Systemic lupus erythematosus (SLE) is a poorly understood autoimmune syndrome characterized by significant morbidity and mortality. Clinical trials in SLE have largely been unsuccessful, and improved understanding of disease heterogeneity and underlying pathogenic factors will be required for efficient intervention in the disease process. The pathogenesis of SLE is driven by a combination of genetic risk factors and environmental influences which lead to an irreversible break in immunologic self-tolerance. Recent genetic studies in SLE have identified numerous novel susceptibility loci, most of which have a modest overall effect on disease susceptibility (odds ratios 1.2-1.3). These studies have used a standard case-control design, studying very large cohorts at high cost. New approaches are needed, as the cohort size required to detect genes with odds ratios <1.2 increases exponentially, easily exceeding the current number of SLE samples available. The next major challenge in unraveling human SLE genetics lies in novel methods for gene discovery, as we are reaching the limit of feasibility with case-control designs. Many lines of evidence support the idea that increased interferon alpha (IFNa) pathway signaling is causal in human lupus. High serum IFNa is a heritable risk factor for SLE, and some established IFNa pathway SLE-risk genes are associated with higher serum IFNa in SLE patients. These data support the idea that gain-of-function variants in the IFNa pathway underlie SLE pathogenesis. Genetic studies of quantitative protein-level phenotypes are characterized by much greater statistical power for discovery than traditional case-control studies. In this proposal, we will use a number of novel techniques which employ IFNa as a quantitative trait to greatly increase the power of genetic analyses, enabling novel gene discovery in existing SLE cohorts. We will validate IFNa-associated candidate genes from a local case-case design genome- wide screen of SLE patients, re-analyze available SLE genome-wide single nucleotide polymorphism (SNP) data to detect associations with serum IFNa, and use gene expression databases to select and test candidate SNPs for association with serum IFNa in SLE patients. The IFNa pathway is one of the most consistently dysregulated causal pathways in human SLE, and defining the genetics of this pathway dysregulation will provide one of our best chances to define the molecular events underlying initial disease pathogenesis. Unmeasured heterogeneity in the molecular pathogenesis in SLE has likely limited the success of interventional drug trials to date. Detailed knowledge of the functional genetic factors present in a given patient could be of great utility in individualizing therapy, and may allow for the development of preventive strategies.

Public Health Relevance

Standard case-control study designs in the complex human disease lupus are reaching the limit of feasibility, as the cohort size required to detect genes with odds ratios for association lower than those currently known increases exponentially and easily exceeds the current number of SLE samples available. The IFNa pathway is one of the most consistently dysregulated causal pathways in human SLE, and defining the genetics of this pathway represents one of our best chances to provide insight into the molecular events underlying initial disease pathogenesis. In this proposal, we will use a number of novel techniques which employ IFNa as a quantitative trait to greatly increase the power of genetic analyses, enabling novel gene discovery in existing cohorts.

Agency
National Institute of Health (NIH)
Institute
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Type
Research Project (R01)
Project #
7R01AR060861-03
Application #
8466930
Study Section
Genetics of Health and Disease Study Section (GHD)
Program Officer
Wang, Yan Z
Project Start
2011-08-01
Project End
2016-04-30
Budget Start
2013-05-01
Budget End
2014-04-30
Support Year
3
Fiscal Year
2013
Total Cost
$339,863
Indirect Cost
$126,113
Name
Mayo Clinic, Rochester
Department
Type
DUNS #
006471700
City
Rochester
State
MN
Country
United States
Zip Code
55905
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