{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:13:04Z","timestamp":1781107984743,"version":"3.54.1"},"reference-count":30,"publisher":"National Academy of Sciences","issue":"1","content-domain":{"domain":["www.pnas.org"],"crossmark-restriction":true},"short-container-title":["Proc. Natl. Acad. Sci. U.S.A."],"published-print":{"date-parts":[[2005,1,4]]},"abstract":"<jats:p>\n                    The abundance of genotype data generated by individual and international efforts carries the promise of revolutionizing disease studies and the association of phenotypes with individual polymorphisms. A key challenge is providing an accurate resolution (phasing) of the genotypes into haplotypes. We present here results on a method for genotype phasing in the presence of recombination. Our analysis is based on a stochastic model for recombination-poor regions (\u201dblocks\u201d), in which haplotypes are generated from a small number of core haplotypes, allowing for mutations, rare recombinations, and errors. We formulate genotype resolution and block partitioning as a maximum-likelihood problem and solve it by an expectation-maximization algorithm. The algorithm was implemented in a software package called\n                    <jats:sc>gerbil<\/jats:sc>\n                    (genotype resolution and block identification using likelihood), which is efficient and simple to use. We tested\n                    <jats:sc>gerbil<\/jats:sc>\n                    on four large-scale sets of genotypes. It outperformed two state-of-the-art phasing algorithms. The\n                    <jats:sc>phase<\/jats:sc>\n                    algorithm was slightly more accurate than\n                    <jats:sc>gerbil<\/jats:sc>\n                    when allowed to run with default parameters, but required two orders of magnitude more time. When using comparable running times,\n                    <jats:sc>gerbil<\/jats:sc>\n                    was consistently more accurate. For data sets with hundreds of genotypes, the time required by\n                    <jats:sc>phase<\/jats:sc>\n                    becomes prohibitive. We conclude that\n                    <jats:sc>gerbil<\/jats:sc>\n                    has a clear advantage for studies that include many hundreds of genotypes and, in particular, for large-scale disease studies.\n                  <\/jats:p>","DOI":"10.1073\/pnas.0404730102","type":"journal-article","created":{"date-parts":[[2004,12,22]],"date-time":"2004-12-22T20:24:20Z","timestamp":1103747060000},"page":"158-162","update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":100,"title":["<scp>gerbil<\/scp>\n                    : Genotype resolution and block identification using likelihood"],"prefix":"10.1073","volume":"102","author":[{"given":"Gad","family":"Kimmel","sequence":"first","affiliation":[{"name":"School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ron","family":"Shamir","sequence":"additional","affiliation":[{"name":"School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"341","published-online":{"date-parts":[[2004,12,22]]},"reference":[{"key":"e_1_3_2_1_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1065573"},{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1069424"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/85776"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1086\/303003"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1002\/gepi.10200"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.102186799"},{"key":"e_1_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Zhang K. 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