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Ulirsch, Jacob

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Ulirsch

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Jacob

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Ulirsch, Jacob

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Now showing 1 - 3 of 3
  • Publication

    Genome-Wide Enhancer Maps Link Risk Variants to Disease Genes

    (Springer Science and Business Media LLC, 2021-04-07) Nasser, Joseph; Bergman, Drew T.; Fulco, Charles P.; Guckelberger, Philine; Doughty, Benjamin; Patwardhan, Tejal A.; Jones, Thouis; Nguyen, Tung; Ulirsch, Jacob; Lekschas, Fritz; Mualim, Kristy; Natri, Heini M.; Weeks, Elle M.; Munson, Glen; Kane, Michael; Kang, Helen Y.; Cui, Ang; Ray, John P.; Eisenhaure, Thomas M.; Collins, Ryan; Dey, Kushal; Pfister, Hanspeter; Price, Alkes; Epstein, Charles; Kundaje, Anshul; Xavier, Ramnik; Daly, Mark; Huang, Hailiang; Finucane, Hilary; Hacohen, Nir; Lander, Eric; Engreitz, Jesse
  • Publication

    Functionally informed fine-mapping and polygenic localization of complex trait heritability

    (Springer Science and Business Media LLC, 2020-11-16) Weissbrod, Omer; Hormozdiari, Farhad; Benner, Christian; Cui, Ran; Ulirsch, Jacob; Gazal, Steven; Schoech, Armin; van de Geijn, Bryce; Reshef, Yakir; Márquez-Luna, Carla; O’Connor, Luke; Pirinen, Matti; Finucane, Hilary; Price, Alkes

    Fine-mapping aims to identify causal variants impacting complex traits. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy by leveraging functional annotations across the entire genome-not just genome-wide-significant loci-to specify prior probabilities for fine-mapping methods such as SuSiE or FINEMAP. In simulations, PolyFun + SuSiE and PolyFun + FINEMAP were well calibrated and identified >20% more variants with a posterior causal probability >0.95 than identified in their nonfunctionally informed counterparts. In analyses of 49 UK Biobank traits (average n = 318,000), PolyFun + SuSiE identified 3,025 fine-mapped variant-trait pairs with posterior causal probability >0.95, a >32% improvement versus SuSiE. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.

  • Publication

    Direct Characterization of Cis-Regulatory Elements and Functional Dissection of Complex Genetic Associations Using HCR–FlowFISH

    (Springer Science and Business Media LLC, 2021-07-29) Reilly, Steven; Gosai, Sager J.; Gutierrez, Alan; Mackay-Smith, Ava; Ulirsch, Jacob; Kanai, Masahiro; Mouri, Kousuke; Berenzy, Daniel; Kales, Susan; Butler, Gina M.; Gladden-Young, Adrianne; Bhuiyan, Redwan M.; Stitzel, Michael L.; Finucane, Hilary K.; Sabeti, Pardis; Tewhey, Ryan

    Abstract: Effective interpretation of genome function and genetic variation requires a shift from epigenetic mapping of cis-regulatory elements (CREs) to characterization of endogenous function. We developed HCR-FlowFISH, a broadly applicable approach to characterize CRISPR-perturbed CREs via accurate quantification of native transcripts, alongside CASA (CRISPR Activity Screen Analysis), a hierarchical Bayesian model to quantify CRE activity. Across >325,000 perturbations, we provide evidence that CREs can regulate multiple genes, skip over the nearest gene, and can display activating and/or silencing effects. At the cholesterol-level associated FADS locus, we combine endogenous screens with reporter assays to exhaustively characterize multiple genome-wide association signals, functionally nominating causal variants and identifying their target genes.