes of trios. Resolved haplotypes were divided into blocks using the Haploview program [24], providing abundant haplotype data sets. Among the identified haplotype blocks, we randomly selected 500 blocks to evaluate haplotyping method performances. To evaluate the performances of haplotype methods on the data with larger sample sizes, we conducted some simulation studies under different scenarios. In our first set of simulations, we generated haplotypes based on real data on the X-chromosome. In our second set of simulations, we generated haplotypes using Hudson's coalescent program [25] to investigate how much efficiency is gained by assuming coalescence prior in PHASE compared to empirical methods without assuming a prior. Programs used for comparisons are PHASE (version 2.1) [26] for the model-based Bayesian method [20], HAPLOTYPER [27] for the empirical Bayesian method [19], PL-EM [28] for the EM method [14], and HPlus [29] for the EE method [15]. Because the accurate estimation of haplotype frequencies and inference of individual haplotypes are both critical in assessing haplotype association with disease phenotypes [30–34], our comparisons focus on evaluating method performance from these two angles.
Methods
HapMap Trio Data
We used X-chromosome genotype data of 30 European and 30 African trios from the HapMap project [1]. With trio data, mother haplotypes can be resolved unambiguously from her offspring's and the father of her offspring's genotype data. The mother's two chromosomes are separated at each locus as transmitted or not transmitted to her child. If the child is male, the child's X-chromosome is transmitted from his mother, therefore mother's allele that matches the child's allele is the transmitted allele. If the child is female, one chromosome is from her mother and the other is from her father. Using the father's allele on the X-chromosome, we can deduce which allele is transmitted from the mother. Hence, 30 mother haplotype pairs are determined by sorting out transmitted alleles from untransmitted alleles, and these sixty (= 30 × 2) represent the true haplotypes, which are not readily obtainable for any autosome chromosomes.
Applying this procedure to the HapMap Phase II data (July 2005 release), we obtained phase-resolved X-chromosome SNP data. We further divided these SNPs into haplotype blocks using Haploview software [24], by specifying for the method described in [35], the parameters of confidence interval minima 0.8 and 0.5 for strong LD, upper confidence interval maximum 0.6 for strong recombination, the fraction of strong LD in informative comparisons to be at least 0.95, and excluding the SNPs with MAF less than 0.05. We chose the parameters to be less stringent than default values to get larger blocks that are still in high LD. The block identification was done separately for European and African mother haplotypes. Within each population, we randomly chose 500 haplotype blocks to compare haplotyping methods. Among the 500 European mother haplotype blocks, the number of SNPs ranges from 2 to 195 with mean of 13 and median of 7. Among the 500 African mother haplotype blocks, the number of SNPs ranges from 2 to 33 with mean of 5 and median of 3. It had been shown that African populations have shorter haplotype blocks than European populations [1].
Notations
Consider a sample of n unrelated individuals from a study population. From each individual, we observe q SNP-genotypes on a specific region in the genome. Let ${\underset{\u02dc}{g}}_{i}$ = (g_{i 1}, …, g_{ iq }) denote the q SNP-genotypes for the i th individual. When genotype g_{ ij }is heterozygous, the phase (parental origin of the two alleles) becomes ambiguous and has two solutions denoted by p_{ ij }. Let ${\underset{\u02dc}{p}}_{i}$ = (p_{i 1}, …, p_{ iq }) denote the phase of ${\underset{\u02dc}{g}}_{i}$. Given phase ${\underset{\u02dc}{p}}_{i}$, genotype ${\underset{\u02dc}{g}}_{i}$ uniquely determines a diplotype (a pair of compatible haplotypes), H_{ i }= (H_{i 1}, H_{i 2}), i.e. ${\underset{\u02dc}{g}}_{i}$|${\underset{\u02dc}{p}}_{i}$ = (H_{i 1}, H_{i 2}). Therefore, for a genotype with m heterozygous loci, there are 2^{m-1}possible resolutions for phase and diplotypes. Let $\underset{\u02dc}{\theta}$ = (θ_{1}, θ_{2}, …, θ_{ T }) denote population haplotype frequencies where T is the total number of haplotypes.
