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149 Network-based analysis of clinical and molecular data in a multiethnic lupus cohort identifies molecular associations with serological manifestations
  1. Ishan Paranjpe1,
  2. Cristina Lanata2,
  3. Joanne Nititham2,
  4. Kimberly Taylor2,
  5. Milena A Gianfrancesco2,
  6. Manish Paranjpe3,
  7. Shan Andrews4,
  8. Sharon Chung2,
  9. Brooke Rhead5,
  10. Lisa Barcellos5,
  11. Laura Trupin6,
  12. Patricia Katz3,
  13. Maria C DallEra6,
  14. Jinoos Yazdany6,
  15. Marina Sirota4 and
  16. Lindsey Criswell2
  1. 1Icahn School of Medicine at Mount Sinai
  2. 2Division of Rheumatology, Department of Medicine, University of California, San Francisco
  3. 3UCSF
  4. 4Baker Institute for Computational Health Sciences, University of California, San Francisco
  5. 5UC Berkeley
  6. 6UC San Francisco


Background Systemic Lupus Erythematous (SLE) is a chronic autoimmune disease with heterogeneous disease manifestations and outcomes. Previous work has found associations between DNA methylation at specific CpG sites and lupus nephritis, serologies, and SLEDAI score. However, these methods examine single CpGs and do not capture the full biological complexity. Using an integrative network-based approach, we aim to define how DNA methylation and genetic variation underlie this clinical heterogeneity in a well-phenotyped multiethnic cohort of SLE patients.

Methods 333 SLE participants from diverse ethnic backgrounds were recruited as part of this study. From peripheral blood, DNA methylation was measured using the Illumina EPIC Beadchip and single nucleotide polymorphism (SNP) genotype data was generated on the Affymetrix LAT1 World Array. Weighted gene correlation network analysis (WGCNA) was applied to the DNA methylation data. The resulting CpG networks were associated with relevant SLE clinical features ina multivariate linear regression model adjusting for population stratification, cell composition, sex, smoking history, medications.

Results We identified one WGCNA CpG module significantly associated with SLEDAI score, anti-Sm, and anti-dsDNA serologies (FDR<0.05). This network consisted of 303 CpGs and was hypomethylated in patients with higher SLEDAI scores and positive anti-Sm and anti-dsDNA serologies. Pathway analysis of this module revealed significant enrichment of genes in the Type I interferon pathway. We also performed a cis-meQTL analysis to determine whether any of the network CpGs were under genetic control. Of the 303 CpGs in the network, 54 CpGs were under proximal genetic control (FDR<0.01), suggesting that specific genetic variants play a role in epigenetic regulation of interferon related gene expression in the context of SLE autoantibody production.

Abstract 149 Figure 1

Associations between WGCNA CpG network and SLE features. WGCNA was applied to methylation data and associated networks with relevant SLE clinical criteria. Top: Colors in squares represent Pearson correlations between individual phenotypes and module eigengenes with significant associations (FDR<0.05) represented by a black circle. Bottom: Distribution of WGCNA eigengene for the identified network shows hypomethylation in patients with anti-Sm and anti-dsDNA serologies.

Conclusions Overall, we performed a network-based analysis of DNA methylation and identified a network of CpGs significantly associated with SLE serologies and SLEDAI score. Our approach identifies large-scale epigenetic remodeling that drives SLE pathology rather than single CpG associations as in previous studies that may be influenced by stochastic variation. By applying an integrative computational approach, our method serves to reveal the epigenetic and genetic role of the Type I interferon pathway in SLE.

Funding Source(s): This study was funded through the following grants: P30 AR070155 (MS, IP, LC), P60AR053308 (LC), K01LM012381 (MS), F32 AR070585 NIAMS (MG), U01DP005120 CDC (LC, CL, JY, MD, LT, PK), the Rheumatology Research Foundation 128849A (CL), and the Lupus Research Alliance (LC).

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