A compendium of promoter-centered long-range chromatin interactions in the human genome

Model-based Analysis of ChIP-Seq (MACS)

Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386

A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping

Rao, S. S. P., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., Robinson, J. T., Sanborn, A. L., Machol, I., Omer, A. D., Lander, E. S., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.021 · Cited by 9,212

Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome

Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) · Science · doi:10.1126/science.1181369 · Cited by 8,898

Topological domains in mammalian genomes identified by analysis of chromatin interactions

Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J. S., & Ren, B. (2012) · Nature · doi:10.1038/nature11082 · Cited by 6,829

Integrative analysis of 111 reference human epigenomes

Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368

The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans

The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062

Master Transcription Factors and Mediator Establish Super-Enhancers at Key Cell Identity Genes

Whyte, W. A., Orlando, D. A., Hnisz, D., Abraham, B. J., Lin, C. Y., Kagey, M. H., Rahl, P. B., Lee, T. I., & Young, R. A. (2013) · Cell · doi:10.1016/j.cell.2013.03.035 · Cited by 4,143

Potential etiologic and functional implications of genome-wide association loci for human diseases and traits

Hindorff, L. A., Sethupathy, P., Junkins, H. A., Ramos, E. M., Mehta, J. P., Collins, F. S., & Manolio, T. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0903103106 · Cited by 4,013

Systematic Localization of Common Disease-Associated Variation in Regulatory DNA

Maurano, M. T., Humbert, R., Rynes, E., Thurman, R. E., Haugen, E., Wang, H., Reynolds, A. P., Sandstrom, R., Qu, H., Brody, J., et al. (2012) · Science · doi:10.1126/science.1222794 · Cited by 3,716

The NHGRI GWAS Catalog, a curated resource of SNP-trait associations

Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., Hindorff, L., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1229 · Cited by 2,707

Clinical and immunological signatures of severe COVID-19 in previously healthy patients with clonal hematopoiesisPreprint

Kang, C. K., Choi, B., Kim, S., Park, S., Yoon, S. H., Lee, D., Lee, A., Ko, Y., Chang, E., Jung, J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.05.463271 · Cited by 1

Sex significantly impacts the function of major depression-linked variants in vivoPreprint

Mulvey, B., Selmanovic, D., & Dougherty, J. D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.01.466849 · Cited by 1

Learning the histone codes of gene regulation with large genomic windows and three-dimensional chromatin interactions using transformerPreprint

Lee, D., Yang, J., & Kim, S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.30.472333 · Cited by 1

Low input capture Hi-C (liCHi-C) identifies promoter-enhancer interactions at high-resolutionPreprint

Tomás-Daza, L., Rovirosa, L., López-Martí, P., Nieto-Aliseda, A., Serra, F., Planas‐Riverola, A., Molina, Ò., McDonald, R., Ghevaert, C., Cuatrecasas, E., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.04.515239 · Cited by 1

Genome-wide analysis of heart failure yields insights into disease heterogeneity and enables prognostic prediction in the Japanese populationPreprint

Enzan, N., Miyazawa, K., Koyama, S., Kurosawa, R., Ieki, H., Yoshida, H., Takechi, F., Fukuyama, M., Osako, R., Tomizuka, K., et al. (2024) · medRxiv · doi:10.1101/2024.11.14.24317249 · Cited by 1

Heterogeneous epigenetic variation converges on splicing dysregulation in opioid addictionPreprint

Qureshi, F. A., Apere, C., Okeke, C., Kassim, B. S., Iskhakova, M., Sallari, R. C., Chakraborty, M., Morgan, L., Barnard, Z., Luna, X., et al. (2025) · medRxiv · doi:10.64898/2025.12.20.25342745 · Cited by 1

A meta-analysis of clinically ascertained lipoedema cohorts from the UK and Spain identifies overlapping susceptibility loci with the UK BiobankPreprint

Dobbins, S. E., Forner‐Cordero, I., Amigo, R., Southgate, L., Hobbs, K., Moy, R., Adjei, M., Muntané, G., Vilella, E., Martorell, L., et al. (2026) · medRxiv · doi:10.64898/2026.02.11.26345915 · Cited by 1

Regulatory plasticity within a complex cytokine-sensing mammary enhancer during lactationPreprint

Lee, H., Liu, C., & Hennighausen, L. G. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.04.134429

Demystifying Cancer Etiology via 3D Genome Mapping

Feng, Y., & Pauklin, S. (2020) · Trends in Genetics · doi:10.1016/j.tig.2020.06.001

RNA Binding Proteins Shape Latent KSHV Genomic Structure: Identification of KSHV Episome Tethering Sites on Host ChromosomesPreprint

