Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning

Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning Most genetic variants in the human genome reside in non-coding regions, where they can perturb regulatory element activity to influence gene expression, thereby contributing to various phenotypes and diseases. However, identifying functionally relevant non-coding genetic variation remains challenging. Here we integrate personal genomics, allele-specific gene regulation, and deep learning predictions to map the impact of non-coding variation in its native allelic and regulatory context. We identify and validate hundreds of cell-type-specific transcription factor binding events disrupted by genetic variants, revealing known and novel mechanisms that underlie allele-specific chromatin accessibility and gene expression. Using this framework, we discovered a rare variant that disrupted an OCT2 binding site within a distal enhancer, thereby modulating the expression of PIK3R5 gene. Our study establishes a generalisable strategy for interpreting non-coding regulatory variation, enabling systematic dissection of variant effects across diverse biological systems and offering a framework to investigate disease mechanisms. This dataset contains the following folders:- variants: selection of samples, variants data processing- 10X: 10X linked-reads data processing- HiC: Hi-C data processing- simulations: TELL-seq and stLFR data processing, data subsampling for phasing quality estimation- personal_genomes: whole-chromosome haplotyping, phased heterozygous variants, and personal genomes construction- ATACseq: ATAC-seq data processing, quantification, and analysis- TTseq: TT-seq data processing, quantification, and analysis- ChIPseq: ChIP-seq data processing- links: putative regulatory links analysis- ChromBPNet: trained models, predicted contribution scores, and disrupted motifs analysis- validation: validation data for the rs545467951 variant Sequencing data have been deposited at GEO database (GSE308298). Proteomics data have been deposited at PRIDE database (PXD069116). Supporting data and original code to reproduce the results presented in this manuscript have been deposited in this release on Zenodo and is also available on Github: https://github.com/magnitov/hap_phen.