Identification of Breast Cancer Associated Variants That Modulate Transcription Factor Binding
GWAS have discovered thousands of genomic loci that are associated with disease risk and quantitative traits, but most of the variants responsible for risk remain uncharacterized. The vast majority of GWAS-identified loci contain non-coding SNPs and defining molecular mechanism of risk is challenging. Many non-coding causal SNPs are hypothesized to alter Transcription Factor (TF) binding sites as the mechanism by which they affect organismal phenotypes. We employed an integrative genomics approa...
Provenance — who produced it, who reused it
Linked to 5 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
4 further papers cite this accession but reuse could not be confirmed.
Deep data QC
metadata only · no data-level QC for this typeStandardized, field-standard QC computed by touching the data — every metric states how it was obtained
No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently
Scientific quality
Based on hands-on reproduction of the papers that use this dataset. A reproducible paper that stands on this data is positive evidence; a flagged one is a prompt to look closer — never a verdict on the dataset itself without the evidence.