Quantifying the effect of sequence variation on regulatory interactions

Abstract

ABSTRACT:The increasing amount of sequence data provides new opportunities and challenges to derive mechanistic models that can link sequence variations to phenotypic diversity. Here we introduce a new computational framework to suggest possible consequences of sequence variations on regulatory networks. Our method, called sTRAP (strap.molgen.mpg.de), analyses variations in the DNA sequence and predicts quantitative changes to the binding strength of any transcription factor for which there is a binding model. We have tested the method against a set of known associations between SNPs and their regulatory consequences. Our predictions are robust with respect to different parameters and model assumptions. Importantly we set an objective and quantifiable benchmark against which future improvements can be compared. Given the good performance of our method, we developed a publicly available tool that can serve as an important starting point for routine analysis of disease-associated sequence regions.

Natural variation of histone modification and its impact on gene expression in the rat genome

“…strong sequence variants in cis-regulated H3K4me3 regions affect the affinity of a TF by altering the sequence of its binding site (Manke et al 2010). For each motif of the TRANSFAC database (Wingender et al 1996; http://www.gene-regulation.com), we tested whether high scores for differential TF binding occurred more often in cis-regulated regions compared with the remaining regions.…”

“…For each PWM from the TRANSFAC database set (Wingender et al 1996; http://www.generegulation.com) of nonredundant PWMs, we scored the difference of binding affinities of the reference and alternative alleles using the sTRAP method (Manke et al 2010). We then classified the SNPs into two classes: SNPs in cis-regulated regions (FDR < 0.1) and SNPs in other regions.…”

Predicting functional regulatory SNPs in the human antimicrobial peptide genes DEFB1 and CAMP in tuberculosis and HIV/AIDS

“…Recent projects such as the 1000 Genomes Project (1kGP) (Abecasis et al, 2012) attempt to systematically map genome-wide variants, while the ENCODE Project database's (Bernstein et al, 2012) main aim is to find DNA functional elements in the human genome. These have spurred a scientific trend for creating algorithms that select rSNPs and quantify their functional impact (Lower et al, 2013, Chen et al, 2014, Bryzgalov et al, 2013, Li et al, 2013, Macintyre et al, 2010, Manke et al, 2010, Teng et al, 2012, Ward & Kellis 2012, Holm et al, 2010. There are several rSNP databases (Guo et al, 2014, Ning et al, 2014 which could have an application in diagnostics and personalized medicine.…”

“…There are many pitfalls and limitations from the existing algorithms and databases for achieving an accurate prediction of the transcription regulation: (1) the disease-associated SNPs in GWAS datasets (Bryzgalov et al, 2013, Li et al, 2013, Teng et al, 2012, Ward & Kellis 2012 may not be in fact causal, since its selection is due to the linkage to other causal SNPs (Levo & Segal 2014); (2) chromatin accessibility datasets lack many specific cell lines (Bryzgalov et al, 2013, Macintyre et al, 2010, Manke et al, 2010, Teng et al, 2012 or are limited in their use , Li et al, 2013, Ward & Kellis 2012; (3) there are few resources integrating genetic variant databases and expression profile information (Yang et al 2010, Holm, Melum, Franke, & Karlsen, 2010, though there is a recent effort in providing tissue-specific gene expression and genotype information (GTEx Consortium, 2015); and (4) the absence of the quantification of the impact of multiple TFBSs of the same TF in the regulatory region (also called homotypic redundancy) , Bryzgalov et al, 2013, Macintyre et al, 2010, Teng et al, 2012, Ward & Kellis 2012, which is an important feature in the regulation of gene expression (Gotea et al, 2010, Spivakov et al, 2012. This is because it has been suggested that the more TFBSs for a single TF in the same region, the less impact a single TFBS perturbation will have (Sharon et al 2012, Smith et al 2013.…”

A CpG-SNP Located within the <b><i>ARPC3</i></b> Gene Promoter Is Associated with Hypertriglyceridemia in Severely Obese Patients

“…To investigate potential regulatory SNPs, we used sTRAP [27] , which calculates the difference in binding affinity between common and rare allele-containing sequences. Sequences overlapping a differentially methylated CpG site (5 bp before and after) were used as input sequence.…”