AI Article Synopsis

  • Researchers analyzed blood samples from nearly 7,000 individuals from Europe and South Asia to explore how DNA sequence variation affects DNA methylation, identifying over 11 million SNP-CpG associations connected to gene regulation.
  • They found that these methylation quantitative trait loci (meQTL) are not only linked to gene expression and metabolic traits but also associated with clinical conditions, revealing pathways that influence traits like body mass index, rheumatoid arthritis, and blood pressure.
  • The study also highlighted specific meQTL with significant effects in certain populations, such as a variant affecting fatty acid metabolism and immune response in T cells, enhancing our understanding of genetic influences on human health.

Article Abstract

We determined the relationships between DNA sequence variation and DNA methylation using blood samples from 3,799 Europeans and 3,195 South Asians. We identify 11,165,559 SNP-CpG associations (methylation quantitative trait loci (meQTL), P < 10), including 467,915 meQTL that operate in trans. The meQTL are enriched for functionally relevant characteristics, including shared chromatin state, High-throuhgput chromosome conformation interaction, and association with gene expression, metabolic variation and clinical traits. We use molecular interaction and colocalization analyses to identify multiple nuclear regulatory pathways linking meQTL loci to phenotypic variation, including UBASH3B (body mass index), NFKBIE (rheumatoid arthritis), MGA (blood pressure) and COMMD7 (white cell counts). For rs6511961 , chromatin immunoprecipitation followed by sequencing (ChIP-seq) validates zinc finger protein (ZNF)333 as the likely trans acting effector protein. Finally, we used interaction analyses to identify population- and lineage-specific meQTL, including rs174548 in FADS1, with the strongest effect in CD8 T cells, thus linking fatty acid metabolism with immune dysregulation and asthma. Our study advances understanding of the potential pathways linking genetic variation to human phenotype.

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Source
http://dx.doi.org/10.1038/s41588-021-00969-xDOI Listing

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