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A phenotypic and genomics approach in a multi-ethnic cohort to subtype systemic lupus erythematosus

Author

Listed:
  • Cristina M. Lanata

    (University of California San Francisco)

  • Ishan Paranjpe

    (University of California
    Icahn School of Medicine at Mount Sinai)

  • Joanne Nititham

    (University of California San Francisco)

  • Kimberly E. Taylor

    (University of California San Francisco)

  • Milena Gianfrancesco

    (University of California San Francisco)

  • Manish Paranjpe

    (University of California)

  • Shan Andrews

    (University of California)

  • Sharon A. Chung

    (University of California San Francisco)

  • Brooke Rhead

    (University of California)

  • Lisa F. Barcellos

    (University of California)

  • Laura Trupin

    (University of California San Francisco)

  • Patricia Katz

    (University of California San Francisco)

  • Maria Dall’Era

    (University of California San Francisco)

  • Jinoos Yazdany

    (University of California San Francisco)

  • Marina Sirota

    (University of California)

  • Lindsey A. Criswell

    (University of California San Francisco
    University of California, San Francisco)

Abstract

Systemic lupus erythematous (SLE) is a heterogeneous autoimmune disease in which outcomes vary among different racial groups. Here, we aim to identify SLE subgroups within a multiethnic cohort using an unsupervised clustering approach based on the American College of Rheumatology (ACR) classification criteria. We identify three patient clusters that vary according to disease severity. Methylation association analysis identifies a set of 256 differentially methylated CpGs across clusters, including 101 CpGs in genes in the Type I Interferon pathway, and we validate these associations in an external cohort. A cis-methylation quantitative trait loci analysis identifies 744 significant CpG-SNP pairs. The methylation signature is enriched for ethnic-associated CpGs suggesting that genetic and non-genetic factors may drive outcomes and ethnic-associated methylation differences. Our computational approach highlights molecular differences associated with clusters rather than single outcome measures. This work demonstrates the utility of applying integrative methods to address clinical heterogeneity in multifactorial multi-ethnic disease settings.

Suggested Citation

  • Cristina M. Lanata & Ishan Paranjpe & Joanne Nititham & Kimberly E. Taylor & Milena Gianfrancesco & Manish Paranjpe & Shan Andrews & Sharon A. Chung & Brooke Rhead & Lisa F. Barcellos & Laura Trupin &, 2019. "A phenotypic and genomics approach in a multi-ethnic cohort to subtype systemic lupus erythematosus," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11845-y
    DOI: 10.1038/s41467-019-11845-y
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    Cited by:

    1. Mike Thompson & Mary Grace Gordon & Andrew Lu & Anchit Tandon & Eran Halperin & Alexander Gusev & Chun Jimmie Ye & Brunilda Balliu & Noah Zaitlen, 2022. "Multi-context genetic modeling of transcriptional regulation resolves novel disease loci," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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