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

Author

Lanata CM1, Paranjpe I2,3, Nititham J1, Taylor KE1, Gianfrancesco M1, Paranjpe M2, Andrews S2, Chung SA1, Rhead B4, Barcellos LF4, Trupin L1, Katz P1, Dall'Era M1, Yazdany J1, Sirota M2, Criswell LA5,6. Nat Commun. 2019 Aug 29;10(1):3902. doi: 10.1038/s41467-019-11845-y.

Author Information

1 Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.

2 Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.

3 Icahn School of Medicine at Mount Sinai, New York, NY, USA.

4 University of California, Berkeley, CA, USA.

5 Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, San Francisco, CA, USA. Lindsey.Criswell@ucsf.edu.

6 Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA. Lindsey.Criswell@ucsf.edu.

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.