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

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, Patricia Katz, Maria Dall’Era, Jinoos Yazdany, Marina Sirota and Lindsey A. Criswell ()
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Cristina M. Lanata: University of California San Francisco
Ishan Paranjpe: University of California
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

Nature Communications, 2019, vol. 10, issue 1, 1-13

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-11845-y

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