All methods compared in this paper use the maximum likelihood approach or its variation. They all shared the same likelihood function of haplotype frequency $\underset{\u02dc}{\theta}$ = (θ_{1}, θ_{2}, …, θ_{ T }), which can be written as
$L(\underset{\u02dc}{\theta})={\displaystyle \prod _{i=1}^{n}f({\underset{\u02dc}{g}}_{i}|\underset{\u02dc}{\theta})={\displaystyle \prod _{i=1}^{n}{\displaystyle \sum _{{\underset{\u02dc}{p}}_{i}}f({\underset{\u02dc}{g}}_{i}|{\underset{\u02dc}{p}}_{i},\underset{\u02dc}{\theta})f({\underset{\u02dc}{p}}_{i})=}}}{\displaystyle \prod _{i=1}^{n}{\displaystyle \sum _{{\underset{\u02dc}{p}}_{i}}f({H}_{i1}|\underset{\u02dc}{\theta})f({H}_{i2}|\underset{\u02dc}{\theta})f({\underset{\u02dc}{p}}_{i})}},\phantom{\rule{0.5em}{0ex}}[1]$
where f(H_{ ij }|$\underset{\u02dc}{\theta}$) is the probability of haplotype H_{ ij }given the population's haplotype frequencies; f(${\underset{\u02dc}{p}}_{i}$) is the prior probability of phase.
Empirical Methods
The estimation-maximization (EM) algorithm was used to obtain maximum likelihood estimates of haplotype frequencies, $\underset{\u02dc}{\theta}$[11]. To avoid trapping in a local maximum, the programs implementing EM algorithm require multiple initial values to ensure the global maximum. Excoffier and Slatkin [11] used bootstrapping to estimate standard errors of estimates of haplotype frequencies, and implemented the method in ARLEQIN. Qin et al. [14] implemented Louis' method [36] and implemented the method in PL-EM. Applying estimation equation technique, Li et al. [15] efficiently estimated the haplotype frequencies and their standard errors and implemented the method in HPlus.
Bayesian methods
Different from the empirical approaches described above, the model-based Bayesian method [16] further assumes that haplotypes are coalescent so future-sampled individual haplotypes H_{ i }is assumed to be more similar to the previously sampled haplotypes, H_{-i}[37]. This Bayesian method was implemented in PHASE software program. Another Bayesian method [19] assumes that prior distribution of haplotype frequency $\underset{\u02dc}{\theta}$ follows a Dirichlet distribution with hyperparameter $\underset{\u02dc}{\beta}$ = (β_{1}, …, β_{ T }. Using Gibbs sampling algorithm, Niu et al. sampled a pair of compatible haplotypes for each individual and estimate the haplotype frequencies, and this method was implemented in HAPLOTYPER.
Comparison Measurements
Accurately estimating haplotype frequencies and inferring individual haplotypes are both critical in assessing haplotype association with disease phenotypes [30–34]. Here we consider two measures to evaluate the accuracy of haplotype frequency estimates and the inferred individual haplotypes. The first measure is the similarity index [11] defined as $I(\underset{\u02dc}{\widehat{\theta}};\underset{\u02dc}{\theta})=1-0.5{\displaystyle \sum _{j=1}^{T}\left|{\widehat{\theta}}_{j}-{\theta}_{j}\right|}$, where θ_{ j }and ${\widehat{\theta}}_{j}$ are the true and the estimated frequency of the j th haplotype, to measure the overall similarity between the estimated and the sample haplotype frequencies and the value of the similarity index ranges from zero to one. The second measure is the prediction rate that measures the percent of correct predictions for all haplotypes from their genotypes compared to the sampled haplotypes. Since HAPLOTYPER gives only one pair of compatible haplotypes for each individual, we calculated the prediction rate based on the best prediction for each individual. The prediction rate weighted by the posterior probability of a pair of inferred haplotypes was also used to evaluate other programs. The results are similar between the two prediction rates. Running time is recorded to measure the computational efficiency of the implemented algorithms. All computer programs were run under their default or recommended settings on computer with a dual Pentium III 800 MHz with 2 GB RAM.
