<!DOCTYPE article PUBLIC "-//NLM//DTD Journal Archiving and Interchange DTD v2.3 20070202//EN" "http://dtd.nlm.nih.gov/publishing/2.3/journalpublishing.dtd">
<article article-type="research-article" xmlns:xlink="http://www.w3.org/1999/xlink">
   <front>
      <journal-meta>
         <journal-id journal-id-type="nlm-ta">BMC Bioinformatics</journal-id>
         <journal-title>BMC Bioinformatics</journal-title>
         <issn pub-type="epub">1471-2105</issn>
         <publisher>
            <publisher-name>BioMed Central</publisher-name>
            <publisher-loc>London</publisher-loc>
         </publisher>
      </journal-meta>
      <article-meta>
         <article-id pub-id-type="publisher-id">1471-2105-6-303</article-id>
         <article-id pub-id-type="pmid">16356172</article-id>
         <article-id pub-id-type="doi">10.1186/1471-2105-6-303</article-id>
         <article-categories>
            <subj-group subj-group-type="heading">
               <subject>Research Article</subject>
            </subj-group>
         </article-categories>
         <title-group>
            <article-title>Analysis of concordance of different haplotype block partitioning algorithms</article-title>
         </title-group>
         <contrib-group>
            <contrib contrib-type="author" corresp="yes" id="A1">
               <name>
                  <surname>Indap</surname>
                  <given-names>Amit R</given-names>
               </name>
               <xref ref-type="aff" rid="I1">1</xref>
               <email>aindap@yahoo.com</email>
            </contrib>
            <contrib contrib-type="author" id="A2">
               <name>
                  <surname>Marth</surname>
                  <given-names>Gabor T</given-names>
               </name>
               <xref ref-type="aff" rid="I2">2</xref>
               <email>marth@bc.edu</email>
            </contrib>
            <contrib contrib-type="author" id="A3">
               <name>
                  <surname>Struble</surname>
                  <given-names>Craig A</given-names>
               </name>
               <xref ref-type="aff" rid="I3">3</xref>
               <email>craig.struble@marquette.edu</email>
            </contrib>
            <contrib contrib-type="author" id="A4">
               <name>
                  <surname>Tonellato</surname>
                  <given-names>Peter</given-names>
               </name>
               <xref ref-type="aff" rid="I1">1</xref>
               <email>tone@mcw.edu</email>
            </contrib>
            <contrib contrib-type="author" id="A5">
               <name>
                  <surname>Olivier</surname>
                  <given-names>Michael</given-names>
               </name>
               <xref ref-type="aff" rid="I1">1</xref>
               <email>molivier@mcw.edu</email>
            </contrib>
         </contrib-group>
         <aff id="I1"><label>1</label>Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, USA</aff>
         <aff id="I2"><label>2</label>Department of Biology, Boston College, Chestnut Hill, USA</aff>
         <aff id="I3"><label>3</label>Department of Mathematics, Statistics, and Computer Science, Marquette University, Milwaukee, USA</aff>
         <pub-date pub-type="collection">
            <year>2005</year>
         </pub-date>
         <pub-date pub-type="epub">
            <day>15</day>
            <month>12</month>
            <year>2005</year>
         </pub-date>
         <volume>6</volume>
         <fpage>303</fpage>
         <lpage>303</lpage>
         <ext-link ext-link-type="uri" xlink:href="http://www.biomedcentral.com/1471-2105/6/303"/>
         <history>
            <date date-type="received">
               <day>24</day>
               <month>6</month>
               <year>2005</year>
            </date>
            <date date-type="accepted">
               <day>15</day>
               <month>12</month>
               <year>2005</year>
            </date>
         </history>
         <permissions>
            <copyright-statement>Copyright &#x000a9; 2005 Indap et al; licensee BioMed Central Ltd.</copyright-statement>
            <copyright-year>2005</copyright-year>
            <copyright-holder>Indap et al; licensee BioMed Central Ltd.</copyright-holder>
            <license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0">
               <p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri"
                     xlink:href="http://creativecommons.org/licenses/by/2.0"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
                  cited.</p>
               <!--<rdf xmlns="http://web.resource.org/cc/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1" xmlns:dcterms="http://purl.org/dc/terms"><Work xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" rdf:about=""><license rdf:resource="http://creativecommons.org/licenses/by/2.0"/><dc:type rdf:resource="http://purl.org/dc/dcmitype/Text"/><dc:author>
               Indap
               R
               Amit
               
               aindap@yahoo.com
            </dc:author><dc:title>
            Analysis of concordance of different haplotype block partitioning algorithms
         </dc:title><dc:date>2005</dc:date><dcterms:bibliographicCitation>BMC Bioinformatics 6(1): 303-. (2005)</dcterms:bibliographicCitation><dc:identifier type="sici">1471-2105(2005)6:1&#x0003c;303&#x0003e;</dc:identifier><dcterms:isPartOf>urn:ISSN:1471-2105</dcterms:isPartOf><License rdf:about="http://creativecommons.org/licenses/by/2.0"><permits rdf:resource="http://web.resource.org/cc/Reproduction" xmlns=""/><permits rdf:resource="http://web.resource.org/cc/Distribution" xmlns=""/><requires rdf:resource="http://web.resource.org/cc/Notice" xmlns=""/><requires rdf:resource="http://web.resource.org/cc/Attribution" xmlns=""/><permits rdf:resource="http://web.resource.org/cc/DerivativeWorks" xmlns=""/></License></Work></rdf>-->
            </license>
         </permissions>
         <abstract>
            <sec id="id1">
               <title>Background</title>
               <p>Different classes of haplotype block algorithms exist and the ideal dataset to assess their performance would be to comprehensively re-sequence a large genomic region in a large
                  population. Such data sets are expensive to collect. Alternatively, we performed coalescent simulations to generate haplotypes with a high marker density and compared block
                  partitioning results from diversity based, LD based, and information theoretic algorithms under different values of SNP density and allele frequency.</p>
            </sec>
            <sec id="id2">
               <title>Results</title>
               <p>We simulated 1000 haplotypes using the standard coalescent for three world populations &#x02013; European, African American, and East Asian &#x02013; and applied three
                  classes of block partitioning algorithms &#x02013; diversity based, LD based, and information theoretic. We assessed algorithm differences in number, size, and coverage of blocks
                  inferred under different conditions of SNP density, allele frequency, and sample size.</p>
               <p>Each algorithm inferred blocks differing in number, size, and coverage under different density and allele frequency conditions. Different partitions had few if any matching block
                  boundaries. However they still overlapped and a high percentage of total chromosomal region was common to all methods. This percentage was generally higher with a higher density of
                  SNPs and when rarer markers were included.</p>
            </sec>
            <sec id="id3">
               <title>Conclusion</title>
               <p>A gold standard definition of a haplotype block is difficult to achieve, but collecting haplotypes covered with a high density of SNPs, partitioning them with a variety of block
                  algorithms, and identifying regions common to all methods may be the best way to identify genomic regions that harbor SNP variants that cause disease.</p>
            </sec>
         </abstract>
      </article-meta>
   </front>
   <body>
      <sec id="id4">
         <title>Background</title>
         <p>Single Nucleotide Polymorphisms (SNPs) are single base pair differences between individuals in a population. The recent completion of the Human Genome Project has helped facilitate the
            discovery of millions of SNPs and their use in genetic association studies for human disease [<xref ref-type="bibr" rid="B1">1</xref>]. Association studies work on the premise that SNP
            genotypes are correlated with a disease phenotype. Individual SNPs are genotyped and the frequency of alleles are compared between groups of affected and un-affected individuals. SNPs that
            are tested for association either must be the causative allele or be in <italic>linkage disequilibrium </italic>(LD) with the causative allele. LD is the non-random association of alleles
            between adjacent loci [<xref ref-type="bibr" rid="B2">2</xref>]. SNPs that are in LD with causative allele serve as a proxy and the association with the disease phenotype is maintained.</p>
         <p>Numerous studies have shown that the human genome contains regions of high LD with low haplotype diversity [<xref ref-type="bibr" rid="B3">3</xref>-<xref ref-type="bibr" rid="B6"
            >6</xref>]. These regions are called <italic>haplotype blocks</italic>. The existence of haplotype blocks reduces the number of SNPs required in association studies by identifying and
            typing only the subset of tag SNPs which uniquely identify common haplotypes present in a block. The frequencies of these haplotypes can be compared in groups of affected and unaffected
            individuals [<xref ref-type="bibr" rid="B7">7</xref>].</p>
         <p>Haplotype blocks are defined computationally by various algorithms and can be classified into three categories: diversity based [<xref ref-type="bibr" rid="B3">3</xref>,<xref
               ref-type="bibr" rid="B8">8</xref>], LD-based [<xref ref-type="bibr" rid="B6">6</xref>], and information-theoretic [<xref ref-type="bibr" rid="B9">9</xref>]. Patil et al. [<xref
               ref-type="bibr" rid="B3">3</xref>] used a diversity based greedy algorithm to partition Chromosome 21 into haplotype blocks in a sample of 20 re-sequenced chromosomes. Their algorithm
            considers all blocks of consecutive SNPs of one SNP or larger, and defines a haplotype block boundary where at least 80% of observed haplotypes within a block are represented at least one
            or more times in their sample of chromosomes. Overlapping block boundaries were eliminated by choosing the block with the maximum ratio of SNPs in the block to the number of SNPs required
            to discriminate all haplotypes represented in the block. The process was repeated until the entire length of the chromosome was partitioned into haplotype blocks. Zhang et al. [<xref
               ref-type="bibr" rid="B8">8</xref>] subsequently provided a dynamic programming implementation for this approach in their software HapBlock [<xref ref-type="bibr" rid="B10">10</xref>].</p>
         <p>Gabriel et al. [<xref ref-type="bibr" rid="B6">6</xref>] used a LD-based algorithm to define haplotype blocks in a worldwide sample of chromosomes from Africa, Asia, and Europe. The
            authors computed confidence bounds of the value of <italic>D'</italic>, a standard measurement of LD [<xref ref-type="bibr" rid="B11">11</xref>], and defined pairs of SNPs to be in strong
            LD (little evidence of recombination) if the one-sided 95% <italic>D' </italic>confidence bound is between 0.7 and 0.98. The authors defined a haplotype block if least 95% of pairwise SNP
            comparisons in a region show little evidence of recombination based upon their <italic>D' </italic>confidence bounds. The program Haploview [<xref ref-type="bibr" rid="B12">12</xref>]
            implements this method of Gabriel et al.</p>
         <p>Anderson and Novembre [<xref ref-type="bibr" rid="B9">9</xref>] use the Minimum Description Length (MDL) principle for defining haplotype blocks which incorporates LD decay between blocks
            and haplotype diversity within blocks [<xref ref-type="bibr" rid="B9">9</xref>]. The MDL principle is an application of information theory to statistical modeling which searches for
            patterns in data [<xref ref-type="bibr" rid="B13">13</xref>]. The description length of a data set is a function of the length with which data can be encoded in binary digits, or bits
               [<xref ref-type="bibr" rid="B9">9</xref>]. The best set of block boundaries defined by Anderson and Novembre's method is the set of block boundaries that has the shortest description
            length for a set of SNP genotypes that span a genomic region. The authors use a dynamic programming algorithm they call the <italic>iterative dynamic programming algorithm </italic>(IDP)
            and a faster, but approximate, dynamic programming algorithm called <italic>iterative approximate dynamic programming algorithm </italic>(IADP) to find the minimum description length for a
            set of haplotypes. Their method is implemented in the program MDBlocks [<xref ref-type="bibr" rid="B9">9</xref>].</p>
         <p>Previous studies on the empirical performance of block partitioning methods have focused on data sets with differing minor allele frequency cutoffs. The studies of Daly et al. [<xref
               ref-type="bibr" rid="B5">5</xref>], Patil et al. [<xref ref-type="bibr" rid="B3">3</xref>], and Gabriel et al. [<xref ref-type="bibr" rid="B6">6</xref>] used minor allele frequency
            cutoffs of 5%, 10%, and 20%, respectfully. Schulze et al. [<xref ref-type="bibr" rid="B14">14</xref>] assessed the effects of varying the minor allele frequency cutoff on the number of
            blocks and tag SNPs inferred by the LD based method of Gabriel et al. [<xref ref-type="bibr" rid="B6">6</xref>] and diversity based method of Zhang et al. [<xref ref-type="bibr" rid="B8"
            >8</xref>]. As rarer SNPs were removed and the allele frequency cutoff raised, the number of blocks inferred decreased for both methods, showing that the block structure is highly
            influenced by the allele frequency of SNPs used in their analysis.</p>
         <p>Ke et al. [<xref ref-type="bibr" rid="B15">15</xref>] studied the impact of SNP density on block boundaries from three different partitioning algorithms: the previously discussed LD
