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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

Associations between traits and biological processes. (A) Histogram of number of associations to biological processes (BPs) per trait. (B) Histogram of number of associations to traits per biological process.
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Fig2: Associations between traits and biological processes. (A) Histogram of number of associations to biological processes (BPs) per trait. (B) Histogram of number of associations to traits per biological process.

Mentions: From a list of 179 different traits in rice, for 153 traits 2519 associations with BP terms were obtained. For only 26 traits, no association with any BP was obtained at all. For most traits (134 out of 179, i.e. 75%) twenty or less BP term associations were obtained (Figure 2A). The detailed associations between traits and biological processes are given in (Additional file 2: Table S1) and summarized data are given in Table 1. In total, 918 BP terms (60%) were involved in at least one association to a trait (Figure 2B).Figure 2


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)

Associations between traits and biological processes. (A) Histogram of number of associations to biological processes (BPs) per trait. (B) Histogram of number of associations to traits per biological process.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig2: Associations between traits and biological processes. (A) Histogram of number of associations to biological processes (BPs) per trait. (B) Histogram of number of associations to traits per biological process.
Mentions: From a list of 179 different traits in rice, for 153 traits 2519 associations with BP terms were obtained. For only 26 traits, no association with any BP was obtained at all. For most traits (134 out of 179, i.e. 75%) twenty or less BP term associations were obtained (Figure 2A). The detailed associations between traits and biological processes are given in (Additional file 2: Table S1) and summarized data are given in Table 1. In total, 918 BP terms (60%) were involved in at least one association to a trait (Figure 2B).Figure 2

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