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DNA methylation profiling identifies a high effect genetic variant for lipoprotein(a) levels.

ABSTRACT: Changes in whole blood DNA methylation levels at several CpG sites have been associated with circulating blood lipids, specifically high-density lipoprotein and triglycerides. This study performs a discovery and validation epigenome-wide association study (EWAS) for circulating lipoprotein(a) [Lp(a)], an independent risk factor for cardiovascular diseases. Whole-blood DNA methylation profiles were assessed in a cohort of 1020 elderly individuals using the Illumina EPIC array and independent validation in 359 elderly males using the Illumina 450 k array. Plasma Lp(a) was measured using an apolipoprotein(a)-size-independent ELISA. Epigenome-wide rank regression analysis identified and validated a single CpG site, cg17028067 located in intron 1 of the LPA gene, that was significantly associated with plasma Lp(a) levels after correction for multiple testing. Genotyping of the site identified a relatively uncommon SNP (rs76735376, MAF <0.02) at the CpG site that largely explained the observed methylation effect. Rs76735376 is an expression quantitative trait loci for the LPA gene and could affect expression by altering enhancer activity. This EWAS for plasma Lp(a) identified a single CpG site within LPA. This association is due to an uncommon, but highly effective genetic variant, which was not in significant linkage disequilibrium with other variants known to influence Lp(a) levels or apo(a) isoform size. This study highlights the utility of CpG site methylation to identify potentially important genetic associations that would not be readily apparent in a comparable size genetic association study.

SUBMITTER: Jones GT

PROVIDER: S-EPMC7518699 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Even with identification of multiple causal genetic variants for common human diseases, understanding the molecular processes mediating the causal variants' effect on the disease remains a challenge. This understanding is crucial for the development of therapeutic strategies to prevent and treat disease. While static profiling of gene expression is primarily used to get insights into the biological bases of diseases, it makes differentiating the causative from the correlative effects difficult, as the dynamics of the underlying biological processes are not monitored. Using yeast as a model, we studied genome-wide gene expression dynamics in the presence of a causal variant as the sole genetic determinant, and performed allele-specific functional validation to delineate the causal effects of the genetic variant on the phenotype. Here, we characterized the precise genetic effects of a functional MKT1 allelic variant in sporulation efficiency variation. A mathematical model describing meiotic landmark events and conditional activation of MKT1 expression during sporulation specified an early meiotic role of this variant. By analyzing the early meiotic genome-wide transcriptional response, we demonstrate an MKT1-dependent role of novel modulators, namely, RTG1/3, regulators of mitochondrial retrograde signaling, and DAL82, regulator of nitrogen starvation, in additively effecting sporulation efficiency. In the presence of functional MKT1 allele, better respiration during early sporulation was observed, which was dependent on the mitochondrial retrograde regulator, RTG3. Furthermore, our approach showed that MKT1 contributes to sporulation independent of Puf3, an RNA-binding protein that steady-state transcription profiling studies have suggested to mediate MKT1-pleiotropic effects during mitotic growth. These results uncover interesting regulatory links between meiosis and mitochondrial retrograde signaling. In this study, we highlight the advantage of analyzing allele-specific transcriptional dynamics of mediating genes. Applications in higher eukaryotes can be valuable for inferring causal molecular pathways underlying complex dynamic processes, such as development, physiology and disease progression.

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DNA methylation profiling identifies a high effect genetic variant for lipoprotein(a) levels.

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