Abstract
OBJECTIVE: CD4(+) T-cells mediate inflammation in atherosclerosis, but additive genetic effects on associated pathways of Th1 and Th2 immune response have not been described. We sought to characterize heritability, pleiotropy, and QTL effects on the expression of genes implicated in Th1 and Th2 immune response in a baboon model of risk factors for atherosclerosis. METHODS: We employed a maximum likelihood-based variance decomposition approach to estimate additive genetic effects on transcript levels generated from a gene expression profile of lymphocytes in 499 pedigreed baboons maintained on a basal diet. Transcript levels for 57 genes implicated in Th1 and Th2 immune response were selected for analysis based on significant heritability in this profile. Multipoint whole genome scans were conducted on heritable transcript levels to localize QTLs influencing these measures. To evaluate pleiotropic effects on transcript levels, we estimated genetic and phenotypic correlations among transcript measures, and assessed their correspondence using a Mantel test. Network analysis using GeneGo's MetaCore™ software was conducted to characterize known interaction among coded proteins. RESULTS: Heritabilities for candidate gene transcript levels ranged from 0.092-0.786 (median h(2)=0.278, P=4.72×10(-4)). Linkage analyses yielded significant evidence (LOD≥2.73) for 14 eQTLs (LOD score range 2.76-14.87, genome-wide P=4.9×10(-2)-1.03×10(-14)). Estimates of genetic correlation supported shared additive genetic effects incorporating all 57 transcripts (null hypothesis of ρ(G)=0 rejected at FDR≤0.05 for 522 of 1596 estimates), and accounted for most of the observed phenotypic correlation among transcripts (Mantel test, r([ρP],)([ρG])=0.781, P<0.0001). Network analysis revealed direct interactions among 54 of the 57 coded proteins. CONCLUSIONS: We conclude that major genetic effects influence expression levels of multiple genes implicated in Th1 and Th2 immune response. Additionally, we find that expression levels of these candidate genes are characterized by extensive pleiotropy, consistent with known interaction among their coded proteins, many of which are independently associated with atherosclerosis.