            approach of Gabriel et al., the four-gamete test [<xref ref-type="bibr" rid="B16">16</xref>], and a <italic>D' </italic>threshold approach of Phillips et al [<xref ref-type="bibr"
               rid="B17">17</xref>]. The author's study genotyped over 5000 SNPs in a 10 Mb region of chromosome 20 in four different populations: CEPH families, U.K. Caucasians, African Americans,
            and East Asians. Block boundaries of the algorithms were assessed with differing marker densities starting at 2 kb and going to 10 kb. Their results show that longer blocks at sparser
            densities are broken into smaller blocks as more SNPs are added in. Other studies describing the LD block structure of the human genome also used varying marker densities. The study by
            Phillips et al. [<xref ref-type="bibr" rid="B17">17</xref>] on chromosome 19 used an average marker density of one SNP per 17.65 kb with a median value of 5.5 kb. Gabriel et al. [<xref
               ref-type="bibr" rid="B6">6</xref>] used an average density of one SNP every 2 kb. Daly et al. [<xref ref-type="bibr" rid="B5">5</xref>] used a density of one marker approximately every
            5 kb. Patil et al. [<xref ref-type="bibr" rid="B3">3</xref>] used a higher density of SNPs with one SNP every 1.3 kb. This study was also the only one that completely re-sequenced the
            entire chromosome for all 20 samples.</p>
         <p>The ideal data set to fully assess the performance of block partitioning algorithms would be a comprehensively re-sequenced large genomic region in a large number of independent
            chromosomes. Unfortunately, such data are not available at this time. Only a limited number of samples have been re-sequenced extensively. In addition to the study by Patil et al., as of
            June 2005 the SeattleSNPs [<xref ref-type="bibr" rid="B18">18</xref>] data set has re-sequenced 234 human genes in 24 African-American and 23 European CEPH samples spanning a total of 4868
            kb of sequence. The ENCODE project [<xref ref-type="bibr" rid="B19">19</xref>] intends to re-sequence five 500 kb genomic regions in the 48 individuals of the HapMap Consortium data set
               [<xref ref-type="bibr" rid="B20">20</xref>].</p>
         <p>Therefore, to fully assess the performance of block partitioning algorithms we generated three populations consisting of 1000 haplotypes using the coalescent, a stochastic technique that
            simulates the genetic history of a sample of chromosomes [<xref ref-type="bibr" rid="B11">11</xref>]. Haplotypes representing a 200 kb chromosomal region for three world populations
            &#x02013; European, African American, and East Asian &#x02013; were simulated using an implementation of the coalescent that uses a population-specific demographic history. The
            population specific profiles we used were previously published in Marth et al. [<xref ref-type="bibr" rid="B21">21</xref>], where the authors derive a closed mathematical formula for
            computing the allele frequency spectrum for a specified demographic profile. The demographic profiles for each of the populations were derived by computing allele frequency spectra
            predicted by Marth's equation for numerous demographic scenarios and testing the fit between it and the observed spectra from the SNP Consortium data set [<xref ref-type="bibr" rid="B1"
            >1</xref>] for each respective population.</p>
         <p>In the study presented here, we partitioned our coalescent-derived haplotypes into blocks using the three algorithms described above (diversity based, LD based, information theoretic). We
            assessed algorithm differences in number, size, and coverage of blocks under different values of marker density, allele frequency, and sample size on the performance of block partitioning
            algorithms. Our results show a great divergence in haplotype blocks predicted by each method, and supports the notion that it may be advisable to use multiple algorithms in parallel to
            comprehensively account for all haplotype blocks in the human genome.</p>
      </sec>
      <sec id="id5">
         <title>Results</title>
         <sec id="id6">
            <title>Data simulation and block partitioning</title>
            <p>One thousand haplotypes representing a 200 kb region were generated via the standard coalescent with population specific demographic profiles for three world populations: European,
               African American, and East Asian. All datasets were analyzed with the three block partitioning algorithms described in the Methods section.</p>
            <p>In addition to the complete dataset of 1000 haplotypes, 1000 bootstrap sub-sample replicates of 24 or 96 haplotypes were sampled and filtered for different SNP density (all markers, one
               marker approximately every 1 kb, one marker approximately every 5 kb) and minor allele frequency (MAF) cutoff values (0.1%, 5%, and 10%). Each bootstrap replicate was partitioned using
               three methods (HapBlock, Gabriel's method, and MDBlocks). Computer memory constraints prevented MDBlocks from partitioning all 1000 chromosomes using all SNPs for each
               coalescent-derived population. For the same reason we were only able to analyze 200 bootstrap subsamples of 24 or 96 chromosomes with MDBlocks. More details on coalescent simulations
               and bootstrap sampling is given in the Methods section of the paper.</p>
         </sec>
         <sec id="id7">
            <title>European population partitions using all chromosomes</title>
            <p>All 1000 European chromosomes were analyzed with HapBlock and Gabriel's method. There were 1349 polymorphic sites with an average SNP density of one SNP per 147 bp. Figure <xref
                  ref-type="fig" rid="F1">1</xref> displays the resulting block partitions using all SNPs from the two methods, with the HapBlock partition denoted as HB and Gabriel's method denoted
               as GA. Table <xref ref-type="table" rid="T2">2</xref> displays descriptive statistics for the HapBlock and Gabriel's method population partitions. No matching block boundaries existed
               between HapBlock and Gabriel's method. HapBlock inferred a larger number of blocks of smaller physical length than Gabriel's method, but 74% of the sequence was common to blocks
               inferred by both methods. Both algorithms gave similiar values of coverage, which is defined as sum of the physical haplotype block lengths in base pairs divided by total length of
               region [<xref ref-type="bibr" rid="B22">22</xref>], with values of 85.6% for HapBlock and 86.1% for Gabriel's method, respectively.</p>
            <fig id="F1" position="float">
               <label>Figure 1</label>
               <caption>
                  <p><bold>European population partitions</bold>. European population partitions for all 1000 haplotypes are shown. Block algorithms are abbreviated as HB (HapBlock), GA (Gabriel's
                     method), MD (MDBlocks). The first two tracks show resulting block partitions from HapBlock and Gabriel's method using all SNPs. Next set of three tracks display resulting block
                     partitions using all SNPs with a 10% MAF. The shared blocks track shows chromosomal regions common to all three block partitions using SNPs with a 10% MAF. The last two tracks
                     show SNP positions of all SNPs and SNPs with at least a 10% MAF.</p>
               </caption>
               <graphic xlink:href="1471-2105-6-303-1"/>
            </fig>
            <table-wrap id="T2" position="float">
               <label>Table 2</label>
               <caption>
                  <p>Descriptive block statistics for all 1000 European, African American, and East Asian haplotypes using all SNPs and all SNPs with a MAF of at least 10%</p>
               </caption>
               <table frame="hsides" rules="groups">
                  <thead>
                     <tr>
                        <td align="center">European haplotypes</td>
                        <td/>
                        <td/>
                        <td/>
                        <td/>
                        <td/>
                     </tr>
                  </thead>
                  <tbody>
                     <tr>
                        <td align="center">Method</td>
                        <td align="center">MAF</td>
                        <td align="center">Number of Blocks</td>
                        <td align="center">Mean bp/block</td>
                        <td align="center">Mean SNPs/block</td>
                        <td align="center">% coverage</td>
                     </tr>
                     <tr>
                        <td colspan="6">
                           <hr/>
                        </td>
                     </tr>
                     <tr>
                        <td align="center">HapBlock</td>
                        <td align="center">0.1%</td>
                        <td align="center">180</td>
                        <td align="center">951.63</td>
                        <td align="center">7.49</td>
                        <td align="center">85.6%</td>
                     </tr>
                     <tr>
                        <td align="center">HapBlock</td>
                        <td align="center">10%</td>
                        <td align="center">48</td>
                        <td align="center">3363.60</td>
                        <td align="center">7.64</td>
                        <td align="center">80.7%</td>
                     </tr>
                     <tr>
                        <td align="center">Gabriel</td>
                        <td align="center">0.1%</td>
                        <td align="center">39</td>
                        <td align="center">4414.28</td>
                        <td align="center">31.41</td>
                        <td align="center">86.1%</td>
                     </tr>
                     <tr>
                        <td align="center">Gabriel</td>
                        <td align="center">10%</td>
                        <td align="center">33</td>
                        <td align="center">4837.52</td>
                        <td align="center">10.84</td>
                        <td align="center">79.8%</td>
                     </tr>
                     <tr>
                        <td align="center">MDBlocks</td>
                        <td align="center">0.1%</td>
                        <td align="center">-</td>
                        <td align="center">-</td>
                        <td align="center">-</td>
                        <td align="center">-</td>
                     </tr>
                     <tr>
                        <td align="center">MDBlocks</td>
                        <td align="center">10%</td>
                        <td align="center">18</td>
                        <td align="center">9348.78</td>
                        <td align="center">20.39</td>
                        <td align="center">84.4%</td>
                     </tr>
                     <tr>
                        <td/>
                        <td/>
                        <td/>
                        <td/>
                        <td/>
                        <td/>
                     </tr>
                     <tr>
                        <td colspan="6">
                           <hr/>
                        </td>
                     </tr>
                     <tr>
                        <td align="center">African American haplotypes</td>
                        <td/>
                        <td/>
                        <td/>
                        <td/>
                        <td/>
                     </tr>
                     <tr>
                        <td colspan="6">
                           <hr/>
                        </td>
                     </tr>
                     <tr>
                        <td align="center">Method</td>
                        <td align="center">MAF</td>
                        <td align="center">Number of Blocks</td>
                        <td align="center">Mean bp/block</td>
                        <td align="center">Mean SNPs/block</td>
                        <td align="center">% coverage</td>
                     </tr>
                     <tr>
                        <td colspan="6">
                           <hr/>
                        </td>
                     </tr>
                     <tr>
                        <td align="center">HapBlock</td>
                        <td align="center">0.1%</td>
                        <td align="center">232</td>
                        <td align="center">733.52</td>
                        <td align="center">7.13</td>
                        <td align="center">85%</td>
                     </tr>
                     <tr>
                        <td align="center">HapBlock</td>
                        <td align="center">10%</td>
                        <td align="center">61</td>
                        <td align="center">2499.93</td>
                        <td align="center">6.26</td>
                        <td align="center">76.2%</td>
                     </tr>
                     <tr>
                        <td align="center">Gabriel</td>
                        <td align="center">0.1%</td>
                        <td align="center">40</td>
                        <td align="center">4433.62</td>
                        <td align="center">37.85</td>
                        <td align="center">88.6%</td>
                     </tr>
                     <tr>
                        <td align="center">Gabriel</td>
                        <td align="center">10%</td>
                        <td align="center">36</td>
                        <td align="center">4416.75</td>
                        <td align="center">10.25</td>
                        <td align="center">80%</td>
                     </tr>
                     <tr>
                        <td align="center">MDBlocks</td>
                        <td align="center">0.1%</td>
                        <td align="center">-</td>
                        <td align="center">-</td>
                        <td align="center">-</td>
                        <td align="center">-</td>
                     </tr>
                     <tr>
                        <td align="center">MDBlocks</td>
                        <td align="center">10%</td>
                        <td align="center">18</td>
                        <td align="center">10004.44</td>
                        <td align="center">21.22</td>
                        <td align="center">90%</td>