Results
Genotype data on the X-chromosome from the International HapMap project
Performances of haplotyping methods on analyzing 500 randomly selected haplotype blocks of the 30 European mothers' genotypes on X-chromosome from the HapMap data.
Performances | Empirical Method | Bayesian Method | ||
---|---|---|---|---|
PL-EM | HPlus | PHASE | HAPLOTYPER* | |
Similarity Index | ||||
Mean | 0.989 | 0.990 | 0.986 | 0.991 |
Median | 1.0 | 1.0 | 1.0 | 1.0 |
Standard deviation | 0.029 | 0.024 | 0.040 | 0.024 |
Range | (0.733, 1.0) | (0.833, 1.0) | (0.292,1.0) | (0.733,1.0) |
Prediction Rate | ||||
Mean | 0.989 | 0.990 | 0.990 | 0.991 |
Median | 1.0 | 1.0 | 1.0 | 1.0 |
Standard deviation | 0.029 | 0.025 | 0.040 | 0.025 |
Range | (0.733, 1.0) | (0.833, 1.0) | (0.283, 1.0) | (0.733, 1.0) |
Running Time (in second) | 98 | 20 | 9935 | 422 |
Performances of haplotyping programs on analyzing 500 randomly selected haplotype blocks of the 30 African mothers' genotypes on X-chromosome from the HapMap data.
Performances | Empirical Method | Bayesian Method | ||
---|---|---|---|---|
PL-EM | HPlus | PHASE | HAPLOTYPER* | |
Similarity Index | ||||
Mean | 0.988 | 0.988 | 0.987 | 0.998 |
Median | 1.0 | 1.0 | 1.0 | 1.0 |
Standard deviation | 0.025 | 0.024 | 0.027 | 0.025 |
Range | (0.833, 1.0) | (0.833, 1.0) | (0.689,1.0) | (0.833,1.0) |
Prediction Rate | ||||
Mean | 0.987 | 0.987 | 0.987 | 0.987 |
Median | 1.0 | 1.0 | 1.0 | 1.0 |
Standard deviation | 0.027 | 0.027 | 0.030 | 0.027 |
Range | (0.800, 1.0) | (0.800, 1.0) | (0.683, 1.0) | (0.800, 1.0) |
Running Time (in second) | 15 | 7 | 1174 | 37 |
Simulated Data
Performances of haplotyping programs on simulated data based on some selected genotypes from the 30 European mothers on X-chromosome from the HapMap data.
#SNP | Sample size | Similarity Index | Prediction Rate | Average Running Time (in seconds) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Empirical | Bayesian | Empirical | Bayesian | Empirical | Bayesian | ||||||||
PL-EM | HPlus | PHASE | HAPLO^{+} | PL-EM | HPlus | PHASE | HAPLO^{+} | PL-EM | HPlus | PHASE | HAPLO^{+} | ||
13 | 30* | 0.783 | 0.776 | 0.822 | 0.683 | 0.767 | 0.767 | 0.800 | 0.633 | ||||
100 | 0.969 | 0.972 | 0.985 | 0.979 | 0.966 | 0.970 | 0.977 | 0.978 | 0.64 | 0.16 | 53.26 | 2.61 | |
150 | 0.981 | 0.982 | 0.989 | 0.981 | 0.973 | 0.978 | 0.989 | 0.980 | 0.69 | 0.23 | 88.57 | 2.64 | |
200 | 0.986 | 0.987 | 0.993 | 0.984 | 0.985 | 0.986 | 0.992 | 0.982 | 0.86 | 0.30 | 114.88 | 5.25 | |
