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Intra- and inter-individual genetic differences in gene expression.

Cowley MJ, Cotsapas CJ, Williams RB, Chan EK, Pulvers JN, Liu MY, Luo OJ, Nott DJ, Little PF - Mamm. Genome (2009)

Bottom Line: We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals.Thus, this class of genetic variation can result in complex inter- and intraindividual differences.This will create substantial challenges in humans, where multiple tissues are not readily available.

View Article: PubMed Central - PubMed

Affiliation: School of Biotechnology and Biomolecular Sciences, The University of New South Wales, NSW, Australia.

ABSTRACT
Genetic variation is known to influence the amount of mRNA produced by a gene. Because molecular machines control mRNA levels of multiple genes, we expect genetic variation in components of these machines would influence multiple genes in a similar fashion. We show that this assumption is correct by using correlation of mRNA levels measured from multiple tissues in mouse strain panels to detect shared genetic influences. These correlating groups of genes (CGGs) have collective properties that on average account for 52-79% of the variability of their constituent genes and can contain genes that encode functionally related proteins. We show that the genetic influences are essentially tissue-specific and, consequently, the same genetic variations in one animal may upregulate a CGG in one tissue but downregulate the CGG in a second tissue. We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals. Thus, this class of genetic variation can result in complex inter- and intraindividual differences. This will create substantial challenges in humans, where multiple tissues are not readily available.

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Coherency analysis. a Coherency overview: An example CGG containing 12 genes is identified by correlation analysis in the 31 BXD strains; the expression ratios from a comparison of two mouse strains for each of these 12 genes are shown (most genes are upregulated). The coherency score is calculated and statistical significance is determined via permutation (see Methods subsection “Coherency test statistic”). The resulting coherency and statistical significance are displayed as an annotated histogram. This process is repeated for all CGGs in expression data from all three tissues. b Intraindividual coherency: We plot the coherency scores for each CGG in the brain, kidney, and liver for DBA/2J vs. C57BL/6J in the first row (blue, green, and red, respectively) and for SJL/J vs. C57BL/6J in the second row (light blue, light green, and orange, respectively). c Interindividual coherency: The same data from panel B but reordered so that the tissues are grouped together. Stars indicate the degree of statistical significance (*P < 0.05, **P = 0.001)
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Fig5: Coherency analysis. a Coherency overview: An example CGG containing 12 genes is identified by correlation analysis in the 31 BXD strains; the expression ratios from a comparison of two mouse strains for each of these 12 genes are shown (most genes are upregulated). The coherency score is calculated and statistical significance is determined via permutation (see Methods subsection “Coherency test statistic”). The resulting coherency and statistical significance are displayed as an annotated histogram. This process is repeated for all CGGs in expression data from all three tissues. b Intraindividual coherency: We plot the coherency scores for each CGG in the brain, kidney, and liver for DBA/2J vs. C57BL/6J in the first row (blue, green, and red, respectively) and for SJL/J vs. C57BL/6J in the second row (light blue, light green, and orange, respectively). c Interindividual coherency: The same data from panel B but reordered so that the tissues are grouped together. Stars indicate the degree of statistical significance (*P < 0.05, **P = 0.001)

Mentions: Having identified CGGs based on their expression patterns in three tissues across a panel of BXD mice, we sought independent evidence that the expression of these groups of genes are being influenced in a coordinated fashion, due to the effects of genetic as opposed to other sources of variation. Within each individual BXD animal, all genes in a CGG should be coordinately regulated, even if this differs across tissues. If these levels are indeed due to genetic differences in the regulatory factors controlling the ultimate mRNA level, then we would expect that CGG members should display similar correlated expression patterns across different genetic backgrounds. However, the multiple, complex changes in genetic background implicit in this experiment are unlikely to result in exactly the same mRNA levels in any two individuals; therefore, rather than test for the identical expression level of all genes in the CGG, we designed a test to detect for the identical direction of mRNA levels: relatively up- or downregulated, compared to a suitable reference or baseline. This coordinated expression over all genes in a CGG can be summarised as a coherency statistic: the proportion of genes whose mRNA levels are upregulated (or downregulated) relative to the reference (see Fig. 5a for an overview, and Methods subsection “Coherency test statistic” for details). We performed simulation studies to assess the performance of the coherency statistic with respect to both the number of genes in a CGG and the magnitude and variability of the expression changes (see supplementary results). Simulating the conditions of our experiment, we identified that the score is adequately powered to detect coherent directionality of expression for CGGs of at least ten genes (at permuted P < 0.05). For groups of genes with less than ten genes, the score had little power, even in the case of maximal coherency.Fig. 5


Intra- and inter-individual genetic differences in gene expression.