                     </tr>
                     <tr>
                        <td/>
                        <td/>
                        <td/>
                        <td/>
                        <td/>
                        <td/>
                     </tr>
                     <tr>
                        <td colspan="6">
                           <hr/>
                        </td>
                     </tr>
                     <tr>
                        <td align="center">East Asian haplotypes</td>
                        <td/>
                        <td/>
                        <td/>
                        <td/>
                        <td/>
                     </tr>
                     <tr>
                        <td colspan="6">
                           <hr/>
                        </td>
                     </tr>
                     <tr>
                        <td align="center">Method</td>
                        <td align="center">MAF</td>
                        <td align="center">Number of Blocks</td>
                        <td align="center">Mean bp/block</td>
                        <td align="center">Mean SNPs/block</td>
                        <td align="center">% coverage</td>
                     </tr>
                     <tr>
                        <td colspan="6">
                           <hr/>
                        </td>
                     </tr>
                     <tr>
                        <td align="center">HapBlock</td>
                        <td align="center">0.1%</td>
                        <td align="center">208</td>
                        <td align="center">831.83</td>
                        <td align="center">7.93</td>
                        <td align="center">86.5%</td>
                     </tr>
                     <tr>
                        <td align="center">HapBlock</td>
                        <td align="center">10%</td>
                        <td align="center">57</td>
                        <td align="center">2596.73</td>
                        <td align="center">5.84</td>
                        <td align="center">74%</td>
                     </tr>
                     <tr>
                        <td align="center">Gabriel</td>
                        <td align="center">0.1%</td>
                        <td align="center">41</td>
                        <td align="center">3966.02</td>
                        <td align="center">24.12</td>
                        <td align="center">81.3%</td>
                     </tr>
                     <tr>
                        <td align="center">Gabriel</td>
                        <td align="center">10%</td>
                        <td align="center">38</td>
                        <td align="center">3437.13</td>
                        <td align="center">8.34</td>
                        <td align="center">65.3%</td>
                     </tr>
                     <tr>
                        <td align="center">MDBlocks</td>
                        <td align="center">0.1%</td>
                        <td align="center">-</td>
                        <td align="center">-</td>
                        <td align="center">-</td>
                        <td align="center">-</td>
                     </tr>
                     <tr>
                        <td align="center">MDBlocks</td>
                        <td align="center">10%</td>
                        <td align="center">18</td>
                        <td align="center">10006.11</td>
                        <td align="center">18.50</td>
                        <td align="center">90%</td>
                     </tr>
                  </tbody>
               </table>
            </table-wrap>
            <p>When analyzing all chromosomes using only SNPs with a MAF of 10% or greater, the total number of markers was reduced to 367 with an average of one SNP every 540 bp. Table <xref
                  ref-type="table" rid="T2">2</xref> also shows descriptive statistics using only SNPs with a MAF of 10% or higher. The number of inferred blocks for HapBlock dropped dramatically from
               180 to 48. For Gabriel's method the change was not as large, with 33 blocks inferred. MDBlocks inferred 18 blocks which had the largest physical size. HapBlock, Gabriel's method, and
               MDBlocks covered 80.7%, 79.8%, and 84.4% of the 200 kb region in blocks. HapBlock again inferred a greater number of blocks of smaller size when compared to the other two methods. Of
               each possible pair of partitions, only Gabriel's method and MDBlocks contained one set of matching boundaries. Still, a large fraction of sequence, 57%, was common to all three
               partitions. Table <xref ref-type="table" rid="T1">1</xref> shows percentage of total sequence common to all population block partitions with this population and condition, as well as
               other populations examined in this study. Figure <xref ref-type="fig" rid="F1">1</xref> shows the population partitions for all three methods using only SNPs with at least a MAF 10%,
               and block regions common for all three algorithms.</p>
            <table-wrap id="T1" position="float">
               <label>Table 1</label>
               <caption>
                  <p>Population partition block overlaps. The table shows the percentage of total sequence common to all three partitions inferred from each algorithm (HapBlock, Gabriel's method, and
                     MDBlocks) for each population studied.</p>
               </caption>
               <table frame="hsides" rules="groups">
                  <thead>
                     <tr>
                        <td align="center">Density</td>
                        <td align="center">MAF</td>
                        <td align="center">European</td>
                        <td align="center">African American</td>
                        <td align="center">East Asian</td>
                     </tr>
                  </thead>
                  <tbody>
                     <tr>
                        <td align="center">all</td>
                        <td align="center">5%</td>
                        <td align="center">61%</td>
                        <td align="center">53%</td>
                        <td align="center">44%</td>
                     </tr>
                     <tr>
                        <td align="center">all</td>
                        <td align="center">10%</td>
                        <td align="center">57%</td>
                        <td align="center">60%</td>
                        <td align="center">46%</td>
                     </tr>
                     <tr>
                        <td align="center">1 kb</td>
                        <td align="center">0.1%</td>
                        <td align="center">43%</td>
                        <td align="center">21%</td>
                        <td align="center">30%</td>
                     </tr>
                     <tr>
                        <td align="center">1 kb</td>
                        <td align="center">5%</td>
                        <td align="center">60%</td>
                        <td align="center">14%</td>
                        <td align="center">22%</td>
                     </tr>
                     <tr>
                        <td align="center">1 kb</td>
                        <td align="center">10%</td>
                        <td align="center">53%</td>
                        <td align="center">17%</td>
                        <td align="center">26%</td>
                     </tr>
                     <tr>
                        <td align="center">5 kb</td>
                        <td align="center">0.1%</td>
                        <td align="center">13%</td>
                        <td align="center">3%</td>
                        <td align="center">10%</td>
                     </tr>
                     <tr>
                        <td align="center">5 kb</td>
                        <td align="center">5%</td>
                        <td align="center">29%</td>
                        <td align="center">0</td>
                        <td align="center">13%</td>
                     </tr>
                     <tr>
                        <td align="center">5 kb</td>
                        <td align="center">10%</td>
                        <td align="center">33%</td>
                        <td align="center">3%</td>
                        <td align="center">12%</td>
                     </tr>
                  </tbody>
               </table>
            </table-wrap>
            <p>Next, we compared the population partitions of HapBlock and Gabriel's method using all markers vs. all markers with a MAF of at least 10%. For HapBlock, 46% of the blocks inferred with
               SNPs with the higher MAF were broken up with the addition of rarer markers however, 70.8% of the chromosome is common to both partitions. For Gabriel's method 73% of the sequence is
               common to partitions resulting from the two differing allele frequency conditions. Only 3% of Gabriel's method blocks were broken into smaller markers with the additon of rarer
            SNPs.</p>
         </sec>
         <sec id="id8">
            <title>African American population partitions using all chromosomes</title>
            <p>A total of of 1653 polymorphic sites with an average density of one SNP every 119 bp defined the 1000 haplotypes in our sample. Table <xref ref-type="table" rid="T2">2</xref> contains
               descriptive statistics for HapBlock and Gabriel's method partitions using all SNPs. HapBlock identified 232 blocks while Gabriel's method identified 40. Figure <xref ref-type="fig"
                  rid="F2">2</xref> displays the HapBlock and Gabriel's method population partitions using all SNPs. Gabriel's method resulted in a slightly larger sequence coverage of 88.6% compared
               with 85% for HapBlock. HapBlock identified a larger number of blocks of smaller size, however 76%, of the sequence was common to both partitions with no exact matching boundaries
               between them.</p>
            <fig id="F2" position="float">
               <label>Figure 2</label>
               <caption>
                  <p><bold>African American population partitions</bold>. African American population partitions for all 1000 haplotypes are shown. Block algorithms are abbreviated as HB (HapBlock),
                     GA (Gabriel's method), MD (MDBlocks). The first two tracks show resulting block partitions from HapBlock and Gabriel's method using all SNPs. Next set of three tracks display
                     resulting block partitions using all SNPs with a 10% MAF. The shared blocks track shows chromosomal regions common to all three block partitions using SNPs with a 10% MAF. The
                     last two tracks show SNP positions of all SNPs and SNPs with at least a 10% MAF.</p>
               </caption>
               <graphic xlink:href="1471-2105-6-303-2"/>
            </fig>
            <p>Using only SNPs with a frequency of at least 10% resulted in a total of 382 markers with an average spacing of one SNP every 521 bp. Table <xref ref-type="table" rid="T2">2</xref> also
               displays descriptive statistics for these block partitions. The number of blocks inferred by HapBlock dropped sharply to 61. For Gabriel's method the difference was smaller with a total
               of 36 blocks inferred. MDBlocks inferred the smallest number of blocks with 18, but had the largest average size. Percent coverage dropped for HapBlock and Gabriel's method to 76.2% and
               80%, respectively. MDBlocks still included 90% of the region in blocks. When comparing all the partitions, 60% of the 200 kb region was common to blocks inferred by all three methods
               (see Table <xref ref-type="table" rid="T1">1</xref>). HapBlock and Gabriel's method shared two matching boundaries, and HapBlock and MDBlocks shared one matching boundary. Figure <xref
                  ref-type="fig" rid="F2">2</xref> displays all three block partitions and shared block regions between each partition. Comparing the HapBlock and Gabriel partitions with the full
               marker set to the corresponding partition of the same method with rarer SNPs filtered out shows that there were common regions identified in both. For HapBlock 67% of the 200 kb region
               was common to blocks for both conditions. For Gabriel's method 74% of the sequence is included in both partitions.</p>
         </sec>
         <sec id="id9">
            <title>East Asian population partitions using all chromosomes</title>
            <p>A total of 1649 SNPs with an average spacing of one SNP every 120 bp defined the 1000 Asian haplotypes. Table <xref ref-type="table" rid="T2">2</xref> shows descriptive statistics for
               HapBlock and Gabriel partitions. HapBlock identified 208 blocks, Gabriel's method inferred 41, and 70% of the chromosome was common to block regions inferred by both methods. No
               matching boundaries existed between the two partitions. Figure <xref ref-type="fig" rid="F3">3</xref> shows HapBlock and Gabriel's method block partitions. The HapBlock partition
               inferred a larger number of blocks of smaller size. Coverage values for HapBlock and Gabriel's method were 86.5% and 81.3%, respectively.</p>
            <fig id="F3" position="float">
               <label>Figure 3</label>
               <caption>
                  <p><bold>East Asian population partitions</bold>. East Asian population partitions for all 1000 haplotypes are shown. Block algorithms are abbreviated as HB (HapBlock), GA (Gabriel's
                     method), MD (MDBlocks). First two tracks show resulting block partitions from HapBlock and Gabriel's method using all SNPs. Next set of three tracks display resulting block
                     partitions using all SNPs with a 10% MAF. The shared blocks track shows chromosomal regions common to all three block partitions using SNPs with a 10% MAF. The last two tracks
                     show SNP positions of all SNPs and SNPs with at least a 10% MAF.</p>
               </caption>
               <graphic xlink:href="1471-2105-6-303-3"/>
            </fig>
            <p>Removing rarer SNPs and using only markers with a MAF of 10% or higher left 333 markers. There was a sharp drop in the number of blocks inferred by HapBlock with 57 blocks compared to
               208 when using the full marker set. Gabriel's method inferred 38 blocks. MDBlocks inferred the fewest with 18. None of the partitions shared the same set of SNPs for a block boundary.
