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Genome-wide classification of epigenetic activity reveals regions of enriched heritability in immune-related traits.
Stricker, Miriam; Zhang, Weijiao; Cheng, Wei-Yi; Gazal, Steven; Dendrou, Calliope; Nahkuri, Satu; Palamara, Pier Francesco.
  • Stricker M; Department of Statistics, University of Oxford, Oxford, UK.
  • Zhang W; Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
  • Cheng WY; Data & Analytics, Roche Pharma Research & Early Development, Roche Innovation Center New York, Little Falls, NJ, USA.
  • Gazal S; Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Dendrou C; Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
  • Nahkuri S; Data & Analytics, Roche Pharma Research & Early Development, Roche Innovation Center Zürich, Zürich, Switzerland. Electronic address: satu.nahkuri@roche.com.
  • Palamara PF; Department of Statistics, University of Oxford, Oxford, UK; Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK. Electronic address: palamara@stats.ox.ac.uk.
Cell Genom ; 4(1): 100469, 2024 Jan 10.
Article en En | MEDLINE | ID: mdl-38190103
ABSTRACT
Epigenetics underpins the regulation of genes known to play a key role in the adaptive and innate immune system (AIIS). We developed a method, EpiNN, that leverages epigenetic data to detect AIIS-relevant genomic regions and used it to detect 2,765 putative AIIS loci. Experimental validation of one of these loci, DNMT1, provided evidence for a novel AIIS-specific transcription start site. We built a genome-wide AIIS annotation and used linkage disequilibrium (LD) score regression to test whether it predicts regional heritability using association statistics for 176 traits. We detected significant heritability effects (average |τ∗|=1.65) for 20 out of 26 immune-relevant traits. In a meta-analysis, immune-relevant traits and diseases were 4.45× more enriched for heritability than other traits. The EpiNN annotation was also depleted of trans-ancestry genetic correlation, indicating ancestry-specific effects. These results underscore the effectiveness of leveraging supervised learning algorithms and epigenetic data to detect loci implicated in specific classes of traits and diseases.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Genómica / Sitios de Carácter Cuantitativo Tipo de estudio: Prognostic_studies / Systematic_reviews Idioma: En Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Genómica / Sitios de Carácter Cuantitativo Tipo de estudio: Prognostic_studies / Systematic_reviews Idioma: En Año: 2024 Tipo del documento: Article