Genetic Mapping in the Presence of Genotyping Errors

Genetic maps are built using the genotypes of many related individuals. Genotyping errors in these data sets can distort genetic maps, especially by inflating the distances. We have extended the traditional likelihood model used for genetic mapping to include the possibility of genotyping errors. Ea...

Full description

Saved in:
Bibliographic Details
Published in:Genetics (Austin) 2007-08, Vol.176 (4), p.2521-2527
Main Authors: Cartwright, Dustin A, Troggio, Michela, Velasco, Riccardo, Gutin, Alexander
Format: Article
Language:English
Subjects:
Advantages
Algorithms
Chromosome Mapping - statistics & numerical data
Data Interpretation, Statistical
Errors
Genetic Markers
Genetics
Genotype
Genotype & phenotype
Investigations
Likelihood Functions
Methods
Models, Genetic
Monte Carlo Method
Software
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c528t-41174971e221639b8755a5927215a94b1aba0d4d727476bda98ed3ed6dd284f63
cites cdi_FETCH-LOGICAL-c528t-41174971e221639b8755a5927215a94b1aba0d4d727476bda98ed3ed6dd284f63
container_end_page 2527
container_issue 4
container_start_page 2521
container_title Genetics (Austin)
container_volume 176
creator Cartwright, Dustin A
Troggio, Michela
Velasco, Riccardo
Gutin, Alexander
description Genetic maps are built using the genotypes of many related individuals. Genotyping errors in these data sets can distort genetic maps, especially by inflating the distances. We have extended the traditional likelihood model used for genetic mapping to include the possibility of genotyping errors. Each individual marker is assigned an error rate, which is inferred from the data, just as the genetic distances are. We have developed a software package, called TMAP, which uses this model to find maximum-likelihood maps for phase-known pedigrees. We have tested our methods using a data set in Vitis and on simulated data and confirmed that our method dramatically reduces the inflationary effect caused by increasing the number of markers and leads to more accurate orders.
doi_str_mv 10.1534/genetics.106.063982
format article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_1950651</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1330334131</sourcerecordid><originalsourceid>FETCH-LOGICAL-c528t-41174971e221639b8755a5927215a94b1aba0d4d727476bda98ed3ed6dd284f63</originalsourceid><addsrcrecordid>eNpdkU9PGzEQxa0KBCnwCSqhVQ_taYPHf9eXSgilaSUQHOBseXcnidFmHexNI759TTdtgdNI837z9EaPkE9ApyC5uFhij4Nv0hSomlLFTcU-kAkYwUumOByQCaWgSqU5HJOPKT1SSpWR1RE5Bs205lpMiJiPLsWN22x8vyx8XwwrLO4iJuwbLMKiyEgYnv-osxhDTKfkcOG6hGf7eUIevs_ur36U17fzn1eX12UjWTWUAkALowEZgxyvrrSUThqmGUhnRA2udrQVbQ4jtKpbZypsObaqbVklFoqfkG-j72Zbr7FtsB-i6-wm-rWLzzY4b98qvV_ZZfhlwUiqJGSDL3uDGJ62mAa79qnBrnM9hm2yqgKjGIgMfn4HPoZt7PNzNsvApdQvcfgINTGkFHHxLwlQ-1KJ_VtJXig7VpKvzl8_8f9m30EGvo7Ayi9XOx_RprXruoyD3e12oJUVlkkG_DdAOZYN</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>214135576</pqid></control><display><type>article</type><title>Genetic Mapping in the Presence of Genotyping Errors</title><source>Oxford Journals Online</source><source>Alma/SFX Local Collection</source><source>Freely Accessible Science Journals at publisher websites</source><creator>Cartwright, Dustin A ; Troggio, Michela ; Velasco, Riccardo ; Gutin, Alexander</creator><creatorcontrib>Cartwright, Dustin A ; Troggio, Michela ; Velasco, Riccardo ; Gutin, Alexander</creatorcontrib><description>Genetic maps are built using the genotypes of many related individuals. Genotyping errors in these data sets can distort genetic maps, especially by inflating the distances. We have extended the traditional likelihood model used for genetic mapping to include the possibility of genotyping errors. Each individual marker is assigned an error rate, which is inferred from the data, just as the genetic distances are. We have developed a software package, called TMAP, which uses this model to find maximum-likelihood maps for phase-known pedigrees. We have tested our methods using a data set in Vitis and on simulated data and confirmed that our method dramatically reduces the inflationary effect caused by increasing the number of markers and leads to more accurate orders.