               Table <xref ref-type="table" rid="T2">2</xref> shows descriptive statistics for the resulting block partitions. The amount of sequence coverage drops for two of the methods: 74% for
               HapBlock and 65.3% for Gabriel's method. Coverage for MDBlocks remains at 90%. The shared block regions between all three methods shown in Figure <xref ref-type="fig" rid="F3">3</xref>
               account for 46% of the chromosomal region.</p>
         </sec>
         <sec id="id10">
            <title>Population partitions at other conditions</title>
            <p>Descriptive statistics for population partitions of each method at other density and MAF conditions are shown in <xref ref-type="supplementary-material" rid="S7">additional file
               7</xref>.</p>
         </sec>
         <sec id="id11">
            <title>Bootstrap partitions using all markers with frequency of &#x02265;10%</title>
            <p>To assess variation in block structure on more realistic sample sizes (i.e. sample sizes that are being obtained by re-sequencing) we bootstrap subsampled 96 or 24 chromosomes from our
               original set 1000 times. Figure <xref ref-type="fig" rid="F4">4</xref> shows the block partitions resulting from HapBlock for the first 50 individual bootstrap subsamples of size 96
               using all SNPs with a MAF of at least 10%. It was clearly evident that the block structure varied between the bootstrap subsamples and the population partition. To find SNPs that were
               consistently inferred together in blocks above a threshold frequency across all bootstrap subsamples we defined consensus block partitions for HapBlock for threshold values from 100 to
               50 percent. (For more details on consensus blocks see Methods.) As the threshold for defining a consensus block is lowered, the physical length of a block increases monotonically and
               blocks defined at higher thresholds are combined. Table <xref ref-type="table" rid="T3">3</xref> shows the percentage of chromosomal region common to both the population partition and
               consensus block partitions of HapBlock using only SNPs with a MAF of at least 10%.</p>
            <fig id="F4" position="float">
               <label>Figure 4</label>
               <caption>
                  <p><bold>European HapBlock consensus and bootstrap block partitions</bold>. European HapBlock consensus and bootstrap partitions using all SNPs with at least a 10% MAF are shown. The
                     first track shows the population partition using all 1000 chromosomes followed by consensus blocks defined at thresholds of 100-50% from bootstrap samples of size 96. The next set
                     of tracks are the first 50 individual bootstrap HapBlock partitions.</p>
               </caption>
               <graphic xlink:href="1471-2105-6-303-4"/>
            </fig>
            <table-wrap id="T3" position="float">
               <label>Table 3</label>
               <caption>
                  <p>European population and consensus block overlap. Percentage of total sequence common to each method's consensus blocks defined from bootstrap subsamples of 96 chromosomes and
                     population partition using all SNPs with at least a 10% MAF.</p>
               </caption>
               <table frame="hsides" rules="groups">
                  <thead>
                     <tr>
                        <td align="center">consensus threshold</td>
                        <td align="center">HapBlock</td>
                        <td align="center">Gabriel's Method</td>
                        <td align="center">MDBlocks</td>
                     </tr>
                  </thead>
                  <tbody>
                     <tr>
                        <td align="center">100</td>
                        <td align="center">20%</td>
                        <td align="center">17%</td>
                        <td align="center">31%</td>
                     </tr>
                     <tr>
                        <td align="center">90</td>
                        <td align="center">55%</td>
                        <td align="center">45%</td>
                        <td align="center">66%</td>
                     </tr>
                     <tr>
                        <td align="center">80</td>
                        <td align="center">72%</td>
                        <td align="center">53%</td>
                        <td align="center">76%</td>
                     </tr>
                     <tr>
                        <td align="center">70</td>
                        <td align="center">77%</td>
                        <td align="center">57%</td>
                        <td align="center">78%</td>
                     </tr>
                     <tr>
                        <td align="center">60</td>
                        <td align="center">78%</td>
                        <td align="center">61%</td>
                        <td align="center">81%</td>
                     </tr>
                     <tr>
                        <td align="center">50</td>
                        <td align="center">79%</td>
                        <td align="center">65%</td>
                        <td align="center">81%</td>
                     </tr>
                  </tbody>
               </table>
            </table-wrap>
            <p>Gabriel's method and MDBlocks partitions also showed within population variation in block structure. (See additional files <xref ref-type="supplementary-material" rid="S1">1</xref> and
                  <xref ref-type="supplementary-material" rid="S2">2</xref>.) Table <xref ref-type="table" rid="T3">3</xref> also contains the percentage of total sequence common between the
               population partitions of Gabriel's method and MDBlocks, and each consensus block definition. Similar to the HapBlock results as the threshold for defining a consensus block is lowered,
               the amount block regions common to both partitions increased. Of the three methods, MDBlocks consensus blocks had the greatest amount of total sequence in common with the population
               partition. Table <xref ref-type="table" rid="T4">4</xref> shows the percentage total sequence common to all three consensus block definitions at each threshold value. Figure <xref
                  ref-type="fig" rid="F5">5</xref> displays consensus blocks from each algorithm defined at a 80% threshold, and block regions common to all three consensus blocks. While these common
               block regions cover only 40% of the 200 kb region in blocks, it was encouraging to find that our consenus block partitions overlapped.</p>
            <table-wrap id="T4" position="float">
               <label>Table 4</label>
               <caption>
                  <p>Consensus block overlap. Percentage of total sequence common to all three European consensus blocks defined from bootstrap subsamples of 96 European haplotypes using all SNPs with
                     at least 10% MAF.</p>
               </caption>
               <table frame="hsides" rules="groups">
                  <thead>
                     <tr>
                        <td align="center">consensus threshold</td>
                        <td align="center">% common sequence</td>
                     </tr>
                  </thead>
                  <tbody>
                     <tr>
                        <td align="center">100</td>
                        <td align="center">9%</td>
                     </tr>
                     <tr>
                        <td align="center">90</td>
                        <td align="center">30%</td>
                     </tr>
                     <tr>
                        <td align="center">80</td>
                        <td align="center">40%</td>
                     </tr>
                     <tr>
                        <td align="center">70</td>
                        <td align="center">45%</td>
                     </tr>
                     <tr>
                        <td align="center">60</td>
                        <td align="center">51%</td>
                     </tr>
                     <tr>
                        <td align="center">50</td>
                        <td align="center">57%</td>
                     </tr>
                  </tbody>
               </table>
            </table-wrap>
            <fig id="F5" position="float">
               <label>Figure 5</label>
               <caption>
                  <p><bold>Common European consensus block regions</bold>. Overlapping consensus block regions from each consensus block defined from MDBlocks (MD), HapBlock (HB), and Gabriel's method
                     (GA). Consensus blocks shown from each method are defined at a threshold of 80% using all SNPs with at least a 10% MAF. The SNP positions are shown in the last track.</p>
               </caption>
               <graphic xlink:href="1471-2105-6-303-5"/>
            </fig>
            <p>Figures <xref ref-type="fig" rid="F6">6</xref> and <xref ref-type="fig" rid="F7">7</xref> show the average number of blocks and base pairs per blocks of each partitioning algorithm
               tested for European haplotype bootstrap subsample sizes of 24 and 96 chromosomes for other density and MAF conditions. There is an inverse relationship between the number of blocks
               inferred and their average size in base pairs per blocks as SNP density increased. Coverage generally increased with an increased density of SNPs (see supplementary Figure <xref
                  ref-type="fig" rid="F3">3</xref>).</p>
            <fig id="F6" position="float">
               <label>Figure 6</label>
               <caption>
                  <p><bold>Average number of blocks inferred European bootstrap subsamples</bold>. Figure 6 shows 3-d bar plots of the average number of blocks inferred for HapBlock, Gabriel's method,
                     and MDBlocks partitions on European bootstrap replicates of sizes 96 and 24 at each SNP density and MAF condition.</p>
               </caption>
               <graphic xlink:href="1471-2105-6-303-6"/>
            </fig>
            <fig id="F7" position="float">
               <label>Figure 7</label>
               <caption>
                  <p><bold>Average bp/block European bootstrap subsamples</bold>. Figure 7 shows 3-d bar plots of the average number of base pairs per block inferred for HapBlock, Gabriel's method,
                     and MDBlocks partitions on European bootstrap replicates of sizes 96 and 24 at each SNP density and MAF condition.</p>
               </caption>
               <graphic xlink:href="1471-2105-6-303-7"/>
            </fig>
            <p>Similar patterns for African American and East Asian bootstraps were found. Variation in block structure between bootstrap samples existed. The same pattern of an inverse relationship
               between the number of blocks and their average size as SNP density increased, remained. As the threshold for a consensus block is lowered, the percentage of sequence common between the
               population block partition increased monotonically. Also as the threshold is lowered, there is a greater percentage of total sequence common to all consensus blocks defined from each
               method. (Data not shown).</p>
         </sec>
      </sec>
      <sec id="id12">
         <title>Discussion</title>
         <p>We generated three populations of haplotypes via coalescent simulations to assess the performance of three block partitioning algorithms under different marker density and allele frequency
            conditions. Each of the block algorithms employed in this study partitions a genomic region into haplotype blocks using vastly different approaches. In addition to the three algorithms
            described here, there are other definitions for haplotype blocks not examined [<xref ref-type="bibr" rid="B16">16</xref>,<xref ref-type="bibr" rid="B17">17</xref>]. Despite all these
            algorithms, there is no widely accepted definition of how to best define haplotype blocks [<xref ref-type="bibr" rid="B23">23</xref>].</p>
         <p>The descriptive statistics of each population block partition using all 1000 chromosomes clearly show that results are different in number, size, and coverage of inferred blocks,
            particularly with a higher density of markers. HapBlock generally inferred the largest number of blocks of smallest size and MDBlocks inferred the fewest number of blocks of largest size.
            While there are few exact matching block boundaries between different partitions, there is a large amount of common block regions between them. Increasing the density of markers had a more
            dramatic effect on the percent coverage for Gabriel's method than the other two methods due to fact that LD patterns are sensitive to marker density and can change with the addition of
            more markers [<xref ref-type="bibr" rid="B15">15</xref>]. The amount of coverage, in turn, influences the percentage of total sequence common to all partitions since there is a greater
            chance of overlap between them.</p>
         <p>To assess within population variation in block structure we bootstrap subsampled haplotypes of sizes 24 or 96 chromosomes. The descriptive statistics of the bootstrap partitions indicate
            that the number of inferred blocks increases as a higher density of markers is used. Also, the average number of base pairs per blocks decreases with a higher density of markers, hence
            there is an inverse relationship between the number of blocks inferred and their physical size. Removing rarer SNPs does not necessarily decrease the number of blocks inferred for each of
            the methods when conditioning on a density value. This result is in contrast to the results of Schulze et al. [<xref ref-type="bibr" rid="B14">14</xref>], who found that removing rarer
            SNPs decreased the number of blocks inferred by the HapBlock and Gabriel's method. This maybe a result of the stochastic nature of the coalescent. To get a clearer picture of the effect of
            allele frequency on the performance of block partitioning algorithms, it may require the simulation of many more genealogies.</p>
         <p>Our consensus block definitions attempt to identify SNPs consistently inferred together in blocks across all bootstrap replicates. The amount of common block regions between the population
            partition and consensus block definitions from bootstrap samples depends heavily on the threshold to define a consensus block, as well as the percent coverage of the bootstrap and
            population partitions. If a significant proportion of the chromosome is inferred in blocks in both the population and consensus definitions, there is a greater chance of finding common
            block regions. However as discussed earlier, this attribute is influenced by SNP density and allele frequency of markers.</p>
         <p>For Gabriel's method, the number, size, and coverage of inferred blocks varied dramatically between the bootstrap samples and population partitions. The average number of blocks inferred
            from Gabriel's method for bootstrap samples of size 24 and 96 of European haplotypes, using all markers with a MAF of at least 10% was 18.06 and 30.76, respectively. On average 20.1% and
            68.1% of the 200 kb region were inferred in blocks. These numbers differ from the population partition numbers of 33 blocks and 79.8%. These disparate numbers illustrate the effect sample
            size has on estimating confidence bounds of <italic>D'</italic>. This also explains the fact that in certain bootstrap samples, Gabriel's method failed to infer any blocks. The percent
            overlap between the consensus blocks defined from bootstrap samples of size 24 and the population partition never exceed 16%, even at the most liberal consensus threshold of 50%. For
            consensus blocks defined from bootstrap sample sizes of 96, the percent intersection with the population partition is 53% even at the fairly high threshold of 80%. For HapBlock and
            MDBlocks, differences between average coverage values from the bootstrap experiments to the population partition are not as large, and they showed a larger percentage of sequence
            intersection between the consensus blocks.</p>
         <p>Comparing the consensus blocks for one method to the population partition of the same method addresses the block structure variation within a particular algorithm. To find common block
            regions in bootstrap subsamples from differing algorithms, we found the overlapping boundaries between consensus block regions from each algorithm. For certain density, MAF conditions, and
            consensus block thresholds, there was very low or non-existent overlap. These numbers can be severely reduced if a particular method fails to infer a large number of blocks covering a
            significant portion of sequence, as was the case for Gabriel's method at the sparsest marker density of 5 kb. When using all SNPs with a 10% MAF for European haplotypes, the percentage
            overlap between all consensus blocks ranges from 9&#x02013;57% depending on the consensus threshold. Finding block regions common to all three methods is an encouraging sign because
            each algorithm takes a different approach to the block partitioning problem. If the haplotype block paradigm is an accurate description of underlying LD patterns of the human genome,
            different algorithms should find common block regions since the three methods base their algorithms on various attributes of the paradigm.</p>
         <p>Rather than searching for exact matching boundaries using Schwartz concordance test statistic as a measure of block concordance [<xref ref-type="bibr" rid="B24">24</xref>], we chose to
            compute the percentage of common block regions between two different block partitions as our metric of concordance. While using the block concordance test statistic is a valid approach,
            the method cannot assess the significance of block boundaries which may differ by few SNPs, but still have a significant degree of overlap between block regions. There was only one
            matching boundary between each possible of pair of partitions using all 1000 European haplotypes using all SNPs with a MAF of 10%. However, 57% of the 200 kb region was common to blocks
            defined from all three methods.</p>
         <p>In our analysis we focused on the number, size, and coverage of haplotype blocks inferred by three different algorithms. We do not discuss tag SNPs identification because we view it as a
            separate problem. However, it should be pointed out that the dynamic programming approach of HapBlock is closely tied to tag SNPs because it defines blocks which minimize the number of
            SNPs needed to distinguish common haplotypes within a block. Recently, a method formulated by Halldorsson et al. [<xref ref-type="bibr" rid="B25">25</xref>] selects tag SNPs which does not
            require a haplotype block definition. Also, the tag SNP algorithm LDselect [<xref ref-type="bibr" rid="B26">26</xref>] chooses tag SNPs independent of chosen haplotype block boundaries.</p>
         <p>Another point to address in our study design is that the simulated haplotypes used were derived from a single realization of a coalescent simulation, hence our study does not address
            genetic sampling [<xref ref-type="bibr" rid="B27">27</xref>]. Since we bootstrap subsampled 96 or 24 individuals from a population of 1000, we fix the genetic history of our data set and
            focus on the statistical sampling on the performance of block partitioning algorithms used in this study. We also chose not to vary recombination rate or incorporate recombination hotspots
            in our simulations since we only analyzed a 200 kb region. Due to these limitations, we did not compare populations to each other. Rather, we examined the trends seen in each population
            and used coalescent simulations with three different population histories to ensure that the results from the three block partitioning algorithms were not due to the coalescent parameters
            chosen. The recent study of Ding et al. [<xref ref-type="bibr" rid="B28">28</xref>] address the affects of population genetic parameters, such as the mutation and recombination rate, on
            the diversity and LD based algorithms discussed here for multiple realizations of coalescent genealogies.</p>
      </sec>
      <sec id="id13">
         <title>Conclusion</title>
         <p>In summary, our results show that for the population partitions using all 1000 chromosomes, there is a varied range of number, size, and coverage of blocks between the different methods.
