Sarcoidosis is a systemic granulomatous disease of unknown etiology that likely involves exposure to some environmental agent in a genetically susceptible host. We propose to identify sarcoidosis susceptibility genes and determine how these genes and environmental risk factors interact to cause sarcoidosis. This will be accomplished by organizing a multicenter consortium to recruit an adequate sample of sarcoidosis families for analysis. We plan to use affecting sibling pair linkage analysis to scan the genome for linked chromosomal regions, transmission disequilibrium testing to evaluate candidate genes in those regions with evidence for linkage and an environmental questionnaire to collect data to test for possible interactions of susceptibility genes with exogenous risk factors. This application offers the Department of Biostatistics and Research Epidemiology, Henry Ford Health System as the Data Coordinating Center (DCC) for the project. The DCC will provide administrative coordination, develop study documents, develop recruitment strategies, provide study tracking, establish a central data base, conduct data quality assurance, and collaborate with other members of the consortium in analysis of the results of the study. The Data Coordinating Center (DCC) will be an independent unit within the consortium, and will take guidance from the Steering Committee. The DCC team is headed by Principal Investigator Sarah Fowler, PhD, a senior biostatistical who specializes in coordinating centers for multi-center studies, and coordinating center biostatistician and Co-PI Mei Lu, PhD, who will sere as project manger for the DCC. DCC Co-Investigator Marvella Ford, PhD, will advise the collaborative group on the recruitment and retention of African American subjects and their families. The Coordinating Center team also includes a support staff consisting of individuals (programmer, data coordinator and secretary) experienced in and dedicated to methodologies for multi-center studies.

Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Henry Ford Health System
United States
Zip Code
Lareau, C A; DeWeese, C F; Adrianto, I et al. (2017) Polygenic risk assessment reveals pleiotropy between sarcoidosis and inflammatory disorders in the context of genetic ancestry. Genes Immun 18:88-94
Levin, Albert M; Adrianto, Indra; Datta, Indrani et al. (2015) Association of HLA-DRB1 with Sarcoidosis Susceptibility and Progression in African Americans. Am J Respir Cell Mol Biol 53:206-16
Li, Jia; Yang, James; Levin, Albert M et al. (2014) Efficient generalized least squares method for mixed population and family-based samples in genome-wide association studies. Genet Epidemiol 38:430-8
Levin, Albert M; Iannuzzi, Michael C; Montgomery, Courtney G et al. (2014) Admixture fine-mapping in African Americans implicates XAF1 as a possible sarcoidosis risk gene. PLoS One 9:e92646
Levin, Albert M; Adrianto, Indra; Datta, Indrani et al. (2014) Performance of HLA allele prediction methods in African Americans for class II genes HLA-DRB1, -DQB1, and -DPB1. BMC Genet 15:72
Adrianto, Indra; Lin, Chee Paul; Hale, Jessica J et al. (2012) Genome-wide association study of African and European Americans implicates multiple shared and ethnic specific loci in sarcoidosis susceptibility. PLoS One 7:e43907
Iannuzzi, Michael C; Rybicki, Benjamin A (2007) Genetics of sarcoidosis: candidate genes and genome scans. Proc Am Thorac Soc 4:108-16
Iannuzzi, Michael C; Rybicki, Benjamin A; Teirstein, Alvin S (2007) Sarcoidosis. N Engl J Med 357:2153-65
Rybicki, B A; Sinha, R; Iyengar, S et al. (2007) Genetic linkage analysis of sarcoidosis phenotypes: the sarcoidosis genetic analysis (SAGA) study. Genes Immun 8:379-86
Thompson, Cheryl L; Rybicki, Benjamin A; Iannuzzi, Michael C et al. (2006) Reduction of sample heterogeneity through use of population substructure: an example from a population of African American families with sarcoidosis. Am J Hum Genet 79:606-13

Showing the most recent 10 out of 17 publications