Genetics of plasma cytokine variation in healthy baboons and humans

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Proffitt, John Michael

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The goal of this project was to investigate the genetic regulation of plasma cytokine variation in healthy baboons and humans. The first objective was to estimate the heritabilities of plasma levels of inflammatory cytokines in healthy baboons. In Study I, levels of the inflammatory cytokines TNF-α, GM-CSF, IL-1β, and IL-6, as well as sTNFR1, and sTNFR2 were measured in 370 related baboons. All of the traits exhibited significant evidence for genetic regulation. The heritabilities ranged from 61% for TNF-α to 14% for sTNFR1. The second objective was to identify chromosomal regions governing the production of resistin in healthy baboons. In Study II, resistin levels were measured in 416 related baboons. Variation in the plasma concentration of resistin was significantly linked to chromosome 18, between the markers D18S475 and D18S172 on the cytoband 18q12 (LOD = 4.0). The third objective of this study was to locate quantitative trait loci (QTLs) that affect circulating levels of plasma TNF-α and IL-1β levels in healthy humans. Study III examined plasma levels of TNF-α and IL-1β in a population of Caucasian Americans from the Midwestern United States. Quantitative trait loci were identified for TNF-α and IL-1β (LOD = 3.0 and LOD = 4.0, respectively) on chromosome 18 between markers ATA82B02 and D18S1371. The fourth objective was to detect QTLs affecting plasma levels of CRP, and to investigate its association with obesity phenotypes in healthy humans. Study IV revealed that chromosome 12 harbors a QTL regulating circulating CRP levels between markers D12S375 and D12S1052 (LOD = 4.1). CRP was genetically correlated with parameters of adiposity. The results from these baboon and human studies suggest that circulating cytokine levels in healthy animals are under significant genetic control. Chromosomes 12 and 18 appear to contain genetic differences that influence inflammation. Future work should aim to resolve the specific genetic elements through fine mapping and the positional candidate gene approach.




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