            The percentage total sequence common to all three partitioning algorithms ranges from 3&#x02013;61% depending on the population and is generally higher using a high density of SNPs
            with a wide range of MAF. Bootstrap sampling of haplotypes from the population shows there is within population variation in block structure for all three methods. Our consensus block
            definition attempts to define blocks based on sets of SNPs consistently found together in blocks across all bootstrap replicates. Using a higher density of markers there is an increased
            percentage of total sequence in common with consensus blocks and population partitions. The percentage of common block regions between consensus blocks defined from all three methods is
            influenced by the percent coverage of individual partitions, which itself is influenced by the density and allele frequency of markers that comprise the haplotypes to be partitioned. It is
            evident that each algorithm gave a different picture of haplotype block structure at differing density and MAF values and few, if any exactly matching block boundaries existed. An open
            question that remains is how best to merge or integrate block definitions from different algorithms. For empirical studies, it is advisable to subject collected data to a variety of block
            algorithms and identify common block regions. If distinct partitioning algorithms show a large portion of overlap in inferred block regions, then these genomic regions can be investigated
            further to identify genetic variants causing disease.</p>
      </sec>
      <sec id="id14" sec-type="methods">
         <title>Methods</title>
         <sec id="id15">
            <title>Coalescent simulations</title>
            <p>Haplotypes representing a 200 kb chromosomal region for three world populations, European, African American, and East Asian were generated using an implementation of the standard
               coalescent with uniform recombination, which uses a population-specific demographic history. The demographic profiles for each of the three populations considered in this study were as
               determined by Marth et al. from The SNP Consortium genotype data [<xref ref-type="bibr" rid="B21">21</xref>]. These profiles are characterized by 3 effective population size epochs. For
               example, for the European population, we used an ancestral effective population size of 10,000 individuals, followed by a bottleneck phase of an effective population size of 2,000
               lasting 500 generations, then an expansion to an effective population size of 20,000 starting 3,000 generations ago. The average number of mutations occurring along a branch of the
               genealogy (lineage) is Poisson distributed and proportional to the branch length. The value of the (constant) mutation rate <italic>&#x003bc; </italic>in the simulations was 2.5
               &#x000d7; 10-8. Since the effective population size is different within each of the three epochs of the demographic profiles, there is not a single value of
               <italic>&#x003b8;</italic>, the scaled mutation rate. The equivalent values of <italic>&#x003b8; </italic>that take into account the fluctuating effective population size are,
               for the European population: 9.85 &#x000d7; 10-4; for African American and East Asian populations: 1.29 &#x000d7; 10-3 and 1.03 &#x000d7; 10-3, respectively. For each
               population, the haplotypes we used to examine the partitioning algorithms were drawn from a single realization of the coalescent.</p>
            <p>The coalescent simulation software was implemented in Perl and run on a Sun Blade 1000 with dual 750 MHz Ultra Sparc III processors and 4.5 GB of RAM. To validate the correctness of the
               program 200 genealogies of 41 individuals for a 200 kb region were simulated. The average frequency spectrum was tabulated from these 200 simulations and plotted against the predicted
               spectra from Marth's mathematical formula. The results of the observed and predicted spectra for each population is shown in supplementary figures <xref ref-type="fig" rid="F4"
               >4</xref>, <xref ref-type="fig" rid="F5">5</xref>, and <xref ref-type="fig" rid="F6">6</xref>.</p>
         </sec>
         <sec id="id16">
            <title>Haplotype block partitioning algorithms</title>
            <p>Three categories of block partitioning algorithms were used in the study: diversity based, LD based, and information theoretic. The software programs that implement each method are
               described below. All three programs were run on a Sun Blade 1000 with dual 750 MHz Ultra Sparc III processors and 4.5 GB of RAM.</p>
            <sec id="id17">
               <title>HapBlock</title>
               <p>HapBlock v2.1 is a diversity based algorithm that minimizes the number SNPs that distinguish at least <italic>&#x003b1; </italic>percent of common haplotypes [<xref
                     ref-type="bibr" rid="B8">8</xref>]. A haplotype block comprised of at least one SNP is defined if the number of common haplotypes represents at least <italic>&#x003b1;
                  </italic>percent of all the observed haplotypes. A haplotype can be designated common either by its frequency or the number of times represented in the set of observed haplotypes. We
                  chose to designate a haplotype as common if it had a frequency of at least 10%. Hence, for our study we set <italic>&#x003b1; </italic>and <italic>&#x003b2; </italic>to 0.80
                  and 0.10, respectively. The program is available for download here: <ext-link ext-link-type="uri" xlink:href="http://www.cmb.usc.edu/msms/HapBlock/"/>.</p>
            </sec>
            <sec id="id18">
               <title>Gabriel's Method</title>
               <p>Gabriel's method [<xref ref-type="bibr" rid="B6">6</xref>] is implemented in the software Haploview v3.11 [<xref ref-type="bibr" rid="B12">12</xref>]. Gabriel's method defines pairs
                  of SNPs to be in strong LD if the one-sided 95% <italic>D' </italic>confidence bound is between 0.7 and 0.98. The method defines a block if 95% of pairwise SNP comparisons are in
                  strong LD. For our study Haploview was executed in command line mode to obtain partitions from Gabriel's method. Executing Gabriel's method on certain bootstrap samples generated a
                  software error. Corresponding with the author for Haploview, we were not able to identify the cause of the error (Jeffery Barrett personal communication). But for all bootstrap
                  samples of haplotypes analyzed, this error was encountered on less than 1% of the time. Haploview is available for download here: <ext-link ext-link-type="uri"
                     xlink:href="http://www.broad.mit.edu/mpg/haploview/index.php"/>.</p>
            </sec>
            <sec id="id19">
               <title>MDBlocks</title>
               <p>MDBlocks vl.0 uses the Minimum Description Length (MDL) principle for defining blocks [<xref ref-type="bibr" rid="B9">9</xref>]. It considers the set of all possible block boundaries
                  and finds the one with the minimum description length using two versions of a dynamic programming algorithm. The first is called the <italic>iterative dynamic programming algorithm
                  </italic>(IDP) The second is a faster, but approximate method called the <italic>iterative approximate dynamic programming algorithm </italic>(IADP). Due to the number and size of
                  haplotypes analyzed, we used the IADP option. MDBlocks ran out of computer memory when attempting to partition all 1000 haplotypes using all SNPs for each population when using the
                  IADP algorithm. MDBlocks is available for download here: <ext-link ext-link-type="uri" xlink:href="http://ib.berkeley.edu/labs/slatkin/eriq/software/mdb_web/"/>.</p>
            </sec>
         </sec>
         <sec id="id20">
            <title>Bootstrap subsampling</title>
            <p>To assess variation in the number and size of blocks inferred by the three partitioning algorithms used in the study under differing values of sample size, SNP density, and MAF cutoffs,
               1000 bootstrap subsamples of sizes 24 or 96 were drawn with replacement from the population. A true bootstrap sample is one that is the same size as the original sample (1000). Since we
               are making smaller samples of 96 or 24 chromosomes, it is more properly called a bootstrap subsample. Initially each bootstrap subsample contained the full set of SNPs, and was
               progressively filtered for each possible pair of SNP density and MAF cutoff values.</p>
            <p>Hence, the same set of individuals that make up a particular bootstrap subsample can be compared at differing density and MAF conditions. If a subsample contained a monomorphic site, it
               was removed prior to the initial filtering of density and allele frequency conditions. Since monomorphic sites do not contain any information, in an information theoretic sense, and
               information theory forms the basis for MDBlocks, the program would crash. Removing monomorphic SNPs in our bootstrapping routine solved this problem (Eric C. Anderson, personal
               communication).</p>
         </sec>
         <sec id="id21">
            <title>Consensus block definition</title>
            <p>To identify SNPs that are consistently inferred together in the same block across all bootstrap subsamples, we introduce the idea of a <italic>consensus block</italic>. Let the
               collection <italic>P </italic>= <italic>p</italic><sub>1</sub>, ..., <italic>p</italic><sub>1000 </sub>be the collection of bootstrap partitions resulting from a particular method. Let
                  <italic>S </italic>be the set of SNPs that comprise the haplotypes. For each <italic>SNP</italic><sub>
                  <italic>i </italic>
               </sub>and <italic>SNP</italic><sub><italic>i</italic>+1</sub>, we calculate how often they are assigned to the same block across all bootstrap samples. We call this the neighbor
               probability. We define a <italic>consensus block </italic>as collection of consecutive SNPs whose neighbor probability is greater than or equal to some threshold percentage
               <italic>t</italic>, for <italic>t </italic>= 100 90 80 70 60 50. Consensus blocks were defined for each of the density and MAF conditions for bootstrap subsamples of sizes 24 and 96. As
               described in the previous section, if a bootstrap subsample initially contained a monomorphic site, it was removed. However, this leads to the situation that not all bootstrap
               replicates may contain the same SNPs. To calculate consensus blocks, then we take the union of all markers used across all bootstrap replicates and then proceed to calculate neighbor
               probability. If a particular SNP was not used in a particular bootstrap, and is a member of a the union set of SNPs, its block assignment was treated as missing data and imputed in the
               following way. If the adjacent markers to the left and to the right of the missing marker were assigned to the same block number, then the missing SNP in question was assigned to the
               same block.</p>
         </sec>
         <sec id="id22">
            <title>Data storage</title>
            <p>All data regarding coalescent-derived haplotypes (SNP positions, allele frequencies, etc), block partitions (number of blocks inferred, block boundaries, etc), and consensus block
               definitions were stored in tables in a MySQL v3.23 database.</p>
         </sec>
         <sec id="id23">
            <title>Visualization of block partitions</title>
            <p>Block partitions were visualized in the UCSC Genome Browser [<xref ref-type="bibr" rid="B29">29</xref>].</p>
         </sec>
         <sec id="id24">
            <title>Block partition intersection</title>
            <p>Finding the common regions between different block partitions was achieved by executing the appropriate MySQL query on tables holding information for block partition boundaries. For a
               subset of density and MAF conditions (all SNPs, all SNPs with a 10% MAF) correctness of the database query was verified by using sequence intersection feature of the UCSC Table Browser
                  [<xref ref-type="bibr" rid="B30">30</xref>].</p>
         </sec>
      </sec>
      <sec id="id25">
         <title>Authors' contributions</title>
         <p>MO conceived the original experimental question. GTM provided the source code for coalescent simulations. ARI, PT, and MO formulated the idea of consensus blocks. CAS offered helpful
            advice on data analysis and implementation. ARI executed the block partitioning programs, collected and analyzed the data, and wrote the paper with editorial comments and modifications
            from MO, GTM, CAS, and PT.</p>
      </sec>
      <sec id="id26" sec-type="supplementary-material">
         <title>Supplementary Material</title>
         <supplementary-material content-type="local-data" id="S7">
            <caption>
               <title>Additional File 7</title>
               <p>Supplementary file 7 is an Excel sheet containing descriptive statistics for population partitions using all 1000 haplotypes for European, African American, East Asian populations
                  for all SNP density and MAF conditions.</p>
            </caption>
            <media mime-subtype="vnd.ms-excel" mimetype="application" xlink:href="1471-2105-6-303-S7.xls">
               <caption>
                  <p>Click here for file</p>
               </caption>
            </media>
         </supplementary-material>
         <supplementary-material content-type="local-data" id="S1">
            <caption>
               <title>Additional File 1</title>
               <p>Supplementary Figure <xref ref-type="fig" rid="F1">1</xref> shows Gabriel's method consensus and bootstrap partitions using all SNPs with at least a 10% MAF for European haplotypes.
