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

Zych K, Li Y, van der Velde JK, Joosen RV, Ligterink W, Jansen RC, Arends D - BMC Bioinformatics (2015)

Bottom Line: 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".

View Article: PubMed Central - PubMed

Affiliation: University of Groningen, Groningen Bioinformatics Centre, Nijenborgh 7, Groningen, 9747, AG, The Netherlands. konradzych2@gmail.com.

ABSTRACT

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 .

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Related in: MedlinePlus

Comparison of QTL detection power.a) LOD scores on the original and the saturated map. QTL mapping was performed on all 10,801 tiling array probes showing differential expression between parents (p<0.01 Student t-test) using the original and saturated maps. 5,837 out of 10,801 probes show a QTL with a LOD>5 on the original map. Blue dots - represent 3,943 probes (67.6%) that show an increased LOD score on the new saturated map. Moreover, 210 new QTLs were detected on the saturated map. Red dots - probes showing a decrease in LOD score on the saturated map. Green circles - are probes used to saturate the map. b) Changing LOD scores. For each of the phenotypes the top QTL peak was selected. If the peaks measured on the original and saturated maps shared a location, then the difference between the LOD scores was calculated. Solid green line - shows median of differences between QTL peaks from chromosome 4, calculated inside a sliding 10 cM window stepped across the chromosome with a step of 1 cM. For each of the windows the value was plotted in the middle of the compartment (thus no value for the first and the last 5 cM). Ticks on the x-axis show the position of the markers: tall gray ticks - show original markers; short green ticks - show markers selected by Pheno2Geno. Only one region, in which no new markers were added (75-80 cM), does not show an increase in power.
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Fig3: Comparison of QTL detection power.a) LOD scores on the original and the saturated map. QTL mapping was performed on all 10,801 tiling array probes showing differential expression between parents (p<0.01 Student t-test) using the original and saturated maps. 5,837 out of 10,801 probes show a QTL with a LOD>5 on the original map. Blue dots - represent 3,943 probes (67.6%) that show an increased LOD score on the new saturated map. Moreover, 210 new QTLs were detected on the saturated map. Red dots - probes showing a decrease in LOD score on the saturated map. Green circles - are probes used to saturate the map. b) Changing LOD scores. For each of the phenotypes the top QTL peak was selected. If the peaks measured on the original and saturated maps shared a location, then the difference between the LOD scores was calculated. Solid green line - shows median of differences between QTL peaks from chromosome 4, calculated inside a sliding 10 cM window stepped across the chromosome with a step of 1 cM. For each of the windows the value was plotted in the middle of the compartment (thus no value for the first and the last 5 cM). Ticks on the x-axis show the position of the markers: tall gray ticks - show original markers; short green ticks - show markers selected by Pheno2Geno. Only one region, in which no new markers were added (75-80 cM), does not show an increase in power.

Mentions: Finally, a QTL mapping of all the gene expression probes showing differential expression between parents (10,801 probes) was performed, and 5,837 probes had a significant (LOD>5) QTL on the original map. Out of these, 3,943 probes (67.6%) showed an increase in QTL likelihood on the saturated map (Figure 3) and an additional 210 new significant QTLs were detected on the saturated map.Figure 3


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

Zych K, Li Y, van der Velde JK, Joosen RV, Ligterink W, Jansen RC, Arends D - BMC Bioinformatics (2015)

Comparison of QTL detection power.a) LOD scores on the original and the saturated map. QTL mapping was performed on all 10,801 tiling array probes showing differential expression between parents (p<0.01 Student t-test) using the original and saturated maps. 5,837 out of 10,801 probes show a QTL with a LOD>5 on the original map. Blue dots - represent 3,943 probes (67.6%) that show an increased LOD score on the new saturated map. Moreover, 210 new QTLs were detected on the saturated map. Red dots - probes showing a decrease in LOD score on the saturated map. Green circles - are probes used to saturate the map. b) Changing LOD scores. For each of the phenotypes the top QTL peak was selected. If the peaks measured on the original and saturated maps shared a location, then the difference between the LOD scores was calculated. Solid green line - shows median of differences between QTL peaks from chromosome 4, calculated inside a sliding 10 cM window stepped across the chromosome with a step of 1 cM. For each of the windows the value was plotted in the middle of the compartment (thus no value for the first and the last 5 cM). Ticks on the x-axis show the position of the markers: tall gray ticks - show original markers; short green ticks - show markers selected by Pheno2Geno. Only one region, in which no new markers were added (75-80 cM), does not show an increase in power.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4339742&req=5

Fig3: Comparison of QTL detection power.a) LOD scores on the original and the saturated map. QTL mapping was performed on all 10,801 tiling array probes showing differential expression between parents (p<0.01 Student t-test) using the original and saturated maps. 5,837 out of 10,801 probes show a QTL with a LOD>5 on the original map. Blue dots - represent 3,943 probes (67.6%) that show an increased LOD score on the new saturated map. Moreover, 210 new QTLs were detected on the saturated map. Red dots - probes showing a decrease in LOD score on the saturated map. Green circles - are probes used to saturate the map. b) Changing LOD scores. For each of the phenotypes the top QTL peak was selected. If the peaks measured on the original and saturated maps shared a location, then the difference between the LOD scores was calculated. Solid green line - shows median of differences between QTL peaks from chromosome 4, calculated inside a sliding 10 cM window stepped across the chromosome with a step of 1 cM. For each of the windows the value was plotted in the middle of the compartment (thus no value for the first and the last 5 cM). Ticks on the x-axis show the position of the markers: tall gray ticks - show original markers; short green ticks - show markers selected by Pheno2Geno. Only one region, in which no new markers were added (75-80 cM), does not show an increase in power.
Mentions: Finally, a QTL mapping of all the gene expression probes showing differential expression between parents (10,801 probes) was performed, and 5,837 probes had a significant (LOD>5) QTL on the original map. Out of these, 3,943 probes (67.6%) showed an increase in QTL likelihood on the saturated map (Figure 3) and an additional 210 new significant QTLs were detected on the saturated map.Figure 3

Bottom Line: 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".

View Article: PubMed Central - PubMed

Affiliation: University of Groningen, Groningen Bioinformatics Centre, Nijenborgh 7, Groningen, 9747, AG, The Netherlands. konradzych2@gmail.com.

ABSTRACT

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 .

Show MeSH
Related in: MedlinePlus