Person: Gazal, Steven
Loading...
Email Address
AA Acceptance Date
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
Gazal
First Name
Steven
Name
Gazal, Steven
6 results
Search Results
Now showing 1 - 6 of 6
Publication Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity(Springer Science and Business Media LLC, 2022-06) Gazal, Steven; Weissbrod, Omer; Hormozdiari, Farhad; Dey, Kushal; Nasser, Joseph; Jagadeesh, Karthik; Weiner, Daniel; Shi, Huwenbo; Fulco, Charles; O’Connor, Luke; Pasaniuc, Bogdan; Engreitz, Jesse M.; Price, Alkes L.Disease-associated single-nucleotide polymorphisms (SNPs) generally do not implicate target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis. Here, we developed a heritability-based framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk. Our optimal combined S2G strategy (cS2G) included seven constituent S2G strategies and achieved a precision of 0.75 and a recall of 0.33, more than doubling the recall of any individual strategy. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 5,095 causal SNP–gene-disease triplets (with S2G-derived functional interpretation) with high confidence. We further applied cS2G to provide an empirical assessment of disease omnigenicity; we determined that the top 1% of genes explained roughly half of the SNP heritability linked to all genes and that gene-level architectures vary with variant allele frequency.Publication Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases(Springer Science and Business Media LLC, 2020-06-08) Ishigaki, Kazuyoshi; Akiyama, Masato; Kanai, Masahiro; Takahashi, Atsushi; Kawakami, Eiryo; Sugishita, Hiroki; Sakaue, Saori; Matoba, Nana; Low, Siew-Kee; Okada, Yukinori; Terao, Chikashi; Amariuta, Tiffany; Gazal, Steven; Kochi, Yuta; Horikoshi, Momoko; Suzuki, Ken; Ito, Kaoru; Koyama, Satoshi; Ozaki, Kouichi; Niida, Shumpei; Sakata, Yasushi; Sakata, Yasuhiko; Kohno, Takashi; Shiraishi, Kouya; Momozawa, Yukihide; Hirata, Makoto; Matsuda, Koichi; Ikeda, Masashi; Iwata, Nakao; Ikegawa, Shiro; Kou, Ikuyo; Tanaka, Toshihiro; Nakagawa, Hidewaki; Suzuki, Akari; Hirota, Tomomitsu; Tamari, Mayumi; Chayama, Kazuaki; Miki, Daiki; Mori, Masaki; Nagayama, Satoshi; Daigo, Yataro; Miki, Yoshio; Katagiri, Toyomasa; Ogawa, Osamu; Obara, Wataru; Ito, Hidemi; Yoshida, Teruhiko; Imoto, Issei; Takahashi, Takashi; Tanikawa, Chizu; Suzuki, Takao; Sinozaki, Nobuaki; Minami, Shiro; Yamaguchi, Hiroki; Asai, Satoshi; Takahashi, Yasuo; Yamaji, Ken; Takahashi, Kazuhisa; Fujioka, Tomoaki; Takata, Ryo; Yanai, Hideki; Masumoto, Akihide; Koretsune, Yukihiro; Kutsumi, Hiromu; Higashiyama, Masahiko; Murayama, Shigeo; Minegishi, Naoko; Suzuki, Kichiya; Tanno, Kozo; Shimizu, Atsushi; Yamaji, Taiki; Iwasaki, Motoki; Sawada, Norie; Uemura, Hirokazu; Tanaka, Keitaro; Naito, Mariko; Sasaki, Makoto; Wakai, Kenji; Tsugane, Shoichiro; Yamamoto, Masayuki; Yamamoto, Kazuhiko; Murakami, Yoshinori; Nakamura, Yusuke; Raychaudhuri, Soumya; Inazawa, Johji; Yamauchi, Toshimasa; Kadowaki, Takashi; Kubo, Michiaki; Kamatani, YoichiroThe overwhelming majority of participants in current genetic studies are of European ancestry1–3, limiting our genetic understanding of complex disease in non-European populations. To address this, we aimed to elucidate polygenic disease biology in the East Asian population by conducting a genome-wide association study (GWAS) with 212,453 Japanese individuals across 42 diseases. We detected 320 independent signals in 276 loci for 27 diseases, among which 25 loci were novel (P < 9.58 x 10-9, an empirically estimated significance threshold). East Asian-specific missense variants were identified as candidate causal variants for three novel loci, and we successfully replicated two of them by analyzing independent Japanese cohorts; p.R220W of ATG16L2 associated with coronary artery disease and p.V326A of POT1 associated with lung cancer. We further investigated enrichment of heritability within 2,868 annotations of genome-wide