Simultaneous SNP Identification and Assessment of Allele-Specific Bias From ChiP-Seq Data
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Single nucleotide polymorphisms (SNPs) have been associated with many aspects of human development and disease, and many non-coding SNPs associated with disease risk are presumed to affect gene regulation. We have previously shown that SNPs within transcription factor binding sites can affect transcription factor binding in an allele-specific and heritable manner. However, such analysis has relied on prior whole-genome genotypes provided by large external projects such as HapMap and the 1000 Genomes Project. This requirement limits the study of allele-specific effects of SNPs in primary patient samples from diseases of interest, where complete genotypes are not readily available. Results: In this study, we show that we are able to identify SNPs de novo and accurately from ChIP-seq data generated in the ENCODE Project. Our de novo identified SNPs from ChIP-seq data are highly concordant with published genotypes. Independent experimental verification of more than 100 sites estimates our false discovery rate at less than 5%. Analysis of transcription factor binding at de novo identified SNPs revealed widespread heritable allele-specific binding, confirming previous observations. SNPs identified from ChIP-seq datasets were significantly enriched for disease-associated variants, and we identified dozens of allele-specific binding events in non-coding regions that could distinguish between disease and normal haplotypes. Conclusions: Our approach combines SNP discovery, genotyping and allele-specific analysis, but is selectively focused on functional regulatory elements occupied by transcription factors or epigenetic marks, and will therefore be valuable for identifying the functional regulatory consequences of non-coding SNPs in primary disease samples.