250 | 0.988 | 0.989 | 0.994 | 0.979 | 0.986 | 0.987 | 0.992 | 0.977 | 0.92 | 0.38 | 157.55 | 6.45 | |
300 | 0.927 | 0.992 | 0.996 | 0.984 | 0.991 | 0.992 | 0.992 | 0.982 | 0.89 | 0.40 | 194.63 | 7.77 | |
16 | 30* | 0.774 | 0.807 | 0.904 | 0.733 | 0.700 | 0.733 | 0.800 | 0.667 | ||||
100 | 0.931 | 0.933 | 0.945 | 0.930 | 0.897 | 0.901 | 0.893 | 0.905 | 3.38 | 1.34 | 108.74 | 3.31 | |
150 | 0.948 | 0.951 | 0.958 | 0.943 | 0.909 | 0.913 | 0.894 | 0.919 | 12.02 | 1.83 | 180.08 | 4.78 | |
200 | 0.961 | 0.963 | 0.967 | 0.955 | 0.923 | 0.926 | 0.907 | 0.932 | 34.81 | 2.21 | 249.74 | 6.06 | |
250 | 0.968 | 0.968 | 0.970 | 0.956 | 0.930 | 0.931 | 0.912 | 0.934 | 61.47 | 2.46 | 332.92 | 7.27 | |
300 | 0.956 | 0.971 | 0.972 | 0.956 | 0.928 | 0.928 | 0.895 | 0.932 | 64.17 | 2.85 | 421.54 | 8.67 | |
12 | 30* | 0.554 | 0.597 | 0.614 | 0.600 | 0.533 | 0.567 | 0.567 | 0.567 | ||||
100 | 0.908 | 0.918 | 0.925 | 0.913 | 0.866 | 0.873 | 0.859 | 0.884 | 1.46 | 0.36 | 76.49 | 3.49 | |
150 | 0.940 | 0.943 | 0.945 | 0.933 | 0.896 | 0.899 | 0.862 | 0.899 | 1.52 | 0.49 | 127.48 | 5.08 | |
200 | 0.956 | 0.958 | 0.957 | 0.938 | 0.904 | 0.906 | 0.880 | 0.901 | 1.61 | 0.55 | 173.51 | 6.61 | |
250 | 0.964 | 0.964 | 0.963 | 0.944 | 0.915 | 0.917 | 0.897 | 0.909 | 1.64 | 0.69 | 202.33 | 8.85 | |
300 | 0.971 | 0.971 | 0.969 | 0.948 | 0.921 | 0.921 | 0.893 | 0.914 | 1.67 | 0.75 | 278.34 | 10.13 |
Performances of haplotyping programs on simulated data based on a coalescence model with mutation rate of 4 (= 4N_{e}θ).
Average #SNP | Sample size | Similarity Index | Prediction Rate | Average Running Time (in seconds) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Empirical | Bayesian | Empirical | Bayesian | Empirical | Bayesian | ||||||||
PL-EM | Hplus | PHASE | HAPLO^{+} | PL-EM | HPlus | PHASE | HAPLO^{+} | PL-EM | HPlus | PHASE | HAPLO^{+} | ||
25 | 100 | 0.943 | 0.947 | 0.982 | 0.976 | 0.941 | 0.945 | 0.981 | 0.976 | 1.39 | 0.18 | 122.89 | 2.52 |
25 | 150 | 0.955 | 0.960 | 0.988 | 0.986 | 0.952 | 0.957 | 0.988 | 0.986 | 2.56 | 0.23 | 185.86 | 3.09 |
26 | 200 | 0.967 | 0.971 | 0.991 | 0.988 | 0.964 | 0.968 | 0.988 | 0.988 | 5.51 | 0.29 | 283.28 | 4.36 |
29 | 250 | 0.974 | 0.977 | 0.992 | 0.986 | 0.973 | 0.976 | 0.993 | 0.980 | 12.37 | 0.36 | 429.03 | 5.75 |
In both Tables 3 and 4, average running times are recorded on the far right for comparison purposes. For all simulations, PHASE requires much more computational time than others and HPlus requires the least computational time among the four.
