Pheno2Geno : high-throughput generation of genetic markers and maps from molecular phenotypes for crosses between inbred strains

2015
journal article
article
7
dc.abstract.enBackground: Genetic markers and maps are instrumental in quantitative trait locus (QTL) mapping in segregating populations. The resolution of QTL localization depends on the number of informative recombinations in the population and how well they are tagged by markers. Larger populations and denser marker maps are better for detecting and locating QTLs. Marker maps that are initially too sparse can be saturated or derived de novo from high-throughput omics data, (e.g. gene expression, protein or metabolite abundance). If these molecular phenotypes are affected by genetic variation due to a major QTL they will show a clear multimodal distribution. Using this information, phenotypes can be converted into genetic markers. Results: The Pheno2Geno tool uses mixture modeling to select phenotypes and transform them into genetic markers suitable for construction and/or saturation of a genetic map. Pheno2Geno excludes candidate genetic markers that show evidence for multiple possibly epistatically interacting QTL and/or interaction with the environment, in order to provide a set of robust markers for follow-up QTL mapping. We demonstrate the use of Pheno2Geno on gene expression data of 370,000 probes in 148 A. thaliana recombinant inbred lines. Pheno2Geno is able to saturate the existing genetic map, decreasing the average distance between markers from 7.1 cM to 0.89 cM, close to the theoretical limit of 0.68 cM (with 148 individuals we expect a recombination every 100/148=0.68 cM); this pinpointed almost all of the informative recombinations in the population. Conclusion: The Pheno2Geno package makes use of genome-wide molecular profiling and provides a tool for high-throughput de novo map construction and saturation of existing genetic maps. Processing of the showcase dataset takes less than 30 minutes on an average desktop PC. Pheno2Geno improves QTL mapping results at no additional laboratory cost and with minimum computational effort. Its results are formatted for direct use in R/qtl, the leading R package for QTL studies. Pheno2Geno is freely available on CRAN under “GNU GPL v3”. The Pheno2Geno package as well as the tutorial can also be found at: http://pheno2geno.nlpl
dc.affiliationWydział Biochemii, Biofizyki i Biotechnologii : Zakład Biofizyki Obliczeniowej i Bioinformatykipl
dc.contributor.authorZych, Konradpl
dc.contributor.authorLi, Yangpl
dc.contributor.authorvan der Velde, Joeri Kpl
dc.contributor.authorJoosen, Ronny V. L.pl
dc.contributor.authorLigterink, Wilcopl
dc.contributor.authorJansen, Ritsert C.pl
dc.contributor.authorArends, Dannypl
dc.date.accessioned2015-06-26T09:22:54Z
dc.date.available2015-06-26T09:22:54Z
dc.date.issued2015pl
dc.date.openaccess0
dc.description.accesstimew momencie opublikowania
dc.description.admin[AB] Zych, Konrad 50000143
dc.description.versionostateczna wersja wydawcy
dc.description.volume16pl
dc.identifier.articleid51pl
dc.identifier.doi10.1186/s12859-015-0475-6pl
dc.identifier.eissn1471-2105pl
dc.identifier.urihttp://ruj.uj.edu.pl/xmlui/handle/item/10454
dc.languageengpl
dc.language.containerengpl
dc.rightsDodaję tylko opis bibliograficzny*
dc.rights.licenceCC-BY
dc.rights.uri*
dc.share.typeotwarte czasopismo
dc.subject.engenotypingpl
dc.subject.enQTL mappingpl
dc.subject.enrecombinationpl
dc.subject.enquantitative geneticspl
dc.subject.encomputational genomicspl
dc.subject.enSNPspl
dc.subject.entiling arrayspl
dc.subtypeArticlepl
dc.titlePheno2Geno : high-throughput generation of genetic markers and maps from molecular phenotypes for crosses between inbred strainspl
dc.title.journalBMC Bioinformaticspl
dc.typeJournalArticlepl
