.. using paired multiome data without QTL information. This improvement further enhanced gene regulatory network inference, leading to the identification of 128 additional transcription factor (TF)–target gene pairs (a 22% increase), some of which show druggable potential. (11/n)

The genetic impact on chromatin accessibility not only shows cell type-specific patterns but also varies across cell states. We further detected 3,080 caQTLs whose allelic effects showed significant interaction with epi-genetic age. (9/n)

Next, we ask why do GWAS hits often miss eQTLs? We integrated 60 GWAS from disease and blood traits with eQTLs and caQTLs and found caQTL integration yields 9.8–30% more colocalizations than eQTLs alone, particularly at distal elements or loci with multiple causal variants. (7/n)

We highlight an interesting example where a chromatin peak chr10:45592479-45592785 shows a negative effect on the gene expression level of MARCH8 in NK cells but a positive effect in Conventional Dendritic Cell 2 (cDC2). (6/n)

More than half of caQTLs show cell type–specific patterns. For example, the chromatin peak chr13:24670806–24672096 contains caQTLs in CD4 TCM and CD14 monocytes, and their top variants (13:24671328:T:C in CD4 TCM and 13:24570579:C:A in CD14 Mono) are independent (LD ≈ 0). (4/n)

We curated one of the largest population-level (n = 1,042) scATAC-seq data from peripheral blood with WGS data, which enabled us to characterize 440,996 chromatin peaks across 28 immune cell types and mapped 243,273 caQTLs. (3/n)