                  The first track shows the population partition using all 1000 chromosomes followed by consensus blocks defined at thresholds of 100-50% from bootstrap samples of size 96. The next
                  set of tracks are the first 50 individual bootstrap Gabriel's method partitions.</p>
            </caption>
            <media mime-subtype="vnd.ms-powerpoint" mimetype="application" xlink:href="1471-2105-6-303-S1.ppt">
               <caption>
                  <p>Click here for file</p>
               </caption>
            </media>
         </supplementary-material>
         <supplementary-material content-type="local-data" id="S2">
            <caption>
               <title>Additional File 2</title>
               <p>Supplementary Figure <xref ref-type="fig" rid="F2">2</xref> shows MDBlocks consensus and bootstrap partitions using all SNPs with at least a 10% MAF for European haplotypes. The
                  first track shows the population partition using all 1000 chromosomes followed by consensus blocks defined at thresholds of 100-50% from bootstrap samples of size 96. The next set of
                  tracks are the first 50 individual bootstrap MDBlocks partitions.</p>
            </caption>
            <media mime-subtype="vnd.ms-powerpoint" mimetype="application" xlink:href="1471-2105-6-303-S2.ppt">
               <caption>
                  <p>Click here for file</p>
               </caption>
            </media>
         </supplementary-material>
         <supplementary-material content-type="local-data" id="S3">
            <caption>
               <title>Additional File 3</title>
               <p>Supplementary Figure <xref ref-type="fig" rid="F3">3</xref> shows 3-d bar plots of the average coverage of HapBlock, Gabriel's method, and MDBlocks partitions on European bootstrap
                  replicates of sizes 96 and 24 at each SNP density and MAF condition.</p>
            </caption>
            <media mime-subtype="vnd.ms-powerpoint" mimetype="application" xlink:href="1471-2105-6-303-S3.ppt">
               <caption>
                  <p>Click here for file</p>
               </caption>
            </media>
         </supplementary-material>
         <supplementary-material content-type="local-data" id="S4">
            <caption>
               <title>Additional File 4</title>
               <p>Supplementary Figure <xref ref-type="fig" rid="F4">4</xref> shows the validated European allele frequency spectrum (AFS). The average folded AFS from 200 coalescent genealogies of 41
                  individuals is plotted in green. The predicted AFS from Marth's mathematical formula is shown in red.</p>
            </caption>
            <media mime-subtype="vnd.ms-powerpoint" mimetype="application" xlink:href="1471-2105-6-303-S4.ppt">
               <caption>
                  <p>Click here for file</p>
               </caption>
            </media>
         </supplementary-material>
         <supplementary-material content-type="local-data" id="S5">
            <caption>
               <title>Additional File 5</title>
               <p>Supplementary figure <xref ref-type="fig" rid="F5">5</xref> shows the validated African American allele frequency specturm (AFS). The average folded AFS from 200 coalescent
                  genealogies of 41 individuals is plotted in green. The predicted AFS from Marth's mathematical formula is shown in red.</p>
            </caption>
            <media mime-subtype="vnd.ms-powerpoint" mimetype="application" xlink:href="1471-2105-6-303-S5.ppt">
               <caption>
                  <p>Click here for file</p>
               </caption>
            </media>
         </supplementary-material>
         <supplementary-material content-type="local-data" id="S6">
            <caption>
               <title>Additional File 6</title>
               <p>Supplementary figure <xref ref-type="fig" rid="F6">6</xref> shows the validated East Asian allele frequency specturm (AFS). The average folded AFS from 200 coalescent genealogies of
                  41 individuals is plotted in green. The predicted AFS from Marth's mathematical formula is shown in red.</p>
            </caption>
            <media mime-subtype="vnd.ms-powerpoint" mimetype="application" xlink:href="1471-2105-6-303-S6.ppt">
               <caption>
                  <p>Click here for file</p>
               </caption>
            </media>
         </supplementary-material>
      </sec>
   </body>
   <back>
      <ack>
         <sec id="id27">
            <title>Acknowledgements</title>
            <p>We thank Vasant Marar and Meeta Oberoi for excellent computer support.</p>
         </sec>
      </ack>
      <ref-list>
         <ref id="B1">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Sachidanandam</surname>
                     <given-names>R</given-names>
                  </name>
                  <name>
                     <surname>Weissman</surname>
                     <given-names>D</given-names>
                  </name>
                  <name>
                     <surname>Schmidt</surname>
                     <given-names>SC</given-names>
                  </name>
                  <name>
                     <surname>Kakol</surname>
                     <given-names>JM</given-names>
                  </name>
                  <name>
                     <surname>Stein</surname>
                     <given-names>LD</given-names>
                  </name>
                  <name>
                     <surname>Marth</surname>
                     <given-names>G</given-names>
                  </name>
                  <name>
                     <surname>Sherry</surname>
                     <given-names>S</given-names>
                  </name>
               </person-group>
               <article-title>A map of genome sequence information containing 1.42 million single nucleotide polymorphisms</article-title>
               <source>Nature</source>
               <year>2001</year>
               <volume>409</volume>
               <fpage>187</fpage>
               <lpage>196</lpage>
            </citation>
         </ref>
         <ref id="B2">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Weiss</surname>
                     <given-names>KM</given-names>
                  </name>
                  <name>
                     <surname>Clark</surname>
                     <given-names>AG</given-names>
                  </name>
               </person-group>
               <article-title>Linkage disequilibrium and mapping of complex human traits</article-title>
               <source>Trends in Genetics</source>
               <year>2002</year>
               <volume>18</volume>
               <fpage>19</fpage>
               <lpage>24</lpage>
               <pub-id pub-id-type="pmid">11750696</pub-id>
            </citation>
         </ref>
         <ref id="B3">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Patil</surname>
                     <given-names>N</given-names>
                  </name>
                  <name>
                     <surname>Berno</surname>
                     <given-names>AJ</given-names>
                  </name>
                  <name>
                     <surname>Hinds</surname>
                     <given-names>DA</given-names>
                  </name>
                  <name>
                     <surname>Barrett</surname>
                     <given-names>WA</given-names>
                  </name>
                  <name>
                     <surname>Doshi</surname>
                     <given-names>JM</given-names>
                  </name>
                  <name>
                     <surname>Hacker</surname>
                     <given-names>CR</given-names>
                  </name>
                  <name>
                     <surname>Kautzer</surname>
                     <given-names>CR</given-names>
                  </name>
                  <name>
                     <surname>Lee</surname>
                     <given-names>DH</given-names>
                  </name>
                  <name>
                     <surname>Marjoribanks</surname>
                     <given-names>C</given-names>
                  </name>
                  <name>
                     <surname>McDonough</surname>
                     <given-names>DP</given-names>
                  </name>
                  <name>
                     <surname>Nguyen</surname>
                     <given-names>BTN</given-names>
                  </name>
                  <name>
                     <surname>Norris</surname>
                     <given-names>MC</given-names>
                  </name>
                  <name>
                     <surname>Sheehan</surname>
                     <given-names>JB</given-names>
                  </name>
                  <name>
                     <surname>Shen</surname>
                     <given-names>N</given-names>
                  </name>
                  <name>
                     <surname>Stern</surname>
                     <given-names>D</given-names>
                  </name>
                  <name>
                     <surname>Stokowski</surname>
                     <given-names>RP</given-names>
                  </name>
                  <name>
                     <surname>Thomas</surname>
                     <given-names>DJ</given-names>
                  </name>
                  <name>
                     <surname>Trulson</surname>
                     <given-names>MO</given-names>
                  </name>
                  <name>
                     <surname>Vyas</surname>
                     <given-names>KR</given-names>
                  </name>
                  <name>
                     <surname>Frazer</surname>
                     <given-names>KA</given-names>
                  </name>
                  <name>
                     <surname>Fodor</surname>
                     <given-names>SPA</given-names>
                  </name>
                  <name>
                     <surname>Cox</surname>
                     <given-names>DR</given-names>
                  </name>
               </person-group>
               <article-title>Blocks of Limited Haplotype Diversity Revealed by High-Resolution Scanning of Human Chromosome 21</article-title>
               <source>Science</source>
               <year>2003</year>
               <volume>294</volume>
               <fpage>1719</fpage>
               <lpage>1723</lpage>
               <pub-id pub-id-type="pmid">11721056</pub-id>
            </citation>
         </ref>
         <ref id="B4">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Olivier</surname>
                     <given-names>M</given-names>
                  </name>
                  <name>
                     <surname>Bustos</surname>
                     <given-names>VI</given-names>
                  </name>
                  <name>
                     <surname>Levy</surname>
                     <given-names>MR</given-names>
                  </name>
                  <name>
                     <surname>Smick</surname>
                     <given-names>GA</given-names>
                  </name>
                  <name>
                     <surname>Moreno</surname>
                     <given-names>I</given-names>
                  </name>
                  <name>
                     <surname>Bushard</surname>
                     <given-names>JM</given-names>
                  </name>
                  <name>
                     <surname>Almendras</surname>
                     <given-names>AA</given-names>
                  </name>
                  <name>
                     <surname>Sheppard</surname>
                     <given-names>K</given-names>
                  </name>
                  <name>
                     <surname>Zierten</surname>
                     <given-names>DL</given-names>
                  </name>
                  <name>
                     <surname>Aggarwal</surname>
                     <given-names>A</given-names>
                  </name>
                  <name>
                     <surname>Carlson</surname>
                     <given-names>CS</given-names>
                  </name>
                  <name>
                     <surname>Foster</surname>
                     <given-names>BD</given-names>
                  </name>
                  <name>
                     <surname>Vo</surname>
                     <given-names>N</given-names>
                  </name>
                  <name>
                     <surname>Kelly</surname>
                     <given-names>L</given-names>
                  </name>
                  <name>
                     <surname>Liu</surname>
                     <given-names>X</given-names>
                  </name>
                  <name>
                     <surname>Cox</surname>
                     <given-names>DR</given-names>
                  </name>
               </person-group>
               <article-title>Complex High-Resolution Linkage Disequilibrium and Haplotype Patterns of Single-Nucleotide Polymorphisms in 2.5 Mb of Sequence on Human Chromosome 21</article-title>
               <source>Genomics</source>
               <year>2001</year>
               <volume>78</volume>
               <fpage>64</fpage>
               <lpage>72</lpage>
               <pub-id pub-id-type="pmid">11707074</pub-id>
            </citation>
         </ref>
         <ref id="B5">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Daly</surname>
                     <given-names>MJ</given-names>
                  </name>
                  <name>
                     <surname>Rioux</surname>
                     <given-names>JD</given-names>
                  </name>
                  <name>
                     <surname>Schaffner</surname>
                     <given-names>SF</given-names>
                  </name>
                  <name>
                     <surname>Hudson</surname>
                     <given-names>TJ</given-names>
                  </name>
                  <name>
                     <surname>Lander</surname>
                     <given-names>ES</given-names>
                  </name>
               </person-group>
               <article-title>High-resolution haplotype structure in the human genome</article-title>
               <source>Nature Genetics</source>
               <year>2001</year>
               <volume>29</volume>
               <fpage>229</fpage>
               <lpage>232</lpage>
               <pub-id pub-id-type="pmid">11586305</pub-id>
            </citation>
         </ref>
         <ref id="B6">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Gabriel</surname>
                     <given-names>SB</given-names>
                  </name>
                  <name>
                     <surname>Schaffner</surname>
                     <given-names>SF</given-names>
                  </name>
                  <name>
                     <surname>Nguyen</surname>
                     <given-names>H</given-names>
                  </name>
                  <name>
                     <surname>Moore</surname>
                     <given-names>JM</given-names>
                  </name>
                  <name>
                     <surname>Roy</surname>
                     <given-names>J</given-names>
                  </name>
                  <name>
                     <surname>Blumenstiel</surname>
                     <given-names>B</given-names>
                  </name>
                  <name>
                     <surname>Higgins</surname>
                     <given-names>J</given-names>
                  </name>
                  <name>
                     <surname>DeFelice</surname>
                     <given-names>M</given-names>
                  </name>
                  <name>
                     <surname>Lochner</surname>
                     <given-names>A</given-names>
                  </name>
                  <name>
                     <surname>Faggart</surname>
                     <given-names>M</given-names>
                  </name>
                  <name>
                     <surname>Liu-Cordero</surname>
                     <given-names>SN</given-names>
                  </name>
                  <name>
                     <surname>Rotimi</surname>
                     <given-names>C</given-names>
                  </name>
                  <name>
                     <surname>Adeyemo</surname>
                     <given-names>A</given-names>
                  </name>
                  <name>
                     <surname>Cooper</surname>
                     <given-names>R</given-names>
                  </name>
                  <name>
                     <surname>Ward</surname>
                     <given-names>R</given-names>
                  </name>
                  <name>
                     <surname>Lander</surname>
                     <given-names>ES</given-names>
                  </name>
                  <name>
                     <surname>Daly</surname>
                     <given-names>MJ</given-names>
                  </name>
