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Identifying the genetic determinants of transcription factor activity.

Lee E, Bussemaker HJ - Mol. Syst. Biol. (2010)

Bottom Line: In budding yeast, our method identifies six times as many locus-TF associations and more than twice as many trans-acting loci as all existing methods combined.Application to mouse data from an F2 intercross identified an aQTL on chromosome VII modulating the activity of Zscan4 in liver cells.Our method has greatly improved statistical power over existing methods, is mechanism based, strictly causal, computationally efficient, and generally applicable.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Columbia University, New York, NY 10027, USA.

ABSTRACT
Analysis of parallel genotyping and expression profiling data has shown that mRNA expression levels are highly heritable. Currently, only a tiny fraction of this genetic variance can be mechanistically accounted for. The influence of trans-acting polymorphisms on gene expression traits is often mediated by transcription factors (TFs). We present a method that exploits prior knowledge about the in vitro DNA-binding specificity of a TF in order to map the loci ('aQTLs') whose inheritance modulates its protein-level regulatory activity. Genome-wide regression of differential mRNA expression on predicted promoter affinity is used to estimate segregant-specific TF activity, which is subsequently mapped as a quantitative phenotype. In budding yeast, our method identifies six times as many locus-TF associations and more than twice as many trans-acting loci as all existing methods combined. Application to mouse data from an F2 intercross identified an aQTL on chromosome VII modulating the activity of Zscan4 in liver cells. Our method has greatly improved statistical power over existing methods, is mechanism based, strictly causal, computationally efficient, and generally applicable.

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(A) Inferred activity of Zscan4p across all F2 mouse population. Each column shows the activity of Zscan4 in homozygous C57BL/6J (BB), heterozygous (BD), and homozygous DBA/2J (DD) mice at aQTL positions, respectively. (B) LOD score profile for Zscan4p.
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f6: (A) Inferred activity of Zscan4p across all F2 mouse population. Each column shows the activity of Zscan4 in homozygous C57BL/6J (BB), heterozygous (BD), and homozygous DBA/2J (DD) mice at aQTL positions, respectively. (B) LOD score profile for Zscan4p.

Mentions: To determine whether our method could map aQTLs for mammalian TFs, we applied it to parallel genotyping and liver cell expression data for an F2 mouse population (Schadt et al, 2003). Weight matrices derived from protein-binding microarray (PBM) data for 104 mouse TFs were used (Badis et al, 2009). The model we used to analyze the yeast segregants contains ‘cis' coefficients, which explicitly model changes in expression because of allelic variation in promoter sequence, in addition to the ‘trans' coefficient that model the changes in TF activity. However, we found that a simpler ‘trans-only' model performed equally well in terms of mapping aQTLs when applied to the yeast segregant data (Supplementary Figure S7). This gave us confidence to use a ‘trans-only' model in mouse, where the density of markers is too low to assign gene-specific promoter sequences. We identified an aQTL for Zscan4, a TF containing four zinc finger domains and a SCAN domain, which is also known as the leucine-rich region (Williams et al, 1995) (Figure 6). Using a multivariate linear model to analyze the homozygous C57BL/6J (BB), homozygous DBA/2J (DD), and heterozygous (BD) genotype at the aQTL locus (Figure 6A), we found the behavior of the aQTL to be additive and show no significant dominant effect (see Materials and methods). A highly significant linkage (LOD score=10.8) with Zscan4 activity occurs between 43 and 66 cM on mouse chromosome 7 (Figure 6B). This region contains >500 genes, which makes it difficult to predict the causal polymorphism. Limited information is available about protein–protein interaction (PPI) for mouse, and we could not detect any direct interaction between genes within this region and Zscan4p. However, our result demonstrates that TF activity can also be inferred and mapped in mammalian cells using our method, and provides a starting point for further dissection of trans-acting regulatory variation mediated by Zscan4p.


Identifying the genetic determinants of transcription factor activity.