Discussion
The key difference between the Bayesian and empirical methods compared in this paper is the use of priors (the approximate coalescent prior by PHASE and the Dirichlet prior by HAPLOTYPER). If the prior approximates real haplotype data, Bayesian methods gain some efficiency using the prior. On the other hand, efficiency may be lost because of a wrong prior. The influence of the prior is non-negligible when the sample size is small. In this case, because the real haplotypes tend to coalesce, PHASE using the approximate coalescent prior is likely to produce more efficient estimates, and HAPLOTYPER using the Dirichlet prior may produce less efficient estimates than the empirical methods. This phenomenon was observed when inferring haplotypes from the simulated genotypes using Hudson's coalescent program [25]. However, the superior performance of PHASE diminishes when the sample size increases (Table 4). For genotype data with low LD, PHASE using the approximate coalescent prior would gain some efficiency when the sample size is small, such as 30, which we investigated here, and 104, which Marroni et al. [23] investigated. However, when the sample increases to 150 or larger, PL-EM and HPlus implementing empirical methods can perform as well as PHASE (Table 3).
Recently, Kimmel and Shamir [39] developed a new likelihood method to infer haplotypes and identify haplotype blocks. Their likelihood uses not only the parameters of haplotype frequencies but also the parameters of the probability of observing a variant allele in each locus and each haplotype. Using the EM algorithm, they estimated haplotype frequencies. It deserves debate whether this new likelihood is better than the one used in most methods. In terms of performance, Kimmel and Shamir [39] claimed that PHASE performed slightly better using its default setting and was a hundred times slower than GERBIL implementing the new likelihood method. Their results comparing PHASE and the empirical methods are similar to ours.
Conclusion
The recent advent of genotyping technologies is rapidly transforming genetic association studies by providing more SNPs (more than 500,000 SNPs) on arrays and by reducing the cost of genotyping individual samples (around $500~1000 per sample). The next-generation genome wide studies will likely use several hundreds or thousands of SNPs on hundreds or thousands of individuals. To gain both statistical and computational efficiency, haplotype-based analyses will be increasingly used, especially for those regions with high LD. With such massive data, as we show in this paper, the empirical methods such as EM and EE can infer haplotypes as accurately as a time-consuming method, such as the model-based Bayesian method that PHASE implement, and they can be easily incorporated into downstream haplotype-based analyses. The empirical methods had already been used in many haplotype-based association methods [32, 34, 40].
Declarations
Acknowledgements
Authors thank Dr. Peter Donnelly for suggesting X-chromosome genotypes from the HapMap Project as the basis for comparison, and thank Mike Feolo for initial discussions on the need for comparing different methods. This work is supported in part by grants from National Institute of Health (5RO1 CA106320-05, 1RO1 CA112512-01, 2 P01 DK053369-07A1, and 5 PO1 CA53996-24).
Authors’ Affiliations
References
- The International HapMap Consortium: A haplotype map of the human genome. Nature. 2005, 437 (27): 1299-1320. 10.1038/nature04226.PubMed CentralView ArticleGoogle Scholar
- Reich DE, Cargill M, Bolk S, Ireland J, Sabeti PC, Richter DJ, Lavery T, Kouyoumjian R, Farhadian SF, Ward R, Lander ES: Linkage disequilibrium in the human genome. Nature. 2001, 411 (6834): 199-204. 10.1038/35075590.View ArticlePubMedGoogle Scholar