                  <name>
                     <surname>Altshuler</surname>
                     <given-names>D</given-names>
                  </name>
               </person-group>
               <article-title>The Structure of Haplotype Blocks in the Human Genome</article-title>
               <source>Science</source>
               <year>2003</year>
               <volume>296</volume>
               <fpage>2225</fpage>
               <lpage>2229</lpage>
               <pub-id pub-id-type="pmid">12029063</pub-id>
            </citation>
         </ref>
         <ref id="B7">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Hirschorn</surname>
                     <given-names>JN</given-names>
                  </name>
                  <name>
                     <surname>Daly</surname>
                     <given-names>MJ</given-names>
                  </name>
               </person-group>
               <article-title>Genome-wide Association Studies for Common Diseases and Complex Traits</article-title>
               <source>Nature Reviews Genetics</source>
               <year>2005</year>
               <volume>6</volume>
               <fpage>95</fpage>
               <lpage>108</lpage>
               <pub-id pub-id-type="pmid">15716906</pub-id>
            </citation>
         </ref>
         <ref id="B8">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Zhang</surname>
                     <given-names>K</given-names>
                  </name>
                  <name>
                     <surname>Deng</surname>
                     <given-names>M</given-names>
                  </name>
                  <name>
                     <surname>Chen</surname>
                     <given-names>T</given-names>
                  </name>
                  <name>
                     <surname>Waterman</surname>
                     <given-names>MS</given-names>
                  </name>
                  <name>
                     <surname>Sun</surname>
                     <given-names>F</given-names>
                  </name>
               </person-group>
               <article-title>A dynamic programming algorithm for haplotype block partitioning</article-title>
               <source>PNAS</source>
               <year>2002</year>
               <volume>99</volume>
               <fpage>7335</fpage>
               <lpage>7339</lpage>
               <pub-id pub-id-type="pmid">12032283</pub-id>
            </citation>
         </ref>
         <ref id="B9">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Anderson</surname>
                     <given-names>EC</given-names>
                  </name>
                  <name>
                     <surname>Novembre</surname>
                     <given-names>J</given-names>
                  </name>
               </person-group>
               <article-title>Finding Haplotype Block Boundaries by Using the Minimum-Description-Length Principle</article-title>
               <source>American Journal of Human Genetics</source>
               <year>2003</year>
               <volume>73</volume>
               <fpage>336</fpage>
               <lpage>354</lpage>
               <pub-id pub-id-type="pmid">12858289</pub-id>
            </citation>
         </ref>
         <ref id="B10">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Zhang</surname>
                     <given-names>K</given-names>
                  </name>
                  <name>
                     <surname>Qin</surname>
                     <given-names>Z</given-names>
                  </name>
                  <name>
                     <surname>Chen</surname>
                     <given-names>T</given-names>
                  </name>
                  <name>
                     <surname>Liu</surname>
                     <given-names>JS</given-names>
                  </name>
                  <name>
                     <surname>Waterman</surname>
                     <given-names>MS</given-names>
                  </name>
                  <name>
                     <surname>Sun</surname>
                     <given-names>F</given-names>
                  </name>
               </person-group>
               <article-title>HapBlock: haplotype block partitioning and tag SNP selection software using a set of dynamic programming algorithms</article-title>
               <source>Bioinformatics</source>
               <year>2005</year>
               <volume>21</volume>
               <fpage>131</fpage>
               <lpage>134</lpage>
               <pub-id pub-id-type="pmid">15333454</pub-id>
            </citation>
         </ref>
         <ref id="B11">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Nordborg</surname>
                     <given-names>M</given-names>
                  </name>
                  <name>
                     <surname>Tavare</surname>
                     <given-names>S</given-names>
                  </name>
               </person-group>
               <article-title>Linkage disequilibrium: What history has to tell us</article-title>
               <source>Trends in Genetics</source>
               <year>2002</year>
               <volume>18</volume>
               <fpage>83</fpage>
               <lpage>90</lpage>
               <pub-id pub-id-type="pmid">11818140</pub-id>
            </citation>
         </ref>
         <ref id="B12">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Barrett</surname>
                     <given-names>JC</given-names>
                  </name>
                  <name>
                     <surname>Fry</surname>
                     <given-names>B</given-names>
                  </name>
                  <name>
                     <surname>Mailer</surname>
                     <given-names>J</given-names>
                  </name>
                  <name>
                     <surname>Daly</surname>
                     <given-names>MJ</given-names>
                  </name>
               </person-group>
               <article-title>Haploview: analysis and visualization of LD and haplotype maps</article-title>
               <source>Bioinformatics</source>
               <year>2005</year>
               <volume>21</volume>
               <fpage>263</fpage>
               <lpage>265</lpage>
               <pub-id pub-id-type="pmid">15297300</pub-id>
            </citation>
         </ref>
         <ref id="B13">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Hansen</surname>
                     <given-names>MH</given-names>
                  </name>
                  <name>
                     <surname>Yu</surname>
                     <given-names>B</given-names>
                  </name>
               </person-group>
               <article-title>Model Selection and the Principle of Minimum Description Length</article-title>
               <source>Journal of the American Statistical Association</source>
               <year>2001</year>
               <volume>96</volume>
               <fpage>746</fpage>
               <lpage>774</lpage>
            </citation>
         </ref>
         <ref id="B14">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Schulze</surname>
                     <given-names>TG</given-names>
                  </name>
                  <name>
                     <surname>Zhang</surname>
                     <given-names>K</given-names>
                  </name>
                  <name>
                     <surname>Chen</surname>
                     <given-names>YS</given-names>
                  </name>
                  <name>
                     <surname>Akula</surname>
                     <given-names>N</given-names>
                  </name>
                  <name>
                     <surname>Sun</surname>
                     <given-names>F</given-names>
                  </name>
                  <name>
                     <surname>McMahon</surname>
                     <given-names>FJ</given-names>
                  </name>
               </person-group>
               <article-title>Defining haplotype blocks and tag single-nucleotide polymorphisms in the human genome</article-title>
               <source>Human Molecular Genetics</source>
               <year>2004</year>
               <volume>13</volume>
               <fpage>335</fpage>
               <lpage>342</lpage>
               <pub-id pub-id-type="pmid">14681300</pub-id>
            </citation>
         </ref>
         <ref id="B15">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Ke</surname>
                     <given-names>X</given-names>
                  </name>
                  <name>
                     <surname>Hunt</surname>
                     <given-names>S</given-names>
                  </name>
                  <name>
                     <surname>Tapper</surname>
                     <given-names>W</given-names>
                  </name>
                  <name>
                     <surname>Lawrence</surname>
                     <given-names>R</given-names>
                  </name>
                  <name>
                     <surname>Stavrides</surname>
                     <given-names>G</given-names>
                  </name>
                  <name>
                     <surname>Ghori</surname>
                     <given-names>J</given-names>
                  </name>
                  <name>
                     <surname>Whittaker</surname>
                     <given-names>P</given-names>
                  </name>
                  <name>
                     <surname>Collins</surname>
                     <given-names>A</given-names>
                  </name>
                  <name>
                     <surname>Morris</surname>
                     <given-names>AP</given-names>
                  </name>
                  <name>
                     <surname>Bentley</surname>
                     <given-names>D</given-names>
                  </name>
                  <name>
                     <surname>Cardon</surname>
                     <given-names>LR</given-names>
                  </name>
                  <name>
                     <surname>Deloukas</surname>
                     <given-names>P</given-names>
                  </name>
               </person-group>
               <article-title>The impact of SNP density on fine-scale patterns of linkage disequilibrium</article-title>
               <source>Human Molecular Genetics</source>
               <year>2004</year>
               <volume>13</volume>
               <fpage>577</fpage>
               <lpage>588</lpage>
               <pub-id pub-id-type="pmid">14734624</pub-id>
            </citation>
         </ref>
         <ref id="B16">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Wang</surname>
                     <given-names>N</given-names>
                  </name>
                  <name>
                     <surname>Akey</surname>
                     <given-names>JM</given-names>
                  </name>
                  <name>
                     <surname>Zhang</surname>
                     <given-names>K</given-names>
                  </name>
                  <name>
                     <surname>Chakraborty</surname>
                     <given-names>R</given-names>
                  </name>
                  <name>
                     <surname>Jin</surname>
                     <given-names>L</given-names>
                  </name>
               </person-group>
               <article-title>Distribution of Recombination Crossovers and the Origin of Haplotype Blocks: The Interplay of Population History, Recombination, and Mutation</article-title>
               <source>American Journal of Human Genetics</source>
               <year>2002</year>
               <volume>71</volume>
               <fpage>1227</fpage>
               <lpage>1234</lpage>
               <pub-id pub-id-type="pmid">12384857</pub-id>
            </citation>
         </ref>
         <ref id="B17">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Phillips</surname>
                     <given-names>M</given-names>
                  </name>
                  <name>
                     <surname>Lawrence</surname>
                     <given-names>R</given-names>
                  </name>
                  <name>
                     <surname>Sachidanandam</surname>
                     <given-names>R</given-names>
                  </name>
                  <name>
                     <surname>Morris</surname>
                     <given-names>A</given-names>
                  </name>
                  <name>
                     <surname>Balding</surname>
                     <given-names>D</given-names>
                  </name>
                  <name>
                     <surname>Donaldson</surname>
                     <given-names>M</given-names>
                  </name>
                  <name>
                     <surname>Studebaker</surname>
                     <given-names>J</given-names>
                  </name>
                  <name>
                     <surname>Ankener</surname>
                     <given-names>W</given-names>
                  </name>
                  <name>
                     <surname>Alfisi</surname>
                     <given-names>S</given-names>
                  </name>
                  <name>
                     <surname>Kuo</surname>
                     <given-names>FS</given-names>
                  </name>
                  <name>
                     <surname>Camisa</surname>
                     <given-names>A</given-names>
                  </name>
                  <name>
                     <surname>Pazorov</surname>
                     <given-names>V</given-names>
                  </name>
                  <name>
                     <surname>Scott</surname>
                     <given-names>K</given-names>
                  </name>
                  <name>
                     <surname>Carey</surname>
                     <given-names>B</given-names>
                  </name>
                  <name>
                     <surname>Faith</surname>
                     <given-names>J</given-names>
                  </name>
                  <name>
                     <surname>Katari</surname>
                     <given-names>G</given-names>
                  </name>
                  <name>
                     <surname>Bhatti</surname>
                     <given-names>H</given-names>
                  </name>
                  <name>
                     <surname>Cyr</surname>
                     <given-names>J</given-names>
                  </name>
                  <name>
                     <surname>Derohannessian</surname>
                     <given-names>V</given-names>
                  </name>
                  <name>
                     <surname>Elosua</surname>
                     <given-names>C</given-names>
                  </name>
                  <name>
                     <surname>Forman</surname>
                     <given-names>A</given-names>
                  </name>
                  <name>
                     <surname>Grecco</surname>
                     <given-names>N</given-names>
                  </name>
                  <name>
                     <surname>Hock</surname>
                     <given-names>C</given-names>
                  </name>
                  <name>
                     <surname>Kuebler</surname>
                     <given-names>J</given-names>
                  </name>
                  <name>
                     <surname>Lathrop</surname>
                     <given-names>J</given-names>
                  </name>
                  <name>
                     <surname>Mockler</surname>
                     <given-names>M</given-names>
                  </name>
                  <name>
                     <surname>Nachtman</surname>
                     <given-names>E</given-names>
                  </name>
                  <name>
                     <surname>Restine</surname>
                     <given-names>S</given-names>
                  </name>
                  <name>
                     <surname>Varde</surname>
                     <given-names>S</given-names>
                  </name>
                  <name>
                     <surname>Hozza</surname>
                     <given-names>M</given-names>
                  </name>
                  <name>
                     <surname>Gelfand</surname>
                     <given-names>C</given-names>
                  </name>
                  <name>
                     <surname>Broxholme</surname>
                     <given-names>J</given-names>
                  </name>
                  <name>
                     <surname>Abecasis</surname>
                     <given-names>G</given-names>
                  </name>
                  <name>
                     <surname>Boyce-Jacino</surname>
                     <given-names>M</given-names>
                  </name>
                  <name>
                     <surname>Cardon</surname>
                     <given-names>L</given-names>
                  </name>
               </person-group>
