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Prioritization of candidate genes in QTL regions based on associations between traits and biological processes.

Bargsten JW, Nap JP, Sanchez-Perez GF, van Dijk AD - BMC Plant Biol. (2014)

Bottom Line: The average reduction of the number of genes was over ten-fold.Comparison with various types of experimental datasets (including QTL fine-mapping and Genome Wide Association Study results) indicated both statistical significance and biological relevance of the obtained connections between genes and traits.This way it capitalizes on QTL data to uncover how individual genes influence trait variation.

View Article: PubMed Central - PubMed

ABSTRACT

Background: Elucidation of genotype-to-phenotype relationships is a major challenge in biology. In plants, it is the basis for molecular breeding. Quantitative Trait Locus (QTL) mapping enables to link variation at the trait level to variation at the genomic level. However, QTL regions typically contain tens to hundreds of genes. In order to prioritize such candidate genes, we show that we can identify potentially causal genes for a trait based on overrepresentation of biological processes (gene functions) for the candidate genes in the QTL regions of that trait.

Results: The prioritization method was applied to rice QTL data, using gene functions predicted on the basis of sequence- and expression-information. The average reduction of the number of genes was over ten-fold. Comparison with various types of experimental datasets (including QTL fine-mapping and Genome Wide Association Study results) indicated both statistical significance and biological relevance of the obtained connections between genes and traits. A detailed analysis of flowering time QTLs illustrates that genes with completely unknown function are likely to play a role in this important trait.

Conclusions: Our approach can guide further experimentation and validation of causal genes for quantitative traits. This way it capitalizes on QTL data to uncover how individual genes influence trait variation.

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Related in: MedlinePlus

Transcription factors as potentially causal genes. Specific TF families (horizontal axis) were found associated with specific traits (vertical axis). Heatmap shows which percentage of the associated TFs belongs to various TF subfamilies for traits with at least ten associated TFs, and at least one TF subfamily which constitutes more than 25% of all associated TFs for that trait. Only TF subfamilies which for at least one trait constituted more than 25% of all TFs, are shown.
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Fig3: Transcription factors as potentially causal genes. Specific TF families (horizontal axis) were found associated with specific traits (vertical axis). Heatmap shows which percentage of the associated TFs belongs to various TF subfamilies for traits with at least ten associated TFs, and at least one TF subfamily which constitutes more than 25% of all associated TFs for that trait. Only TF subfamilies which for at least one trait constituted more than 25% of all TFs, are shown.

Mentions: In addition to the overall higher number of transcription factors among the prioritized candidate genes, there are also clearly different types of transcription factors associated with specific traits (FigureĀ 3). For several of these associations evidence exists in the literature. For example, the trait chlorophyll content is associated by our analysis with MICK MADS domain transcription factors; this is in line with the fact that targets of the tomato MADS TF RIN are involved in chlorophyll degradation [61]. The traits blast disease resistance and leaf angle are associated with NAC transcription factors by our analysis; experimental evidence indicates that these TFs are indeed involved in pathogen responses [62] and in waterlogging-induced upward bending of leaves [63]. Finally, the trait tiller number is associated with ERF transcription factors, and indeed the rice ERF TF OsEATB is known to be involved in regulation of tillering [64]. This preference of particular types of TFs to be relevant for specific traits will be useful in further prioritization of candidate genes for such traits.Figure 3


Prioritization of candidate genes in QTL regions based on associations between traits and biological processes.

Bargsten JW, Nap JP, Sanchez-Perez GF, van Dijk AD - BMC Plant Biol. (2014)

Transcription factors as potentially causal genes. Specific TF families (horizontal axis) were found associated with specific traits (vertical axis). Heatmap shows which percentage of the associated TFs belongs to various TF subfamilies for traits with at least ten associated TFs, and at least one TF subfamily which constitutes more than 25% of all associated TFs for that trait. Only TF subfamilies which for at least one trait constituted more than 25% of all TFs, are shown.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4274756&req=5

Fig3: Transcription factors as potentially causal genes. Specific TF families (horizontal axis) were found associated with specific traits (vertical axis). Heatmap shows which percentage of the associated TFs belongs to various TF subfamilies for traits with at least ten associated TFs, and at least one TF subfamily which constitutes more than 25% of all associated TFs for that trait. Only TF subfamilies which for at least one trait constituted more than 25% of all TFs, are shown.
Mentions: In addition to the overall higher number of transcription factors among the prioritized candidate genes, there are also clearly different types of transcription factors associated with specific traits (FigureĀ 3). For several of these associations evidence exists in the literature. For example, the trait chlorophyll content is associated by our analysis with MICK MADS domain transcription factors; this is in line with the fact that targets of the tomato MADS TF RIN are involved in chlorophyll degradation [61]. The traits blast disease resistance and leaf angle are associated with NAC transcription factors by our analysis; experimental evidence indicates that these TFs are indeed involved in pathogen responses [62] and in waterlogging-induced upward bending of leaves [63]. Finally, the trait tiller number is associated with ERF transcription factors, and indeed the rice ERF TF OsEATB is known to be involved in regulation of tillering [64]. This preference of particular types of TFs to be relevant for specific traits will be useful in further prioritization of candidate genes for such traits.Figure 3

Bottom Line: The average reduction of the number of genes was over ten-fold.Comparison with various types of experimental datasets (including QTL fine-mapping and Genome Wide Association Study results) indicated both statistical significance and biological relevance of the obtained connections between genes and traits.This way it capitalizes on QTL data to uncover how individual genes influence trait variation.

View Article: PubMed Central - PubMed

ABSTRACT

Background: Elucidation of genotype-to-phenotype relationships is a major challenge in biology. In plants, it is the basis for molecular breeding. Quantitative Trait Locus (QTL) mapping enables to link variation at the trait level to variation at the genomic level. However, QTL regions typically contain tens to hundreds of genes. In order to prioritize such candidate genes, we show that we can identify potentially causal genes for a trait based on overrepresentation of biological processes (gene functions) for the candidate genes in the QTL regions of that trait.

Results: The prioritization method was applied to rice QTL data, using gene functions predicted on the basis of sequence- and expression-information. The average reduction of the number of genes was over ten-fold. Comparison with various types of experimental datasets (including QTL fine-mapping and Genome Wide Association Study results) indicated both statistical significance and biological relevance of the obtained connections between genes and traits. A detailed analysis of flowering time QTLs illustrates that genes with completely unknown function are likely to play a role in this important trait.

Conclusions: Our approach can guide further experimentation and validation of causal genes for quantitative traits. This way it capitalizes on QTL data to uncover how individual genes influence trait variation.

Show MeSH
Related in: MedlinePlus