The current model offers a potential mechanism to explain in part why genetic variants discovered so far do not explain much of the expected genetic variability. Although part of the unexplained variability may be due to rare genetic polymorphisms still to be found,4
the model predicts that person-to-person variation in the use of alternative promoters would reduce the observed genetic variance of a genetic system. Thus, even a complete knowledge of all the genetic variants involved in a particular phenotypic trait would be no enough to explain the whole genetic variance of the trait.
Three major factors explain the reduction of the genetic variance according to the model discussed in the present work. First, the observed additive effects of the SNPs inside each of the alternative promoters is attenuated in comparison to their actual effects. For example, because the allele A1 of the G1 SNP exerts its effect only when the promoter P1 is being used, its observed additive effect would be reduced by a factor equal to f1 relative to its actual effect. The same situation applies for the B1 allele of the G2 SNP whose observed additive effect would be attenuated by a factor equal to f2. Second, because the use of alternative promoters is not being measured (e.g. in current genetic epidemiology studies such scenario is not even considered as a possibility) the dimensionality of the observed data would be always lower than the actual dimensionality of the population data. The number of observed chromosomes will be less than the number of actual chromosomes in the population. Third, different promoters may have different baseline levels of the phenotypic trait under study further reducing the proportion of the actual variance that is due to measured genetic polymorphisms.
Recent published evidence supports the proposed hypothesis of person-to-person variation in the use of alternative promoters. Turner et al. reported the presence of high inter-individual variability in the methylation patterns of alternative promoters of the glucocorticoid receptor (NR3C1
) gene in twenty-six healthy subjects, suggesting person-to-person variation in epigenetic regulatory mechanisms.7
A small study that measured promoter activity of the aromatase (CYP19A1
) gene in skin fibroblasts from 4 normal volunteers found that one subject showed increased activity of the promoters I.3 and II in response to cAMP, in contrast to the other 3 subjects who expressed the cAMP-unresponsive promoter I.4.8
In non-malignant lung tissue from 15 patients with non-small cell lung cancer, two cases used mostly promoters I.3 and II of the CYP19A1
gene and the rest of patients used the promoter I.4.9
It is noteworthy that may even exist ethnic differences in the use of alternative promoters. A recent study in 101 women with uterine leiomyoma (31 African American, 34 white American, and 36 Japanese women) reported that leiomyoma tissue from African American women expressed the promoter II in higher proportion compared to Japanese women.10
At last, the CD36
gene showed inter-individual variability in the use of four out of five alternative promoters in cultured monocytes from 10 subjects.11
The present results, published evidence about variability in the use of alternative promoters, and the fact that more than half of human genes have alternative promoters,12
with a mean of 3.1 promoters per gene13
stress the need to carry out extensive studies in human populations to determine and quantify inter-individual variation in the use of alternative promoters. To date there are few approaches to assess the use of alternative promoters in a genome-wide scale. Singer et al.14
developed a promoter tiling array that can identify about 35,000 alternative promoters from almost 7,000 human genes, and Jacox et al.15
described a computational approach to determine alternative promoter usage in nearly 1,500 genes using the Affymetrix Exon 1.0 array. Although those microarrays only interrogates a subset of genes in the genome (i.e. those genes with known alternative promoters) they would provide enough data to test the proposed hypothesis in a genome-wide scale. A comprehensive assessment should ideally measure person-to-person variation across different types of tissue.
The present model can be easily extended to include cases of genes with more than two promoters and more than one SNP in each of the promoters. In a gene with multiple promoters, the observed additive effect of a particular SNP would be reduced by a factor equal to the proportion of chromosomes in the population using the promoter in which the SNP is located. The model may also be used for other types of alternative regulatory elements such as multiple enhancers affecting gene expression; the so-called shadow enhancers.16-18
A limitation of the presented model is that depends on the knowledge about alternative promoters or regulatory elements in general. More experimental work such as chromatin immunoprecipitation (ChIP)-chip assays validated with transgenic models is needed to identify new regulatory elements.
In summary, the present report shows that in presence of inter-individual variation in the use of alternative promoters the observable effects of genetic variants will be lower than their actual effects. The proposed model may explain in part why GWAS-identified variants are in most part poor predictors of human complex traits. Future studies are needed to determine and quantify the person-to-person variability in the use of alternative promoters as well as to identify new regulatory elements in the human genome.