The applicant's career goal is to become an independently funded investigator in the genetics of rheumatic diseases, particularly rheumatoid arthritis (RA). To meet this goal, the applicant proposes a career development plan with emphasis on didactic training in the principles of epidemiology and statistical genetics methods by pursing courses towards a Masters of Science in Public Health in Clinical Research. In addition, she will obtain hands-on training in bioinformatics. She will become well versed in using state-of-the-art statistical techniques that can accommodate the unique challenges posed by large genetic data sets. The candidate has assembled a strong interdisciplinary mentoring team with proven track record of successful mentorship and academic accomplishment in rheumatology, epidemiology and statistical genetics. The research component of this mentored patient-oriented research career development award proposes to identify genetic associations with risk and severity of RA in African Americans and to build predictive models for RA risk and outcome using clinical and genomic data. Data from ~ 1000 African Americans RA participants and ~1700 African American controls is available from the NIH-funded Consortium for the Longitudinal Evaluation of African Americans with RA (CLEAR) Registry and from local and national collaborations. Genotyping data will be available from the ImmunoChip, a custom array of ~200,000 rare and common single nucleotide polymorphisms (SNPs) developed by the ImmunoChip Consortium. The ImmunoChip contains SNPs selected from the most strongly associated makers discovered in Caucasian and Asian populations through GWAS strategy in autoimmune diseases including RA, type 1 diabetes, lupus, celiac disease, autoimmune thyroid disease, inflammatory bowel disease, ankylosing spondylitis, psoriasis and other diseases. Genotyping is currently underway (see letter from Dr. Peter Gregersen) using institutional funds from UAB provided by Dr. Lou Bridges and will be available by the requested start date of this K23 application. The scientific aims of the study are: 1) to determine whether genetic markers of autoimmunity validated in European and Asian populations influence the risk of developing RA in African Americans; 2) to determine whether the genetic markers of susceptibility to RA play a role in radiographic severity of RA in African Americans; 3) to develop and test predictive models for RA risk and outcome using clinical and genetic data in African Americans. This project will greatly improve our understanding of the genetic influences on the RA risk and outcome and will provide essential training for Dr. Danila' development as an independent investigator.

Public Health Relevance

Rheumatoid arthritis is an autoimmune disease of unknown etiology, but both genetics and environmental factors are thought to play a role. There are racial/ethnic differences in the genetics underlying the disease. In this application, we will seek to discover whether known autoimmune genetic loci are associated with RA susceptibility and radiographic severity in African Americans, a minority group that has been underrepresented in RA research. Finally, we will be able to explore population based predictors of risk and severity of RA in African Americans, which may have diagnostic and therapeutic implication in African Americans with RA.

Agency
National Institute of Health (NIH)
Institute
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Type
Mentored Patient-Oriented Research Career Development Award (K23)
Project #
5K23AR062100-04
Application #
8874113
Study Section
Arthritis and Musculoskeletal and Skin Diseases Special Grants Review Committee (AMS)
Program Officer
Wang, Yan Z
Project Start
2012-06-01
Project End
2016-05-31
Budget Start
2015-06-01
Budget End
2016-05-31
Support Year
4
Fiscal Year
2015
Total Cost
Indirect Cost
Name
University of Alabama Birmingham
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
063690705
City
Birmingham
State
AL
Country
United States
Zip Code
35294
Danila, Maria I; Outman, Ryan C; Rahn, Elizabeth J et al. (2018) Evaluation of a Multimodal, Direct-to-Patient Educational Intervention Targeting Barriers to Osteoporosis Care: A Randomized Clinical Trial. J Bone Miner Res 33:763-772
Stirratt, Michael J; Curtis, Jeffrey R; Danila, Maria I et al. (2018) Advancing the Science and Practice of Medication Adherence. J Gen Intern Med 33:216-222
Stoll, Matthew L; Weiss, Pamela F; Weiss, Jennifer E et al. (2018) Age and fecal microbial strain-specific differences in patients with spondyloarthritis. Arthritis Res Ther 20:14
Wright, N C; Foster, P J; Mudano, A S et al. (2017) Erratum to: Assessing the feasibility of the Effectiveness of Discontinuing Bisphosphonates trial: a pilot study. Osteoporos Int 28:2505
Danila, Maria I; Laufer, Vincent Albert; Reynolds, Richard J et al. (2017) Dense Genotyping of Immune-Related Regions Identifies Loci for Rheumatoid Arthritis Risk and Damage in African Americans. Mol Med 23:177-187
Wright, N C; Foster, P J; Mudano, A S et al. (2017) Assessing the feasibility of the Effectiveness of Discontinuing Bisphosphonates trial: a pilot study. Osteoporos Int 28:2495-2503
Wampler Muskardin, Theresa; Vashisht, Priyanka; Dorschner, Jessica M et al. (2016) Increased pretreatment serum IFN-?/? ratio predicts non-response to tumour necrosis factor ? inhibition in rheumatoid arthritis. Ann Rheum Dis 75:1757-62
Danila, Maria I; Outman, Ryan C; Rahn, Elizabeth J et al. (2016) A multi-modal intervention for Activating Patients at Risk for Osteoporosis (APROPOS): Rationale, design, and uptake of online study intervention material. Contemp Clin Trials Commun 4:14-24
Navarro-Millán, Iris; Darrah, Erika; Westfall, Andrew O et al. (2016) Association of anti-peptidyl arginine deiminase antibodies with radiographic severity of rheumatoid arthritis in African Americans. Arthritis Res Ther 18:241
Cui, Xiangqin; Yu, Shaohua; Tamhane, Ashutosh et al. (2015) Simple regression for correcting ýýCt bias in RT-qPCR low-density array data normalization. BMC Genomics 16:82

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