Cowley MJ, Cotsapas CJ, Williams RB, Chan EK, Pulvers JN, Liu MY, Luo OJ, Nott DJ, Little PF - Mamm. Genome (2009)

Coherency analysis. a Coherency overview: An example CGG containing 12 genes is identified by correlation analysis in the 31 BXD strains; the expression ratios from a comparison of two mouse strains for each of these 12 genes are shown (most genes are upregulated). The coherency score is calculated and statistical significance is determined via permutation (see Methods subsection “Coherency test statistic”). The resulting coherency and statistical significance are displayed as an annotated histogram. This process is repeated for all CGGs in expression data from all three tissues. b Intraindividual coherency: We plot the coherency scores for each CGG in the brain, kidney, and liver for DBA/2J vs. C57BL/6J in the first row (blue, green, and red, respectively) and for SJL/J vs. C57BL/6J in the second row (light blue, light green, and orange, respectively). c Interindividual coherency: The same data from panel B but reordered so that the tissues are grouped together. Stars indicate the degree of statistical significance (*P < 0.05, **P = 0.001)
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Related In: Results  -  Collection

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Fig5: Coherency analysis. a Coherency overview: An example CGG containing 12 genes is identified by correlation analysis in the 31 BXD strains; the expression ratios from a comparison of two mouse strains for each of these 12 genes are shown (most genes are upregulated). The coherency score is calculated and statistical significance is determined via permutation (see Methods subsection “Coherency test statistic”). The resulting coherency and statistical significance are displayed as an annotated histogram. This process is repeated for all CGGs in expression data from all three tissues. b Intraindividual coherency: We plot the coherency scores for each CGG in the brain, kidney, and liver for DBA/2J vs. C57BL/6J in the first row (blue, green, and red, respectively) and for SJL/J vs. C57BL/6J in the second row (light blue, light green, and orange, respectively). c Interindividual coherency: The same data from panel B but reordered so that the tissues are grouped together. Stars indicate the degree of statistical significance (*P < 0.05, **P = 0.001)
Mentions: Having identified CGGs based on their expression patterns in three tissues across a panel of BXD mice, we sought independent evidence that the expression of these groups of genes are being influenced in a coordinated fashion, due to the effects of genetic as opposed to other sources of variation. Within each individual BXD animal, all genes in a CGG should be coordinately regulated, even if this differs across tissues. If these levels are indeed due to genetic differences in the regulatory factors controlling the ultimate mRNA level, then we would expect that CGG members should display similar correlated expression patterns across different genetic backgrounds. However, the multiple, complex changes in genetic background implicit in this experiment are unlikely to result in exactly the same mRNA levels in any two individuals; therefore, rather than test for the identical expression level of all genes in the CGG, we designed a test to detect for the identical direction of mRNA levels: relatively up- or downregulated, compared to a suitable reference or baseline. This coordinated expression over all genes in a CGG can be summarised as a coherency statistic: the proportion of genes whose mRNA levels are upregulated (or downregulated) relative to the reference (see Fig. 5a for an overview, and Methods subsection “Coherency test statistic” for details). We performed simulation studies to assess the performance of the coherency statistic with respect to both the number of genes in a CGG and the magnitude and variability of the expression changes (see supplementary results). Simulating the conditions of our experiment, we identified that the score is adequately powered to detect coherent directionality of expression for CGGs of at least ten genes (at permuted P < 0.05). For groups of genes with less than ten genes, the score had little power, even in the case of maximal coherency.Fig. 5

Bottom Line: We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals.Thus, this class of genetic variation can result in complex inter- and intraindividual differences.This will create substantial challenges in humans, where multiple tissues are not readily available.

View Article: PubMed Central - PubMed

Affiliation: School of Biotechnology and Biomolecular Sciences, The University of New South Wales, NSW, Australia.

ABSTRACT
Genetic variation is known to influence the amount of mRNA produced by a gene. Because molecular machines control mRNA levels of multiple genes, we expect genetic variation in components of these machines would influence multiple genes in a similar fashion. We show that this assumption is correct by using correlation of mRNA levels measured from multiple tissues in mouse strain panels to detect shared genetic influences. These correlating groups of genes (CGGs) have collective properties that on average account for 52-79% of the variability of their constituent genes and can contain genes that encode functionally related proteins. We show that the genetic influences are essentially tissue-specific and, consequently, the same genetic variations in one animal may upregulate a CGG in one tissue but downregulate the CGG in a second tissue. We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals. Thus, this class of genetic variation can result in complex inter- and intraindividual differences. This will create substantial challenges in humans, where multiple tissues are not readily available.

Show MeSH
Related in: MedlinePlus