</description><identifier>ISSN: 0016-6731</identifier><identifier>ISSN: 1943-2631</identifier><identifier>EISSN: 1943-2631</identifier><identifier>DOI: 10.1534/genetics.106.063982</identifier><identifier>PMID: 17277374</identifier><identifier>CODEN: GENTAE</identifier><language>eng</language><publisher>United States: Genetics Soc America</publisher><subject>Advantages ; Algorithms ; Chromosome Mapping - statistics &amp; numerical data ; Data Interpretation, Statistical ; Errors ; Genetic Markers ; Genetics ; Genotype ; Genotype &amp; phenotype ; Investigations ; Likelihood Functions ; Methods ; Models, Genetic ; Monte Carlo Method ; Software ; Studies</subject><ispartof>Genetics (Austin), 2007-08, Vol.176 (4), p.2521-2527</ispartof><rights>Copyright Genetics Society of America Aug 2007</rights><rights>Copyright © 2007 by the Genetics Society of America 2007</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c528t-41174971e221639b8755a5927215a94b1aba0d4d727476bda98ed3ed6dd284f63</citedby><cites>FETCH-LOGICAL-c528t-41174971e221639b8755a5927215a94b1aba0d4d727476bda98ed3ed6dd284f63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,777,781,882,27905,27906</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17277374$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cartwright, Dustin A</creatorcontrib><creatorcontrib>Troggio, Michela</creatorcontrib><creatorcontrib>Velasco, Riccardo</creatorcontrib><creatorcontrib>Gutin, Alexander</creatorcontrib><title>Genetic Mapping in the Presence of Genotyping Errors</title><title>Genetics (Austin)</title><addtitle>Genetics</addtitle><description>Genetic maps are built using the genotypes of many related individuals. Genotyping errors in these data sets can distort genetic maps, especially by inflating the distances. We have extended the traditional likelihood model used for genetic mapping to include the possibility of genotyping errors. Each individual marker is assigned an error rate, which is inferred from the data, just as the genetic distances are. We have developed a software package, called TMAP, which uses this model to find maximum-likelihood maps for phase-known pedigrees. We have tested our methods using a data set in Vitis and on simulated data and confirmed that our method dramatically reduces the inflationary effect caused by increasing the number of markers and leads to more accurate orders.</description><subject>Advantages</subject><subject>Algorithms</subject><subject>Chromosome Mapping - statistics &amp; numerical data</subject><subject>Data Interpretation, Statistical</subject><subject>Errors</subject><subject>Genetic Markers</subject><subject>Genetics</subject><subject>Genotype</subject><subject>Genotype &amp; phenotype</subject><subject>Investigations</subject><subject>Likelihood Functions</subject><subject>Methods</subject><subject>Models, Genetic</subject><subject>Monte Carlo Method</subject><subject>Software</subject><subject>Studies</subject><issn>0016-6731</issn><issn>1943-2631</issn><issn>1943-2631</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><recordid>eNpdkU9PGzEQxa0KBCnwCSqhVQ_taYPHf9eXSgilaSUQHOBseXcnidFmHexNI759TTdtgdNI837z9EaPkE9ApyC5uFhij4Nv0hSomlLFTcU-kAkYwUumOByQCaWgSqU5HJOPKT1SSpWR1RE5Bs205lpMiJiPLsWN22x8vyx8XwwrLO4iJuwbLMKiyEgYnv-osxhDTKfkcOG6hGf7eUIevs_ur36U17fzn1eX12UjWTWUAkALowEZgxyvrrSUThqmGUhnRA2udrQVbQ4jtKpbZypsObaqbVklFoqfkG-j72Zbr7FtsB-i6-wm-rWLzzY4b98qvV_ZZfhlwUiqJGSDL3uDGJ62mAa79qnBrnM9hm2yqgKjGIgMfn4HPoZt7PNzNsvApdQvcfgINTGkFHHxLwlQ-1KJ_VtJXig7VpKvzl8_8f9m30EGvo7Ayi9XOx_RprXruoyD3e12oJUVlkkG_DdAOZYN</recordid><startdate>20070801</startdate><enddate>20070801</enddate><creator>Cartwright, Dustin A</creator><creator>Troggio, Michela</creator><creator>Velasco, Riccardo</creator><creator>Gutin, Alexander</creator><general>Genetics Soc America</general><general>Genetics Society of America</general><general>Copyright © 2007 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>7X8</scope><scope>5PM</scope></search><sort><creationdate>20070801</creationdate><title>Genetic Mapping in the Presence of Genotyping Errors</title><author>Cartwright, Dustin