Functional dissection of complex and molecular trait variants at single nucleotide resolution
Siraj, L.; Castro, R. I.; Dewey, H.; Kales, S.; Nguyen, T. T. L.; Kanai, M.; Berenzy, D.; Mouri, K.; Wang, Q.; McCaw, Z. R.; Gosai, S. J.; Aguet, F.; Cui, R.; Vockley, C. M.; Lareau, C. A.; Okada, Y.; Gusev, A.; Jones, T. R.; Lander, E. S.; Sabeti, P. C.; Finucane, H. K.; Reilly, S. K.; Ulirsch, J. C.; Tewhey, R.
10.1101/2024.05.05.592437 bioRxivShow abstract
Identifying the causal variants and mechanisms that drive complex traits and diseases remains a core problem in human genetics. The majority of these variants have individually weak effects and lie in non-coding gene-regulatory elements where we lack a complete understanding of how single nucleotide alterations modulate transcriptional processes to affect human phenotypes. To address this, we measured the activity of 221,412 trait-associated variants that had been statistically fine-mapped using a Massively Parallel Reporter Assay (MPRA) in 5 diverse cell-types. We show that MPRA is able to discriminate between likely causal variants and controls, identifying 12,025 regulatory variants with high precision. Although the effects of these variants largely agree with orthogonal measures of function, only 69% can plausibly be explained by the disruption of a known transcription factor (TF) binding motif. We dissect the mechanisms of 136 variants using saturation mutagenesis and assign impacted TFs for 91% of variants without a clear canonical mechanism. Finally, we provide evidence that epistasis is prevalent for variants in close proximity and identify multiple functional variants on the same haplotype at a small, but important, subset of trait-associated loci. Overall, our study provides a systematic functional characterization of likely causal common variants underlying complex and molecular human traits, enabling new insights into the regulatory grammar underlying disease risk.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles 98%
- DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of enhancers 98%
- Genotyping sequence-resolved copy number variationusing pangenomes reveals paralog-specific global diversityand expression divergence of duplicated genes 98%
Similar papers in this journal
- Shared and distinct molecular effects of regulatory genetic variants provide insight into mechanisms of distal enhancer-promoter communication 98%
- A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders 98%
- Histone exchange sensors reveal variant specific dynamics in mouse embryonic stem cells 98%
Similar papers in this journal
- A genome-wide mutational constraint map quantified from variation in 76,156 human genomes 99%
- Massively parallel characterization of transcriptional regulatory elements in three diverse human cell types 98%
- A familial, telomere-to-telomere reference for human de novo mutation and recombination from a four-generation pedigree 97%
Similar papers in this journal
- Impact of disease-associated chromatin accessibility QTLs across immune cell types and contexts 98%
- Binding domain mutations provide insight into CTCF's relationship with chromatin and its contribution to gene regulation 97%
- Meta-analysis fine-mapping is often miscalibrated at single-variant resolution 97%
Similar papers in this journal
- Saturation mapping of MUTYH variant effects using DNA repair reporters 97%
- Misexpression of inactive genes in whole blood is associated with nearby rare structural variants 97%
- Characterization of non-coding variants associated with transcription factor binding through ATAC-seq-defined footprint QTLs in liver 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.