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Removing reference mapping biases using limited or no genotype data identifies allelic differences in protein binding at disease-associated loci

Overview of attention for article published in BMC Medical Genomics, July 2015
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Title
Removing reference mapping biases using limited or no genotype data identifies allelic differences in protein binding at disease-associated loci
Published in
BMC Medical Genomics, July 2015
DOI 10.1186/s12920-015-0117-x
Pubmed ID
Authors

Martin L. Buchkovich, Karl Eklund, Qing Duan, Yun Li, Karen L. Mohlke, Terrence S. Furey

Abstract

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. 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. 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. 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.

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Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 6%
China 1 3%
Unknown 28 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 32%
Researcher 7 23%
Professor > Associate Professor 3 10%
Student > Master 3 10%
Unspecified 1 3%
Other 4 13%
Unknown 3 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 52%
Biochemistry, Genetics and Molecular Biology 9 29%
Unspecified 1 3%
Immunology and Microbiology 1 3%
Medicine and Dentistry 1 3%
Other 0 0%
Unknown 3 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 July 2015.
All research outputs
#3,805,014
of 5,407,305 outputs
Outputs from BMC Medical Genomics
#285
of 358 outputs
Outputs of similar age
#131,130
of 189,285 outputs
Outputs of similar age from BMC Medical Genomics
#25
of 27 outputs
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