A phenotypic and genomics approach in a multi-ethnic cohort to subtype systemic lupus erythematosus.
Cristina M LanataIshan ParanjpeJoanne NitithamKimberly E TaylorMilena GianfrancescoManish ParanjpeShan AndrewsSharon A ChungBrooke RheadLisa F BarcellosLaura TrupinPatricia KatzMaria Dall'EraJinoos YazdanyMarina SirotaLindsey A CriswellPublished in: Nature communications (2019)
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.
Keyphrases
- genome wide
- systemic lupus erythematosus
- dna methylation
- disease activity
- copy number
- single cell
- machine learning
- gene expression
- multiple sclerosis
- type diabetes
- rheumatoid arthritis
- dendritic cells
- metabolic syndrome
- high resolution
- case report
- juvenile idiopathic arthritis
- mass spectrometry
- rna seq
- skeletal muscle
- african american
- transcription factor
- insulin resistance