Lee E, Bussemaker HJ - Mol. Syst. Biol. (2010)

(A) Inferred activity of Zscan4p across all F2 mouse population. Each column shows the activity of Zscan4 in homozygous C57BL/6J (BB), heterozygous (BD), and homozygous DBA/2J (DD) mice at aQTL positions, respectively. (B) LOD score profile for Zscan4p.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2964119&req=5

f6: (A) Inferred activity of Zscan4p across all F2 mouse population. Each column shows the activity of Zscan4 in homozygous C57BL/6J (BB), heterozygous (BD), and homozygous DBA/2J (DD) mice at aQTL positions, respectively. (B) LOD score profile for Zscan4p.
Mentions: To determine whether our method could map aQTLs for mammalian TFs, we applied it to parallel genotyping and liver cell expression data for an F2 mouse population (Schadt et al, 2003). Weight matrices derived from protein-binding microarray (PBM) data for 104 mouse TFs were used (Badis et al, 2009). The model we used to analyze the yeast segregants contains ‘cis' coefficients, which explicitly model changes in expression because of allelic variation in promoter sequence, in addition to the ‘trans' coefficient that model the changes in TF activity. However, we found that a simpler ‘trans-only' model performed equally well in terms of mapping aQTLs when applied to the yeast segregant data (Supplementary Figure S7). This gave us confidence to use a ‘trans-only' model in mouse, where the density of markers is too low to assign gene-specific promoter sequences. We identified an aQTL for Zscan4, a TF containing four zinc finger domains and a SCAN domain, which is also known as the leucine-rich region (Williams et al, 1995) (Figure 6). Using a multivariate linear model to analyze the homozygous C57BL/6J (BB), homozygous DBA/2J (DD), and heterozygous (BD) genotype at the aQTL locus (Figure 6A), we found the behavior of the aQTL to be additive and show no significant dominant effect (see Materials and methods). A highly significant linkage (LOD score=10.8) with Zscan4 activity occurs between 43 and 66 cM on mouse chromosome 7 (Figure 6B). This region contains >500 genes, which makes it difficult to predict the causal polymorphism. Limited information is available about protein–protein interaction (PPI) for mouse, and we could not detect any direct interaction between genes within this region and Zscan4p. However, our result demonstrates that TF activity can also be inferred and mapped in mammalian cells using our method, and provides a starting point for further dissection of trans-acting regulatory variation mediated by Zscan4p.

Bottom Line: In budding yeast, our method identifies six times as many locus-TF associations and more than twice as many trans-acting loci as all existing methods combined.Application to mouse data from an F2 intercross identified an aQTL on chromosome VII modulating the activity of Zscan4 in liver cells.Our method has greatly improved statistical power over existing methods, is mechanism based, strictly causal, computationally efficient, and generally applicable.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Columbia University, New York, NY 10027, USA.

ABSTRACT
Analysis of parallel genotyping and expression profiling data has shown that mRNA expression levels are highly heritable. Currently, only a tiny fraction of this genetic variance can be mechanistically accounted for. The influence of trans-acting polymorphisms on gene expression traits is often mediated by transcription factors (TFs). We present a method that exploits prior knowledge about the in vitro DNA-binding specificity of a TF in order to map the loci ('aQTLs') whose inheritance modulates its protein-level regulatory activity. Genome-wide regression of differential mRNA expression on predicted promoter affinity is used to estimate segregant-specific TF activity, which is subsequently mapped as a quantitative phenotype. In budding yeast, our method identifies six times as many locus-TF associations and more than twice as many trans-acting loci as all existing methods combined. Application to mouse data from an F2 intercross identified an aQTL on chromosome VII modulating the activity of Zscan4 in liver cells. Our method has greatly improved statistical power over existing methods, is mechanism based, strictly causal, computationally efficient, and generally applicable.

Show MeSH
Related in: MedlinePlus