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Mixture modeling of transcript abundance classes in natural populations.

Hsieh WP, Passador-Gurgel G, Stone EA, Gibson G - Genome Biol. (2007)

Bottom Line: Similar results are observed in reported data on gene expression in human lymphoblast cell lines, in which analysis of association with linked polymorphisms implies that cis-acting single nucleotide polymorphisms make only a modest contribution to bimodal distributions of transcript abundance.Population surveys of gene expression may complement genetical genomics as a general approach to quantifying sources of transcriptional variation.Differential expression of transcripts among individuals is due to a complex interplay of cis-acting and trans-acting factors.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Genetics, Gardner Hall, North Carolina State University, Raleigh, North Carolina 27695-7614, USA.

ABSTRACT

Background: Populations diverge in genotype and phenotype under the influence of such evolutionary processes as genetic drift, mutation accumulation, and natural selection. Because genotype maps onto phenotype by way of transcription, it is of interest to evaluate how these evolutionary factors influence the structure of variation at the level of transcription. Here, we explore the distributions of cis-acting and trans-acting factors and their relative contributions to expression of transcripts that exhibit two or more classes of abundance among individuals within populations.

Results: Expression profiling using cDNA microarrays was conducted in Drosophila melanogaster adult female heads for 58 nearly isogenic lines from a North Carolina population and 50 from a California population. Using a mixture modeling approach, transcripts were identified that exhibit more than one mode of transcript abundance across the samples. Power studies indicate that sample sizes of 50 individuals will generally be sufficient to detect divergent transcript abundance classes. The distribution of transcript abundance classes is skewed toward low frequency minor classes, which is reminiscent of the typical skew in genotype frequencies. Similar results are observed in reported data on gene expression in human lymphoblast cell lines, in which analysis of association with linked polymorphisms implies that cis-acting single nucleotide polymorphisms make only a modest contribution to bimodal distributions of transcript abundance.

Conclusion: Population surveys of gene expression may complement genetical genomics as a general approach to quantifying sources of transcriptional variation. Differential expression of transcripts among individuals is due to a complex interplay of cis-acting and trans-acting factors.

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Strength of association between cis-SNPs and transcript abundance. Frequency histograms in bins of increasing order of magnitude of significance, with number of genes indicated on the y-axis. (a) The distribution of significance measures (negative logarithm of the P value) for the most strongly associated single nucleotide polymorphism (SNP) within 100 kilobases of each of the 818 bimodally expressed transcripts. (b) The same distribution for SNPs linked to a set of 835 randomly selected transcripts. Note the excess of outliers in the bimodal sample. (c) The distribution of strongest associations for a typical permutation of SNPs against unlinked transcripts, clearly showing much reduced significance relative to those observed for linked SNPs. (d) The 'best possible' distribution of associations, assuming that a single SNP explains all of the observed bimodality of each transcript. Single dots in panels A and D represent outlier significance values.
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Figure 7: Strength of association between cis-SNPs and transcript abundance. Frequency histograms in bins of increasing order of magnitude of significance, with number of genes indicated on the y-axis. (a) The distribution of significance measures (negative logarithm of the P value) for the most strongly associated single nucleotide polymorphism (SNP) within 100 kilobases of each of the 818 bimodally expressed transcripts. (b) The same distribution for SNPs linked to a set of 835 randomly selected transcripts. Note the excess of outliers in the bimodal sample. (c) The distribution of strongest associations for a typical permutation of SNPs against unlinked transcripts, clearly showing much reduced significance relative to those observed for linked SNPs. (d) The 'best possible' distribution of associations, assuming that a single SNP explains all of the observed bimodality of each transcript. Single dots in panels A and D represent outlier significance values.

Mentions: On the other hand, comparison with the distribution of cis associations in the set of bimodal TACs implies some enrichment for locally acting regulatory polymorphisms. Figure 7 shows the observed quantile distributions of the strongest association statistic for each gene in (panel a) our sample of 818 bimodal transcripts, (panel b) a random sample of 838 transcripts, (panel c) a random permutation of genotypes against transcripts, and (panel d) the best possible TAC associations, assuming that each TAC is due to a single genotype class (see Materials and methods, below). The distributions in panels a and b are similar overall, expect for the long tail encompassing the top 2.5% of the bimodal TAC sample, identifying 20 genes for which the two TACs are largely explained by single cis-acting SNPs. By contrast with panel c, random sets of genes are also heavily enriched for cis-acting SNPs, whose effects are not strong enough to exceed an experiment-wide significance threshold, but nevertheless strongly suggest that the majority of genes are regulated in part by cis-SNPs that have stronger associations than are observed if genotypes are randomly matched to transcript frequencies. Figure 7d indicates that most of the detected associations only explain a small portion of the bimodality of transcript abundance, because the association statistics are in general much smaller than would be observed if there were tight correspondence between genotype and transcript abundance.


Mixture modeling of transcript abundance classes in natural populations.

