The EN-TEx resource of multi-tissue personal epigenomes &amp; variant-impact models

Rozowsky, Joel; Gao, Jiahao; Borsari, Beatrice; Yang, Yucheng; Galeev, Timur R.; Gürsoy, Gamze; Epstein, Charles B.; Xiong, Kun; Xu, Jinrui; Li, Tianxiao; Liu, Jason; Yu, Keyang; Berthel, Ana; Chen, Zhanlin; Navarro, Fábio C. P.; Sun, Maxwell S.; Wright, James C.; Chang, Justin; Cameron, Christopher J.F.; Shoresh, Noam; Gaskell, Elizabeth; Drenkow, Jörg; Adrian, Jessika; Aganezov, Sergey; Aguet, François; Balderrama-Gutierrez, Gabriela; Banskota, Samridhi; Corona, Guillermo Barreto; Chee, Sora; Chhetri, Surya B.; Martins, Gabriel Conte Cortez; Danyko, Cassidy; Davis, Carrie A.; Farid, Daniel; Farrell, Nina; Gabdank, Idan; Gofin, Yoel; Gorkin, David U.; Gu, Mengting; Hecht, Vivian; Hitz, Benjamin; Issner, Robbyn; Jiang, Yunzhe; Kirsche, Melanie; Kong, Xiangmeng; Lam, Bonita R.; Li, Shantao; Li, Bian; Li, Xiqi; Lin, Khine; Luo, Ruibang; Mackiewicz, Mark; Wang, Mengqi; Moore, Jill E.; Mudge, Jonathan M.; Nelson, Nicholas M.; Nusbaum, Chad; Popov, Ioann; Pratt, Henry E.; Qiu, Yunjiang; Ramakrishnan, S.; Raymond, Joe; Salichos, Leonidas; Scavelli, Alexandra; Schreiber, Jacob; Sedlazeck, Fritz J.; See, Lei Hoon; Sherman, Rachel M.; Shi, Xu; Shi, Minyi; Sloan, Cricket A.; Strattan, J. Seth; Tan, Zhen; Tanaka, Forrest; Vlasova, Anna; Wang, Jun; Werner, Jonathan; Williams, Brian A.; Xu, Min; Yan, Chengfei; Lu, Yu; Zaleski, Christopher; Zhang, Jing; Ardlie, Kristin; Cherry, J. Michael; Mendenhall, Eric M.; Noble, William Stafford; Weng, Zhiping; Levine, Morgan E.; Dobin, Alexander; Wold, B; Mortazavi, A; Ren, Bing; Gillis, Jesse; Myers, R; Snyder, M; Choudhary, Jyoti; Milosavljevic, Aleksandar; Schatz, Michael C.; Bernstein, B; Guigó, Roderic; Gingeras, T; Gerstein, Mark

doi:10.1016/j.cell.2023.02.018

Cited by 28 publications

(14 citation statements)

References 169 publications

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“…The binding sites of several cardiac TF markers (i.e., MEF2 TFs) were enriched in the 1,641 caPeaks that overlapped haPeaks, and the 2,593 caPeaks not overlapping haPeaks (Figure 2f). Our observations show that caPeaks harbor different predicted TFBSs than non-caPeaks, which is consistent with previous findings that regulatory variation impacts the binding of some TFs more than others 25,26 .…”

Section: Resultssupporting

confidence: 93%

See 1 more Smart Citation

Multi-omic QTL mapping in early developmental tissues reveals phenotypic and temporal complexity of regulatory variants underlying GWAS loci

Arthur,

Nguyen,

D’Antonio-Chronowska

et al. 2024

Preprint

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Most GWAS loci are presumed to affect gene regulation, however, only ∼43% colocalize with expression quantitative trait loci (eQTLs). To address this colocalization gap, we identify eQTLs, chromatin accessibility QTLs (caQTLs), and histone acetylation QTLs (haQTLs) using molecular samples from three early developmental (EDev) tissues. Through colocalization, we annotate 586 GWAS loci for 17 traits by QTL complexity, QTL phenotype, and QTL temporal specificity. We show that GWAS loci are highly enriched for colocalization with complex QTL modules that affect multiple elements (genes and/or peaks). We also demonstrate that caQTLs and haQTLs capture regulatory variations not associated with eQTLs and explain ∼49% of the functionally annotated GWAS loci. Additionally, we show that EDev-unique QTLs are strongly depleted for colocalizing with GWAS loci. By conducting one of the largest multi-omic QTL studies to date, we demonstrate that many GWAS loci exhibit phenotypic complexity and therefore, are missed by traditional eQTL analyses.