Kumar, A., Lyu, Y., Yanagihashi, Y., Chantarasrivong, C., Majerčiak, V., Salemi, M., Wang, K.-H., Chuang, F. Y. S., Davis, R. R., Tepper, C. G., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-136834/v1

3DFAACTS-SNP: Using Regulatory T Cell-specific Epigenomics Data to Uncover Candidate Mechanisms of Type-1 Diabetes (T1D) riskPreprint

Liu, N., Sadlon, T. J., Wong, Y. Y., Pederson, S., Breen, J., & Barry, S. C. (2021) · Research Square · doi:10.21203/rs.3.rs-1175572/v1

β-catenin programs a tissue-specific epigenetic vulnerability in aggressive adrenocortical carcinomaPreprint

Mohan, D. R., Borges, K. S., Finco, I., LaPensee, C. R., Rege, J., Solon, A. L., Little, D. W., Else, T., Almeida, M. Q., Dang, D., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.02.497654

Genetic Risk Factors for ME/CFS Identified using Combinatorial AnalysisPreprint

Das, S., Taylor, K., Kozubek, J. A., Sardell, J. M., & Gardner, S. (2022) · Research Square · doi:10.21203/rs.3.rs-2168850/v1

Development of digital Hi-C assayPreprint

Mori, A., & Schweikert, G. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.31.522397

TCF4 Is a Key Mediator of Cell Identity and Oncogenesis in Neuroblastoma

Aljouda, N. A. (2023) · doi:10.21007/etd.cghs.2023.0627

scHi-CNN: a Computational Method for Statistically Significant Single-cell Hi-C Chromatin Interactions with Nearest Neighbors

Bulathsinghalage, C., & Liu, L. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385614

Additional file 3 of Hi-C profiling of cancer spheroids identifies 3D-growth-specific chromatin interactions in breast cancer endocrine resistance

Li, J., Fang, K., Choppavarapu, L., Yang, K., Yang, Y., Wang, J., Cao, R., Jatoi, I., & Jin, V. X. (2021) · Figshare · doi:10.6084/m9.figshare.16639797

Additional file 5 of Chromatin architecture reveals cell type-specific target genes for kidney disease risk variants

Duan, A., Wang, H., Zhu, Y., Wang, Q., Zhang, J., Hou, Q., Xing, Y., Shi, J., Hou, J., Qin, Z. S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14111126

Additional file 3 of Chromatin architecture reveals cell type-specific target genes for kidney disease risk variants

Duan, A., Wang, H., Zhu, Y., Wang, Q., Zhang, J., Hou, Q., Xing, Y., Shi, J., Hou, J., Qin, Z. S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14111120

Additional file 2 of Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data

Qin, T., Lee, C., Li, S., Cavalcante, R. G., Orchard, P., Yao, H., Zhang, H., Wang, S., Patil, S. B., Boyle, A. P., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19663872

Additional file 4 of Chromatin architecture reveals cell type-specific target genes for kidney disease risk variants

Duan, A., Wang, H., Zhu, Y., Wang, Q., Zhang, J., Hou, Q., Xing, Y., Shi, J., Hou, J., Qin, Z. S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14111123

Additional file 4 of 3DFAACTS-SNP: using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of type 1 diabetes (T1D) risk

Liu, N., Sadlon, T. J., Wong, Y. Y., Pederson, S., Breen, J., & Barry, S. C. (2022) · Figshare · doi:10.6084/m9.figshare.20206409

Additional file 4 of Hi-C profiling of cancer spheroids identifies 3D-growth-specific chromatin interactions in breast cancer endocrine resistance

Li, J., Fang, K., Choppavarapu, L., Yang, K., Yang, Y., Wang, J., Cao, R., Jatoi, I., & Jin, V. X. (2021) · Figshare · doi:10.6084/m9.figshare.16639803

Additional file 3 of 3DFAACTS-SNP: using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of type 1 diabetes (T1D) risk

Liu, N., Sadlon, T. J., Wong, Y. Y., Pederson, S., Breen, J., & Barry, S. C. (2022) · Figshare · doi:10.6084/m9.figshare.20206406

Additional file 2 of 3DFAACTS-SNP: using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of type 1 diabetes (T1D) risk

Liu, N., Sadlon, T. J., Wong, Y. Y., Pederson, S., Breen, J., & Barry, S. C. (2022) · Figshare · doi:10.6084/m9.figshare.20206403

Additional file 2 of Hi-C profiling of cancer spheroids identifies 3D-growth-specific chromatin interactions in breast cancer endocrine resistance

