Methods that analyze single-cell paired RNA-seq and ATAC-seq multiome data have shown great promise in linking regulatory elements to genes. However, existing methods differ in their modeling assumptions and approaches to account for biological and technical noise-leading to low concordance in their linking scores-and do not capture the effects of genomic distance. We propose pgBoost, an integrative modeling framework that trains a non-linear combination of existing linking strategies (including genomic distance) on fine-mapped eQTL data to assign a probabilistic score to each candidate SNP-gene link. We applied pgBoost to single-cell multiome data from 85k cells representing 6 major immune/blood cell types. pgBoost attained higher enrichment for fine-mapped eSNP-eGene pairs (e.g. 21x at distance >10kb) than existing methods (1.2-10x; p-value for difference = 5e-13 vs. distance-based method and < 4e-35 for each other method), with larger improvements at larger distances (e.g. 35x vs. 0.89-6.6x at distance >100kb; p-value for difference < 0.002 vs. each other method). pgBoost also outperformed existing methods in enrichment for CRISPR-validated links (e.g. 4.8x vs. 1.6-4.1x at distance >10kb; p-value for difference = 0.25 vs. distance-based method and < 2e-5 for each other method), with larger improvements at larger distances (e.g. 15x vs. 1.6-2.5x at distance >100kb; p-value for difference < 0.009 for each other method). Similar improvements in enrichment were observed for links derived from Activity-By-Contact (ABC) scores and GWAS data. We further determined that restricting pgBoost to features from a focal cell type improved the identification of SNP-gene links relevant to that cell type. We highlight several examples where pgBoost linked fine-mapped GWAS variants to experimentally validated or biologically plausible target genes that were not implicated by other methods. In conclusion, a non-linear combination of linking strategies, including genomic distance, improves power to identify target genes underlying GWAS associations.

The authors have declared no competing interest.

This research was funded by NIH grants U01 HG012009, R56 HG013083, R01 MH101244, R37 MH107649, R01 HG006399, and R01 MH115676.

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All single-cell multiome data sets analyzed are publicly available. The 10x PBMC data set is available at https://www.10xgenomics.com/datasets. The Luecken BMMC and SHARE-Seq LCL data sets are available at Gene Expression Omnibus (accession codes GSE194122 and GSE140203, respectively). Linking scores and percentiles for pgBoost and constituent methods have been made publicly available at 10.5281/zenodo.11211926. Fine-mapped eQTL data are available at https://www.finucanelab.org/data. ABC scores are available on the ENCODE portal (https://www.encodeproject.org/). Biosample IDs and file accessions are listed in Supplementary Table 7. The CRISPR data set is available at https://github.com/EngreitzLab/CRISPR_comparison/blob/main/resources/crispr_data/EPCrisprBenchmark_ensemble_data_GRCh38.tsv.gz. GWAS derived SNP gene links are available at https://github.com/Deylab999/GWAS_benchmark_IGVF/blob/main/UKBiobank.ABCGene.anyabc.tsv. GWAS fine-mapping results are available at https://www.finucanelab.org/data.