               <article-title>Chromosome-wide distribution of haplotype blocks and the role of recombination hot spots</article-title>
               <source>Nature Genetics</source>
               <year>2003</year>
               <volume>33</volume>
               <fpage>382</fpage>
               <lpage>387</lpage>
               <pub-id pub-id-type="pmid">12590262</pub-id>
            </citation>
         </ref>
         <ref id="B18">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Carlson</surname>
                     <given-names>CS</given-names>
                  </name>
                  <name>
                     <surname>Eberle</surname>
                     <given-names>MA</given-names>
                  </name>
                  <name>
                     <surname>Rieder</surname>
                     <given-names>MJ</given-names>
                  </name>
                  <name>
                     <surname>Smith</surname>
                     <given-names>JD</given-names>
                  </name>
                  <name>
                     <surname>Kruglyak</surname>
                     <given-names>L</given-names>
                  </name>
                  <name>
                     <surname>Nickerson</surname>
                     <given-names>DA</given-names>
                  </name>
               </person-group>
               <article-title>Additional SNPs and linkage-disequilibrium analyses are necessary for whole-genome association studies in humans</article-title>
               <source>Nature Genetics</source>
               <year>2003</year>
               <volume>33</volume>
               <fpage>518</fpage>
               <lpage>521</lpage>
               <pub-id pub-id-type="pmid">12652300</pub-id>
            </citation>
         </ref>
         <ref id="B19">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <collab>Consortium TEP</collab>
               </person-group>
               <article-title>The ENCODE (ENCyclopedia Of DNA Elements) Project</article-title>
               <source>Science</source>
               <year>2003</year>
               <volume>306</volume>
               <fpage>636</fpage>
               <lpage>640</lpage>
            </citation>
         </ref>
         <ref id="B20">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <collab>International HapMap Consortium T</collab>
               </person-group>
               <article-title>The International HapMap Project</article-title>
               <source>Nature</source>
               <year>2003</year>
               <volume>426</volume>
               <fpage>789</fpage>
               <lpage>796</lpage>
               <pub-id pub-id-type="pmid">14685227</pub-id>
            </citation>
         </ref>
         <ref id="B21">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Marth</surname>
                     <given-names>G</given-names>
                  </name>
                  <name>
                     <surname>Schuler</surname>
                     <given-names>G</given-names>
                  </name>
                  <name>
                     <surname>Yeh</surname>
                     <given-names>R</given-names>
                  </name>
                  <name>
                     <surname>Davenport</surname>
                     <given-names>R</given-names>
                  </name>
                  <name>
                     <surname>Agarwala</surname>
                     <given-names>R</given-names>
                  </name>
                  <name>
                     <surname>Church</surname>
                     <given-names>D</given-names>
                  </name>
                  <name>
                     <surname>Wheelan</surname>
                     <given-names>S</given-names>
                  </name>
                  <name>
                     <surname>Baker</surname>
                     <given-names>J</given-names>
                  </name>
                  <name>
                     <surname>Ward</surname>
                     <given-names>M</given-names>
                  </name>
                  <name>
                     <surname>Kholodov</surname>
                     <given-names>M</given-names>
                  </name>
                  <name>
                     <surname>Phan</surname>
                     <given-names>L</given-names>
                  </name>
                  <name>
                     <surname>Czabarka</surname>
                     <given-names>E</given-names>
                  </name>
                  <name>
                     <surname>Murvai</surname>
                     <given-names>J</given-names>
                  </name>
                  <name>
                     <surname>Cutler</surname>
                     <given-names>D</given-names>
                  </name>
                  <name>
                     <surname>Wooding</surname>
                     <given-names>S</given-names>
                  </name>
                  <name>
                     <surname>Rogers</surname>
                     <given-names>A</given-names>
                  </name>
                  <name>
                     <surname>Chakravarti</surname>
                     <given-names>A</given-names>
                  </name>
                  <name>
                     <surname>Harpending</surname>
                     <given-names>HC</given-names>
                  </name>
                  <name>
                     <surname>Kwok</surname>
                     <given-names>PY</given-names>
                  </name>
                  <name>
                     <surname>Sherry</surname>
                     <given-names>ST</given-names>
                  </name>
               </person-group>
               <article-title>Sequence variations in the public genome data reflect a bottlenecked population history</article-title>
               <source>PNAS</source>
               <year>2003</year>
               <volume>100</volume>
               <fpage>376</fpage>
               <lpage>381</lpage>
               <pub-id pub-id-type="pmid">12502794</pub-id>
            </citation>
         </ref>
         <ref id="B22">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Pritchard</surname>
                     <given-names>JK</given-names>
                  </name>
                  <name>
                     <surname>Wall</surname>
                     <given-names>J</given-names>
                  </name>
               </person-group>
               <article-title>Assessing the Performance of the Haplotype Block Model of Linkage Disequilibrium</article-title>
               <source>American Journal of Human Genetics</source>
               <year>2003</year>
               <volume>73</volume>
               <fpage>502</fpage>
               <lpage>515</lpage>
               <pub-id pub-id-type="pmid">12916017</pub-id>
            </citation>
         </ref>
         <ref id="B23">
            <citation citation-type="other">
               <person-group person-group-type="author">
                  <name>
                     <surname>Bafna</surname>
                     <given-names>V</given-names>
                  </name>
                  <name>
                     <surname>Halldorsson</surname>
                     <given-names>BV</given-names>
                  </name>
                  <name>
                     <surname>Schwartz</surname>
                     <given-names>R</given-names>
                  </name>
                  <name>
                     <surname>Clark</surname>
                     <given-names>AG</given-names>
                  </name>
                  <name>
                     <surname>Istrail</surname>
                     <given-names>S</given-names>
                  </name>
               </person-group>
               <article-title>Haplotypes and Informative SNP Selection Algorithms: Don't Block Out Information</article-title>
               <source>RECOMB</source>
               <year>2003</year>
               <fpage>19</fpage>
               <lpage>27</lpage>
            </citation>
         </ref>
         <ref id="B24">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Schwartz</surname>
                     <given-names>R</given-names>
                  </name>
                  <name>
                     <surname>Halldorsson</surname>
                     <given-names>BV</given-names>
                  </name>
                  <name>
                     <surname>Bafna</surname>
                     <given-names>V</given-names>
                  </name>
                  <name>
                     <surname>Clark</surname>
                     <given-names>AG</given-names>
                  </name>
                  <name>
                     <surname>Istrail</surname>
                     <given-names>S</given-names>
                  </name>
               </person-group>
               <article-title>Robustness of Inference of Haplotype Block Structure</article-title>
               <source>Journal of Computational Biology</source>
               <year>2003</year>
               <volume>10</volume>
               <fpage>13</fpage>
               <lpage>19</lpage>
               <pub-id pub-id-type="pmid">12676048</pub-id>
            </citation>
         </ref>
         <ref id="B25">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Halldorsson</surname>
                     <given-names>BV</given-names>
                  </name>
                  <name>
                     <surname>Bafna</surname>
                     <given-names>V</given-names>
                  </name>
                  <name>
                     <surname>Lippert</surname>
                     <given-names>R</given-names>
                  </name>
                  <name>
                     <surname>Schwartz</surname>
                     <given-names>R</given-names>
                  </name>
                  <name>
                     <surname>Vega</surname>
                     <given-names>FMDL</given-names>
                  </name>
                  <name>
                     <surname>Clark</surname>
                     <given-names>AG</given-names>
                  </name>
                  <name>
                     <surname>Istrail</surname>
                     <given-names>S</given-names>
                  </name>
               </person-group>
               <article-title>Optimal Haplotype Block-Free Selection of Tagging SNPs for Genome-Wide Association Studies</article-title>
               <source>Genome Research</source>
               <year>2004</year>
               <volume>14</volume>
               <fpage>1633</fpage>
               <lpage>1640</lpage>
               <pub-id pub-id-type="pmid">15289481</pub-id>
            </citation>
         </ref>
         <ref id="B26">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Carlson</surname>
                     <given-names>CS</given-names>
                  </name>
                  <name>
                     <surname>Eberle</surname>
                     <given-names>MA</given-names>
                  </name>
                  <name>
                     <surname>Rieder</surname>
                     <given-names>MJ</given-names>
                  </name>
                  <name>
                     <surname>Yi</surname>
                     <given-names>Q</given-names>
                  </name>
                  <name>
                     <surname>Kruglyak</surname>
                     <given-names>L</given-names>
                  </name>
                  <name>
                     <surname>Nickerson</surname>
                     <given-names>DA</given-names>
                  </name>
               </person-group>
               <article-title>Selecting a Maximally Informative Set of Single-Nucleotide Polymorphisms for Association Analyses Using Linkage Disequilibrium</article-title>
               <source>The American Journal of Human Genetics</source>
               <year>2004</year>
               <volume>74</volume>
               <fpage>106</fpage>
               <lpage>120</lpage>
               <pub-id pub-id-type="pmid">14681826</pub-id>
            </citation>
         </ref>
         <ref id="B27">
            <citation citation-type="book">
               <person-group person-group-type="author">
                  <name>
                     <surname>Weir</surname>
                     <given-names>BS</given-names>
                  </name>
               </person-group>
               <source>Genetic Data Analysis II</source>
               <year>1996</year>
               <publisher-name>Sinauer Associates</publisher-name>
            </citation>
         </ref>
         <ref id="B28">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Ding</surname>
                     <given-names>K</given-names>
                  </name>
                  <name>
                     <surname>Zhou</surname>
                     <given-names>K</given-names>
                  </name>
                  <name>
                     <surname>Zhang</surname>
                     <given-names>J</given-names>
                  </name>
                  <name>
                     <surname>Knight</surname>
                     <given-names>J</given-names>
                  </name>
                  <name>
                     <surname>Zhang</surname>
                     <given-names>X</given-names>
                  </name>
                  <name>
                     <surname>Shen</surname>
                     <given-names>Y</given-names>
                  </name>
               </person-group>
               <article-title>The Effect of Haplotype-Block Definitions on Inference of Haplotype-Block Structure and htSNPs Selection</article-title>
               <source>Molecular Biology and Evolution</source>
               <year>2005</year>
               <volume>22</volume>
               <fpage>148</fpage>
               <lpage>159</lpage>
               <pub-id pub-id-type="pmid">15371531</pub-id>
            </citation>
         </ref>
         <ref id="B29">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Kent</surname>
                     <given-names>WJ</given-names>
                  </name>
                  <name>
                     <surname>Sugnet</surname>
                     <given-names>CW</given-names>
                  </name>
                  <name>
                     <surname>Furey</surname>
                     <given-names>TS</given-names>
                  </name>
                  <name>
                     <surname>Roskin</surname>
                     <given-names>KM</given-names>
                  </name>
                  <name>
                     <surname>Pringle</surname>
                     <given-names>TH</given-names>
                  </name>
                  <name>
                     <surname>Zahler</surname>
                     <given-names>AM</given-names>
                  </name>
                  <name>
                     <surname>Haussler</surname>
                     <given-names>D</given-names>
                  </name>
               </person-group>
               <article-title>The Human Genome Browser at UCSC</article-title>
               <source>Genome Research</source>
               <year>2002</year>
               <volume>12</volume>
               <fpage>996</fpage>
               <lpage>1006</lpage>
               <pub-id pub-id-type="pmid">12045153</pub-id>
            </citation>
         </ref>
         <ref id="B30">
            <citation citation-type="journal">
               <person-group person-group-type="author">
                  <name>
                     <surname>Karolchik</surname>
                     <given-names>D</given-names>
                  </name>
                  <name>
                     <surname>Hinrichs</surname>
                     <given-names>AS</given-names>
                  </name>
                  <name>
                     <surname>Furey</surname>
                     <given-names>TS</given-names>
                  </name>
                  <name>
                     <surname>Roskin</surname>
                     <given-names>KM</given-names>
                  </name>
                  <name>
                     <surname>Sugnet</surname>
                     <given-names>CW</given-names>
                  </name>
                  <name>
                     <surname>Haussler</surname>
                     <given-names>D</given-names>
                  </name>
                  <name>
                     <surname>Kent</surname>
                     <given-names>WJ</given-names>
                  </name>
               </person-group>
               <article-title>The UCSC Table Browser data retrieval tool</article-title>
               <source>Nucleic Acids Research</source>
               <year>2004</year>
               <volume>32</volume>
               <fpage>D493</fpage>
               <lpage>D496</lpage>
               <pub-id pub-id-type="pmid">14681465</pub-id>
            </citation>
         </ref>
      </ref-list>
   </back>
</article>
