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Application of statistical and functional methodologies for the investigation of genetic determinants of coronary heart disease biomarkers: lipoprotein lipase genotype and plasma triglycerides as an exemplar

Authors
Journal
Human Molecular Genetics
0964-6906
Publisher
Oxford University Press
Publication Date
Volume
19
Issue
20
Identifiers
DOI: 10.1093/hmg/ddq308
Keywords
  • Articles
Disciplines
  • Biology
  • Medicine

Abstract

Genome-wide association studies have proved very successful in identifying novel single-nucleotide polymorphisms (SNPs) associated with disease or traits, but the related, functional SNP is usually unknown. In this paper, we describe a methodology to locate and validate candidate functional SNPs using lipoprotein lipase (LPL), a gene previously associated with triglyceride levels, as an exemplar. Two thousand seven hundred and eighty-six healthy middle-aged men from the NPHSII UK prospective study (with up to six measures of plasma lipid levels) were genotyped for 20 LPL tagging (t)SNPs using Illumina Bead technology. Using model-selection procedures and haplotypes, we identified eight SNPs that consistently maximized the fit of the model to the phenotype. Fifteen SNPs in high linkage disequilibrium with these were identified, and functional assays were carried out on all 23 SNPs. Electrophoretic mobility shift assay (EMSA) was used to identify SNPs that had the potential to alter DNA–protein interactions, reducing the number to eight possible candidate SNPs. These were examined for ability to alter expression using a luciferase reporter assay, and two regulatory SNPs, showing genotype differences, rs327 and rs3289, were identified. Finally, multiplexed-competitor-EMSA (MC-EMSA) and supershift EMSA identified FOXA2 to rs327T, and CREB-binding protein (CBP) and CCAAT displacement protein (CDP) to rs3289C as the factors responsible for transcription binding. We have identified two novel candidate functional SNPs in LPL and presented a procedure aimed to efficiently detect SNPs potentially causal to genetic association. We believe that this methodology could be successfully applied to future re-sequencing data.

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