Li, J., Fang, K., Choppavarapu, L., Yang, K., Yang, Y., Wang, J., Cao, R., Jatoi, I., & Jin, V. X. (2021) · Figshare · doi:10.6084/m9.figshare.16639794

Additional file 6 of Chromatin architecture reveals cell type-specific target genes for kidney disease risk variants

Duan, A., Wang, H., Zhu, Y., Wang, Q., Zhang, J., Hou, Q., Xing, Y., Shi, J., Hou, J., Qin, Z. S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14111129

Additional file 2 of Chromatin architecture reveals cell type-specific target genes for kidney disease risk variants

Duan, A., Wang, H., Zhu, Y., Wang, Q., Zhang, J., Hou, Q., Xing, Y., Shi, J., Hou, J., Qin, Z. S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14111117

Additional file 7 of Chromatin architecture reveals cell type-specific target genes for kidney disease risk variants

Duan, A., Wang, H., Zhu, Y., Wang, Q., Zhang, J., Hou, Q., Xing, Y., Shi, J., Hou, J., Qin, Z. S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14111132

A technique for preserving network structure in randomized Hi-C data

Sizovs, A., Melkus, G., Ručevskis, P., Silina, S., Lāce, L., Celms, E., & Vīksna, J. (2024) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720024400018

Identification of Novel Gastric Adenocarcinoma Susceptibility Loci Via Genome-Wide Association Analysis in West Asians and Large-Scale Replication in East AsiansPreprint

Latifi‐Navid, S., Abdi, E., Wang, T., Pourfarzi, F., Yazdanbod, A., Salami, S. A., Safaralizadeh, R., Amjadi, O., Latifi‐Navid, H., Safaralizadeh, B., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4819283

Identification of multi-cancer risk genes

Rivera, S. (2024) · Queensland University of Technology · doi:10.5204/thesis.eprints.248736

Exploration and Visualization Methods for Chromatin Interaction Data

Sizovs, A., Silina, S., Melkus, G., Ručevskis, P., Lāce, L., Celms, E., & Vīksna, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5128-0_9

CHARMER: detecting and harmonizing high-confidence chromatin interactions across tissues and Hi-C protocolsPreprint

Cole, S., Kuksa, P. P., Cifello, J., Valladares, O., Leung, Y. Y., & Wang, L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.25.625258

Biologically targeted discovery-replication scan identifies G×G interaction in relation to risk of Barrett’s esophagus and esophageal adenocarcinoma

Yan, L., He, Q., Verma, S. P., Zhang, X., Giel, A.-S., Maj, C., Graz, K., Naderi, E., Chen, J., Ali, M. W., et al. (2025) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2025.100399

Insufficient gene expression and lost gene regulatory network may underlie the mechanism of Hirschsprung Disease in 5p–syndrome

Luan, Y., Li, P., Luo, Y., Zhang, H., Zhu, X.-C., Zhang, Y., Yin, A., Wu, Q., & Chai, C. (2025) · Heliyon · doi:10.1016/j.heliyon.2025.e42079

GEM-Finder: Dissecting GWAS Variants via Long-Range Interacting Cis-Regulatory Elements with Differentiationspecific Genes

Park, G., Lee, A., & Jung, I. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-90252-9_26

Gene Expression Variability Linked to Chromatin Clique Configurations and cis-Regulatory Elements

Melkus, G., Sizovs, A., Silina, S., Ručevskis, P., Lāce, L., Celms, E., & Vīksna, J. (2024) · International Conference on Computational Biology and Bioinformatics (ICCBB) · doi:10.1145/3715020.3715050

A gene regulatory element modulates myosin expression and controls cardiomyocyte response to stressPreprint

Anglen, T., Kaplow, I. M., Choi, B., Dewars, E. R., Perelli, R. M., Hagy, K. T., Tran, D., Ramaker, M. E., Shah, S. H., Jung, I., et al. (2025) · Genome Research · doi:10.1101/gr.280825.125

Advances in dietary bioactives: Unlocking chromatin accessibility

Huang, W., Lin, S., Yuan, Y., Farag, M. A., Wang, H., & Xiao, J. (2025) · Phytochemistry Reviews · doi:10.1007/s11101-025-10196-5

A combined genomic arrhythmia propensity score delineates cumulative risk

Monroe, T. O., Puckelwartz, M. J., Pesce, L. L., Kearns, S. D., Page, P., Ibrahim, N., Weber, Z., Ugwor, E. I., Nóbrega, M. A., Kannankeril, P. J., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102455

ETNet: an interpretable transformer framework for enhancer–enhancer interaction prediction with cross-context transferability

Wang, S., Chen, T., Yang, Z., Liang, Z., & Shen, Y. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf634

