Genome-Wide Scan for Copy Number Variation Association with Biomarker Quantitative Trait Loci in Aging:

European Journal of Inflammation

K.Szigeti¹ 

关键词: copy number variation;    biomarker;    aging;   

DOI  :  10.1177/1721727X1401200206

学科分类：医学（综合）

来源: Sage Journals

【 摘 要 】

Biomarkers are emerging as important tools in the detection and monitoring of various diseases. A major limitation and challenge to effectively utilize biomarker signals is the limited understanding of factors contributing to their variance. As genetic variation is a major contributor to phenotypic variation, exploring genetic contributions is of great importance. Copy number variants (CNVs) offer an alternative genomic framework to understand contributions to phenotypic variance. A copy-number variation genome-wide association study was performed using 116 serum inflammatory biomarkers as quantitative trait in elderly normal controls to test the hypothesis that CNVs contribute to the phenotypic heterogeneity of serum biomarkers. Three chromosomal regions were associated with four biomarkers in trans. Transforming growth factor alpha (TG-alpha) serum levels were associated with CNV dosage at chr11:5,788 kb, soluble levels of receptor for advanced glycation endproducts (sRAGE) was associated with CNV dosage at chr8:40,183 kb and both thrombospondin-1 and tissue inhibitor of metalloproteinase 1 (TIMP-1) were associated with CNV dosage at chr11:18,961 kb. The CNV at chr11:5,788 kb harbors 2 olfactory genes and the introns of Tripartite motif-containing (TRIM) gene cluster TRIM5&22 while the CNV at chr11:18,961 includes the Mas-related G-protein coupled receptor member X1. These trans associations may identify novel relationships in the relevant pathways and suggest that genetic variation can contribute to biomarker levels. The detected trans-association between MRGPRX1 and thrombospondin-1/TIMP-1 could implicate a novel pathway between pain/itching and inflammation. Cataloguing all genetic variants with an effect on biomarkers will serve as a tool to interpret epidemiological studies and establish causal relationships through Mendelian randomization.

【 授权许可】

CC BY-NC