- Daly MJ, Rioux JD, Schaffner SF, Hudson TJ, Lander ES: High-resolution haplotype structure in the human genome. Nature Genetics. 2001, 29: 229-232. 10.1038/ng1001-229.View ArticlePubMedGoogle Scholar
- Goldstein DB: Islands of linkage disequilibrium. Nature Genetics. 2001, 29: 109-111. 10.1038/ng1001-109.View ArticlePubMedGoogle Scholar
- Patil N, Berno AJ, Hinds DA, Barrett WA, Doshi JM, Hacker CR, Kautzer CR, Lee DH, Marjoribanks C, McDonough DP, Nguyen BT, Norris MC, Sheehan JB, Shen N, Stern D, Stokowski RP, Thomas DJ, Trulson MO, Vyas KR, Frazer KA, Fodor SP, Cox DR: Blocks of limited haplotype diversity revealed by high-resolution scanning of human chromosome 21. Science. 2001, 294 (5547): 1719-1723. 10.1126/science.1065573.View ArticlePubMedGoogle Scholar
- Schaid DJ: Genetic epidemiology and haplotypes. Genetic Epidemiology. 2004, 27: 317-320. 10.1002/gepi.20046.View ArticlePubMedGoogle Scholar
- Clark AG: The role of haplotypes in candidate gene studies. Genetic Epidemiology. 2004, 27: 321-333. 10.1002/gepi.20025.View ArticlePubMedGoogle Scholar
- Green ED, Cox DR, Myers RM: The human genome project and its impact on the study of human disease. The genetic basis of human cancer. Edited by: Vogelstein B, Kinzler KW. 1998, New York , McGraw-Hill, Health professional division, 33-63.Google Scholar
- Wijsman EM: A deductive method of haplotype analysis in pedigrees. American Journal of Human Genetics. 1987, 41: 356-373.PubMed CentralPubMedGoogle Scholar
- Clark AG: Inference of haplotypes from PCR-amplified samples of diploid populations. Mol Biol Evol. 1990, 7: 111-122.PubMedGoogle Scholar
- Excoffier L, Slatkin M: Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population. Mol Biol Evol. 1995, 12 (5): 921-927.PubMedGoogle Scholar
- Hawley ME, Kidd KK: HAPLO: a program using the EM algorithm to estimate the frequencies of multi-site haplotypes. Journal Heredity. 1995, 86: 409-411.Google Scholar
- Long JC, Williams RC, Urbanek M: An E-M algorithm and testing strategy for multiple-locus haplotypes. American Journal of Human Genetics. 1995, 56: 799-810.PubMed CentralPubMedGoogle Scholar
- Qin ZS, Niu T, Liu JS: Partition-ligation-expectation-maximization algorithm for haplotype inference with single-nucleotide polymorphisms. American Journal of Human Genetics. 2002, 71: 1242-1247. 10.1086/344207.PubMed CentralView ArticlePubMedGoogle Scholar
- Li SS, Khalid N, Carlson C, Zhao LP: Estimating Haplotype Frequencies and Standard Errors for Multiple Single Nucleotide Polymorphisms. Biostatistics. 2003, 4 (4): 513-522. 10.1093/biostatistics/4.4.513.View ArticlePubMedGoogle Scholar
- Stephens M, Smith NJ, Donnelly P: A new statistical method for haplotype reconstruction from population data. American Journal of Human Genetics. 2001, 68 (4): 978-989. 10.1086/319501.PubMed CentralView ArticlePubMedGoogle Scholar
- Lin S, Cutler DJ, Zwick ME, Chakravarti A: Haplotype inference in random population samples. American Journal of Human Genetics. 2002, 71 (5): 1129-1137. 10.1086/344347.PubMed CentralView ArticlePubMedGoogle Scholar
- Storey JD: The positive false discovery rate: A Bayesian interpretation and the q-value. The Annals of Statistics. 2003, 31 (6): 2013-2035. 10.1214/aos/1074290335.View ArticleGoogle Scholar
- Niu T, Qin ZS, Xu X, Liu JS: Bayesian haplotype inference for multiple linked single-nucleotide polymorphisms. American Journal of Human Genetics. 2002, 70 (1): 157-169. 10.1086/338446.PubMed CentralView ArticlePubMedGoogle Scholar
- Stephens M, Donnelly P: A comparison of Bayesian methods for haplotype reconstruction from population genotype data. American Journal of Human Genetics. 2003, 73: 1162-1169. 10.1086/379378.PubMed CentralView ArticlePubMedGoogle Scholar
- Xu H, Wu X, Spitz M, Shete S: Comparison of haplotype inference methods using genotypic data from unrelated individuals. Human Heredity. 2004, 58: 63-68. 10.1159/000083026.View ArticlePubMedGoogle Scholar
