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Bayesian hierarchical modeling of means and covariances of gene expression data within families.

Pique-Regi R, Morrison J, Thomas DC - BMC Proc (2007)

Bottom Line: The latter provides a way of testing for cis and trans effects.The method was applied to data on 116 SNPs and 189 genes on chromosome 11, for which Morley et al. (Nature 2004, 430: 743-747) had previously reported linkage.We were able to confirm the association of the expression of HSD17B12 with a SNP in the same region reported by Morley et al., and also detected a SNP that appeared to affect the expression of many genes on this chromosome.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Preventive Medicine, University of Southern California, 1540 Alcazar Street, CHP-220, Los Angeles, California 90089, USA. piquereg@usc.edu

ABSTRACT
We describe a hierarchical Bayes model for the influence of constitutional genotypes from a linkage scan on the expression of a large number of genes. The model comprises linear regression models for the means in relation to genotypes and for the covariances between pairs of related individuals in relation to their identity-by-descent estimates. The matrices of regression coefficients for all possible pairs of single-nucleotide polymorphisms (SNPs) by all possible expressed genes are in turn modeled as a mixture of values and a normal distribution of non- values, with probabilities and means given by a third-level model of SNP and trait random effects and a spatial regression on the distance between the SNP and the expressed gene. The latter provides a way of testing for cis and trans effects. The method was applied to data on 116 SNPs and 189 genes on chromosome 11, for which Morley et al. (Nature 2004, 430: 743-747) had previously reported linkage. We were able to confirm the association of the expression of HSD17B12 with a SNP in the same region reported by Morley et al., and also detected a SNP that appeared to affect the expression of many genes on this chromosome. The approach appears to be a promising way to address the huge multiple comparisons problem for relating genome-wide genotype x expression data.

No MeSH data available.


Related in: MedlinePlus

Gene expression × Genotype associations and residual linkage summary. Left, Image describing the mean value of the association parameters πnm between the gene expression phenotypes (rows) and the SNP genotypes (columns). The matrix shows that the interactions are very sparse (dark spots), meaning that phenotypes are controlled by small number of SNPs, with no apparent concentration along the cis region delimited by blue lines. However, there exist some SNPs (columns) that seem to be correlated with a large set of phenotypes, potentially indicating a master regulatory region. Right, Image describing the posterior probability of the linkage locus after removing the association effect from the covariance.
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Figure 2: Gene expression × Genotype associations and residual linkage summary. Left, Image describing the mean value of the association parameters πnm between the gene expression phenotypes (rows) and the SNP genotypes (columns). The matrix shows that the interactions are very sparse (dark spots), meaning that phenotypes are controlled by small number of SNPs, with no apparent concentration along the cis region delimited by blue lines. However, there exist some SNPs (columns) that seem to be correlated with a large set of phenotypes, potentially indicating a master regulatory region. Right, Image describing the posterior probability of the linkage locus after removing the association effect from the covariance.

Mentions: After convergence has been reached, the number of regression coefficients with nonzero coefficients in Eq. (1) is very small. This is because in the mixture model employed in Eq. (3), a large number of the probabilities are close to 0 (Figure 2).


Bayesian hierarchical modeling of means and covariances of gene expression data within families.

Pique-Regi R, Morrison J, Thomas DC - BMC Proc (2007)

Gene expression × Genotype associations and residual linkage summary. Left, Image describing the mean value of the association parameters πnm between the gene expression phenotypes (rows) and the SNP genotypes (columns). The matrix shows that the interactions are very sparse (dark spots), meaning that phenotypes are controlled by small number of SNPs, with no apparent concentration along the cis region delimited by blue lines. However, there exist some SNPs (columns) that seem to be correlated with a large set of phenotypes, potentially indicating a master regulatory region. Right, Image describing the posterior probability of the linkage locus after removing the association effect from the covariance.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2367475&req=5

Figure 2: Gene expression × Genotype associations and residual linkage summary. Left, Image describing the mean value of the association parameters πnm between the gene expression phenotypes (rows) and the SNP genotypes (columns). The matrix shows that the interactions are very sparse (dark spots), meaning that phenotypes are controlled by small number of SNPs, with no apparent concentration along the cis region delimited by blue lines. However, there exist some SNPs (columns) that seem to be correlated with a large set of phenotypes, potentially indicating a master regulatory region. Right, Image describing the posterior probability of the linkage locus after removing the association effect from the covariance.
Mentions: After convergence has been reached, the number of regression coefficients with nonzero coefficients in Eq. (1) is very small. This is because in the mixture model employed in Eq. (3), a large number of the probabilities are close to 0 (Figure 2).

Bottom Line: The latter provides a way of testing for cis and trans effects.The method was applied to data on 116 SNPs and 189 genes on chromosome 11, for which Morley et al. (Nature 2004, 430: 743-747) had previously reported linkage.We were able to confirm the association of the expression of HSD17B12 with a SNP in the same region reported by Morley et al., and also detected a SNP that appeared to affect the expression of many genes on this chromosome.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Preventive Medicine, University of Southern California, 1540 Alcazar Street, CHP-220, Los Angeles, California 90089, USA. piquereg@usc.edu

ABSTRACT
We describe a hierarchical Bayes model for the influence of constitutional genotypes from a linkage scan on the expression of a large number of genes. The model comprises linear regression models for the means in relation to genotypes and for the covariances between pairs of related individuals in relation to their identity-by-descent estimates. The matrices of regression coefficients for all possible pairs of single-nucleotide polymorphisms (SNPs) by all possible expressed genes are in turn modeled as a mixture of values and a normal distribution of non- values, with probabilities and means given by a third-level model of SNP and trait random effects and a spatial regression on the distance between the SNP and the expressed gene. The latter provides a way of testing for cis and trans effects. The method was applied to data on 116 SNPs and 189 genes on chromosome 11, for which Morley et al. (Nature 2004, 430: 743-747) had previously reported linkage. We were able to confirm the association of the expression of HSD17B12 with a SNP in the same region reported by Morley et al., and also detected a SNP that appeared to affect the expression of many genes on this chromosome. The approach appears to be a promising way to address the huge multiple comparisons problem for relating genome-wide genotype x expression data.

No MeSH data available.


Related in: MedlinePlus