Additional file 1 of 3DFAACTS-SNP: using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of type 1 diabetes (T1D) risk

Liu, N., Sadlon, T. J., Wong, Y. Y., Pederson, S., Breen, J., & Barry, S. C. (2022) · Figshare · doi:10.6084/m9.figshare.20206400

Additional file 1 of Genomics and epigenetics guided identification of tissue-specific genomic safe harbors

Shrestha, D., Bag, A., Wu, R., Zhang, Y., Tang, X., Qi, Q., Xing, J., & Cheng, Y. (2022) · Figshare · doi:10.6084/m9.figshare.21184212

Additional file 3 of Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data

Qin, T., Lee, C., Li, S., Cavalcante, R. G., Orchard, P., Yao, H., Zhang, H., Wang, S., Patil, S., Boyle, A. P., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19663875

Additional file 1 of Integrative epigenomic and high-throughput functional enhancer profiling reveals determinants of enhancer heterogeneity in gastric cancer

Sheng, T., Ho, S. W. T., Ooi, W. F., Xu, C., Xing, M., Padmanabhan, N., Huang, K. K., Ma, L., Ray, M., Guo, Y. A., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16786857

Additional file 10 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

Roller, M., Stamper, E. L., Villar, D., Izuogu, O. G., Martin, F., Redmond, A. M., Ramachanderan, R., Harewood, L., Odom, D. T., & Flicek, P. (2021) · Figshare · doi:10.6084/m9.figshare.14059943

Additional file 1 of Genetic risk factors for ME/CFS identified using combinatorial analysis

Das, S., Taylor, K., Kozubek, J. A., Sardell, J., & Gardner, S. (2024) · Figshare · doi:10.6084/m9.figshare.26554556

Additional file 1 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

Roller, M., Stamper, E. L., Villar, D., Izuogu, O. G., Martin, F., Redmond, A. M., Ramachanderan, R., Harewood, L., Odom, D. T., & Flicek, P. (2021) · Figshare · doi:10.6084/m9.figshare.14059946

Additional file 1 of Integration of single-cell multi-omics data by regression analysis on unpaired observations

Yuan, Q., & Duren, Z. (2022) · Figshare · doi:10.6084/m9.figshare.20342394

Additional file 1 of Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data

Qin, T., Lee, C., Li, S., Cavalcante, R. G., Orchard, P., Yao, H., Zhang, H., Wang, S., Patil, S., Boyle, A. P., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19663869

Additional file 1 of INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants

Dong, C., Simonett, S. P., Shin, S., Stapleton, D. S., Schueler, K. L., Churchill, G. A., Lu, L., Liu, X., Jin, F., Li, Y., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16417367

Additional file 1 of Functional and genetic determinants of mutation rate variability in regulatory elements of cancer genomes

Lee, C. A., Abd-Rabbo, D., & Reimand, J. (2021) · Figshare · doi:10.6084/m9.figshare.14532252

Additional file 1 of Chromatin architecture reveals cell type-specific target genes for kidney disease risk variants

Duan, A., Wang, H., Zhu, Y., Wang, Q., Zhang, J., Hou, Q., Xing, Y., Shi, J., Hou, J., Qin, Z., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14111114

Additional file 3 of Functional and genetic determinants of mutation rate variability in regulatory elements of cancer genomes

Lee, C. A., Abd-Rabbo, D., & Reimand, J. (2021) · Figshare · doi:10.6084/m9.figshare.14532258

Additional file 3 of Integration of single-cell multi-omics data by regression analysis on unpaired observations

Yuan, Q., & Duren, Z. (2022) · Figshare · doi:10.6084/m9.figshare.20342400

Additional file 2 of Integrative epigenomic and high-throughput functional enhancer profiling reveals determinants of enhancer heterogeneity in gastric cancer

Sheng, T., Ho, S. W. T., Ooi, W. F., Xu, C., Xing, M., Padmanabhan, N., Huang, K. K., Ma, L., Ray, M., Guo, Y. A., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16786860

Additional file 10 of Genomics and epigenetics guided identification of tissue-specific genomic safe harbors

Shrestha, D., Bag, A., Wu, R., Zhang, Y., Tang, X., Qi, Q., Xing, J., & Cheng, Y. (2022) · Figshare · doi:10.6084/m9.figshare.21184209

Additional file 1 of Hi-C profiling of cancer spheroids identifies 3D-growth-specific chromatin interactions in breast cancer endocrine resistance

Li, J., Fang, K., Choppavarapu, L., Yang, K., Yang, Y., Wang, J., Cao, R., Jatoi, I., & Jin, V. X. (2021) · Figshare · doi:10.6084/m9.figshare.16639791

