DNA methylation quantitative trait locus (mQTL) analyses on 32,851 participants identify genetic variants associated with DNA methylation at 420,509 sites in blood, resulting in a database of >... Show moreDNA methylation quantitative trait locus (mQTL) analyses on 32,851 participants identify genetic variants associated with DNA methylation at 420,509 sites in blood, resulting in a database of >270,000 independent mQTLs.Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated. Show less
In this thesis, we aimed to better understand how genetic variation affect the processes underlying health and disease, as trait-associated genetic variants are often located in non-coding... Show moreIn this thesis, we aimed to better understand how genetic variation affect the processes underlying health and disease, as trait-associated genetic variants are often located in non-coding regions. This hampers their interpretability, and has prompted the exploration of their effects on transcriptional regulation, a process that is crucial in the development of common and complex diseases. To do this, we have used a variety of omics data in a large collection of individuals from the general population. Using these data, we have investigated the local and distal effects of genetic variants on other molecular phenotypes, such as gene expression levels and DNA methylation levels of CpG sites, and the underlying mechanisms. This has resulted in a framework enabling the exploration of causal hypotheses about transcriptional regulation using genetics as a causal anchor. The approaches used in this thesis have yielded insight into transcriptional (dys)regulation and several underlying mechanisms. This will be helpful in better understanding how transcriptional regulation contributes to complex phenotypes related to health and disease, such as common diseases. Show less
