Background: Genetic variant landscape of coronary artery disease is dominated by noncoding variants among which many occur within putative enhancers regulating the expression levels of relevant genes. It is crucial to assign the genetic variants to their correct genes both to gain insights into perturbed functions and better assess the risk of disease. Methods: In this study, we generated high-resolution genomic interaction maps (≈750 bases) in aortic endothelial, smooth muscle cells and THP-1 (human leukemia monocytic cell line) macrophages stimulated with lipopolysaccharide using Hi-C coupled with sequence capture targeting 25 429 features, including variants associated with coronary artery disease. We also sequenced their transcriptomes and mapped putative enhancers using chromatin immunoprecipitation with an antibody against H3K27Ac. Results: The regions interacting with promoters showed strong enrichment for enhancer elements and validated several previously known interactions and enhancers. We detected interactions for 727 risk variants obtained by genome-wide association studies and identified novel, as well as established genes and functions associated with cardiovascular diseases. We were able to assign potential target genes for additional 398 genome-wide association studies variants using haplotype information, thereby identifying additional relevant genes and functions. Importantly, we discovered that a subset of risk variants interact with multiple promoters and their expression levels were strongly correlated. Conclusions: In summary, we present a catalog of candidate genes regulated by coronary artery disease–related variants and think that it will be an invaluable resource to further the investigation of cardiovascular pathologies and disease.

Linked-read sequencing promises a one-method approach for genome-wide insights including single nucleotide variants (SNVs), structural variants, and haplotyping. We introduce Barcode Linked Reads (BLR), an open-source haplotyping pipeline capable of handling millions of barcodes and data from multiple linked-read technologies including DBS, 10× Genomics, TELL-seq and stLFR. Running BLR on DBS linked-reads yielded megabase-scale phasing with low (<0.2%) switch error rates. Of 13616 protein-coding genes phased in the GIAB benchmark set (v4.2.1), 98.6% matched the BLR phasing. In addition, large structural variants showed concordance with HPRC-HG002 reference assembly calls. Compared to diploid assembly with PacBio HiFi reads, BLR phasing was more continuous when considering switch errors. We further show that integrating long reads at low coverage (∼10×) can improve phasing contiguity and reduce switch errors in tandem repeats. When compared to Long Ranger on 10× Genomics data, BLR showed an increase in phase block N50 with low switch-error rates. For TELL-Seq and stLFR linked reads, BLR generated longer or similar phase block lengths and low switch error rates compared to results presented in the original publications. In conclusion, BLR provides a flexible workflow for comprehensive haplotype analysis of linked reads from multiple platforms.

Element Biosciences’ avidity sequencing has emerged as a competing technology to Illumina’s short read sequencing platform. Prior benchmarks of avidity sequencing have not included the latest Illumina NovaSeq X/X Plus instruments with XLEAP chemistry. Here, we have run PCR-free whole genome sequencing on four human tumor cell lines using both Illumina NovaSeq X Plus and Element AVITI instruments. AVITI showed low duplication rates and reported higher base qualities, the latter contributed to improved mapping confidence and fewer spurious variant candidates. Both platforms were found to be highly comparable when benchmarking variant calling, with AVITI only providing a minor improvement on INDELs at lower coverages. Stratifying by genomic context revealed further differences, where AVITI genome coverage and variant calls were superior in high GC regions while being inferior in GC homopolymers. Error rate analysis highlighted further differences between the platforms, in particular AVITI in some instances displayed an increased error rate on read 2 related to short fragments. AVITI error rate was also found to be more stable downstream of repetitive regions, except for GC homopolymers. We further found that AVITI sequencing was sensitive to G-quadruplex motifs. Overall, despite these identified differences, both platforms performed highly comparable for variant analysis.

Element Biosciences’ avidity sequencing has emerged as a competing technology to Illumina’s short-read sequencing platform. Prior benchmarks of avidity sequencing have not included the latest Illumina NovaSeq X/X Plus instruments with XLEAP chemistry. Here, we have run polymerase chain reaction-free whole-genome sequencing on four human tumor cell lines using both Illumina NovaSeq X Plus and Element AVITI instruments. AVITI showed low duplication rates and reported higher base qualities; the latter contributed to improved mapping confidence and fewer spurious variant candidates. Both platforms were found to be highly comparable when benchmarking variant calling, with AVITI only providing a minor improvement on INDELs at lower coverages. Stratifying by genomic context revealed further differences, where AVITI genome coverage and variant calls were superior in high-GC regions while being inferior in GC homopolymers. Error-rate analysis highlighted further differences between the platforms; in particular, AVITI in some instances displayed an increased error rate on read 2 related to short fragments. AVITI error rate was also found to be more stable downstream of repetitive regions, except for GC homopolymers. We further found that AVITI sequencing was sensitive to G-quadruplex motifs. Overall, despite these identified differences, both platforms performed highly comparable for variant analysis.

Tissue-resident macrophages receive signals from their microenvironment that coordinate the activity of transcription factors (TFs) to establish distinct transcriptional profiles and identities. However, the molecular mechanisms whereby interactions with other cells of the niche imprint a distinct macrophage identity remain poorly understood. Here, we report that retinoid X receptors (RXRs) determine the differentiation and identity of alveolar macrophages (AM) by regulating chromatin accessibility and transcriptional activity of AM-core and function genes, enabling PPARγ-dependent programs. AM differentiation and maintenance in vivo require RXR upregulation in response to tissue-derived δ-like canonical Notch ligand 4 (DLL4), GM-CSF, and TGF β (TGFβ). Interplay among these signals leads to cooperation between RXRα, RBPJ, STAT5, and SMAD4 for the transcriptional and epigenetic regulation of key AM-core genes. These results underscore the role of RXRs as key TFs that cooperate with other regulatory elements to establish the AM population and determine AM identity.

Precision medicine is increasingly applied in the cancer clinic, adapting treatment to genomic alterations of the tumor. However, whether alterations disrupt the function of a protein can depend on if both alleles of a gene are altered. While massively parallel sequencing technologies can identify sequence aberrations, they are limited in resolving the corresponding haplotype information. In this proof-of-concept case study, we applied the linked-read droplet barcode sequencing (DBS) technology to resolve the haplotype complexity of colorectal cancer genomes on paired tumor and normal samples. Several cancer-related genes carried multiple mutations in either one or both haplotypes. Additionally, a number of haplotype-resolved large structural variants and copy number alterations were detected and phased with short somatic variants. Nearly all characterized oncogenic pathways harbored some of the identified short somatic variants. The study demonstrates that linked-read DBS technology can characterize complex genetic variations in a haplotype context and may provide essential information for personalized approaches.