A ; Troggio, Michela ; Velasco, Riccardo ; Gutin, Alexander</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c528t-41174971e221639b8755a5927215a94b1aba0d4d727476bda98ed3ed6dd284f63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Advantages</topic><topic>Algorithms</topic><topic>Chromosome Mapping - statistics &amp; numerical data</topic><topic>Data Interpretation, Statistical</topic><topic>Errors</topic><topic>Genetic Markers</topic><topic>Genetics</topic><topic>Genotype</topic><topic>Genotype &amp; phenotype</topic><topic>Investigations</topic><topic>Likelihood Functions</topic><topic>Methods</topic><topic>Models, Genetic</topic><topic>Monte Carlo Method</topic><topic>Software</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cartwright, Dustin A</creatorcontrib><creatorcontrib>Troggio, Michela</creatorcontrib><creatorcontrib>Velasco, Riccardo</creatorcontrib><creatorcontrib>Gutin, Alexander</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Docstoc</collection><collection>University Readers</collection><collection>Calcium &amp; Calcified Tissue Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Biological Sciences</collection><collection>Agriculture Science Database</collection><collection>Consumer Health Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - 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>Cartwright, Dustin A</au><au>Troggio, Michela</au><au>Velasco, Riccardo</au><au>Gutin, Alexander</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Genetic Mapping in the Presence of Genotyping Errors</atitle><jtitle>Genetics (Austin)</jtitle><addtitle>Genetics</addtitle><date>2007-08-01</date><risdate>2007</risdate><volume>176</volume><issue>4</issue><spage>2521</spage><epage>2527</epage><pages>2521-2527</pages><issn>0016-6731</issn><issn>1943-2631</issn><eissn>1943-2631</eissn><coden>GENTAE</coden><abstract>Genetic maps are built using the genotypes of many related individuals. Genotyping errors in these data sets can distort genetic maps, especially by inflating the distances. We have extended the traditional likelihood model used for genetic mapping to include the possibility of genotyping errors. Each individual marker is assigned an error rate, which is inferred from the data, just as the genetic distances are. We have developed a software package, called TMAP, which uses this model to find maximum-likelihood maps for phase-known pedigrees. We have tested our methods using a data set in Vitis and on simulated data and confirmed that our method dramatically reduces the inflationary effect caused by increasing the number of markers and leads to more accurate orders.</abstract><cop>United States</cop><pub>Genetics Soc America</pub><pmid>17277374</pmid><doi>10.1534/genetics.106.063982</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0016-6731
ispartof Genetics (Austin), 2007-08, Vol.176 (4), p.2521-2527
issn 0016-6731
1943-2631
1943-2631
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_1950651
source Oxford Journals Online; Alma/SFX Local Collection; Freely Accessible Science Journals at publisher websites
subjects Advantages
Algorithms
Chromosome Mapping - statistics & numerical data
Data Interpretation, Statistical
Errors
Genetic Markers
Genetics
Genotype
Genotype & phenotype
Investigations
Likelihood Functions
Methods
Models, Genetic
Monte Carlo Method
Software
Studies
title Genetic Mapping in the Presence of Genotyping Errors
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-20T17%3A59%3A43IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Genetic%20Mapping%20in%20the%20Presence%20of%20Genotyping%20Errors&rft.jtitle=Genetics%20(Austin)&rft.au=Cartwright,%20Dustin%20A&rft.date=2007-08-01&rft.volume=176&rft.issue=4&rft.spage=2521&rft.epage=2527&rft.pages=2521-2527&rft.issn=0016-6731&rft.eissn=1943-2631&rft.coden=GENTAE&rft_id=info:doi/10.1534/genetics.106.063982&rft_dat=%3Cproquest_pubme%3E1330334131%3C/proquest_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c528t-41174971e221639b8755a5927215a94b1aba0d4d727476bda98ed3ed6dd284f63%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=214135576&rft_id=info:pmid/17277374&rfr_iscdi=true