Integrated associations of genotypes with multiple blood biomarkers linked to coronary heart disease risk.


Drenos, F; Talmud, PJ; Casas, JP; Smeeth, L; Palmen, J; Humphries, SE; Hingorani, AD; (2009) Integrated associations of genotypes with multiple blood biomarkers linked to coronary heart disease risk. Human molecular genetics, 18 (12). pp. 2305-16. ISSN 0964-6906 DOI: https://doi.org/10.1093/hmg/ddp159

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Abstract

Individuals at risk of coronary heart disease (CHD) show multiple correlations across blood biomarkers. Single nucleotide polymorphisms (SNPs) indexing biomarker differences could help distinguish causal from confounded associations because of their random allocation prior to disease. We examined the association of 948 SNPs in 122 candidate genes with 12 CHD-associated phenotypes in 2775 middle aged men (a genic scan). Of these, 140 SNPs indexed differences in HDL- and LDL-cholesterol, triglycerides, C-reactive protein, fibrinogen, factor VII, apolipoproteins AI and B, lipoprotein-associated phospholipase A2, homocysteine or folate, some with large effect sizes and highly significant P-values (e.g. 2.15 standard deviations at P = 9.2 x 10(-140) for F7 rs6046 and FVII levels). Top ranking SNPs were then tested for association with additional biomarkers correlated with the index phenotype (phenome scan). Several SNPs (e.g. in APOE, CETP, LPL, APOB and LDLR) influenced multiple phenotypes, while others (e.g. in F7, CRP and FBB) showed restricted association to the index marker. SNPs influencing six blood proteins were used to evaluate the nature of the associations between correlated blood proteins utilizing Mendelian randomization. Multiple SNPs were associated with CHD-related quantitative traits, with some associations restricted to a single marker and others exerting a wider genetic 'footprint'. SNPs indexing biomarkers provide new tools for investigating biological relationships and causal links with disease. Broader and deeper integrated analyses, linking genomic with transcriptomic, proteomic and metabolomic analysis, as well as clinical events could, in principle, better delineate CHD causing pathways amenable to treatment.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology
Research Centre: Centre for Global Non-Communicable Diseases (NCDs)
PubMed ID: 19336475
Web of Science ID: 266349400018
URI: http://researchonline.lshtm.ac.uk/id/eprint/5443

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