Using approximate Bayesian computation to estimate tuberculosis transmission parameters from genotype data
Tuberculosis can be studied at the population level by genotyping strains of Mycobacterium tuberculosis isolated from patients. We use an approximate Bayesian computational method in combination with a stochastic model of tuberculosis transmission and mutation of a molecular marker to estimate the n...
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| Veröffentlicht in: | Genetics (Austin) 2006-07, Vol.173 (3), p.1511-1520 |
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| creator | Tanaka, Mark M Francis, Andrew R Luciani, Fabio Sisson, S A |
| description | Tuberculosis can be studied at the population level by genotyping strains of Mycobacterium tuberculosis isolated from patients. We use an approximate Bayesian computational method in combination with a stochastic model of tuberculosis transmission and mutation of a molecular marker to estimate the net transmission rate, the doubling time, and the reproductive value of the pathogen. This method is applied to a published data set from San Francisco of tuberculosis genotypes based on the marker IS6110. The mutation rate of this marker has previously been studied, and we use those estimates to form a prior distribution of mutation rates in the inference procedure. The posterior point estimates of the key parameters of interest for these data are as follows: net transmission rate, 0.69/year [95% credibility interval (C.I.) 0.38, 1.08]; doubling time, 1.08 years (95% C.I. 0.64, 1.82); and reproductive value 3.4 (95% C.I. 1.4, 79.7). These figures suggest a rapidly spreading epidemic, consistent with observations of the resurgence of tuberculosis in the United States in the 1980s and 1990s. |
| doi_str_mv | 10.1534/genetics.106.055574 |
| format | Article |
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We use an approximate Bayesian computational method in combination with a stochastic model of tuberculosis transmission and mutation of a molecular marker to estimate the net transmission rate, the doubling time, and the reproductive value of the pathogen. This method is applied to a published data set from San Francisco of tuberculosis genotypes based on the marker IS6110. The mutation rate of this marker has previously been studied, and we use those estimates to form a prior distribution of mutation rates in the inference procedure. The posterior point estimates of the key parameters of interest for these data are as follows: net transmission rate, 0.69/year [95% credibility interval (C.I.) 0.38, 1.08]; doubling time, 1.08 years (95% C.I. 0.64, 1.82); and reproductive value 3.4 (95% C.I. 1.4, 79.7). These figures suggest a rapidly spreading epidemic, consistent with observations of the resurgence of tuberculosis in the United States in the 1980s and 1990s.</description><identifier>ISSN: 0016-6731</identifier><identifier>ISSN: 1943-2631</identifier><identifier>EISSN: 1943-2631</identifier><identifier>DOI: 10.1534/genetics.106.055574</identifier><identifier>PMID: 16624908</identifier><identifier>CODEN: GENTAE</identifier><language>eng</language><publisher>United States: Genetics Society of America</publisher><subject>Algorithms ; Bayes Theorem ; Bayesian analysis ; Computational Biology - methods ; Disease transmission ; DNA fingerprints ; Genetic Markers ; Genotype ; Genotype & phenotype ; Humans ; Investigations ; Methods ; Mutation ; Mycobacterium tuberculosis ; Risk Factors ; San Francisco - epidemiology ; Studies ; Tuberculosis ; Tuberculosis - epidemiology ; Tuberculosis - genetics ; Tuberculosis - transmission</subject><ispartof>Genetics (Austin), 2006-07, Vol.173 (3), p.1511-1520</ispartof><rights>Copyright Genetics Society of America Jul 2006</rights><rights>Copyright © 2006 by the Genetics Society of America 2006</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c492t-9dcd4ce0d3a79bd06713e1631b07f08826b7db2fc1368b85c9bb34436521391c3</citedby><cites>FETCH-LOGICAL-c492t-9dcd4ce0d3a79bd06713e1631b07f08826b7db2fc1368b85c9bb34436521391c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16624908$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tanaka, Mark M</creatorcontrib><creatorcontrib>Francis, Andrew R</creatorcontrib><creatorcontrib>Luciani, Fabio</creatorcontrib><creatorcontrib>Sisson, S A</creatorcontrib><title>Using approximate Bayesian computation to estimate tuberculosis transmission parameters from genotype data</title><title>Genetics (Austin)</title><addtitle>Genetics</addtitle><description>Tuberculosis can be studied at the population level by genotyping strains of Mycobacterium tuberculosis isolated from patients. We use an approximate Bayesian computational method in combination with a stochastic model of tuberculosis transmission and mutation of a molecular marker to estimate the net transmission rate, the doubling time, and the reproductive value of the pathogen. This method is applied to a published data set from San Francisco of tuberculosis genotypes based on the marker IS6110. The mutation rate of this marker has previously been studied, and we use those estimates to form a prior distribution of mutation rates in the inference procedure. The posterior point estimates of the key parameters of interest for these data are as follows: net transmission rate, 0.69/year [95% credibility interval (C.I.) 0.38, 1.08]; doubling time, 1.08 years (95% C.I. 0.64, 1.82); and reproductive value 3.4 (95% C.I. 1.4, 79.7). These figures suggest a rapidly spreading epidemic, consistent with observations of the resurgence of tuberculosis in the United States in the 1980s and 1990s.