# Epigenetic and transcriptomic determinants of Sjogren's Syndrome subtypes utilizing data from the Sjogren's International Collaborative Clinical Alliance (SICCA) cohort

> **NIH NIH R03** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2020 · $161,500

## Abstract

ABSTRACT
 Sjögren's Syndrome (SS) is a systemic autoimmune disease affecting the exocrine system, with hallmark
symptoms of dry mouth and/or dry eyes caused by the dysfunction of salivary and/or lacrimal glands,
respectively. While Genome-Wide Association Studies (GWAS) and other studies have increased our knowledge
of genetic risk factors for SS, the disease etiology remains not well understood, and such risk factors have not
been translatable to any immunological treatment options for SS. The NIH-NIDCR-funded Sjögren's International
Collaborative Clinical Alliance (SICCA) was established to improve the understanding, diagnosis and treatment
of patients with SS by developing/validating standardized classification criteria for SS; and developing a rich
biospecimen repository with clinical data to be used for future epidemiologic, pathogenesis, and genetic studies
of SS.[1, 2] For this project, we will focus on genomic data and measures of the 2016 ACR-EULAR classification
criteria, involving ocular, oral, and autoantibody manifestations. As shown in our previous work, the genetics of
SS varies with ancestry; thus, we will cluster patients by both the criteria subphenotypes and genetic ancestry.
We believe that accounting for disease heterogeneity in this way will enable us to more precisely identify disease
pathways and mechanisms. Using previously secured funding, we are completing DNA methylation typing on
LSGs in 373 SICCA patients and single-cell RNA sequencing (scRNAseq) on PBMCs of 86 SICCA patients who
also have DNA methylation profiling. This data provides a unique opportunity for multi-omics analysis to
determine correlates between LSG tissue epigenetics, peripheral blood cell-type distributions and cell-specific
gene expression by SS subsets. First, using GWAS data and DNA methylation data from LSG biopsies,
we will identify genetic and epigenetic modifications associated with subtypes of SS in SICCA patients.
We will then examine the relationships between them by testing for genotype-specific methylation and
expression, and utilizing mendelian randomization and causal inference testing to investigate causality between
these measures. Second, we will analyze scRNAseq data to identify how cell types, states and cell-
specific gene expression correlate with SS subtypes. Finally, we will integrate genetics, epigenetics, and
transcriptomics to determine multi-omics profiles associated with SS subtypes. We will jointly model
associated features from the genomic data to investigate causal pathways via correlation networks, conditional
analysis, and machine learning. We anticipate that SS subtypes will exhibit specific relationships within the multi-
omics data and that this will advance our understanding of SS disease processes, leading to better treatment
targets.

## Key facts

- **NIH application ID:** 10041649
- **Project number:** 1R03DE029800-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** CAROLINE Helene SHIBOSKI
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $161,500
- **Award type:** 1
- **Project period:** 2020-09-09 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10041649

## Citation

> US National Institutes of Health, RePORTER application 10041649, Epigenetic and transcriptomic determinants of Sjogren's Syndrome subtypes utilizing data from the Sjogren's International Collaborative Clinical Alliance (SICCA) cohort (1R03DE029800-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10041649. Licensed CC0.

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