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dc.contributor.authorBuchkovich, Martin L
dc.contributor.authorEklund, Karl
dc.contributor.authorDuan, Qing
dc.contributor.authorLi, Yun
dc.contributor.authorMohlke, Karen L
dc.contributor.authorFurey, Terrence S
dc.date.accessioned2015-09-01T17:53:16Z
dc.date.available2015-09-01T17:53:16Z
dc.date.issued2015-07-26
dc.identifier.citationBMC Medical Genomics. 2015 Jul 26;8(1):43
dc.identifier.urihttp://dx.doi.org/10.1186/s12920-015-0117-x
dc.identifier.urihttp://hdl.handle.net/10724/31900
dc.description.abstractAbstract Background Genetic variation can alter transcriptional regulatory activity contributing to variation in complex traits and risk of disease, but identifying individual variants that affect regulatory activity has been challenging. Quantitative sequence-based experiments such as ChIP-seq and DNase-seq can detect sites of allelic imbalance where alleles contribute disproportionately to the overall signal suggesting allelic differences in regulatory activity. Methods We created an allelic imbalance detection pipeline, AA-ALIGNER, to remove reference mapping biases influencing allelic imbalance detection and evaluate accuracy of allelic imbalance predictions in the absence of complete genotype data. Using the sequence aligner, GSNAP, and varying amounts of genotype information to remove mapping biases we investigated the accuracy of allelic imbalance detection (binomial test) in CREB1 ChIP-seq reads from the GM12878 cell line. Additionally we thoroughly evaluated the influence of experimental and analytical parameters on imbalance detection. Results Compared to imbalances identified using complete genotypes, using imputed partial sample genotypes, AA-ALIGNER detected >95 % of imbalances with >90 % accuracy. AA-ALIGNER performed nearly as well using common variants when genotypes were unknown. In contrast, predicting additional heterozygous sites and imbalances using the sequence data led to >50 % false positive rates. We evaluated effects of experimental data characteristics and key analytical parameter settings on imbalance detection. Overall, total base coverage and signal dispersion across the genome most affected our ability to detect imbalances, while parameters such as imbalance significance, imputation quality thresholds, and alignment mismatches had little effect. To assess the biological relevance of imbalance predictions, we used electrophoretic mobility shift assays to functionally test for predicted allelic differences in CREB1 binding in the GM12878 lymphoblast cell line. Six of nine tested variants exhibited allelic differences in binding. Two of these variants, rs2382818 and rs713875, are located within inflammatory bowel disease-associated loci. Conclusions AA-ALIGNER accurately detects allelic imbalance in quantitative sequence data using partial genotypes or common variants filling a critical methodological gap in these analyses, as full genotypes are rarely available. Importantly, we demonstrate how experimental and analytical features impact imbalance detection providing guidance for similar future studies.
dc.titleRemoving reference mapping biases using limited or no genotype data identifies allelic differences in protein binding at disease-associated loci
dc.typeJournal Article
dc.date.updated2015-07-29T18:57:57Z
dc.language.rfc3066en
dc.rights.holderBuchkovich et al.


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