</description><subject>Algorithms</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Computational Biology - methods</subject><subject>Disease transmission</subject><subject>DNA fingerprints</subject><subject>Genetic Markers</subject><subject>Genotype</subject><subject>Genotype & phenotype</subject><subject>Humans</subject><subject>Investigations</subject><subject>Methods</subject><subject>Mutation</subject><subject>Mycobacterium tuberculosis</subject><subject>Risk Factors</subject><subject>San Francisco - epidemiology</subject><subject>Studies</subject><subject>Tuberculosis</subject><subject>Tuberculosis - epidemiology</subject><subject>Tuberculosis - genetics</subject><subject>Tuberculosis - transmission</subject><issn>0016-6731</issn><issn>1943-2631</issn><issn>1943-2631</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqNkkuLFDEUhYMoTjv6CwQpXLirnty8sxF08DEw4MZZhySVaqupqpRJarD_vRm6fW5mVoGb7x7OuRyEXgLeAqfsYhfmUAaft4DFFnPOJXuENqAZbYmg8BhtMAbRCknhDD3LeY8xFpqrp-gMhCBMY7VB-5s8zLvGLkuKP4bJltC8t4eQBzs3Pk7LWmwZ4tyU2IRcjkBZXUh-HWMeclOSnfM05HxHLTbZKZSQctOnODXVYiyHJTSdLfY5etLbMYcXp_cc3Xz88PXyc3v95dPV5bvr1jNNSqs73zEfcEet1K7DQgINUAM5LHusFBFOdo70HqhQTnGvnaOMUcEJUA2enqO3R91ldVPofJirx9EsqbpPBxPtYP79mYdvZhdvDXAiJGZV4M1JIMXva41taj4fxtHOIa7ZCCWk5EDuBUFrpUHRB4BcY651BV__B-7jmuZ6LkOAASNCyQrRI-RTzDmF_nc2wOauGuZXNepAmGM16tarv8_yZ-fUBfoTowa6JQ</recordid><startdate>20060701</startdate><enddate>20060701</enddate><creator>Tanaka, Mark M</creator><creator>Francis, Andrew R</creator><creator>Luciani, Fabio</creator><creator>Sisson, S A</creator><general>Genetics Society of America</general><general>Copyright © 2006 by the Genetics Society of America</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>4T-</scope><scope>4U-</scope><scope>7QP</scope><scope>7SS</scope><scope>7TK</scope><scope>7TM</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K9-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0K</scope><scope>M0R</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M2P</scope><scope>M7N</scope><scope>M7P</scope><scope>MBDVC</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7QL</scope><scope>C1K</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20060701</creationdate><title>Using approximate Bayesian computation to estimate tuberculosis transmission parameters from genotype data</title><author>Tanaka, Mark M ; Francis, Andrew R ; Luciani, Fabio ; Sisson, S A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c492t-9dcd4ce0d3a79bd06713e1631b07f08826b7db2fc1368b85c9bb34436521391c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithms</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Computational Biology - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Genetics (Austin)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tanaka, Mark M</au><au>Francis, Andrew R</au><au>Luciani, Fabio</au><au>Sisson, S A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using approximate Bayesian computation to estimate tuberculosis transmission parameters from genotype data</atitle><jtitle>Genetics (Austin)</jtitle><addtitle>Genetics</addtitle><date>2006-07-01</date><risdate>2006</risdate><volume>173</volume><issue>3</issue><spage>1511</spage><epage>1520</epage><pages>1511-1520</pages><issn>0016-6731</issn><issn>1943-2631</issn><eissn>1943-2631</eissn><coden>GENTAE</coden><abstract>Tuberculosis can be studied at the population level by genotyping strains of Mycobacterium tuberculosis isolated from patients. We use an approximate Bayesian computational method in combination with a stochastic model of tuberculosis transmission and mutation of a molecular marker to estimate the net transmission rate, the doubling time, and the reproductive value of the pathogen. This method is applied to a published data set from San Francisco of tuberculosis genotypes based on the marker IS6110. The mutation rate of this marker has previously been studied, and we use those estimates to form a prior distribution of mutation rates in the inference procedure. The posterior point estimates of the key parameters of interest for these data are as follows: net transmission rate, 0.69/year [95% credibility interval (C.I.) 0.38, 1.08]; doubling time, 1.08 years (95% C.I. 0.64, 1.82); and reproductive value 3.4 (95% C.I. 1.4, 79.7). These figures suggest a rapidly spreading epidemic, consistent with observations of the resurgence of tuberculosis in the United States in the 1980s and 1990s.</abstract><cop>United States</cop><pub>Genetics Society of America</pub><pmid>16624908</pmid><doi>10.1534/genetics.106.055574</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 0016-6731 |
| ispartof | Genetics (Austin), 2006-07, Vol.173 (3), p.1511-1520 |
| issn | 0016-6731 1943-2631 1943-2631 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_1526704 |
| source | Oxford University Press Journals All Titles (1996-Current); MEDLINE; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Algorithms Bayes Theorem Bayesian analysis Computational Biology - methods Disease transmission DNA fingerprints Genetic Markers Genotype Genotype & phenotype Humans Investigations Methods Mutation Mycobacterium tuberculosis Risk Factors San Francisco - epidemiology Studies Tuberculosis Tuberculosis - epidemiology Tuberculosis - genetics Tuberculosis - transmission |
| title | Using approximate Bayesian computation to estimate tuberculosis transmission parameters from genotype data |
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