- Niu T: Algorithms for infering haplotypes. Genetic Epidemiology. 2004, 27: 334-347. 10.1002/gepi.20024.View ArticlePubMedGoogle Scholar
- Marroni F, Toni C, Pennato B, Tsai YY, Duggal P, Bailey-Wilson J, Presciuttini S: Haplotypic structure of the X chromosome in the COGA population sample and the quality of its reconstruction by extant software packages. BMC Genetics. 2005, 6(Suppl I):S77: 1-5.Google Scholar
- Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005, 21: 263-265. 10.1093/bioinformatics/bth457.View ArticlePubMedGoogle Scholar
- Hudson RR: Generating samples under a Wright-Fisher neutral model of genetic variation. Bioinformatics. 2002, 18: 337-338. 10.1093/bioinformatics/18.2.337.View ArticlePubMedGoogle Scholar
- Stephens M, Smith NJ, Donnelly P: PHASE 2.1. [http://www.stat.washington.edu/stephens/software.html]
- Liu J, Qin S, Niu T: Haplotyper 1.0. [http://www.people.fas.harvard.edu/~junliu/Haplo/click.html]
- Liu J, Qin S, Niu T: PL-EM. [http://www.people.fas.harvard.edu/~junliu/plem/click.html]
- Li SS, Laws RJ, Zhao LP: HPlus 2.5. [http://qge.fhcrc.org/hplus/]
- Schaid DJ: Evaluating associations of haplotypes with traits. Genetic Epidemiology. 2004, 27: 348-364. 10.1002/gepi.20037.View ArticlePubMedGoogle Scholar
- Fallin D, Cohen A, Essioux L, Chumakov I, Blumenfeld M, Cohen D, Schork NJ: Genetic analysis of case/control data using estimated haplotype frequencies: application to APOE locus variation and Alzheimer's disease. Genome Research. 2001, 11 (1): 143-151. 10.1101/gr.148401.PubMed CentralView ArticlePubMedGoogle Scholar
- Schaid DJ, Rowland CM, Tines DE, Jacobson RM, Poland GA: Score Tests for Association between Traits and Haplotypes when Linkage Phase Is Ambiguous. American Journal of Human Genetics. 2002, 70: 425-434. 10.1086/338688.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhao LP, Li SS, Khalid N: A Method for Assessing Disease Associations with SNP Haplotypes and Environmental Variables in Case-Control Studies. American Journal of Human Genetics. 2003, 72: 1231-1250. 10.1086/375140.PubMed CentralView ArticlePubMedGoogle Scholar
- Stram DO, Pearce CL, Bretsky P, Freedman M, Hirschhorn JN, Altschuler D, Kolonel LN, Henderson BE, Thomas DC: Modeling and E-M estimation of haplotype-specific relative risks from genotype data for a case-control study of unrelated individuals. Human Heredity. 2003, 55: 179-190. 10.1159/000073202.View ArticlePubMedGoogle Scholar
- Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B, Higgins J, DeFelice M, Lochner A, Faggart M, Liu-Cordero SN, Rotimi C, Adeyemo A, Cooper R, Ward R, Lander ES, Daly MJ, Altshuler D: The structure of haplotype blocks in the human genome. Science. 2002, 296 (5576): 2225-2229. 10.1126/science.1069424.View ArticlePubMedGoogle Scholar
- Louis TA: Finding observed information using the EM algorithm. Journal of the Royal Statistical Society B. 1982, 44: 98-130.Google Scholar
- Hudson RR: Gene genealogies and the coalescent process. Surveys in evolutionary biology. Edited by: Futuyma D, Antonovics J. 1991, Oxford , Oxford University Press, 7: 1-44.Google Scholar
- Liu Z, Lin S: Multilocus LD measure and tagging SNP selection with generalized mutual information. Genetic Epidemiology. 2005, 29 (4): 353-364. 10.1002/gepi.20092.PubMed CentralView ArticlePubMedGoogle Scholar
- Kimmel G, Shamir R: GERBIL: genotype resolution and block identification using likelihood. PNAS. 2005, 102 (1): 158-162. 10.1073/pnas.0404730102.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhao H, Pfeiffer R, Gail MH: Haplotype analysis in population genetics and association studies. Phamacogenomics. 2003, 4 (2): 171-178. 10.1517/phgs.4.2.171.22636.View ArticleGoogle Scholar
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