transcription factor occupancy, and identified 378 significant enrichments across nine diseases (FDR < 0.05) (e.g. NF-κB for immune-related diseases). This large-scale GWAS in a Japanese population provides insights into the etiology of common complex diseases and highlights the importance of performing GWAS in non-European populations.Publication Comparison of Methods That Use Whole Genome Data to Estimate the Heritability and Genetic Architecture of Complex Traits(Springer Science and Business Media LLC, 2018-05) Evans, Luke M.; Tahmasbi, Rasool; Vrieze, Scott I.; Abecasis, Gonçalo R.; Das, Sayantan; Gazal, Steven; Bjelland, Douglas W.; de Candia, Teresa R.; Goddard, Michael E.; Neale, Benjamin; Yang, Jian; Visscher, Peter M.; Keller, Matthew C.Multiple methods have been developed to estimate narrow-sense heritability, h2, using single nucleotide polymorphisms (SNPs) in unrelated individuals. However, a comprehensive evaluation of these methods has not yet been performed, leading to confusion and discrepancy in the literature. We present the most thorough and realistic comparison of these methods to date. We used thousands of real whole-genome sequences to simulate phenotypes under varying genetic architectures and confounding variables, and we used array, imputed, or whole genome sequence SNPs to obtain 'SNP-heritability' estimates. We show that SNP-heritability can be highly sensitive to assumptions about the frequencies, effect sizes, and levels of linkage disequilibrium of underlying causal variants, but that methods that bin SNPs according to minor allele frequency and linkage disequilibrium are less sensitive to these assumptions across a wide range of genetic architectures and possible confounding factors. These findings provide guidance for best practices and proper interpretation of published estimates.Publication Functional Architecture of Low-Frequency Variants Highlights Strength of Negative Selection Across Coding and Non-Coding Annotations(Springer Science and Business Media LLC, 2018-11) Gazal, Steven; Loh, Po-Ru; Finucane, Hilary K.; Ganna, Andrea; Schoech, Armin; Sunyaev, Shamil; Price, AlkesCommon variant heritability has been widely reported to be concentrated in variants within cell-type-specific non-coding functional annotations, but little is known about low-frequency variant functional architectures. We partitioned the heritability of both low-frequency (0.5%≤ minor allele frequency <5%) and common (minor allele frequency ≥5%) variants in 40 UK Biobank traits across a broad set of functional annotations. We determined that non-synonymous coding variants explain 17 ± 1% of low-frequency variant heritability ([Formula: see text]) versus 2.1 ± 0.2% of common variant heritability ([Formula: see text]). Cell-type-specific non-coding annotations that were significantly enriched for [Formula: see text] of corresponding traits were similarly enriched for [Formula: see text] for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain dorsolateral prefrontal cortex explain 57 ± 12% of [Formula: see text] versus 12 ± 2% of [Formula: see text] for neuroticism. Forward simulations confirmed that low-frequency variant enrichment depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict effect size variance of causal rare variants (minor allele frequency <0.5%).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, AlkesFine-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 Reconciling S-LDSC and LDAK functional enrichment estimates(Springer Science and Business Media LLC, 2019-07-08) Gazal, Steven; Marquez-Luna, Carla; Finucane, Hilary; Price, AlkesRecent work has highlighted the importance of accounting for linkage disequilibrium (LD)-dependent genetic architectures in analyses of heritability. Two models incorporating LD-dependent architectures have been proposed for analyses of functional enrichment: the baseline-LD model4 used by stratified LD-score regression (S-LDSC) and the LDAK model.