show abstract

Section: Resultssupporting

confidence: 93%

“…Recent studies have shown that some TFs are more likely than other TFs to have their binding sites impacted by variants 25,26 . To investigate if different TFs are bound to CVPC caPeaks and non-caPeaks (i.e., peaks not associated with a caQTL), we examined the 55,269 CVPC ATAC-seq peaks with at least one predicted TFBS.…”

Section: Resultsmentioning

confidence: 99%

Multi-omic QTL mapping in early developmental tissues reveals phenotypic and temporal complexity of regulatory variants underlying GWAS loci

Arthur,

Nguyen,

D’Antonio-Chronowska

et al. 2024

Preprint

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show abstract

“…Future improvements in DL models’ ability to predict interindividual differences could be achieved by incorporating cross-individual data in the training process. An example of such data is the EN-TEX(Rozowsky et al, 2023) dataset, which aligns functional genomic peaks with the specific individuals and haplotypes they correspond to.…”

Section: Discussionmentioning

confidence: 99%

Haplotype Function Score improves biological interpretation and cross-ancestry polygenic prediction of human complex traits

Song

Shi

Lin

2023

Preprint

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We proposed a new framework for human genetic association study: at each locus, use deep learning model (in this study, Sei) to calculate functional genomic activity score for two haplotypes per individual, and use this score, defined as haplotype function score (HFS), to replace original genotype in association study. Applying HFS framework to 14 complex traits in UK Biobank, we identified 3,619 independent HFS-trait association with p<5×10−8. Fine-mapping revealed 2,699 causal associations, corresponded to a median of 63 more causal findings per trait compared with SNP-based analysis. HFS-based enrichment analysis revealed 727 pathway-trait association and 153 tissue-trait association with strong biological interpretability, including “circadian pathway-chronotype” and “Arachnoid acid-intelligence”. Lastly, we trained neural network and applied transfer learning to integrate with SNP-based polygenic risk score, which showed 4.1% to 28.8% improvement in the cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding genetic basis of human complex traits.

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“…Despite the amount of attention SNV have garnered, they are not the only variant type that differentiates two genomes 5,6 . Recently, an increasing number of studies incorporate structural variation (SV) [7][8][9] into their analysis. SVs are often defined to be 50bp or larger and lead to deletions, insertions, amplifications, or rearrangements of a genome 7 .…”

Section: Introductionmentioning

confidence: 99%

Comprehensive and accurate genome analysis at scale using DRAGEN accelerated algorithms

Behera,

Catreux,

Rossi

et al. 2024

Preprint

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Research and medical genomics require comprehensive and scalable solutions to drive the discovery of novel disease targets, evolutionary drivers, and genetic markers with clinical significance. This necessitates a framework to identify all types of variants independent of their size (e.g., SNV/SV) or location (e.g., repeats). Here we present DRAGEN that utilizes novel methods based on multigenomes, hardware acceleration, and machine learning based variant detection to provide novel insights into individual genomes with ∼30min computation time (from raw reads to variant detection). DRAGEN outperforms all other state-of-the-art methods in speed and accuracy across all variant types (SNV, indel, STR, SV, CNV) and further incorporates specialized methods to obtain key insights in medically relevant genes (e.g., HLA, SMN, GBA). We showcase DRAGEN across 3,202 genomes and demonstrate its scalability, accuracy, and innovations to further advance the integration of comprehensive genomics for research and medical applications.

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The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

Cited by 28 publications

References 169 publications

Multi-omic QTL mapping in early developmental tissues reveals phenotypic and temporal complexity of regulatory variants underlying GWAS loci

Multi-omic QTL mapping in early developmental tissues reveals phenotypic and temporal complexity of regulatory variants underlying GWAS loci

Haplotype Function Score improves biological interpretation and cross-ancestry polygenic prediction of human complex traits

Comprehensive and accurate genome analysis at scale using DRAGEN accelerated algorithms

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