Hsieh WP, Passador-Gurgel G, Stone EA, Gibson G - Genome Biol. (2007)

Strength of association between cis-SNPs and transcript abundance. Frequency histograms in bins of increasing order of magnitude of significance, with number of genes indicated on the y-axis. (a) The distribution of significance measures (negative logarithm of the P value) for the most strongly associated single nucleotide polymorphism (SNP) within 100 kilobases of each of the 818 bimodally expressed transcripts. (b) The same distribution for SNPs linked to a set of 835 randomly selected transcripts. Note the excess of outliers in the bimodal sample. (c) The distribution of strongest associations for a typical permutation of SNPs against unlinked transcripts, clearly showing much reduced significance relative to those observed for linked SNPs. (d) The 'best possible' distribution of associations, assuming that a single SNP explains all of the observed bimodality of each transcript. Single dots in panels A and D represent outlier significance values.
© Copyright Policy - open-access
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC2394757&req=5

Figure 7: Strength of association between cis-SNPs and transcript abundance. Frequency histograms in bins of increasing order of magnitude of significance, with number of genes indicated on the y-axis. (a) The distribution of significance measures (negative logarithm of the P value) for the most strongly associated single nucleotide polymorphism (SNP) within 100 kilobases of each of the 818 bimodally expressed transcripts. (b) The same distribution for SNPs linked to a set of 835 randomly selected transcripts. Note the excess of outliers in the bimodal sample. (c) The distribution of strongest associations for a typical permutation of SNPs against unlinked transcripts, clearly showing much reduced significance relative to those observed for linked SNPs. (d) The 'best possible' distribution of associations, assuming that a single SNP explains all of the observed bimodality of each transcript. Single dots in panels A and D represent outlier significance values.
Mentions: On the other hand, comparison with the distribution of cis associations in the set of bimodal TACs implies some enrichment for locally acting regulatory polymorphisms. Figure 7 shows the observed quantile distributions of the strongest association statistic for each gene in (panel a) our sample of 818 bimodal transcripts, (panel b) a random sample of 838 transcripts, (panel c) a random permutation of genotypes against transcripts, and (panel d) the best possible TAC associations, assuming that each TAC is due to a single genotype class (see Materials and methods, below). The distributions in panels a and b are similar overall, expect for the long tail encompassing the top 2.5% of the bimodal TAC sample, identifying 20 genes for which the two TACs are largely explained by single cis-acting SNPs. By contrast with panel c, random sets of genes are also heavily enriched for cis-acting SNPs, whose effects are not strong enough to exceed an experiment-wide significance threshold, but nevertheless strongly suggest that the majority of genes are regulated in part by cis-SNPs that have stronger associations than are observed if genotypes are randomly matched to transcript frequencies. Figure 7d indicates that most of the detected associations only explain a small portion of the bimodality of transcript abundance, because the association statistics are in general much smaller than would be observed if there were tight correspondence between genotype and transcript abundance.

Bottom Line: Similar results are observed in reported data on gene expression in human lymphoblast cell lines, in which analysis of association with linked polymorphisms implies that cis-acting single nucleotide polymorphisms make only a modest contribution to bimodal distributions of transcript abundance.Population surveys of gene expression may complement genetical genomics as a general approach to quantifying sources of transcriptional variation.Differential expression of transcripts among individuals is due to a complex interplay of cis-acting and trans-acting factors.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Genetics, Gardner Hall, North Carolina State University, Raleigh, North Carolina 27695-7614, USA.

ABSTRACT

Background: Populations diverge in genotype and phenotype under the influence of such evolutionary processes as genetic drift, mutation accumulation, and natural selection. Because genotype maps onto phenotype by way of transcription, it is of interest to evaluate how these evolutionary factors influence the structure of variation at the level of transcription. Here, we explore the distributions of cis-acting and trans-acting factors and their relative contributions to expression of transcripts that exhibit two or more classes of abundance among individuals within populations.

Results: Expression profiling using cDNA microarrays was conducted in Drosophila melanogaster adult female heads for 58 nearly isogenic lines from a North Carolina population and 50 from a California population. Using a mixture modeling approach, transcripts were identified that exhibit more than one mode of transcript abundance across the samples. Power studies indicate that sample sizes of 50 individuals will generally be sufficient to detect divergent transcript abundance classes. The distribution of transcript abundance classes is skewed toward low frequency minor classes, which is reminiscent of the typical skew in genotype frequencies. Similar results are observed in reported data on gene expression in human lymphoblast cell lines, in which analysis of association with linked polymorphisms implies that cis-acting single nucleotide polymorphisms make only a modest contribution to bimodal distributions of transcript abundance.

Conclusion: Population surveys of gene expression may complement genetical genomics as a general approach to quantifying sources of transcriptional variation. Differential expression of transcripts among individuals is due to a complex interplay of cis-acting and trans-acting factors.

Show MeSH
Related in: MedlinePlus