Additional file 2 of Integration of single-cell multi-omics data by regression analysis on unpaired observations

Yuan, Q., & Duren, Z. (2022) · Figshare · doi:10.6084/m9.figshare.20342397

Additional file 2 of INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants

Dong, C., Simonett, S. P., Shin, S., Stapleton, D. S., Schueler, K. L., Churchill, G. A., Lu, L., Liu, X., Jin, F., Li, Y., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16417370

A compendium of chromatin interaction maps in the Giant Panda genome

Liu, P., Zhang, J., Cai, K., Wang, J., Liu, H., Zhang, L., Xu, W. F., Liu, Y., Shen, F., Hou, R., et al. (2026) · Communications Biology · doi:10.1038/s42003-026-09522-0

Genomics link obesity and type 2 diabetes to Alzheimer’s disease to unveil novel biological insightsPreprint

Cunha, C., García‐Ureña, M., Martínez, R., Romero-Lado, M. J., Fernández, M. V., Andreassen, O. A., Sims, R., Tsolaki, M., Sleegers, K., Hiltunen, M. O., et al. (2026) · medRxiv · doi:10.64898/2026.02.10.26344393

Genetic influences on haematopoiesis

Poeschla, M., & Sankaran, V. G. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-00947-1 · Cited by 1

Dissection of coordinated interferon response by promoter elements with enhancer activity

Santiago‐Algarra, D. (2020) · theses.fr (ABES) · doi:10.70675/a064ee17zbea2z412azb9b6z12839deb2851

Comprehensive mapping of human genetic variation at Epromoters

Wan, J. (2024) · theses.fr (ABES) · doi:10.70675/a2e4bbffze012z41baz8c31z2f84f271295e

Genetic Architecture and Myocardial Fibrotic Remodeling in Mitral Valve ProlapsePreprint

Small, A. M., Yu, M., Berrandou, T.-E., Georges, A. B., Huff, M. L., Morningstar, J. E., Rand, S. A., Koyama, S., Lee, J., Vy, H. M. T., et al. (2026) · medRxiv · doi:10.64898/2026.04.09.26350328

Hypertrophic cardiomyopathy: a genome-wide association meta-analysis and polygenic risk score

Lopes, L. R., Aung, N., van Duijvenboden, S., Nicholls, H., Burns, R., Jager, J., Lorenzini, M., Akhtar, M. M., Protonotarios, A., Barbeito‐Caamaño, C., et al. (2026) · Journal of Medical Genetics · doi:10.1136/jmg-2026-111722

Functionally informed annotation influences pathway-specific polygenic risk and disease inference in Alzheimer’s diseasePreprint

Bazemore, K., Iqbal, T., Kuzma, A. B., Grant, S. F. A., Schellenberg, G. D., Wang, L., Chesi, A., Jin, J., & Naj, A. C. (2026) · medRxiv · doi:10.64898/2026.05.25.26353905

Topological Patterns of 3-Cliques in Chromatin Interaction Networks

Vīksna, J., Sizovs, A., Melkus, G., Ručevskis, P., Celms, E., Lāce, L., & Silina, S. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-34456-4_8

mV2G: a multiomic atlas for tissue-specific variant-to-gene prioritization

Zhang, D. Y., Zhou, H., Sheth, M. U., Gschwind, A. R., Engreitz, J., Lin, X., & Liu, H. (2026) · Nucleic Acids Research · doi:10.64898/2026.08.11.743997

Origins and consequences of oncogenic 3D chromatin remodelling

Franceschini, G. M., Georgakopoulou, K., Ciriello, G., & Oricchio, E. (2026) · Nature reviews. Cancer · doi:10.1038/s41568-026-00969-1

Epromoter 3D interaction-associated regulation in T-acute Lymphoblastic LeukemiaPreprint

Paixão, J. F. R., Manosalva, I., Pinton, A., Cieślak, A., Cardone, C., Sakakini, N., Sadouni, N., Zanzoni, A., Andrieu, G. P., Asnafi, V., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.25.746923

Functional Annotation of Alzheimer’s Disease-Associated SNPs Based on Three-Dimensional Chromatin Structure

Li, H., Wang, Z., Liu, X., & Sun, X. S. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27188396

A Hybrid Large Language Model and Diffusion Framework for Rational Promoter Design

Lei, X., Wang, X., Liu, Y. T., Zhang, Y., Huang, J., Peng, T., Liang, C., Li, Q., Jiang, H., & Xiong, W. (2026) · ACS Synthetic Biology · doi:10.1021/acssynbio.6c00128

Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters

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