dspace.entity.typePublication
dc.abstract.enpl
Background: Genetic markers and maps are instrumental in quantitative trait locus (QTL) mapping in segregating populations. The resolution of QTL localization depends on the number of informative recombinations in the population and how well they are tagged by markers. Larger populations and denser marker maps are better for detecting and locating QTLs. Marker maps that are initially too sparse can be saturated or derived de novo from high-throughput omics data, (e.g. gene expression, protein or metabolite abundance). If these molecular phenotypes are affected by genetic variation due to a major QTL they will show a clear multimodal distribution. Using this information, phenotypes can be converted into genetic markers. Results: The Pheno2Geno tool uses mixture modeling to select phenotypes and transform them into genetic markers suitable for construction and/or saturation of a genetic map. Pheno2Geno excludes candidate genetic markers that show evidence for multiple possibly epistatically interacting QTL and/or interaction with the environment, in order to provide a set of robust markers for follow-up QTL mapping. We demonstrate the use of Pheno2Geno on gene expression data of 370,000 probes in 148 A. thaliana recombinant inbred lines. Pheno2Geno is able to saturate the existing genetic map, decreasing the average distance between markers from 7.1 cM to 0.89 cM, close to the theoretical limit of 0.68 cM (with 148 individuals we expect a recombination every 100/148=0.68 cM); this pinpointed almost all of the informative recombinations in the population. Conclusion: The Pheno2Geno package makes use of genome-wide molecular profiling and provides a tool for high-throughput de novo map construction and saturation of existing genetic maps. Processing of the showcase dataset takes less than 30 minutes on an average desktop PC. Pheno2Geno improves QTL mapping results at no additional laboratory cost and with minimum computational effort. Its results are formatted for direct use in R/qtl, the leading R package for QTL studies. Pheno2Geno is freely available on CRAN under “GNU GPL v3”. The Pheno2Geno package as well as the tutorial can also be found at: http://pheno2geno.nl
dc.affiliationpl
Wydział Biochemii, Biofizyki i Biotechnologii : Zakład Biofizyki Obliczeniowej i Bioinformatyki
dc.contributor.authorpl
Zych, Konrad
dc.contributor.authorpl
Li, Yang
dc.contributor.authorpl
van der Velde, Joeri K
dc.contributor.authorpl
Joosen, Ronny V. L.
dc.contributor.authorpl
Ligterink, Wilco
dc.contributor.authorpl
Jansen, Ritsert C.
dc.contributor.authorpl
Arends, Danny
dc.date.accessioned
2015-06-26T09:22:54Z
dc.date.available
2015-06-26T09:22:54Z
dc.date.issuedpl
2015
dc.date.openaccess
0
dc.description.accesstime
w momencie opublikowania
dc.description.admin
[AB] Zych, Konrad 50000143
dc.description.version
ostateczna wersja wydawcy
dc.description.volumepl
16
dc.identifier.articleidpl
51
dc.identifier.doipl
10.1186/s12859-015-0475-6
dc.identifier.eissnpl
1471-2105
dc.identifier.uri
http://ruj.uj.edu.pl/xmlui/handle/item/10454
dc.languagepl
eng
dc.language.containerpl
eng
dc.rights*
Dodaję tylko opis bibliograficzny
dc.rights.licence
CC-BY
dc.rights.uri*
dc.share.type
otwarte czasopismo
dc.subject.enpl
genotyping
dc.subject.enpl
QTL mapping
dc.subject.enpl
recombination
dc.subject.enpl
quantitative genetics
dc.subject.enpl
computational genomics
dc.subject.enpl
SNPs
dc.subject.enpl
tiling arrays
dc.subtypepl
Article
dc.titlepl
Pheno2Geno : high-throughput generation of genetic markers and maps from molecular phenotypes for crosses between inbred strains
dc.title.journalpl
BMC Bioinformatics
dc.typepl
JournalArticle
dspace.entity.type
Publication
Affiliations

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